December 2008 

EPA/600/R-08/082F 

Integrated Science Assessment 

for Oxides of Nitrogen and Sulfur – 

Ecological Criteria 

National Center for Environmental Assessment-RTP Division 

Office of Research and Development 

U.S. Environmental Protection Agency 

Research Triangle Park, NC 
Disclaimer 

This document has been reviewed in accordance with U.S. Environmental Protection Agency 

policy and approved for publication. Mention of trade names or commercial products does not constitute 

endorsement or recommendation for use. 
Executive Summary Integrated Science Assessment

Oxides of Nitrogen and Sulfur 

Ecological Criteria 

December 2008 ■ EPA/600/R-08/082F 

U.S. Environmental Protection Agency | Office of Research and Development | National Center for Environmental Assessment 

http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=201485 

Introduction 

his Integrated Science Assessment 

 (ISA) is a synthesis and evaluation of 

 the most policy-relevant science 

that forms the scientific foundation for the 

review of the secondary (welfare-based) 

national ambient air quality standards 

(NAAQS) for oxides of nitrogen (NOX) and 

oxides of sulfur (SOX). The Clean Air Act definition of welfare effects includes, but is not 

limited to, effects on soils, water, wildlife, 

vegetation, visibility, weather, and climate, 

as well as effects on man-made materials, 

economic values, and personal comfort 

and well-being. The current secondary 

NAAQS for SOX, set in 1973, is a 3-hour average of 0.5 ppm1 sulfur dioxide (SO2), not to 

be exceeded 

more than 

once per year. 

The secondary 

NAAQS for 

NOX is identical to the primary standard 

set in 1971: an 

 

1 1ppm= 1000 ppb 

annual average not to exceed 0.053 ppm 

nitrogen dioxide (NO2). The current secondary NAAQS were set to protect against direct damage to vegetation by exposure to T gas-phase SOX or NOX. 

Scope 

 his

 ef

 tio

gen (N) a

ent enrich

 ISA is focused on ecological 

fects resulting from current deposin of compounds containing nitrond sulfur (S). Acidification, N nutriment and effects of sulfate (SO42–) 

on methylation of mercury (Hg) are highlighted in the document. The following figure illustrates the scope of the document. 

T
 

ISA for Oxides of Nitrogen and Sulfur Executive Summary

Both N and S contribute to acidification of 

ecosystems. This ISA considers several 

chemical forms that contribute to acidifying 

deposition, including gases and particles 

derived from SOX, NOX, and reduced nitrogen (NHX). 

Deposition of N contributes to N-nutrient enrichment and eutrophication. An assessment of the complex ecological effects of 

atmospheric N deposition requires consideration of many different chemical forms of 

reactive N (Nr). For this reason, the ISA includes evaluation of data on the most 

common reduced inorganic forms of N, 

ammonia (NH3) and ammonium (NH4

+

); on 

oxidized inorganic forms including nitric oxide (NO) and NO2, nitrate (NO3

–

), nitric acid 

(HNO3), and nitrous oxide (N2O); and on organic N compounds including peroxyacetyl 

nitrate (PAN). 

Other welfare effects addressed in the ISA 

include effects of SO42- deposition on Hg 

methylation, along with evidence related to 

direct exposure to gas-phase NOX and SOX. 

The key conclusions of the ISA follow. 

2
 

ISA for Oxides of Nitrogen and Sulfur Executive Summary

Current concentrations and deposition 

 mbient annual NOX and SOX con- 

 centrations as reported in 

 the routine national networks have 

decreased substantially owing to controls 

enacted since the 1970s. NOX decreased 

~35% in the period 1990-2005, to current annual average concentrations of ~15 ppb. 

Emissions of SOX have been substantially reduced in recent years: annual average 

ambient SOX concentrations have decreased ~50% in the period 1990–2005 and 

now stand at ~4 ppb for both aggregate 

annual and 24-hour average concentrations nation-wide. 

A Emitted NOX, SOX, NHX and other pollutants 

can be transported vertically by convection 

into the upper part of the mixed layer on 

one day, then transported overnight in a 

layer of high concentrations. Once pollutants are lofted to the middle and upper troposphere, they typically have a much 

longer lifetime and, with the generally 

stronger winds at these altitudes, can be 

transported long distances from their source 

regions. The length scale of this transport is 

highly variable owing to differing chemical 

and meteorological conditions encountered along the transport path. 

3
 

ISA for Oxides of Nitrogen and Sulfur Executive Summary

Numerical chemical-transport models 

(CTMs) are the prime tools for computing 

emissions and interactions among pollutants 

like NOX, SOX, and NHX, their transport and 

transformation including production of secondary aerosols like ammonium nitrate and 

ammonium sulfate, the evolution of particle 

size distributions, the resulting atmospheric 

concentrations and the deposition of these 

pollutants to the surface. CTMs are driven by 

calculated emissions for primary species 

such as NOX, SOX, NH3, and primary particulate matter, and by the meteorological 

fields produced by other numerical prediction models. As such, CTMs are the chief 

means of relating emitted pollutants with 

deposited ones. 

The emitted, transported, and transformed 

pollutants reach the surface where they can 

have ecological effects largely through 

deposition. Direct and indirect wet and dry 

deposition to specific locations like watersheds depend on air pollutant emissions and 

concentrations in the airshed above the watershed, but the shape and areal extent of 

the airshed is quite different from that of the 

watershed owing to the transport and transformation of emitted pollutants described 

above. 

Deposition is spatially heterogeneous across 

the U.S. In the years 2004-2006, routine national monitoring networks reported mean S 

deposition in the U.S. highest east of the Mississippi River with the highest reported deposition, 21 kg S/ha/yr, in the Ohio River Valley 

where most recording stations reported 

three-year averages for this period of more 

than 10 kg S/ha/yr. Numerous other stations 

in the eastern U.S. reported S deposition 

greater than 5 kg S/ha/yr. Data are sparse 

for the central U.S. between the 100th meridian and the Mississippi River; but, where 

available, deposition values there were 

lower than in most of the eastern U.S., ranging from 4 to over 5 kg S/ha/yr. Total S deposition in the U.S. west of the 100th meridian is 

lower than in the East or upper Midwest, owing to lower densities of high-emitting 

sources in the West. In the years 2004-2006, 

all routine recording stations in the West reported less than 2 kg S/ha/yr and many reported less than 1 kg S/ha/yr. S was primarily 

deposited in the form of wet SO42-, followed 

by a smaller proportion of dry SO2, and a 

much smaller proportion of dry SO42-. 

Expanding urbanization, agricultural intensification, and industrial production during the 

previous 100 years have produced a nearly 

10-fold increase in total N deposited from 

the atmosphere compared to pre-industrial 

levels. NOX, chiefly from fossil fuel combustion, often dominates total N pollution in the 

U.S. and comprises from 50 to 75% of current 

4
 

ISA for Oxides of Nitrogen and Sulfur Executive Summary

total N atmospheric deposition. This wet and 

dry atmospheric N deposition is spatially 

heterogeneous, too, owing to the influence 

of meteorology, transport, precipitation patterns and land use. 

For 2004–2006, routine national monitoring 

networks reported the highest mean N 

deposition totals in the U.S. in the Ohio River 

Valley, specifically in the states of Indiana 

and Ohio, with values greater than 9 kg 

N/ha/yr. N deposition was lower in other 

parts of the East, including the Southeast 

and in northern New England. In the central 

U.S., the highest N annual average deposition totals were on the order of 6 to 7 kg 

N/ha/yr. Measured concentrations and inferred deposition totals were dominated by 

wet NO3

–

 and NH4

+

 species, followed by 

dry HNO3, dry NH4

+

, and dry NO3

–

. NH3 is not 

yet measured routinely in any national networks; however, smaller-scale intensive 

monitoring and numerical air quality modeling both indicate that it may account for 

more than 80% of the dry reduced N deposition total. 

Although S and N deposition in most areas 

of the U.S. occurred as wet deposition, 

there were some exceptions, including parts 

of California where N deposition was primarily dry. 

The thin coverage of monitoring sites in 

many locations, especially in the rural West, 

means that limited data exist on deposition 

totals in a large number of potentially sensitive places. Numerical modeling experiments can help fill in these data gaps and 

suggest that local and even regional areas 

of high ambient concentration and deposition exist where measured data are unavailable. Model-predicted values for N 

deposition in some regions of the Adirondacks in New York are greater than 20 kg 

N/ha/yr; other model estimates as high as 

32 kg N/ha/yr have been made for a region 

of southern California, where more than half 

of that total was predicted to come from 

NO and NO2. 

The ISA concludes that the national-scale 

networks routinely monitoring N deposition 

are inadequate to characterize both the full 

range of reduced and oxidized forms of N 

deposition and the substantial regional heterogeneity across the landscape of the U.S. 

5
 

ISA for Oxides of Nitrogen and Sulfur Executive Summary

The evidence is sufficient to infer a causal 

relationship between acidifying deposition 

and effects on: 

(1) biogeochemistry related to terrestrial and 

aquatic ecosystems; 

(2) biota in terrestrial and aquatic ecosystems. 

 he effects of acidifying deposition 

 on ecosystems have been well 

 studied over the past several decades and vulnerable areas have been identified in the U.S. The wealth of data has led 

to the development of widely used ecological models for predicting soil and surface 

water acidification. Regional and ecosystem vulnerability to acidification results from 

inherent sensitivity and exposure to acidifying deposition. 

Sensitivity of terrestrial and aquatic ecosystems to acidification from S and N deposition is regional and predominantly governed 

by surficial geology. Other factors contributing to the sensitivity of soils and surface waters to acidifying deposition include topography, vegetation, soil chemistry, land use, 

and hydrologic flowpath. 

Soil acidification is a natural process, 

but is often accelerated by acidifying 

deposition, which can decrease concentrations of exchangeable base 

cations in soils. Biological effects of 

acidification on terrestrial ecosystems 

are generally attributable to Al toxicity and decreased ability of plant 

roots to take up base cations. Areas 

most sensitive to terrestrial effects from acidifying deposition include forests in the Adirondack Mountains of New York, the Green 

Mountains of Vermont, the White Mountains 

of New Hampshire, the Allegheny Plateau of 

Pennsylvania, and high-elevation forest 

ecosystems in the central and southern Appalachians. While studies show some recovery of surface waters, there are widespread 

areas of ongoing depletion of exchangeable base cations in forest soils in the northeastern U.S., despite recent decreases in 

acidifying deposition. 

T

Ecological effects of acidification 

In aquatic systems, consistent and coherent 

evidence from multiple studies of many 

species shows that acidification can cause 

the loss of acid-sensitive species, and that 

more species are lost with greater acidification. These effects are linked to changes in 

6
 

ISA for Oxides of Nitrogen and Sulfur Executive Summary

surface water chemistry, including concentrations of SO4

2-, NO3–

, inorganic Al, and calcium (Ca2+), surface water pH, sum of base 

cations, acid neutralizing capacity (ANC), 

and base cation surplus. These effects are 

also influenced by historical inputs to these 

systems. Decreases in ANC and pH and increases in inorganic Al concentration contribute to declines in zooplankton, macroinvertebrates, and fish species richness. These 

effects on species richness may also affect 

ecosystem services, such as biodiversity and 

cultural services such as fishing and tourism. 

Although both N and S deposition can 

cause terrestrial and aquatic acidification, S 

deposition is generally the primary cause of 

chronic acidification, with secondary contributions from N deposition. Following decreases in S deposition in the 1980s and 

1990s, one quarter to one third of the 

chronically acidic lakes and streams in the 

U.S. were no longer acidic during baseflow 

in the year 2000. A number of lakes and 

streams, however, remain acidic even 

though wet SO42- deposition has decreased 

by as much as 30% since 1989. N deposition, 

which has also decreased in the years since 

1990 in most places in the U.S. with routine 

monitoring, is the primary cause of episodic 

acidification which, despite its short duration, 

has been shown to cause long-term biological effects. 

Many of the surface waters most sensitive to 

acidification in the U.S. are found in the 

Examples of biogeochemical indicators of 

effects from acidifying deposition on ecosystems 

Ecosystem Biogeochemical Indicator 

Terrestrial 

• Soil base saturation 

• Inorganic Aluminum concentration 

in soil water 

• Soil carbon-to-nitrogen ratio 

Aquatic 

• Sulfate 

• Nitrate 

• Base cations 

• Acid neutralizing capacity 

• Surface water inorganic Aluminum 

• pH 

Examples of biological indicators of 

effects from acidifying deposition on ecosystems 

Indicator Measure 

Terrestrial Ecosystems 

Red Spruce • Percent dieback of canopy 

trees 

Sugar 

Maple 

• Dead basal area, crown 

vigor index, fine twig dieback 

Aquatic Ecosystems 

Fishes, zooplankton, crustaceans, rotifers • Presence/absence 

• Fish condition factor 

• Biodiversity 

7
 

ISA for Oxides of Nitrogen and Sulfur Executive Summary

Number of fish species per lake vs. acidity status, expressed 

as acid neutralizing capacity (ANC), for Adirondack lakes. 

Source: Sullivan et al., 2006b

Regions of the northern and eastern U.S. that contain appreciable numbers of lakes 

and streams sensitive to deleterious effects from acidifying deposition. 

Source: Stoddard et al., 2003

Northeast, the Southeast, and the mountainous West. In the West, acidic surface 

waters are rare and the extent of chronic 

surface water acidification that has occurred to date has been limited. However, 

episodic acidification does occur. In both 

the mountainous West and the Northeast, 

the most severe acidification of surface waters generally occurs during spring snowmelt. 

The ISA highlights evidence from two wellstudied areas to provide more detail on 

how acidification affects ecosystems: The 

Adirondacks (NY) and Shenandoah National Park (VA). In the Adirondacks, the current rates of N and S deposition exceed the 

amount that would allow recovery of the 

most acid sensitive lakes. In the Shenandoah, past SO42- has accumulated in the soil 

and is slowly released from the soil into 

stream water where it causes acidification, 

making parts of this 

region sensitive to even 

the current lower 

deposition loadings. 

Numerical models specifically calibrated to 

these locations and 

conditions suggest that 

the number of acidic 

streams will increase 

even under current 

deposition loads. 

8
 

ISA for Oxides of Nitrogen and Sulfur Executive Summary

Ecological effects of nitrogen deposition

 he

 of

 so

identified

re are many well-studied effects 

 N deposition on ecosystems and 

me vulnerable areas have been 

 in the U.S. However, the full extent 

of ecosystem vulnerability is still unknown. 

Substantial empirical information from specific ecosystems and for specific endpoints 

is available, but given the complexity of the 

N cycle, a broadly applicable and welltested predictive model of the ecological 

effects of N deposition is not yet available. 

Though the sensitivity of ecosystems to N 

deposition across the U.S. varies, a large 

body of evidence clearly demonstrates a 

relationship between N deposition and a 

broad range of ecological effects. 

 here are many well-studied effects 

 of N deposition on ecosystems and 

 some vulnerable areas have been 

identified in the U.S. However, the full extent 

of ecosystem vulnerability is still unknown. 

Substantial empirical information from specific ecosystems and for specific endpoints 

is available, but given the complexity of the 

N cycle, a broadly applicable and welltested predictive model of the ecological 

effects of N deposition is not yet available. 

Though the sensitivity of ecosystems to N 

deposition across the U.S. varies, a large 

body of evidence clearly demonstrates a 

relationship between N deposition and a 

broad range of ecological effects. 

T

The contribution of N deposition to total N 

load varies among ecosystems. Atmospheric N deposition is the main source of 

new N to most headwater streams, high 

elevation lakes, and low-order streams. Atmospheric N deposition contributes to the 

total N load in terrestrial, wetland, freshwater, and estuarine ecosystems that receive N 

through multiple pathways (i.e. biological Nfixation, agricultural land runoff and waste 

water effluent). There are multiple biogeochemical indicators of N deposition effects. 

The contribution of N deposition to total N 

load varies among ecosystems. Atmospheric N deposition is the main source of 

new N to most headwater streams, high 

elevation lakes, and low-order streams. Atmospheric N deposition contributes to the 

total N load in terrestrial, wetland, freshwater, and estuarine ecosystems that receive N 

through multiple pathways (i.e. biological Nfixation, agricultural land runoff and waste 

water effluent). There are multiple biogeochemical indicators of N deposition effects. 

The evidence is sufficient to infer a causal relationship between N deposition, to which NOX

and NHX contribute, and the alteration of the 

following: 

(1) biogeochemical cycling of N and carbon 

(C) in terrestrial, wetland, freshwater aquatic, 

and coastal marine ecosystems; 

(2) biogenic flux of methane (CH4), and N2O in 

terrestrial and wetland ecosystems; 

(3) species richness, species composition, and 

biodiversity in terrestrial, wetland, freshwater 

aquatic and coastal marine ecosystems. 

Examples of biogeochemical indicators of 

effects from reactive N deposition on ecosystems 

Ecosystem Biogeochemical Indicator 

Terrestrial 

and 

Wetland 

• NO3

–

 leaching 

• Nitrification 

• Denitrification 

• N2O emissions 

• CH4 emissions 

• Soil C:N ratio 

• Foliar / plant tissue [N], C:N, 

N:magnesium, N:phosphorus 

• Soil water [NO3

–

] and [NH4

+

] 

Freshwater 

and 

Estuarine 

• Chlorophyll a

• Water [NO3

–

] 

• Dissolved inorganic N 

• Dissolved oxygen 

• N:P 

9
 

ISA for Oxides of Nitrogen and Sulfur Executive Summary

In terrestrial ecosystems, the onset of NO3

–

leaching is one of the best documented 

biogeochemical indicators that an ecosystem receives more N than it uses and is able 

to retain. N removal by ecosystems is a 

valuable ecosystem service regulating water quality. When atmospheric deposition of 

N impairs the ability of terrestrial and 

aquatic ecosystems to retain and remove 

N, NO3

–

 leaching occurs and the degradation of water quality can occur. The onset of 

leaching was calculated to occur with 

deposition levels between 5.5 and 10 kg 

N/ha/yr for sensitive eastern forests. In the 

mixed conifer forests of the Sierra Nevada 

and San Bernardino mountains, the onset of 

increased NO3

–

 leaching was calculated to 

be 17 kg N/ha/yr. Several studies in the 

Rocky Mountains indicate that the capacity 

of alpine catchments to retain N was exceeded at levels greater than 5-10 kg 

N/ha/yr. 

In terrestrial ecosystems, the onset of NO3

–

leaching is one of the best documented 

biogeochemical indicators that an ecosystem receives more N than it uses and is able 

to retain. N removal by ecosystems is a 

valuable ecosystem service regulating water quality. When atmospheric deposition of 

N impairs the ability of terrestrial and 

aquatic ecosystems to retain and remove 

N, NO3

–

 leaching occurs and the degradation of water quality can occur. The onset of 

leaching was calculated to occur with 

deposition levels between 5.5 and 10 kg 

N/ha/yr for sensitive eastern forests. In the 

mixed conifer forests of the Sierra Nevada 

and San Bernardino mountains, the onset of 

increased NO3

–

 leaching was calculated to 

be 17 kg N/ha/yr. Several studies in the 

Rocky Mountains indicate that the capacity 

of alpine catchments to retain N was exceeded at levels greater than 5-10 kg 

N/ha/yr. 

N deposition alters the biogenic sources and 

sinks of two greenhouse gases (GHGs), CH4

and N2O, in terrestrial and wetland ecosystems, resulting in increased emissions to the 

atmosphere. Non-flooded upland soil is the 

largest biological sink and takes up about 

6% of atmospheric CH4. N addition decreases CH4 uptake in coniferous and deciduous forests, and N addition increases 

N deposition alters the biogenic sources and 

sinks of two greenhouse gases (GHGs), CH4

and N2O, in terrestrial and wetland ecosystems, resulting in increased emissions to the 

atmosphere. Non-flooded upland soil is the 

largest biological sink and takes up about 

6% of atmospheric CH4. N addition decreases CH4 uptake in coniferous and deciduous forests, and N addition increases 

CH4 production in wetlands. Soil is the largest source of N2O, accounting for 60% of 

global emissions. N deposition increases the 

biogenic emission of N2O in coniferous forest, deciduous forests, grasslands, and wetlands. Although N addition can cause a 

general stimulation of biogenic CH4 and 

N2O emissions from soils, it is difficult to generalize a dose-response relationship between the amount of N addition and the 

changes in GHG flux on a large heterogeneous landscape. This is because GHG production is influenced by multiple environmental factors (e.g., soil, vegetation and 

climate), which vary greatly over small spatial and temporal scales. 

CH4 production in wetlands. Soil is the largest source of N2O, accounting for 60% of 

global emissions. N deposition increases the 

biogenic emission of N2O in coniferous forest, deciduous forests, grasslands, and wetlands. Although N addition can cause a 

general stimulation of biogenic CH4 and 

N2O emissions from soils, it is difficult to generalize a dose-response relationship between the amount of N addition and the 

changes in GHG flux on a large heterogeneous landscape. This is because GHG production is influenced by multiple environmental factors (e.g., soil, vegetation and 

climate), which vary greatly over small spatial and temporal scales. 

N is often the most limiting nutrient to growth 

in ecosystems. N deposition thus often increases primary productivity, thereby altering the biogeochemical cycling of C. N 

Examples of biological indicators of 

effects from N deposition on ecosystems 

Ecosystem Biological Indicators 

• Altered community composition, biodiversity and/or population decline. Taxa affected 

include: diatoms, lichen, mycorrhizae, moss, grasses and 

other herbaceous plants 

• Plant root: shoot ratio 

Terrestrial and 

Wetlands 

• Terrestrial plant biomass/production 

Freshwater 

and Estuarine 

• Phytoplankton biomass/production 

• Toxic or nuisance algae 

blooms 

• Submerged aquatic vegetation

• Fauna from higher trophic levels 

10
 

ISA for Oxides of Nitrogen and Sulfur Executive Summary

deposition can cause changes in ecosystem C budgets. However, whether N deposition increases or decreases ecosystem Csequestration remains unclear. A limited 

number of studies suggest that N deposition 

may increase C-sequestration in some forests, but has no apparent effect on Csequestration in non-forest ecosystems. 

In terrestrial ecosystems, N deposition can 

accelerate plant growth and change C allocation patterns (e.g. shoot:root ratio), 

which can increase susceptibility to severe 

fires, drought, and wind damage. These effects have been shown in studies conducted in the western U.S. and Europe. The 

alteration of primary productivity can also 

alter competitive interactions among plant 

species. The increase in growth is greater for 

some species than others, leading to possible shifts in population dynamics, species 

composition, community structure, and in 

few instances, ecosystem type. 

There are numerous sensitive terrestrial biota 

and ecosystems that are affected by N 

deposition. Acidophytic lichens are among 

the most sensitive terrestrial taxa to N deposition, with adverse effects occurring with 

exposures as low as 3 kg N/ha/yr in the 

Pacific Northwest and southern California. 

The onset of declining biodiversity in grasslands has been estimated to be 5 kg 

N/ha/yr in Minnesota and the European Union. Altered community composition of alpine ecosystems in the Rocky Mountains 

and forest encroachment into temperate 

grasslands in Southern Canada is estimated 

to be 10 kg N/ha/yr. 

The productivity of many freshwater ecosystems is N-limited. N deposition can alter 

species assemblages and cause eutrophication of aquatic ecosystems to the extent 

that N is the growth-limiting nutrient. In the 

Rocky Mountains, deposition loads of approximately 1.5-2 kg N/ha/yr are reported to 

alter species composition in the diatom 

communities in some freshwater lakes, an 

indicator of impaired water quality. 

11
 

ISA for Oxides of Nitrogen and Sulfur Executive Summary

In estuarine ecosystems, N from atmospheric 

and non-atmospheric sources contributes to 

increased phytoplankton and algal productivity, leading to eutrophication. Estuary eutrophication is an ecological problem indicated by water quality deterioration, 

resulting in numerous adverse effects including hypoxic zones, species mortality, and 

harmful algal blooms. The calculated contribution of atmospheric deposition to total 

N loads can be as high as 72% in estuaries. 

The Chesapeake Bay is an example of a 

large, well-studied, and severely eutrophic 

estuary that is calculated to receive as 

much as 30% of its total N load from the atmosphere. 

In estuarine ecosystems, N from atmospheric 

and non-atmospheric sources contributes to 

increased phytoplankton and algal productivity, leading to eutrophication. Estuary eutrophication is an ecological problem indicated by water quality deterioration, 

resulting in numerous adverse effects including hypoxic zones, species mortality, and 

harmful algal blooms. The calculated contribution of atmospheric deposition to total 

N loads can be as high as 72% in estuaries. 

The Chesapeake Bay is an example of a 

large, well-studied, and severely eutrophic 

estuary that is calculated to receive as 

much as 30% of its total N load from the atmosphere. 

Other welfare effects: 

Mercury methylation 

Hg is highly neurotoxic and once methylated, principally by S-reducing bacteria, it 

can be taken up by microorganisms, zooplankton and macroinvertebrates, and 

concentrated in higher trophic levels, including fish eaten by humans. In 2006, 3,080 

fish consumption advisories were issued because of methylmercury (MeHg), and as of 

July 2007, 23 states had issued statewide 

advisories. The production of meaningful 

amounts of MeHg requires the presence of 

SO42- and Hg, and where Hg is present, increased availability of SO42- results in increased 

production 

of MeHg. 

The 

amount of 

MeHg 

produced 

varies with 

oxygen content, temperature, pH, and supply of labile organic C. Watersheds with 

conditions known to be conducive to Hg 

methylation can be found in the northeastern U.S. and southeastern Canada, but biotic Hg accumulation has been widely observed in other regions that have not been 

studied as extensively, and where a different 

set of conditions may exist. 

Hg is highly neurotoxic and once methylated, principally by S-reducing bacteria, it 

can be taken up by microorganisms, zooplankton and macroinvertebrates, and 

concentrated in higher trophic levels, including fish eaten by humans. In 2006, 3,080 

fish consumption advisories were issued because of methylmercury (MeHg), and as of 

July 2007, 23 states had issued statewide 

advisories. The production of meaningful 

amounts of MeHg requires the presence of 

SO42- and Hg, and where Hg is present, increased availability of SO42- results in increased 

production 

of MeHg. 

The 

amount of 

MeHg 

produced 

varies with 

oxygen content, temperature, pH, and supply of labile organic C. Watersheds with 

conditions known to be conducive to Hg 

methylation can be found in the northeastern U.S. and southeastern Canada, but biotic Hg accumulation has been widely observed in other regions that have not been 

studied as extensively, and where a different 

set of conditions may exist. 

Examples of quantified relationships between 

deposition levels and ecological effects 

Kg 

N/ha/yr Ecological effect 

~1.5 

Altered diatom communities in 

high elevation freshwater lakes 

and elevated N in tree leaf tissue 

high elevation forests in the western U.S. 

3.1 Decline of some lichen species in 

the western U.S. 

4 

Altered growth and coverage of 

alpine plant species in the western 

U.S. 

5 

Onset of decline of species richness in grasslands of the U.S. and 

U.K. 

5.5 - 10 Onset of nitrate leaching in Eastern 

forests of the U.S. 

5-10 Multiple effects in tundra, bogs 

and freshwater lakes in Europe 

5-15 

Multiple effects in arctic, alpine, 

subalpine and scrub habitats in 

Europe 

The evidence is sufficient to 

infer a causal relationship between S deposition and increased Hg methylation in 

wetlands and aquatic environments. 

12
 

13

ISA for Oxides of Nitrogen and Sulfur Executive Summary

Other welfare effects: 

Direct phytotoxic 

Acute and chronic exposures to SO2 have 

phytotoxic effects on vegetation which include foliar injury, decreased photosynthesis, and decreased growth. Acute exposures 

to NO2, NO, PAN, and HNO3 cause plant 

foliar injury and decreased growth. However, the majority of studies have been performed at concentrations of these gasphase species above current ambient conditions observed in the U.S. Consequently, 

there is little evidence that current concentrations of gas-phase S or N oxides are high 

enough to cause phytotoxic effects. One 

exception is 

that 

some 

studies 

indicate 

that current 

HNO3 concentrations may be contributing 

to the decline in lichen species in the Los 

Angeles basin. 

Acute and chronic exposures to SO2 have 

phytotoxic effects on vegetation which include foliar injury, decreased photosynthesis, and decreased growth. Acute exposures 

to NO2, NO, PAN, and HNO3 cause plant 

foliar injury and decreased growth. However, the majority of studies have been performed at concentrations of these gasphase species above current ambient conditions observed in the U.S. Consequently, 

there is little evidence that current concentrations of gas-phase S or N oxides are high 

enough to cause phytotoxic effects. One 

exception is 

that 

some 

studies 

indicate 

that current 

HNO3 concentrations may be contributing 

to the decline in lichen species in the Los 

Angeles basin. 

The evidence is sufficient to 

infer a causal relationship 

between exposure to SO2, 

NO, NO2, PAN, and HNO3

and injury to vegetation. 

Conclusion 

The main effects of N and S pollution assessed in the ISA are acidification, N enrichment, and Hg methylation. Acidification of ecosystems is driven primarily by 

deposition resulting from SOX, NOX, and NHX pollution. Acidification from the 

deposition resulting from current emission levels causes a cascade of effects 

that harm susceptible aquatic and terrestrial ecosystems, including slower 

growth and injury to forests and localized extinction of fishes and other aquatic 

species. In addition to acidification, atmospheric deposition of reactive N resulting from current NOX and NHX emissions along with other non-atmospheric 

sources (e.g., fertilizers and wastewater), causes a suite of ecological changes 

within sensitive ecosystems. These include increased primary productivity in 

most N-limited ecosystems, biodiversity losses, changes in C cycling, and eutrophication and harmful algal blooms in freshwater, estuarine, and ocean ecosystems. In some watersheds, additional SO42– from atmospheric deposition increases Hg methylation rates by increasing both the number and activity of Sreducing bacteria. Methylmercury is a powerful toxin that can bioaccumulate 

to toxic amounts in food webs at higher trophic levels (e.g. bass, perch, otters, 

or kingfishers). 
Contents 

List of Tables________________________________________________________________________ xi

List of Figures ______________________________________________________________________ xv

Acronyms and Abbreviations ________________________________________________________ xxiii

Authors, Contributors, Reviewers_____________________________________________________ xxix

Project Team _____________________________________________________________________ xxxiii

Clean Air Scientific Advisory Committee ______________________________________________ xxxvi

Chapter 1. Introduction ______________________________________________________________ 1-1

1.1. Scope____________________________________________________________________________________________ 1-1

1.2. History of the NOX and SOX Review ____________________________________________________________________ 1-3

1.3. History of the Current Review _________________________________________________________________________ 1-4

1.4. Development of the Integrated Science Assessment _______________________________________________________ 1-5

1.5. Organization of the Integrated Science Assessment________________________________________________________ 1-5

1.6. Causality Framework ________________________________________________________________________________ 1-6

1.6.1. First Step: Determination of Causality ______________________________________________________________ 1-7

1.6.2. Second Step: Evaluation of Ecological Response_____________________________________________________ 1-8

Chapter 2. Source to Deposition _______________________________________________________ 2-1

2.1. Introduction _______________________________________________________________________________________ 2-1

2.2. Sources and Emissions of Tropospheric NOX _____________________________________________________________ 2-2

2.2.1. Major Anthropogenic Sources ____________________________________________________________________ 2-2

2.2.2. Major Biogenic Sources_________________________________________________________________________ 2-7

2.2.2.1. Soils _________________________________________________________________________________ 2-7

2.2.2.2. Live Vegetation_________________________________________________________________________ 2-9

2.2.2.3. Biomass Burning _______________________________________________________________________ 2-9

2.2.2.4. Lightning______________________________________________________________________________ 2-9

2.2.3. Anthropogenic and Biogenic Sources of N2O _______________________________________________________ 2-10

2.3. Sources and Emissions of Tropospheric SOX ____________________________________________________________ 2-11

2.3.1. Major Anthropogenic Sources ___________________________________________________________________ 2-11

2.3.2. Major Biogenic Sources________________________________________________________________________ 2-16

2.4. NHX Emissions____________________________________________________________________________________ 2-17

2.5. Evaluating Emissions Inventories _____________________________________________________________________ 2-20

2.5.1. Emissions for Historical Modeling ________________________________________________________________ 2-23

2.6. NOX-SOX-NHX Chemistry in the Troposphere ____________________________________________________________ 2-23

 i
2.6.1. Introduction _________________________________________________________________________________ 2-23

2.6.2. NOX Chemistry_______________________________________________________________________________ 2-25

2.6.2.1. NOX and Ozone Formation_______________________________________________________________ 2-30

2.6.2.2. Multiphase Interactions _________________________________________________________________ 2-30

2.6.3. SOX Chemistry_______________________________________________________________________________ 2-32

2.6.3.1. Multi-Phase SOX Chemistry ______________________________________________________________ 2-34

2.6.4. Multiphase NOX, SOX, and NHX Interactions________________________________________________________ 2-38

2.6.5. Transport-Related Effects on Chemistry ___________________________________________________________ 2-41

2.7. Sampling and Analysis Techniques____________________________________________________________________ 2-43

2.7.1. Methods for Relevant Gas-Phase N Species _______________________________________________________ 2-43

2.7.1.1. NO and NO2 __________________________________________________________________________ 2-43

2.7.1.2. Total NOY ____________________________________________________________________________ 2-47

2.7.1.3. Nitric Acid ____________________________________________________________________________ 2-47

2.7.1.4. Other Nitrates _________________________________________________________________________ 2-49

2.7.1.5. Ammonia ____________________________________________________________________________ 2-49

2.7.2. Methods for Relevant Gas-Phase Sulfur Species ____________________________________________________ 2-53

2.7.2.1. Positive Interference____________________________________________________________________ 2-53

2.7.2.2. Negative Interference___________________________________________________________________ 2-54

2.7.2.3. Other Methods ________________________________________________________________________ 2-54

2.7.3. Methods for Relevant Aerosol-Phase Nitrogen and Sulfur Species ______________________________________ 2-54

2.7.3.1. Artifacts _____________________________________________________________________________ 2-56

2.7.3.2. Other Methods ________________________________________________________________________ 2-58

2.8. Methods to Compute NOX and SOX Concentrations, Chemical Interactions, and Deposition _______________________ 2-59

2.8.1. Chemical Transport Models_____________________________________________________________________ 2-59

2.8.1.1. Global Scale __________________________________________________________________________ 2-61

2.8.1.2. Regional Scale ________________________________________________________________________ 2-62

2.8.1.3. Sub-Regional Scale ____________________________________________________________________ 2-63

2.8.1.4. Modeling Effects of Convection for Chemical Transport ________________________________________ 2-64

2.8.2. Computed Deposition _________________________________________________________________________ 2-65

2.8.2.1. Deposition Forms ______________________________________________________________________ 2-66

2.8.2.2. Methods for Estimating Dry Deposition _____________________________________________________ 2-67

2.8.2.3. Factors Affecting Dry Deposition Rates and Totals ____________________________________________ 2-68

2.8.2.4. Nitrogen Deposition and Flux with Biota ____________________________________________________ 2-70

2.8.3. Air Quality Model Evaluation ____________________________________________________________________ 2-73

2.8.3.1. Ground-based Comparisons of Photochemical Dynamics ______________________________________ 2-74

2.8.3.2. Predicted Chemistry for Nitrates and Related Compounds ______________________________________ 2-82

2.8.3.3. Evaluating Deposition with CTMs _________________________________________________________ 2-88

2.8.3.4. Regional CTM Performance______________________________________________________________ 2-89

2.8.4. Computing Atmospheric Deposition to Specific Locations _____________________________________________ 2-93

2.8.5. Policy Relevant Background Concentrations of NOX and SOX _________________________________________ 2-103

2.9. Ambient Monitoring and Reported Concentrations of Relevant Nitrogen and Sulfur Species ______________________ 2-109

2.9.1. Routine Air Monitoring Networks in North America __________________________________________________ 2-110

2.9.1.1. Pollutant Categories___________________________________________________________________ 2-114

2.9.2. Intensive Field Campaigns ____________________________________________________________________ 2-116

2.9.3. Satellite-Based Air Quality Observing Systems_____________________________________________________ 2-118

2.9.3.1. Satellite Coverages ___________________________________________________________________ 2-122

2.9.3.2. Measurement Issues __________________________________________________________________ 2-122

2.9.4. European Air Monitoring Networks ______________________________________________________________ 2-122

2.9.5. Ambient Concentrations of Relevant N Compounds_________________________________________________ 2-125

2.9.5.1. NO and NO2 _________________________________________________________________________ 2-125

2.9.5.2. NOY and NOZ ________________________________________________________________________ 2-137

2.9.5.3. Nitro-PAHs __________________________________________________________________________ 2-142

2.9.5.4. Ammonia ___________________________________________________________________________ 2-142

2.9.5.5. NH4NO3 ____________________________________________________________________________ 2-146

2.9.6. Ambient Concentrations of Relevant Sulfur Compounds _____________________________________________ 2-151

2.9.6.1. SO2 and SO4

2– Near Urban Areas ________________________________________________________ 2-151

2.9.6.2. SO2 and SO4

2− in Rural and Remote Areas_________________________________________________ 2-167

2.10. Deposition of Nitrogen and Sulfur Species Across the Landscape __________________________________________ 2-175

2.10.1. Nitrogen __________________________________________________________________________________ 2-177

2.10.1.1. Example of NO2 and HNO3 Deposition and Flux Data from Harvard Forest _______________________ 2-179

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2.10.2. Sulfur ____________________________________________________________________________________ 2-185

2.11. Summary ______________________________________________________________________________________ 2-188

2.11.1. Emissions and Atmospheric Concentrations______________________________________________________ 2-188

2.11.2. Field Sampling and Analysis __________________________________________________________________ 2-189

2.11.3. Nitrogen and Sulfur Deposition ________________________________________________________________ 2-189

Chapter 3. Ecological and Other Welfare Effects _________________________________________ 3-1

3.1. Introduction to Ecological Concepts ____________________________________________________________________ 3-1

3.1.1. Critical Loads as an Organizing Principle for Ecological Effects of Atmospheric Deposition ____________________ 3-1

3.1.2. Ecosystem Scale, Function, and Structure __________________________________________________________ 3-2

3.1.3. Ecosystem Services ___________________________________________________________________________ 3-3

3.2. Ecological Effects of Acidification ______________________________________________________________________ 3-3

3.2.1. Effects on Major Biogeochemical Processes ________________________________________________________ 3-4

3.2.1.1. Soil Acidification ________________________________________________________________________ 3-5

3.2.1.2. Sulfur Accumulation and SO4

2− Leaching ____________________________________________________ 3-7

3.2.1.3. Nitrogen Accumulation and NO3

– Leaching ___________________________________________________ 3-8

3.2.1.4. Base-Cation Leaching ___________________________________________________________________ 3-9

3.2.1.5. Aluminum Leaching ____________________________________________________________________ 3-10

3.2.1.6. Episodic Acidification ___________________________________________________________________ 3-11

3.2.2. Terrestrial Ecosystems ________________________________________________________________________ 3-16

3.2.2.1. Chemical Effects ______________________________________________________________________ 3-16

3.2.2.2. Summary of Biogeochemistry and Chemical Effects ___________________________________________ 3-20

3.2.2.3. Biological Effects ______________________________________________________________________ 3-20

3.2.2.4. Summary of Biological Effects ____________________________________________________________ 3-28

3.2.3. Aquatic Ecosystems __________________________________________________________________________ 3-29

3.2.3.1. Chemical Effects ______________________________________________________________________ 3-29

3.2.3.2. Summary of Biogeochemistry and Chemical Effects ___________________________________________ 3-45

3.2.3.3. Biological Effects ______________________________________________________________________ 3-47

3.2.3.4. Summary of Biological Effects ____________________________________________________________ 3-54

3.2.4. Most Sensitive and Most Affected Ecosystems and Regions ___________________________________________ 3-55

3.2.4.1. Characteristics of Sensitive Ecosystems ____________________________________________________ 3-55

3.2.4.2. Extent and Distribution of Sensitive Ecosystems______________________________________________ 3-56

3.2.4.3. Levels of Deposition at Which Effects are Manifested__________________________________________ 3-62

3.2.4.4. Acidification Case Study #1: Adirondack Region of New York ___________________________________ 3-68

3.2.4.5. Acidification Case Study #2: Shenandoah National Park, Virginia ________________________________ 3-78

3.2.5. Ecosystem Services __________________________________________________________________________ 3-84

3.3. Nutrient Enrichment Effects from Nitrogen Deposition _____________________________________________________ 3-84

3.3.1. Reactive Nitrogen and the Nitrogen Cascade _______________________________________________________ 3-85

3.3.2. Nitrogen Enrichment Effects on N Cycling _________________________________________________________ 3-87

3.3.2.1. Terrestrial Ecosystems__________________________________________________________________ 3-88

3.3.2.2. Wetland Ecosystems ___________________________________________________________________ 3-99

3.3.2.3. Freshwater Aquatic Ecosystems _________________________________________________________ 3-101

3.3.2.4. Estuarine and Coastal Marine Ecosystems _________________________________________________ 3-106

3.3.2.5. Summary of N Effects on Biogeochemical Cycling of N and Associated Chemical Indicators __________ 3-109

3.3.3. N Deposition Effects on Productivity and C Budgets ________________________________________________ 3-110

3.3.3.1. Terrestrial Ecosystems_________________________________________________________________ 3-110

3.3.3.2. Freshwater Aquatic ___________________________________________________________________ 3-122

3.3.3.3. Estuarine and Marine __________________________________________________________________ 3-125

3.3.3.4. Summary of Nitrogen Effects on Carbon Cycling_____________________________________________ 3-130

3.3.4. Biogenic Trace Gases: Nitrous Oxide, Methane, Nitric Oxide and VOCs_________________________________ 3-131

3.3.4.1. Methane ____________________________________________________________________________ 3-131

3.3.4.2. Nitrous Oxide ________________________________________________________________________ 3-134

3.3.4.3. Nitric Oxide and VOCs _________________________________________________________________ 3-137

3.3.4.4. Volatile Organic Compounds (VOCs) _____________________________________________________ 3-138

3.3.4.5. Summary of N Effects on Biogenic Trace Gases_____________________________________________ 3-138

3.3.5. Species Composition, Species Richness and Biodiversity ____________________________________________ 3-139

3.3.5.1. Terrestrial Ecosystem Biodiversity ________________________________________________________ 3-139

3.3.5.2. Transitional Ecosystems _______________________________________________________________ 3-152

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3.3.5.3. Freshwater Aquatic Ecosystems _________________________________________________________ 3-155

3.3.5.4. Estuarine and Marine Ecosystems________________________________________________________ 3-157

3.3.3.5. Summary of N Effects on Species Composition, Species Richness and Biodiversity_________________ 3-159

3.3.6. N Deposition Effects on NO3

− Toxicity____________________________________________________________ 3-161

3.3.7. Critical Loads and Other Quantified Relationships between Deposition Levels and Ecological Effects__________ 3-161

3.3.7.1. Empirical Critical Loads for Europe _______________________________________________________ 3-161

3.3.7.2. Empirical Critical Loads for U.S. _________________________________________________________ 3-163

3.3.8. Characterization of Sensitivity and Vulnerability ____________________________________________________ 3-167

3.3.8.1. Extent and Distribution of Sensitive and Vulnerable Ecosystems ________________________________ 3-167

3.3.8.2. Case Study: Alpine and Subalpine Communities of the Eastern Slope of the Rocky Mountains ________ 3-173

3.3.8.3. Case Study: Chesapeake Bay ___________________________________________________________ 3-176

3.3.8.4. Case Study: San Bernardino ____________________________________________________________ 3-177

3.3.9. Ecosystem Services _________________________________________________________________________ 3-179

3.4. Other Welfare Effects _____________________________________________________________________________ 3-182

3.4.1. Non-Acidification Effects of Sulfur _______________________________________________________________ 3-182

3.4.1.1. Biological Role of Sulfur ________________________________________________________________ 3-182

3.4.1.2. Cycling and Storage of Sulfur ___________________________________________________________ 3-185

3.4.1.3. Export of Sulfur_______________________________________________________________________ 3-188

3.4.1.4. Sulfur and Methylation of Mercury ________________________________________________________ 3-190

3.4.1.5. Summary of S and Methylation of Mercury _________________________________________________ 3-196

3.4.1.6. S Nutrient Enrichment Case Study: Interactive Effects of S and Hg in Little Rock Lake, WI ___________ 3-197

3.4.2. Direct Phytotoxic Effects of Gaseous N and S on Vegetation__________________________________________ 3-197

3.4.2.1. Direct Phytotoxic Effects of SO2 on Vegetation ______________________________________________ 3-198

3.4.2.2. Direct Phytotoxic Effects of NO, NO2 and PAN ______________________________________________ 3-200

3.4.2.3. Direct Phytotoxic Effects of HNO3 ________________________________________________________ 3-202

3.4.2.4. Summary of Phytotoxic Effects of Gaseous Nitrogen and Sulfur on Vegetation _____________________ 3-203

Chapter 4. Summary and Conclusions __________________________________________________ 4-1

4.1. Source to Deposition ________________________________________________________________________________ 4-1

4.1.1. Chemical Families and Constituent Species _________________________________________________________ 4-1

4.1.2. Transport and Transformation ____________________________________________________________________ 4-1

4.1.3. Emissions and Atmospheric Concentrations_________________________________________________________ 4-2

4.1.4. Deposition ___________________________________________________________________________________ 4-3

4.1.5. Field Sampling and Analysis _____________________________________________________________________ 4-4

4.2. Acidification _______________________________________________________________________________________ 4-4

4.2.1. Terrestrial____________________________________________________________________________________ 4-5

4.2.1.1. Biogeochemistry and Chemical Effects ______________________________________________________ 4-5

4.2.1.2. Biological Effects _______________________________________________________________________ 4-6

4.2.1.3. Regional Vulnerability and Sensitivity _______________________________________________________ 4-7

4.2.2. Aquatic______________________________________________________________________________________ 4-7

4.2.2.1. Biogeochemistry and Chemical Effects ______________________________________________________ 4-8

4.2.2.2. Biological Effects ______________________________________________________________________ 4-11

4.2.2.3 Regional Vulnerability and Sensitivity _______________________________________________________ 4-12

4.2.3. Ecosystem Services __________________________________________________________________________ 4-13

4.3. Nitrogen Nutrient Enrichment ________________________________________________________________________ 4-13

4.3.1. Terrestrial___________________________________________________________________________________ 4-14

4.3.1.1. Biogeochemical Effects _________________________________________________________________ 4-15

4.3.1.2. Species Richness, Composition and Biodiversity _____________________________________________ 4-16

4.2.3. Transitional _________________________________________________________________________________ 4-19

4.3.2.1. Biogeochemical Effects _________________________________________________________________ 4-19

4.3.2.2. Biological Effects ______________________________________________________________________ 4-21

4.3.2.3. Regional Vculnerability and Sensitivity _____________________________________________________ 4-21

4.3.3. Freshwater Aquatic ___________________________________________________________________________ 4-21

4.3.3.1. Biogeochemical Effects _________________________________________________________________ 4-22

4.3.3.2. Biological Effects ______________________________________________________________________ 4-22

4.3.3.3. Regional Vulnerability and Sensitivity ______________________________________________________ 4-23

4.3.4. Estuarine Aquatic_____________________________________________________________________________ 4-23

4.3.4.1. Biogeochemical Effects _________________________________________________________________ 4-23

 iv
4.3.4.2. Biological Effects ______________________________________________________________________ 4-25

4.3.4.3. Regional Vulnerability and Sensitivity ______________________________________________________ 4-25

4.3.5. Ecosystem Services __________________________________________________________________________ 4-26

4.4. Direct Phytotoxic Effects ____________________________________________________________________________ 4-26

4.4.1. Sulfur Dioxide _______________________________________________________________________________ 4-26

4.4.2. NO, NO2 and PAN ____________________________________________________________________________ 4-27

4.4.3. HNO3 ______________________________________________________________________________________ 4-27

4.5. Mercury Methylation _______________________________________________________________________________ 4-27

Annex A. Ecosystem Monitoring and Models ____________________________________________A-1

A.1. Introduction _______________________________________________________________________________________ A-1

A.2. Ecosystem Monitoring _______________________________________________________________________________ A-2

A.2.1. Environmental Monitoring and Assessment Program__________________________________________________ A-2

A.2.2. Surface Water Chemistry Monitoring ______________________________________________________________ A-4

A.2.2.1. TIME Project __________________________________________________________________________ A-4

A.2.2.2. Long-Term Monitoring Project _____________________________________________________________ A-5

A.2.3. USGS Monitoring Programs _____________________________________________________________________ A-7

A.2.3.1. National Water Quality Assessment Program _________________________________________________ A-7

A.2.3.2. Hydrologic Benchmark Network ___________________________________________________________ A-8

A.2.3.3. New York City Water Quality Network _______________________________________________________ A-8

A.2.3.4. Catskill Long-Term Monitoring Sites ________________________________________________________ A-9

A.2.3.5. Buck Creek, New York___________________________________________________________________ A-9

A.2.4. NSF Long-Term Ecological Research Network ______________________________________________________ A-9

A.2.4.1. Hubbard Brook Experimental Forest _______________________________________________________ A-13

A.2.4.2. Coweeta_____________________________________________________________________________ A-17

A.2.4.3. Walker Branch ________________________________________________________________________ A-18

A.2.5. Water, Energy, and Biogeochemical Budgets Program _______________________________________________ A-18

A.2.5.1. Sleepers River ________________________________________________________________________ A-19

A.2.5.2. Loch Vale ____________________________________________________________________________ A-21

A.2.6. Other Monitoring Programs_____________________________________________________________________ A-22

A.2.6.1. Bear Brook ___________________________________________________________________________ A-22

A.2.6.2. Shenandoah Watershed Study ___________________________________________________________ A-23

A.2.6.3. Fernow ______________________________________________________________________________ A-25

A.2.6.4. National Ecological Observatory Network ___________________________________________________ A-26

A.3. Modeling ________________________________________________________________________________________ A-27

A.3.1. Principal Ecosystem Models Used in the U.S. ______________________________________________________ A-27

A.3.1.1. MAGIC ______________________________________________________________________________ A-27

A.3.1.2. NuCM_______________________________________________________________________________ A-30

A.3.1.3. PnET-BGC ___________________________________________________________________________ A-31

A.3.1.4. DayCent-Chem _______________________________________________________________________ A-34

A.3.1.5. SPARROW __________________________________________________________________________ A-35

A.3.1.6. WATERSN ___________________________________________________________________________ A-39

A.3.2. Additional Effects Models Used Widely in Europe ___________________________________________________ A-44

A.3.2.1. The Very Simple Dynamic Model _________________________________________________________ A-44

A.3.2.2. SMART _____________________________________________________________________________ A-44

A.3.2.3. SAFE _______________________________________________________________________________ A-45

A.3.3. Other Models________________________________________________________________________________ A-45

Annex B. Acidification Effects_________________________________________________________B-1

B.1. Effects on Biogeochemical Processes along Acidification Pathways ___________________________________________ B-1

B.1.1. Atmospheric Deposition and Canopy Interaction _____________________________________________________ B-1

B.1.2. Interactions with Soil ___________________________________________________________________________ B-1

B.1.2.1. Sulfur Retention and Release _____________________________________________________________ B-1

B.1.2.2. Base Cation Depletion ___________________________________________________________________ B-3

B.1.2.3. Aluminum Mobilization___________________________________________________________________ B-5

 v
B.1.2.4. Soil Acidification________________________________________________________________________ B-6

B.1.2.5. Nitrogen Saturation _____________________________________________________________________ B-7

B.1.2.6. Nitrate Leaching_______________________________________________________________________ B-11

B.1.3. Interactions with Transitional Ecosystems _________________________________________________________ B-12

B.1.3.1. Sulfur Storage and Release in Transitional Ecosystems________________________________________ B-12

B.1.3.2. Organic Acidity in Transitional Ecosystems and Downstream Surface Waters_______________________ B-12

B.2. Factors That Determine Ecosystem Sensitivity___________________________________________________________ B-16

B.2.1. Transitional Ecosystems _______________________________________________________________________ B-16

B.2.1.1. Wetlands and Peatlands ________________________________________________________________ B-16

B.2.1.2. Ponds_______________________________________________________________________________ B-17

B.2.2. Streams and Lakes ___________________________________________________________________________ B-17

B.2.3. Other Types of Ecosystems ____________________________________________________________________ B-18

B.3. Distribution and Extent of Ecosystem Effects ____________________________________________________________ B-18

B.3.1. Terrestrial Ecosystems ________________________________________________________________________ B-18

B.3.2. Transitional Ecosystems _______________________________________________________________________ B-20

B.3.3. Aquatic Ecosystems __________________________________________________________________________ B-20

B.3.3.1. Status of Surface Waters – Regional Overview_______________________________________________ B-21

B.3.3.2. Recent Changes in Surface Water Chemistry________________________________________________ B-22

B.3.4. Regional Assessments ________________________________________________________________________ B-24

B.3.4.1. Northeastern Surface Waters ____________________________________________________________ B-24

B.3.4.2. Southeastern Surface Waters ____________________________________________________________ B-34

B.3.4.3. Upper Midwest________________________________________________________________________ B-39

B.3.4.4. West________________________________________________________________________________ B-41

B.3.4.5. Temporal Variability in Water Chemistry ____________________________________________________ B-46

B.4. Effects on Biota ___________________________________________________________________________________ B-54

B.4.1. Types of Effects of Acidification on Biota __________________________________________________________ B-55

B.4.1.1. Individual Condition Factor ______________________________________________________________ B-55

B.4.1.2. Species Composition ___________________________________________________________________ B-57

B.4.1.3. Taxonomic Richness ___________________________________________________________________ B-57

B.4.1.4. Community Structure ___________________________________________________________________ B-62

B.4.1.5. Indices of Ecological Effects _____________________________________________________________ B-63

B.4.2. Timing of Effects _____________________________________________________________________________ B-63

B.4.2.1. Life Stage Differences in Sensitivity _______________________________________________________ B-63

B.4.2.2. Biological Effects of Episodes ____________________________________________________________ B-65

B.4.2.3. Timing of Recovery from Acidification ______________________________________________________ B-68

B.4.3. Effects by Ecosystem Type_____________________________________________________________________ B-71

B.4.3.1. Terrestrial Ecosystems _________________________________________________________________ B-71

B.4.3.2. Aquatic Ecosystem ____________________________________________________________________ B-73

B.5. Effects on Watersheds and Landscapes________________________________________________________________ B-80

B.5.1. Interactions among Terrestrial, Transitional, and Aquatic Ecosystems ___________________________________ B-80

B.5.2. Interactions with Land Use and Disturbance________________________________________________________ B-81

B.5.2.1. Timber Harvest _______________________________________________________________________ B-83

B.5.2.2. Insect Infestation ______________________________________________________________________ B-84

B.5.3. Wind or Ice Storm Damage_____________________________________________________________________ B-85

B.5.3.1. Fire_________________________________________________________________________________ B-86

B.5.3.2. Multiple Stress Response _______________________________________________________________ B-86

B.6. Ecological indicators of acidification ___________________________________________________________________ B-86

B.6.1. Biological Indicators __________________________________________________________________________ B-86

B.6.1.1. Phytoplankton ________________________________________________________________________ B-88

B.6.1.2. Zooplankton __________________________________________________________________________ B-88

B.6.1.3. Benthic Invertebrates___________________________________________________________________ B-89

B.6.1.4. Fish ________________________________________________________________________________ B-91

B.6.1.5. Amphibians __________________________________________________________________________ B-94

B.6.1.6. Fish-Eating Birds ______________________________________________________________________ B-95

Annex C. Nutrient Enrichment Effects from Nitrogen ______________________________________C-1

C.1. Effects on Biogeochemical Pathways and Cycles _________________________________________________________ C-1

 vi
C.1.1. Nitrogen Cycling in Terrestrial Ecosystems _________________________________________________________ C-1

C.1.1.1. Nitrogen Deposition Effects on DON Leaching ________________________________________________ C-1

C.1.1.2. Interactions Between Snow Melt and Nitrate Leaching__________________________________________ C-1

C.1.1.3. Denitrification: NO and N2O Flux___________________________________________________________ C-1

C.1.1.4. Climate and N2O Interactions _____________________________________________________________ C-2

C.1.2. Nitrogen Cycling in Transitional Ecosystems ________________________________________________________ C-3

C.1.2.1. Denitrification: Measurement Techniques ____________________________________________________ C-3

C.1.2.2. Nitrogen Deposition Effects on Methane_____________________________________________________ C-3

C.1.3. Nitrogen Cycling in Estuarine Ecosystems __________________________________________________________ C-4

C.1.3.1. Denitrification and Anammox in Estuarine Ecosystems _________________________________________ C-4

C.1.3.2. Nitrogen Budgets_______________________________________________________________________ C-4

C.1.4. Timing of Chemical Change _____________________________________________________________________ C-6

C.1.4.1. Interannual Change: Nitrate Leaching_______________________________________________________ C-6

C.1.4.2. Episodic Change _______________________________________________________________________ C-6

C.1.4.3. Reversibility of Impacts __________________________________________________________________ C-7

C.1.5. Tables Supporting Cross Ecosystem Evaluation of N2O, CH4 and CO2 Flux________________________________ C-8

C.2. Terrestrial Ecosystems _____________________________________________________________________________ C-15

C.2.1. General C Cyling_____________________________________________________________________________ C-16

C.2.2. Forest Growth Interactions with Herbivores ________________________________________________________ C-16

C.2.3. Southern California Coniferous Forest ____________________________________________________________ C-16

C.2.4. Boreal Forests_______________________________________________________________________________ C-17

C.2.5. Alpine _____________________________________________________________________________________ C-17

C.2.6. Arctic Tundra________________________________________________________________________________ C-20

C.2.7. Arid Land___________________________________________________________________________________ C-20

C.2.8. Lichens ____________________________________________________________________________________ C-20

C.3. Transitional Ecosystems ____________________________________________________________________________ C-22

C.4. Aquatic Ecosystems _______________________________________________________________________________ C-23

C.4.1. History of Evaluating Nitrogen Enrichment in Freshwater Aquatic Ecosystems_____________________________ C-23

C.4.2. Interactions between Nitrogen and P loading _______________________________________________________ C-24

C.4.3. Aquatic Species Affected ______________________________________________________________________ C-25

C.4.3.1. Phytoplankton and Plants _______________________________________________________________ C-26

C.4.3.2. Seasonal Nitrogen Input and Cyanobacteria_________________________________________________ C-27

C.4.3.3. Nitrate Toxicity: Invertebrates ____________________________________________________________ C-27

C.4.4. Nitrate Toxicity: Amphibians and Fish_____________________________________________________________ C-28

C.5. Estuary and Coastal Ecosystems _____________________________________________________________________ C-32

C.5.1. Interacting Factors with Productivity ______________________________________________________________ C-33

C.5.2. Hydrology Interactions with Phytoplankton Biomass _________________________________________________ C-33

C.6. Watersheds, Landscapes and Disturbance _____________________________________________________________ C-33

C.6.1. Interactions among Terrestrial, Transitional, and Aquatic Ecosystems ___________________________________ C-33

C.6.2. Interactions with Land Use and Disturbance _______________________________________________________ C-38

C.6.3. Timber Harvest and Fire _______________________________________________________________________ C-39

C.6.4. Insect Infestation and Disease __________________________________________________________________ C-40

C.6.5. Urbanization ________________________________________________________________________________ C-41

C.6.6. Agriculture __________________________________________________________________________________ C-42

C.6.7. Other Disturbances ___________________________________________________________________________ C-42

C.6.8. Multiple Stress Response ______________________________________________________________________ C-43

Annex D. Critical Loads ______________________________________________________________D-1

D.1. Background _______________________________________________________________________________________ D-1

D.1.1. The Critical Load Process_______________________________________________________________________ D-2

D.1.2. Organization of this Annex ______________________________________________________________________ D-3

D.2. Definitions and Conceptual Approach___________________________________________________________________ D-4

D.2.1. Critical Load Definitions ________________________________________________________________________ D-4

D.2.2. Critical Load Analysis Procedures ________________________________________________________________ D-5

D.2.3. Target Load Definition__________________________________________________________________________ D-7

D.3. Time Frame of Response ____________________________________________________________________________ D-8

 vii
D.3.1. Steady State Critical Loads______________________________________________________________________ D-9

D.3.2. Dynamic Critical Loads _________________________________________________________________________ D-9

D.3.3. Receptor Responses__________________________________________________________________________ D-10

D.3.4. Deposition Schedules _________________________________________________________________________ D-10

D.3.5. Long-Term Implications________________________________________________________________________ D-13

D.3.6. Steady State and Dynamic Critical Loads: Complementary Information __________________________________ D-14

D.4. Calculation of Critical Loads _________________________________________________________________________ D-14

D.4.1. Empirical Models_____________________________________________________________________________ D-15

D.4.2. Acidification Effects of Sulfur and Nitrogen_________________________________________________________ D-15

D.4.3. Nutrient Effects of Nitrogen_____________________________________________________________________ D-15

D.4.4. Process-Based Models ________________________________________________________________________ D-16

D.4.5. Steady State Models__________________________________________________________________________ D-16

D.4.6. Dynamic Models _____________________________________________________________________________ D-16

D.5. Use of Critical Loads in the U.S. – Current Status ________________________________________________________ D-18

D.5.1. Current Recommendations on Critical Loads Uses in the U.S. _________________________________________ D-18

D.5.2. Questions and Limitations Regarding Critical Loads Uses in the U.S.____________________________________ D-19

D.5.3. Critical Loads Research and Monitoring Needs _____________________________________________________ D-19

D.5.3.1. Emissions and Deposition _______________________________________________________________ D-19

D.5.3.2. Soils ________________________________________________________________________________ D-20

D.5.3.3. Surface Waters _______________________________________________________________________ D-20

D.5.3.4. Biological Effects ______________________________________________________________________ D-20

D.5.3.5. Critical Loads Models __________________________________________________________________ D-21

Annex E. Effects of NOY, NHX, and SOX on Structures and Materials _________________________E-1

E.1. Introduction _______________________________________________________________________________________ E-1

E.2. Environmental Exposures of Materials __________________________________________________________________ E-2

E.2.1. Mechanisms of Materials Damage ________________________________________________________________ E-2

E.2.2. Deposition Processes __________________________________________________________________________ E-2

E.2.3. Chemical Interactions of Nitrogen and Sulfur Oxide Species ____________________________________________ E-3

E.2.4. Materials Damage Experimental Techniques ________________________________________________________ E-3

E.3. Effects on Dyes and Textiles__________________________________________________________________________ E-4

E.3.1. Fading of Dyes _______________________________________________________________________________ E-4

E.3.2. Degradation of Textile Fibers ____________________________________________________________________ E-4

E.4. Effects on Plastics and Elastomers_____________________________________________________________________ E-4

E.5. Effects on Metals___________________________________________________________________________________ E-5

E.5.1. Role of NOY, NHX, and SOX in the Corrosion Process _________________________________________________ E-9

E.5.2. Effect on Economically Important Metals __________________________________________________________ E-10

E.5.3. Effects on Electronics _________________________________________________________________________ E-11

E.6. Effects on Paints __________________________________________________________________________________ E-11

E.7. Effects on Stone and Concrete _______________________________________________________________________ E-12

E.8. Effects of NOX on Paper and Archival Materials __________________________________________________________ E-15

E.9. Costs of Materials Damage from NOY, NHX, and SOX _____________________________________________________ E-16

E.10. Summary _______________________________________________________________________________________ E-16

Annex F. Valuation of the Environmental Effects of N and S (non-materials) __________________F-1

F.1. Introduction _______________________________________________________________________________________ F-1

F.1.1. Valuation in the Context of NOX and SOX ___________________________________________________________ F-1

F.1.1.1. Ecosystem Services_____________________________________________________________________ F-1

F.1.1.2. Use of the Valuation Literature to Define Adversity _____________________________________________ F-3

F.1.1.3. Methods for Selecting Literature for this Assessment ___________________________________________ F-4

F.2. Conceptual Framework ______________________________________________________________________________ F-6

 viii
 ix

F.2.1. Taxonomy of Values for Environmental Goods and Services____________________________________________ F-6

F.2.2. Welfare Economics ____________________________________________________________________________ F-7

F.2.3. Benefit Estimation Approaches ___________________________________________________________________ F-9

F.3. Valuation of Forests and Terrestrial Ecosystems _________________________________________________________ F-11

F.3.1. Use Values _________________________________________________________________________________ F-11

F.3.2. Total Values_________________________________________________________________________________ F-14

F.3.3. National-Scale Valuation _______________________________________________________________________ F-15

F.3.4. Valuation of Degrees of Injury ___________________________________________________________________ F-15

F.3.5. Limitations and Uncertainties ___________________________________________________________________ F-17

F.4. Valuation of Transitional Ecosystems __________________________________________________________________ F-18

F.4.1. Use and Non-Use Values ______________________________________________________________________ F-18

F.4.2. Limitations and Uncertainties ___________________________________________________________________ F-20

F.5. Valuation of Aquatic Ecosystems _____________________________________________________________________ F-20

F.5.1. Acidification _________________________________________________________________________________ F-21

F.5.2. Eutrophication _______________________________________________________________________________ F-22

F.5.2.1. Recreation ___________________________________________________________________________ F-22

F.5.2.2. Commercial Fisheries __________________________________________________________________ F-24

F.5.2.3. Water Clarity _________________________________________________________________________ F-25

F.5.3. Avoided Costs _______________________________________________________________________________ F-25

F.5.4. Limitations and Uncertainties ___________________________________________________________________ F-25

F.6. Summary ________________________________________________________________________________________ F-26

Glossary__________________________________________________________________________ G-1

References_________________________________________________________________________R-1
List of Tables 

Table 1-1. Aspects to aid in judging causality.______________________________________________________________________ 1-6

Table 1-2. Weight of evidence for causal determination.______________________________________________________________ 1-8

Table 2-1. Emissions of NOX, NH3, and SO2 in the U.S. by source and category, 2002. _____________________________________ 2-2

Table 2-2. Total and non-EGU SO2 emissions densities for selected U.S. counties, 2001. __________________________________ 2-13

Table 2-3. Relative contributions of various gas and aqueous phase reactions to aqueous NO3 formation within a sunlit cloud, 

10 minutes after cloud formation. ______________________________________________________________________ 2-31

Table 2-4. Atmospheric lifetimes (τ) of SO2 and reduced S species with respect to reaction with OH, NO3, and Cl radicals. ________ 2-33

Table 2-5. Relative contributions of various reactions to the total S(IV) oxidation rate within a sunlit cloud, 10 minutes after 

cloud formation.____________________________________________________________________________________ 2-37

Table 2-6. Satellite instruments used to retrieve tropospheric NO2 columns. _____________________________________________ 2-46

Table 2-7. Verified ambient NH3 monitors.________________________________________________________________________ 2-50

Table 2-8. Performance characteristics of the 7 U.S. EPA ETV tested NH3 methods. ______________________________________ 2-51

Table 2-9. Site codes and locations of passive NH3 samplers in the U.S. EPA and Lake Michigan Air Directors and Illinois 

State Water Survey Consortium Project. ________________________________________________________________ 2-52

Table 2-10. Atmospheric N loads relative to total N loads in selected great waters.* ________________________________________ 2-94

Table 2-11. Natural and anthropogenic sources of atmospheric N compounds.____________________________________________ 2-96

Table 2-12. Characteristics of oxidized-nitrogen airsheds. ____________________________________________________________ 2-97

Table 2-13. Characteristics of principal airsheds for reduced-N (Red-N) deposition. ________________________________________ 2-98

Table 2-14. Major routine operating air monitoring networks. _________________________________________________________ 2-110

Table 2-15. Air monitoring networks/campaigns for non-routine special intensive studies conducted since the mid-1990s. _________ 2-117

Table 2-16. Satellite-based air quality observing systems.1,4__________________________________________________________ 2-119

Table 2-17. Key atmospheric chemistry and dynamics data sets at the NASA Goddard DAAC. ______________________________ 2-121

Table 2-18. International and European air monitoring programs. _____________________________________________________ 2-124

Table 2-19. Ambient NH3 concentrations summarized by study._______________________________________________________ 2-143

Table 2-20. Number of monitors in California and San Diego County, 2005. _____________________________________________ 2-152

Table 2-21. Number of monitors in Ohio and Cuyahoga County, 2005. _________________________________________________ 2-152

Table 2-22. Regional distribution of SO2 and SO4

2− ambient concentrations, averaged for 2003−2005. ________________________ 2-159

Table 2-23. Distributions of temporal averaging inside and outside CMSAs. _____________________________________________ 2-159

Table 2-24. Range of mean annual SO2 concentrations and Pearson correlation coefficients in urban areas having at least four 

regulatory monitors, 2003–2005. _____________________________________________________________________ 2-160

Table 2-25. Regional changes in wet and dry N and S atmospheric concentrations and deposition, 1989-1991 and 2003-2005. ____ 2-190

Table 3-1. An example of the matrix of information that must be considered in the definition and calculation of critical loads 

(see discussion in text)._______________________________________________________________________________ 3-2

Table 3-2. Examples of chemical indicators of effects from acidifying deposition to terrestrial ecosystems. _____________________ 3-16

Table 3-3. Example biological effects indicators in terrestrial ecosystems._______________________________________________ 3-21

Table 3-4. Examples of chemical indicators of effects from acidifying deposition to aquatic ecosystems. _______________________ 3-30

Table 3-5. Estimates of change in number and proportion of acidic surface waters in acid-sensitive regions of the North and 

East, based on applying current rates of change in Gran ANC to past estimates of population characteristics from 

probability surveys. _________________________________________________________________________________ 3-37

 xi
Table 3-6. Regional trend results for long-term monitoring lakes and streams for the period 1990 through 2000._________________ 3-38

Table 3-7. Model projections of surface water SO4

2- and associated ANC, shown as changes between dates, for Adirondack 

and Shenandoah streams. ___________________________________________________________________________ 3-44

Table 3-8. General summary of biological changes anticipated with surface water acidification, expressed as a decrease in 

surface water pH. __________________________________________________________________________________ 3-64

Table 3-9. Studies that either did or did not yield evidence that acidifying deposition affected certain species of birds. ____________ 3-67

Table 3-10. Observed relationships between zooplankton species richness and lakewater ANC in the Adirondack Mountains._______ 3-74

Table 3-11. Summary of biogeochemical indicators of N deposition to terrestrial ecosystems. ________________________________ 3-93

Table 3-12. Effects of fire on nutrient concentrations in forests in Nevada and California. ____________________________________ 3-97

Table 3-13. Summary of N cycling studies for wetlands. _____________________________________________________________ 3-100

Table 3-14. Summary of studies on the effects of N deposition on freshwater aquatic ecosystems. ___________________________ 3-105

Table 3-15. Summary of N effects on forest carbon cycling. __________________________________________________________ 3-113

Table 3-16. Summary of additional evidence of N effects on productivity of freshwater ecosystems. __________________________ 3-124

Table 3-17. Summary of N effects on grassland biodiversity. _________________________________________________________ 3-142

Table 3-18. Summary of N effects on arid and semi-arid ecosystems. __________________________________________________ 3-144

Table 3-19. Summary of N effects on desert ecosystems. ___________________________________________________________ 3-147

Table 3-20. Summary of N effects on lichens. _____________________________________________________________________ 3-149

Table 3-21. Summary of N effects on alpine ecosystems.____________________________________________________________ 3-151

Table 3-22. Summarized responses of coastal marshes ecosystem to N fertilization. ______________________________________ 3-154

Table 3-23. N effects on species composition and biodiversity ________________________________________________________ 3-157

Table 3-24 Biological indicators for the effects of elevated N deposition and related empirical critical loads for major ecosystem 

types (according to the eunis classification) occurring in Europe. ____________________________________________ 3-162

Table 3-25. Summary of dose-response curves for N deposition and ecological indicators. _________________________________ 3-164

Table 3-26. Changes in aquatic ecosystems associated with elevated N loadings in the Western U.S. ________________________ 3-171

Table 3-27. Primary Goods and Services Provided by Ecosystems ____________________________________________________ 3-181

Table 3-28. Summary of recent studies of SO2 exposure to plants. ____________________________________________________ 3-204

Table 4-1. Chemical indicators of acidification to terrestrial ecosystems. _________________________________________________ 4-6

Table 4-2. Chemical indicators of acidification in surface water. _______________________________________________________ 4-10

Table 4-3. Indicators of estuarine eutrophication. __________________________________________________________________ 4-25

Table 4-4. Summary of N deposition levels and corresponding ecological effects._________________________________________ 4-28

Table A-1. LTER site locations and basic site description information. __________________________________________________ A-10

Table A-2. Current long-term monitoring data sets developed through the Hubbard Brook Ecosystem Study ____________________ A-14

Table A-3. Study watersheds at HBEF. __________________________________________________________________________ A-15

Table A-4. Parameter estimates, probability levels, and regression results of parametric and bootstrap regressions of total 

nitrogen at 414 national stream quality accounting network stations on basin attributes, for the Chesapeake Bay 

total nitrogen SPARROW model. ______________________________________________________________________ A-37

Table A-5. Effect of spatial referencing on measures of regression model performance for predicting total N flux using the 

sparrow model. ____________________________________________________________________________________ A-38

Table A-6. Parameter estimates, probability levels, and regression results for the Chesapeake Bay total N SPARROW model______ A-39

Table A-7. Summary of N retention rates used in recent WATERSN studies._____________________________________________ A-42

Table A-8. Some examples of models that could contribute to development of a better understanding of the ecological efforts 

of atmospheric S and N deposition, but that are not explicitly addressed in this annex. ____________________________ A-45

 xii
Table B-1. N-saturated forests in North America, including estimated N inputs and outputs. _________________________________ B-96

Table B-2 Summary of measured ANC, pH, and Al concentrations compared with reference values in the six high-interest 

areas. ___________________________________________________________________________________________ B-97

Table B-3 Sources of data and sample sizes for datasets analyzed by Stoddard et al. (2003), along with estimates of the 

condition of surface waters in each region in the 1980s. ____________________________________________________ B-98

Table B-4 Estimates of change in number and proportion of acidic surface waters in acid-sensitive regions of the North 

and East, based on applying current rates of change in Gran ANC to past estimates of population characteristics 

from probability surveys. _____________________________________________________________________________ B-99

Table B-5 Regional trend results for long-term monitoring sites for the period 1990 through 2000.____________________________ B-99

Table B-6 Slopes of trends in Gran ANC in acidic, low, and moderate ANC lakes and streams, 1990–2000.___________________ B-100

Table B-7 Changes in key chemical characteristics during periods of record in Maine aquatic systems. ______________________ B-100

Table B-8 Projected changes (μeq/L) in median values of streamwater chemistry at the regional modeling sites from 1995 to 

2040 in each of the three emissions control strategies, stratified into two segments of the SAMI region (northeast 

and southwest) and by physiographic province.__________________________________________________________ B-100

Table B-9. Population estimates of water chemistry percentiles for selected lake populations in the western U.S.a ______________ B-101

Table B-10. Population estimates of the percentage of lakes in selected subregions of the West with ANC and NO3

− within 

defined ranges. ___________________________________________________________________________________ B-102

Table B-11. Median streamwater ANC and watershed area of streams in Shenandoah National Park that have water chemistry 

and fish species richness data._______________________________________________________________________ B-102

Table B-12. Reference levels for the Acidic Stress Index based on logistic regression of fish presence as a function of the 

sensitive intermediate and tolerant ASI values for brown bullhead, brook trout, lake trout, and common shiner. ________ B-103

Table B-13. General summary of biological changes anticipated with surface water acidification, expressed as a decrease in 

surface water pH. _________________________________________________________________________________ B-103

Table B-14. Estimated percentage of Adirondack lakes with and Acidic Stress Index exceeding the reference levels for effects 

on fish populations, based on diatom-inferred historical (pre-industrial) chemistry and present-day measured and 

inferred acid-base chemistry. ________________________________________________________________________ B-104

Table B-15. Estimated percentage of Adirondack lakes with acid-base chemistry unsuitable for fish population survival, based 

on diatom-inferred historical (pre-industrial) chemistry and present-day measured and inferred acid-base chemistry. ___ B-104

Table B-16. Estimated percentage of the lakes in the Northeast and Upper Midwest, ELS/NSWS target population 

with an Acidic Stress Index exceeding the reference levels for fish populations defined in Table C-12. _______________ B-105

Table B-17. Estimated percentage of lakes in the Northeast, ELS/NSWS target populations with acid-base chemistry unsuitable 

for fish population survival. __________________________________________________________________________ B-105

Table B-18. Distribution of acidic stress index values among the NSS-1 Target populations for the mid-Appalachian region. _______ B-106

Table B-19. Distribution of acidic stress index values among the NSS-1 target populations for the interior Southeast region. _______ B-107

Table B-20. Comparison of solution and tissue chemistries at threshold treatment levels where significant impacts on tree 

growth or nutrient content were first observed.___________________________________________________________ B-108

Table B-21. Overview of selected major processes by which landscape change can alter drainage water acid-base chemistry______ B-111

Table B-22. Observed relationships between zooplankton species richness (R) and lakewater ANC. __________________________ B-111

Table B-23. Threshold response of increased mortality of fish to low pH listed from least sensitive to most sensitive. _____________ B-112

Table B-24. Threshold values of pH for various taxa and effects. ______________________________________________________ B-113

Table B-25. Threshold values of Al for various species and effects (form of Al not specified for most studies).___________________ B-114

Table B-26. The effects of increasing Ca2+ to ameliorate low pH and high Al. ____________________________________________ B-115

Table B-27. Brook trout acidification response categories developed by Bulger et al. (2000) for streams in Virginia. ______________ B-115

Table B-28. Partial listing of bioassays demonstrating decreased fish survival in waters with low pH and (or) elevated aluminum. ___ B-116

Table B-29. Mills et al., 1987. Shows effect of various pH on fish forage fish and lake trout. _________________________________ B-117

 xiii
Table B-30. Range of minimum pH of fish species occurrence in 11 lake surveys. ________________________________________ B-117

Table B-31. Studiesa that either did (yes) or did not (no) yield evidence that acidic deposition affected certain species of birds______ B-119

Table B-32. Predicted habitat suitability for lakes in the Algona Model Dataset ___________________________________________ B-119

Table B-33. Summary statistics of biological data layers for mercury (Hg) concentrations in fish and wildlife (μg/g) in the 

northeastern U.S. and southeastern Canada. ___________________________________________________________ B-120

Table B-34. Mercury concentrations in avian eggs and tissues and related effects. ________________________________________ B-120

Table C-1. Estimated percent of total N load to Delaware Bay and Hudson River/Raritan Bay contributed 

by atmospheric deposition. ____________________________________________________________________________ C-5

Table C-2 The study site, experimental condition, ecosystem type, N form, amount of N addition and citations is presented for 

all studies used in NEE, EC, CH4 uptake, CH4 emission and N2O emission meta analyses. _________________________ C-8

Table C-3. Principal Air Quality Indicator Lichen Species in Oregon and Washington ______________________________________ C-21

Table C-4. Contribution of fens to support of plant species diversity in selected states. _____________________________________ C-22

Table C-5. Summary of additional evidence for N limitation on productivity of freshwater ecosystems. _________________________ C-25

Table C-6. Summary of effects of N enrichment on aquatic biota in freshwater ecosystems. _________________________________ C-28

Table C-7. Essential ecological attributes and reporting categories. ____________________________________________________ C-34

Table C-8. Primary goods and services provided by ecosystems.______________________________________________________ C-36

Table C-9. Ecological effects of N deposition described for study sites in the Western U.S.__________________________________ C-44

Table D-1 An example of the matrix of information that must be considered in the definition and calculation of critical loads. ________ D-5

Table D-2. Biological indicators for the effects of elevated N deposition and related empirical critical loads for major ecosystem 

types (according to the Eunis classification) occurring in Europe. _____________________________________________ D-21

Table E-1. Studies on corrosive effects of NOY/NH3/SOX effects on metals. _______________________________________________ E-6

Table E-2. Studies on corrosive effects of NOY/NH3/SOX on stone. ____________________________________________________ E-13

Table F-1. Commonly adopted environmental valuation methods based on revealed or state preferences. _____________________ F-27

Table F-2. Economic effects of ozone and other pollutants on agriculture, as reported in the 1996 ozone criteria document. _______ F-28

Table F-3. Economic effects of ozone on marketable benefits from forests. ______________________________________________ F-29

Table F-4. Forecasted average values for select activities, per day per person in 1996. ____________________________________ F-30

Table F-5. Typical impacts of specific pollutants on the visual quality of forests. __________________________________________ F-30

Table F-6. Economic valuation studies related to forest aesthetics. ____________________________________________________ F-31

Table F-7. Summary of the monetized estimates of the annual value of forest quality changes_______________________________ F-33

Table F-8. Estimated value of avoiding forest damage in the U.S. _____________________________________________________ F-33

Table F-9. Ecological wetland functions, economic goods and services, types of value, and applicable valuation methods. ________ F-34

Table F-10. Economic valuation studies related to acidification and eutrophication in aquatic ecosystems. ______________________ F-35

 xiv
List of Figures 

Figure 1-1. Replace Biogeochemical cycles of NOX and SOX. __________________________________________________________ 1-2

Figure 2-1. 2001 county-level total U.S. NOX (NO and NO2) emissions. __________________________________________________ 2-4

Figure 2-2. 2001 county-level total U.S. NOX (NO and NO2) emissions densities (tons per square mile). ________________________ 2-5

Figure 2-3. 2001 county-level total U.S. NOX (NO and NO2) emissions densities (tons per square mile) from electric-generating 

utilities (EGUs). 1 ____________________________________________________________________________________ 2-5

Figure 2-4. 2001 county-level total U.S. NOX (NO and NO2) emissions densities (tons per square mile) from on-road mobile 

sources.___________________________________________________________________________________________ 2-6

Figure 2-5. 2001 county-level total U.S. SO2 emissions. _____________________________________________________________ 2-12

Figure 2-6. 2001 county-level total U.S. SO2 emissions densities (tons per square mile).1 ___________________________________ 2-12

Figure 2-7. 2001 county-level SO2 emissions densities (tons per square mile) from EGUs. __________________________________ 2-13

Figure 2-8. 2001 county-level SO2 emissions densities (tons per square mile) from on-road mobile sources. ____________________ 2-14

Figure 2-9. 2001 county-level SO2 emissions densities (tons per square mile) from off-road mobile and other transportation 

sources.__________________________________________________________________________________________ 2-14

Figure 2-10. State-level SO2 emissions, 1990-2005. _________________________________________________________________ 2-15

Figure 2-11. 2001 county-level total U.S. NH3 emissions. _____________________________________________________________ 2-18

Figure 2-12. 2001 county-level total U.S. NH3 emissions densities.1 _____________________________________________________ 2-18

Figure 2-13. 2001 county-level NH3 emissions densities from on-road mobile sources. ______________________________________ 2-19

Figure 2-14. 2001 county-level NH3 emissions densities from EGUs. ____________________________________________________ 2-19

Figure 2-15. 2001 county-level NH3 emissions densities from miscellaneous and biogenic sources. ____________________________ 2-20

Figure 2-16. Atmospheric cycle of reactive oxidized N species. _________________________________________________________ 2-24

Figure 2-17. The combined NOX + SOX + NHX system showing how atmospheric fates and lifetimes of reduced and oxidized N 

components are linked.______________________________________________________________________________ 2-25

Figure 2-18. Atmospheric cycle of S compounds.____________________________________________________________________ 2-34

Figure 2-19. Comparison of aqueous-phase oxidation paths. __________________________________________________________ 2-36

Figure 2-20. RH effects on deliquescence and efflorescence points for a NaCl+ Na2SO4 particle, indicating deliquescence at 

~72% relative humidity and re-crystallization at ~52% RH. __________________________________________________ 2-38

Figure 2-21. Predicted isolines of particulate NO3 concentrations (μg/m3) as a function of total HNO3 and NH3 at 293 K and 80% 

relative humidity, and with 25 μg/m3 SO4

2− and 2 μg/m3 total Cl−._____________________________________________ 2-40

Figure 2-22. Predicted particulate NO3 concentration as a function of RH for a typical environment. ____________________________ 2-40

Figure 2-23. Tropospheric NO2 column estimates (molecules NO2/cm2) retrieved from the SCIAMACHY satellite instrument for 

2004–2005. _______________________________________________________________________________________ 2-47

Figure 2-24. Average ambient NH3 concentrations from the NH3 passive samplers trial network, 2007-2008. ____________________ 2-52

Figure 2-25. Schematic of the resistance-in-series analogy for atmospheric deposition. Function of wind speed, solar radiation, 

plant characteristics, precipitation/moisture, and soil/air temperature.__________________________________________ 2-65

Figure 2-26. The relationship between particle diameter and deposition velocity for particles. _________________________________ 2-69

Figure 2-27. 8 km southeast U.S. CMAQ domain zoomed over Tampa Bay, FL. ___________________________________________ 2-74

Figure 2-28. 2 km southeast U.S. CMAQ domain zoomed over Tampa Bay, FL. ___________________________________________ 2-74

Figure 2-29. Hourly averages for May 1-31, 2002. CMAQ 8 km and 2 km results and measured concentrations of NO (top), NO2

(middle), and total NOX (bottom). ______________________________________________________________________ 2-75

 xv
Figure 2-30. May 2002 daily concentrations and 8 km CMAQ predictions for ethene at Sydney, FL. ____________________________ 2-76

Figure 2-31. May 2002 daily concentrations and 8 km CMAQ predictions for isoprene at Sydney, FL.___________________________ 2-77

Figure 2-32. Observed hourly PM2.5 concentrations at St. Petersburg, FL and results from CMAQ 8 km. ________________________ 2-78

Figure 2-33. Observed and modeled ratios of O3 to NOX.______________________________________________________________ 2-79

Figure 2-34. Observed and CMAQ 8 km and 2 km predicted formaldehyde concentrations.___________________________________ 2-80

Figure 2-35. Hourly concentrations of hydrogen peroxide, observed and predicted by CMAQ 8 km and 2 km, May 1-31, 2002 at 

Sydney, FL. _______________________________________________________________________________________ 2-81

Figure 2-36. Hourly and CMAQ-UCD-predicted total pNO3 concentrations at Tampa Bay, FL, and observations at Sydney, FL 

May 1-31, 2002. ___________________________________________________________________________________ 2-83

Figure 2-37. Hourly and CMAQ-predicted HNO3 concentrations at Sydney, FL, May 1-31, 2002. ______________________________ 2-84

Figure 2-38. Hourly and CMAQ-UCD-predicted ratio of HNO3 to total NO3 at Tampa Bay, FL and observations at Sydney, FL, 

May 1-31, 2002. ___________________________________________________________________________________ 2-85

Figure 2-39. CMAQ-UCD predicted fractions and totals of total NO3 for days in May 2002 with measurements in Tampa Bay, FL. ____ 2-86

Figure 2-40. Hourly and CMAQ-predicted NH3 concentrations at Sydney, FL, May 1-31, 2002. ________________________________ 2-87

Figure 2-41. Scatter plot of total nitrate (HNO3 plus pNO3) wet deposition (mg N/m2/yr) of the model mean versus measurements 

for the North American Deposition Program (NADP) network.________________________________________________ 2-88

Figure 2-42. Scatter plot of total SO4

2– wet deposition (mg S/m2/yr) of the model mean versus measurements for the National 

Atmospheric Deposition Program (NADP) network. ________________________________________________________ 2-89

Figure 2-43. CMAQ modeling domains for the OAQPS risk and exposure assessments: 36 km outer parent domain in black; 12 

km western U.S. (WUS) domain in red; 12 km eastern U.S. (EUS) domain in blue. _______________________________ 2-89

Figure 2-44. 12-km EUS Summer sulfate PM, each data point represents a paired monthly averaged (June/July/August) 

observation and CMAQ prediction at a particular IMPROVE, STN, and CASTNet site. ____________________________ 2-90

Figure 2-45. 12-km EUS Winter nitrate PM, each data point represents a paired monthly averaged 

(December/January/February) observation and CMAQ prediction at a particular IMPROVE and STN site._____________ 2-91

Figure 2-46. 12-km EUS Winter total nitrate (HNO3 + total pNO3), each data point represents a paired monthly averaged 

(December/January/February) observation and CMAQ prediction at a particular CASTNet site. _____________________ 2-91

Figure 2-47. 12-km EUS annual sulfate wet deposition, each data point represents an annual average paired observation and 

CMAQ prediction at a particular NADP site. ______________________________________________________________ 2-92

Figure 2-48. 12-km EUS annual nitrate wet deposition, each data point represents an annual average paired observation and 

CMAQ prediction at a particular NADP site. ______________________________________________________________ 2-92

Figure 2-49. Principal airsheds and watersheds NOX for these estuaries: _________________________________________________ 2-93

Figure 2-50. Typical surface layer cell and total column structure and processes represented in CMAQ._________________________ 2-98

Figure 2-51. CMAQ vs. measured air concentrations from east-coast sites in the IMPROVE, CSN (labeled STN), and CASTNet 

networks in the summer of 2002: (right) sulfate and (left) ammonium.__________________________________________ 2-99

Figure 2-52. Comparison of CMAQ-predicted and NADP-measured NH4

+ wet deposition. ___________________________________ 2-100

Figure 2-53. CMAQ-predicted (red symbols and lines) and 12-h measured (blue symbols and lines) NH3 and SO4

2- surface 

concentrations at high and low concentration cells in North Carolina in July 2004._______________________________ 2-101

Figure 2-54. Surface cell (layer 1) analysis of the sensitivity of NHX deposition and transport to the change in NH3 Vd in CMAQ._____ 2-101

Figure 2-55. Total column analysis for NH3 (left) and NHX (right) showing modeled NH3 emissions, transformation, and transport 

throughout the mixed layer and up to the free troposphere._________________________________________________ 2-102

Figure 2-56. Range of influence (where 50% of emitted NH3 deposits) from the high concentration Sampson County cell in the 

June 2002 CMAQ simulation of Vd sensitivities.__________________________________________________________ 2-102

Figure 2-57. Areal extent of the change in NHX range of influence as predicted by CMAQ for the Sampson County high 

concentration cell (center of range circles) in June 2002 using the base case and sensitivity case Vd. _______________ 2-103

Figure 2-58. Annual mean concentrations of NO2 (ppb) in surface air over the U.S. in the present-day (upper panel) and policy 

relevant background (middle panel) MOZART-2 simulations. _______________________________________________ 2-104

 xvi
Figure 2-59. Annual mean concentrations of SO2 (ppb) in surface air over the U.S. in the present-day (upper panel) and policy 

relevant background (middle panel) MOZART-2 simulations. _______________________________________________ 2-105

Figure 2-60. Annual mean concentrations of wet and dry deposition of NHNO3, NH4NO3, NOX, HO2NO2, and organic nitrates 

(mg N/m2/yr) in surface air over the U.S. in the present-day (upper panel) and policy relevant background (middle 

panel) MOZART-2 simulations._______________________________________________________________________ 2-107

Figure 2-61. Annual mean concentrations of SOX deposition (SO2 + pSO4) (mg S/m2/yr) in surface air over the U.S. in the 

present-day (upper panel) and policy relevant background (middle panel) MOZART-2 simulations. _________________ 2-108

Figure 2-62. July mean soil NO emissions (upper panels; 1 × 109 molecules/cm2/s) and surface PRB NOX concentrations (lower 

panels; ppt) over the U.S. from MOZART-2 (left) and GEOS-Chem (right) model simulations in which anthropogenic 

O3 precursor emissions were set to zero in North America. _________________________________________________ 2-109

Figure 2-63. Aggregate map of most routine U.S. monitoring stations. __________________________________________________ 2-112

Figure 2-64. Trends in regional chemical composition of PM2.5 aerosols based on urban speciation sites and averaged over the 

entire 2006 sampling period._________________________________________________________________________ 2-113

Figure 2-65. Original 3−tiered NCore design (left) and proposed site locations for Level 2 multiple pollutant sites. ________________ 2-113

Figure 2-66. Maps illustrating coverage of PM2.5 FRM and FEM and O3 network (left); and PM2.5 continuous samplers (right). ______ 2-115

Figure 2-67. Routinely operating North American precipitation and surface water networks. _________________________________ 2-116

Figure 2-68. Correlation surfaces between MODIS AOD and hourly PM2.5 surface sites from April-September 2002. ______________ 2-123

Figure 2-69. Location of ambient-level NO2 monitors for NAAQS compliance in 2007. Shaded states have NO2 monitors; 

unshaded states have none._________________________________________________________________________ 2-125

Figure 2-70. Ambient concentrations of NO2 measured at all monitoring sites located within Metropolitan Statistical Areas 

(MSAs) in the U.S. from 2003 through 2005. ____________________________________________________________ 2-126

Figure 2-71. Monthly average NO2 concentrations (ppb) for January 2002 (left panel) and July 2002 (right panel) calculated by 

CMAQ (36 X 36 km horizontal resolution). ______________________________________________________________ 2-127

Figure 2-72. Nationwide trend in NO2 concentrations. _______________________________________________________________ 2-127

Figure 2-73. Time series of 24-h average NO2 concentrations at individual monitoring sites in Atlanta, GA from 2003 through 

2005. A natural spline function (with 9 degrees of freedom) was fit and overlaid to the data (dark solid line). __________ 2-128

Figure 2-74. Time series of 24-h average NO2 concentrations at individual monitoring sites in New York City from 2003 through 

2005. A natural spline function (with 9 degrees of freedom) was fit and overlaid to the data (dark solid line). __________ 2-129

Figure 2-75. Time series of 24-h average NO2 concentrations at individual monitoring sites in Chicago, IL from 2003 through 

2005. ___________________________________________________________________________________________ 2-130

Figure 2-76. Time series of 24-h average NO2 concentrations at individual monitoring sites in Baton Rouge, LA from 2003 

through 2005. ____________________________________________________________________________________ 2-131

Figure 2-77. Time series of 24-h average NO2 concentrations at individual monitoring sites in Houston, TX from 2003 through 

2005. ___________________________________________________________________________________________ 2-132

Figure 2-78. Time series of 24-h average NO2 concentrations at individual monitoring sites in Los Angeles, CA from 2003 

through 2005. ____________________________________________________________________________________ 2-133

Figure 2-79. Time series of 24-h average NO2 concentrations at individual monitoring sites in Los Angeles, CA from 2003 

through 2005. ____________________________________________________________________________________ 2-134

Figure 2-80. Time series of 24-h average NO2 concentrations at individual monitoring sites in Riverside, CA from 2003 through 

2005. A natural spline function (with 9 degrees of freedom) was fit and overlaid to the data (dark solid line). __________ 2-135

Figure 2-81. Time series of 24-h average NO2 concentrations at individual monitoring sites in Riverside, CA from 2003 through 

2005. ___________________________________________________________________________________________ 2-136

Figure 2-82. Mean hourly NO2 concentrations on weekdays and weekends measured at two sites in Atlanta, GA. ________________ 2-137

Figure 2-83. Measured O3 (ppb by volume) versus PAN (ppt by volume) in Tennessee, including (a) aircraft measurements, and 

(b, c, and d) suburban sites near Nashville. _____________________________________________________________ 2-138

Figure 2-84. Ratios of PAN to NO2 observed at Silwood Park, Ascot, Berkshire, U.K. from July 24 to August 12, 1999. ____________ 2-139

 xvii
Figure 2-85. Ratios of HNO2 to NO2 observed in a street canyon (Marylebone Road) in London, U.K. from 11 a.m. to midnight 

during October 1999. Data points reflect 15–min average concentrations of HONO and NO2.______________________ 2-140

Figure 2-86. Annual average gas-phase HNO3 concentrations, 2004–2006. ______________________________________________ 2-141

Figure 2-87. Annual average gas-phase NO3 concentrations, 2004–2006. _______________________________________________ 2-142

Figure 2-88 County-scale NH3 emissions densities from the CMU inventory model. _______________________________________ 2-144

Figure 2-89. Ambient NH3 concentration as a function of county scale NH3 emissions. _____________________________________ 2-145

Figure 2-90. Estimated county-scale ambient NH3 concentrations. _____________________________________________________ 2-145

Figure 2-91. IMPROVE network measured annual averaged NH4NO3 concentration for 2000 (left) and for 2004 (right).____________ 2-147

Figure 2-92. IMPROVE and CSN (labeled STN) monitored mean NH4NO3 concentrations for 2000 through 2004. ________________ 2-147

Figure 2-93. Regional and local contributions to annual average PM2.5 by pNO3 for select urban areas based on paired 

IMPROVE and CSN monitoring sites.__________________________________________________________________ 2-148

Figure 2-94. Maps of spatial patterns for average annual pNO3 measurements (top), and for NH3 emissions for April 2002 from 

the WRAP emissions inventory (bottom). _______________________________________________________________ 2-149

Figure 2-95. CMAQ simulation of January monthly averaged pNO3 concentration using 1996 emissions (left), and for a 50% 

decrease in NH3 emissions (right). ____________________________________________________________________ 2-150

Figure 2-96. Particulate NO3 source attribution by region using CAMx modeling for six western remote area monitoring sites _______ 2-150

Figure 2-97. Criteria pollutant monitor locations (A) and SO2 monitor locations (B), California, 2005.___________________________ 2-153

Figure 2-98. Criteria pollutant monitor locations (A) and SO2 monitor locations (B), Ohio, 2005. ______________________________ 2-154

Figure 2-99. Criteria pollutant monitor locations (A) and SO2 monitor locations (B), Arizona, 2005. ____________________________ 2-155

Figure 2-100. Criteria pollutant monitor locations (A) and SO2 monitor locations (B), Pennsylvania, 2005. _______________________ 2-156

Figure 2-101. Criteria pollutant monitor locations (A) and SO2 monitor locations (B), New York, 2005. __________________________ 2-157

Figure 2-102. Criteria pollutant monitor locations (A) and SO2 monitor locations (B), Massachusetts, 2005. ______________________ 2-158

Figure 2-103. Steubenville, OH, 2003–2005. _______________________________________________________________________ 2-162

Figure 2-104. Philadelphia, 2003–2005. ___________________________________________________________________________ 2-163

Figure 2-105. Los Angeles, 2003–2005. ___________________________________________________________________________ 2-164

Figure 2-106. Riverside, CA, 2003–2005. __________________________________________________________________________ 2-165

Figure 2-107. Phoenix, 2003–2005. ______________________________________________________________________________ 2-166

Figure 2-108. Annual mean ambient SO2 concentration, 1989 through 1991 (top), and for 2003 through 2005 (bottom). ____________ 2-168

Figure 2-109. Annual mean ambient SO4

2− concentration, 1989 through 1991 (top), and 2003 through 2005 (bottom). _____________ 2-169

Figure 2-110. IMPROVE network measured annual averaged pSO4 concentration for 2000 (top) and for 2004 (bottom). ____________ 2-170

Figure 2-111. IMPROVE mean (NH4)2SO4

2− concentrations for 2000 through 2004._________________________________________ 2-171

Figure 2-112. IMPROVE and CSN (labeled STN) monitored mean (NH4)2SO4

2− concentrations for 2000 through 2004._____________ 2-171

Figure 2-113. Regional and local contributions to annual average PM2.5 by pSO4 for select urban areas based on paired 

IMPROVE and CSN monitoring sites.__________________________________________________________________ 2-172

Figure 2-114. Contributions of the Pacific Coast area to the (NH4)2SO4 (μg/m3) at 84 remote-area monitoring sites in western U.S. 

based on trajectory regression. Dots denote locations of the IMPROVE aerosol monitoring sites.___________________ 2-173

Figure 2-115. pSO4 source attribution by region using CAMx modeling for six western remote area monitoring sites. _______________ 2-174

Figure 2-116. Trends, 1990–2005 in S (left) and N (right) deposition for 34 sites in the eastern U.S. ____________________________ 2-175

Figure 2-117. Total average yearly wet and dry inorganic N deposition, excepting NH3, for 2004–2006 (top) and 1989–1991 

(bottom). ________________________________________________________________________________________ 2-176

Figure 2-118. Total average yearly inorganic N deposition by species, excepting NH3, for 2004–2006 (top) and 1989–1991 

(bottom). ________________________________________________________________________________________ 2-177

 xviii
Figure 2-119. NO3

– concentration in NADP wet deposition samples, 2004–2006.___________________________________________ 2-178

Figure 2-120. Average NO3

− concentration in NADP wet deposition samples, 2004–2006. ___________________________________ 2-179

Figure 2-121. Diel cycles of median concentrations (upper panels) and fluxes (lower panels) for the northwest clean sector, left 

panels) and southwest (polluted sector, right panels) wind sectors at Harvard Forest, April-November, 2000, for 

NO, NO2, and O3/10._______________________________________________________________________________ 2-180

Figure 2-122. Simple NOX photochemical canopy model outputs. _______________________________________________________ 2-181

Figure 2-123. Hourly (dots) and median nightly (pluses) NO2 flux vs. concentration, with results of least squares fit on the hourly 

data (curve). _____________________________________________________________________________________ 2-182

Figure 2-124. Averaged profiles at Harvard Forest give some evidence of some NO2 input near the canopy top from lightmediated ambient reactions, or emission from open stomates. ______________________________________________ 2-183

Figure 2-125. Summer (June-August) 2000 median concentrations (upper panels), fractions of NOY (middle panels), and fluxes 

(lower panels) of NOY and component species separated by wind direction (northwest on the left and southwest on 

the right). ________________________________________________________________________________________ 2-184

Figure 2-126. Total average yearly wet and dry S deposition for 2004–2006 (top) and 1989–1991 (bottom). _____________________ 2-186

Figure 2-127. Total average yearly S deposition by species for 2004–2006 (top) and 1989–1991 (bottom). ______________________ 2-187

Figure 3-1. Illustration of major fluxes of ions associated with S-driven acidification of drainage water. __________________________ 3-4

Figure 3-2. Diagram illustrates soil horizons commonly found.__________________________________________________________ 3-6

Figure 3-3. Results of an in situ bioassay during a period of episodic acidification in Buck Creek, Adirondack Mountains, in 

spring 1990. ______________________________________________________________________________________ 3-13

Figure 3-4. Relationship between mean summer acid neutralizing capacity (ANC) and the mean of minimum spring ANC 

values at long-term monitoring lake and stream sites in New England, the Adirondacks, and the Northern 

Appalachian Plateau. _______________________________________________________________________________ 3-14

Figure 3-5. Diagram based on Fenn et al. (2006) shows indicators of forest physiological function, growth and structure that are 

linked to biogeochemical cycles through processes that control rates of Ca supply._______________________________ 3-18

Figure 3-6. Distribution of red spruce (rose) and sugar maple (green) in the eastern U.S. ___________________________________ 3-22

Figure 3-7. Mean (+ standard error bars) of current-year red spruce needle winter injury in reference and calcium-addition 

watersheds and among crown classes, expressed as foliar injury (A) and bud mortality (B). ________________________ 3-23

Figure 3-8. Conceptual diagram outlining the current understanding of sugar maple decline._________________________________ 3-25

Figure 3-9. Native range of flowering dogwood (Cornus florida) (dk. gray) and the documented range of dogwood anthracnose 

in the eastern U.S. (red)._____________________________________________________________________________ 3-26

Figure 3-10. Surface water alkalinity in the conterminous U.S. _________________________________________________________ 3-30

Figure 3-11. Summary of regional trends in surface water chemistry from 1990 to 2000 in regions covered by the Stoddard et al. 

(2003) report. _____________________________________________________________________________________ 3-33

Figure 3-12. Concentration of inorganic Al in Adirondack streams as a function of the calculated base cation surplus.______________ 3-35

Figure 3-13. F-factors calculated from PnET-BGC model results for the period 1850 to 1980 as a function of simulated ANC in 

1980 for 44 EMAP lakes in the Adirondack region of New York. ______________________________________________ 3-40

Figure 3-14. Median and range of projected change in ANC (μeq/L) of Adirondack lakes for 50-year MAGIC simulations versus 

median future change in sulfur deposition (kg S/ha/yr) for each deposition scenario. ______________________________ 3-42

Figure 3-15. Number of fish species as a function of mean stream ANC among 13 streams in Shenandoah National Park, 

Virginia. __________________________________________________________________________________________ 3-52

Figure 3-16. Number of fish species per lake versus acidity status, expressed as ANC, for Adirondack lakes. ____________________ 3-53

Figure 3-17. Regions of the northern and eastern U.S. that contain appreciable numbers of lakes and streams that are sensitive 

to acidification from acidifying deposition.________________________________________________________________ 3-58

Figure 3-18. Spatial patterns in predicted wet SO4

2− and NO3− deposition in the Adirondack Park during the period 1988 to 1999. ____ 3-69

Figure 3-19. Measured wet deposition of sulfur at the Huntington Forest NADP/NTN monitoring station. ________________________ 3-70

Figure 3-20. Estimated time series of S deposition at one example watershed in the southwestern Adirondack Mountains. __________ 3-71 

 xix
Figure 3-21. Cumulative distribution functions of selected major ions (μeq/L), calculated ANC of lakewater (μeq/L), and B 

horizon soil % base saturation for the MAGIC and PnET-BGC models. 

Figure 3-22. Time series data for SO4

2−, NO3−, base cations [Ca plus Mg], Gran ANC, pH, and DOC in one example of long-term 

monitoring in Darts Lake in the Adirondack Park.__________________________________________________________ 3-76

Figure 3-23. Mean rates of change in solute concentration in 16 lakes of the Adirondack Long-Term Monitoring (ALTM) program 

from 1982 to 2000. _________________________________________________________________________________ 3-77

Figure 3-24. Simulated cumulative frequency distributions of lakewater ANC at three dates for the population of Adirondack 

lakes, based on MAGIC model simulations reported by Sullivan et al., 2006b. ___________________________________ 3-78

Figure 3-25. Wet sulfur deposition for the period of record at the Big Meadows NADP/NTN monitoring station in Shenandoah 

National Park. _____________________________________________________________________________________ 3-79

Figure 3-26. Length-adjusted condition factor (K), a measure of body size in blacknose dace (Rhinichthys atratulus) compared 

with mean stream pH among 11 populations (n = 442) in Shenandoah National Park._____________________________ 3-82

Figure 3-27. Illustration of the N cascade showing the movement of the human-produced Nr as it cycles through the various 

environmental reservoirs in the atmosphere, terrestrial ecosystems, and aquatic ecosystems. ______________________ 3-86

Figure 3-28. N cycle (dotted lines indicated processes altered by N saturation). ____________________________________________ 3-87

Figure 3-29. Schematic illustration of the response of temperate forest ecosystems to long-term, chronic N additions.______________ 3-89

Figure 3-30. Surface water NO3

− concentrations as a function of N deposition at the base of each watershed in summer and 

spring. ___________________________________________________________________________________________ 3-90

Figure 3-31. a) N export in stream water as a function of N deposition at the base of sampled watersheds. ______________________ 3-91

Figure 3-32. Mean annual NO3

− concentrations in 230 lakes and streams across the northeastern U.S. ________________________ 3-102

Figure 3-33. NO3

− concentrations in high-elevation lakes in western North America. _______________________________________ 3-103

Figure 3-34. A conceptualization of the relationship between overall eutrophic conditions, associated eutrophic symptoms, and 

influencing factors (N loads and susceptibility). __________________________________________________________ 3-106

Figure 3-35. Estimated anthropogenic N inputs to the estuaries of the northeastern U.S., in kg N/ha/yr. ________________________ 3-108

Figure 3-36. Interactions between the C and N cycles._______________________________________________________________ 3-111

Figure 3-37. Mean 5-year radial increment from 31,606 core samples from Picea abies during the period 1945 to 1996 for three 

atmospheric N deposition zones (high, medium, and low wet N-deposition in 1990), respectively. __________________ 3-115

Figure 3-38. Effects of N addition on forest ecosystem C content. ______________________________________________________ 3-118

Figure 3-39. Effects of N addition on NEE of non-forest ecosystems. ___________________________________________________ 3-121

Figure 3-40. N cycle in freshwater ecosystem. _____________________________________________________________________ 3-122

Figure 3-41. Description of the eutrophic symptoms included in the national estuary condition assessment. _____________________ 3-127

Figure 3-42. A high Chl a rating was observed in a large number of the nation’s estuaries. __________________________________ 3-128

Figure 3-43. Frequency of hypoxia in Long Island Sound, 1994 to 2002._________________________________________________ 3-129

Figure 3-44. Effects of N addition on biogenic CH4 emission.__________________________________________________________ 3-132

Figure 3-45. Effects of N addition on biological CH4 uptake. __________________________________________________________ 3-133

Figure 3-46. Effects of N addition on biological CH4 uptake. __________________________________________________________ 3-134

Figure 3-47. Effects of N addition on biogenic N2O emission. _________________________________________________________ 3-135

Figure 3-48. Effects of N addition on biogenic N2O emission. _________________________________________________________ 3-136

Figure 3-49. The relationship between N2O emission and N deposition. _________________________________________________ 3-137

Figure 3-50. Diatom assemblage sediment patterns in Emerald Lake, WY._______________________________________________ 3-156

Figure 3-51. Microscopic counts of phytoplankton species composition in the Neuse River Estuary, NC following 36-h in situ 

bioassays to manipulate available forms of N. ___________________________________________________________ 3-159

Figure 3-52. Map of the western U.S. showing the primary geographic areas where N deposition effects have been reported. ______ 3-168

Figure 3-53. Map of location of wetlands in the eastern U.S. __________________________________________________________ 3-169

 xx
Figure 3-54. Overall eutrophication condition on a national scale. ______________________________________________________ 3-172

Figure 3-55. Changes in plant species composition associated with N addition treatments in an alpine dry meadow of the 

Colorado Front Range. _____________________________________________________________________________ 3-175

Figure 3-56. Diagram of multiple factors contributing to forest susceptibility to wildfire.______________________________________ 3-178

Figure 3-57. Diagram of relationships of human actions, N loading, and ecosystem services. ________________________________ 3-179

Figure 3-58. Representation of the S cycle in forest ecosystems. ______________________________________________________ 3-186

Figure 3-59. Simplified cycle of mercury, showing the role of sulfur. ____________________________________________________ 3-191

Figure 3-60. (A) SO4

2– and (B) MeHg concentrations as a function of time in sediment slurries made from Quabbin Reservoir 

littoral sediments. _________________________________________________________________________________ 3-192

Figure 3-61. MeHg produced in sediment cores incubated two weeks under artificial lake water containing 3–1040 μM Na2SO4. ____ 3-194

Figure 3-62. The microarchitecture of a dicot leaf. __________________________________________________________________ 3-198

Figure A-1. Location of acid-sensitive regions of the northern and eastern U.S. ____________________________________________ A-3

Figure A-2. Location LTM sites used in the 2003 Surface Water report.___________________________________________________ A-5

Figure A-3. Long-term record of SO4

2− concentration in streamwater and precipitation at Watershed 6 of HBEF. _________________ A-16

Figure A-4. Location of sampling stations in Sleepers River watershed, Vermont.__________________________________________ A-20

Figure A-5. SWAS-VTSS Program Study Sites. ____________________________________________________________________ A-24

Figure A-6. Conceptual structure of the MAGIC model showing major pools and fluxes included in simulation of effects of S and 

N deposition. ______________________________________________________________________________________ A-28

Figure A-7. The PnET-BGC model illustrating the compartments and flow paths of carbon and nutrients (C/Nut) within the 

model. ___________________________________________________________________________________________ A-33

Figure A-8. Mathematical form of the SPARROW model._____________________________________________________________ A-36

Figure A-9. Schematic diagram of the WATERSN approach to estimate the contribution made by different N sources to the total 

N inputs an estuary. ________________________________________________________________________________ A-41

Figure A-10. WATERSN model estimates of anthropogenic N inputs to the estuaries of the northeastern U.S., in kilograms per 

hectare per year.___________________________________________________________________________________ A-43

Figure B-1. MAGIC model hindcast estimates of pre-industrial pH versus diatom-inferred pH for 33 statistically selected 

Adirondack lakes: __________________________________________________________________________________ B-14

Figure B-3. Location and percentage of acidic surface waters in U.S. high-interest subpopulations with respect to acidic 

deposition effects. __________________________________________________________________________________ B-22

Figure B-4. Measured concentration of SO4

2− in selected representative lakes and streams in six regions of the U.S. during the 

past approximately 15 years. _________________________________________________________________________ B-29

Figure B-5. Summary of regional trends in surface water chemistry from 1990 through 2000 in regions covered by the Stoddard 

et al. (2003) report. _________________________________________________________________________________ B-30

Figure B-6. Estimated time series of S deposition at one example watershed in the SW Adirondack Mountains used by Sullivan 

et al. (2006b) as input to the MAGIC model for projecting past and future changes in lakewater chemistry 

attributable to acidic deposition. _______________________________________________________________________ B-32

Figure B-7. Simulated cumulative frequency distributions of lakewater ANC at three points in time for the population of 

Adirondack lakes. __________________________________________________________________________________ B-33

Figure B-8. Map showing simulated changes in streamwater ANC from 1995 to 2040 in response to the SAMI A2 emmissions 

control strategy, representing existing emissions control regulations. __________________________________________ B-37

Figure B-9. Major geomorphic units and locations of lakes sampled in the Western Lake Survey. Those areas known to contain 

sensitive lake resources are shaded with cross-hatching. ___________________________________________________ B-42

Figure B-10. Estimated percent changes in the total deposition of sulfur, reduced nitrogen, and nitrate-nitrogen at MAGIC 

modeling sites from 1995 to 2040 under each of the emissions control strategies ________________________________ B-43

Figure B-11. Relationship between mean summer and spring ANC values at LTM sites in New England, the Adirondacks, and 

the Northern Appalachian Plateau. _____________________________________________________________________ B-49

 xxi
 xxii

Figure B-12. Minimum streamwater ANC sampled at each site during each year versus median spring ANC for all samples 

collected at that site during that spring season. ___________________________________________________________ B-50

Figure B-13 Relationship between ANC and runoff for streamwater samples collected at intensively studied sites in Shenandoah 

National Park. _____________________________________________________________________________________ B-52

Figure B-14. Decrease in ANC and pH and increase in dissolved aluminum in response to a sharp increase in streamflow in 

three watersheds within Shenandoah National Park during a hydrological episode in 1995. ________________________ B-53

Figure B-15. Length-adjusted condition factor (K), a measure of body size in blacknose dace (Rhinichthys atratulus) compared 

with mean stream pH among 11 populations (n = 442) in Shenandoah National Park._____________________________ B-56

Figure B-16. Mean residual number of species per lake for lakes in Ontario, by pH interval.___________________________________ B-59

Figure B-17. Number of fish species per lake or stream versus acidity statues, expressed either as pH or ANC.___________________ B-60

Figure B-18. Number of fish species among 13 streams in Shenandoah National Park. Values of ANC are means based on 

quarterly measurements, 1987–94. ____________________________________________________________________ B-61

Figure B-19. Life stages of brook trout. ____________________________________________________________________________ B-64

Figure B-20. Example model application. __________________________________________________________________________ B-71

Figure B-21. Time series data for SO4

2−, NO3−, base cations [Ca2+ + Mg2+], Gran ANC, pH, and DOC in example Long Term 

Monitoring Lakes and streams that have relatively low ANC._________________________________________________ B-76

Figure C-1. Schematic representation of the response of vegetation to nutrient addition. ____________________________________ C-16

Figure C-2. Distribution of alpine vegetation in three western regions that are in close proximity to urban and agricultural 

sources of atmospheric N emissions ___________________________________________________________________ C-18

Figure C-3. Number of nationally rare species versus standing crop in each of 401 quadrants from wetlands in Ontario, 

Quebec, and Nova Scotia. ___________________________________________________________________________ C-22

Figure C-4. Sample stressors and the essential ecological attributes they affect. __________________________________________ C-36

Figure C-5. Linkages among various ecosystem goods and services (food, water, biodiversity, forest products) and other 

driving forces (climate change). _______________________________________________________________________ C-38

Figure C-6. Effect of watershed defoliation by the gypsy moth caterpillar on NO3

− flux in streamwater. _________________________ C-41

Figure D-1. Conceptual patterns of pollutant deposition effects on a chemical indicator and a corresponding biological indicator 

during increasing and decreasing deposition._____________________________________________________________ D-11

Figure D-2. Pollutant deposition patterns for defining the temporal parameters of dynamic critical loads analyses. ________________ D-12

Figure F-1. Illustration chart of the assessment. _____________________________________________________________________ F-2

Figure F-2. Reviewed studies by ecosystem addressed. ______________________________________________________________ F-5

Figure F-3. Reviewed studies by ecological endpoint. ________________________________________________________________ F-6

Figure F-4. Taxonomy of values for environmental goods and services. __________________________________________________ F-7

Figure F-5. Linkages from emissions to forest aesthetics. ____________________________________________________________ F-13

Figure F-6. Geographic distribution of forested areas historically affected by air pollution. ___________________________________ F-14
Acronyms and Abbreviations 

ACCENT Atmospheric Composition Change: the 

European Network of excellence 

AIRMoN Atmospheric Integrated Research Monitoring 

Network 

AIRS Atmospheric Infrared Sounder (instrument) 

Al aluminum 

Al3+ aluminum ion 

Ali inorganic aluminum 

Aln+ aluminum ion 

Alo organic aluminum 

Al(OH)3 aluminum hydroxide 

ALSC Adirondack Lake Survey Corporation 

ALTM Adirondack Long Term Monitoring 

AMD acid mine drainage 

ANC acid neutralizing capacity 

AOD aerosol optical depth 

AQCD Air Quality Criteria Document 

AQEG Air Quality Expert Group 

AQI Air Quality Index 

AQS Air Quality System (database) 

Ar argon 

ARS Agricultural Research Service 

As arsenic 

ASI Acid Stress Index 

asl above sea level 

ATMOS Atmospheric Trace Molecule Spectroscopy 

ATTILA type of Lagrangian model 

AUSPEX Atmospheric Utility Signatures, Predictions, 

and Experiments 

AVIRIS Airborne Visible and Infrared Imaging 

Spectrometer 

Ba barium 

BBW Bear Brook Watershed 

BBWM Bear Brook Watershed, Maine 

BC black carbon 

BCS base-cation surplus 

BGC Biogeochemical (model) 

B-IBI benthic index of biological integrity 

BMPs best management practices 

BNF bacterial nitrogen fertilization 

Br bromine 

Br− bromine ion 

Br2 molecular bromine 

BrCl bromine chloride 

BrO bromine oxide 

BUV Backscatter Ultraviolet Spectrometer 

BUVD Beneficial Use Values Database 

C carbon; concentration 

12C carbon-12, stable isotope of carbon 

13C carbon-13, stable isotope of carbon 

Ca ambient air concentration 

Ca calcium 

Ca2+ calcium ion 

CAA Clean Air Act 

CAAA Amendments to the Clean Air Act 

CAAAC Clean Air Act Advisory Committee 

CaCl2 calcium chloride 

CaCO3 calcium carbonate 

CALIPSO Cloud-Aerosol Lidar and Infrared Pathfinder 

Satellite Observation (satellite) 

Ca(NO3)2 calcium nitrate 

Ca(OH)2 calcium hydroxide 

CAPMoN Canadian Air and Precipitation Monitoring 

Network 

CaSO4·2H2O gypsum 

CASTNet Clean Air Status and Trends Network 

CB4 Carbon Bond 4 (model) 

Cd cadmium 

CEC cation exchange capacity 

CENTURY model that simulates carbon, nitrogen, 

phosphorus, sulfur, and water dynamics in the 

soil-plant system at monthly intervals over time 

scales of centuries and millennia 

CFCs chlorinated fluorocarbons 

CG cloud-to-ground (lightning flash) 

Chl a chlorophyll a

CH4 methane 

C2H4 ethene 

C2H6 ethane 

C5H8 isoprene 

CH3CHO acetaldehyde 

CH3C(O) acetyl radical 

CH3C(O)OO acetyl peroxy radical 

CH2I2 diiodomethane 

CH2O formaldehyde 

CH3OOH methyl hydroperoxide 

CH3-S-CH3 dimethylsulfide, DMS 

CH3-S-H methyl mercaptan 

(CH3)2SO dimethyl sulfoxide, DMSO 

CH3SO3H methanesulfonic acid 

CH3-S-S-CH3 dimethyl disulfide, DMDS 

Ci interstitial air concentration 

CL critical load 

Cl chlorine 

Cl− chlorine ion 

Cl2 molecular chlorine 

 xxiii 
CLaMS type of Lagrangian model 

CloudSat NASA Earth observation satellite 

ClNO2 nitryl chloride 

CMAQ Community Multiscale Air Quality (modeling 

system) 

CMSA consolidated metropolitan statistical area 

CO carbon monoxide 

CO2 carbon dioxide 

CO3

− carbonate 

CONUS continental U.S. 

CPUE catch per unit effort 

CRREL U.S. Army Cold Regions Research and 

Engineering Laboratory 

CS Consumer surplus 

CS2 carbon disulfide 

CSS coastal sage scrub (ecosystem) 

CTM chemical transport model 

Cu copper 

CV contingent valuation 

CVM contingent valuation method 

Δ, δ delta, difference; change 

DayCent model for daily biogeochemistry for forest, 

grassland, cropland, and savanna systems 

DayCent-Chem combination of DayCent-Chem and PHREEQC 

models 

DC dichotomous choice 

DDRP Direct Delayed Response Project 

DDT Damage Delay Time 

DECOMP decomposition model based on soil-plant 

system dynamics 

DEP Department of Environmental Protection 

DIC dissolved inorganic carbon 

DIN dissolved inorganic nitrogen 

DMDS dimethyl disulfide, CH3-S-S-CH3 

DMS dimethyl sulfide, CH3-S-CH3 

DMSO dimethylsulfoxide 

DNDC Denitrification-Decomposition (model) 

DO dissolved oxygen 

DOC dissolved organic carbon 

DON dissolved organic nitrogen 

EBB East Bear Brook 

EC elemental carbon 

EEAs Essential Ecological Attributes 

ELA Experimental Lakes Area 

ELS Eastern Lakes Survey 

EMAP Environmental Monitoring and Assessment 

Program 

EMEFS Eulerian Model Evaluation Field Study 

EMEP Co-operative Programme for Monitoring and 

Evaluation of the Long-range Transmission of 

Air Pollutants in Europe 

EMF ectomycorrhizal fungi 

EOS Earth Observation System 

EPA U.S. Environmental Protection Agency 

EPT Ephemeroptera-Plecoptera-Tricoptera (index) 

ERP Episodic Response Project 

ESA European Space Agency 

EVRI Environmental Valuation Reference Inventory 

F flux 

F− fluorine ion 

FAB First-order Acidity Balance model 

FACE free-air CO2 enrichment (studies) 

Fe iron 

FePO4 iron phosphate 

FeS iron sulfide 

F-factor fraction of the change in mineral acid anions 

that is neutralized by base cation release 

FHM Forest Health Monitoring 

FIA Forest Inventory and Analysis (program) 

FISH Fish in Sensitive Habitats (project) 

FLEXPART type of Lagrangian model 

ForSAFE three-component model using nitrogen, carbon 

cycling, and soil chemistry 

FRM Federal Reference Method 

FTIR Fourier Transform Infrared Spectroscopy 

FW2 black carbon soot 

Fx flux 

γN2O5 reaction potential coefficient for N2O5

GAW Global Atmospheric Watch (program) 

GCE Goddard Cumulus Ensemble (model) 

GDP gross domestic product 

GEOS Goddard Earth Observing System 

GEOS-Chem Goddard Earth Observing System (with global 

chemical transport model) 

GEOS-1DAS Goddard Earth Observing System Data 

Assimilation System 

GFED Global Fire Emissions Database 

GHG greenhouse gas 

GOES Geostationary Operational Environmental 

Satellites 

GOME Global Ozone Monitoring Experiment 

gs stomatal conductance 

GtC global ton carbon 

Gton global ton 

GWP global warming potential 

H hydrogen; hydrogen atom 

2H hydrogen-2, deuterium, stable isotope of 

hydrogen 

H+ proton, hydrogen ion; relative acidity 

ha hectare 

HAPs hazardous air pollutants 

HBEF Hubbard Brook Experimental Forest 

HBES Hubbard Brook Ecosystem Study 

HBN Hydrologic Benchmark Network 

HC hydrocarbon 

HCHO formaldehyde 

HCl hydrochloric acid 

 xxiv 
Hg mercury 

HNO2, HONO nitrous acid 

HNO3, HOONO nitric acid 

HNO4 pernitric acid 

HO2 hydroperoxyl radical 

H2O2 hydrogen peroxide 

HO2NO2 peroxynitric acid 

HOBr hypobromous acid 

HOCl hypochlorous acid 

HOX hypohalous acid 

HP hedonic pricing 

HSO3

− bisulfate ion 

HSO4

− sulfuric acid ion 

H2S hydrogen sulfide 

H2SO3 sulfurous acid 

H2SO4 sulfuric acid 

hν photon with energy at wavelength ν

I iodine 

I2 molecular iodine 

IA Integrated Assessment 

IADN Integrated Atmospheric Monitoring Deposition 

Network 

IC intracloud (lightning flash) 

ILWAS Integrated Lake-Watershed Acidification Study 

IPC International Cooperative Programme 

IEc Industrial Economicsym 

IIASA International Institute for Applied Systems 

Analysis 

IMPROVE Interagency Monitoring of Protected Visual 

Environments 

INO3 iodine nitrate 

INTEX-NA Intercontinental Chemical Transport 

Experiment - North America 

IO iodine oxide 

IPCC Intergovernmental Panel on Climate Change 

IPCC-AR4 Intergovernmental Panel on Climate Change 

4th Assessment Report 

IPCC-TAR Intergovernmental Panel on Climate Change 

3rd Assessment Report 

IQR interquartile range 

IR infrared 

ISA Integrated Science Assessment 

J flux from a leaf, deposition flux (g/cm/second) 

JPL Jet Propulsion Laboratory 

JRGCE Jasper Ridge Global Climate Change 

Experiment 

K potassium 

K+ potassium ion 

Ka dissociation constant 

Kb dissociation constant 

KH Henry’s Law constant in M/atm (M•atm-1) 

KNO3 potassium nitrate 

Kw ion product of water 

LAF Lake Acidification and Fisheries 

LAR leaf-area ratio 

LB laboratory bioassay 

LC0.01 lethal concentration at which 0.01% of exposed 

animals die 

LD33 lethal dose at which 33% of exposed animals 

die 

LDH lactic acid dehydrogenase 

LIDAR Light Detection and Ranging (remote sensing 

system) 

LIF laser-induced fluorescence 

LIMS Limb Infrared Monitor of the Stratosphere 

LOD limit of detection 

LP long-path 

LRTAP Long Range Transport of Air Pollution 

LTER Long-Term Ecological Research (program) 

LTM Long-Term Monitoring (project) 

M air molecule 

MA Millennium Ecosystem Assessment 

MAGIC Model of Acidification of Groundwater in 

Catchments (model) 

MAHA Mid-Atlantic Highlands Assessment of streams 

MAQSIP Multiscale Air Quality Simulation Platform 

(model) 

MAT moist acidic tundra 

MAX-DOAS multiple axis differential optical absorption 

spectroscopy 

MBL marine boundary layer 

MDN Mercury Deposition Network 

MeHg methylmercury 

MEM model ensemble mean 

μeq microequivalent 

Mg magnesium 

Mg2+ magnesium ion 

MIMS membrane inlet mass spectrometry 

MM5 National Center for Atmospheric 

Research/Penn State Mesoscale Model, 

version 5 

Mn manganese 

MOBILE6 Highway Vehicle Emission Factor Model 

MODIS Moderate Resolution Imaging 

Spectroradiometer 

MOPITT Measurement of Pollution in the Troposphere 

MOZAIC Measurement of Ozone and Water Vapor by 

Airbus In-Service Aircraft 

MOZART Model for Ozone and Related Chemical 

Tracers 

MPAN peroxymethacrylic nitrate 

MSA metropolitan statistical area 

Mt million tons 

N nitrogen 

N, n number of observations 

14N nitrogen-14, stable isotope of nitrogen 

15N nitrogen-15, stable isotope of nitrogen 

 xxv 
N2 molecular nitrogen; nonreactive nitrogen 

NA not available; insufficient data 

Na sodium 

Na+ sodium ion 

NAAQS National Ambient Air Quality Standards 

NaCl sodium chloride 

NADP National Atmospheric Deposition Program 

Na2MoO4 sodium molybdate 

NAMS National Air Monitoring Stations 

NANI Net anthropogenic nitrogen inputs 

NAPAP National Acid Precipitation Assessment 

Program 

NASQAN National Stream Quality Accounting Network 

NARSTO program formerly known as North American 

Regional Strategy for Atmospheric Ozone 

NAS National Academy of Sciences 

NASA National Aeronautics and Space Administration 

Na2SO4 sodium sulfate 

NASQAN National Stream Quality Accounting Network 

NATTS National Air Toxics Trends (network) 

NAWQA National Water Quality Assessment (program) 

NCore National Core Monitoring Network 

NEE net ecosystem exchange 

NEG/ECP New England Governors and Eastern 

Canadian Premiers 

NEI National Emissions Inventory 

NEON National Ecological Observatory Network 

NEP net ecosystem productivity 

NFI net factor income 

NH3 ammonia 

NH2 amino (chemical group) 

NH4

+ ammonium ion 

NH4Cl ammonium chloride 

NH4NO3 ammonium nitrate 

(NH4)2SO4 ammonium sulfate 

NHX category label for NH3 plus NH4

+

NHY total reduced nitrogen 

Ni nickel 

NILU Norwegian Institute for Air Research 

NITREX Nitrogen saturation Experiments 

nitro-PAH nitro-polycyclic aromatic hydrocarbon 

NLCD National Land Cover Data 

NMOC nonmethane organic compound 

NO nitric oxide 

NO2 nitrogen dioxide 

NO2

− nitrite 

NO3

− nitrate 

N2O nitrous oxide 

N2O5 dinitrogen pentoxide 

NOAA U.S. National Oceanic and Atmospheric 

Administration 

NOAA-ARL U.S. National Oceanic and Atmospheric 

Administration Air Resources Laboratory 

NOAEL no-observed-adverse-effect level 

NOEC no-observed-effect concentration 

NOX sum of NO and NO2

NOY sum of NOX and NOZ; odd nitrogen species; 

total oxidized nitrogen 

NOZ sum of all inorganic and organic reaction 

products of NOX (HONO, HNO3, HNO4, organic 

nitrates, particulate nitrate, nitro-PAHs, etc.) 

NPOESS National Polar-orbiting Operational 

Environmental Satellite System 

NPP net primary production 

NPS National Park Service 

Nr reactive nitrogen 

NRC National Research Council 

NS nonsignificant 

NSF National Science Foundation 

NSS National Stream Survey 

nss non-sea salt 

NSTC National Science and Technology Council 

NSWS National Surface Water Survey 

NTN National Trends Network 

NuCM nutrient cycling model 

O2 molecular oxygen 

O3 ozone 

16O oxygen-16, stable isotope of oxygen 

18O oxygen-18, stable isotope of oxygen 

19O oxygen-19, radioactive isotope of oxygen 

OC organic carbon 

OCO Orbiting Carbon Observatory 

OCS carbonyl sulfide 

O(1D) electronically excited oxygen atom 

OH hydroxyl radical 

OMI Ozone Monitoring Instrument 

O(3P) ground-state oxygen atom 

P phosphorus 

P, p probability value 

P1 1st percentile 

P5 5th percentile 

P95 95th percentile 

P99 99th percentile 

PAHs polycyclic aromatic hydrocarbons 

PAMS Photochemical Assessment Monitoring 

Stations 

PAN peroxyacetyl nitrate 

PANs peroxyacyl nitrates 

PARASOL Polarization and Anisotropy of Reflectances for 

Atmospheric Sciences coupled with 

Observations from a Lidar (satellite) 

Pb lead 

PBL planetary boundary layer 

PC payment card 

PCBs polychlorinated biphenyl compounds 

pH relative acidity 

 xxvi 
P(HNO3) production of nitric acid 

PHREEQC model for soil and water geochemical 

equilibrium 

PIRLA Paleocological Investigation of Recent Lake 

Acidification (projects) 

pKa dissociation constant 

PM particulate matter 

PM2.5 particulate matter with aerodynamic diameter 

of #2.5 μm 

PM10 particulate matter with aerodynamic diameter 

#10 μm 

PM10-2.5 particulate matter with aerodynamic diameter 

between 10 and 2.5 μm 

PM-CAMx Comprehensive Air Quality Model with 

extensions and with particulate matter 

chemistry 

PnET Photosynthesis and EvapoTranspiration 

(model) 

PnET-BGC Photosynthesis and EvapoTranspirationBiogeochemical (model) 

PnET-CN Photosynthesis and EvapoTranspiration model 

of C, water, and N balances 

PnET-N-DNDC Photosynthesis and EvapoTranspirationDenitrification-Decomposition (model) 

pNO3

− particulate nitrate 

P(O3) production of O3

PO4

−, PO43− phosphate 

POPs persistent organic pollutants 

ppb parts per billion 

PPN peroxypropionyl nitrate 

ppt parts per trillion 

PRB policy relevant background 

PRE-STORM Preliminary Regional Experiment for STORM 

PROFILE model using soil mineralogy as input 

PS producer surplus 

pSO4

2− particulate sulfate 

P(SO4

2−) production of sulfate 

Q flow rate; discharge 

Q10 temperature coefficient 

QAPP Quality Assurance Project Plan 

R generic organic group attached to a molecule 

R2 coefficient of determination 

r

2 correlation coefficient 

Ra aerodynamic resistance 

Rb boundary layer resistance 

Rc internal resistance 

RADM Regional Acid Deposition Model 

RAMS Regional Atmospheric Modeling System 

RAPS Regional Air Pollution Study 

RCOO-s strongly acidic organic anions 

RC(O)OO organic peroxy radical 

RDT Recovery Delay Time 

REMAP Regional Environmental Monitoring and 

Assessment Program 

RH relative humidity 

RLTM Regional Long-Term Monitoring 

RMCC Research and Monitoring Coordinating 

Committee 

RMSE root mean squared error 

RO2 organic peroxyl; organic peroxy 

RONO2 organic nitrate 

RO2NO2 peroxynitrate 

RP revealed preferences 

RRX lognormal-transformed response ratio 

RuBisCO ribulose-1,5-bisphosphate 

carboxylase/oxygenase 

s second 

S sulfur 

32S sulfur-32, stable isotope of sulfur 

34S sulfur-34, stable isotope of sulfur 

35S sulfur-35, radioactive isotope of sulfur 

SAA sum of mineral acid anion concentrations 

SAFE Soil Acidification in Forest Ecosystems (model) 

SAMAB Southern Appalachian Man and the Biosphere 

(program) 

SAMI Southern Appalachian Mountains Initiative 

SAO Smithsonian Astrophysical Observatory 

SAPRAC Statewide Air Pollution Research Center 

SBC sum of base cation concentrations 

SBUV Solar Backscatter Ultraviolet Spectrometer 

SC safe concentration 

SCAQS Southern California Air Quality Study 

SCIAMACHY Scanning Imaging Absorption Spectrometer for 

Atmospheric Chartography 

Se selenium; standard error 

SEARCH Southeastern Aerosol Research and 

Characterization Study (monitoring program) 

Si silicon 

SIP State Implementation Plan 

SJAQS San Joaquin Valley Air Quality Study 

SLA specific leaf area 

SLAMS State and Local Air Monitoring Stations 

SMART Simulation Model for Acidification’s Regional 

Trends (model) 

SMB Simple Mass Balance (model) 

SO sulfur monoxide 

SO2 sulfur dioxide 

SO3 sulfur trioxide 

SO3

2− sulfite 

SO4

2− sulfate ion 

S2O disulfur monoxide 

SONEX Subsonics Assessment Ozone and Nitrogen 

Oxides Experiment 

SOS Southern Oxidant Study 

SOS/T State of Science/Technology (report) 

SOX sulfur oxides 

SP stated preferences 

 xxvii 
 xxviii 

SPARROW SPAtially Referenced Regressions on 

Watershed Attributes (model) 

Sr strontium 

86Sr strontium-86, stable isotope of strontium 

87Sr strontium-87, stable isotope of strontium 

SRB sulfate-reducing bacteria 

SRP soluble reactive phosphorus 

SSWC Steady State Water Chemistry (model) 

STE stratospheric-tropospheric exchange 

STN Speciation Trends Network 

SUM06 seasonal sum of all hourly average 

concentrations ≥ 0.06 ppm 

SVOC semivolatile organic compound 

SWAS Shenandoah Watershed Study 

Τ, τ tau, atmospheric lifetime 

T time; duration of exposure 

TAF Tracking and Analysis Framework (model) 

Tair air temperature 

TAMM Timber Assessment Market Model 

TAR Third Assessment Report 

TC total carbon; travel cost 

TCM travel cost method 

TDLAS Tunable Diode Laser Absorption Spectrometer 

Tg teragram 

TIME Temporally Integrated Monitoring of 

Ecosystems (program) 

TN total nitrogen 

TOMS Total Ozone Mapping Spectrometer 

TOR tropospheric ozone residual 

TP total phosphorus 

TRACE-P Transport and Chemical Evolution over the 

Pacific 

TSI timber-stand improvement 

TSS total suspended solids 

Twater water temperature 

UMD-CTM University of Maryland Chemical Transport 

Model 

UNECE United Nations Economic Commission for 

Europe 

USDA U.S. Department of Agriculture 

USFS U.S. Forest Service 

USGS U.S. Geological Survey 

UV ultraviolet 

UV-A ultraviolet radiation of wavelengths from 320 to 

400 nm 

UV-B ultraviolet radiation of wavelengths from 280 to 

320 nm 

Vd deposition rate, deposition velocity (cm/s) 

VOC volatile organic compound 

VSD Very Simple Dynamic (soil acidification model) 

VTSSS Virginia Trout Stream Sensitivity Study 

WARMS Waterfowl Acidification Response Modeling 

System 

WATERSN Watershed Assessment Tool for Evaluating 

Reduction Scenarios for Nitrogen 

WBB West Bear Brook 

WEBB Water, Energy, and Biogeochemical Budgets 

WFPS water-filled pore space 

WGE Working Group on Effects 

WLS Western Lakes Survey 

WMO World Meteorological Organization 

WMP Watershed Manipulation Project 

WSA Wadeable Stream Assessment (survey) 

wt % percent by weight 

WTA willingness-to-accept 

WTP willingness-to-pay 

XNO3 nitrate halogen-X salt 

XO halogen-X oxide 

Zn zinc 

ZnO zinc oxide 
Authors, Contributors, Reviewers 

Authors 

Dr. Tara Greaver (NOX and SOX Project Manager)—National Center for Environmental Assessment, 

Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, 

NC 

Dr. Jeffrey R. Arnold—National Center for Environmental Assessment, Office of Research and 

Development, U.S. Environmental Protection Agency, Research Triangle Park, NC 

Dr. Jill S. Baron—U.S. Geological Survey, Natural Resource Ecology Laboratory, Colorado State 

University, Fort Collins CO 

Dr. Jana Compton—National Health and Environmental Effects Research Laboratory, Office of Research 

and Development, U.S. Environmental Protection Agency, Corvallis, OR 

Dr. Bernard J. Cosby, Jr.—Department of Environmental Sciences, University of Virginia, 

Charlottesville, VA 

Dr. Ila Cote—National Center for Environmental Assessment, Office of Research and Development, 

U.S. Environmental Protection Agency, Research Triangle Park, NC 

Ms. Rebecca Daniels— National Health and Environmental Effects Research Laboratory, Office of 

Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC 

Dr. Jean-Jacques B. Dubois—National Center for Environmental Assessment, Office of Research and 

Development, U.S. Environmental Protection Agency, Research Triangle Park, NC 

Dr. Christine L. Goodale—Department of Ecology and Evolutionary Biology, Cornell University, 

Ithaca, NY 

Dr. Alan T. Herlihy—Department of Fisheries & Wildlife, Oregon State University, Corvallis, OR 

Dr. Jeffrey D. Herrick—National Center for Environmental Assessment, Office of Research and 

Development, U.S. Environmental Protection Agency, Research Triangle Park, NC 

Dr. Alan J. Krupnick—Resources for the Future, Washington, DC 

Dr. Kathleen Fallon Lambert—Ecologic: Analysis and Communications, Woodstock, VT 

Dr. Gregory B. Lawrence—U.S. Geological Survey Troy, NY 

Dr. Lingli Liu—National Center for Environmental Assessment, Office of Research and Development, 

U.S. Environmental Protection Agency, Research Triangle Park, NC 

Dr. Todd C. McDonnell—E&S Environmental Chemistry, Inc., Corvallis, OR 

Dr. Kristopher Novak—National Center for Environmental Assessment, Office of Research and 

Development, U.S. Environmental Protection Agency, Research Triangle Park, NC 

Dr. Joseph Pinto—National Center for Environmental Assessment, Office of Research and Development, 

U.S. Environmental Protection Agency, Research Triangle Park, NC 

 xxix
Dr. Mary A. Ross—National Center for Environmental Assessment, Office of Research and 

Development, U.S. Environmental Protection Agency, Research Triangle Park, NC 

Mr. Rich Scheffe—Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency, 

Research Triangle Park, NC 

Dr. Juha Siikamaki—Resources for the Future, Washington, DC 

Dr. Timothy J. Sullivan—E&S Environmental Chemistry, Inc., Corvallis, OR 

Dr. Helga Van Miegroet—Department of Wildland Resources, Department of Watershed Sciences, Utah 

State University, Logan, UT 

Dr. Paul F. Wagner—National Center for Environmental Assessment, Office of Research and 

Development, U.S. Environmental Protection Agency, Research Triangle Park, NC 

Contributors 

Dr. Robin L. Dennis—National Effects Research Laboratory, Office of Research and Development, U.S. 

Environmental Protection Agency, Research Triangle Park, NC 

Dr. Russell Dickerson—Dept. of Atmospheric and Oceanic Sciences, University of Maryland, College 

Park, MD 

Dr. Tina Fan— Environmental and Occupational Health Sciences Institute/ University of Medicine and 

Dentistry: New Jersey, Piscataway, NJ 

Dr. Arlene Fiore—Geophysical Fluid Dynamics Laboratory/NOAA, Princeton, NJ 

Dr. Larry Horowitz—Geophysical Fluid Dynamics Laboratory/NOAA, Princeton, NJ 

Dr. William Keene—Dept. of Environmental Sciences, University of Virginia, Charlottesville, VA 

Dr. Randall Martin—Dept. of Physics, Dalhousie University, Halifax, Nova Scotia, Canada 

Dr. William Munger—Center for Earth and Planetary Physics, Harvard University, Cambridge, MA 

Dr. Sandy Sillman—Dept. of Atmospheric and Oceanic Sciences, University of Michigan, Ann Arbor, 

MI 

Dr. John T. Walker—National Risk Management Research Laboratory, Office of Research and 

Development, U.S. Environmental Protection Agency, Research Triangle Park, NC 

Reviewers 

Dr. Lawrence Band—Department of Geography, UNC Chapel Hill, Chapel Hill, NC 

Dr. Tamara Blett—National Park Service, Lakewood, CO 

Dr. Jana Compton—National Health and Environmental Effects Research Laboratory, Office of Research 

and Development, U.S. Environmental Protection Agency, Corvallis, OR 

Dr. Russell Dickerson—Department of Chemistry & Chemical Physics, University or Maryland, 

College Park, MD 

 xxx
Dr. Dave A. Evans—Office of Policy, Economics, and Innovation, Washington, DC 

Dr. Dale Evarts—Office of Air and Radiation, Office of Air Quality Planning and Standards, 

U.S. Environmental Protection Agency, Research Triangle Park, NC 

Dr. Jacqueline Geoghegan—Department of Economics, Clark University, Worcester, MA 

Dr. D. Alan Hansen—Electric Power Research Institute, Palo Alto, CA 

Dr. Brooke Hemming—National Center for Environmental Assessment, Office of Research and 

Development, U.S. Environmental Protection Agency, Research Triangle Park, NC 

Dr. Brian Heninger—Office of Policy, Economics, and Innovation, Washington, DC 

Dr. William Hogsett—National Health and Environmental Effects Research Laboratory, Office of 

Research and Development, U.S. Environmental Protection Agency, Corvallis, OR 

Dr. Bryan Hubbell—Office of Air and Radiation, Office of Air Quality Planning and Standards, 

U.S. Environmental Protection Agency, Research Triangle Park, NC 

Dr. Gary Lear—Office of Air and Radiation, Office of Atmospheric Programs Washington, DC 

Dr. John Lehrter—National Health and Environmental Effects Research Laboratory, Office of Research 

and Development, U.S. Environmental Protection Agency, Gulf Breeze, FL 

Dr. Jason Lynch—Office of Air and Radiation, Office of Atmospheric Programs, U.S. Environmental 

Protection Agency, Washington, DC 

Dr. William Malm—National Park Service, Fort Collins, CO 

Dr. Steve McNulty—USDA Forest Service, Raleigh, NC 

Ms. Connie Meacham—National Center for Environmental Assessment, Office of Research and 

Development, U.S. Environmental Protection Agency, Research Triangle Park, NC 

Mr. Greg Miller—Office of Policy, Economics, and Innovation, Washington, DC 

Dr. James Morris—Belle W. Baruch Institute for Marine & Coastal Sciences, University of South 

Carolina, Columbia, SC 

Dr. William Munger—School of Engineering and Applied Science Harvard University, Cambridge, MA 

Dr. Knute Nadelhoffer—University of Michigan Biological Station, Pellston, MI 

Dr. Christine Negra—The Heinz Center, Washington, DC 

Dr. William Orem—U.S. Geological Survey, Reston, VA 

Dr. Ellen Porter—National Park Service, Denver, CO 

Dr. Anne Rea—Office of Air and Radiation, Office of Air Quality Planning and Standards, 

U.S. Environmental Protection Agency, Research Triangle Park, NC 

Dr. Paul Ringold—National Health and Environmental Effects Research Laboratory, Office of Research 

and Development, U.S. Environmental Protection Agency, Corvallis, OR 

Ms. Vicki Sandiford—Office of Air and Radiation, Office of Air Quality Planning and Standards, 

U.S. Environmental Protection Agency, Research Triangle Park, NC 

 xxxi
 xxxii

Dr. William Showers—Dept. of Marine, Earth and Atmospheric Sciences, North Carolina State 

University, Raleigh, NC 

Dr. Gail Tonnesen—University of California, Denver, CO 

Dr. John Vandenberg (Division Director) —National Center for Environmental Assessment-RTP 

Division, Office of Research and Development, U.S. Environmental Protection Agency, Research 

Triangle Park, NC 

Dr. Lisa Wainger—TN and Associates, Hollywood, MD 

Mr. Randy Waite—Office of Air and Radiation, Office of Air Quality Planning and Standards, U.S. 

Environmental Protection Agency, Research Triangle Park, NC 

Ms. Debra Walsh (Deputy Division Director)— National Center for Environmental Assessment-RTP 

Division, Office of Research and Development, U.S. Environmental Protection Agency, Research 

Triangle Park, NC 

Dr. Shaun Watmough—Dept. of Environmental and Resource Studies, Trent University, Peterborough, 

Ontario 

Ms. Lydia Wegman—Office of Air and Radiation, Office of Air Quality Planning and Standards 

U.S. Environmental Protection Agency, Research Triangle Park, NC 

Dr. Jason West—Dept. of Environmental Sciences & Engineering, University of North Carolina, 

Chapel Hill, NC 

Dr. William Wilson—National Center for Environmental Assessment, Office of Research and 

Development, U.S. Environmental Protection Agency, Research Triangle Park, NC 
Project Team 

Executive Direction 

Dr. Ila Cote (Acting Division Director)—National Center for Environmental Assessment-RTP Division, 

Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, 

NC 

Dr. John Vandenberg (Division Director) —National Center for Environmental Assessment-RTP 

Division, Office of Research and Development, U.S. Environmental Protection Agency, Research 

Triangle Park, NC 

Ms. Debra Walsh (Deputy Division Director)— National Center for Environmental Assessment-RTP 

Division, Office of Research and Development, U.S. Environmental Protection Agency, Research 

Triangle Park, NC 

Dr. Mary A. Ross (Branch Chief)—National Center for Environmental Assessment, Office of Research 

and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC 

Scientific Staff 

Dr. Tara Greaver (NOX and SOX Project Manager)—National Center for Environmental Assessment, 

Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, 

NC 

Dr. Jeffrey R. Arnold—National Center for Environmental Assessment, Office of Research and 

Development, U.S. Environmental Protection Agency, Research Triangle Park, NC 

Dr. Jean-Jacques Dubois—Oak Ridge Institute for Science and Education, Postdoctoral Research Fellow 

to National Center for Environmental Assessment, Office of Research and Development, U.S. 

Environmental Protection Agency, Research Triangle Park, NC 

Dr. Jeffrey D. Herrick—National Center for Environmental Assessment, Office of Research and 

Development, U.S. Environmental Protection Agency, Research Triangle Park, NC 

Dr. Lingli Liu— Oak Ridge Institute for Science and Education, Postdoctoral Research Fellow to 

National Center for National Center for Environmental Assessment, Office of Research and 

Development, U.S. Environmental Protection Agency, Research Triangle Park, NC 

Dr. Kristopher Novak—National Center for Environmental Assessment, U.S. Environmental Protection 

Agency, Research Triangle Park, NC 

Dr. Paul F. Wagner—National Center for Environmental Assessment, Office of Research and 

Development, U.S. Environmental Protection Agency, Research Triangle Park, NC 

 xxxiii
 xxxiv

Technical Support Staff 

Ms. Ellen Lorang—National Center for Environmental Assessment, Office of Research and 

Development, U.S. Environmental Protection Agency, Research Triangle Park, NC 

Ms. Connie Meacham—National Center for Environmental Assessment, Office of Research and 

Development, U.S. Environmental Protection Agency, Research Triangle Park, NC 

Ms. Deborah Wales—National Center for Environmental Assessment, Office of Research and 

Development, U.S. Environmental Protection Agency, Research Triangle Park, NC 

Mr. Richard Wilson—National Center for Environmental Assessment, Office of Research and 

Development, U.S. Environmental Protection Agency, Research Triangle Park, NC
 xxxvi

Clean Air Scientific Advisory Committee 

NOX and SOX Secondary NAAQS Review Panel 

Dr. Armistead (Ted) Russell* (Chair)—Georgia Power Distinguished Professor of Environmental 

Engineering, Environmental Engineering Group, School of Civil and Environmental Engineering, 

Georgia Institute of Technology, Atlanta, GA 

Dr. Praveen Amar, Director, Science and Policy, Northeast States for Coordinated Air Use Management, 

Boston, MA 

Dr. Andrzej Bytnerowicz, Senior Scientist, Pacific Southwest Research Station, USDA Forest Service, 

Riverside, CA 

Ms. Lauraine Chestnut, Managing Economist, Stratus Consulting Inc., Boulder, CO 

Dr. Ellis B. Cowling*, Emeritus Professor, Colleges of Natural Resources and Agriculture and Life 

Sciences, North Carolina State University, Raleigh, NC 

Dr. Douglas Crawford-Brown*, Professor and Director, Department of Environmental Sciences and 

Engineering, Carolina Environmental Program, University of North Carolina at Chapel Hill, Chapel Hill, 

NC 

Dr. Charles T. Driscoll, Jr., Professor, Environmental Systems Engineering, College of Engineering and 

Computer Science, Syracuse University, Syracuse, NY 

Dr. Paul J. Hanson, Distinguished R&D Staff Member, Environmental Sciences Division, Oak Ridge 

National Laboratory, Oak Ridge, TN 

Dr. Rudolf Husar, Professor and Director, Mechanical Engineering, Engineering and Applied Science, 

Center for Air Pollution Impact and Trend Analysis, Washington University, St. Louis, MO 

Dr. Dale Johnson, Professor, Department of Environmental and Resource Sciences, College of 

Agriculture, University of Nevada, Reno, NV 

Dr. Donna Kenski*, Director, Lake Michigan Air Directors Consortium, Rosemont, IL 

Dr. Naresh Kumar, Senior Program Manager, Environment Division, Electric Power Research Institute, 

Palo Alto, CA 

Dr. Myron Mitchell, Distinguished Professor and Director of Council on Hydrologic Systems Science, 

College of Environmental and Forestry, State University of New York, Syracuse, NY 

Mr. Richard L. Poirot, Environmental Analyst, Air Pollution Control Division, Department of 

Environmental Conservation, Vermont Agency of Natural Resources, Waterbury, VT 

Mr. David J. Shaw, Director, Division of Air Resources, New York State Department of Environmental 

Conservation, Albany, NY 

Dr. Kathleen Weathers, Senior Scientist, Cary Institute of Ecosystem Studies, Millbrook, NY 

* Members of the statutory Clean Air Scientific Advisory committee (CASAC) appointed by the United States (U.S.) Environmental Protection 

Agency (EPA) Administrator 
Chapter 1. Introduction 

This Integrated Science Assessment (ISA) synthesizes and evaluates the most policy-relevant 

science to help form the scientific foundation for the review of the secondary (welfare-based) National 

Ambient Air Quality Standards (NAAQS) for oxides of nitrogen (NOX) and sulfur oxides (SOX). The 

Clean Air Act (CAA) definition of welfare effects includes, but is not limited to, effects on soils, water, 

wildlife, vegetation, visibility, weather, and climate, as well as effects on materials, economic values, and 

personal comfort and well-being. 

The intent of the ISA, according to the CAA, is to “accurately reflect the latest scientific 

knowledge expected from the presence of [a] pollutant in ambient air” (U.S. Code, 1970a, 1970b). It 

includes scientific research from atmospheric sciences, exposure and deposition, biogeochemistry, 

hydrology, soil science, marine science, plant physiology, animal physiology, and ecology conducted at 

multiple scales (e.g., population, community, ecosystem, landscape levels). Key information and 

judgments formerly found in the Air Quality Criteria Documents (AQCDs) for NOX and SOX are 

included; Annexes provide a more detailed discussion of the most pertinent scientific literature. Together, 

the ISA and Annexes serve to update and revise the last NOX and SOX AQCDs that were published in 

1993 and 1982, respectively. 

As discussed in the Integrated Plan for the Review of the Secondary NAAQS for Nitrogen Dioxide 

and Sulfur Dioxide (U.S. EPA, 2007a) a series of policy-relevant questions frames this review of the 

scientific evidence used to provide a scientific basis for evaluation of the secondary NAAQS for NO2

(0.053 parts per million [ppm], annual average) and SO2 (0.5 ppm, 3-h average). The framing questions 

considered are: 

1. What are the known or anticipated welfare effects influenced by ambient NOX and SOX? For 

which effects is there sufficient information available to be useful as a basis for considering 

distinct secondary standards? 

2. What is the nature and magnitude of ecosystem responses to NOX and SOX that are understood to 

have known or anticipated adverse effects? What is the variability associated with these responses 

(including ecosystem type, climatic conditions, environmental effects, and interactions with other 

environmental factors and pollutants)? 

3. To what extent do the current standards provide the requisite protection for the public welfare 

effects associated with NOX and SOX? 

4. Which biotic species are most vulnerable to the adverse effects of NOX and SOX air pollution? 

How is adversity defined? 

5. What ecosystems are most sensitive to NOX and SOX pollution? 

6. How does NOX and SOX pollution impact ecosystem services? 

7. What are the most appropriate spatial and temporal scales to evaluate impacts on ecosystems? 

8. What is the relationship between ecological vulnerability to NOX and SOX pollution and 

variations in current meteorology or gradients in climate? 

1.1. Scope 

The U.S. EPA is integrating the science assessment for these two criteria air pollutants due to their 

combined effects on atmospheric chemistry, deposition processes, and public welfare effects. The focus of 

 1-1
this assessment is primarily on effects related to the deposition of nitrogen (N)- and sulfur (S)-containing 

compounds. Ecological effects from acidification and N-nutrient enrichment have been studied most 

extensively in the ecological literature. An assessment of the complex ecological effects of N deposition 

requires consideration of multiple forms of N. Thus, this assessment includes evaluation of data on 

inorganic reduced forms of N (e.g., ammonia [NH3] and ammonium ion [NH4

+

]), inorganic oxidized 

forms (e.g., NOX, nitric acid [HNO3], nitrous oxide [N2O], nitrate [NO3

−

]), and organic N compounds 

(e.g., urea, amines, proteins, nucleic acids). In addition to acidification and N-nutrient enrichment, other 

welfare effects related to deposition of N- and S-containing compounds are discussed, such as SOX

interactions with mercury (Hg) methylation. In addition, this assessment includes evidence related to 

direct ecological effects of gas-phase NOX and SOX since the direct effects of gas-phase SOX on 

vegetation formed a primary basis for the initial establishment of the secondary NAAQS for SO2. The 

contribution of gas-phase NOX as greenhouse gases (GHG), particularly N2O, is considered, chiefly in the 

response of soils to reactive nitrogen (Nr) enrichment. 

A review of the particulate matter (PM) NAAQS is underway. Recent data on the welfare effects of 

airborne particulate NOX and SOX in the ambient air will be evaluated in the PM ISA. These effects 

include visibility impairment, soiling and damage to materials, and effects of ambient PM on climate. 

(For more information, see http://www.epa.gov/ttn/naaqs/standards/pm/s_pm_index.html.) 

Gas-phase and particulate NOX and SOX compounds can affect ecosystems and alter numerous 

linked biogeochemical cycles. A simplified diagram of the combined NOX and SOX cycle is presented in 

Figure 1-1. The ISA includes additional figures that provide more detail on the interactions among 

biogeochemical cycles, and the locations of those figures are indicated in the diagram. These figures 

include atmospheric cycling, interactions between the N cycle and carbon (C), the N cycle and 

phosphorous (P), and the S cycle and Hg. 

Figure 1-1. Biogeochemical cycles of NOX and SOX. 

 1-2
1.2. History of the NOX and SOX Review 

Nitrogen Oxides 

In 1971, U.S. EPA promulgated identical primary and secondary NAAQS for NO2: 0.053 ppm as 

an annual average (36 FR 8186). The scientific bases for these NAAQS were provided in the AQCD for 

NOX (U.S. EPA, 1971). 

In 1984, U.S. EPA proposed to retain these standards (49 FR 6866), and after the public comment 

period, finalized that decision in 1985 (50 FR 25532); the scientific basis for this review was provided by 

the 1982 AQCD for NOX (U.S. EPA, 1982a). 

In 1991, U.S. EPA released an updated draft AQCD for the Clean Air Scientific Advisory 

Committee (CASAC) and public review and comment (56 FR 59285). CASAC reviewed the document 

and concluded it “provides a scientifically balanced and defensible summary of current knowledge of the 

effects of this pollutant and provides an adequate basis for U.S. EPA to make a decision as to the 

appropriate NAAQS for NO2” (Wolff, 1993). 

The U.S. EPA also prepared a draft Staff Paper that summarized and integrated the key studies and 

scientific evidence contained in the revised AQCD and identified the critical elements to be considered in 

the review of the NO2 NAAQS. In September 1995, U.S. EPA finalized the Staff Paper, Review of the 

National Ambient Air Quality Standards for Nitrogen Dioxide: Assessment of Scientific and Technical 

Information (U.S. EPA, 1995b). The Administrator made a final determination that no revisions to the 

primary and secondary NAAQS for NO2 were appropriate at that time (61 FR 52852, October 8, 1996). 

The level for both the existing primary and secondary NAAQS for NO2 remains 0.053 ppm (equivalent to 

100 micrograms per cubic meter of air [μg/m3

]) in annual arithmetic average, calculated as the arithmetic 

mean of the 1-h NO2 concentrations. 

Sulfur Oxides 

Based on the 1970 SOX AQCD (U.S. Department of Health, Education and Welfare, 1970), U.S. 

EPA promulgated primary and secondary NAAQS for SO2, under Section 109 of the CAA on April 30, 

1971 (36 FR 8186). The secondary standard was set at 0.02 ppm in an annual arithmetic mean and a 3-h 

average of 0.5 ppm, not to be exceeded more than once per year. These standards were established solely 

based on vegetation effects evidence. In 1973, revisions made to Chapter 5 “Effects of Sulfur Oxide in the 

Atmosphere on Vegetation” of the SOX AQCD (U.S. EPA, 1973), indicated that it could not properly be 

concluded that the reported vegetation injury resulted from the average SO2 exposure over the growing 

season rather than from short-term peak concentrations. U.S. EPA, therefore, proposed (38 FR 11355) and 

then finalized a revocation of the annual mean secondary standard (38 FR 25678). 

In 1979, U.S. EPA announced that it was revising the SOX AQCD concurrently with the PM review 

and would produce a combined PM SOX AQCD. Following review of the draft revised criteria document 

in August 1980, CASAC concluded acidic deposition was a topic of extreme scientific complexity 

because of the difficulty in establishing firm quantitative relationships among (a) emissions of relevant 

pollutants (e.g., SO2 and NOX), (b) formation of acidic wet and dry deposition products, and (c) effects on 

terrestrial and aquatic ecosystems. CASAC also noted that acidic deposition involves, at a minimum, 

several different criteria pollutants (i.e., SOX, NOX, and the fine particulate fraction of suspended 

particles). CASAC recommended that any document on this subject should address both wet and dry 

deposition, because dry deposition was believed to account for at least half of the total acid deposition 

problem. 

The U.S. EPA proposed not to revise the existing primary and secondary standards on April 26, 

1988 (53 FR 14926). Regarding the secondary SO2 NAAQS, the U.S. EPA Administrator concluded that 

based upon then-current scientific understanding of the acidic deposition problem, it would be premature 

and unwise to prescribe any regulatory control program at that time, and when the fundamental scientific 

uncertainties had been reduced through ongoing research efforts, U.S. EPA would draft and support an 

 1-3
appropriate set of control measures. On May 22, 1996, U.S. EPA’s final decision, that revisions of the 

NAAQS for SOX were not appropriate at that time, was announced in the Federal Register (61 FR 25566). 

Acidic Deposition Assessments 

Based upon their conclusions from the AQCD review discussed above, CASAC recommended that 

a separate, comprehensive document on acidic deposition be prepared before any regulatory consideration 

for the control of acidic deposition. CASAC also suggested that a discussion of acidic deposition be 

included in the AQCDs for both NOX and PM-SOX. Following CASAC closure on the criteria document 

for SO2 in 1981, U.S. EPA’s Office of Air Quality Planning and Standards (OAQPS) published a Staff 

Paper (U.S. EPA, 1982c); it did not, however, directly address this issue. U.S. EPA followed CASAC 

guidance and subsequently prepared the following documents: The Acidic Deposition Phenomenon and 

Its Effects: Critical Assessment Review Papers, Volumes I and II (U.S. EPA, 1984a, 1984b) and The 

Acidic Deposition Phenomenon and Its Effects: Critical Assessment Document (U.S. EPA, 1985). These 

documents, though they were not considered criteria documents and did not undergo CASAC review, 

represented the most comprehensive summary of relevant scientific information completed by the U.S. 

EPA at that point. 

Assessment of the ecological effects of NOX and SOX has been conducted under the U.S. EPA acid 

precipitation control program. In the 1990 CAA Amendments (CAAA), Title IV was to reduce emissions 

of SO2 and NOX from fossil fuel-burning power plants to protect ecosystems suffering damage from acid 

deposition and to improve air quality. The National Acid Precipitation Assessment Program (NAPAP) has 

periodically assessed and reported to Congress on the implementation of the Acid Rain Program, recent 

scientific knowledge surrounding acid deposition and its effects, and the reduction in acid deposition 

necessary to prevent adverse ecological effects. These assessments were to be reported to Congress 

quadrennially, beginning in 1996. The most recent in this series of reports is the National Acid 

Precipitation Assessment Program Report to Congress: An Integrated Assessment that was submitted to 

Congress in 2005 (NAPAP, 2005). 

The 1990 CAAA also required U.S. EPA to conduct a study on the feasibility and effectiveness of 

an acid deposition standard or standards to protect “sensitive and critically sensitive aquatic and terrestrial 

resources.” In 1995, the U.S. EPA submitted to Congress its report titled Acid Deposition Standard 

Feasibility Study: Report to Congress (U.S. EPA, 1995a) in fulfillment of this requirement. The Acid 

Deposition Standard Feasibility Study Report to Congress concluded establishing acid deposition 

standards for S and N deposition may at some point in the future be technically feasible although 

appropriate deposition loads for these acidifying chemicals could not defined with reasonable certainty at 

that time. 

1.3. History of the Current Review 

U.S. EPA’s National Center for Environmental Assessment in Research Triangle Park, NC 

announced the official initiation of the current periodic review of air quality criteria for NOX on 

December 9, 2005 (70 FR 73236), and for SOX on May 15, 2006 (71 FR 28023), with a call for 

information. A workshop to inform the Agency’s review of the secondary standards for these two 

pollutants was held on July 17-19, 2007 (72 FR 11960). The first ISA external review draft was published 

in December 2007 (72 FR 72719) and reviewed by CASAC at a public meeting on April 2-3, 2008. The 

second ISA external review draft was published in August 2008 (73 FR 46908) and reviewed by the 

CASAC at a public meeting on October 1-2, 2008. This final ISA (December 2008) includes revisions to 

address comments from CASAC and the public. 

 1-4
1.4. Development of the Integrated Science Assessment 

An extensive search and review of the literature is the initial step in preparing the ISA. Additional 

publications were identified by U.S. EPA scientists in a variety of disciplines. In addition to peer-reviewed 

literature, previous U.S. EPA reports and materials identified in reviewing reference lists were examined. 

Further publications have been identified through the peer review process by CASAC, other experts, and 

the public. The focus of this ISA is on literature published since the 1993 NOX AQCD and the 1982 SOX

AQCD. Key findings and conclusions from the 1993 and 1982 reviews are discussed in conjunction with 

recent studies. In addition, analyses of air quality and emissions data, and studies on atmospheric 

chemistry, transport, and fate of these emissions were scrutinized. 

Emphasis was placed on studies that evaluated effects near ambient levels and studies that consider 

NOX and SOX as components of a complex mixture of air pollutants. Studies conducted in any country 

that contribute significantly to the knowledge base were considered for inclusion. In evaluating 

quantitative exposure-response relationships, emphasis was placed on findings from studies conducted in 

the U.S. and Canada as having ecological and climatic conditions most relevant for review of the 

NAAQS. In assessing the relative scientific quality of studies reviewed here and to assist in interpreting 

the findings, the following were considered: 

1. To what extent are the aerometric data/exposure metrics of adequate quality and sufficiently 

representative to serve as credible exposure indicators? 

2. Were the study populations well-defined and adequately selected to allow for meaningful 

comparisons between study groups? 

3. Were the ecological assessment endpoints reliable and policy-relevant? 

4. Were the statistical analyses used appropriately and properly performed and interpreted? 

5. Were likely important covariates (e.g., potential confounders or effect modifiers) adequately 

controlled or taken into account in the study design and statistical analyses? 

6. Were the reported findings consistent, biologically plausible, and coherent in terms of consistency 

with other known facts? 

These guidelines provide benchmarks for evaluating various studies and for focusing on the highest 

quality studies in assessing the body of environmental effects evidence. Detailed critical analysis of all 

NOX and SOX environmental effects studies, especially in relation to the above considerations, is beyond 

the scope of the ISA and Annexes. Studies providing qualitative or quantitative information on exposureresponse relationships for the environmental effects associated with current ambient air concentrations of 

NOX and SOX or deposition levels likely to be encountered in the U.S. were considered most relevant. 

1.5. Organization of the Integrated Science Assessment 

This ISA has four chapters. Chapter 1 provides background information on the purpose of the 

document, explains how policy-relevant scientific studies are identified and selected for inclusion, and 

introduces the causality framework used in U.S. EPA’s assessments. Chapter 2 presents fundamental and 

applied atmospheric science data to support assessing the environmental exposures and effects associated 

with N and S oxides. Information relevant to the review of the welfare effects of NOX and SOX is 

integrated and evaluated in Chapter 3. Findings are organized into three categories: ecological effects of 

acidification, ecological effects of N nutrient pollution, and other welfare effects, which address several 

minor welfare effects, including gas-phase foliar toxicity and the role of S in Hg methylation. Finally, 

summary and conclusions are found in Chapter 4. Supplementary Annexes provide additional details. 

 1-5
1.6. Causality Framework 

U.S. EPA uses a two-step approach to evaluate the scientific evidence on welfare effects of criteria 

pollutants, similar to the approach it uses for health effects. The steps address two general policy-relevant 

questions: 

1. Given the total body of evidence, what, if any, are the welfare effects of NOX and SOX? 

2. Can levels of exposure at which welfare effects of concern occur be defined? 

The first step determines the weight of evidence in support of causation, and characterizes the 

strength of any resulting causal classification. The second step includes further evaluation of the 

quantitative evidence with respect to concentration-response relationships and the levels, duration, and 

pattern of exposures at which effects are observed. 

The most widely cited aspects of causality in public health were articulated by Sir Austin Bradford 

Hill (1965), and have been widely used (e.g., IARC, 2006; Samet and Bodurow, 2008). Several 

adaptations of the Hill aspects have been used in aiding causality judgments in the ecological sciences 

(Adams, 2003; Buck et al., 2000; Collier, 2003; Fox, 1991; Gerritsen et al., 1998). Based on these 

adaptations, the U.S. EPA uses eight aspects in judging causality (see Table 1-1). The broad national scale 

of this assessment differs from the site-specific scale of ecological assessment for which applications of 

the Hill aspects have been published. The following aspects were developed to meet the scope of this 

ISA: 

Table 1-1. Aspects to aid in judging causality. 

 CONSISTENCY of the observed association. The inference of causality is strengthened when the 

same association between agent and effect is observed across similar, independent studies. The 

reproducibility of findings constitutes one of the strongest arguments for causality. If there are 

discordant results among comparable investigations, possible reasons such as differences in 

exposure, confounding factors, and the power of the study are considered. 

 STRENGTH of the observed association. The finding of large, well-demarcated effects increases 

confidence that the association is causal. However, given a truly causal agent, a small magnitude 

in the effect could follow from a lower level of exposure, a lower potency, or the prevalence of 

other agents causing similar effects. While large effects support causality, modest effects, 

therefore, do not preclude it. 

 SPECIFICITY of the observed association. The effect is only observed after exposure to that agent, 

and the agent produces only that effect. Hill (1965), and subsequent authors, consider specificity 

a weak aspect. At the scale of ecosystems, as in epidemiology, complexity is such that single 

agents causing single effects, and single effects following single causes, are extremely unlikely. 

The absence of specificity cannot be used to exclude causality, especially at those scales. 

However, if specificity can be demonstrated, as in some laboratory or other experimental studies, 

it does add strong support to causality. 

 TEMPORALITY of the observed association. Evidence of a temporal sequence between the 

introduction of an agent and appearance of the effect constitutes another argument in favor of 

causality. 

 GRADIENT. A clear exposure-response relationship (e.g., increasing effects associated with 

greater exposure) strongly suggests cause and effect. 

 1-6
 PLAUSIBILITY. A credible ecological basis for the observed association adds strength to an 

inference of causality. A proposed mechanistic linking between an effect, and exposure to the 

agent, is an important source of support for causality, especially when data establishing the 

existence and functioning of those mechanistic links are available. A lack of biological 

understanding, however, is not sufficient reason to reject causality. 

 EXPERIMENTAL evidence. Controlled exposure to the agents provides results that support the 

proposed causal relationship. The practical limits on control as the number of potential interacting 

factors increases are such that the most compelling experiments can only be conducted at the 

scale of a laboratory, growth chamber, or, at most, mesocosm. Therefore, since a judgment of 

causality derived from experimental evidence often cannot be extended very far beyond the scale 

at which the experiment was conducted, experimental evidence is generally only one element of 

the information that comes to bear in determining causality at the ecosystem, regional, or greater 

scales. 

 COHERENCE. Given the scale and complexity of the environment and of ecosystems, 

determinations of causality are usually based on many lines of evidence, considered jointly. 

Evidence may be drawn from a variety of experimental approaches (e.g., greenhouse, laboratory, 

field) and subdisciplines of ecology (e.g., community ecology, biogeochemistry, 

paleological/historical reconstructions). The coherence of the available sources is a critical aspect 

of assessing the strength of a causal association. The coherence of evidence from various fields, 

and at various scales, greatly adds to the strength of an inference of causality. 

While these aspects provide a framework for assessing the evidence, they are not simple formulas 

or fixed rules of evidence leading to conclusions about causality (Hill, 1965). The aspects in Table 1-1 

cannot be used as a strict checklist, but rather to determine the weight of the evidence for inferring 

causality. In particular, the absence of one or more of the aspects does not automatically exclude a study 

from consideration (e.g., see discussion in CDC, 2004). For example, one cannot simply count the 

number of studies reporting statistically significant or nonsignificant results, and reach credible 

conclusions about the relative weight of the evidence and the likelihood of causality. Rather, the aspects 

are an important part of the assessment, whose goal is to produce an objective appraisal of the evidence, 

and is informed by peer and public comment and advice, including weighing of alternative views on 

controversial issues. 

1.6.1. First Step: Determination of Causality 

In this ISA, U.S. EPA evaluated publications available since the previous NAAQS reviews. This 

evaluation builds upon evidence available and conclusions drawn in the previous reviews to draw 

conclusions on the causal relationships between relevant pollutant exposures and welfare outcomes. A 

five-level hierarchy is used to classify the weight of evidence for causation, as assessed by the reviewing 

group with input from peers, CASAC, and the public. After integration of the evidence from all relevant 

disciplines or types of studies (laboratory studies, ecosystem experiments, simulation models and 

observational studies), the weight of evidence in support of causality is expressed using one of the five 

descriptors (see Table 1-2). In this multi-pollutant assessment, the effects may be due to a combination of 

pollutants (e.g., in acidifying deposition or N deposition). To the extent possible, U.S. EPA will identify 

the pollutants that are “significant contributing factors” to the relationship being evaluated. 

 1-7
 1-8

Table 1-2. Weight of evidence for causal determination. 

Relationship Description 

Causal relationship Evidence is sufficient to conclude that there is a causal relationship between relevant pollutant exposure 

and the outcome. Causality is supported when an association has been observed between the pollutant 

and the outcome in studies in which chance, bias, and confounding could be ruled out with reasonable 

confidence. Controlled exposure (laboratory or small- to medium-scale field studies) provides the 

strongest evidence for causality, but the scope of inference may be limited. Generally, determination is 

based on multiple studies conducted by multiple research groups, and evidence that is considered 

sufficient to infer a causal relationship is usually obtained from the joint consideration of many lines of 

evidence that reinforce each other. 

Likely to be a causal 

relationship 

Evidence is sufficient to conclude that there is a likely causal association between relevant pollutant 

exposures and the outcome. That is, an association has been observed between the pollutant and the 

outcome in studies in which chance, bias and confounding are minimized, but uncertainties remain. For 

example, field studies show a relationship, but suspected interacting factors cannot be controlled, and 

other lines of evidence are limited or inconsistent. Generally, determination is based on multiple studies 

in multiple research groups. 

Suggestive of a causal 

relationship 

Evidence is suggestive of an association between relevant pollutant exposures and the outcome, but 

chance, bias and confounding cannot be ruled out. For example, at least one high-quality study shows an 

association, but the results of other studies are inconsistent. 

Inadequate to infer a causal 

relationship 

The available studies are of insufficient quality, consistency or statistical power to permit a conclusion 

regarding the presence or absence of an association between relevant pollutant exposure and the 

outcome. 

Suggestive of no causal 

relationship 

Several adequate studies, examining relationships between relevant exposures and outcomes, are 

consistent in failing to show an association between exposure and the outcome at any level of exposure. 

1.6.2. Second Step: Evaluation of Ecological Response 

Beyond judgments regarding causality are questions relevant to characterizing exposure 

concentration response and risk to ecosystems (e.g., the levels and loads of pollution at which ecological 

effects occur). Such questions include: 

1. What elements of the ecosystem (e.g., types, regions, taxonomic groups, populations, functions) 

appear to be affected, and/or are more susceptible to effects? 

2. Under what exposure conditions (amount or concentration, duration and pattern) are effects seen? 

3. What is the shape of the concentration-response or exposure-response relationship? 

Causal and likely causal claims typically characterize how the probability of ecological effects 

changes in response to exposure. The ecological scale at which those quantitative considerations are valid 

is a concern. Initially, responses are evaluated within the range of observation, but ecological data for 

concentration-response analyses are often not available at the national or even regional scale. They are, 

therefore, typically presented site by site. Where greenhouse or animal ecotoxicological studies are 

available, they may be used to aid in characterizing concentration-response relations, particularly those 

relative to mechanisms of action and characteristics of sensitive biota. 
Chapter 2.

 

Source to Deposition 

This chapter provides fundamental and applied atmospheric science data to support assessing the 

environmental exposures and effects associated with N and S oxides. More specifically, these data relate 

to N and S emissions sources and rates, atmospheric transformation and transport, total atmospheric 

loadings, measurement and modeling techniques, and deposition issues relevant to this review of the 

NAAQS. These data are prologue for the detailed descriptions of the evidence of environmental effects 

from N and S oxides that follow in Chapter 3, and a source of information to help interpret those effects 

when integrated with these data on atmospheric concentrations and exposures. 

2.1. Introduction 

As noted in Chapter 1, the definition of NOX appearing in the NAAQS enabling legislation differs 

from the one used by atmospheric scientists and air quality control experts. The atmospheric sciences 

community defines NOX as the sum of NO and NO2. However, in the Federal Register Notice (FRN) 

(October 8, 1996) for the “National Ambient Air Quality Standards for NO2: Final Rule” (61 FR 52852), 

the term “nitrogen oxides” was used to “describe the sum of NO, NO2, and other oxides of nitrogen.” This 

ISA uses the legal, rather than the technical definition; hence, the terms “oxides of nitrogen” and 

“nitrogen oxides” here refer to all forms of oxidized N compounds, including NO, NO2, and all other 

oxidized N-containing compounds transformed from NO and NO2.

1

 Additionally, because some of the 

constituent members of the NOX family of chemical species interact with particulate-phase chemical 

species and change phase themselves, the chemistry, concentrations, and deposition of particulate N 

compounds are also considered in this assessment. 

Oxides of sulfur (SOX) is defined here to include sulfur monoxide (SO), sulfur dioxide (SO2 [the 

largest component of SOX and the U.S. EPA Criteria Air Pollutant]), sulfur trioxide (SO3), and disulfur 

monoxide (S2O). Of these, only SO2 is present in the lower troposphere at concentrations relevant for 

environmental considerations. Moreover, some gas-phase sulfur oxides interact with particles and change 

phase themselves, just as do some constituent members of the N family of gas-phase chemical species; 

hence, particulate-phase S compounds are also assessed here. 

NH3 is included in this ISA both because its oxidation can be a minor source of NOX and because it 

is the precursor for ammonium ion (NH4

+

), which plays a key role in neutralizing acidity in ambient 

particles produced from NO2 and SO2 and in cloud, fog, and rain water. (NH3 and NH4

+

 are conventionally 

grouped together under the category label NHX.) Excess NH3 is also an actor in nitrification of aqueous 

and terrestrial ecosystems, participating alone and together with NOX in the N cascade (Galloway et al., 

2003). Additionally, NH3 is involved in the ternary nucleation of new particles and reacts with gas-phase 

HNO3 to form ammonium nitrate (NH4NO3), a major component of N deposition in many areas of the 

contiguous U.S. (CONUS). 

1

 This follows usage in the Clean Air Act, Section 108(c): “Such criteria [for oxides of nitrogen] shall include a discussion of nitric and nitrous 

acids, nitrites, nitrates, nitrosamines, and other carcinogenic and potentially carcinogenic derivatives of oxides of nitrogen.” (U.S. Code 1970a) 

By contrast, within the air pollution research and control communities, the terms “oxides of nitrogen” and “nitrogen oxides” are restricted to refer 

only to the sum of NO and NO2, and this sum is commonly abbreviated as NOX. The category label used by this air pollution research and control 

community for the sum of all oxidized N compounds, including those listed in Section 108(c), is NOY. 

 2-1
 2-2

2.2. Sources and Emissions of Tropospheric NOX

Tropospheric NOX emissions sources can be anthropogenic, resulting from human activity, or 

biogenic and natural, resulting from the activity of non-human organisms, though sometimes with the 

addition of human activities, as with production from livestock or agriculture, and from other smaller 

miscellaneous non-biological sources. However, anthropogenic sources contribute substantially more 

mass than biogenic ones. The anthropogenic and biogenic sources of NOX are described in detail and their 

emissions totals are provided below. 

2.2.1. Major Anthropogenic Sources 

Anthropogenic NOX emissions are dominated by fossil fuel combustion sources that release NOX

predominantly in the form of NO with variable amounts of NO2. In 2002, anthropogenic NOX emissions 

in the U.S. totaled 23.19 teragram/year (Tg/yr). Table 2-1 lists fractions and totals from anthropogenic 

NOX sources collected for the 2002 National Emissions Inventory (NEI) (U.S. EPA, 2006a). 

Table 2-1. Emissions of NOX, NH3, and SO2 in the U.S. by source and category, 2002. 

2002 Emissions (Tg/yr) NOX1 NH3 SO2

Total All Sources 23.19 4.08 16.87

Fuel Combustion Total 9.11 0.02 14.47

Fuel Combustion Electrical Utilities 5.16 <0.01 11.31

Coal 4.50 <0.01 10.70

 Bituminous 2.90 8.04

 Subbituminous 1.42 2.14

 Anthracite & Lignite 0.18 0.51

 Other <0.01 

Oil 0.14 <0.01 0.38

 Residual 0.13 0.36

 Distillate 0.01 0.01

Gas 0.30 <0.01 0.01

 Natural 0.29 

 Process 0.01 

 Other 0.05 <0.01 0.21

Internal Combustion 0.17 <0.01 0.01

Fuel Combustion Industrial 3.15 <0.01 2.53

Coal 0.49 <0.01 1.26

 Bituminous 0.25 0.70

 Subbituminous 0.07 0.10

 Anthracite & Lignite 0.04 0.13

 Other 0.13 0.33

Oil 0.19 <0.01 0.59

Residual 0.09 0.40

 Distillate 0.09 0.16

 Other 0.01 0.02

Gas 1.16 <0.01 0.52

 Natural 0.92 

 Process 0.24 

 Other <0.01 

Other 0.16 <0.01 0.15

 Wood/Bark Waste 0.11 

2002 Emissions (Tg/yr) NOX1 NH3 SO2

 Liquid Waste 0.01 

 Other 0.04 

Internal Combustion 1.15 <0.01 0.01

Fuel Combustion Other 0.80 <0.01 0.63

Commercial / Institutional Coal 0.04 <0.01 0.16

Commercial / Institutional Oil 0.08 <0.01 0.28

Commercial / Institutional Gas 0.25 <0.01 0.02

Misc. Fuel Combust. (Exc. Resident.) 0.03 <0.01 0.01

Residential Wood 0.03 <0.01

Residential Other 0.36 0.16

 Distillate Oil 0.06 0.15

 Bituminous/Subbituminous 0.26 <0.01

 Other 0.04 <0.01

Industrial Process Total 1.10 0.21 1.54

Chemical & Allied Product Mfg 0.12 0.02 0.36

Organic Chemical Mfg 0.02 <0.01 0.01

Inorganic Chemical Mfg 0.01 <0.01 0.18

Sulfur Compounds 0.17

Other 0.02

Polymer & Resin Mfg <0.01 <0.01 <0.01

Agricultural Chemical Mfg 0.05 0.02 0.05

Ammonium Nitrate/Urea Mfg. <0.01

Other 0.02

Paint, Varnish, Lacquer, Enamel Mfg 0.00 0.00

Pharmaceutical Mfg 0.00 0.00

Other Chemical Mfg 0.03 <0.01 0.12

Metals Processing 0.09 <0.01 0.30

Non-Ferrous Metals Processing 0.01 <0.01 0.17

Copper 0.04

Lead 0.07

Zinc 0.01

Other <0.01
 2-3 

2002 Emissions (Tg/yr) NOX1 NH3 SO2

Ferrous Metals Processing 0.07 <0.01 0.11

Metals Processing 0.01 <0.01 0.02

Petroleum & Related Industries 0.16 <0.01 0.38

Oil & Gas Production 0.07 <0.01 0.11

Natural Gas 0.11

Other 0.01

Petrol. Refineries & Related Industries 0.05 <0.01 0.26

Fluid Catalytic Cracking Units <0.01 0.16

Other <0.01 0.07

Asphalt Manufacturing 0.04 0.01

Other Industrial Processes 0.54 0.05 0.46

Agriculture, Food, & Kindred Products 0.01 <0.01 0.01

Textiles, Leather, & Apparel Products <0.01 <0.01 <0.01

Wood, Pulp & Paper, Publish. Prods. 0.09 <0.01 0.10

Rubber & Misc. Plastic Products <0.01 <0.01 <0.01

Mineral Products 0.42 <0.01 0.33

Cement Mfg 0.24 0.19

Glass Mfg 0.01 

Other 0.10 0.09

Machinery Products <0.01 <0.01 <0.01

Electronic Equipment <0.01 <0.01 <0.01

Transportation Equipment <0.01 <0.01

Miscellaneous Industrial Processes 0.01 0.05 0.02

Solvent Utilization 0.01 <0.01 <0.01

Degreasing <0.01 <0.01 <0.01

Graphic Arts <0.01 <0.01 <0.01

Dry Cleaning <0.01 <0.01 <0.01

Surface Coating <0.01 <0.01 <0.01

Other Industrial <0.01 <0.01 <0.01

Nonindustrial <0.01 

Solvent Utilization <0.01 

Storage & Transport <0.01 <0.01 0.01

Bulk Terminals & Plants <0.01 <0.01 <0.01

Petrol. & Petrol. Product Storage <0.01 <0.01 <0.01

Petrol. & Petrol. Product Transport <0.01 <0.01 <0.01

Service Stations: Stage II <0.01 <0.01

Organic Chemical Storage <0.01 <0.01 <0.01

Organic Chemical Transport 0.01 <0.01

Inorganic Chemical Storage <0.01 <0.01 <0.01

Inorganic Chemical Transport <0.01 <0.01

Bulk Materials Storage 0.01 <0.01 <0.01

Waste Disposal & Recycling 0.17 0.14 0.03

Incineration 0.06 <0.01 0.02

Industrial 

Other <0.01

Open Burning 0.10 <0.01 <0.01

Industrial <0.01

Land Clearing Debris 

Other <0.01

Public Operating Treatment Works <0.01 0.14 <0.01

Industrial Waste Water <0.01 <0.01 <0.01

Treatment, Storage, Disposal Facility <0.01 <0.01 <0.01

Landfills <0.01 <0.01 <0.01

Industrial <0.01

Other <0.01

Other <0.01 <0.01 <0.01

Transportation Total 12.58 0.32 0.76

2002 Emissions (Tg/yr) NOX1 NH3 SO2

Highway Vehicles 8.09 0.32 0.30

Light-Duty Gas Vehicles & Motorcycles 2.38 0.20 0.10

 Light-Duty Gas Vehicles 2.36 0.10

 Motorcycles 0.02 0.00

Light-Duty Gas Trucks 1.54 0.10 0.07

 Light-Duty Gas Trucks 1 1.07 0.05

 Light-Duty Gas Trucks 2 0.47 0.02

Heavy-Duty Gas Vehicles 0.44 <0.01 0.01

Diesels 3.73 <0.01 0.12

 Heavy-Duty Diesel Vehicles 3.71 

 Light-Duty Diesel Trucks 0.01 

 Light-Duty Diesel Vehicles 0.01 

Off-Highway 4.49 <0.01 0.46

Non-Road Gasoline 0.23 <0.01 0.01

 Recreational 0.01 

 Construction 0.01 

 Industrial 0.01 

 Lawn & Garden 0.10 

 Farm 0.01 

 Light Commercial 0.04 

 Logging <0.01 

 Airport Service <0.01 

 Railway Maintenance <0.01 

 Recreational Marine Vessels 0.05 

Non-Road Diesel 1.76 <0.01 0.22

 Recreational 0.00 

 Construction 0.84 

 Industrial 0.15 

 Lawn & Garden 0.05 

 Farm 0.57 

 Light Commercial 0.08 

 Logging 0.02 

 Airport Service 0.01 

 Railway Maintenance <0.01 

 Recreational Marine Vessels 0.03 

Aircraft 0.09 0.01

Marine Vessels 1.11 0.18

 Diesel 1.11 

 Residual Oil 

 Other 

Railroads 0.98 0.05

Other 0.32 <0.01 0.00

 Liquefied Petroleum Gas 0.29 

 Compressed Natural Gas 0.04 

Miscellaneous 0.39 3.53 0.10

Agriculture & Forestry <0.01 3.45 <0.01

Agricultural Crops <0.01

Agricultural Livestock 2.66

Other Combustion 0.08 0.10

Health Services 

Cooling Towers 

Fugitive Dust 

Other 

Natural Sources 3.10 0.03

1 Emissions are expressed in terms of NO2. Note: Subcategory values may not sum to 

category totals due to rounding. Source: U.S. EPA (2006a) 
Of this total, emissions from all types of transportation accounted for ~56% of NOX, or 12.58 Tg, 

with on-road highway vehicles representing the major mobile source component, 8.09 Tg. Roughly onehalf of these on-road emissions have diesel engine sources and one-half have gasoline engine sources. 

(Sawyer et al. [2000] reviewed in detail the factors associated with NOX emissions by mobile sources.) 

The next largest source category, electric-generating utilities (EGUs), accounted for ~22%, or 5.16 Tg of 

total NOX in 2002. Stationary engines, non-road vehicles, and industrial facilities also emit NOX, but 

because they are fewer in number or burn less fuel, their mass contributions to total NOX are less than 

transportation and EGUs. 

The values in Table 2-1 are U.S. national averages and so may not reflect differences in the relative 

contributions of NOX sources to ambient mass loadings at any particular location; hence, these values are 

not likely to be useful predictors of any particular localized environmental exposures to NOX. As a partial 

refinement of scale, county-level NOX emissions are depicted in Figure 2-1.1 A further refinement appears 

in Figure 2-2, where the same 2001 NOX emissions data are plotted as area-normalized intensities in tons 

per square mile. This normalized emissions intensity base is also used to show the separate contributions 

from EGUs and on-road mobile sources in Figures 2-3 and 2-4, respectively. 

Source: U.S. EPA (2006a) 

Figure 2-1. 2001 county-level total U.S. NOX (NO and NO2) emissions.1

 

1

 Range values: White, 0 or no reported value; Blue, from the smallest non-zero to the 10th percentile value; Green, from above the 10th to the 25th

percentile; Yellow, from above the 25th to the 50th percentile; Pink, from above the 50th to the 75th percentile; Red, from above the 75th to the 90th

percentile; Brown, from above the 90th percentile to the highest reported value. 

 2-4
Source: U.S. EPA (2006a) 

Figure 2-2. 2001 county-level total U.S. NOX (NO and NO2) emissions densities (tons per square mile). 1

Source: U.S. EPA (2006a) 

Figure 2-3. 2001 county-level total U.S. NOX (NO and NO2) emissions densities (tons per square mile) from 

electric-generating utilities (EGUs). 1 

 

1

 Range values: White, 0 or no reported value; Blue, from the smallest non-zero to the 10th percentile value; Green, from above the 10th to the 25th

percentile; Yellow, from above the 25th to the 50th percentile; Pink, from above the 50th to the 75th percentile; Red, from above the 75th to the 90th

percentile; Brown, from above the 90th percentile to the highest reported value. 

 2-5
Source: U.S. EPA (2006a) 

Figure 2-4. 2001 county-level total U.S. NOX (NO and NO2) emissions densities (tons per square mile) from 

on-road mobile sources. 1

Emissions of NOX from combustion are derived from both fuel N and atmospheric N. Combustionzone temperatures >~1300 K are required to fix atmospheric N2 by the reaction. 

ON 22 ⎯+ ⎯→ 2NO

Reaction 1 

Below this temperature, NO can be formed from fuel N by the reaction 

dcba ONOHC 2 ⎯+ ⎯→ 2 2 ++ zNOOyHxCO

Reaction 2 

Both Reaction 1 and Reaction 2 have temperature dependencies and vary with concentrations of hydroxyl 

radical (OH), hydroperoxy radical (HO2), and O2. 

The N content in fossil fuels and its specific chemical form vary strongly with source type, fuel, 

engine emissions controls, and running conditions. N content in fuel stocks ranges from 0.05% by weight 

(wt %) in light distillates such as diesel fuel (Samet and Bodurow, 2008) to 1.5 wt % in heavy fuel oils, 

and from 0.5 to 2.0 wt % in coal, as surveyed by the United Kingdom (U.K. AQEG, 2004) Air Quality 

Expert Group. 

On-road mobile source emissions constitute the largest type of emissions from all transportation 

sources. Significant variability attaches to these emissions. For example, the ratio of NO2 to total NOX in 

 

1

 Range values: White, 0 or no reported value; Blue, from the smallest non-zero to the 10th percentile value; Green, from above the 10th to the 25th

percentile; Yellow, from above the 25th to the 50th percentile; Pink, from above the 50th to the 75th percentile; Red, from above the 75th to the 90th

percentile; Brown, from above the 90th percentile to the highest reported value. 

 2-6
exhaust gases in primary emissions ranges from 1 to 3% from gasoline engines tested on dynamometers 

(Heeb et al., 2008; Hilliard and Wheeler, 1979). On the other hand, some European studies have reported 

NO2-to-NOX ratios ≥ 15% from gasoline vehicles based on integrated measurements from Tedlar bags 

(Lenner, 1987; Soltic and Weilenmann, 2003). However, subsequent studies suggesting that NO-to-NO2

conversion will occur within a bag sample of diluted exhaust if not properly handled have led groups 

performing these measurements to revise their measurement techniques to avoid use of Tedlar bag 

samples (Alvarez et al., 2008). As a result, dynamometer-based measurements generally indicate that in 

the absence of post-tailpipe transformation, NO2 comprises, at most, only a few percent of the total NOX

in current-generation gasoline engine exhaust. 

The emissions ratio of NO2 to NOX ranges between 5 and 12% from heavy-duty diesel truck 

engines, although some emission control devices used for diesel engines in Europe increase the fraction of 

exhaust NOX emitted as NO2 to >20% (Carslaw and Beevers, 2005; Carslaw, 2005; Carslaw and Carslaw, 

2007; Kessler et al., 2006). In the U.S., on-road experiments with diesel engines propelling heavy buses 

in congested urban areas like New York City have shown that engines equipped with emissions control 

devices similar to those in the European studies increased the NO2-to-NOX ratio from ~10% before 

addition of the new controls to ~30% after controls were added (Shorter et al., 2005). In a second type of 

experiment in a different setting, Kittelson et al. (2006) used an on-road laboratory to sample exhaust 

plumes of a truck equipped with the European-style emissions control device under highway cruise 

conditions and found the NO2-to-NOX ratios for this exhaust under highway cruise conditions ranged 

from 59 to 70%. The wide range revealed by comparing these two studies illustrates the significant 

differences in NOX exhaust under different conditions of engine load and ambient temperature. 

As for other combustion sources, NO2-to-NOX emissions ratios for compressed natural gas engines 

range between 5 and 10%, and between 5 and 10% from most stationary sources. The NO2-to-NOX ratios 

in emissions from turbine jet engines are as high as 35% during taxi and takeoff (U.S. EPA, 2006a). 

In addition to NO and NO2, mobile sources emit other forms of oxidized N including nitrous acid 

(HNO2); measured ratios of HNO2 to NOX range from a low of 0.3% in the Caldecott Tunnel, San 

Francisco, CA (Kirchstetter and Harley, 1996), up to as much as 0.5 and 1.0% in studies in the U.K. 

(U.K. AQEG, 2004). 

Marine transport represents an additional source of NOX in the U.S., especially for coastal cities 

with large ports, but constitutes a larger source in Europe where it is expected to represent more than 60% 

of land-based NOX sources (U.K. AQEG, 2004). 

The anthropogenic sources of NOX are distributed with height such that some, like on-road mobile 

sources, are nearer to ground level than others, like the emissions stacks from EGUs and some industrial 

emitters. Emissions height is an important consideration because the prevailing winds aloft are generally 

stronger than those at the surface. The result is that emissions from elevated sources can be distributed 

over a wider area than those emitted at the surface and hence can be diluted to lower mixing ratios than 

those emitted nearer their sources. 

2.2.2.

2.2.2.1.

Major Biogenic Sources 

Soils 

Nitrification and denitrification processes in soils produce two gas-phase intermediates, NO and 

N2O, which can evolve from soil microbes before reaching their reaction endpoint, N2. N2O is not among 

the nitrogen oxides important for urban and regional air quality either for human health concerns or 

environmental effects because its reaction potential on these spatio-temporal scales in the troposphere is 

insignificant. As a result, NO from soil metabolism is the prime, but not exclusive, form of atmospheric 

NOX from the biosphere relevant to this ISA. 

 2-7
Biogenic NOX emissions are predominately the result of incomplete bacterial denitrification and 

nitrification processes, as described above. Denitrification is a reduction process performed by particular 

groups of heterotrophic bacteria having the ability to use nitrate ion (NO3

−

) as an electron acceptor during 

anaerobic respiration, thereby converting NO3

−

 in soils and water to gas-phase forms (Firestone and 

Davidson, 1989). At low O2 concentrations, these microbial communities may use NO3

−

, nitrite (NO2

−

), or 

N2O as alternative electron acceptors to O2 (Davidson and Schimel, 1995). 

The basic outlines of these reaction pathways are known, but uncertainty remains concerning the 

conditions favoring production of the various products of the NO3

−

 transformations. Groups of aerobic 

bacteria use most NH4

+

 in soils as an energy source, oxidizing it to NO2 and then NO3

−

. Oxidized N 

products from nitrification may undergo denitrification and thus also drive production of NOX. Some 

bacteria are known to be nitrifiers and denitrifiers and can change depending on environmental 

conditions, including high loadings of exogenous N. 

Soil emissions of NOX can be increased by agricultural practices and activities, including the use of 

synthetic and organic fertilizers, production of N-fixing crops, cultivation of soils with high organic 

content, and the application of livestock manure to croplands and pasture. All of these practices directly 

add exogenous N to soils, of which a portion will then be converted to NO or N2O on the pathway to full 

conversion to N2. Additionally, indirect additions of N to soils can also result in NOX emissions from 

agricultural and non-agricultural systems. Indirect additions include processes by which atmospheric NOX

is deposited directly to a region or N from applied fertilizer or manure volatilizes to NH3 and is oxidized 

to NOX and then is ultimately re-deposited onto soils as NH4NO3, HNO3, or NOX (U.S. EPA, 2006c). 

N metabolism in soils is strongly dependent on soil substrate concentrations and physical 

conditions. Where N is limiting, it is efficiently retained and little gas-phase N is released; where N is in 

excess of demand, N emissions increase. As a consequence, soil NO emissions are highest from fertilized 

agricultural lands and tropical soils (Davidson and Kingerlee, 1997; Williams et al., 1992). In addition, 

temperature, soil moisture, and O2 concentrations control both the rates of reaction and the partitioning 

between NO and N2O. In flooded soils where O2 concentrations are low, N2O is the dominant soil N gas; 

as soils dry, more O2 diffuses in and NO emissions increase. In very dry soils, microbial activity is 

inhibited and emissions of both N2O and NO decrease. 

Emission rates of NO from cultivated soils depend largely on fertilization levels and soil 

temperature. Production of NO from agriculture results from the oxidation of NH3 emitted both by 

livestock and by soils after fertilization with NH4NO3. Estimates of biogenic N emissions are far less 

certain than those of anthropogenic emissions sources. Uncertainty on the order of a factor of 3 or more is 

introduced by the variation within biomes to which fertilizer is applied, such as between shortgrass and 

tallgrass prairie for example (Davidson and Kingerlee, 1997; Williams et al., 1992; Yienger and Levy, 

1995). The contribution of soil emissions to the global NOX budget is approximately 10% (Finlayson-Pitts 

and Pitts, 2000; Seinfeld and Pandis, 1998; Van Aardenne et al., 2001), but NOX emissions from fertilized 

fields are highly variable. Soil NO emissions can be estimated from the fraction of the applied fertilizer N 

emitted as NOX, for example, but the flux depends strongly on land use type and temperature. Estimates 

of globally averaged fractional-applied N lost as NO vary from a low of 0.3% (Skiba et al., 1997) up to 

2.5% (Yienger and Levy, 1995). 

The spatial scales of these N fluxes are also significant. Local contributions to soil NOX can be 

much greater than the global average, particularly in summer, and especially where corn is grown 

extensively. Approximately 60% of total NOX emitted by soils in the U.S. occurs in the central corn belt. 

Nitrification of fertilizer NH3 to NO3

−

 in aerobic soils appears to be the dominant pathway to soil NOX

emissions, but the mass and chemical form of N applied to soils, the vegetative cover, the temperature and 

soil moisture characteristics, and the agricultural practices such as tillage all influence the amount of 

fertilizer N converted and released as NOX. On sub-national scales these emissions can be large and 

highly variable. Williams et al. (1992) estimated that NOX from soils in Illinois was ~25% of the total 

NOX emissions from industrial and commercial processes in that state. In Iowa, Kansas, Minnesota, 

Nebraska, and South Dakota—states with smaller human populations than Illinois—soil emissions may, 

in fact, dominate the NOX budget. 

 2-8
Emissions of NOX from soils often peak in summer when ozone (O3) formation is also at a 

maximum. The significance of agricultural emission sources of NO and NH3 among other air pollutants 

was described in detail in a recent National Research Council report (NRC, 2002). That report 

recommended immediate implementation of best management practices to control these emissions, and 

called for additional research to quantify the magnitude of emissions and the effects of agriculture on air 

quality. The effects of such changes in management practice can be dramatic: Civerolo and Dickerson 

(1998) reported that the use of no-till cultivation techniques on a fertilized cornfield in Maryland reduced 

NO emissions by a factor of 7. 

2.2.2.2. Live Vegetation 

Extensive work on N inputs from the atmosphere to forests was conducted in the 1980s as part of 

the Integrated Forest Study, summarized by Johnson and Lindberg (1992b). As noted below and in 

Chapter 4, our understanding of NO2 exchange with vegetation suggests that NO2 should be emitted from 

foliage when ambient concentrations are below the compensation point of ~1 ppb. However, Lerdau et al. 

(2000) noted that current understanding of the global distribution of NOX is not consistent with the large 

source that would be expected in remote forests if NO2 emissions were significant when atmospheric 

concentrations were below the 1 ppb compensation point. 

2.2.2.3. Biomass Burning 

During biomass burning, N is derived mainly from fuel N and not from atmospheric N2, since 

temperatures required to fix atmospheric N2 are likely to be found only in the flaming crowns of the most 

intense boreal forest fires. N is present in plants mostly as amine (NH2) groups in amino acids. During 

combustion, N is released in many forms, mostly unidentified and presumably as N2, leaving very little N 

remaining in the fuel ash. Emissions of NOX are estimated to be ~0.2 to 0.3% of the total biomass burned 

(e.g., Andreae, 1991; Radke et al., 1991). The most abundant NOX species in biomass burning plumes is 

NO, emissions of which account for ~10 to 20% of the total fuel N loadings (Lobert et al., 1991); other 

N-containing species such as NO2, nitriles, and NH3 together account for a similar amount. Westerling 

et al. (2006) noted that the frequency and intensity of wildfires in the western U.S. increased substantially 

since 1970, lending added importance to consideration of all NOX emissions from this sector. 

2.2.2.4. Lightning 

Annual global production of NO by lightning is the most uncertain source of atmospheric N. In the 

last decade, literature values of the global average production rate ranged from 2 to 20 Tg N/yr. Most 

recent estimates, however, are in the range of 3 to 8 Tg N/yr. This large and persistent uncertainty stems 

from several factors: a wide range of as much as two orders of magnitude in NO production rates per 

meter of flash length; uncertainty over whether cloud-to-ground (CG) and intracloud (IC) flashes produce 

substantially different NO levels; the global average flash rate; and the ratio of IC to CG flashes. 

Estimates of the NO concentration produced per flash have been made from theoretical 

considerations (e.g., Price et al., 1997), laboratory experiments (e.g., Wang et al., 1998), and field 

experiments (Huntrieser et al., 2002, 2007; Stith et al., 1999), and with a hybrid method of cloudresolving model simulations, observed lightning flash rates, and measurements of NO concentrations in 

cloud anvils (DeCaria et al., 2000, 2005; Ott et al., 2007). A series of midlatitude and subtropical 

thunderstorm events were simulated with the model of DeCaria et al. (2005) and the derived NO 

production per CG flash was, on average, 500 moles/flash, while production per IC flash was 

425 moles/flash on average (Ott et al., 2007). The hybrid method had earlier been used by Pickering et al. 

(1998) who showed that only ~5 to 20% of the total NO produced by lightning in a given storm exists in 

 2-9
the planetary boundary layer (PBL) at the end of a thunderstorm event, thereby reducing its importance as 

a direct emissions source to the urban and regional troposphere. 

2.2.3. Anthropogenic and Biogenic Sources of N2O 

N2O has an atmospheric lifetime (τ) of ~114 years, resulting from its having effectively no 

chemistry in the lower troposphere on urban and regional scales. The chief N2O loss pathway with a 

quantum yield of ~1 is the photodissociation process 

)(1

2 ⎯⎯→ 2 + DONON

Reaction 3 

driven by the short wavelength UV present only in the stratosphere. 

However, N2O is also a GHG with a global warming potential (GWP) on the conventional 100–

year time horizon of ~296; i.e., 1 molecule of N2O is nearly 300 times more effective at trapping heat in 

the atmosphere than 1 molecule of carbon dioxide (CO2) over a 100–year period (IPCC, 2001b). The high 

GWP of N2O results from its combination of direct and indirect radiative forcing climate effects in the 

stratosphere. By comparison, the primary climate effects of NO and NO2 are indirect and result from their 

role in promoting the production of O3 (P(O3)) in the troposphere and, to a lesser degree, in the lower 

stratosphere where NOX has positive radiative forcing effects. Additional complications for calculating 

NOX GWPs ensue owing to the fact that NOX emissions from high-altitude aircraft are also likely to 

decrease methane (CH4) concentrations, a negative radiative forcing effect (IPCC, 1996), and that 

particulate nitrate (pNO3

−

) transformed from NOX also has negative radiative forcing effects. U.S. EPA 

does not calculate GWPs for total NOX or for SOX or for the other atmospheric constituents for which no 

agreed-upon method exists to estimate the contributions from these gases that are short-lived in the 

atmosphere, have strong spatial variability, or have only indirect effects on radiative forcing. 

Thus, because there are no tropospheric reactions or effects to consider, N2O is not a significant 

component of NOX for this ISA review of the NOX and SOX secondary effects related to the NAAQS. 

However, the role of N2O as an intermediate product along with NO from the complex soil metabolism 

described in Section 2.2.2.1 means that a brief description of its emissions strengths and its component 

part of the total budget of U.S. GHGs will be useful, and so appears just below. 

N2O is a contributor to the total U.S. GHG budget, with 6.5% of total GHG on a Tg CO2

equivalents basis (CO2e) in 2005 (U.S. EPA, 2007b). CO2, by comparison, accounted for 83.9% in the 

same year, and CH4 for 7.4% (U.S. EPA, 2007b). Although atmospheric concentrations of N2O have 

increased globally by ~18% to a current value of ~315 ppb due to western industrialization since the year 

1750 C.E. (Hofmann et al., 2004), there is considerable interannual variation in N2O emissions which 

remains largely unexplained (IPCC, 2001a). N2O emissions in the U.S., for example, decreased by 2.8%, 

or 13.4 Tg CO2e, between 1990 and 2005 (U.S. EPA, 2007a). 

N2O is produced by biological processes occurring in the soil and water, as described in 

Section 2.2.2 above, and by a variety of anthropogenic activities in the agricultural, energy, industrial, and 

waste management sectors. The chief anthropogenic activities producing N2O in the U.S. are agricultural 

soil management, fuel combustion in motor vehicles, manure management, production of adipic acid 

(nylon) and HNO3, wastewater treatment, and stationary fuel combustion. 

N2O emissions from anthropogenic activities in the U.S. were 386.7 Tg CO2e/yr between 1990 and 

2004 (U.S. EPA, 2007a). These emissions resulted from the fuel combustion, industrial practices, and 

stimulation of biogenic sources through agricultural practices listed above. In 2005, N2O emissions from 

mobile sources were 38.0 Tg CO2e, or ~8% of the U.S. N2O emissions total (U.S. EPA, 2007a). In the 

period between 1990 and 1998, control technologies on mobile sources reduced on-road vehicle NO and 

NO2 emissions at the expense of increasing N2O emissions by 10%. The overall reduction in N2O mobile 

 2-10
source emissions between 1998 and 2005 (when totals were last available), however, has been 13% owing 

to more efficient controls used after 1998. 

Biogenic production of N2O stimulated through soil management accounted for >75% of all U.S. 

N2O emissions in 2005 (U.S. EPA, 2007a). N2O emissions from these sources have shown no significant 

long-term trend because the biogenic emitters are highly sensitive to the concentrations and forms of N 

applied to soils, and these applications have been largely constant (U.S. EPA, 2007a). 

Aquatic sources of N2O may also be stimulated by environmental conditions. In some ocean areas, 

large areas of surface water can become depleted in O2, allowing active denitrification in open water, and 

potentially increasing N2O emissions as described in Section 2.2.2. In addition, oceanic N2O can also 

arise from denitrification in marine sediments, particularly in nutrient-rich areas like estuaries. 

2.3. Sources and Emissions of Tropospheric SOX

Emissions of SO2, the chief component of SOX, are due mostly to combustion of fossil fuels by 

EGUs and industrial processes, with transportation-related sources making smaller but significant 

contributions. 

2.3.1. Major Anthropogenic Sources 

Table 2-1 shows that for 2002, fossil fuel combustion at EGUs accounted for ~66% of total SO2

emissions in the U.S., or 11.31 Tg of the total 16.87 Tg. All transportation sources accounted for ~5% of 

the total U.S. SO2 emissions in 2002, or 0.76 Tg. On-road vehicles produced ~40% of the transportationrelated total SO2 emissions in 2002, with off-road diesel and marine traffic together accounting for the 

remainder. Thus, most SO2 emissions originate from point sources having well-known locations and 

identifiable fuel streams. 

Since nearly all S in fuels is released in volatile components, either SO2 or SO3, during combustion, 

total S emissions from these point sources can be computed from the known S content in fuel stocks with 

greater accuracy than can total NOX emissions from point sources. However, just as for the NOX

emissions totals described above, total SOX emissions estimates are national-scale averages and so cannot 

accurately reflect the contribution of local sources to selected environmental exposures to SOX at specific 

locations and times. To refine those national estimates, county-level average SO2 emissions for 2001 are 

shown in Figure 2-5; and normalized emissions intensities per square mile like those shown above for 

NOX are shown for SO2 in Figure 2-6. 

Figure 2-6 illustrates the west-to-east increasing gradient in SO2 emissions densities, with most 

counties east of the Mississippi River in warmer colors (greater emissions densities) than most counties in 

the West. The upper end of the SO2 emissions density distribution represented here includes many 

counties in the eastern U.S.—primarily in the Ohio River Valley—with 2001 SO2 emissions densities 

significantly greater than 20. Examples of these high densities (in tons per square mile) are Hillsborough 

County, FL, 80; Grant County, WV, 156; Indiana County, PA, 190; Washington County, OH, 273; and 

Armstrong County, PA, 292. In these counties, SO2 emissions were due mostly to EGU fuel combustion, 

as shown in Table 2-2. For the non-EGU emissions densities and the total SO2 densities in Figure 2-6, the 

upper end of the density distribution compresses a wide range; see Table 2-2. Thus, for the five counties 

considered above, non-EGU emissions were <5% of total SO2 emissions in Washington County, OH, 

and <1% in Indiana County, PA, Armstrong County, PA, and Grant County, WV. Hillsborough County, 

FL, is an exception, where 17% of the 2001 SO2 emissions density came from non-EGU sources, the 

largest of which was chemical and allied product manufacturing. 

 2-11
Source: U.S. EPA (2006a) 

Figure 2-5. 2001 county-level total U.S. SO2 emissions.1

Source: U.S. EPA (2006a) 

Figure 2-6. 2001 county-level total U.S. SO2 emissions densities (tons per square mile).1 

 

1

Range values: White, 0 or no reported value; Blue, from the smallest non-zero to the 10th percentile value; Green, from above the 10th to the 25th

percentile; Yellow, from above the 25th to the 50th percentile; Pink, from above the 50th to the 75th percentile; Red, from above the 75th to the 90th

percentile; Brown, from above the 90th percentile to the highest reported value. 

 2-12
Source: U.S. EPA (2006a) 

Figure 2-7. 2001 county-level SO2 emissions densities (tons per square mile) from EGUs. 1 

Although on-road mobile sources in 2001 contributed <5% to SO2 emissions totals on the national 

scale, their fraction of county-level emissions densities varies widely. Generally, however, on-road mobile 

source SO2 emissions reflect the west-to-east increasing gradient in the densities of both total SO2

emissions and U.S. population, as shown in Figure 2-8. In areas such as Wayne County, MI, and Bronx 

County, NY, for example, 2001 SO2 emissions densities from on-road mobile sources were 3 and 8.8 tons 

per square mile out of totals of 98 and 160 tons per square mile, total SO2, respectively. In other areas like 

Dallas County, TX, and DeKalb County, GA, however, the on-road fraction of total SO2 emissions 

densities in 2001 was substantially greater: 1.5 out of the total 4.1 tons per square mile in Dallas County, 

and 3.5 out of the total 6.5 tons per square mile in DeKalb County. 

Table 2-2. Total and non-EGU SO2 emissions densities for selected U.S. counties, 2001. 

County SO2 Emissions Density (tons/mile2) Non-EGU Emissions Density Fraction (%) 

Hillsborough, FL 80 17 

Grant, WV 156 <1 

Indiana, PA 190 <1 

Washington, OH 273 <5 

Armstrong, PA 292 <1 

 

1

Range values: White, 0 or no reported value; Blue, from the smallest non-zero to the 10th percentile value; Green, from above the 10th to the 25th

percentile; Yellow, from above the 25th to the 50th percentile; Pink, from above the 50th to the 75th percentile; Red, from above the 75th to the 90th

percentile; Brown, from above the 90th percentile to the highest reported value. 

 2-13
Source: U.S. EPA (2006a) 

Figure 2-8. 2001 county-level SO2 emissions densities (tons per square mile) from on-road mobile 

sources.1

Source: U.S. EPA (2006a) 

Figure 2-9. 2001 county-level SO2 emissions densities (tons per square mile) from off-road mobile and 

other transportation sources. 1

 

1

Range values: White, 0 or no reported value; Blue, from the smallest non-zero to the 10th percentile value; Green, from above the 10th to the 25th

percentile; Yellow, from above the 25th to the 50th percentile; Pink, from above the 50th to the 75th percentile; Red, from above the 75th to the 90th

percentile; Brown, from above the 90th percentile to the highest reported value. 

 2-14
An additional source of SO2 emissions of concern in particular locations not immediately obvious 

from national-scale averages and totals are transit and in-port activities in areas with substantial shipping 

traffic (Wang et al., 2007). Because of the importance of these SO2 emissions, the ports of Long Beach 

and Los Angeles, CA, for example, are part of a Sulfur Emissions Control Area in which S contents of 

fuels are not to exceed 1.5%. Figure 2-9 shows SO2 emissions densities combined for all non-road 

transportation-related emitters in which coastal areas with ports and shipping routes, such as the 

Mississippi River, are easily discerned. In Los Angeles County, CA, for example, off-road transportation 

including shipping and port traffic contributed 1.4 of the total 4.1 tons of SO2 per square mile in 2001; in 

King County (including the city of Seattle), WA, the off-road transportation fraction was 42% of the total 

SO2 emissions density, or 1.2 of the total 2.8 tons per square mile. Emissions density data at finer scales 

more specific to the ports are not available in the routine emissions inventories and some confusion 

attends estimates of the actual SO2 loads from these sources. Modeling studies by Vutukuru and Dabdub 

(2008) for southern California ports, for example, have shown that ships contribute <2 ppb to the 24-h 

average SO2 concentration in Long Beach, CA, in 2002 and <0.5 ppb farther inland. 

SO2 data collected from the State and Local Air Monitoring Stations (SLAMS) and National Air 

Monitoring Stations (NAMS) networks show that the decline in SO2 emissions following controls placed 

on electric generating utilities in the previous 15 years has improved air quality. There has not been a 

single monitored exceedance of the SO2 annual ambient air quality standard in the U.S. since 2000, 

(U.S. EPA, 2006e). U.S. EPA trends data (www.epa.gov/airtrends) reveal that the national composite 

average SO2 annual mean ambient concentration decreased by ~48% from 1990 to 2005, with the largest 

single-year reduction coming in 1994-1995, the Acid Rain Program’s (ARP) first operating year (U.S. 

EPA, 2006e). Figure 2-10 depicts data for SO2 emissions in the CONUS in these years that reflect this 

reduction using individual state-level totals. Note that SOX emissions have changed over this period both 

temporally and spatially, with some areas in the southeast U.S. such as North Carolina and Georgia 

realizing increased SOX emissions since 2000. SOX emissions from the largest emitters in the states of the 

Ohio River Valley, however, have mostly decreased, in some cases by very large fractions. 

Figure 2-10. State-level SO2 emissions, 1990-2005. 

 2-15
These trends in emissions data are consistent with the trends in the observed ambient 

concentrations from the Clean Air Status and Trends Network (CASTNet). Following implementation of 

the Phase I controls on ARP sources between 1995 and 2000, significant reductions in SO2 concentrations 

and ambient SO4

2– concentrations were observed at CASTNet sites throughout the eastern U.S. 

2.3.2. Major Biogenic Sources 

Emissions of SOX from natural sources are small compared to industrial emissions within the U.S. 

(see Table 2-1). However, important exceptions occur locally as the result of volcanic activity, wildfires, 

and in certain coastal zones as described above. 

The major biogenic sources of SO2 are volcanoes, biomass burning, wildfires, and dimethylsulfide 

(DMS) oxidation over the oceans. Although SO2 constitutes a relatively minor fraction of 0.005% by 

volume of total volcanic emissions (Holland, 1978), concentrations in volcanic plumes can range from 

several to tens of ppm. The ratio of hydrogen sulfide (H2S) to SO2 is highly variable in volcanic gases, 

typically <1, as in the Mount St. Helens eruption in the Washington Cascade Range (46o20’N, 122o18’W, 

summit 2549 m asl) (Turco et al., 1983). However, in addition to being degassed from magma, H2S can be 

produced if ground waters, especially those containing organic matter, come into contact with volcanic 

gases. In this case, the ratio of H2S to SO2 can be >1. H2S produced this way would more likely be 

emitted through side vents than through eruption columns (Pinto et al., 1989). Primary particulate sulfate 

(pSO4) is a component of marine aerosol and is also produced by wind erosion of surface soils. 

Since 1980, the Mount St. Helens volcano has been a variable source of SO2. Its major effects came 

in the explosive eruptions of 1980, which primarily affected the northern part of the mountainous western 

half of the U.S. The Augustine volcano near the mouth of the Cook Inlet in southwestern Alaska 

(59o36’N, 153o43’W, summit 1252 m asl) has had variable SO2 emissions since its last major eruptions in 

1986. Volcanoes in the Kamchatka peninsula of the eastern region of Siberian Russia do not significantly 

affect surface SO2 concentrations in northwestern North America. The most serious effects from volcanic 

SO2 in the U.S. occur on the island of Hawaii. Nearly continuous venting of SO2 from Mauna Loa and 

Kilauea produces SO2 in such large amounts that >100 km downwind of the island, levels of SO2 can 

exceed 30 ppb (Thornton and Bandy, 1993). 

Emissions of SO2 from burning vegetation are generally in the range of 1 to 2% of the biomass 

burned (see e.g., Levine et al., 1999). S is bound in amino acids in vegetation, and ~50% of this 

organically–bound S is released during combustion, leaving the remainder in the ash (Delmas, 1982). 

Gas-phase emissions are mainly in the form of SO2, with much smaller amounts of H2S and carbonyl 

sulfide (OCS). The ratio of reduced S species such as H2S to more oxidized forms such as SO2, increases 

as the fire conditions change from flaming to smoldering phases of combustion because emissions of 

reduced species are favored by lower temperatures and decreased O2 availability. 

SO2 is also produced by the photochemical oxidation of reduced S compounds such as DMS, H2S, 

carbon disulfide (CS2), OCS, methyl mercaptan (CH3-S-H), and dimethyl disulfide (CH3-S-S-CH3). The 

sources for these compounds are mainly biogenic (see Table 2-1). Emissions of reduced S species are 

associated typically with marine organisms living either in pelagic or coastal zones and with anaerobic 

bacteria in marshes and estuaries. Emissions of DMS from marine plankton represent the largest single 

source of reduced S species to the atmosphere (Berresheim et al., 1995). Other sources such as wetlands 

and terrestrial plants and soils account for <5% of the DMS global flux, with most of this coming from 

wetlands. 

Other than OCS, which is lost mainly by photolysis with a τ of ~6 months, SOX species are lost 

mainly by reaction with OH and NO3

−

 and are relatively short-lived, with τ ranging from a few hours to a 

few days. Reaction with NO3

−

 at night most likely represents the major loss process for DMS and methyl 

mercaptan. Although the mechanisms for the oxidation of DMS are not known with certainty, excess 

SO4

2−

 in marine aerosol appears related mainly to production of SO2 from the oxidation of DMS. Because 

 2-16
OCS is relatively long-lived, it can survive oxidation in the troposphere and be transported upward into 

the stratosphere. Crutzen (1976) proposed that its oxidation to SO4

2−

 in the stratosphere serves as the 

major source of the stratospheric aerosol layer. However, Myhre et al. (2004) proposed that SO2

transported upward from the troposphere by deep convection is the most likely source, since the flux of 

OCS is too small to account for current atmospheric loadings. In addition, in situ measurements of the 

isotopic composition of S in stratospheric SO4

2−

 do not match those of OCS (Leung et al., 2002). Thus, in 

addition to biogenic OCS, anthropogenic SO2 emissions could be important precursors to the formation of 

the stratospheric aerosol layer. 

The coastal and wetland sources of DMS have a dormant period in the fall and winter from plant 

senescence. Marshes die back in fall and winter, so DMS emissions from them are lower, and lower light 

levels in winter at mid-to-high latitudes lessen phytoplankton growth also tend to lower DMS emissions. 

Western coasts at mid-to-high latitudes have lower actinic flux to drive photochemical production and 

oxidation of DMS. Freezing at mid and high latitudes affects the release of biogenic S gases, particularly 

in the nutrient-rich regions around Alaska. Transport of SO2 from regions of biomass burning seems to be 

limited by heterogeneous losses that accompany convective processes that ventilate the surface layer and 

the lower boundary layer (Thornton et al., 1996). 

Reduced S species are also produced by several anthropogenic industrial sources: DMS is used in 

petroleum refining; and in petrochemical production processes to control the formation of coke and CO; 

to control dusting in steel mills; in a range of organic syntheses; as a food flavoring component; and can 

also be oxidized by natural or artificial means to dimethyl sulfoxide, a widely-used industrial solvent. 

2.4. NHX Emissions 

NH3 can be emitted from or deposited to soils, water, or vegetation depending on the ratio of the 

atmospheric NH3 concentration to the compensation point of the underlying surface. The compensation 

point, χ, generally is governed by the form, concentration, and acidity of N at the surface of exchange, 

and hence changes over time as these variables change. For most of the year, large areas of the U.S. are 

very near the nominal χ of 1 μg/m3

, with the result that the NH3 air-surface flux is very often highly 

dynamic. Figure 2-11 and Figure 2-12 show county-level annual total NH3 emissions for 2001 in tons, 

and the spatially normalized county-level emissions in tons per square mile, respectively. 

Total emissions of NH3 on a national scale show a strikingly different pattern from those of NOX or 

SO2 as comparison of these figures to their NOX and SOX analogs above illustrates. Anthropogenic NH3

emissions from mobile sources are small since the three-way catalysts used in motor vehicles emit only 

small amounts of NH3 as a byproduct during the reduction of NOX; in 2002, this totaled ~8% of the 

national NH3 total of ~3.7 metric short tons. Stationary combustion sources including EGUs make even 

smaller contributions to emissions of NH3 because their efficient combustion favors NOX formation and 

NH3 is produced during combustion largely by inefficient, low-temperature burning. In 2002, the total 

from all stationary source fuel combustion processes amounted to <2% of total NH3 emissions and 

chemical production added only ~0.7% more. Hence, NH3 emissions totals are dominated by biogenic 

production from agriculture, chiefly from livestock management and fertilizer applications to soils. In 

2002, these sources accounted for ~86% of U.S. total emissions. 

As with NOX and SOX emissions, however, these national-scale emissions totals obscure important 

variability at finer scales. To illustrate this point, Figures 2-13 through 2-15 show county-level NH3

emissions densities separately for emissions from on-road mobile sources, EGUs, and miscellaneous and 

biogenics, respectively. 

 2-17
Source: U.S. EPA (2006a) 

Figure 2-11. 2001 county-level total U.S. NH3 emissions.1

Source: U.S. EPA (2006a) 

Figure 2-12. 2001 county-level total U.S. NH3 emissions densities.1

 

1

 Range values: White, 0 or no reported value; Blue, from the smallest non-zero to the 10th percentile value; Green, from above the 10th to the 25th

percentile; Yellow, from above the 25th to the 50th percentile; Pink, from above the 50th to the 75th percentile; Red, from above the 75th to the 90th

percentile; Brown, from above the 90th percentile to the highest reported value. 

 2-18
Source: U.S. EPA (2006a) 

Figure 2-13. 2001 county-level NH3 emissions densities from on-road mobile sources. 

Source: U.S. EPA (2006a) 

Figure 2-14. 2001 county-level NH3 emissions densities from EGUs.1

 

1

 Range values: White, 0 or no reported value; Blue, from the smallest non-zero to the 10th percentile value; Green, from above the 10th to the 25th

percentile; Yellow, from above the 25th to the 50th percentile; Pink, from above the 50th to the 75th percentile; Red, from above the 75th to the 90th

percentile; Brown, from above the 90th percentile to the highest reported value. 

 2-19
Source: U.S. EPA (2006a) 

Figure 2-15. 2001 county-level NH3 emissions densities from miscellaneous and biogenic sources. 1

2.5. Evaluating Emissions Inventories 

Emissions inventories are very complex and highly changeable conjoined forms built from 

measurements and production and transfer rates, some measured directly, others indirectly, and others 

merely assumed, combined with model predictions. National-scale emissions inventories like the ones 

illustrated in county-level maps here have uncertainties embedded in them owing to unknown emission 

factors, unknown and varying emission rates, generalized or depleted profiles, and the like. Substantial 

effort is applied at national, state, and local scales to test these terms in the final emissions totals, and to 

assure their quality. 

One means for evaluating emissions inventories has been to compare predictions in the inventories 

to measured long-term trends or to ratios of pollutants in ambient air. Comparisons of emissions model 

predictions with observations have been performed in a number of environments. Very often emissions 

inventories for NOX and SOX are evaluated in relation to CO emissions because the low reactivity of CO 

on urban and regional scales means it can be treated as largely conserved. Using the distinction between 

mobile sources which emit NOX and CO but little SO2, and power plants which emit NOX and SO2 but 

little CO, Stehr et al. (2000) evaluated emissions estimates for the eastern U.S. Results indicated that coal 

combustion contributes 25 to 35% of the total area NOX emissions in rough agreement with the U.S. EPA 

NEI (2006a). Studies using ratios of CO concentrations to NOX concentrations, and concentrations of 

nonmethane organic compounds (NMOC) to NOX carried out in the early 1990s in tunnels and ambient 

air indicated that emissions of CO and NMOC were systematically underestimated in emissions 

inventories at that time. More details are available in the 2000 CO AQCD (U.S. EPA, 2000a). 

These reconciliation studies depend on the assumption that NOX emissions are predicted correctly 

by emissions factor models which are merely mean and aggregate descriptions of the highly variable U.S. 

 

1

Range values: White, 0 or no reported value; Blue, from the smallest non-zero to the 10th percentile value; Green, from above the 10th to the 25th

percentile; Yellow, from above the 25th to the 50th percentile; Pink, from above the 50th to the 75th percentile; Red, from above the 75th to the 90th

percentile; Brown, from above the 90th percentile to the highest reported value. 

 2-20
mobile source fleet. Roadside remote sensing data have indicated that >50% of non-methane 

hydrocarbons (NMHC) and CO emissions are produced by less than 10% of vehicles (Stedman et al., 

1991), typically the poorly maintained super-emitters. 

Parrish et al. (1998) and Parrish and Fehsenfeld (2000) proposed methods to derive emission rates 

by examining measured ambient ratios among individual volatile organic compounds (VOCs), NOX, and 

NOY. Typically, strong correlations exist among measured values for these species because emission 

sources are geographically co-located, even when individual sources are different. Correlations can be 

used to derive emissions ratios between species, including adjustments for the effect of photochemical 

aging. Examples of this type include using correlations between CO and NOY (e.g., Parrish et al., 1991), 

between individual VOC species and NOY (Goldan et al., 1995, 2000) and among various VOC species 

(McKeen and Liu, 1993; McKeen et al., 1996). Many of these studies were summarized in Parrish et al. 

(1998), Parrish and Fehsenfeld (2000), and Trainer et al. (2000). 

Other methods for emissions evaluation exist. Buhr et al., (1992) derived emission estimates from 

principal component analysis (PCA) and other statistical methods. Goldstein and Schade (2000) also used 

species correlations to identify the relative effects of anthropogenic and biogenic emissions. Chang et al. 

(1996, 1997), and Mendoza-Dominguez and Russell (2000) used inverse modeling to derive emission 

rates in conjunction with results from chemical-transport models (CTMs). 

A decadal field study of ambient CO at a rural site in the eastern U.S. (Hallock-Waters et al., 1999) 

indicated a downward trend consistent with the downward trend in estimated emissions over the period 

1988 to 1999 (U.S. EPA, 2000e), even when the global downward trend was taken into account. 

Measurements at two urban areas in the U.S. confirmed the decrease in CO emissions (Parrish et al., 

2002). That study also indicated that the ratio of CO to NOX emissions decreased by approximately a 

factor of 3 over 12 years. NEI estimates (U.S. EPA, 1997b) indicated a much smaller decrease in this 

ratio, suggesting that NOX emissions from mobile sources may have been underestimated or increasing or 

both. Parrish et al. (2002) concluded that O3 photochemistry in U.S. urban areas may have become more 

NOX-limited over the past decade. (See Section 2.6.2.1 for a discussion of NOX and its role in enhancing 

and limiting O3 formation.) 

Results from these recent emissions evaluation studies have been mixed, with some studies 

showing agreement to within ± 50% (U.S. EPA, 2000e). However, Pokharel et al. (2002) employed 

remotely sensed emissions from on-road vehicles and fuel use data to estimate emissions in Denver. Their 

calculations indicated a continual decrease in CO, hydrocarbons (HC), and NO emissions from mobile 

sources over the 6–year study period, 1996 through 2001. Inventories based on the ambient data were 30 

to 70% lower for CO, 40% higher for HC, and 40 to 80% lower for NO than those predicted by the 

MOBILE6 on-road mobile source emissions model. (See http://www.epa.gov/otaq/m6.htm for 

information on MOBILE6). 

Satellite data also have proved useful for optimizing estimates of NO2 emissions (Jaeglé et al., 

2005; Leue et al., 2001; Martin et al., 2003). Satellite-borne instruments such as the Global Ozone 

Monitoring Experiment (GOME) (see, e.g., Martin et al., 2003, and references therein) and the Scanning 

Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY) retrieve tropospheric 

NO2 columns that can be combined with model-derived τ of NOX to yield emissions of NOX. 

Top-down inference of NOX emission inventories from the satellite observations of NO2

concentrations columns by mass balance requires at minimum three pieces of information: the retrieved 

tropospheric NO2 column; the tropospheric NOX-to-NO2 ratio in the columns; and the NOX τ against 

reaction losses to stable chemical reservoirs. (See the discussion of these chemical reservoirs in Section 

2.6.) A photochemical model has been used to provide information on the latter two pieces of information. 

The method is most often applied to land surface emissions, excluding lightning. Tropospheric NO2

columns are largely insensitive to lightning NOX emissions since most of the lightning NOX in the upper 

troposphere is present as NO at the time of the satellite measurements (Ridley et al., 1996) owing to the 

slower reactions of NO with O3 at the altitude where lightning production is most prevalent. 

Using satellite data, Bertram et al. (2005) found clear signals in the SCIAMACHY observations of 

short, intense NOX pulses following springtime fertilizer application and subsequent precipitation over 

 2-21
agricultural regions of the western U.S. For the agricultural region in north-central Montana, they 

calculated an annual SCIAMACHY top-down estimate that is 60% greater than a commonly-used model 

of soil NOX emissions by Yienger and Levy (1995). 

Jaeglé et al. (2005) applied additional information on the spatial distribution of emissions and fire 

activity to partition NOX emissions into sources from fossil fuel combustion, soils, and biomass burning. 

Global a posteriori estimates of soil NOX emissions were 68% larger than the a priori estimates. Large 

increases were found for the agricultural region of the western U. S. during summer, increasing total U.S. 

soil NOX emissions by a factor of 2. 

Martin et al. (2006) retrieved tropospheric NO2 columns for May 2004 to April 2005 from the 

SCIAMACHY satellite instrument to derive top-down NOX emissions estimates via inverse modeling 

with the GEOS-Chem global chemical transport model. (See http://www.as.harvard.edu/ctm/geos/ for 

more information on GEOS-Chem.) The top-down emissions were combined with a priori information 

from a bottom-up emissions inventory with error weighting to achieve an improved a posteriori estimate 

of the global distribution of surface NOX emissions. Their a posteriori inventory improved GEOS-Chem 

simulations of NOX, peroxyacetyl nitrate (PAN), and HNO3 as compared against airborne in situ 

measurements over and downwind of New York City. Their a posteriori inventory also showed lower 

NOX emissions from the Ohio River Valley in summer than winter, reflecting recent controls on NOX

emissions from EGUs there. Their a posteriori global inventory was highly consistent with the NEI 1999 

(http://www.epa.gov/ttn/chief/net/1999inventory.html) (R2

 = 0.82, bias = 3%); however, it was 68% 

greater than a recent inventory by Streets et al. (2003) for East Asia for the year 2000. 

Significant uncertainties attach to estimates of the magnitude and spatial and temporal variability of 

NH3 emissions. A strong seasonal pattern should be evident in NH3 emissions profiles to correspond with 

the overwhelmingly agricultural sources of NH3 and strong seasonal temperature differences in NH3

volatility, for example, but this pattern has not appeared in previous emissions factors and inventories. 

The magnitude of these temporal differences is large: Heber et al. (2001) showed that NH3 flux from two 

swine finishing buildings were ~70% higher in June than in fall and winter months, and Aneja et al. 

(2000) found fluxes from hog waste lagoons ~80 to 90% higher in summer as compared to winter. 

The value of inverse modeling techniques using large-scale Eulerian air quality models (AQMs) 

has been successfully demonstrated for several aspects of emissions inventories; see, for example, 

Mendoza-Dominguez and Russell (2000, 2001a, b). Gilliland (2001), Gilliland et al. (2001), and Pinder 

et al. (2006) have worked extensively with Kalman filter inverse modeling and the U.S. EPA Community 

Multiscale Air Quality (CMAQ) modeling system (Byun and Ching, 1999), to reduce uncertainties 

specifically in NH3 emissions. NH3 is an especially good case for emissions estimate evaluation with 

inverse modeling techniques because the modeled response in NH4

+

 wet deposition is strongly linear with 

changes in NH3 emissions. Correcting the NH3 emissions estimates was also shown to be an essential step 

for reasonable model predictions of other N compounds (Gilliland et al., 2003). Results can be highly 

significant. For example, the a posteriori R value of CMAQ predictions against measured wet NH4

+

concentrations from the National Atmospheric Deposition Program (NADP) sites in the U.S. was 0.98, 

increased from the a priori value of 0.12. Pinder et al. (2004) provided the first farm-level model for NH3

emissions from dairy cattle, and this has been coupled with the seasonally varying fertilizer inventory for 

NH3 from Goebes et al. (2003) and with the inverse modeling results of Gilliland et al. (2003) to correct 

the NEI NH3 emissions totals. The estimate of Gilliland et al. (2003) was that the annual NEI NH3 was 

~37% too high to optimize the modeled wet NH4

+

 concentration. Following earlier work by Gilliland and 

others in this vein, U.S. EPA (2006a), in fact, reported its intention to decrease total NH3 emissions in the 

NEI by 23% by altering emissions factors for nondairy cows and swine. 

Holland et al. (2005) estimated wet and dry deposition of NHX based on measurements over the 

CONUS and reported that NH3 emissions in the 1999 NEI were underestimated by a factor of ~2 or 3. 

Possible reasons for this error included under-representation of deposition monitoring sites in populated 

areas and the neglect of offshore transport in the NEI. The use of fixed deposition velocities (Vd) not 

reflective of local conditions at the time of measurement introduces additional uncertainty into estimates 

of dry deposition to which NH3 is particularly sensitive. 

 2-22
2.5.1.

2.6.1.

Emissions for Historical Modeling 

Rigorous emissions inventories require careful analysis of very large data sets on fuel use and types 

and activity patterns for them to be a reliable basis for atmospheric concentration and deposition 

calculations. Sections 2.2 to 2.5 make clear that even with current best estimates for these input data, 

present-day emissions inventories can be substantially in error. 

However, estimates of biological effects from long-term acidifying deposition require computation 

of historical emissions. Such historical estimates of emissions generally use some divisions of economic 

sectors together with estimates of fuel type and S content, for example. Schopp et al. (2003) have pursued 

this method for estimating acid deposition in Europe between 1880 and 2030 using the International 

Institute for Applied Systems Analysis (IIASA) Regional Acidification and Information System (RAINS) 

model; see Alcamo et al. (1990) and Asman et al. (1988) for descriptions and applications of RAINS. 

Mylonda (1996) used similar tools and methods to compute estimates of SO2 emissions and atmospheric 

concentrations and deposition in Europe showing that emissions, which peaked in Europe in the 1960s 

and 1970s had increased by a factor of 10 since the 1880s. Similarly, modeled concentrations and 

deposition increased by factors of 2 to 6, depending on location in the same time period. 

Historical emissions and concentration estimates are often not resolved to more than decadal 

resolution because of the very large uncertainties inherent in their computation, which very often includes 

no information on variability in the meteorological state of the atmosphere or its oxidizing capacity, both 

of which change over time. Spatial resolution, however, has extended from continental scale, like those 

using RAINS, up to global scale estimates and down to subregional ones. A survey of methods and 

cautions for interpretation was made by Galloway (1995). 

Global estimates like the ones by Lefohn et al. (1999) use methods similar to those at the 

continental scale, though with more specific factors on S content in fuels, for example, where those are 

known. Lefohn et al. (1999), for example, estimated the global average anthropogenic S emissions in 

1850 to be <2% of current levels. At finer scales, Driscoll et al. (2001b) used U.S. EPA (2000e) estimates 

of emissions trends between 1900 and 1998 together with biogeochemical modeling to estimate historical 

loadings to the northeast U.S. to calculate potential recovery times in affected lakes and streams. 

Among the finest-scale historical estimates for N emissions and deposition are those by Bowen and 

Valiela (2001) for Cape Cod, MA. Bowen and Valiela (2001) computed decadal changes in several 

oxidized and reduced nitrogen species for the time period between 1910 and 1995 to conclude that total N 

deposition at Cape Cod has been increasing at the rate of 0.26 kg N/ha/decade. 

2.6. NOX-SOX-NHX Chemistry in the Troposphere 

Introduction 

NOX, VOCs, and CO are precursors in the formation of O3 and other elements of photochemical 

smog and PM. The role of NOX in producing O3, the factors controlling P(O3) efficiency and methods for 

calculating O3 from its NOX and VOC precursors were all reviewed in Section 2.2 of the 2006 O3 AQCD 

(U.S. EPA, 2006b) and are available in numerous texts including Jacob, 2000; Jacobson, 2002; and 

Seinfeld and Pandis, 1998. More specific details on the chemistry and transformation of SOX can be 

found in the 2008 SOX ISA (U.S. EPA 2008b). Hence, those topics are only briefly recounted here with 

special reference to the secondary NOX and SOX NAAQS. 

 2-23
Figure 2-16. Atmospheric cycle of reactive oxidized N species. NOY refers to all the species shown within 

the inner and outer box: NOX to NO and NO2 (in the inner box), and NOZ to all the species 

outside of the inner box. IN refers to inorganic particulate species (e.g., sodium [Na+], calcium 

[Ca2+]), MPP to multiphase processes, hν to a solar photon, and R to an organic radical. 

Particle-phase RONO2 are formed from the species shown on the right side. For the purposes 

of this document, “NOX” is defined as the group of all N-containing compounds inside the 

large dashed-line box, the same group generally termed “NOY” by atmospheric scientists. 

Important compounds, reactions, and cycles are schematized in Figure 2-16. Figure 2-16 also 

illustrates that NO2, itself an oxidant, can react to form other photochemical oxidants including organic 

nitrates (RONO2) like the PANs shown in Figure 2-16, and can react with toxic compounds like the 

polycyclic aromatic hydrocarbons (PAHs) to form nitro-PAHs, some of which demonstrate greater 

toxicity than either reactant alone. NO2 can also be further transformed to HNO3 and can contribute in 

that form to the acidity of cloud, fog, and rain water and can form ambient NO3

− particles (pNO3). 

The only gas-phase forms of SOX of interest in tropospheric chemistry are SO2, SO3, and H2SO4. 

SO3 can be emitted from the stacks of power plants and factories; however, it reacts extremely rapidly 

with H2O in the stacks or immediately after release into the atmosphere to form sulfuric acid (H2SO4). 

H2SO4 in turn mainly condenses onto existing particles when particle loadings are high, or nucleates to 

form new particles under lower concentration conditions. Thus, of the gas-phase SOX species, only SO2 is 

emitted in the tropospheric boundary layer at concentrations of concern for environmental exposures. 

NH3, the gas-phase precursor for NH4

+

, plays a key role in neutralizing acidity in ambient particles 

and in cloud, fog, and rain water. NH3 is also involved in the ternary nucleation of new particles and 

reacts with gas-phase HNO3 to form NH4NO3, and with SO4

2−

 to form ammonium bisulfate (NH4HSO4) 

and ammonium sulfate ((NH4)2SO4), three significant components of N and S deposition across the 

landscape. The NOX-SOX-NHX cycles and phase-changes are schematized in Figure 2-17. 

 2-24
Source: Adapted from Meng and Seinfeld (1996) and Warneck (1999a); used with permission), by R. Mathur and R. Dennis, U.S. EPA / 

Office of Research and Development (ORD) / National Environmental Research Laboratory (NERL) / Atmospheric Modeling Division. 

Figure 2-17. The combined NOX + SOX + NHX system showing how atmospheric fates and lifetimes of 

reduced and oxidized N components are linked. 

2.6.2. NOX Chemistry 

As described in Section 2.2, NOX is emitted by combustion sources mainly as NO with ~5 to 10% 

NO2. The rapid photochemical cycle in the troposphere linking NO and NO2 involves photolysis of NO2

by UV-A radiation to yield NO and a ground-state oxygen atom, O(3

P). 

)(3

2 hNO ν ⎯+ ⎯→ + PONO

Reaction 4 

This ground state oxygen atom, O(3P), can then combine with molecular oxygen (O2) to form O3; and, 

colliding with M, any molecule from the surrounding air (M = N2, O2, etc.), the newly formed O3

molecule transfers excess energy and is stabilized 

2 MOPO ⎯++ ⎯→ 3 + MO 3 )(

Reaction 5 

NO and O3 react to re-form NO2. 

 2-25
ONO 3 ⎯+ ⎯→ 2 + ONO 2

Reaction 6 

Reaction 6 is responsible for O3 decreases and NO2 increases found near sources of NO (like highways or 

power plant plumes). The falloff of NO2 with distance from a road depends on: wind speed and direction; 

the local structure of turbulent mixing; temperature through the temperature dependence of Reaction 6; 

and the amount of UVA-flux through Reaction 4. 

Oxidation of reactive VOCs leads to formation of reactive radical species that allow conversion of 

NO to NO2 without participation of O3 as in Reaction 6. 

2

, NO 22 NO ⎯⎯ →ROHO

⎯⎯

Reaction 7 

O3, therefore, can accumulate as NO2 photolyzes as in Reaction 4, followed by Reaction 5. Specific 

mechanisms for the oxidation of a number of VOCs were discussed in the 2006 O3 AQCD (U.S. EPA, 

2006b). 

It is often convenient to speak about families of chemical species defined in terms of members that 

interconvert rapidly on time scales shorter than those for formation or destruction of the family as a 

whole. For example, an odd oxygen (OX) family can be defined as 

= ∑ )()(( +++ ) 3 2

3 1 OX NOODOPO

Equation 1 

In much the same way, NOX is sometimes referred to as odd nitrogen. Hence, production of OX

occurs by Equation 1, while the sequence of reactions given by Reactions 4–6 represents no net 

production of OX. (Definitions of species families and methods for constructing families are discussed in 

Jacobson [1999] and references therein). Other families including N-containing species used later in this 

chapter are 

2 ( ) NOZ = HNO2 HNO3 HNO4 NO3 52 +++++ 3 −− NOOOCHOCHPANON 2

−

∑

+ other organic nitrates + halogen nitrates + particulate nitrates 

Equation 2 

and 

Y = NONO X + NOZ

Equation 3 

and 

X += HOOHHO 2

Equation 4 

NOZ refers to the sum of all oxidation products of NOX without the original NO and NO2. 

The reaction of NO2 with O3 leads to formation of the NO3 radical. 

2 ONO 3 ⎯+ ⎯→ 3 + ONO 2

Reaction 8 

 2-26
However, NO3 radical reacts rapidly, having a τ of ~5 seconds during the photochemically most active 

period of the day near local solar noon, by two pathways (Atkinson et al., 1992): 

3 ONO 2 %)10( hNO ν ⎯+ ⎯→ +

Reaction 9

%)90(

3

3 2 hvNO ⎯+ ⎯→ + PONO )(

Reaction 10 

Because of this, NO3 concentrations remain low during daylight hours but can increase after sunset 

to nighttime concentrations of <5 × 107

 to 1 × 1010 molecules/cm3 or <2 to 430 parts per trillion (ppt) over 

continental areas influenced by anthropogenic emissions of NOX (Atkinson et al., 1986). At night, NO3, 

rather than OH, is most often the primary oxidant in polluted tropospheric systems. Moreover, NO3 can 

combine with NO2 to form dinitrogen pentoxide (N2O5) 

3 NONO 2 ON 52 ←+ ⎯→M

Reaction 11 

and N2O5 then both photolyzes and thermally decomposes back to NO2 and NO3 during the day; however, 

N2O5 concentrations can accumulate during the night to ppb levels in polluted urban atmospheres. 

The tropospheric chemical removal processes for NOX include reaction of NO2 with the OH radical 

and hydrolysis of N2O5 in aqueous aerosol solutions if there is no organic coating. Both of these reactions 

produce HNO3

NOOH 2 HNO3 ⎯+ ⎯→M

Reaction 12

3

)1(

52 2 ON 2 HNO ⎯⎯ → OH

⎯

Reaction 13 

The gas-phase reaction of OH with NO2 (Reaction 12) is one of the major and ultimate removal 

processes for NOX in the troposphere. This reaction removes OH and NO2 in one step and competes with 

HC for OH in areas characterized by high NOX concentrations such as urban centers. The τ for conversion 

of NOX to HNO3 in the PBL at 40oN latitude ranges from ~4 hours in July to ~16 hours in January. The 

corresponding range in τ at 25oN latitude is between 4 and 5 hours, while at 50oN latitude, HNO3 τ ranges 

from about 4 to 20 hours (Martin et al., 2003). 

In addition to gas-phase HNO3, Golden and Smith (2000) have shown on the basis of theoretical 

studies that pernitrous acid (HO2NO) is also produced by the reaction of NO2 and OH; however, this 

production channel most likely represents a minor yield of ~15% at the surface (JPL, 2003). Pernitrous 

acid will also thermally decompose and photolyze. Gas-phase HNO3 formed from Reaction 12 and 

Reaction 13 undergoes wet and dry deposition to the surface and uptake by ambient aerosol particles. 

 2-27
In addition to uptake on particles and in cloud droplets, HNO3, photolyzes and reacts with OH 

3 hHNO ν ⎯+ ⎯→ + NOOH 2

Reaction 14

3 hHNO ν ⎯+ ⎯→ + HNOO 2

Reaction 15

3 hHNO ν ⎯+ ⎯→ + NOH 3

Reaction 16 

and 

HNO3 OH ⎯+ ⎯→ 3 + 2OHNO

Reaction 17 

Margitan and Watson (1982) established that Reaction 14 has a quantum yield of ~1, with only very small 

contributions from the two other possible photolytic pathways (Reactions 15 and 16). The τ of HNO3 with 

respect to these two reactions is long enough for HNO3 to act somewhat as a reservoir species for NOX

during long-range transport, contributing in this way to NO2 levels in areas remote from the source region 

of the NOX that formed this HNO3. 

The contribution of Reaction 13 to HNO3 formation is highly uncertain during both winter and 

summer. The importance of Reaction 13 could be much higher during winter than during summer because 

of the much lower concentration of OH and the enhanced stability of N2O5 due to lower temperatures and 

UV flux. Geyer and Platt (2002) concluded that Reaction 16 constituted about 10% of the removal of 

NOX at a site near Berlin, Germany during spring and summer. However, Dentener and Crutzen (1993) 

estimated this to be 20% in summer and 80% in winter. A modeling study by Tonnesen and Dennis (2000) 

reported; 16 to 31% of summer HNO3 production from Reaction 13. Recent work in the northeastern U.S. 

(Brown et al., 2006a, b; Frost et al., 2006a) indicates that this reaction proceeds at a faster rate in power 

plant plumes than in urban plumes. 

OH also reacts with NO to produce nitrous acid (HNO2). 

NOOH HNO2 ⎯+ ⎯→M

Reaction 18 

In sunlight, HNO2 is rapidly photolyzed back to the original reactants. 

2 hHNO ν ⎯+ ⎯→ + NOOH

Reaction 19 

Reaction 18 is, however, a negligible source of HNO2, which is formed mainly by multiphase processes. 

At night, heterogeneous reactions of NO2 in aerosols or at Earth’s surface result in accumulation of HNO2

(Harris et al., 1982; Jacob, 2000). Harris et al., (1982) suggested that photolysis of HNO2 at sunrise could 

provide an important early-morning source of OH necessary to drive P(O3). 

 2-28
HO2 can react with NO2 to produce pernitric acid (HNO4) 

2 2 MNOHO ⎯++ ⎯→ 4 + MHNO

Reaction 20 

which can thermally decompose and photolyze back to its original reactants. The acids formed in these 

gas-phase reactions are all water soluble; thus, they can be incorporated into cloud droplets and in the 

aqueous phase of particles. 

A broad range of organic N compounds are directly emitted by combustion sources or formed in 

the atmosphere from NOX emissions. Organic N compounds include the PANs, nitrosamines, nitro-PAHs, 

and the more recently identified nitrated quinones. Oxidation of VOCs produces organic peroxy radicals 

(RO2). Reaction of RO2 radicals with NO and NO2 produces RONO2 and peroxynitrates (RO2NO2) 

2 NORO RONO2 ⎯+ ⎯→M

Reaction 21

2 NORO 2 NORO 22 ⎯+ ⎯→M

Reaction 22 

Reaction 21 is a minor branch for the reaction of RO2 with NO; the major branch produces RO and NO2

as discussed in the next section. However, the RONO2 yield increases with carbon number (Atkinson, 

2000). 

The most important of these organic nitrates is PAN, the dominant member of the broader family of 

peroxyacyl nitrate species (PANs) which includes peroxypropionyl nitrate (PPN) of anthropogenic origin 

and peroxymethacrylic nitrate (MPAN) produced from isoprene oxidation. The PANs are formed by the 

combination reaction of acetyl peroxy radicals with NO2

3 2 3 2 )( NOOOOCCH ⎯+ ⎯→ )( OONOOCCH

Reaction 23 

where the acetyl peroxy radicals are formed mainly during the oxidation of ethane (C2H6). Acetaldehyde 

(CH3CHO) is formed as an intermediate species during the oxidation of ethane. CH3CHO can be 

photolyzed or react with OH to yield acetyl radicals 

3 )( hHOCCH ν ⎯+ ⎯→ 3 )( + HOCCH

Reaction 24

3 )( HOHOCCH ⎯+ ⎯→ 3 )( + 2OHOCCH

Reaction 25 

Acetyl radicals then react with O2 to yield acetyl peroxy radicals. 

3 )( 2 MOOCCH ⎯++ ⎯→ 3 )( + MOOOCCH

Reaction 26 

 2-29
However, acetyl peroxy radicals will react with NO in areas of high NO concentrations 

3 3 2 )( NOOOOCCH ⎯+ ⎯→ )( + NOOOCCH

Reaction 27 

and the acetyl-oxy radicals will then decompose. 

3 3 2 )( OCOCH ⎯⎯→ + COCH

Reaction 28 

Thus, formation of PAN is favored at conditions of high ratios of NO2 to NO, which are most 

typically found under low total NOX concentration conditions. The PANs both thermally decompose and 

photolyze back to their reactants with τ on the order of a few hours during warm sunlit conditions: τ with 

respect to thermal decomposition range from ~1 h at 298 K to ~2.5 days at 273 K and up to several weeks 

at 250 K. Thus, PANs can provide a reversible sink of NOX at cold temperatures and high solar zenith 

angles, allowing release of NO2 as air masses warm, particularly by subsidence from the free troposphere. 

The PANs are also removed by uptake to vegetation (Sparks et al., 2003; Teklemariam and Sparks, 2004). 

RO2NO2 produced by Reaction 22 are thermally unstable and most have very short τ of <100 seconds 

owing to thermal decomposition back to the original reactants. Thus they are not effective permanent 

sinks of NOX, except at lower temepatures. 

2.6.2.1.

2.6.2.2.

NOX and Ozone Formation 

O3 is unlike most other air pollution species whose rates of formation vary directly with the 

emissions of their precursors in that P(O3) changes nonlinearly with the concentrations of its precursors. 

At the low NOX concentrations found in most environments ranging from remote continental areas to 

rural and suburban areas downwind of urban centers, net P(O3) increases with increasing NOX levels. At 

the high NOX concentrations found in downtown metropolitan areas especially near busy streets and 

roadways and in power plant plumes, net destruction of O3 by titration reaction with NO dominates. 

Between these two regimes is a transition stage in which P(O3) shows only a weak dependence on NOX

concentrations. In the high NOX concentration regime, NO2 scavenges OH (Reaction 12) which would 

otherwise oxidize VOCs to produce HO2, which in turn would oxidize NO to NO2 (Reaction 7). In the low 

NOX concentration regime, VOC oxidation generates, or at least does not consume, free radicals, and 

P(O3) varies directly with NOX levels. Sometimes the terms VOC-limited and NOX-limited are used to 

describe these two regimes; also, the terms NOX-limited and NOX-saturated are used (see Jaeglé et al., 

2001). OH chemistry initiates HC oxidation and behaves similarly to that for O3 with respect to NOX

concentrations (Hameed et al., 1979; Pinto et al., 1993; Poppe et al., 1993; Zimmermann and Poppe, 

1993). These considerations introduce a high degree of uncertainty into attempts to relate changes in O3

concentration to precursors emissions. Note that in a NOX-limited or NOX-sensitive regime, P(O3) is not 

insensitive to radical production or the flux of solar UV photons; rather, P(O3) is simply more sensitive to 

the NOX concentrations. For example, global tropospheric O3 is sensitive to CH4, even though the 

troposphere is predominantly NOX-limited. More details on P(O3) are given in the 2008 NOX ISA (U.S. 

EPA, 2008a). 

Multiphase Interactions 

Warneck (1999a) constructed a box model describing the chemistry of the oxidation of NO2

including the interactions of N species and minor processes in sunlit cumulus clouds. The relative 

contributions of different reactions to the oxidation of NO2 to NO3 10 minutes after cloud formation are 

 2-30
given in Table 2-3 where the last two columns show the relative contributions with and without transition 

metal ions. Oxidation of NO2 as delineated in Table 2-3 occurs mainly in the gas phase within clouds, 

implying that gas-phase oxidation of NO2 by OH predominates. 

NOX and Halogen Chemistry Interactions 

Four decades of observational data on O3 in the troposphere have revealed numerous anomalies not 

fully explained by the gas-phase HOX-NOX photochemistry (described above). The best-known example 

is the dramatic decrease in ground-level O3 concentration during polar sunrise due to multiphase catalytic 

cycles involving inorganic Br and Cl radicals (Barrie et al., 1988; Foster et al., 2001; Martinez et al., 

1999). Other examples of anomalies in tropospheric O3 at lower latitudes include O3 concentrations 

<10 ppb in the marine boundary layer (MBL) and overlying free troposphere (FT) at times over large 

portions of the tropical Pacific (Kley et al., 1996), as well as post-sunrise O3 concentration decreases over 

the western subtropical Pacific Ocean (Nagao et al., 1999), the temperate Southern Ocean (Galbally et al., 

2000), and the tropical Indian Ocean (Dickerson et al., 1999). 

Table 2-3. Relative contributions of various gas and aqueous phase reactions to aqueous NO3 formation 

within a sunlit cloud, 10 minutes after cloud formation. 

Reaction % of Totala % of Totalb

GAS PHASE 

OH + NO 57.7 67.4 2 + M 

AQUEOUS PHASE 

N2O5(g) + H2O 8.1 11.2 

NO3

− + Cl− <0.1 0.1 

NO3

− + SO32− 0.7 1.0 

NO3

− + HCOO− 0.6 0.8 

HNO4 + SO3

2− 31.9 20.5 

HOONO + NO3

− 0.8 <0.1 

O <0.1 3 + NO2

− <0.1 

a In the absence of transition metals. b In the presence of iron and copper ions. 

Source: Adapted from Warneck (1999a). Used with permission.

The set of N reactions with aerosol salts in marine atmospheres sketched briefly here was reviewed 

in detail by De Haan et al. (1999). This chemistry remains important not only for halogen cycling and 

atmospheric oxidation reactions (Andreae and Crutzen, 1997), but also because through them NO3

−

 can 

be shifted from gas-phase HNO3 or from fine-mode aerosol after dissociation of NH4NO3, for example, to 

coarse-mode particles, thereby enhancing the potential for local N deposition to coastal regions. The 

actual areal extent of N deposition resulting from gas-to-particle NO3

− conversion, however, is a complex 

function of local wind speeds, as shown by Pryor and Sørensen (2000). With moderate winds of 3.5–10 

m/s (~8 to 22 mph), gas-phase HNO3 Vd exceeded that of an average sodium nitrate (NaNO3) particle, 

whereas at higher and lower wind speeds the reverse was true. This means that as a result of gas-toparticle NO3

− conversion, under commonly moderate winds, less N would be deposited locally and more 

would be available for transport and deposition in a larger area of extent. 

 2-31
2.6.3. SOX Chemistry 

The only forms of monomeric SOX of interest in troposheric chemistry are SO2 and SO3. SO3 can 

be emitted from the stacks of power plants and factories; however, it reacts extremely rapidly with H2O in 

the stacks or immediately after release into the atmosphere to form H2SO4, which mainly condenses onto 

existing particles when particle loadings are high; it can nucleate to form new particles under lower 

concentration conditions. Thus, only SO2 is present in the troposheric boundary layer at concentrations of 

concern for environmental exposures. 

Gas phase oxidation of SO2 is initiated by the reaction 

2 MOHSO ⎯++ ⎯→ 3 + MHSO

 Reaction 29 

followed by 

OHSO 23 ⎯+ ⎯→ 3 + HOSO 2

Reaction 30

3 2 ⎯+ ⎯→ SOHOHSO 42

Reaction 31 

Because the saturation vapor pressure of H2SO4 is extremely low, it will be removed rapidly by 

transfer to the aqueous phase of aerosol particles and cloud droplets. Depending on atmospheric 

conditions and the concentrations of other ambient particles and gas-phase species that can participate in 

new particle formation, it can also nucleate to form new particles. Rate coefficients for the reactions of 

SO2 with either HO2 or NO3 (JPL, 2003) are too low to be significant. 

SO2 is chiefly but not exclusively primary in origin; it is also produced by the photochemical 

oxidation of reduced S compounds such as DMS, H2S, CS2, OCS, and methyl mercaptan, which are all 

mainly biogenic in origin (Their sources are discussed in Section 2.3.2). Table 2-4 lists the τ of reduced S 

species with respect to reaction with various oxidants. Except for OCS, which is lost mainly by photolysis 

with τ ~6 months, these species are lost mainly by reaction with OH and NO3

–

. 

 2-32
Table 2-4. Atmospheric lifetimes (τ) of SO2 and reduced S species with respect to reaction with OH, NO3, 

and Cl radicals. 

OH NO3 CI 

Compound 

k x 1012 τ k x 1012 τ k x 1012 τ

SO2 1.6 7.2 d NA NA 

CH3−S-CH3 5.0 2.3 d 1.0 1.1 h 400 29 d 

H2S 4.7 2.2 d NA 74 157 d 

CS2 1.2 9.6 d <0.0004 >116 day <0.004 Nr 

OCS 0.0019 17 y <0.0001 >1.3 yr <0.0001 Nr 

CH3−S-H 33 8.4 h 0.89 1.2 h 200 58 d 

CH3−S-S-CH3 230 1.2 h 0.53 2.1 h NA 

1Rate coefficients were taken from JPL Chemical Kinetics Evaluation No. 14 (JPL 2003). NA = Reaction rate coefficient not available. OH = 1 × 106/cm3 . NO3 = 2.5 × 108/cm3

Cl = 1 × 108/cm3. Nr = Rate coefficient too low to be relevant as an atmospheric loss mechanism. Rate coefficients were calculated at 298 K and 1 atmosphere. 

Source: Seinfeld and Pandis (1998) 

Because OCS is relatively long-lived in the troposphere, it can be transported upwards into the 

stratosphere. Crutzen (1976) proposed that its oxidation serves as the major source of SO4

2−

 in the 

stratospheric Junge layer during periods when volcanic plumes do not reach the stratosphere. However, 

the flux of OCS into the stratosphere is probably not sufficient to maintain this stratospheric aerosol layer. 

Myhre et al. (2004) proposed instead that SO2 transported upwards from the troposphere is the most likely 

source since the upward flux of OCS is too small to sustain observed SO4

2−

 loadings in the Junge layer. 

In addition, in situ measurements of the isotopic composition of S do not match those of OCS (Leung 

et al., 2002). 

Reaction with NO3 at night most likely represents the major loss process for DMS and methyl 

mercaptan, although the mechanisms are not well understood. Initial attack by NO3 and OH involves H+

abstraction, with a smaller branch leading to OH addition to the S atom. The OH addition branch 

increases in importance as temperatures decrease, becoming the major pathway below temperatures of 

285 K (Ravishankara, 1997). The adduct may either decompose to form methanesulfonic acid (MSA) or 

undergo further reactions in the main pathway to yield DMS (Barnes et al., 1991). Following H+

abstraction from DMS, the main reaction products include MSA and SO2. The ratio of MSA to SO2 is 

strongly temperature dependent, varying from 0.1 in tropical waters to 0.4 in Antarctic waters (Seinfeld 

and Pandis, 1998). SO4

2−

 in excess of that expected from sea salt aerosols is related mainly to production 

of SO2 from the oxidation of DMS. These transformations among atmospheric S compounds are 

summarized in Figure 2-18. 

 2-33
Source: Adapted from Berresheim et al. (1995). Used with permission. 

Figure 2-18. Atmospheric cycle of S compounds. 

2.6.3.1. Multi-Phase SOX Chemistry 

The major S species in clouds are hydrogen sulfite (HSO3

−

) and the sulfite ion (SO3

2−

). Both are 

derived from the dissolution of SO2 in water and are grouped together as S(IV); bisulfate ion (HSO4

−

) and 

SO4

2−

 are grouped together as S(VI). The chief species capable of oxidizing S(IV) to S(VI) in cloud water 

are O3, H2O2 or organic peroxides, OH, and ions of transition metals such as iron (Fe), manganese (Mn) 

and copper (Cu) in the presence of O2. The basic mechanism of aqueous-phase oxidation of SO2 has long 

been studied and can be found in numerous texts on atmospheric chemistry; see for example, FinlaysonPitts and Pitts (2000), Jacob (1999), Jacobson (2002), and Seinfeld and Pandis (1998). Following 

Jacobson (2002), the steps involved in aqueous-phase oxidation of SO2 can be summarized as: dissolution 

of SO2

)( )( 2 2 gSO ←⎯→ aqSO

Reaction 32 

and formation and dissociation of H2SO3. 

+ − + − + ←⎯→ ←⎯→ + ←⎯→ + 2

2 2 32 3 3 aqOHaqSO )()( SOH HSOH 2 SOH

Reaction 33 

 2-34
In the pH range commonly found in rainwater, pH 2 to 6, the most important reaction converting 

S(IV) to S(VI) is 

− + − + HSO HOH ←++ ⎯→ 2 ++ 2HOHSO 2

3 2 4

Reaction 34 

as SO3

2−

 is much less abundant than HSO3

−

. 

Another pathway to aqueous-phase oxidation of S(IV) is reaction with O3

2 2

2 HSO3 3 OHO ⎯++ ⎯→ 4 ++ OOHSO − −

Reaction 35 

But while the gas-phase reaction of SO2 with O3 is slow, the aqueous phase analog of Reaction 35 

is rapid, with rate coefficient increases up to a value of ~5 x 10-3 with increasing pH between 1 and 3 

(Finlayson-Pitts and Pitts, 2000). 

Major pathways for the aqueous-phase oxidation of S(IV) to S(VI) as a function of pH are shown 

in Figure 2-19. For pH up to about 5.3, H2O2 is the dominant oxidant converting S(IV) to S(VI), while at 

pH >5.3, O3 becomes dominant, followed by Fe(III), using the characteristic values found in Seinfeld and 

Pandis (1998). However, differences in concentrations of oxidants result in differences in the pH at which 

this transition occurs. Note that oxidation of SO2 by O3 and O2 tends to be self-limiting: as SO4

2−

 is 

formed, pH decreases and rates of these reactions decrease. Higher pH levels are expected to be found 

mainly in marine aerosols; however, in marine aerosols, the Cl-catalyzed oxidation of S(IV) may be more 

important (Hoppel and Caffrey, 2005; Zhang and Millero, 1991). Because NH4

+

 is so effective in 

neutralizing acidity, it, too, affects the rate of oxidation of S(IV) to S(VI) and the rate of dissolution of 

SO2 in particles and cloud droplets. 

Comparison of the relative rates of oxidation by gas-and-aqueous phase reactions by Warneck 

(1999b) indicated that, on average, only ~20% of SO2 is oxidized by gas-phase reactions; the remainder is 

oxidized by aqueous-phase reactions. Warneck’s box model (1999a) describing the chemistry of the 

oxidation of SO2 and NO2 included interactions of S species and minor processes in sunlit cumulus 

clouds. The relative contributions of different reactions to the oxidation of S(IV) species to S(VI) 10 

minutes after cloud formation are given in Table 2-5 with the last two columns showing the relative 

contributions with and without transition metal ions. As can be seen from Table 2-5, SO2 within a cloud 

(gas + cloud droplets) is oxidized mainly by H2O2 in the aqueous phase, while gas-phase oxidation by OH 

is small by comparison. A much smaller contribution in the aqueous phase is made by methyl 

hydroperoxide (CH3OOH) because it is formed mainly in the gas phase and its Henry’s Law constant is 

several orders of magnitude smaller that of H2O2. After H2O2, HNO4 is the major contributor to S(IV) 

oxidation. 

The values shown in Table 2-5 here and Table 2-3 above for NO3 indicate that gas-phase oxidation 

accounts for only ~20% of SO2 oxidation but ~90% of NO2 oxidation. 

 2-35
Figure 2-19. Comparison of aqueous-phase oxidation paths. The rate of conversion of S(IV) to S(VI) is 

shown as a function of pH. Concentrations assumed are: [SO2(g)] = 5 ppb; [NO2(g)] = 1 ppb; 

[H2O2(g)] = 1 ppb; [O3(g)] = 50 ppb; [Fe(III)(aq)] = 0.3 μM; [Mn(II)(aq)] = 0.3 μM. 

In areas away from strong pollution sources, the SO2 τ is ~7 days, based on measurements of the 

rate constant for Reaction 29 (JPL, 2003) and a nominal OH concentration of 106 molecules/cm3

. 

However, the mechanism of SO2 oxidation at a particular location depends on local environmental 

conditions. For example, near stacks, oxidants such as OH are depleted and almost no SO2 is oxidized in 

the gas phase. Farther downwind, as the plume is diluted with background air, the gas phase oxidation of 

SO2 increases in importance. Finally, even farther downwind when conditions in the plume can become 

more oxidizing than in background air, the SO2 oxidation rate can exceed that in background air. 

SO2 in the PBL is also removed from the atmosphere by dry deposition to moist surfaces, resulting 

in an atmospheric τ with respect to dry deposition of ~1 day to 1 week. Wet deposition of S naturally 

depends on the variable nature of rainfall, but in general results in a τ of ~7 days, too. These two 

processes, oxidation and deposition, lead to an overall τ of SO2 in the atmosphere of 3 to 4 days. 

 2-36
Table 2-5. Relative contributions of various reactions to the total S(IV) oxidation rate within a sunlit 

cloud, 10 minutes after cloud formation. 

Reaction % of Totala % of Totalb

GAS PHASE 

OH + SO2 3.5 3.1 

AQUEOUS PHASE 

 O3 + HSO3

– 0.6 0.7 

 O3 + SO3

2– 7.0 8.2 

 H2O2 + SO3

2– 78.4 82.1 

CH3OOH + HSO3

– 0.1 0.1 

HNO4 + HSO3

– 9.0 4.4 

HOONO + HSO3

– <0.1 <0.1 

HSO5

– + HSO3– 1.2 <0.1 

SO5

– + SO32– <0.1 <0.1 

HSO 0.6 5

– + Fe2+

aIn the absence of transition metals.bIn the presence of iron and copper ions. 

Source: Adapted from Warneck (1999a). Used with permission. 

Multiphase chemical transformations involving inorganic halogenated compounds effect changes in 

the multiphase cycling of SOX in ways analogous to their effects on NOX. Oxidation of DMS by BrO 

produces dimethyl sulfoxide (DMSO) (Barnes et al., 1991; Toumi, 1994), and oxidation by Cl leads to 

formation of SO2 (Keene et al., 1996). DMSO and SO2 are precursors for MSA and H2SO4. In the MBL, 

virtually all H2SO4 and MSA vapor condenses onto existing aerosols or cloud droplets, which 

subsequently evaporate thereby contributing to aerosol growth and acidification. Unlike MSA, H2SO4

also has the potential to produce new particles (Korhonen et al., 1999; Kulmala et al., 2000) which in 

marine regions is thought to occur primarily in the free troposphere. 

Excepting H2SO4, inorganic particles are solid at low relative humidity (RH), and their composition 

determines their deliquescence thresholds for forming saturated aqueous solutions. Crystallization is not 

simply the reverse of deliquescence but is a process subject to hysteresis; see the sodium chloride (NaCl) 

+ Na2SO4 example in Figure 2-20, and Tang and Munkelwitz (1993) for deliquescence RH points of other 

inorganic particles. 

Particles with several components behave similarly to the example of Na2SO4 but with more 

complex curves and generally have deliquescence RH points below those of their constituent components 

(Wexler and Seinfeld, 1991). 

Saiz-Lopez et al. (2004) estimated that observed levels of BrO at Mace Head Atmospheric 

Research Station in Ireland would oxidize (CH3)2S ~6 times faster than OH and thereby substantially 

increase P(H2SO4) and other condensable S species in the MBL. SO2 is also scavenged by deliquesced 

aerosols and oxidized to H2SO4 in the aqueous phase by several strongly pH-dependent pathways 

(Chameides and Stelson, 1992; Keene and Savoie, 1998; Vogt et al., 1996). Model calculations indicate 

that oxidation of S(IV) by O3 dominates in fresh, alkaline sea salt aerosols, whereas oxidation by 

hypohalous acids, primarily HOCl, dominates in moderately acidic solutions. 

 2-37
Source: Ansari and Pandis (2000) 

Figure 2-20. RH effects on deliquescence and efflorescence points for a NaCl+ Na2SO4 particle, indicating 

deliquescence at ~72% relative humidity and re-crystallization at ~52% RH. Points are 

measurements from Tang et al., (1997); solid line is aerosol thermodynamic model prediction 

of Ansari and Pandis (2000). 

Additional non-sea salt (nss) pSO4 is generated by SO2 oxidation in cloud droplets (Clegg and 

Toumi, 1998). Ion-balance calculations indicate that most of the nss pSO4 in short-lived sea salt size 

fractions accumulates in acidic aerosol solutions or in acidic aerosols processed through clouds or both 

(see, e.g., Keene et al., 2004). The production, cycling, and associated radiative effects of S-containing 

aerosols in marine and coastal air are regulated in part by chemical transformations involving inorganic 

halogens (Von Glasow et al., 2002b). These transformations include: dry-deposition fluxes of nss pSO4 in 

marine air dominated, naturally, by the sea salt size fractions (Huebert et al., 1996; Turekian et al., 2001); 

HCl phase partitioning that regulates sea salt pH and associated pH-dependent pathways for S(IV) 

oxidation (Keene et al., 2002; Pszenny et al., 2004); and potentially important oxidative reactions with 

reactive halogens for (CH3)2S and S(IV). However, both the absolute magnitudes and relative importance 

of these processes in MBL S cycling are poorly understood. 

2.6.4. Multiphase NOX, SOX, and NHX Interactions 

Figure 2-17 above illustrated the central role NH3 can play in the atmospheric chemistry of NOX

and SOX. This results in part from its being the most common soluble base in the atmosphere and from a 

range of its possible chemical reactions. OH attack on NH3 proceeds by 

3 OHNH ⎯+ ⎯→ 2 + 2OHNH

Reaction 36 

 2-38
The fate of the NH2 radical is not known with certainty, but in polluted atmospheres can be 

2 ONH 3 ⎯+ ⎯→ ,, NONHONH

Reaction 37 

2 NONH 2 ⎯+ ⎯→ N2

Reaction 38 

2 NONH 2 ⎯+ ⎯→ 2 + 2OHON

Reaction 39 

However, with typical OH concentrations of 1 to 2 x 106 molecules/cm3, the τ of NH3 against the initial 

reaction is ~30 to 70 days, sufficiently long that this is a small sink compared to NH3 uptake by cloud 

droplets where it is reduced to NH4+ 

OHgNH ←+ ⎯→ ⋅ aqOHNH ←⎯→ NH + OH +

3 2 23 4 )( )(

Reaction 40 

Gas-phase NH3 also reacts with gas-phase HNO3 to form particulate NH4NO3

NH3 HNO3 ←+ ⎯→ NONH 34

Reaction 41 

and with aqueous-phase H2SO4 to form particulate and aqueous-phase NH4HSO4 and (NH4)2SO4. 

)( )( ),( 3 42 44 + lSOHgNH ⎯⎯→ lsHSONH

Reaction 42 

)( ),()()( 3 44 424 + lHSONHgNH ⎯⎯→ lsSONH

Reaction 43 

These products are of special note because submicron NH4HSO4 and (NH4)2SO4 can act as cloud 

condensation nuclei, but the H2SO4 + H2O system does not readily undergo nucleation without addition of 

NH4

+

 (Coffman and Hegg, 1995; Kulmala et al., 2007). These two product species are also of interest 

because Reaction 42 and Reaction 43 are not reversible under typical ambient conditions, while 

Reaction 41, resulting in creation of NH4NO3, is reversible. The NH4NO3 is in a thermodynamic 

equilibrium with NH4

+

 and HNO3 in the gas phase such that lower temperatures shift the equilibrium 

toward greater production of NH4NO3. Higher RH also shifts the equilibrium toward liquid-phase 

NH4NO3. For these reasons, and because gas-phase NH3 will neutralize SO4

2−

 preferentially first, 

NH4NO3 can only form when an excess of gas-phase NH3 first exists. 

Along with HNO3 and H2SO4, NH3 can be limiting in the formation of secondary atmospheric 

particles containing NO3 and SO4

2−

. Measurements and thermodynamic models of free and condensedphase precursors have been used to predict the limiting reactant under different atmospheric conditions 

(Blanchard et al., 2000; Dennis et al., 2001; Watson et al., 1994). Figure 2-21 shows results from one 

application of this technique from Blanchard et al. (2000) with isopleths of NO3

–

 concentrations as 

predicted from total HNO3 and NH3 with 25 μg/m3

 SO4

2−

 and 2 μg/m3 total Cl-. Formation of pNO3 is 

limited by total NH3 availability, but not HNO3, where isolines are vertical. NO3

–

 exists predominately in 

 2-39
the condensed phase where isolines are horizontal and formation is not limited by NH3 there. These 

relationships have been confirmed in field measurements like those reported for NO3

– aloft in Arnold and 

Luke (2007). 

Source: Adapted from Blanchard et al. (2000). 

Figure 2-21. Predicted isolines of particulate NO3 concentrations (μg/m3) as a function of total HNO3 and 

NH3 at 293 K and 80% relative humidity, and with 25 μg/m3 SO4

2− and 2 μg/m3 total Cl−. 

Source: Pandis (2004) in McMurry et al. (2004) 

Figure 2-22. Predicted particulate NO3 concentration as a function of RH for a typical environment. Actual

measured values depend on aging characteristics of the particle. 

 2-40
Changing RH and particle water content also change the partitioning between gas and condensed phases 

for semivolatile species like NO3

−

; see Figure 2-22 for the example of NH4NO3. 

The phase partitioning of NH3 with deliquesced aerosol solutions is controlled primarily by the 

thermodynamic properties of the system 

4 w ]H/[K]NH[)]aq(NH[)g(NH K3

K

3 H b + + ←⎯→ ←⎯→ +

Reaction 44 

where KH and Kb are the temperature-dependent Henry’s Law and dissociation constants 62 M/atm, 1.8 × 

10–5 M, respectively, for NH3; and Kw is the ion product of water, 1.0 × 10–14 M (Chameides, 1984). For a 

given NHX concentration, increasing aqueous concentrations of particulate H+

 will shift the partitioning of 

NH3 towards the condensed phase. Consequently, under the more polluted conditions characterized by 

higher pSO4 concentration, ratios of gas-phase NH3 to particulate NH4

+

 decrease (Smith et al., 2007). It 

also follows that in marine air, where aerosol acidity varies substantially as a function of particle size, 

NH3 partitions preferentially to the more acidic submicron size fractions (Keene et al., 2004; Smith et al., 

2007). 

Because the dry Vd of gas-phase NH3 to the surface is substantially greater than that for the 

submicron pSO4 fractions with which most NH4

+

 is associated, dry deposition fluxes of total NH3 are 

dominated by the gas-phase fraction (Russell et al., 2003; Smith et al., 2007). Consequently, partitioning 

with acidic pSO4 effectively increases the τ of total NH3 against dry deposition. 

This shift has important consequences for NH3 cycling in the atmosphere and potential ecological 

effects. In coastal New England during summer, air transported from rural eastern Canada contains 

relatively low concentrations of nss pSO4 and total NH3 (Smith et al., 2007). Under these conditions, the 

roughly equal partitioning of total NH3 between gas and particulate phases sustains substantial dry 

deposition fluxes to the coastal ocean of total NH3 with a median value of 10.7 μM/m2

/day. In contrast, 

heavily polluted air transported from the industrialized midwestern U.S. contains median concentrations 

of nss pSO4 and total NH3, which are a factor of ~3 or greater more than that median value for clean air. 

Under these conditions, >85% of the total NH3 partitions to the acidic pSO4 size fractions, and, 

consequently, the median dry-deposition flux of total NH3 is 30% lower than that under the cleaner, 

northerly flow regime. The relatively longer atmospheric τ of total NH3 against dry deposition under more 

polluted conditions implies that, on average, total NH3 would accumulate to higher atmospheric 

concentrations under these conditions and also be subject to atmospheric transport over longer distances. 

Consequently, the importance of NHX removal via wet deposition would also increase. Because of the 

inherently sporadic character of precipitation, greater heterogeneity may exist in NH3 deposition fields 

leading to similar heterogeneity in any potential responses in sensitive ecosystems downwind of major S 

emissions regions. 

2.6.5. Transport-Related Effects on Chemistry 

Convective processes and small-scale turbulence transport pollutants both upward and downward 

throughout the PBL and the FT. NOX, SOX, and NHX can be transported vertically by convection into the 

upper part of the mixed layer on one day, then transported overnight as a layer of elevated mixing ratios, 

sometimes by a nocturnal low-level jet, and then entrained into a growing convective boundary layer 

downwind and brought back to the surface. 

Because NO and NO2 are only slightly soluble, they can be transported over longer distances in the 

gas phase than can more soluble species which are depleted by deposition to moist surfaces or taken up 

more readily on aqueous surfaces of particles. During transport, emitted N species can be transformed 

into reservoir species such as HNO3, PANs, and N2O5. These species can then contribute to local NOX

concentrations in remote areas. For example, it is now well established that PAN decomposition provides 

 2-41
a major source of NOX in the remote troposphere (Staudt et al., 2003). PAN decomposition in subsiding 

air masses from Asia over the eastern Pacific could make an important contribution to O3 and NOX

enhancement in the U.S. (Hudman et al., 2004; Kotchenruther et al., 2001). Further details about 

mechanisms for transporting O3 and its precursors were described at length in 2006 O3 AQCD (U.S. EPA, 

2006b). 

Major episodes of high pollution concentrations in the eastern U.S. and in Europe are often 

associated with slow-moving high-pressure systems. High-pressure systems during the warmer seasons 

are associated with subsidence, resulting in warm, generally cloudless conditions with light winds. The 

subsidence results in stable conditions near the surface, which inhibit or reduce the vertical mixing of 

primary and secondary pollutants. Photochemical activity is enhanced under these conditions because of 

higher temperatures and the availability of sunlight. 

Crutzen and Gidel (1983), Gidel (1983), and Chatfield and Crutzen (1984) hypothesized that 

convective clouds played an important role in rapid atmospheric vertical transport of trace species and 

first tested simple parameterizations of convective transport in atmospheric chemical models. At nearly 

the same time, evidence was shown of venting the boundary layer by shallow, fair weather cumulus 

clouds (see, e.g., Greenhut et al., 1984; Greenhut, 1986). Field experiments were conducted in 1985, 

which resulted in verification of the hypothesis that deep convective clouds are instrumental in 

atmospheric transport of trace constituents (Dickerson et al., 1987). Once pollutants are lofted to the 

middle and upper troposphere, they typically have a much longer chemical τ and, with the generally 

stronger winds at these altitudes, can be transported long distances from their source regions. Transport of 

NOX from the boundary layer to the upper troposphere by convection tends to dilute concentrations and 

extend the NOX τ from <24 h to several days. Photochemical reactions occur during this long-range 

transport. Pickering et al. (1990) demonstrated that venting of boundary layer NOX by convective clouds 

(shallow and deep) causes enhanced P(O3) in the FT. NOX at the surface can often increase P(O3) 

efficiency. Therefore, convection aids in the transformation of local pollution into a contribution to global 

atmospheric pollution. Downdrafts within thunderstorms tend to bring air with less NOX from the middle 

troposphere into the boundary layer. Lightning produces NO which is directly injected chiefly into the 

middle and upper troposphere. The total global production of NO by lightning remains uncertain, but is 

on the order of 10% of the total. 

The first unequivocal observations of deep convective transport of boundary layer pollutants to the 

upper troposphere were documented by Dickerson et al. (1987). Instrumentation aboard three research 

aircraft measured CO, O3, NO, NOX, NOY, and HCs in the vicinity of an active mesoscale convective 

system near the border of Oklahoma and Arkansas during the 1985 PRE-STORM experiment. Anvil 

penetrations about two hours after cloud maturity found greatly enhanced mixing ratios inside the cloud 

of all of the aforementioned species compared with outside it. NO mixing ratios in the anvil averaged 3 to 

4 ppb, with individual 3-min observations reaching 6 ppb; boundary layer NOX was typically 1.5 ppb or 

less outside the cloud. Therefore, the anvil observations represent a mixture of boundary layer NOX and 

NOX contributed by lightning. Luke et al. (1992) summarized the air chemistry data from all 18 flights 

during PRE-STORM by categorizing each case according to synoptic flow patterns. Storms in the 

maritime tropical flow regime transported large amounts of CO, O3, and NOY into the upper troposphere 

while the midtroposphere remained relatively clean. During frontal passages a combination of stratiform 

and convective clouds mixed pollutants more uniformly into the middle and upper levels. 

Thunderstorm clouds are optically very thick and, therefore, have major effects on radiative fluxes 

and photolysis rates. Madronich (1987) provided modeling estimates of the effects of clouds of various 

optical depths on photolysis rates. In the upper portion of a thunderstorm anvil, photolysis is likely to be 

enhanced by a factor of 2 or more due to multiple reflections off the ice crystals. In the lower portion and 

beneath the cloud, photolysis is substantially decreased. With enhanced photolysis rates, the NO-to-NO2

ratio in the upper troposphere is driven to larger values than under clear-sky conditions. 

Thunderstorm updraft regions, which contain copious amounts of water, are regions where efficient 

scavenging of soluble species can occur (Balkanski et al., 1993). NO2 itself is not very soluble; hence, wet 

scavenging is not a major removal process for it. However, a major NOX reservoir species, HNO3, is 

 2-42
highly soluble. Very few direct field measurements of the amounts of specific trace gases, including 

HNO3, that are scavenged in storms are available. Pickering et al. (2001) used a combination of model 

estimates of soluble species that did not include wet scavenging and observations of these species from 

the upper tropospheric outflow region of a major line of convection observed near Fiji. Over 90% of the 

NOX in the outflow air appeared to have been removed by the storm. 

2.7. Sampling and Analysis Techniques 

2.7.1.

2.7.1.1.

Methods for Relevant Gas-Phase N Species 

Separate sections here on field-deployed measurement techniques focus on current methods and 

promising new technologies so no attempt is made to cover development of these methods or methods no 

longer in widespread use. Rather, the descriptions in this chapter concern chiefly the Federal Reference 

Methods and Federal Equivalent Methods (FRM and FEM, respectively). More detailed discussions of 

the FRM, FEM, and other, newer methods including issues about their field use are found in Clemitshaw 

(2004); Edgerton et al. (2006, 2007); McClenny (2000); Parrish and Fehsenfeld (2000); and U.S. EPA 

(1996a, 2008a, 2008b). 

NO and NO2

Chemiluminescence 

NO can be measured reliably using the principle of gas-phase chemiluminescence (CL) induced by 

the reaction of NO with O3 at low pressure. Modern commercial NOX analyzers have sufficient sensitivity 

for adequate measurement in urban and many rural locations (U.S. EPA, 1993a, 2006b). Research grade 

CL instruments have been compared under realistic field conditions to spectroscopic instruments with 

results that indicate that both methods are reliable at concentrations relevant to smog studies to better than 

15% when using a 95% confidence response time of 60 seconds (see Crosley, 1996). Near-source, urban, 

and rural and remote concentrations of NO are routinely measured using CL. However, Cardelino and 

Chameides (2000) reported that measured NO concentrations during the afternoon were frequently at or 

below the operational limit of detection (LOD), ~1 ppb, of the regulatory NOX instruments even in large 

metropolitan regions such as Washington, DC, Houston, TX, and New York, NY. 

The FRM for NO2 also makes use of this NO detection technique using a prerequisite step to 

reduce NO2 to NO on the surface of a molybdenum oxide (MoOX) substrate heated to ~340 °C. Because 

the FRM monitor cannot detect NO2 directly, the NO2 level is computed as the difference between the 

sample passed over the heated MoOX substrate (the NOX total) and the sample not so reduced (the NO 

alone). Reduction of NO2 to NO on the MoOX substrate is not specific to NO2, however; hence, the CL 

analyzers are subject to unknown and varying levels of interferences produced by the presence in the 

sample of other oxidized N compounds, the NOZ species shown in the outer box of Figure 2-16. This 

interference is often termed a “positive artifact” in the NO2 concentration estimate since the presence of 

NOZ always results in an over-estimate in the reported measurement of the actual NO2 concentration. 

This interference by NOZ compounds has long been known (see Crosley, 1996; Fehsenfeld et al., 

1987; McClenny et al., 2002; Nunnermacker et al., 1998; Parrish and Fehsenfeld, 2000; Rodgers and 

Davis, 1989; Steinbacher et al., 2007; U.S. EPA, 2008a). These studies have relied on intercomparisons of 

measurements using the FRM and other techniques for measuring NO2. The sensitivity of the FRM to 

potential interference by individual NOZ compounds is variable and depends in part on characteristics of 

 2-43
individual monitors, such as design of the instrument inlet, the temperature and composition of the 

reducing substrate, and the interactions of atmospheric species with the reducing substrate. 

Only recently have attempts been made to quantify systematically the magnitude and variability of 

the interference by NOZ species in ambient measurements of NO2. Dunlea et al. (2007) found an average 

of ~22% of ambient NO2 (~9 to 50 ppb) measured in Mexico City was due to interference from NOZ

compounds; that is to say, the true ambient NO2 concentration was ~22% lower than what was reported at 

monitors using the CL difference technique. Although comparable levels of NO2 are found in many 

locations in the U.S., the same comparison for distinct places in the U.S. is difficult to make because 

significant uncertainty remains in determining the concentrations of the higher oxidation NOZ products 

since they are not routinely measured. Dunlea et al. (2007) compared NO2 measured using the 

conventional CL instrument with optical techniques. The main sources of interference were HNO3 and 

various RONO2 which can be converted to NO on the catalyst with varying rates of efficiency. In this 

study, the efficiency of conversion on the catalyst — that is, how much of the compound introduced to the 

catalyst was converted to NO — was estimated to be ~38% for HNO3; ~95% for PAN, and ~95% for 

other RONO2. Peak interference (over-estimation) in the reported estimate of NO2 concentrations from 

the presence of NOZ compounds of up to 50% was found during afternoon hours and was associated with 

O3 and NOZ compounds such as HNO3, and with multifunctional alkyl nitrates. 

In a study in rural Switzerland, Steinbacher et al. (2007) compared measurements of NO2

continuously measured using a conventional CL-NOX monitor and measurements in which NO2 was 

photolyzed to NO. They found that the conventional CL technique overestimated the reported NO2

concentration, on average, by 10% during winter and 50% during summer. 

Another approach to estimating the NO2 measurement interference uses model calculations in 

conjunction with known data on the reduction efficiencies of NOZ species on the MoOX converters. 

Lamsal et al. (2008) used the conversion efficiencies noted above along with output for NOY species from 

the GEOS-Chem CTM to derive seasonal correction factors for the ambient monitoring data across the 

U.S. These factors range from <10% in winter in the East to >80% in the West, with the highest values 

found during summer in relatively unpopulated areas. Lamsal et al. (2008) also used these corrected data 

to determine the feasibility of using satellite data to supplement the ground-based data. However, the 

current generation of satellite monitors are in low earth orbit and so the NO2 values are restricted to time 

of satellite overpass in early afternoon local time. 

Calculations using CMAQ for the mid-Atlantic region in a domain extending from Virginia to 

southern New Jersey (see http://www.mde.state.md.us /Programs/AirPrograms/air_planning/index.asp) 

were made at much higher spatial resolution than the GEOS-Chem simulations by Lamsal et al. (2008). 

The daily average interference for an episode during the summer of 2002 estimated using model-derived 

concentration fields for NOZ species and using the conversion efficiencies for NOZ species given above, 

ranged from ~20% in Baltimore to ~80% in Madison, VA. Highest values were found during the 

afternoon, when photochemical activity is highest and production and accumulation of the higher 

oxidized NOZ compounds is greatest; lowest values occurred during the middle of the night when 

photochemistry stops. The model calculations showed episode averages of the NOZ-to-NO2 ratio ranging 

from 0.26 to 3.6 in rural Virginia, with the highest ratios in rural areas and lowest in urban centers closer 

to sources of fresh NOX emissions. (The capabilities of three-dimensional CTMs such as GEOS-Chem 

and CMAQ and issues associated with their use are presented in Section 2.8 below.) 

In summary, the current CL method of determining ambient NOX and then reporting NO2

concentrations by subtraction of NO is subject to positive interference by NO2 oxidation products, chiefly 

HNO3 and PAN as well as other oxidized N-containing compounds, though the magnitude of this positive 

bias is largely unknown and can be rapidly changing. Measurements of these higher-order oxidation 

products in urban areas are sparse. Concentrations of these oxidation products are expected to peak in the 

afternoon because of the continued oxidation of NO2 from NO emitted during the morning rush hours and 

during conditions conducive to photochemistry in areas well downwind of sources, particularly during 

summer. 

 2-44
Within the urban core of metropolitan areas, where many of the ambient monitors are sited close to 

strong NOX sources such as motor vehicles on busy streets and highways, these positive artifacts due to 

NO2 oxidation products are much smaller than outside the urban core farther downwind, and are 

typically <10%. Conversely, the positive artifacts are larger in locations more distant from NOX sources 

where NO2 concentrations are lowest and can exceed 50% as noted above. Therefore, variable, positive 

artifacts associated with measuring NO2 using the FRM severely hamper its ability to serve as an accurate 

and precise indicator of NO2 concentrations at the typical ambient levels generally encountered in remote 

areas outside of urban cores where they would be most relevant for the environmental exposures of 

concern to a secondary NAAQS. 

Other Methods 

NO has also been successfully measured in ambient air with direct spectroscopic methods; these 

include two-photon laser-induced fluorescence (TPLIF), tunable diode laser absorption spectroscopy 

(TDLAS), and two-tone frequency-modulated spectroscopy (TTFMS). These were reviewed thoroughly 

in the 2008 NOX ISA (U.S. EPA 2008a). The spectroscopic methods demonstrate excellent sensitivity and 

selectivity for NO with detection limits on the order of 10 ppt for integration times of 1 min and 

spectroscopic methods compare well with the CL method for NO in controlled laboratory air, ambient air, 

and heavily polluted air (see Gregory et al., 1990; Kireev et al., 1999; Walega et al., 1984). However, 

these spectroscopic methods remain research grade rather than field deployable due to their complexity, 

size, and cost, but are essential for demonstrating that CL methods are reliable. 

There are approaches to measuring NO2 not affected by the artifacts mentioned above. For 

example, NO2 can be photolytically reduced to NO with an efficiency of ~70% as used in Steinbacher 

et al. (2007). Ryerson et al. (2000) developed a gas-phase CL method using a photolytic converter based 

on a Hg lamp with increased radiant intensity in the region of peak NO2 photolysis, 350 to 400 nm, and 

producing conversion efficiencies of 70% or more in less than 1 second. Metal halide lamps with 

conversion efficiency of ~50% and accuracy on the order of 20% have been used (Nakamura et al., 2003). 

Because the converter produces little radiation at wavelengths less than 350 nm, interferences from HNO3

and PAN are minimal. This method requires additional development to ensure its cost effectiveness and 

reliability for extensive field deployment. 

Optical methods such as those using differential optical absorption spectroscopy (DOAS) or laserinduced fluorescence (LIF) are also available. However, these particular methods are more expensive than 

either the FRM monitors or photolytic reduction technique and require specialized expertise to operate. 

Moreover, the DOAS obtains an area-integrated measurement rather than a point measurement. Cavity 

attenuated phase shift (CAPS) monitors are an alternative optical approach potentially less costly than 

DOAS or LIF (Kebabian et al., 2007). However, this technique is not highly specific to NO2 and is 

subject to interference by other species absorbing at 440 nm, such as the 1,2–dicarbonyl compounds. The 

extent of this interference and the potential of the CAPS technique for extensive field deployment have 

not been evaluated. 

A DOAS system manufactured by OPSIS is designated as an FEM for measuring NO2. DOAS 

systems can also be configured to measure NO, HNO2, and NO3. Typical detection limits are 0.2 to 0.3 

ppb for NO, 0.05 to 0.1 ppb for NO2, 0.05 to 0.1 ppb for HNO2, and 0.001 to 0.002 ppb for NO3, at path 

lengths of 0.2, 5, 5, and 10 km, respectively. The obvious advantage compared to fixed-point 

measurements is that concentrations relevant to a much larger area are obtained, especially if multiple 

targets are used. At the same time, any microenvironmental artifacts are minimized over the long path 

integration. However, comparisons to other measurements made at point locations are difficult to 

interpret. A major limitation in this technique had involved inadequate knowledge of absorption cross 

sections. Harder et al. (1997) conducted an experiment in rural Colorado involving simultaneous 

measurements of NO2 by DOAS and by photolysis followed by CL. They found differences of as much as 

110% in clean air from the west; but for NO2 mixing ratios in excess of 300 ppt, the two methods agreed 

to better than 10%. Stutz et al. (2000) cites two intercomparisons of note. NO was measured by DOAS, 

 2-45
by photolysis of NO2 followed by CL, and by LIF during July 1999 as part of the SOS in Nashville, TN. 

On average, the three methods agreed to within 2%, with some larger differences likely caused by spatial 

variability over the DOAS path. In another study in Europe, a multi-reflection set-up over a 15 km path 

negated the problem of spatial averaging. Here agreement with the CL detector following photolytic 

conversion was excellent (slope = 1.006 ± 0.005; intercept = 0.036 ± 0.019; r2

 = 0.99) over a 

concentration range from about 0.2 to 20 ppb. 

Remote Sensing of NO2

The paucity of specific in situ NO2 measurements motivates the inference of ground-level NO2

concentrations from satellite measurements of tropospheric NO2 columns. This prospect would take 

advantage of the greater sensitivity of tropospheric NO2 columns to NOX in the lower troposphere than in 

the upper troposphere as in Section 2.2 above. Tropospheric NO2 columns show a strong correlation with 

in situ NO2 measurements in northern Italy (Ordonez et al., 2006). Quantitative calculation of surface 

NO2 concentrations from a tropospheric NO2 column requires information on the relative vertical profile. 

Comparison of vertical profiles of NO2 in GEOS-Chem versus in situ measurements over and downwind 

of North America showed a high degree of consistency (Martin et al., 2004, 2006), suggesting that CTMs 

could be used to infer the relationship between surface NO2 concentrations and satellite observations of 

the tropospheric NO2 column. 

Table 2-6 contains an overview of the three satellite instruments used to retrieve tropospheric NO2

columns from measurements of solar backscatter. All three instruments are in polar sun-synchronous 

orbits with global measurements in the late morning and early afternoon local time. The spatial resolution 

of the measurement from SCIAMACHY is 7 times better than that from the GOME; and that from Ozone 

Monitoring Instrument (OMI) is 40 times better than that from GOME. 

Table 2-6. Satellite instruments used to retrieve tropospheric NO2 columns. 

Instrument Coverage Typical U.S. 

Measurement Time 

Typical Resolution 

(km) 

Return Time 

(days)1

Instrument 

Overview 

GOME 1995–2002 10:30–11:30 AM 320 × 40 3 Burrows et al. (1999) 

SCIAMACHY 2002– 10:00–11:00 AM 30 × 60 6 Bovensmann et al. 

(1999) 

OMI 2004– 12:45–1:45 PM 13 × 24 1 Levelt et al. (2006) 

1 Return time is reported here for cloud-free conditions. Note that due to precession of the satellite’s orbit, return measurements are close to but not made over the same location. In 

practice, clouds decrease observation frequency by a factor of 2. 

Figure 2-23 shows tropospheric NO2 columns retrieved from SCIAMACHY. Pronounced 

enhancements are evident over major urban and industrial areas. The high degree of spatial heterogeneity 

over the southwestern U.S. provides empirical evidence that most of the tropospheric NO2 column is 

concentrated in the lower troposphere. Tropospheric NO2 columns are more sensitive to NOX in the lower 

troposphere than in the upper troposphere (Martin et al., 2002). This sensitivity to NOX in the lower 

troposphere is due to 25-fold decrease in the NO2-to-NO ratio from the surface to the upper troposphere 

(Bradshaw et al., 1999), driven by the temperature dependence of the NO + O3 reaction. Martin et al. 

(2004) integrated in situ airborne measurements of NO2 and found that during summer the lower mixed 

layer contains 75% of the tropospheric NO2 column over Houston and Nashville. However, it should be 

noted that these satellite measurements are also sensitive to surface albedo and aerosol loading which 

strongly vary locally. 

 2-46
Source: Martin et al. (2006). Reprinted with permission. 

Figure 2-23. Tropospheric NO2 column estimates (molecules NO2/cm2) retrieved from the SCIAMACHY 

satellite instrument for 2004–2005. 

2.7.1.2.

2.7.1.3.

Total NOY

Gold-catalyzed CO or H2 reduction, or conversion on heated MoOX, have been used for many years 

to reduce total NOY to NO before detection by CL (Crosley, 1996; Fehsenfeld et al., 1987). Both 

techniques offer generally reliable measurements, with response times on the order of 60 seconds and a 

linear dynamic range demonstrated in field intercomparisons from ~10 ppt to 10s of ppb. Under some 

conditions, hydrogen cyanide (HCN), NH3, alkyl nitrates (RNO2), and acetonitrile (CH3CN) can be 

converted to NO; but at normal atmospheric concentrations and RH, and when convertor temperature is 

closely monitored, these are minor interferants. Thermal decomposition followed by LIF has also been 

used for NOY detection. In field comparisons, instruments based on these two principles generally showed 

good agreement (Day et al., 2002) with experimental uncertainty estimated to be on the order of 15 to 

30%. 

Commercially available NOX monitors have been converted to NOY monitors by moving the MoOX

convertor to couple directly to the sample inlet. Because of losses on inlet surfaces and differences in the 

efficiency of reduction of NOZ compounds on the heated MoOX substrate, NOX measured by CL in 

standard mode cannot be considered a surrogate for NOY. However, in settings close to relatively highconcentration fresh emissions like those during urban rush hour, most of the NOY is present as NOX. 

Reliable measurements of NOY and NO2, especially at the low concentrations observed in many areas 

remote from sources remain crucial for evaluating the performance of CTMs; see Section 2-8 for 

additional details on CTM application and evaluation. 

Nitric Acid 

A major issue to be considered when measuring NOX and NOY is the possibility that HNO3, a 

major component of NOY, is sometimes lost in inlet tubes and not measured (Luke et al., 1998; Parrish 

and Fehsenfeld, 2000). This problem is especially important if measured NOY is used to identify NOXlimited versus NOX-saturated conditions. The problem is substantially alleviated, although not necessarily 

completely solved, by using much shorter inlets on NOY monitors than on standard NOX monitors and by 

 2-47
the use of surfaces less likely to take up HNO3. The correlation between O3 and NOY differs for NOXlimited versus NOX-saturated locations, but this difference is driven primarily by differences in the ratio 

of O3 to HNO3. If HNO3 were omitted from the NOY measurements, then the measurements would 

represent a severely biased estimate. 

Accurate measurement of HNO3 has presented a long-standing analytical challenge. To understand 

why, it is useful to consider the major factors that control HNO3 partitioning between the gas and 

deliquesced-particulate phases in ambient air as depicted in Reaction 45. 

][][])([)( 3 3 3

+ − gHNO ←⎯→⎯H aqHNO ←⎯→a + NOH K K

Reaction 45 

where KH is the Henry’s Law constant in M/atm and Ka is the acid dissociation constant in M. Thus, the 

primary controls on HNO3 phase partitioning are its thermodynamic properties, aerosol liquid water 

content (LWC), solution pH, and reaction kinetics. Aerosol LWC and pH are controlled by the relative 

mix of acids and bases in the system, the hygroscopic properties of condensed compounds, and 

meteorological conditions, chiefly RH, temperature, and pressure. 

In the presence of chemically distinct aerosols of varying acidities (e.g., supermicron, 

predominantly sea salt, and submicron, predominantly pSO4), HNO3 should partition preferentially to the 

less-acidic particles, and observations are consistent with this (see. e.g., Huebert et al., 1996; Keene et al., 

1998). The kinetics of this phase partitioning are controlled by atmospheric concentrations of HNO3 vapor 

and pNO3, and the size distribution and τ of the particles against deposition. Submicron diameter aerosols 

typically equilibrate with the gas phase in seconds to minutes while supermicron aerosols require hours to 

a day or more; see, e.g., Erickson et al. (1999) and Meng and Seinfeld (1996). Consequently, smaller 

aerosol size fractions are typically close to thermodynamic equilibrium with respect to HNO3, whereas 

larger size fractions, for which τ against deposition range from hours to a few days, are often 

undersaturated (Erickson et al., 1999; Keene et al., 1998). 

Methods used widely for measuring HNO3 include standard filter packs configured with nylon or 

alkaline-impregnated filters (see, e.g., Bardwell et al., 1990; Goldan et al., 1983), annular denuders like 

U.S. EPA method IP-9, and mist chambers (Talbot et al., 1990), all typically joined to ion chromatography 

detection. Intercomparisons of these measurement techniques by Hering et al. (1988), Tanner et al. 

(1989), and Talbot et al. (1990) reported differences on the order of a factor of 2 or more. In part, this 

variance is due to nonsystematic sampling error. When chemically distinct aerosols with different pH—

for example, sea salt and pSO4—mix together on a bulk filter, the acidity of the bulk mixture will be 

greater than that of the less-acidic aerosols with which most of the NO3 is associated. This change in pH 

may cause the bulk mix to be supersaturated with respect to HNO3 leading to volatilization and, thus, to a 

positive measurement bias in HNO3 sampled downstream. Alternatively, when undersaturated 

supermicron size fractions like sea salt accumulate on a bulk filter and chemically interact over time with 

HNO3 in the sample air stream, scavenging may lead to a negative bias in the HNO3 sampled 

downstream. Because the magnitude of both effects will vary as functions of the overall composition and 

thermodynamic state of the multiphase systems, the combined influence can cause net positive or net 

negative measurement bias in data with unknown frequencies. Pressure drops across particle filters can 

also lead to artifact volatilization and associated positive bias in HNO3 concentrations measured 

downstream. 

Sensitive HNO3 measurements based on the principle of chemical ionization mass spectroscopy 

(CIMS) have become more common; see Huey et al. (1998), Furutani and Akimoto (2002), Mauldin et al. 

(1998), and Neuman et al. (2002). The CIMS relies on selective formation of ions such as silicon 

pentafluoride–nitric acid (SiF5

− · HNO3) or (HSO4− · HNO3) followed by detection via mass spectroscopy. 

Two CIMS techniques and a filter pack technique were intercompared in Boulder, CO (Fehsenfeld et al., 

1998) where results indicated agreement to within 15% between the two CIMS instruments and between 

the CIMS and filterpack methods under relatively clean conditions of 50-400 ppt HNO3. In more polluted 

 2-48
air, the filterpack technique generally yielded higher values than the CIMS, suggesting that interactions 

between chemically distinct particles on bulk filters is a more important source of bias in polluted 

continental air. Differences were also greater at lower temperature when pNO3 accounted for a relatively 

greater fraction of total NO3

–

. 

Three semi-continuous methods for detecting HNO3 were tested against the annular denuder 

filterpack (ADS) integrated collection technique at the Tampa Bay Regional Atmospheric Chemistry 

Experiment (BRACE) Sydney research station ~20 km downwind of the Tampa, FL, urban core (Arnold 

and Luke, 2007). The semi-continuous instruments included: two slightly differing implementations of 

the NOY-NOY* (total oxides of nitrogen minus that total denuded of HNO3) denuder difference technique, 

one from the NOAA Air Resources Lab (ARL), and one from Atmospheric Research and Analysis, Inc. 

(ARA); the parallel plate wet diffusion scrubber online ion chromatography technique from Texas Tech 

University (TTU); and the CIMS from the Georgia Institute of Technology (GIT). Integrated twelve hour 

ADS samples were collected by the University of South Florida (USF). Results for 10 min samples 

computed from the various higher sampling frequencies of each semi-continuous instrument showed good 

agreement (R2

 >0.7) for afternoon periods of the highest production and accumulation of HNO3. Further, 

agreement was within ± 30% for these instruments even at HNO3 concentrations <0.30 ppb. The USF 

ADS results were biased low, however, by 44%, on average, compared to the corporate 12-h aggregated 

means from the semi-continuous methods, and by >600% for the nighttime samples; ADS results were 

below the corporate mean maximum HNO3 concentration by >30% as well. The four instruments using 

semi-continuous methods, by contrast, were all within 10% of each other’s 12-h mean mixing ratios. 

While only ARA employed a formal minimum detection limit at 0.050 ppb, error analysis with the other 

techniques established that at the same level of precision, TTU’s effective limit was approximately the 

same as ARA’s, and that ARL’s limit was 0.030 ppb; analysis for GIT showed no apparent effective limit 

at the levels of HNO3 encountered in this field study. The importance of sample inlet height for HNO3

measurements was indirectly shown through comparison to previous fieldwork at this site when sample 

inlet heights ranged from 1.5–10 m and produced systematic discrepancies in HNO3 concentrations 

correlated with height of more than a factor of 2. 

2.7.1.4.

2.7.1.5.

Other Nitrates 

Methods for sampling and analysis of RONO2 in the atmosphere have been reviewed by Parrish 

and Fehsenfeld (2000). PAN, PPN, and MPAN were typically measured using a gas chromatograph 

followed by electron capture detectors (GC-ECD); see, e.g., Gaffney et al. (1998), although other 

techniques such as Fourrier Transform InfraRed (FTIR) analysis can also be used. Field measurements 

made using GC-ECD have reported a total uncertainty of ± 5 ppt + 15% (Gaffney et al., 1998; Roberts et 

al., 1998). Additional descriptions of specific techniques for RONO2 and some of the issues involved with 

using data taken with them appear below in Section 2.10, accompanying descriptions of the methods used 

routinely to monitor ambient air concentrations and deposition amounts of RONO2, and in the 2008 NOX

ISA (U.S. EPA, 2008a). 

Ammonia 

Because NH3 plays a key role in the atmospheric chemistry of particle formation, several methods 

have been developed for ambient and higher-level concentrations; see Allegrini et al. (1992); Appel et al. 

(1988); Asman et al. (1998); Fehsenfeld et al. (1996); Genfa and Dasgupta (1989); Mennen et al. (1996); 

Pryor et al. (2001); Schwab et al. (2007); Williams et al. (1992); and Wyers et al. (1993). Measurement of 

NH3 is made difficult by its chemistry, whereby it forms strong H bonds with itself and water, and can be 

lost either with hysteresis or irreversibly to many instrument surfaces. Moreover, the range of atmospheric 

 2-49
NH3 concentrations extends over 4 or 5 orders of magnitude. Because of these challenges, many NH3

techniques remain research-grade with steep requirements of time, care, and technical experience. 

U.S. EPA has proposed to include ambient NH3 measurements in its National Core (NCore) 

monitoring network (U.S. EPA, 2005b) and this has motivated additional development and testing of NH3

monitors. The U.S. EPA Environmental Technology Verification (ETV) Program’s Advanced Monitoring 

Systems (AMS) Center, has verified the performance of seven ambient NH3 monitors for use at confined 

animal feeding operations (CAFOs). In collaboration with the U.S. Department of Agriculture (USDA), 

the AMS Center verified the seven ambient NH3 monitors (see Table 2-7) in two phases of testing, each at 

separate CAFOs: Phase I was conducted at a swine finishing farm and Phase II at a cattle feedlot. These 

sites were selected to provide realistic testing conditions and a wide range of NH3 concentrations. 

Table 2-8 summarizes some of the performance data for the individual technologies. (The full verification 

reports can be found at http://www.epa.gov/nrmrl/std/etv/vt-ams.html under the ambient NH3 sensors 

category.) 

Ambient NH3 monitors utilize a wide range of analytical methods, including direct detection by 

spectroscopic techniques; indirect detection of NH3 using selective membrane permeation with 

conductivity detection, catalytic conversion with CL detection; treatment with a chemical dopant followed 

by ion mobility detection; and other techniques. Ambient NH3 monitors also can provide specialized 

features valuable in specific uses, such as long-term monitoring or determining NH3 fluxes and emission 

rates. 

For example, monitors that collect NH3 concentration data with a frequency greater than 1 Hz can 

be used with simultaneous three-dimensional windspeed and direction data to determine NH3 flux. 

Alternatively, open-path monitors can be used to calculate emission rates from CAFOs, since these 

monitors measure the average NH3 concentration over a 1 to 100 m path. Some monitors also are suitable 

for long-term monitoring, since they can be operated without user intervention for weeks at a time. 

Table 2-7. Verified ambient NH3 monitors. 

Technology Name Description 

Aerodyne Research, Inc. QC-TILDAS An infrared laser spectrometer, based on pulsed quantum cascade laser technology; continuous 

measurement 

Bruker Daltonics OPAG 22 Open-Path 

Gas Analyzer 

A broadband, open-path, Fourier transform infrared spectrometer for remote sensing continuous 

measurement 

Molecular Analytics IonPro-IMSNH3

Analyzer 

An ion mobility spectrometer; continuous measurement 

Omnisens SA TGA310 NH3 Analyzer A trace gas analyzer that uses photoacoustic spectrometry; continuous measurement 

Pranalytica, Inc. NitroluxTM 1000 

Ambient NH3 Analyzer 

A resonant photoacoustic spectrometer with a line-tunable CO2 laser; continuous measurement 

Mechatronics Instruments BV 

AiRRmonia NH3 Analyzer 

A single-point monitor composed of a membrane diffusion sampler, a detector block with a diffusion 

membrane, and two conductivity cells; continuous measurement 

Thermo Electron Corp. Model 17C 

NH3 Analyzer 

A CL analyzer that uses NO and ozone (O3) reactions; time-averaged measurement 

 2-50
Table 2-8. Performance characteristics of the 7 U.S. EPA ETV tested NH3 methods. 

Average 

Relative 

Accuracyb 

Response Linearity Comparabilityc 

Relative 

Precisionb Vendora Testing Time 

(95%) Slope Intercept r2 Slope Intercept r2

A Phase I & 

Phase II 

3.7 to 

10.5% 

0.3% 3 to 76 min 0.90 to 

1.03 

-24 to -0.6 1.000 0.86 to 

1.20 

-0.5 to 16 0.984 to 

0.990 

B Phase I & 

Phase II 

2.4 to 34% 0.7 to 2.1% 8 to 20 min 1.02 to 

1.28 

-2.4 to 136 0.9957 to 0.9999 0.41 to 

1.18 

-1.4 to 58 0.538 to 

0.9755 

C Phase I & 

Phase II 

10 to 44% 0.2 to 1.3% 1 to 32 min 0.716 to 

1.25 

-58.5 to 167 0.9854 to 0.9997 0.646 to 

1.83 

-6.7 to 21.6 0.9794 to 

0.9842 

D Phase II 2.2% 0.9% 2 to 2.6 min 0.966 15.9 1.000 1.15 -4.1 0.994 

E Phase II 18.3% 1.0% 2.5 to 17 

min 

0.815 1.08 1.000 1.565 -16.5 0.994 

F Phase II 26% 1.8% 4 to 14 s 0.583 24.9 0.9144 NR NR NR 

G Phase I & 

Phase II 

4.7 to 10% 1.9 to 2.5% 0.8 to 66 s 0.840 to 

0.962 

-8.8 to 35 0.9989 to 0.9998 0.984 to 

1.09 

-9.5 to 14.4 0.9943 to 

0.9982 

a Because the ETV Program does not compare technologies, the performance results shown in this table do not identify the vendor associated with each result and are not in the same 

order as the list of technologies in Table 2-7. b A result of 0% indicates perfect accuracy or precision. c

 The comparability of the verified technology with a standard reference method 

was established by comparing the average NH3 sensors readings with time-integrated NH3 samples collected using citric-acid-coated denuders. The reference samples were collected 

based on procedures described in the U.S. EPA Compendium Method IO-4.2, Determination of Reactive Acidic and Basic Gases and Acidity of Fine Particles (<2.5 μm). Comparability 

between the NH3 sensors results and the reference method results with respect to ambient air was assessed by linear regression using the reference method NH3 concentrations as the 

independent variable and results from the NH3 sensor as the dependent variable. 

In addition to the evaluation by U.S. EPA, ETV, a laboratory-based intercomparison of real-time 

ambient NH3 instruments was conducted and reported by Schwab et al. (2007) with seven instruments 

using six methods. The tunable diode laser (TDL) absorption spectrometer, the wet scrubbing long-path 

absorption photometer (LOPAP), the wet effusive diffusion denuder (WEDD), the ion mobility 

spectrometer (IMS), the Nitrolux laser acousto-optical absorption analyzer, and a modified CL analyzer. 

Schwab et al. (2007) reported that all instruments performed well and agreed to within ~25% of the 

expected calibration value, with the exception of the CL analyzer which suffered from problems related to 

its MoOX conversion of NOZ to NO. (Work with a modification of this technique has been continuing 

with the Aerosol Research Inhalation Epidemiology Study (ARIES); see, e.g., Blanchard and Hidy, 2003.) 

Instrument response time is known to be a crucial feature for ambient NH3 measurements, and 

Schwab et al. (2007) showed response time to be sensitive to measurement history as well as the sample 

handling materials. Shortest response was for the TDL; the Nitrolux and IMS and WEDD instruments had 

unacceptably long time responses under some environmental conditions which rendered correlations 

across instruments meaningless. The TDL and LOPAP reported values closest to delivered concentration 

values; the IMS exhibited bias of ~ +25%; the Nitrolux bias was ~ −25%. Schwab et al. (2007) concluded 

that sub-ppb ambient NH3 measurements can be taken reliably with some of these instruments, but that 

special care must still be exercised to ensure high-quality data. 

These and other recent intercomparisons of ambient NH3 instruments have confirmed that no single 

active sampling technique has yet been identified for automated, fast-response, low-concentration, highquality continuous data. 

Passive samplers for ambient NH3 measurements, however, have been tested for multiple periods 

of 14-day exposures over several years in the small network of sites in the U.S. Upper Midwest listed in 

Table 2-9 and have performed well. 

 2-51
Table 2-9. Site codes and locations of passive NH3 samplers in the U.S. EPA and Lake Michigan Air 

Directors and Illinois State Water Survey Consortium Project. 

Site Code Location 

IL11 Bondville, IL 

IN99 Indianapolis, IN 

MI96 Allen Park, MI 

MN18 Fernberg, MN 

MN29 Blue Mounds S.P., MN 

MN42 Great River Bluffs S.P., MN 

OH02 Athens, OH 

OH27 Cincinnati, OH 

WI07 Mayville, WI 

In this test, the Radiello® samplers demonstrated excellent reproducibility across the three samples 

taken at each site for each 14-day exposure period. Ambient NH3 concentration from each sampler and 

the average value and standard deviation and percent relative standard deviation for each of the 14-day 

exposure periods was reported at every site in 2007 and 2008 (data not shown). 

Figure 2-24. Average ambient NH3 concentrations from the NH3 passive samplers trial network, 2007-2008. 

 2-52
Figure 2-24 shows the average ambient NH3 concentrations from each 14-day exposure period at 

each site from the Radiello® samplers. The irregularly high concentrations at Blue Mounds State Park, 

MN remains unexplained, but may be due to wintertime shifts in the location of a large bison herd nearer 

to the sample site. 

2.7.2.

2.7.2.1.

Methods for Relevant Gas-Phase Sulfur Species 

Currently, ambient SO2 is measured using instruments based on pulsed fluorescence. The UV 

fluorescence monitoring method for atmospheric SO2 was developed to improve upon the flame 

photometric detection (FPD) method for SO2, which in turn had displaced the pararosaniline wet chemical 

method. The pararosaniline method is still the FRM for atmospheric SO2, but is rarely used because of its 

complexity and slow response, even in its automated forms. Both the UV fluorescence and FPD methods 

are designated as FEMs by the U.S. EPA, but UV fluorescence has largely supplanted the FPD approach 

because of the UV method’s inherent linearity, sensitivity, and the absence of consumables, such as the H2

gas needed for FPD. 

The LOD for a non-trace-level SO2 analyzer is 10 ppb (CFR, 2006) However, most commercial 

analyzers report operational detection limits of ~3 ppb. This concentration is very near the current 

ambient annual average concentration of SO2 of ~4 ppb. 

SO2 molecules absorb UV light at one wavelength and emit UV light at longer wavelengths. This 

fluorescence involves excitation of the SO2 molecule to a higher energy (singlet) electronic state. Once 

excited, the molecule decays non-radiatively to a lower energy electronic state from which it then decays 

to the original, or ground, electronic state by emitting a photon of light at a longer wavelength (i.e., lower 

energy) than the original, incident photon. The process can be summarized by the following equations 

∗

2 hSO ν1 ⎯+ ⎯→ SO2

Reaction 46

2 2 ν 2 SO ⎯⎯→ + hSO ∗

Reaction 47 

where SO2* represents the excited state of SO2, hν1, and hν2 represent the energy of the excitation and 

fluorescence photons, respectively, and hν2 <hν1. The intensity of the emitted light is proportional to the 

number of SO2 molecules in the sample gas. Additional information is found in the 2008 SOX ISA (U.S. 

EPA 2008b). 

Positive Interference 

Luke (1997) reported the positive artifacts of a modified pulsed fluorescence detector generated by 

the co-existence of NO, CS2, and a number of highly fluorescent aromatic HCs such as benzene, toluene, 

o-xylene, m-xylene, p-xylene, m-ethyltoluene, ethylbenzene, and 1,2,4–trimethylbenzene. The positive 

artifacts could be reduced by using a HC kicker membrane. At a flow rate of 300 standard cc/min and a 

pressure drop of 645 torr across the kicker, the interference from ppm levels of many aromatic HCs was 

eliminated entirely. More details appear in the 2008 SOX ISA (U.S. EPA, 2008b). 

 2-53
2.7.2.2. Negative Interference 

Nonradiative deactivation (quenching) of excited SO2 molecules can occur from collisions with 

common molecules in air, including N, O2, and H2O. During collisional quenching, the excited SO2

molecule transfers energy, kinetically allowing the SO2 molecule to return to the original lower energy 

state without emitting a photon. Collisional quenching results in a decrease in the SO2 fluorescence and 

results in the underestimation of SO2 concentration in the air sample. The concentrations of N2 and O2 are 

constant in the ambient air, so quenching from those species at a surface site is also constant, but the 

water vapor content of air can vary. Luke (1997) reported that the response of the detector could be 

reduced by about 7% and 15% at water vapor mixing ratios of 1 and 1.5 mole percent (RH = 35 to 50%) 

at 20-25 °C and 1 atm for a modified pulsed fluorescence detector. At very high SO2 concentrations, 

reactions between electronically excited SO2 and ground state SO2 to form SO3 and SO are theoretically 

possible (Calvert et al., 1978), but this possibility has not been examined. 

2.7.2.3. Other Methods 

A more sensitive SO2 measurement method than the UV-fluorescence method was reported by 

Thornton et al. (2002b) using an atmospheric pressure ionization mass spectrometer. The high 

measurement precision and instrument sensitivity were achieved by adding isotopically labeled SO2

(

34S16O2) continuously to the manifold as an internal standard. Field studies showed that the method 

precision was better than 10%. 

SO2 can also be measured by LIF at ~220 nm (Matsumi et al., 2005). Because the laser wavelength 

is alternately tuned to an SO2 absorption peak at 220.6 and a trough at 220.2 nm and the difference signal 

at the two wavelengths is used to extract the SO2 concentration, the technique eliminates interference 

from either absorption or fluorescence by other species and has high sensitivity: 5 ppt in 60 s. 

SO2 can also be measured by the same DOAS instrument that can measure NO2; see the discussion 

of DOAS in Section 2.7.1.1 above. 

Photo-acoustic techniques have been employed for SO2 detection, but generally have detection 

limits sutable only for source monitoring (Gondal, 1997; Gondal and Mastromarino, 2001). 

CIMS techniques for SO2 have been shown to have high sensitivity, 10 ppt or better, with 

uncertainty of ~15% when a charcoal scrubber is used for zeroing and the sensitivity is measured with 

isotopically labeled 34SO2 (Hanke et al., 2003; Hennigan et al., 2006; Huey et al., 2004). 

2.7.3. Methods for Relevant Aerosol-Phase Nitrogen and Sulfur 

Species 

SO4

2−

 is commonly present in PM2.5. Most PM2.5 samplers have a size-separation device to separate 

particles so that only those particles approximately 2.5 μm or less are collected on the sample filter. Air is 

drawn through the sample filter at a controlled flow rate by a pump located downstream of the sample 

filter. The systems have two critical flow rate components for the capture of fine particulates: both the 

flow of air through the sampler must be at a flow rate that ensures the size cut at 2.5 μm; and the flow rate 

must be optimized to capture the desired amount of particulate loading with respect to the analytical 

method detection limits. 

Using the system described above to collect pSO4 sampling artifacts can introduce: positive 

sampling artifacts for pSO4, pNO3, and pNH4 due to chemical reactions, and negative sampling artifacts 

for NO3

–

 and NH4

+

 due to decomposition and evaporation. 

Several traditional and new methods could be used to quantify elemental S collected on filters: 

energy dispersive X-ray fluorescence, synchrotron induced X-ray fluorescence, proton induced X-ray 

 2-54
emission (PIXE), total reflection X-ray fluorescence (XRF), and scanning electron microscopy. Energy 

dispersive X-ray fluorescence (EDXRF) (Method IO-3.3, U.S. EPA, 1997; see U.S. EPA (2004) for 

details) and PIXE are the most commonly used methods. Since sample filters often contain very small 

amounts of particle deposits, preference is given to methods that can accommodate small sample sizes 

and which require little or no sample preparation or operator time after the samples are placed into the 

analyzer. XRF meets these needs and leaves the sample intact after analysis so it can be submitted for 

additional examinations by other methods as needed. To obtain the greatest efficiency and sensitivity, 

XRF typically places the filters in a vacuum which may cause volatile compounds to evaporate. As a 

result, species that can volatilize such as NH4NO3 and certain organic compounds can be lost during the 

analysis. The effects of this volatilization are important if the PTFE filter is to be subjected to subsequent 

analyses of volatile species. 

Polyatomic ions such as SO4

2−

, NO3

–

, and NH4

+

 are quantified by methods such as IC or automated 

colorimetry for NH4

+

. All ion analysis methods require a fraction of the filter to be extracted in deionized 

distilled water for SO4

2−

 and Na2CO3/NaHCO3 solution for NO3 and then filtered to remove insoluble 

residues before analysis. The extraction volume should be as small as possible to avoid over-diluting the 

solution and inhibiting the detection of the desired constituents at levels typical of those found in ambient 

PM2.5 samples. 

Continuous methods for the quantification of S compounds first remove gas-phase S from the 

sample stream by a diffusion tube denuder followed by analysis of pSO4 (Cobourn et al., 1978; Durham 

et al., 1978; Huntzicker et al., 1978; Mueller and Collins, 1980; Tanner et al., 1980). Another approach is 

to measure total S and gas-phase S separately by alternately removing particles from the sample stream. 

pSO4 is obtained as the difference between the total and gas-phase S (Kittelson et al., 1978). The total S 

content is measured by a FPD by introducing the sampling stream into a fuel-rich, H2-air flame (e.g., 

Farwell and Rasmussen, 1976; Stevens et al., 1969) which reduces S compounds and measures the 

intensity of the CL from electronically excited sulfur molecules (S2*). Sensitivities for pSO4 as low as 0.1 

μg/m3

 with time resolution ranging from 1 to 30 min have been reported. Continuous measurements of 

pSO4 content have also been obtained by on-line XRF analysis with resolution of 30 min or less (Jaklevic 

et al., 1981). During a field-intercomparison study of five different S instruments, Camp et al. (1982) 

found four out of five FPD systems agreed to ± 5% during a 1-week sampling period. 

There are two major PM speciation ambient air-monitoring networks in the U.S.: the Chemical 

Speciation Network (CSN), which now includes the former Speciation Trends Network (STN), and the 

Interagency Monitoring of Protected Visual Environments (IMPROVE) network. The current CSN 

samplers sample on a 1-in-3 days cycle using three filters: Teflon for equilibrated mass and elemental 

analysis including elemental S; a HNO3 denuded nylon filter for ion analysis including NO3 and SO4

2−

; a 

quartz-fiber filter for elemental and organic carbon (EC and OC, respectively). The IMPROVE sampler, 

which collects two 24-h samples per week, simultaneously collects one sample of PM10 on a Teflon filter, 

and three samples of PM2.5 on Teflon, nylon, and quartz filters. PM2.5 mass concentrations are determined 

gravimetrically from the PM2.5 Teflon filter sample, which is also used to determine concentrations of 

selected elements. The PM2.5 nylon filter sample, which is preceded by a denuder to remove acidic gases, 

is analyzed to determine pNO3 and pSO4 concentrations. Finally, the PM2.5 quartz filter sample is 

analyzed for OC and EC using the thermal-optical reflectance (TOR) method for IMPROVE and thermaloptical transmittance (TOT) for CSN, though this network is in transition to TOR. 

In a side-by-side comparison of two of the chief aerosol monitoring techniques, PM2.5 mass and the 

major contributing species were moderately well correlated among the different methods with r >0.8 

(Hains et al., 2007). Agreement was good for total mass, SO4

2−

, OC, total carbon (TC), and NH4

+

, while 

NO3

−

 and black carbon (BC) showed less-good fits. Based on reported uncertainties, however, even daily 

concentrations of PM2.5 mass and major contributing species were often significantly different at the 95% 

confidence level. The older STN/CSN methods reported generally higher values of PM2.5 total mass and 

of individual species than did the IMPROVE-like ones. Since these differences can only be partially 

accounted for by known random errors, the authors concluded that the current uncertainty estimates used 

for data analyzed with the older STN protocol may underestimate the actual uncertainty. 

 2-55
2.7.3.1. Artifacts 

The reaction of SO2 and other acid gases with basic sites on glass fiber filters or with basic coarse 

particles on the filter leads to the formation of nonvolatile pSO4, pNO3, and Cl salts. These positive 

artifacts lead to overestimates of concentrations of PM total mass and SO4

2−

 and likely NO3

−

 as well. 

These problems were largely overcome by changing to quartz fiber or Teflon filters and by the separate 

collection of the PM2.5 fraction. However, the possible reaction of acidic gases with basic coarse particles 

remains a possibility, especially with PM10 and PM10-2.5 measurements. These positive artifacts could be 

effectively eliminated by removing acid gases in the sampling line with denuders coated with NaCl or 

Na2CO3. 

Positive sampling artifacts also occur during measurement of pNH4. The reaction of NH3 with acid 

particles 

3 42 424 2 + SOHNH → )( SONH

Reaction 48 

either during sampling or during transportation, storage, and equilibration could lead to an overestimation 

of pNH4 concentrations. Techniques have been developed to overcome this problem, including using a 

denuder coated with hydrofluoric, citric, or phosphoric acid to remove NH3 during sampling and to 

protect the collected PM from NH3 (Brauer et al., 1991; Keck and Wittmaack, 2006; Koutrakis et al., 

1988a, 1988b; Possanzini et al., 1999; Suh et al., 1992, 1994; Winberry et al., 1999). Positive artifacts for 

pNH4 can also develop during sample handling due to contamination with NH3 emitted directly from 

human sweat, breath, and tobacco smoking to form (NH4)2SO4 or NH4HSO4 if the filter is improperly 

handled (Sutton et al., 2000). 

Although pSO4 is relatively stable on a Teflon filter, it is now well known that volatilization losses 

of pNO3 occur during sampling. For pNO3, the effect on the accuracy of atmospheric measurements from 

these volatilization losses is more significant for PM2.5 than for PM10, partly because NO3

−

 contributes a 

smaller fraction to PM10 and partly because NO3

−

 is most often present in a non-volatile form such as 

NaNO3, in the coarse mode. 

Sampling artifacts resulting from the loss of pNO3 species represents a significant problem in areas 

such as southern California that experience high total NO3 loadings. Hering and Cass (1999) discussed 

errors in PM2.5 mass measurements owing to the volatilization of pNO3 using data from two field 

measurement campaigns conducted in southern California: the Southern California Air Quality Study 

(SCAQS) (Lawson, 1990); and the 1986 California Institute of Technology (CalTech) study (Solomon 

et al., 1992). In both studies, side-by-side sampling of PM2.5 was conducted with one sampler collecting 

particles directly onto a Teflon filter and a second using an MgO-coated denuder (Appel et al., 1981) to 

remove gaseous HNO3, followed by a nylon filter to absorb the evaporating HNO3. In both studies, the 

PM2.5 mass lost from NH4NO3 volatilization represented a significant fraction of the total PM2.5 mass, and 

these losses were greater during summer than fall: 17% (summer) versus 9% (fall) during SCAQS, and 

21% (summer) versus 13% (fall) during CalTech. With regard to percentage loss of pNO3, as contrasted to 

percentage loss of mass discussed above, Hering and Cass (1999) found that the amount of pNO3

remaining on the Teflon filter samples was, on average, 28% less than that on the HNO3 denuded nylon 

filters. 

Hering and Cass (1999) also analyzed these data by extending the evaporative model developed by 

Zhang and McMurry (1987). The extended model used by Hering and Cass (1999) takes into account the 

dissociation of collected NH4NO3 on Teflon filters into HNO3 and NH3 via three mechanisms: scrubbing 

of HNO3 and NH3 in the sampler inlet. John et al. (1988) showed that clean PM10 inlet surfaces serve as 

an effective denuder for HNO3; heating of the filter substrate above ambient temperature by sampling; 

and pressure drop across the Teflon filter. For the sampling systems modeled, the flow-induced pressure 

drop was measured to be less than 0.02 atm, and the corresponding change in vapor pressure was 2%, so 

 2-56
losses driven by pressure drop were not considered to be significant in this work. Losses from Teflon 

filters were found to be higher during the summer than during the winter, higher during the day compared 

to night, and were reasonably consistent with modeled predictions. 

Finally, during SCAQS (Lawson, 1990), particulate samples also were collected using a Berner 

impactor and greased Tedlar substrates in size ranges from 0.05 to 10 μm in aerodynamic diameter. The 

Berner impactor PM2.5 NO3

–

 values were much closer to those from the denuded nylon filter than those 

from the Teflon filter; the impactor NO3

–

 values were ~2% lower than the nylon filter NO3 for the fall 

measurements and ~7% lower for the summer measurements. When the impactor collection was 

compared to the Teflon filter collection for nonvolatile species, pSO4, the results were in agreement. 

Chang et al. (2000) discussed reasons for reduced loss of NO3

–

 from impactors. 

Brook and Dann (1999) observed much higher NO3

–

 losses during a study in which they measured 

NO3

–

 in Windsor and Hamilton, Ontario, Canada, by three techniques: a single Teflon filter in a 

dichotomous sampler; the Teflon filter in an ADS; and total NO3

–

 including both the Teflon filter and the 

nylon back-up filter from the ADS. The Teflon filter from the dichotomous sampler averaged only 13% of 

the total NO3, whereas the Teflon filter from the ADS averaged 46% of total NO3

–

. Considerable NO3 was 

lost from the dichotomous sampler filters during handling, which included weighing and XRF 

measurement in a vacuum. 

Kim et al. (1999) also examined pNO3 sampling artifacts by comparing denuded and non-denuded 

quartz and nylon filters during the PM10 Technical Enhancement Program (PTEP) in the California South 

Coast Air Basin. They observed NO3

–

 losses for most measurements; however, for a significant number of 

measurements, they observed positive NO3

–

 artifacts. Kim et al. (1999) pointed out that random 

measurement errors make it difficult to measure true amounts of NO3

–

 loss. 

Diffusion denuder samplers, developed primarily to measure particle strong acidity (Koutrakis 

et al., 1988a, 1992) also can be used to study NO3

−

 volatilization. Measurements were made with two 

versions of the Harvard-U.S. EPA ADS (HEADS) for which HNO3 vapor was removed by a Na2CO3

coated denuder and the remaining pNO3 was reported either as the sum of nonvolatile NO3

–

 collected on a 

Teflon filter, and volatized NO3

−

 collected on a Na2CO3 coated filter downstream of the Teflon filter (full 

HEADS) or on a Nylon filter downstream of the Teflon filter (Nylon HEADS). The full HEADS 

consistently underestimated the total NO3

–

 by ~20% compared to the Nylon HEADS. 

This comparison technique was then used to measure loss of NO3

–

 from Teflon filters in seven U.S. 

cities by Babich et al. (2000) who found significant pNO3 losses in Riverside, CA, Philadelphia, PA, and 

Boston, MA, but not in Bakersfield, CA, Chicago, IL, Dallas, TX, or Phoenix, AZ, where measurements 

were made only during winter. 

Negative sampling artifacts due to decomposition and volatilization are also significant for pNH4, 

more often when it appears as NH4NO3 since (NH4)2SO4 is much more stable. The presence and 

deposition of NH4NO3 is highly sensitive to environmental factors such as temperature, RH, acidity of 

aerosols, as well as to filter type (Keck and Wittmaack, 2005; Spurny, 1999). Any change in these 

parameters during the sampling period influences the position of the equilibrium between the particle and 

gas phases. Keck and Wittmaack (2005) observed that at temperatures <0 oC, acetate–NO3, quartz fiber, 

and Teflon filters could properly collect pNH4, NH3, and Cl–

; but at temperatures >0 oC, the salts were lost 

from quartz fiber and Teflon filters, more so at higher temperatures and with no significant difference 

between quartz fiber and Teflon filters. The salts were lost completely from denuded quartz fiber filters at 

temperatures above ~20 oC, and from non-undenuded quartz fiber and Teflon filters at temperatures above 

~25 oC. It is anticipated that current sampling techniques underestimate pNH4 levels due to volatilization, 

but fine particle mass contains many acidic compounds, and, as a consequence, a fraction of volatilized 

NH4

+

 in the form of NH3 can be retained on the Teflon filter by reactions with them. Owing to these 

positive and negative interference effects, the magnitude of pNH4 remains largely unknown. However, 

techniques have been applied to pNH4 sampling to correct its concentrations due to evaporation using a 

backup filter coated with hydrofluoric acids, citric acid, or phosphorous acids to absorb the evaporated 

NH4 as NH3. Total NH4 concentration then is the sum of the pNH4 collected on the Teflon filter and the 

concentration of the NH3 collected on the backup filter. 

 2-57
Volatile compounds can also leave the filter after sampling and before filter weighing or chemical 

analysis. Losses of NO3

–

, NH4

+

, and Cl– from glass and quartz-fiber filters that were stored in unsealed 

containers at ambient air temperatures for 2 to 4 weeks before analysis exceeded 50% (Witz et al., 1990). 

2.7.3.2. Other Methods 

An integrated collection and vaporization cell was developed by Stolzenburg and Hering (2000) 

that provides automated, 10–min resolution monitoring of fine pNO3. In this system, particles are 

collected by a humidified impaction process and analyzed in place by flash vaporization and CL detection 

of the evolved NOX. In field tests in which the system was co-located with two FRM samplers, the 

automated pNO3 sampler results followed the results from the FRM, but were offset lower. The system 

also was co-located with a HEADS and a SASS speciation sampler (MetOne Instruments). In all these 

tests, the automated sampler was well correlated to other samplers with slopes ranging from 0.95 for the 

FRM to 1.06 for the HEADS, and correlation coefficients ranging from 0.94 to 0.996. During the 

Northern Front Range Air Quality Study in Colorado (Watson et al., 1998), the automated NO3

–

 monitor 

captured the 12–min variability in pNO3 concentrations with a precision of approximately ± 0.5 μg/m3

(Chow et al., 1998). A comparison with denuded filter measurements followed by IC analysis (Chow and 

Watson, 1999) showed agreement within ± 0.6 μg/m3

 for most of the measurements, but exhibited a 

discrepancy of a factor of 2 for the periods of high pNO3 concentrations. More recent intercomparisons 

took place during the 1997 Southern California Ozone Study (SCOS97) in Riverside, CA. Comparisons 

with 14 days of 24-h denuder-filter sampling gave a correlation coefficient of 0.87 and showed no 

significant bias. As currently configured, the system has a detection limit of 0.7 μg/m3

 and a precision of 

0.2 μg/m3

. 

The extent to which sampling artifacts for pNH4 have been adequately addressed in the current 

networks is not clear. Recently, new denuder-filter sampling systems have been developed to measure 

pSO4, pNO3, and pNH4 with an adequate correction of NH4

+

 sampling artifacts. The denuder-filter system, 

Chembcomb Model 3500 speciation sampling cartridge developed by Rupprecht & Patashnick Co, Inc. 

could be used to collect NO3, SO4

2−

, and NH4 

+

 simultaneously. The sampling system contains a singlenozzle size-selective inlet, two honeycomb denuders, the aerosol filter and two backup filters (Keck and 

Wittmaack, 2005). The first denuder in the system is coated with 0.5% sodium carbonate and 1% glycerol 

and collects acid gases such as HCl, SO2, HNO2, and HNO3. The second denuder is coated with 0.5% 

phosphoric acid in methanol for collecting NH3. Backup filters collect the gases behind denuded filters. 

The backup filters are coated with the same solutions as the denuders. A similar system based on the same 

principle was applied by Possanzini et al. (1999). The system contains two NaCl coated annular denuders 

followed by two denuders coated with Na2CO3

+

 glycerol and citric acid, respectively. This configuration 

was adopted to remove HNO3 quantitatively on the first NaCl denuder. The third and forth denuder 

remove SO2 and NH3, respectively. A polyethylene cyclone and a two-stage filter holder containing three 

filters are placed downstream of the denuders. Aerosol fine particles are collected on a Teflon membrane. 

A backup nylon filter and a subsequent citric acid impregnated filter paper collect dissociation products, 

NH4NO3, HNO3 and NH3, evaporated from the filtered particulate matter. 

 2-58
2.8. Methods to Compute NOX and SOX Concentrations, 

Chemical Interactions, and Deposition 

2.8.1. Chemical Transport Models 

CTMs are the prime tools used to compute the interactions among NOX, SOX, other pollutants and 

their precursors, the transport and transformation of air toxics, the production of secondary aerosols, the 

evolution of the particle size distribution, and deposition of pollutants. CTMs are driven by emissions 

inventories for primary species such as NOX, SOX, NH3, and primary PM, and by meteorological fields 

produced by other numerical prediction models. Meteorological quantities such as winds and 

temperatures are taken from operational analyses, reanalyses, or weather circulation models. In most 

cases, these are off-line meteorological analyses, meaning that they are not modified by radiatively active 

species generated by the air quality model (AQM). 

Emissions of precursor compounds can be divided into anthropogenic and biogenic source 

categories, and biogenic sources can be further divided into biotic (vegetation, microbes, animals) and 

abiotic (geogenic biomass burning, lightning) categories as presented in Section 2.2 above. However, the 

distinction between biogenic sources and anthropogenic sources is often difficult to make, as human 

activities affect directly or indirectly emissions from what would have been considered biogenic sources 

during the preindustrial era. Thus, emissions from plants and animals used in agriculture have been 

referred to as anthropogenic or biogenic in different applications. Wildfire emissions may be considered 

to be biogenic, except that forest management practices may have led to the buildup of fuels on the forest 

floor, thereby altering the frequency and severity of forest fires. 

The initial conditions, or starting concentration fields of all species computed by a model, and the 

boundary conditions, or concentrations of species along the horizontal and upper boundaries of the model 

domain throughout the simulation, must be specified at the beginning of the simulation. Both initial and 

boundary conditions can be estimated from models or data or, more generally, model + data hybrids. 

Because data for vertical profiles of most species of interest are sparse, results of model simulations over 

larger, usually global, domains are often used. As might be expected, the influence of boundary conditions 

depends on the τ of the species under consideration and the time scales for transport from the boundaries 

to the interior of the model. 

Each of the model components described above has associated uncertainties and the relative 

importance of these uncertainties varies with the modeling application. The largest errors in 

photochemical modeling are still thought to arise from the meteorological and emissions inputs to the 

model (Russell and Dennis, 2000). Within the model itself, horizontal advection algorithms are still 

thought to be significant sources of uncertainty (see, e.g., Chock and Winkler, 1994), though more 

recently, those errors are thought to have been reduced (see, e.g., Odman and Ingram, 1996). There are 

also indications that problems with mass conservation continue to be present in photochemical and 

meteorological models (see, e.g., Odman and Russell, 1999) and can result in significant simulation 

errors. The effects of errors in initial conditions can be minimized by including several days spin-up time 

in a simulation to allow the model to be driven by emitted species before the simulation of the period of 

interest begins. 

While the effects of poorly specified boundary conditions propagate through the model’s domain, 

the effects of these errors remain undetermined. Because many meteorological processes occur on spatial 

scales smaller than the model grid spacing (either horizontally or vertically) and thus are not calculated 

explicitly, parameterizations of these processes must be used and these introduce additional uncertainty. 

Specific uncertainty also arises in modeling the chemistry of NOX transformations because they are 

strongly nonlinear. Thus, the volume of the grid cell into which emissions are injected is important 

because, for example, O3 production or loss depends in a complicated way on the concentrations of NOX

 2-59
and OH as explained in Section 2.6.2.1 above. Use of ever-finer grid spacing allows more valid separation 

of regions of high NOX concentrations from low NOX regions and from regions where NOX

concentrations are optimal for P(O3). 

The use of grid spacing fine enough to resolve the chemistry in individual power-plant plumes is 

too demanding of computer resources for this to be attempted in most regional air quality simulations. 

Instead, parameterizations of the effects of sub-grid-scale processes such as these must be developed, else 

serious errors can result if emissions are allowed to mix through an excessively large grid volume before 

the chemistry step in a model calculation is performed. In light of the significant differences between 

atmospheric chemistry taking place within and outside of a power plant plume identified by Ryerson et al. 

(1998), inclusion of a separate module for treating large, tight plumes can be useful. Because the 

photochemistry of NOX transformation is nonlinear, emissions correctly modeled in a tight plume may be 

incorrectly modeled in a more dilute plume. Fortunately, it appears that the chemical mechanism used to 

follow a plume’s development need not be as detailed as that used to simulate the rest of the domain, as 

the inorganic reactions are most important in the plume; see e.g., Kumar and Russell (1996). 

Because the chemical production and loss terms in the continuity equations for individual species 

are coupled, the chemical calculations must be performed iteratively until calculated concentrations 

converge to within some preset criterion. The number of iterations and the convergence criteria chosen 

also can introduce error. 

CTMs have been developed for application over a wide range of spatial scales ranging up from 

neighborhood to global. Regional scale CTMs are used to: obtain better understanding of the processes 

controlling the formation, transport, and destruction of gas- and particle-phase criteria and hazardous air 

pollutants; understand the relations between concentrations of secondary pollutant products and 

concentrations of their precursors such as NOX and VOCs and the factors leading to acid deposition and 

possible damage to biota and ecosystems; understand relations among the concentration patterns of 

various pollutants that may exert adverse effects; and evaluate how changes in emissions propagate 

through the atmospheric system to secondary products and deposition. 

CTMs in current use mostly have one of two forms. The first, grid-based or Eulerian air quality 

models subdivide the region to be modeled or the modeling domain, into a three-dimensional array of grid 

cells. Spatial derivatives in the species continuity equations are cast in finite-difference form over this 

grid and a system of equations for the concentrations of all the chemical species in the model are solved 

numerically at each grid point. Finite element Eulerian models also exist and have been exercised, but less 

frequently. Time-dependent continuity or mass conservation equations are solved for each species 

including terms for transport, chemical production and destruction, and emissions and deposition (if 

relevant), in each cell. Chemical processes are simulated with ordinary differential equations, and 

transport processes are simulated with partial differential equations. Because of a number of factors such 

as the different time scales inherent in different processes, the coupled, nonlinear nature of the chemical 

process terms, and computer storage limitations, not all of the terms in the equations are solved 

simultaneously in three dimensions. Instead, operator splitting, in which terms in the continuity equation 

involving individual processes are solved sequentially, is used. 

In the second common CTM formulation, trajectory or Lagrangian models, a number of 

hypothetical air parcels are specified as though following wind trajectories. In these models, the original 

system of partial differential equations is transformed into a system of ordinary differential equations. 

A less common approach is to use a hybrid Lagrangian-Eulerian model, in which certain aspects of 

atmospheric chemistry and transport are treated with a Lagrangian approach and others are treaded in an 

Eulerian manner; see e.g., Stein et al., 2000. 

Each approach has advantages and disadvantages. The Eulerian approach is more general in that it 

includes processes that mix air parcels and allows integrations to be carried out for long periods during 

which individual air parcels lose their identity. There are, however, techniques for including the effects of 

mixing in Lagrangian models such as FLEXPART (Zanis et al., 2003), ATTILA (Reithmeier and Sausen, 

2002), and CLaMS (McKenna et al., 2002). 

 2-60
2.8.1.1. Global Scale 

Global-scale CTMs are used to address issues associated with climate change and stratospheric O3

depletion, and to provide boundary conditions for the regional-scale models. The CTMs include 

simplified mathematical descriptions of atmospheric transport, the transfer of solar radiation through the 

atmosphere, chemical reactions, and removal to the surface by turbulent motions and precipitation for 

pollutants emitted into the model domain. The upper boundaries of the CTMs extend anywhere from the 

top of the mixed layer to the mesopause at ~80 km to obtain more realistic boundary conditions for 

problems involving stratospheric dynamics. 

The importance of global transport of O3 and O3 precursors and their contribution to regional O3

levels in the U.S. is now apparent. There are at present on the order of 20 three-dimensional global 

models developed by various groups to address problems in tropospheric chemistry. These models resolve 

synoptic meteorology, O3-NOX-CO-HC photochemistry, have parameterizations for wet and dry 

deposition, and parameterize sub-grid scale vertical mixing processes such as convection. Global models 

have proven useful for testing and advancing scientific understanding beyond what is possible with 

observations alone. For example, they can calculate quantities of interest that cannot be measured directly, 

such as export of pollution from one continent to the global atmosphere, or the response of the 

atmosphere to future perturbations in anthropogenic emissions. 

Global simulations are typically conducted at a horizontal resolution of 200 km2

 or more. 

Simulations of the effects of transport from long-range transport link multiple horizontal resolutions from 

the global to the local scale. Finer resolution will only improve scientific understanding to the extent that 

the governing processes are more accurately described at that scale. Consequently, there is a critical need 

for observations at the appropriate scales to evaluate the scientific understanding represented by the 

models. 

During the recent IPCC-AR4 tropospheric chemistry study coordinated by the European Union 

Atmospheric Composition Change: the European Network of excellence (ACCENT), 26 atmospheric 

CTMs were used to estimate the impacts of three emissions scenarios on global atmospheric composition, 

climate, and air quality in 2030 (Dentener et al., 2006a, b). All models were required to use anthropogenic 

emissions developed at IIASA (Dentener et al., 2005) and GFED version 1 biomass burning emissions 

(Van der Werf et al., 2003) as described in Stevenson et al. (2006). The base simulations from these 

models were evaluated against a suite of present-day observations. Most relevant to this assessment report 

are the evaluations with NO2 and N deposition (Dentener et al., 2006b; Stevenson et al., 2006), which are 

summarized briefly below. 

A subset of 17 of the 26 models used in the Stevenson et al. (2006) study was used to compare with 

three retrievals of NO2 columns from the GOME instrument (van Noije et al., 2006) for the year 2000. 

The higher resolution models reproduced the observed patterns better, and the correlation among 

simulated and retrieved columns improved for all models when simulated values were smoothed to a 5° × 

5° grid, implying that the models did not accurately reproduce the small-scale features of NO2 (van Noije 

et al., 2006). Van Noije et al. (2006) also suggested that variability in simulated NO2 columns may reflect 

model differences in OH distributions and the resulting NOX lifetimes, as well as differences in vertical 

mixing which strongly affected partitioning between NO and NO2. Overall, the models tended to 

underestimate concentrations in the retrievals in industrial regions including the eastern U.S. and to 

overestimate them in biomass burning regions (van Noije et al., 2006). 

Over the eastern U.S. and in industrial regions more generally, the spread in absolute column 

abundances is generally larger among the retrievals than among the models, with the discrepancy among 

the retrievals particularly pronounced in winter (van Noije et al., 2006), suggesting that the models were 

biased low, or that the European retrievals may be biased high as the Dalhousie SAO retrieval is closer to 

the model estimates. The lack of seasonal variability in fossil fuel combustion emissions may have 

contributed to a wintertime model underestimate (van Noije et al., 2006) manifested most strongly over 

Asia. In biomass burning regions, the models generally reproduce the timing of the seasonal cycle of the 

retrievals, but tend to overestimate the seasonal cycle amplitude, partly due to lower values in the wet 

 2-61
season, which may reflect an underestimate in wet season soil NO emissions (Jaeglé et al., 2004; van 

Noije et al., 2006). 

2.8.1.2. Regional Scale 

Most major modeling efforts within the U.S. EPA use the Community Multiscale Air Quality 

modeling system (CMAQ) (Byun and Schere, 2006; Byun and Ching, 1999). A number of other modeling 

platforms using Lagrangian and Eulerian frameworks were reviewed in the 2006 O3 AQCD (U.S. EPA, 

2006b) and in Russell and Dennis (2000). Evaluations of the performance of CMAQ are given in Appel 

et al. (2005), Arnold et al. (2003), Eder and Yu (2006), and Fuentes and Raftery (2005). CMAQ’s domain 

can extend from several hundred kilometers to the entire hemisphere. CMAQ is most often driven by the 

MM5 mesoscale meteorological model (Seaman, 2000), though it may be driven by other meteorological 

models, including the Regional Atmosphere Modeling System (RAMS); see http://atmet.com. 

Simulations of pollution episodes over regional domains have been performed with a horizontal 

resolution as low as 1 km, and smaller calculations over limited domains have been performed at even 

finer scales. However, simulations at such high resolutions require better parameterizations of 

meteorological processes such as boundary layer fluxes, deep convection, and clouds (Seaman, 2000), as 

well as finer-scale emissions data than are generally available. Finer spatial resolution is necessary to 

resolve features such as urban heat island circulation; sea, bay, and land breezes; mountain and valley 

breezes; and the nocturnal low-level jet; all of which can affect pollutant concentrations. 

The most common approach to setting up the horizontal domain is to nest a finer grid within a 

larger domain of coarser resolution. However, there are other strategies such as the stretched grid (see 

e.g., Fox-Rabinovitz et al., 2002) and the adaptive grid. In a stretched grid, the grid’s resolution 

continuously varies throughout the domain, thereby eliminating any potential problems with the sudden 

change from one resolution to another at the boundary. Caution should be exercised in using such a 

formulation because certain parameterizations like those for convection valid on a relatively coarse grid 

scale may not be valid on finer scales. Adaptive grids are not fixed at the start of the simulation, but 

instead adapt to the needs of the simulation as it evolves (see e.g., Hansen et al., 1994). They have the 

advantage that they can resolve processes at relevant spatial scales. However, they can be very slow if the 

situation to be modeled is complex. Additionally, if adaptive grids are used for separate meteorological, 

emissions, and photochemical models, there is no reason a priori why the resolution of each grid should 

match, and the gains realized from increased resolution in one model will be wasted in the transition to 

another model. The use of finer horizontal resolution in CTMs will necessitate finer-scale inventories of 

land use and better knowledge of the exact paths of roads, locations of factories, and, in general, better 

methods for locating sources and estimating their emissions. 

The vertical resolution of these CTMs is variable, and usually configured to have more layers in the 

PBL, and fewer higher up. Because the height of the boundary layer is of critical importance in 

simulations of air quality, improved resolution of the boundary layer height would likely improve air 

quality simulations. Additionally, current CTMs do not adequately resolve fine scale features such as the 

nocturnal low-level jet in part because little is known about the nighttime boundary layer. 

CTMs require time-dependent, three-dimensional wind fields for the period of simulation. The 

winds may be generated either by a model using initial fields alone or with four-dimensional data 

assimilation to improve the model’s performance; i.e., model equations can be updated periodically or 

nudged to bring results into agreement with observations. Modeling series durations can range from 

simulations of several days’ duration to several months or multiple seasons of the year. 

Chemical kinetics mechanisms (sets of chemical reactions) representing the important reactions 

occurring in the atmosphere are used in CTMs to estimate the rates of chemical formation and destruction 

of each pollutant simulated as a function of time. Because of different approaches to the lumping of 

organic compounds into surrogate groups for computational efficiency, chemical mechanisms can 

produce somewhat different results under similar conditions. The CB-IV chemical mechanism (Gery 

 2-62
et al., 1989), the RADM II mechanism (Stockwell et al., 1990), the SAPRC (e.g., Carter, 1990; Wang 

et al., 2000a, b), and the RACM mechanisms can be used in CMAQ. Jimenez et al. (2003) provided brief 

descriptions of the features of the main mechanisms in use and compared concentrations of several key 

species predicted by seven chemical mechanisms in a box model simulation over 24 h. The average 

deviation from the average of all mechanism predictions for O3 and NO over the daylight period was 

<20%, and was 10% for NO2 for all mechanisms. However, much larger deviations were found for HNO3, 

PAN, HO2, H2O2, ethylene, and isoprene. The large deviations shown for most species imply differences 

between the τ of atmospheric species and the assignment of model simulations to either NOX-limited or 

radical-limited regimes between mechanisms. Gross and Stockwell (2003) found small differences 

between mechanisms for clean conditions, with differences becoming more significant for polluted 

conditions, especially for NO2 and RO2 radicals. 

CMAQ and other state-of-the-science CTMs incorporate processes and interactions of aerosolphase chemistry (Mebust et al., 2003). There have also been several attempts to study the feedbacks of 

chemistry on atmospheric dynamics using meteorological models like MM5 (Grell et al., 2000; Liu et al., 

2001; Lu et al., 1997; Park et al., 2001). This coupling is necessary to simulate accurately feedbacks such 

as may be caused by the heavy aerosol loading found in forest fire plumes (Lu et al., 1997; Park et al., 

2001), or in heavily polluted areas. Photolysis rates in CMAQ can now be calculated interactively with 

model-produced O3, NO2, and aerosol fields (Binkowski et al., 2007). 

Spatial and temporal characterizations of anthropogenic and biogenic precursor emissions must be 

specified as inputs to a CTM. Emissions inventories have been compiled on grids of varying resolution 

for many HCs, aldehydes, ketones, CO, NH3, and NOX. Emissions inventories for many species require 

the application of algorithms for calculating the dependence of emissions on physical variables such as 

temperature and to convert the inventories into formatted emission files that can be used by a CTM. For 

example, emissions data preprocessing for CMAQ is done by the Spare-Matrix Operator Kernel 

Emissions (SMOKE) system; see http://smoke-model.org. For many species, information concerning the 

temporal variability of emissions is lacking, so annual or O3-season averages are used in short-term, 

episodic simulations. Annual emissions estimates are often modified by the emissions model to produce 

emissions more characteristic of the time of day and season. Significant errors in emissions can occur if 

inappropriate time dependence or a default profile is used. 

2.8.1.3. Sub-Regional Scale 

The grid spacing in regional CTMs of between 1 and 12 km2 is usually too coarse to resolve spatial 

variations on the neighborhood scale. The interface between regional scale models and models of smaller 

exposure scales described is provided by smaller scale dispersion models. AERMOD (http://www.epa.gov 

/scram001/dispersion_prefrec.htm) is one example of a steady-state plume model formulated as a 

replacement to the ISC3 dispersion model. In the stable boundary layer (SBL), it assumes the 

concentration distribution to be Gaussian in both the vertical and horizontal dimensions. In the convective 

boundary layer, the horizontal distribution is also assumed to be Gaussian, but the vertical distribution is 

described with a bi-Gaussian probability density function. AERMOD has provisions to be applied to flat 

and complex terrain, and multiple source types (including, point, area and volume sources) in both urban 

and rural areas. It incorporates air dispersion based on PBL turbulence structure and scaling concepts, and 

is meant to treat both surface and elevated sources and simple and complex terrain in rural and urban 

areas. The dispersion of emissions from line sources like highways is treated as the sum of emissions 

from a number of point sources placed side by side. However, emissions are usually not in steady state 

and there are different functional relationships between buoyant plume rise in point and line sources. It 

should be remembered that NO2 is largely secondary in nature. However, AERMOD does not have 

provision for including secondary sources. The more appropriate use of AERMOD would be to simulate 

the total of NO and NO2, or NOX. 

 2-63
There are non-steady state models that incorporate plume rise explicitly from different types of 

sources. For example, CALPUFF (http://www.src.com/calpuff/calpuff1.htm) is a non-steady-state puff 

dispersion model that simulates the effects of time- and space-varying meteorological conditions on 

pollution transport, transformation, and removal and has provisions for calculating dispersion from 

surface sources. However, it should be noted that neither model was designed to treat the dispersion of 

emissions from roads or to include secondary sources. 

2.8.1.4. Modeling Effects of Convection for Chemical Transport 

The effects of deep convection can be simulated using cloud-resolving models or in regional or 

global models in which the convection is parameterized. The Goddard Cumulus Ensemble (GCE) model 

(Tao and Simpson, 1993) has been used by Pickering et al. (1991, 1992a, b, 1993, 1996), Scala et al. 

(1990) and Stenchikov et al. (1996) in the analysis of convective transport of trace gases. The cloud 

model is nonhydrostatic and contains a detailed representation of cloud microphysical processes. Twoand three-dimensional versions of the model have been applied in transport analyses. The initial 

conditions for the model are usually from a sounding of temperature, water vapor, and winds 

representative of the region of storm development. Model-generated wind fields can be used to perform 

air parcel trajectory analyses and tracer advection calculations. 

Such methods were used by Pickering et al. (1992a) to examine transport of urban plumes by deep 

convection. Transport of an Oklahoma City, OK, plume by the 10–11 June 1985 PRE-STORM squall line 

was simulated with the 2–D GCE model. This major squall line passed over the Oklahoma City 

metropolitan area, as well as more rural areas to the north. Chemical observations ahead of the squall line 

were conducted by the PRE-STORM aircraft. In this event, forward trajectories from the boundary layer 

at the leading edge of the storm showed that almost 75% of the low-level inflow was transported to 

altitudes exceeding 8 km. Over 35% of the air parcels reached altitudes over 12 km. Tracer transport 

calculations were performed for CO, NOX, O3, and HCs. Rural boundary layer NOX was only 0.9 ppb, 

whereas the urban plume contained about 3 ppb. In the rural case, mixing ratios of 0.6 ppb were 

transported up to 11 km. Cleaner air descended at the rear of the storm lowering NOX at the surface from 

0.9 to 0.5 ppb. In the urban plume, mixing ratios in the updraft core reached 1 ppb between 14 and 15 km. 

At the surface, the main downdraft lowered the NOX mixing ratios from 3 to 0.7 ppb. 

Regional CTMs have been used for applications such as simulations of photochemical O3

production, acid deposition, and fine PM. Walcek et al. (1990) included a parameterization of cloud-scale 

aqueous chemistry, scavenging, and vertical mixing in the chemistry model of Change et al. (1987). The 

vertical distribution of cloud microphysical properties and the amount of sub-cloud layer air lifted to each 

cloud layer are determined using a simple entrainment hypothesis (Walcek and Taylor, 1986). Vertically 

integrated O3 formation rates over the northeast U. S. were enhanced by ~50% when the in-cloud vertical 

motions were included in the model. 

Global models with parameterized convection and lightning have been run to examine the roles of 

these processes over North America. Lightning contributed 23% of upper tropospheric NOY over the 

SONEX region according to the UMD-CTM modeling analysis of Allen et al. (2000). During the summer 

of 2004 the NASA Intercontinental Chemical Transport Experiment – North America (INTEX-NA) was 

conducted primarily over the eastern two-thirds of the U.S., as a part of the International Consortium for 

Atmospheric Research on Transport and Transformation. Deep convection was prevalent over this region 

during the experimental period. Cooper et al. (2006) used a particle dispersion model simulation for NOX

to show that 69–84% of the upper tropospheric O3 enhancement over the region in summer 2004 was due 

to lightning NOX. The remainder of the enhancement was due to convective transport of O3 from the 

boundary layer or other sources of NOX. Hudman et al. (2007) used a GEOS-Chem model simulation to 

show that lightning was the dominant source of upper tropospheric NOX over this region during this 

period. Approximately 15% of North American boundary layer NOX emissions were shown to have been 

vented to the free troposphere over this region based on both the observations and the model. 

 2-64
2.8.2. Computed Deposition 

Wet and dry deposition are important removal processes for pollutants on urban, regional, and 

global scales and so are included in CTMs. The general approach used to estimate deposition velocity 

(Vd) in most models is the resistance-in-series method described above and represented in Equation 5. 

This approach works for a range of substances, although it is inappropriate for species with substantial reemissions from the surface or for species where deposition to the surface depends on concentrations at the 

surface itself. The approach is also modified somewhat for aerosols in that the terms Rb and Rc are 

replaced with a surface Vd to account for gravitational settling. 

d = + + cba )RRR/(1V

Equation 5 

Source: Courtesy of T. Pierce, USEPA / ORD / NERL / Atmospheric Modeling Division. 

Figure 2-25. Schematic of the resistance-in-series analogy for atmospheric deposition. Function of wind 

speed, solar radiation, plant characteristics, precipitation/moisture, and soil/air temperature. 

where Ra, Rb, and Rc represent the resistance due to atmospheric turbulence, transport in the fluid 

sublayer very near the elements of surface such as leaves or soil, and the resistance to uptake of the 

surface itself, respectively, as shown in Figure 2-25. 

 2-65
Wesley and Hicks (2000) listed several shortcomings of the then-current knowledge of dry 

deposition. Among those shortcomings were difficulties in representing dry deposition over varying 

terrain where horizontal advection plays a significant role in determining the magnitude of Ra and 

difficulties in adequately determining Vd for extremely stable conditions such as those occurring at night; 

see the discussion by Mahrt (1998), for example. Under optimal conditions, when a model is exercised 

over a relatively small area where dry deposition measurements have been made, models still generally 

showed uncertainties on the order of ± 30% (see, e.g., Brook et al., 1996; Massman et al., 1994; Padro, 

1996; Wesely and Hicks, 2000). Wesely and Hicks (2000) concluded that an important result of those 

comparisons was that the level of sophistication of most dry deposition models was relatively low, and 

that deposition estimates, therefore, must rely heavily on empirical data. Still larger uncertainties exist 

when the surface features in the built environment are not well known or when the surface comprises a 

patchwork of different surface types, as is common in the eastern U.S. 

2.8.2.1. Deposition Forms 

Wet Deposition 

Wet deposition results from the incorporation of atmospheric particles and gases into cloud droplets 

and their subsequent precipitation as rain or snow, or from the scavenging of particles and gases by 

raindrops or snowflakes as they fall (Lovett, 1994). Wet deposition depends on precipitation amount and 

ambient pollutant concentrations. Receptor (i.e., vegetation) surface properties have little effect on wet 

deposition, although leaves can retain liquid and solubilized PM. In terrain containing extensive 

vegetative canopies, any material deposited via precipitation to the upper stratum of foliage is likely to be 

intercepted by several foliar surfaces before reaching the soil. This allows such processes as foliar uptake, 

chemical transformation, and re-suspension into the atmosphere to occur. 

Landscape characteristics can affect wet deposition via orographic effects and by the closer 

aerodynamic coupling to the atmosphere of tall forest canopies as compared to the shorter shrub and 

herbaceous canopies. Following wet deposition, humidity and temperature conditions further affect the 

extent of drying versus concentrating of solutions on foliar surfaces, which influence the rate of metabolic 

uptake of surface solutes (Swietlik and Faust, 1984). The net consequence of these factors on direct 

physical effects of wet deposited PM on leaves is not known (U.S. EPA, 2004). 

Rainfall introduces new wet deposition and also redistributes throughout the canopy previously 

dry-deposited particles (Peters and Eiden, 1992). The concentrations of suspended and dissolved materials 

are typically highest at the onset of precipitation and decline with duration of individual precipitation 

events (Hansen et al., 1994). Sustained rainfall removes much of the accumulation of dry-deposited 

particles from foliar surfaces, reducing direct foliar effects and combining the associated chemical burden 

with the wet-deposited material (Lovett, 1994) for transfer to the soil. Intense rainfall may contribute 

substantial total particulate inputs to the soil, but it also removes bioavailable or injurious pollutants from 

foliar surfaces. This washing effect, combined with differential foliar uptake and foliar leaching of 

different chemical constituents from particles, alters the composition of the rainwater that reaches the soil 

and the pollutant burden that is taken-up by plants. Once in the soil, these particle constituents may affect 

biogeochemical cycles of major, minor, and trace elements. Low intensity precipitation events, in 

contrast, may deposit significantly more particulate pollutants to foliar-surfaces than high intensity 

precipitation events. Additionally, low-intensity events may enhance foliar uptake through the hydrating 

of some previously dry-deposited particles (U.S. EPA, 2004). 

Dry Deposition 

Dry particulate deposition, especially of heavy metals, base cations, and organic contaminants, is a 

complex and poorly characterized process. It appears to be controlled primarily by such variables as 

atmospheric stability, macro- and micro-surface roughness, particle diameter, and surface characteristics 

 2-66
(Hosker and Lindberg, 1982). The range of particle sizes, the diversity of canopy surfaces, and the variety 

of chemical constituents in airborne particulates have made it difficult to predict and to estimate dry 

particulate deposition (U.S. EPA, 2004). 

Dry deposition of atmospheric particles to plant and soil surfaces affects all exposed surfaces. 

Larger particles >5 μm diameter are dry deposited mainly by gravitational sedimentation and inertial 

impaction. Smaller particles, especially those with diameters between 0.2 and 2 μm, are not readily drydeposited and may travel long distances in the atmosphere until their eventual deposition, most often via 

precipitation. Plant parts of all types, along with exposed soil and water surfaces, receive steady deposits 

of dry dusts, EC, and heterogeneous secondary particles formed from gaseous precursors (U.S. EPA, 

1982b). 

Estimates of regional particulate dry deposition infer fluxes from the product of variable and 

uncertain measured or modeled particulate concentrations in the atmosphere and even more variable and 

uncertain estimates of Vd parameterized for a variety of specific surfaces (see e.g., Brook et al., 1996). 

Even for specific sites and well-defined particles, uncertainties are large. Modeling the dry deposition of 

particles to vegetation is at a relatively early stage of development, and it is not currently possible to 

identify a best or most generally applicable modeling approach (U.S. EPA, 2004). 

Occult Deposition 

The occurrence of occult deposition tends to be geographically restricted to coastal and high 

mountain areas. Several factors make occult deposition particularly effective for the delivery of dissolved 

and suspended particulates to vegetation. Concentrations of particulate-derived materials are often manyfold higher in cloud or fog water than in precipitation or ambient air due to orographic effects and gasliquid partitioning. In addition, fog and cloud water deliver particulate chemical species in a bioavailablehydrated form to foliar surfaces. This enhances deposition by sedimentation and impaction of submicron 

aerosol particles that exhibit low Vd before fog droplet formation (Fowler et al., 1989). Deposition to 

vegetation in fog droplets is proportional to wind speed, droplet size, concentration, and fog density. In 

some areas, typically along foggy coastlines or at high elevations, occult deposition represents a 

substantial fraction of total deposition to foliar surfaces (Fowler et al., 1991). 

2.8.2.2. Methods for Estimating Dry Deposition 

Methods for estimating dry deposition of particles are more restricted than for gaseous species and 

fall into two major categories: surface analysis methods, which include all types of estimates of 

contaminant accumulation on surfaces of interest; and atmospheric deposition rate methods, which use 

measurements of contaminant concentrations in the atmosphere and descriptions of surrounding surface 

elements to estimate deposition rates (Davidson and Wu, 1990). Surface extraction or washing methods 

characterize the accumulation of particles on natural receptor surfaces of interest or on experimental 

surrogate surfaces. These techniques rely on methods designed specifically to remove only surfacedeposited material. Total surface rinsate may be equated to accumulated deposition or to the difference in 

concentrations in rinsate between exposed and control (sheltered) surfaces and may be used to refine 

estimates of deposition. Foliar extraction techniques may underestimate deposition to leaves because of 

uptake and translocation processes that remove pollutants from the leaf surface (Garten and Hanson, 

1990; Taylor et al., 1988). Foliar extraction methods also cannot distinguish gas from particle-phase 

sources (Bytnerowicz et al., 1987a, 1987b; Dasch, 1987; Lindberg and Lovett, 1985; Van Aalst, 1982). 

The National Dry Deposition Network was established in 1986 to document the magnitude, spatial 

variability, and trends in dry deposition across the U.S.. Currently, the network operates as a component 

of the CASTNet (Clarke et al., 1997). A significant limitation on current capacity to estimate regional 

effects of NOX and SOX deposition is inadequate knowledge of the mechanisms and factors governing 

particle dry deposition to diverse surfaces (U.S. EPA, 2004). 

 2-67
Dry deposition cannot be directly measured. Deposition rates and totals are often calculated as the 

product of measured ambient concentration and a modeled Vd. This method is widely used because 

atmospheric concentrations are easier to measure than are dry deposition rates, and models have been 

developed to estimate Vd. Ambient pollutant concentrations and meteorological conditions required for 

application of inferential models are routinely collected at CASTNet dry deposition sites. Monitored 

chemical species are limited to O3, SO4

2–, NO3–

, NH4

+

, SO2, and HNO3. The temporal resolution for the 

ambient concentration measurements and dry deposition flux calculations is hourly for O3 and weekly for 

the other species (Clarke et al., 1997). 

Collection and analysis of stem flow and throughfall can also provide useful estimates of 

particulate deposition when compared to directly sampled precipitation. The method is most precise for 

particle deposition when gaseous deposition is a small component of the total dry deposition and when 

leaching or uptake of compounds of interest out of or into the foliage is not a significant fraction of the 

deposition because these lead to positive and negative artifacts in the calculated totals. 

Foliar washing, whether using precipitation or experimental lavage, is one of the best available 

methods to determine dry deposition to vegetated ecosystems. Major limitations include the site 

specificity of the measurements and the restriction to elements that are largely conserved within the 

vegetative system. Surrogate surfaces have not been found that can adequately replicate essential features 

of natural surfaces; and therefore do not produce reliable estimates of particle deposition to the landscape. 

Micrometeorological methods employ eddy covariance, eddy accumulation, or flux gradient 

protocols for quantifying dry deposition. These techniques require measurements of particulate 

concentrations and of atmospheric transport processes. They are currently well developed for ideal 

conditions of flat, homogeneous, and extensive landscapes and for chemical species for which accurate 

and rapid sensors are available. Additional studies are needed to extend these techniques to more complex 

terrain and more chemical species. 

2.8.2.3. Factors Affecting Dry Deposition Rates and Totals 

In the size range of ~0.1 to 1.0 μm where Vd is relatively independent of particle diameter as shown 

in Figure 2-26, particulate deposition is controlled by roughness of the surface and by the stability and 

turbulence of the atmospheric surface layer. Impaction and interception dominate over diffusion as dry 

deposition processes, and the Vd is considerably lower than for particles that are either smaller or larger 

than this size range (Shinn, 1978). 

Deposition of particles between 1 and 10 μm diameter is strongly dependent on particle size 

(Shinn, 1978). Larger particles within this size range are collected more efficiently at typical wind speeds 

than are smaller particles (Clough, 1975), suggesting the importance of impaction. Impaction is related to 

wind speed, the square of particle diameter, and the inverse of receptor diameter as a depositing particle 

fails to follow the streamlines of the air in which it is suspended around the receptor. When particle 

trajectory favors a collision, increasing either wind speed or the ratio of particle size to receptor crosssection increases the probability of collision. 

 2-68
Figure 2-26. The relationship between particle diameter and deposition velocity for particles. Values 

measured in wind tunnels by Little and Wiffen (1977) over short grass with wind speed of 2.5 

m/s closely approximate the theoretical distribution determined by Peters and Eiden (1992) for 

a tall spruce forest. These distributions reflect the interaction of Brownian diffusivity 

(descending dashed line), which decreases with particle size and sedimentation velocity 

(ascending dotted line from Stokes Law), which increases with particle size. Intermediatesized particles (.0.1 to 1.0 μm) are influenced strongly by both particle size and sedimentation 

velocity, and deposition is independent of size. Source: U.S. EPA (2004). 

Empirical estimates of Vd for fine particles under wind tunnel and field conditions are often 

several-fold greater than predicted by theory (Unsworth and Wilshaw, 1989). A large number of transport 

phenomena, including streamlining of foliar obstacles, turbulence structure near surfaces, and various 

phoretic transport mechanisms are not well characterized (U.S. EPA, 2004). The discrepancy between 

estimated and predicted values of Vd may reflect model limitations or experimental limitations in the 

specification of the effective size and number of receptor obstacles. Available reviews (e.g., U.S. EPA, 

1996a, 2004) suggest the following generalizations: particles >10 μm exhibit variable Vd between 0.5 and 

1.1 cm/s depending on friction velocities, whereas a minimum particle Vd of 0.03 cm/s exists for particles 

in the size range 0.1 to 1.0 μm; the Vd of particles is approximately a linear function of friction velocity; 

and deposition of particles from the atmosphere to a forest canopy is from 2 to 16 times greater than 

deposition in adjacent open terrain like grasslands or other low vegetation. 

Leaf Surface Effects on Vd

The chemical composition of a particle is not usually considered to be a primary determinant of its 

Vd. Rather, the plant leaf surface has an important influence on the Vd of particles, and therefore on the 

flux of dry deposition to the terrestrial environment. Relevant leaf surface properties include stickiness, 

microscale roughness, and cross-sectional area. These properties affect the probability of impaction and 

particle bounce. The efficiency of deposition to vegetation also varies with leaf shape. Particles impact 

 2-69
more frequently on the adaxial (upper surface) surface than on the abaxial (lower surface). Most particles 

accumulate in the midvein, central portion of leaves. The greatest particle loading on dicotyledonous 

leaves is frequently on the adaxial surface at the base of the blade, just above the petiole junction. 

Precipitation washing probably plays an important role in this distribution pattern (U.S. EPA, 2004). 

Lead particles have been shown to accumulate to a greater extent on older as compared with 

younger needles and twigs of white pine, suggesting that wind and rain may be insufficient to fully wash 

the foliage. Fungal mycelia (derived from windborne spores) were frequently observed in intimate contact 

with other particles on leaves, which may reflect minimal re-entrainment of the spore due to shelter by the 

particles, mycelia development near sources of soluble nutrients provided by the particles, or simply codeposition (Smith and Staskawicz, 1977). 

Leaves with complex shapes tend to collect more particles than do those with shapes that are more 

regular. Conifer needles are more efficient than broad leaves in collecting particles by impaction, 

reflecting the small cross-section of the needles relative to the larger leaf laminae of broadleaves and the 

greater penetration of wind into conifer canopies than broadleaf ones (U.S. EPA, 2004). 

Canopy Surface Effects on Vd

Surface roughness increases particulate deposition, and Vd is usually greater for a forest than for a 

nonforested area and greater for a field than for a water surface. Different size particles have different 

transport properties and Vd. The upwind leading edges of forests, hedgerows, and individual plants are 

primary sites of coarse particle deposition. Impaction at high wind speed and the sedimentation that 

follows the reduction in wind speed and carrying capacity of the air in these areas lead to preferential 

deposition of larger particles (U.S. EPA, 2004). 

Air movement is slowed in proximity to vegetated surfaces. Canopies of uneven age or with a 

diversity of species are typically aerodynamically rougher and receive larger inputs of dry-deposited 

pollutants than do smooth, low, or monoculture vegetation (Garner et al., 1989; U.S. EPA, 2004). 

Canopies on slopes facing the prevailing winds receive larger inputs of pollutants than more sheltered, 

interior canopy regions. 

All foliar surfaces within a forest canopy are not equally exposed to particle deposition. Upper 

canopy foliage tends to receive maximum exposure to coarse and fine particles, but foliage within the 

canopy tends to receive primarily fine particles. 

2.8.2.4. Nitrogen Deposition and Flux with Biota 

Several Nr are species are deposited to vegetation, among them, HNO3, NO2, and PAN and other 

RONO2. 

Field observations based on concentration gradients of HNO3 and using eddy covariance 

techniques demonstrate rapid deposition that approaches the aerodynamic limit (as constrained by 

atmospheric turbulence) in the Weseley and Lesht (1989) formulation based on analogy to resistance; see 

Figure 2-25 and Equation 5. Surface resistance to HNO3 uptake by vegetation is negligible and its 

deposition rates are independent of leaf area or stomatal conductance, implying that deposition occurs to 

branches, soil, and the leaf cuticle as well as leaf surfaces. The HNO3 Vd typically exceeds 1 cm/s and 

exhibits a diel pattern controlled by turbulence characteristics of midday maxima and lower values at 

night in the more stable boundary layer. 

Compared with HNO3, NO2 interaction with vegetation is more difficult to understand in part 

because very fast measurements of NO2 flux are confounded by the rapid interconversion of NO and NO2

with O3 (Gao et al., 1991). Application of 15N-labeled NO2 has demonstrated that NO2 is absorbed and 

metabolized by foliage (Mocker et al., 1998; Segschneider et al., 1995; Siegwolf et al., 2001; Weber et al., 

1995). Exposure to NO2 induces activation of NO3

–

 reductase (Weber et al., 1995, 1998), a necessary 

enzyme for assimilating oxidized N. Current understanding of NO2 interactions with foliage is largely 

based on leaf cuvette and growth chamber studies, which expose foliage or whole plants to controlled 

 2-70
NO2 concentrations, and measure the fraction of NO2 removed from the chamber air. A key finding is that 

the fit of NO2 flux to NO2 concentratin has a non-zero intercept, implying a compensation point or 

internal concentration. In studies at very low NO2 concentrations, emission from foliage is observed 

(Teklemariam and Sparks, 2006). Evidence for a compensation point is not solely based on the fitted 

intercept. The NO2 uptake rate to foliage is clearly related to stomatal conductance. Internal resistance is 

variable, and may be associated with concentrations of reactive species such as ascorbate in the plant 

tissue that react with NO2 (Teklemariam and Sparks, 2006). Foliar NO2 emissions show some dependence 

on N content (Teklemariam and Sparks, 2006). Internal NO2 appears to derive from plant N metabolism. 

Two approaches to modeling NO2 uptake by vegetation are the resistance-in-series analogy which 

considers flux (F) as the product of concentration (C) and Vd, related to the sum of aerodynamic, 

boundary layer, and internal resistances (Ra, Rb, and Rc, respectively, from Equation 5 and Figure 2-25); 

by convention, positive fluxes are in the direction from atmosphere to foliage. Note that this approach is 

the method most often used to predict deposition in AQMs, that of Wesely and Lesht (1989), as described 

above. Typically, the NO2 Vd is less than that for O3 due to the surface’s generally higher resistance to 

NO2 uptake, consistent with NO2’s lower reactivity. 

Alternatively, NO2 exchange with foliage can be modeled from a physiological standpoint where 

the flux from the leaf (J) is related to the stomatal conductance (gs) and a concentration gradient between 

the ambient air NO2 concentration (Ca) and interstitial air NO2 concentration (Ci) in the leaf (Ca – Ci) as 

depicted in Equation 6. 

)CC(gJ = − ias

Equation 6 

This approach best describes results for exchange with individual foliage elements, and is expressed per 

unit leaf or needle area. While this approach provides linkage to leaf physiology, it is not straightforward 

to scale up from the leaf to the ecosystem. This model implicitly associates the compensation point with a 

finite internal concentration. Typically observed compensation points are ~1 ppb; values of internal NO2

concentrations are consistent with metabolic pathways that include NOX. In this formulation, the uptake 

will be linear with NO2, which is typically measured in foliar chamber studies. 

Several studies have shown the UV dependence of NO2 emissions, which implies some photoinduced surface reactions to release NO2. Rather than model this as a UV-dependent internal 

concentration, it would be more realistic to add an additional term to account for emission that is 

dependent on light levels and other surface characteristics 

UVJCCgJ )()( = − = sias

Equation 7 

PAN is phytotoxic and absorbed at the leaf. Observations based on inference from concentration 

gradients and rates of loss at night (Schrimpf et al., 1996; Shepson et al., 1992) and from leaf chamber 

studies (Teklemariam and Sparks, 2004) have indicated that uptake of PAN is slower than that of O3. 

However, recent work in coniferous canopies with direct eddy covariance PAN flux measurements 

indicated a Vd more similar to that of O3. Uptake of PAN is under stomatal control, has non-zero 

deposition at night, and is influenced by leaf wetness (Turnipseed et al., 2006). On the other hand, flux 

measurements determined by gradient methods over a grass surface showed a Vd closer to 0.1 cm/s, with 

uncertainty on the order of a factor of 10 (Doskey et al., 2004). Whether the discrepancies are 

methodological or indicate intrinsic differences between different vegetation is unknown. Uptake of PAN 

is a smaller loss process than its thermal decomposition in all cases. 

The biosphere also interacts with NOX through HC emissions and their subsequent reactions to 

form multi-functional RONO2. Formation of the hydroxyalkyl nitrates occurs after OH attack on VOCs. 

In one sense, this mechanism is simply an alternate pathway for OH to react with NOX to form a rapidly 

 2-71
depositing species. If VOC were not present, OH would be available to react with NO2 when it is present 

to form HNO3. 

Isoprene nitrates are an important class of RONO2. Isoprene reacts with OH to form a radical that 

adds NO2 to form the hydroxyalkyl nitrate. The combination of hydroxyl and NO3 functional group 

makes these compounds especially soluble with low vapor pressures, so they likely deposit rapidly 

(Shepson et al., 1996; Treves et al., 2000). Many other unsaturated HCs react by analogous routes. 

Observations at Harvard Forest show a substantial fraction of the total of all gas-phase forms of oxidized 

N is not accounted for by NO, NO2 and PAN, and the remainder is attributed to the missing fraction of 

RONO2 (Horii et al., 2006; Munger et al., 1998). Furthermore, the total NOY flux exceeds the sum of 

HNO3, NOX, and PAN, which implies that the RONO2 are a substantial fraction of the total N deposition. 

Other observations showing evidence of hydoxyalkyl nitrates include those of Grossenbacher et al. (2001) 

and Day et al. (2003). 

HNO2 formation on vegetative surfaces at night has long been observed based on measurements of 

positive gradients (Harrison and Kitto, 1994). Surface reactions of NO2 enhanced by moisture were 

proposed to explain these results. Production was evident at sites with high ambient NO2; at low 

concentration, uptake of HNO2 exceeded the source. Daytime observations of HNO2 when rapid 

photolysis is expected to deplete ambient concentrations to very low levels implies a substantial source of 

photo-induced HNO2 formation at a variety of forested sites where measurements have been made. 

Estimated source strengths are 200 to 1800 ppt/h in the surface layer (Zhou et al., 2002a, 2003b), which is 

~20 times faster than all nighttime sources. 

HNO2 sources could be important to HOX budgets as HNO2 is rapidly photolyzed by sunlight to 

OH and NO. Additional evidence of light-dependent reactions to produce HNO2 comes from discovery of 

a HNO2 artifact in Pyrex sample inlet lines exposed to ambient light. Either covering the inlet or washing 

it eliminated the HNO2 formation (Zhou et al., 2002b). Similar reactions might serve to explain 

observations of UV-dependent production of NOX in empty foliar cuvettes that had been exposed to 

ambient air (Hari et al., 2003; Raivonen et al., 2003). 

Production of HNO2 in the dark is currently believed to occur via a heterogeneous reaction 

involving NO2 on wet surfaces (He et al., 2006; Jenkin et al., 1988; Pitts et al., 1984; Sakamaki et al., 

1983). It has been proposed that the mechanism has first-order dependence in both NO2 and H2O 

(Kleffmann et al., 1998; Svensson et al., 1987) despite the stoichiometry. However, the molecular 

pathway of the mechanism is still under debate. Jenkin et al. (1988) postulated a H2O•NO2 water complex 

reacting with gas phase NO2 to produce HNO2, which is inconsistent with the formation of an N2O4

intermediate leading to HNO2 as proposed by Finlayson-Pitts et al. (2003). Another uncertainty is whether 

the reaction forming HNO2 is dependent on water vapor (Stutz et al., 2004; Svensson et al., 1987) or 

water adsorbed on surfaces (Kleffmann et al., 1998). Furthermore, the composition of the surface and the 

available amount of surface or surface-to-volume ratio can significantly influence the HNO2 production 

rates (Kaiser and Wu, 1977; Kleffmann et al., 1998; Svensson et al., 1987), which may explain the 

difference in the rates observed between laboratory and atmospheric measurements. 

There is no consensus on a chemical mechanism for photo-induced HNO2 production. Photolysis of 

HNO3 or NO3

−

 absorbed on ice or in surface water films has been proposed (Honrath et al., 2002; 

Ramazan et al., 2004; Zhou et al., 2001, 2003b). Alternative pathways include NO2 interaction with 

organic surfaces such as humic substances (George et al., 2005; Stemmler et al., 2006). Note that either 

NO3

−

 photolysis or heterogeneous reaction of NO2 are routes for recycling deposited NOX back to the 

atmosphere in an active form. NO3

–

 photolysis would return N that heretofore was considered irreversibly 

deposited, while surface reactions between NO2 and water films or organic molecules would decrease the 

effectiveness of observed NO2 deposition if the HNO2 were re-emitted. 

 2-72
2.8.3. Air Quality Model Evaluation 

Urban and regional air quality is determined by a complex system of coupled chemical and 

physical processes including emissions of pollutants and pollutant precursors, complex chemical 

reactions, physical transport and diffusion, and wet and dry deposition. NOX in these systems has long 

been known to act nonlinearly in P(O3) and other secondary pollutants (Dodge, 1977); to extend over 

multiple spatial and temporal scales; and to involve complicated cross-media environmental issues such 

as acidic or nutrient deposition to sensitive biota and degradation of visibility. 

NOY species emitted and transformed from emissions control the production and fate of O3 and 

aerosols by sustaining or suppressing OH cycling. Correctly characterizing the interrelated NOY and OH 

dynamics for O3 formation and fate in the polluted troposphere depends on new techniques using 

combinations of several NOY species for diagnostically probing the complex atmospheric dynamics in 

typical urban and regional airsheds. 

Arnold et al. (1998) described a model evaluation methodology that distinguished several types of 

AQM testing. Two components of that methodology were: operational testing to judge the performance 

and overall behavior of a model over specific attributes; and diagnostic testing to help reveal potential 

compensating error in model inputs or processing. Diagnostic testing is in situ testing of model 

components using data that emphasize atmospheric processes, often with mass balance techniques, special 

species ratios, and process rate and reaction rate information not typically stored by the model for output. 

Some of these probes have been developed through process-oriented studies using theoretical 

assumptions, model-derived explanations, and results from instrumented models ranging from onedimensional box models to the full 4-dimensional photochemical modeling system (Tonnesen and Dennis, 

2000). Additional information on instrumenting AQMs for diagnostic analysis with model process and 

reaction rate information is found in Dennis et al. (2002); information pertaining to the specific 

implementation of these techniques in CMAQ is found in (Gipson and Young, 1999); and results from 

application of diagnostic probes to modeling experiments are found in Arnold and Dennis (2003, 2006). 

Evaluation results from a recent U.S. EPA exercise of CMAQ in the Tampa Bay, FL, airshed are 

presented here as an example of the present level of skill of state-of-the-science AQMs for predicting 

atmospheric concentrations of the relevant NOX, SOX, and NHX species for this NAAQS assessment. This 

modeling series exercised CMAQ version 4.4 and with the University of California at Davis (UCD) 

sectional aerosol module in place of the standard CMAQ modal module and was driven by meteorology 

from MM5 v3.6 and with NEI emissions as augmented by continuous emissions monitoring data where 

available. (The UCD size-segregated module was preferred for this application because of the importance 

of sea salt particles in the bay airshed. Testing of this new engineering extension revealed that its 

performance was very similar to CMAQ’s standard modal; hence, model behavior and performance 

reported here can stand as a general indication of CMAQ’s skill.) 

The CTM was run with 21 vertical layers for the month of May 2002. For this evaluation, CMAQUCD was run in a one-way nested series of three domains with 32 km, 8 km, and 2 km horizontal grid 

spacings from the CONUS (32 km) to central Florida and the eastern Gulf of Mexico (2 km). 

Depictions of the 8 km and 2 km domains used here zoomed over the central Tampa area are shown 

in Figure 2-27 and Figure 2-28. 

 2-73
Figure 2-27. 8 km southeast U.S. CMAQ domain zoomed over Tampa Bay, FL. 

Figure 2-28. 2 km southeast U.S. CMAQ domain zoomed over Tampa Bay, FL. 

2.8.3.1. Ground-based Comparisons of Photochemical Dynamics 

Errors in the NOX concentrations in the model most likely from on-road emissions (Figure 2-29) 

affected NOX predictions, but CMAQ-UCD’s general responses were reasonable. The model also 

replicated well anthropogenic and biogenic VOC emissions; see Figure 2-30 and Figure 2-31, 

 2-74
respectively. After initial errors leading to underprediction in the first 21 days, CMAQ’s predictions of 

hourly PM2.5 concentrations and trends over the whole month also replicated the observed concentrations 

well; see Figure 2-32. 

0

1

2

3

4

5

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

Hours (EST)

NO (ppb) 0

2

4

6

8

10

12

14

16

18

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

Hours (EST)

NO2 (ppb) 20

0

2

4

6

8

10

12

14

16

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

Hours (EST)

NOX (ppb) 18

20

Figure 2-29. Hourly averages for May 1-31, 2002. CMAQ 8 km and 2 km results and measured 

concentrations of NO (top), NO2 (middle), and total NOX (bottom). Legend: blue circles = 

Observations (OBS); green squares = 8 km; red diamonds = 2 km CMAQ solutions. 

 2-75
0

1

2

3

1-May 2-May 3-May 4-May 5-May 6-May 7-May 8-May 9-May 10-

May

Hours (date mark = 0000 EST)

Ethene (ppb)

4

0

1

2

3

11-

May

12-

May

13-

May

14-

May

15-

May

16-

May

17-

May

18-

May

19-

May

20-

May

Hours (date mark = 0000 EST)

Ethene (ppb)

4

0

1

2

3

21-

May

22-

May

23-

May

24-

May

25-

May

26-

May

27-

May

28-

May

29-

May

30-

May

31-

May

Hours (date mark = 0000 EST)

Ethene (ppb)

4

Figure 2-30. May 2002 daily concentrations and 8 km CMAQ predictions for ethene at Sydney, FL. Legend: 

blue circles = OBS; green squares = 8 km CMAQ solutions. 

 2-76
0

2

4

6

1-May 2-May 3-May 4-May 5-May 6-May 7-May 8-May 9-May 10-

May

Hours (date mark = 0000 EST)

Isoprene (ppb)

8

0

2

4

6

11-May12-May13-May14-May15-May16-May17-May18-May19-May20-May

Hours (date mark = 0000 EST)

Isoprene (ppb) 8

0

1

2

3

21-

May

22-

May

23-

May

24-

May

25-

May

26-

May

27-

May

28-

May

29-

May

30-

May

31-

May

Hours (date mark = 0000 EST)

Isoprene (ppb)

(note change of scale) 4

Figure 2-31. May 2002 daily concentrations and 8 km CMAQ predictions for isoprene at Sydney, FL. 

Legend: blue circles = OBS; green squares = 8 km CMAQ solutions.

 2-77
0

10

20

30

40

1-May 2-May 3-May 4-May 5-May 6-May 7-May 8-May 9-May 10-

May

Hours (date mark = 0000 EST)

PM 2.5 (ug/m3

) 50

60

0

10

20

30

40

50

60

11-May12-May13-May14-May15-May16-May17-May18-May19-May20-May

Hours (date mark = 0000 EST)

PM2.5 (ug/m3

)

0

10

20

30

40

21-

May

22-

May

23-

May

24-

May

25-

May

26-

May

27-

May

28-

May

29-

May

30-

May

31-

May

Hours (date mark = 0000 EST)

PM2.5 (ug/m3

) 50

60

Figure 2-32. Observed hourly PM2.5 concentrations at St. Petersburg, FL and results from CMAQ 8 km. 

Legend: blue circles = OBS; green squares = 8 km CMAQ solutions. 

 2-78
0

20

40

60

80

100

120

0.00 2.00 4.00 6.00 8.00 10.0

0

12.0

0

14.0

0

16.0

0

18.0

0

20.0

0

22.0

0

Diurnal Time (EST)

O3/NOX each hour, 1 -- 31 May

140

0

20

40

60

80

100

120

140

160

0.0-0.5 0.5-1.0 1.0-3.0 3.0-5.0 5.0-10 10-15 15-25 >25

O3/NOX ratio

Fraction of total hours, 0700 -- 1900, 1 -- 31180

200

 May

Figure 2-33. Observed and modeled ratios of O3 to NOX. Diel curves from hourly averages over May 1-31, 

2002 (top), and distribution of O3 to NOX ratio values binned to show fractions of total daylight 

hours, May 1–31, 2002 (bottom panel). Legend: blue circles or bars = OBS; green squares or 

bars = 8 km; red triangles or bars = 2 km. 

The P(O3) efficiency curves in the model agreed well with those observed at Sydney, FL; see 

Figure 2-33. However, tests of CH2O (Figure 2-34) and H2O2 (Figure 2-35) seemed to indicate an error in 

the model’s OH chemistry related to these radical reservoir species since both were substantially and 

systematically different from the observations at the ground-based Sydney site. These species have 

historically been very difficult to model well, however, and the overall excellent agreement of CMAQUCD to production curves in relation to NOX processing mean that this error was likely restricted to these 

species and of limited influence in the overall model solutions and for this evaluation. 

 2-79
0

2

4

6

8

0.00 2.00 4.00 6.00 8.00 10.00 12.00 14.00 16.00 18.00 20.00 22.00

Diurnal Time (EST)

HCHO (ppb)

0

2

4

6

0 5 10 15

NOZ (ppb)

HCHO (ppb)

8

Figure 2-34. Observed and CMAQ 8 km and 2 km predicted formaldehyde concentrations. Hourly averages 

from each day, May 1-31, 2002 (top), and formaldehyde concentrations as a function of NOZ

concentrations (bottom). Legend: blue circles = OBS; green squares = 8 km; red triangles or 

diamonds = 2 km. 

 2-80
0

1

2

3

1-May 2-May 3-May 4-May 5-May 6-May 7-May 8-May 9-May 10-

May

Hours (Date Mark = 0000 EST)

H O2 2 (ppb)

4

0

1

2

3

11-May 12-May 13-May 14-May 15-May 16-May 17-May 18-May 19-May 20-May

Hours (Date Mark = 0000 EST)

H O2 2 (ppb) 4

0

1

2

3

21-May22-May23-May24-May25-May26-May27-May28-May29-May30-May31-May

Hours (Date Mark = 0000 EST)

H O2 2 (ppb) 4

Figure 2-35. Hourly concentrations of hydrogen peroxide, observed and predicted by CMAQ 8 km and 2 

km, May 1-31, 2002 at Sydney, FL. Legend: blue circles = OBS; green squares = 8 km; red 

diamonds = 2 km. 

 2-81
2.8.3.2. Predicted Chemistry for Nitrates and Related Compounds 

pNO3 plays a crucial and complex role in the health of aquatic and estuarine ecosystems. Gas-phase 

NO3

–

 replacement of Cl– on sea salt particles is often favored thermodynamically and the Vd of the coarse 

NO3 formed through this replacement is more than an order of magnitude greater than for fine NO3. Over 

open bodies of salt water such as the Gulf of Mexico and Tampa Bay, FL, NO3

–

 from this reaction 

dominates dry deposition and is estimated to be of the same order as NO3

–

 wet deposition. 

However, NO3

–

 concentrations are driven, buffered, and altered by a wide range of photochemical 

gas-phase reactions, heterogeneous reactions, and aerosol dynamics, making them especially difficult to 

model well. Because pNO3 is derived mostly from gas-phase HNO3 and will interact with Na+

, NH4

+

, Cl–, 

and SO4

2–, all these species and the physical parameters governing their creation, transport, 

transformation, and fate must be accurately replicated to predict pNO3 with high fidelity. This has 

historically been a difficult problem for numerical process models, owing not least to the pervasive dearth 

of reliable ambient measurements of NO3

– in its various forms. Normalized mean error (NME) for the 

large-scale Eulerian CTM-predicted pNO3 has typically been on the order of a factor of 3 greater than the 

NME for pSO4 (Odman et al., 2002; Pun et al., 2003). 

SO4

2−

, NH4

+

, Na+, and Cl– were all predicted to within a factor of 2 and with no significant bias 

during the photochemical day in the 8 km CMAQ-UCD solution, although a significant bias in Na+

 and 

Cl– was evident in the 2 km solution for two near water sites. This grid-size dependent bias is still being 

explored. Size segregation maxima were correct to within two size bins every day for which there were 

observations for both SO4

2– and NH4+

 (0.2 to 1.0 μm), and Na+ and Cl– (2.0 to 10.0 μm). Cl– 

concentrations were greatly overpredicted during dark hours, but were nearer to observed values during 

the photochemical day. CMAQ performance for HNO3 and NH3 are shown in Figure 2-37 and Figure 

2-40, respectively. 

Figure 2-36 shows that CMAQ-UCD systematically underpredicted the hourly time series of 

measured pNO3 concentrations at the Sydney supersite, the only location with discrete pNO3 data. These 

time series data establish that CMAQ-UCD’s largest errors were on four days in the first 2 weeks of the 

month, but that the total peak pNO3 concentrations were nearly all underpredicted. 

Since pNO3 is derived in large part from gas-phase HNO3, its underprediction may be due to an 

underprediction of HNO3 concentrations or an underrepresentation of the gas- to aerosol-phase change. At 

Sydney, FL, in fact, both these conditions held. Figure 2-37 depicts the model’s bias for HNO3

underprediction in both the 8 km and 2 km solutions, excepting four days of very large peak 

overpredictions. This trend was especially true overnight; on eight other days the model overpredicted the 

one hour peak concentration as well, though not so substantially, but the chief effect was still one of an 

artificial and inappropriate N limitation in the model. 

A time series molar equivalent ratio of HNO3 to total NO3 depicts which phase stores the NO3

–

 and 

how that storage ratio changes over time. Figure 2-38 shows that at Sydney, FL, CMAQ-UCD stored too 

much NO3

–

in the gas phase as HNO3 (and recall that the daytime HNO3 concentrations were sometimes 

overpredicted by the model) and too little in the gas phase overnight, when the model was regularly low 

against the measurements; compare Figures 2-37 and 2-38. Note again here the self-similarity of the 8 km 

and 2 km solutions in this comparison. 

Interestingly, the 23 hour integrated data did not reveal this important difference in nitrate form 

between the model and measurements as Figure 2-39 shows in the stacked bar percentage plots of fine 

and coarse pNO3 together with gas-phase HNO3. Both the 8 km (Figure 2-39 (middle panel) and the 2 km 

(Figure 2-39 (bottom panel) solutions predicted distributions between the two general ranges of aerosol 

size, and between gas and aerosol phases, with good fidelity to the daily observations (Figure 2-39 (top 

panel)) at Sydney, FL. This result illustrates that while discrete time series data are crucial for diagnosing 

model behavior, on the integrated total daily and longer basis used for computing total annual N loads, 

CMAQ-UCD predicted approximately the correct distributions for pNO3, even though the total NO3

–

concentration prediction was biased low. 

 2-82
0

1

2

3

4

5

1-May 2-May 3-May 4-May 5-May 6-May 7-May 8-May 9-May 10-

May

Hours (date mark = 0000 EST)

Total Particulate NO3 (ug/m3

) 6

0

1

2

3

4

5

11-May 12-May 13-May 14-May 15-May 16-May 17-May 18-May 19-May 20-May

Hours (date mark = 0000 EST)

Total Particulate NO3 (ug/m3

) 6

0

1

2

3

4

5

21-May22-May23-May24-May25-May26-May27-May28-May29-May30-May31-May

Hours (date mark = 0000 EST)

Total Particulate NO3 (ug/m3

) 6

Figure 2-36. Hourly and CMAQ-UCD-predicted total pNO3 concentrations at Tampa Bay, FL, and 

observations at Sydney, FL May 1-31, 2002. Legend: blue circles = OBS; green x’s = 8 km; red 

diamonds = 2 km. 

 2-83
0

1

2

3

4

5

6

1-May 2-May 3-May 4-May 5-May 6-May 7-May 8-May 9-May 10-

May

Hours (Date Mark = 0000 EST)

HNO3 (ppb) 0

1

2

3

4

5

11-May 12-May 13-May 14-May 15-May 16-May 17-May 18-May 19-May 20-May

Hours (Date Mark = 0000 EST)

HNO3 (ppb) 6

0

1

2

3

4

5

21-May22-May23-May24-May25-May26-May27-May28-May29-May30-May31-May

Hours (Date Mark = 0000 EST)

HNO3 (ppb) 6

Figure 2-37. Hourly and CMAQ-predicted HNO3 concentrations at Sydney, FL, May 1-31, 2002. Legend: 

blue circles = OBS; green squares = 8 km; red diamonds = 2 km. 

 2-84
0.0

0.2

0.4

0.6

0.8

1-May 2-May 3-May 4-May 5-May 6-May 7-May 8-May 9-May 10-May

Hours (date mark = 0000 EST)

HNO3 / Total NO3 1.0

0.0

0.2

0.4

0.6

0.8

11-May 12-May 13-May 14-May 15-May 16-May 17-May 18-May 19-May 20-May

Hours (date mark = 0000 EST)

HNO3 / Total NO3 1.0

0.0

0.2

0.4

0.6

0.8

21-May22-May23-May24-May25-May26-May27-May28-May29-May30-May31-May

Hours (date mark = 0000 EST)

HNO3 / Total NO3 1.0

Figure 2-38. Hourly and CMAQ-UCD-predicted ratio of HNO3 to total NO3 at Tampa Bay, FL and 

observations at Sydney, FL, May 1-31, 2002. Legend: blue circles = OBS; green x’s = 8 km; red 

diamonds = 2 km. 

 2-85
MOUDI aerosol NO3 and ARA HNO3 (23 h daily mean)

100%

0%

20%

40%

60%

80%

5/1 5/3 5/5 5/7 5/9 5/11 5/13 5/15 5/17 5/19 5/21 5/23 5/25 5/27 5/29 5/31

Date, 2002

Contribution to Total NO3

< 2.5 um 2.5 - 10 um HNO3

CMAQ-UCD 8 km solution (23 h daily mean)

100%

0%

20%

40%

60%

80%

5/1 5/3 5/5 5/7 5/9 5/11 5/13 5/15 5/17 5/19 5/21 5/23 5/25 5/27 5/29 5/31

Date, 2002

Contribution to Total NO3

< 2.5 um 2.5 - 10 um HNO3

CMAQ-UCD 2 km solution (23 Hour Means)

100%

0%

20%

40%

60%

80%

5/1 5/3 5/5 5/7 5/9 5/11 5/13 5/15 5/17 5/19 5/21 5/23 5/25 5/27 5/29 5/31

Date, 2002

Contribution to Total NO3

< 2.5 um 2.5 - 10 um HNO3

Figure 2-39. CMAQ-UCD predicted fractions and totals of total NO3 for days in May 2002 with 

measurements in Tampa Bay, FL. Legend: red bars = <2.5 μm; blue bars = 2.5–10 μm; green 

bars = HNO3. Measured concentrations (top panel), CMAQ-UCD 8 km solution (middle panel) 

and CMAQ-UCD 2 km solution (bottom panel). 

 2-86
0

2

4

6

8

10

12

14

16

18

20

1-May 2-May 3-May 4-May 5-May 6-May 7-May 8-May 9-May 10-

May

Hours (date mark = 0000 EST)

NH3 (ug/m3

) 22

0

2

4

6

8

10

12

14

16

18

20

11-

May

12-

May

13-

May

14-

May

15-

May

16-

May

17-

May

18-

May

19-

May

20-

May

Hours (date mark = 0000 EST)

NH3 (ug/m3

) 22

0

2

4

6

8

10

12

14

16

18

20

21-May22-May23-May24-May25-May26-May27-May28-May29-May30-May31-May

Hours (date mark = 0000 EST)

NH3 (ug/m3

) 22

Figure 2-40. Hourly and CMAQ-predicted NH3 concentrations at Sydney, FL, May 1-31, 2002. Legend: blue 

circles = OBS; green squares = 8 km; red diamonds = 2 km. 

While the coarse fraction inorganic aerosol anion totals were dominated by NO3

–

, and SO4

2–

dominated the fine fraction aerosol inorganic anions, there was sufficient NHX (NHX = NH3 + NH4

+

) at 

Sydney, FL, to form fine aerosol NH4NO3 in some circumstances. Figure 2-40 depicts the hourly mass 

concentration of NH3 at Sydney, FL, showing again the strong self-similarity of the 8 km and 2 km 

 2-87
solutions. Each solution, however, underpredicted the measured NH3 concentrations consistently, and 

especially for the nine very large excursions of 10–20 μg/m3

 during the month. 

Overall, CMAQ-UCD was found to be operationally sound in this evaluation of its 8 km and 2 km 

solutions for the Tampa Bay airshed using the ground-based and aloft data (not shown here) from the May 

2002 field intensive. Moreover, results from diagnostic tests of the model’s photochemical dynamics were 

generally in excellent agreement with results from the ambient atmosphere. However, CMAQ-UCD was 

biased low in this application for total NO3 and for NO3 present as gas-phase HNO3. In addition, the 

model was biased low for the HOX radical reservoir species CH2O and H2O2, though this bias appeared to 

have been limited to these species. Performance of the new UCD aerosol module was judged to be 

entirely adequate, allocating aerosols by chemical makeup to the appropriate size fractions. Model 

performance for fine-mode aerosols was also judged to be fully adequate. 

2.8.3.3. Evaluating Deposition with CTMs 

Global CTM Performance 

Both wet and dry deposition are of necessity highly parameterized in all CTMs. While all current 

models implement resistance schemes for dry deposition, the Vd generated from different models can vary 

highly across terrain types (Stevenson et al., 2006). The accuracy of wet deposition in global CTMs is tied 

to spatial and temporal distribution of model precipitation and the treatment of chemical scavenging. 

Dentener et al. (2006b) compared wet deposition across 23 models with available measurements around 

the globe. Figure 2-41 and Figure 2-42 extract results of a comparison of the 23-model mean versus 

observations over the eastern U.S. for pNO3 and pSO4 deposition, respectively. The mean model results 

were strongly correlated with the observations (r >0.8), and usually captured the magnitude of wet 

deposition to within a factor of 2 over the eastern U.S. Dentener et al. (2006b) concluded that 60 to 70% 

of the participating models captured the measurements to within 50% in regions with quality controlled 

observations. 

Source: Dentener et al. (2006b). Reprinted with permission. 

Figure 2-41. Scatter plot of total nitrate (HNO3 plus pNO3) wet deposition (mg N/m2/yr) of the model mean 

versus measurements for the North American Deposition Program (NADP) network. Dashed 

lines indicate factor of 2. The gray line is the result of a linear regression fitting through zero. 

 2-88
Source: Dentener et al. (2006b). Reprinted with permission. 

Figure 2-42. Scatter plot of total SO4

2– wet deposition (mg S/m2/yr) of the model mean versus 

measurements for the National Atmospheric Deposition Program (NADP) network. Dashed 

lines indicate factor of 2. The gray line is the result of a linear regression fitting through zero. 

2.8.3.4. Regional CTM Performance 

Regional CTM performance for concentration and deposition of relevant NOX and SOX species is 

illustrated here with examples from CMAQ version 4.6.1 as configured and run for exposure and risk 

assessments reported in the Draft Risk and Exposure Assessment for the Review of the Secondary 

National Ambient Air Quality Standards for Oxides of Nitrogen and Oxides of Sulfur (U.S. EPA 2008c); 

additional details on the model configuration and application are found there. A map of the 36 km parent 

domain and two 12 km (east and west) progeny domains appears in Figure 2-43. 

Figure 2-43. CMAQ modeling domains for the OAQPS risk and exposure assessments: 36 km outer parent 

domain in black; 12 km western U.S. (WUS) domain in red; 12 km eastern U.S. (EUS) domain 

in blue. 

 2-89
Comparisons from the 2002 annual run of CMAQ for the exposure assessment are shown here 

against measured concentrations and deposition totals from nodes in three networks: IMPROVE, CSN 

(labeled STN in the plots) and CASTNet; see the full description of these networks in Section 2.9 below. 

Comparisons were made as model-observation pairs at all sites having sufficient data for the seasonal or 

the 2002 annual time period in the two 12 km east and west domains and were evaluated with these 

descriptive statistics: correlation, root mean square error, normalized mean bias, and normalized mean 

error. 

Summertime pSO4 concentrations are well predicted by CMAQ, to within a factor of 2 at nearly 

every point, and with R2

 >0.8 across all three networks; see Figure 2-44. This result tracks the generally 

well-predicted pSO4 concentrations found in earlier CMAQ evaluations: see Eder and Yu, 2006; Mebust 

et al., 2003; and Tesche et al., 2006. Since pSO4 concentrations are strongly a function of precipitation, 

care must be taken to ensure that the meteorological solution driving individual CMAQ chemical 

applications produces precipitation fields with low bias as discussed by Appel et al., (2008). 

Figure 2-44. 12-km EUS Summer sulfate PM, each data point represents a paired monthly averaged 

(June/July/August) observation and CMAQ prediction at a particular IMPROVE, STN, and 

CASTNet site. Solid lines indicate the factor of 2 around the 1:1 line shown between them. 

Wintertime pNO3 (Figure 2-45) and total NO3 (HNO3+pNO3) (Figure 2-46) concentrations are 

predicted less well by CMAQ; but NO3

–

 is a pervasively difficult species to measure and model for the 

reasons described in detail in Sections 2.6 and 2.7 above. Still, at the CASTNet nodes where the total NO3

concentrations are higher than they are at all but a few of the remote IMPROVE sites, CMAQ predicts 

concentrations for nearly every node to within a factor of 2 and with an R2

 >0.8. 

 2-90
Figure 2-45. 12-km EUS Winter nitrate PM, each data point represents a paired monthly averaged 

(December/January/February) observation and CMAQ prediction at a particular IMPROVE and 

STN site. Solid lines indicate the factor of 2 around the 1:1 line shown between them. 

Figure 2-46. 12-km EUS Winter total nitrate (HNO3 + total pNO3), each data point represents a paired 

monthly averaged (December/January/February) observation and CMAQ prediction at a 

particular CASTNet site. Solid lines indicate the factor of 2 around the 1:1 line shown between 

them. 

These CMAQ-predicted concentrations, coupled with modeled cloud and precipitation fields 

produce wet deposition fields for SO4

2−

 and NO3

−

 in the east domain as shown in Figures 2-47 and 2-48, 

respectively. 

 2-91
Figure 2-47. 12-km EUS annual sulfate wet deposition, each data point represents an annual average 

paired observation and CMAQ prediction at a particular NADP site. Solid lines indicate the 

factor of 2 around the 1:1 line shown between them. 

Figure 2-48. 12-km EUS annual nitrate wet deposition, each data point represents an annual average 

paired observation and CMAQ prediction at a particular NADP site. Solid lines indicate the 

factor of 2 around the 1:1 line shown between them. 

 2-92
2.8.4. Computing Atmospheric Deposition to Specific Locations 

Inputs of new N, i.e., non-recycled, exogenous N mostly anthropogenic in origin, are often key 

factors controlling primary productivity in N-sensitive estuarine and coastal waters (Paerl et al., 2000). 

Increasing trends in urbanization, agricultural intensity, and industrial expansion have led to increases in 

N deposited from the atmosphere on the order of a factor of 10 in the previous 100 years (Swackhamer 

et al., 2004). Direct loadings of atmospheric N to ocean and gulf waters along the northeast and southeast 

U.S. are now roughly equal to or exceed the load of new N from riverine inputs (Duce et al., 1991) at 11, 

5.6, and 5.6 kg N/ha for the northeast Atlantic coast of the U.S., the southeast Atlantic coast of the U.S., 

and the U.S. eastern Gulf of Mexico, respectively (Paerl, 2002). 

This N deposition takes different forms physically and chemically. Physically, deposition can be 

direct, with the loads resulting from air pollutants depositing directly to the surface of a body of water, 

usually a large body of water like an estuary or lake. In addition, there is indirect deposition component 

derived from deposition to the rest of the watershed, both land and water, of which some fraction is 

transported through runoff, rivers, streams, and groundwater to the waterbody of concern. 

Airshed extents developed and used courtesy of R. Dennis, U.S. EPA/ORD/NERL/Atmospheric Modeling Division. 

Figure 2-49. Principal airsheds and watersheds NOX for these estuaries: Hudson/Raritan Bay; Chesapeake 

Bay; Pamlico Sound; and Altamaha Sound. 

 2-93
Direct and indirect depositions to watersheds depend on air pollutant concentrations in the airshed 

above the watershed. The shape and extent of the airshed is quite different from that of the watershed. In a 

watershed, everything that falls in its area, by definition, flows into a single body of water. An airshed, by 

contrast, is a theoretical concept that defines the area containing the emissions contributing a given level, 

often 75%, to the deposition in a particular watershed or to a given waterbody. Hence, airsheds are 

modeled domains containing the sources estimated to contribute a given level of deposition from each 

pollutant of concern. Four U.S. East Coast watersheds and their corresponding NOX airsheds are shown in 

Figure 2-49. 

Table 2-10. Atmospheric N loads relative to total N loads in selected great waters.* 

Waterbody Total N Load 

(million kg/yr) 

Atmospheric N Load 

(million kg/yr) Percent Load from the Atmosphere 

Albemarle-Pamlico Sounds 23 9 38 

Chesapeake Bay 170 36 21 

Delaware Bay 54 8 15 

Long Island Sound 60 12 20 

Narragansett Bay 5 0.6 12 

New York Bight 164 62 38 

BASED ON ADN LOADS FROM THE WATERSHED ONLY (EXCLUDING DIRECT N DEPOSITION TO THE BAY SURFACE): 

Waquoit Bay, MA 0.022 0.0065 29 

BASED ON ADN DIRECTLY TO THE WATERBODY (EXCLUDING ADN LOADS FROM THE WATERSHED): 

Delaware Inland Bays 1.3 0.28 21 

Flanders Bay, NY 0.36 0.027 7 

Guadalupe Estuary, TX 4.2–15.9 0.31 2–8 

Massachusetts Bays 22–30 1.6–6 5–27 

Narragansett Bay 9 0.4 4 

Newport River Coastal Waters, NC 0.27–0.85 0.095–0.68 >35 

Potomac River, MD 35.5 1.9 5 

Sarasota Bay, FL 0.6 0.16 26 

Tampa Bay, FL 3.8 1.1 28 

ADN = atmospheric deposition of N 

Source: U.S. EPA, 2000d 

N inputs have been studied in several East and Gulf Coast estuaries owing to eutrophication 

concerns there. N from atmospheric deposition in these locations is estimated to be 10 to 40% of the total 

input of N to many of these estuaries, and could be higher for some. Estimates of total N loadings to 

estuaries or to other large-scale elements in the landscape are then computed using measurements of wet 

and dry N deposition where these are available and interpolated with or without a set of air quality model 

predictions such as the Extended Regional Acid Deposition Model (Ext-RADM) (Dennis et al., 2001; 

 2-94
Mathur and Dennis, 2003). Ext-RADM has been shown to capture spatial and seasonal variations in N 

deposition; to predict the constituent deposition species correctly; and to simulate the chemistry and 

physics relating reduced and oxidized forms of N with high validity. 

Extensive evaluation by Mathur and Dennis of the performance of Ext-RADM showed that modelpredicted ambient levels, gas-to-particle partitioning ratios, and deposition totals were in good agreement 

with available measurements, having R2

 for both annual and seasonal totals in the range of 0.4 to 0.7 for 

most species. Ext-RADM correctly predicted that most particles in the eastern U.S. are fully neutralized, 

further demonstrating that most modeled chemistry is correct as judged against measurements. 

Experiments with Ext-RADM to characterize atmospheric conditions over the eastern U.S. in the period 

at the end of the 1980s and early 1990s showed that the model predicted that reduced N species were 

contributed 47 ± 8% of total N wet deposition, in excellent agreement with the number inferred from 

measurements, 43 ± 9%. 

Table 2-10 lists several waterbodies for which atmospheric N inputs have been computed and 

ratioed to total N loads. The contribution from the atmosphere ranges from a low of 2–8% for the 

Guadalupe Estuary in South Texas to highs of ~38% in the New York Bight and the Albemarle-Pamlico 

Sound in North Carolina. 

The North Carolina case is a particularly rich example of computing defined airsheds and 

characterizing the contributions from oxidized and reduced forms of N to underlying water bodies; see the 

summary reported by Dennis and Mathur (2001). The Albemarle-Pamlico principal N airsheds were 

computed to be 665,600 km2

 for oxidized N, and 406,400 km2 for reduced N; these are factors of 25 and 

15, respectively, larger than the watershed drainage area. NO emissions from within the oxidized N 

principal airshed was estimated to explain 63% of all oxidized N deposition to the Albemarle-Pamlico 

system, very similar to the total of 60% of all reduced N deposition accounted for by NH3 emissions in 

the reduced N principal airshed. The regional component to these computed N deposition totals varied 

with the form of N such that local NH3 emissions inside North Carolina were estimated to account for 

45% (hence, 55% left from the regional component) of the total reduced N deposition, while local NO 

and NO2 emissions accounted for only 20% of the oxidized N deposition total (hence, leaving a regional 

component of 80%). 

Chemically, N deposited from the atmosphere directly or indirectly can be present as an oxide or in 

reduced form as NH3 and NH4

+

 or as dissolved or particulate organic N; see the listing in Table 2-11 for a 

division of these and an approximate ranking of source strengths. NO and NO2, chiefly from fossil fuel 

combustion, dominate total N pollution in the U.S. at ~50 to 75% of the total; see the descriptions of this 

chemistry in Section 2.6.2 and of sources in Section 2.2. above. 

CAFOs and other intensified agricultural production methods have resulted in greatly increased 

volumes of animal wastes, of which 30 to 70% may be emitted as NH3 (Whitall and Paerl, 2001). The 

increase in reduced N deposition in the U.S. measured as increased NH4

+

 deposition correlates well with 

the local and regional increases in this agricultural intensity (Whitall and Paerl, 2001). Moreover, the 

increases in NH4

+

 deposition in the U.S. track the effects in Europe where animal operations have 

dominated agricultural production for much of the previous 100 years and where NH4

+

 is the dominant 

form of N deposited from the atmosphere (Holland et al., 1999). Tables 2-12 and 2-13 list several 

important watersheds and their respective oxidized (Table 2-12) and reduced (Table 2-13) airsheds. 

Airsheds for oxidized N tend to be larger than those for reduced N owing to differences in the transport 

and deposition of NOX and NHX described above in Section 2.6. 

 2-95
Table 2-11. Natural and anthropogenic sources of atmospheric N compounds. 

Chemical Form Sources (in approximate order of importance) 

Reduced N 

NH3NH4

Agricultural 

Livestock waste (volatilized NH3) 

Chemical fertilizers (volatilized NH3) 

Biomass burning 

Dust from deforestation and land clearing 

Urban and Rural (non-agricultural) 

Wastewater treatment (volatilized NH3) 

Fossil fuel combustion (from automobile catalytic converters) 

Natural 

Biomass burning (forest and grass fire) 

Decomposition of organic matter 

Dust and aerosols 

Volcanism 

Oxidized Nitrogen 

NO, NO2, NO3

−

Urban and Rural (non-agricultural) 

Fossil fuel combustion 

Mobile and stationary engines 

Powerplants and industrial 

Natural 

Biomass burning 

Lightning 

Photolysis of N2O (air, land, water) 

Dust and aerosols generated by storms 

Microbially mediated volatilization 

Organic Nitrogen 

(Dissolved and Particulate) 

Agricultural 

Dust and volatilization of wastes* 

Urban and Rural (non-agricultural) 

Dust or aerosols* 

Natural 

Atmospheric photochemical and lighting 

Biological production in oceans* 

* = possible, but little known about sources (the major chemical forms of atmospheric N compounds are the reduced, oxidized, and organic forms) 

Source: Swackhamer et al. (2004). Reprinted with permission.

Considerable uncertainty attaches to estimates of the third form of atmospherically derived N, 

organic N, in part because convenient methods for measurement and analysis are not widely available; see 

Table 2-11. Intensive studies at individual sites have shown, however, that for the North Carolina coast, 

for example, 30% of rain water N and deposition consisted of organic N, 20–30% of which was then 

available to primary producers on time scales of hours to days (Peierls and Paerl, 1997). 

 2-96
Table 2-12. Characteristics of oxidized-nitrogen airsheds. 

Watershed Size 

(km2) 

Size Factor 

over 

Watershed 

Area 

% Oxidized-N 

Deposition Explained 

Airshed NOX

Emissions as % of 

Eastern North 

America 

Efficiency of 

Deposition: 

% Deposition / 

% Emission 

Casco Bay 624,000 244 47 10 4.7 

Great Bay 547,000 214 60 13 4.6 

Narragansett 

Bay 

595,200 138 73 18 4.1 

Long Island 

Sound 

905,600 22 70 23 3.0 

Hudson/Raritan 

Bay 

912,000 22 62 25 2.5 

Barnegat Bay 505,600 361 67 16 4.2 

Delaware Bay 729,600 22 75 26 2.9 

Delaware Inland 

Bays 

326,400 584 52 12 4.3 

Chesapeake 

Bay 

1,081,600 6.5 76 34 2.2 

Pamlico Sound 665,600 25 63 18 3.5 

Winyah Bay 886,400 19 69 24 2.9 

Charleston 

Harbor 

806,400 20 56 18 3.1 

St. Helena 

Sound 

588,800 48 59 11 5.4 

Altamaha 678,400 18 68 13 5.2 

Tampa Bay 256,000 45 76 5 15.2 

Apalachee Bay 441,600 31 50 9 5.6 

Apalachicola 

Bay 

812,800 16 69 17 4.1 

Mobile Bay 992,000 8.7 68 17 4.0 

Lake 

Pontchartrain 

659,200 17 63 11 5.7 

BaratariaTerrebonne 409,600 55 63 8 7.9 

Source: http://www.epa.gov/AMD/Multimedia/characteristicstable.html. Table generated by and used courtesy of R. Dennis, U.S. EPA/ORD/NERL/Atmospheric Modeling Division 

 2-97
Table 2-13. Characteristics of principal airsheds for reduced-N (Red-N) deposition. 

Principal Red-N 

Airshed Area 

(km2) 

% Red-N Deposition 

Explained by Airshed 

Emissions 

Airshed NH3 Emission as % 

of Eastern North American 

Emissions 

Red-N Area as % 

of OX-N Area Watershed 

Chesapeake Bay 668,000 64% 55% 11% 

Pamlico Sound 406,000 61% 60% 6.8% 

Apalachee Bay 310,000 70% 45–50% est. 4.3% 

Source: http://www.epa.gov/AMD/Multimedia/reducedTable.html. Table generated by and used courtesy of R. Dennis, U.S. EPA/ORD/NERL/Atmospheric Modeling Division 

Although deposition of atmospheric N to most locations is dominated at present by oxidized N 

produced from fuel combustion, reduced N also contributes, perhaps becoming the dominate contributor 

in areas near source regions. However, not all of the reduced N in the form of NH3 is deposited locally 

near to the source, as is often assumed. 

AQMs like CMAQ can be instrumented to provide analytical output on modeled processes 

including emissions, chemical production, and horizontal and vertical transport; see Gipson, 1999. This 

process analysis output makes possible experiments with modeled emissions and transport to explore the 

fraction of species like total NHX deposited locally and the size of the remaining fraction available for 

transport to locations remote from the high concentration sources of NH3. An example of this work for 

areas on the east coast of the U.S. near areas of extremely high NH3 emissions in North Carolina was 

performed by Dennis and Mathur in the U.S. EPA Atmospheric Modeling Division using CMAQ version 

4.5 with 12 km grid spacings. This example is provided here to illustrate the importance of transport to 

calculated local and remote deposition totals. 

CMAQ includes all chemical and physical processes now known to be relevant for building an 

analysis of the vertical and horizontal budget of NH3 for either surface cells in the model’s first layer, 

nominally 38 m deep, or the total column of air above the surface, generally extending through the mixed 

layer into the free troposphere. The diagram in Figure 2-50 illustrates these processes and the surface cell 

and total column as present in the model. 

Gas to Particle 

Conversion 

Gas to Particle 

Conversion 

Free Troposphere 

Mixed Layer

Surface 

38m 

(−2 km) 

NH3 

Emissions 

NH3 

Emissions

Dry Deposition 

Dry Deposition 

Vertical Redistribution 

(mixing, advection, clouds) 

Horizontal 

Advection 

Horizontal 

Advection 

Layer 1 Analysis Total Column Analysis 

Top of Model – 16 km 

Wet Deposition 

Figure 2-50. Typical surface layer cell and total column structure and processes represented in CMAQ. 

 2-98
Importantly, CMAQ captures the chief spatial patterns and magnitudes of air concentrations and 

wet deposition relevant to computing these budgets, as shown in Figures 2-51 for concentrations and 2-52 

for deposition. 

 

Figure 2-51. CMAQ vs. measured air concentrations from east-coast sites in the IMPROVE, CSN (labeled 

STN), and CASTNet networks in the summer of 2002: (left) sulfate and (right) ammonium. 

Solid lines indicate the factor of 2 around the 1:1 line shown between them. 

More specifically, CMAQ’s predictions of NH3 and SO4

2- for both high and low concentration sites 

are well-within the range of measurements there; see Figure 2-53. 

Deposition velocities are difficult to estimate for reasons described in Sections 2.8.2 and 2.8.3 

above Recent work in the U.S. EPA Atmospheric Modeling Division with CMAQ showed that the 

original Vd for NH3 was very likely too high and should be nearer to the values for SO2 deposition, or 

even lower over some land use surface types. A sensitivity study with the model was performed to test the 

effects of changing NH3 Vd on the fraction of NH3 available for transport away from cells with high 

emissions concentrations. Comparisons were made for the surface cells and total column NH3

concentrations similar to the analysis represented in Figure 2-54. 

In the highest emissions cells during June 2002, the surface NHX budget was dominated by 

turbulent transport or vertical mixing moving a majority of the surface NH3 emissions up and away from 

the surface into the mixed layer. Figure 2-54 depicts the NHX budget under the base case (Base Vd) and 

the sensitivity case (SO2 Vd) for which the NH3 Vd was set equal to the SO2 Vd. Lower NH3 Vd decreased 

NHX deposition to the surface from 15 to 8 %, leaving more NHX for transport horizontally, 22% up from 

20% in the base case, and vertically, 69% up from 64% in the base case. 

 2-99
 2-100

200 2003 200

NADP Ammonium Ion Wet Deposition

CMAQ Ammonium Ion Wet Deposition

Figure 2-52. Comparison of CMAQ-predicted and NADP-measured NH4

+ wet deposition. (top left) CMAQ 

prediction; (bottom left) NADP-measurements; (right) regression and smoothed median line 

through CMAQ predictions and NADP measurements with sites in the Chesapeake Bay 

watershed highlighted. 

Typically, ~67% of surface emissions were moved aloft where most was advected away from the 

high emissions cell, with a small fraction converted to pNH4 and an even smaller fraction wet-deposited 

to the surface. The total column analyses for NH3 and NHX are shown in Figure 2-55. 

Local total deposition (wet + dry) is a significant but not dominant loss pathway for surface NH3

emissions. In these simulations, CMAQ deposited ~25% of the NH3 emissions from the single high 

concentration cell in Sampson County, NC, back into that cell. By far, the largest contribution to the local 

deposition total was dry deposition: dry-to-wet deposition ratios for the Sampson County high emissions 

cell and surrounding surface cells ranged from 2 to 10. 

Deposition to cells farther away from the high concentration, immediately surrounding cells was 

significantly affected by the change in NH3 Vd tested in this case. Figure 2-56 depicts the range of 

influence of the high concentration cell, where that range is defined to be the distance by which 50% of 

the emissions attributable to that cell have deposited. The range of influence of the high concentration 

Sampson County cell was extended in the Vd sensitivity tested here from ~180 km in the base case to 

~400 km in the case using the lower, more realistic Vd for NH3. The areal extent of this difference in 

range of influence is mapped in Figure 2-57. 
Kenansville Ammonia July 2004

12-hour Averages: 6am-6pm

0

5

10

15

20

25

30

35

40

45

184 186 188 190 192 194 196 198 200 202 204 206 208 210 212 214

Julian Day: TickMark at Midnight (July 2004)

NH3 (ug/m3)

Kenansville NH3 CMAQ-J4c NH3

Kenansville Sulfate July 2004 12-hour Averages: 6am-6pm

0

2

4

6

8

10

12

14

16

18

20

184 186 188 190 192 194 196 198 200 202 204 206 208 210 212 214

Julian Day: TickMark at Midnight (July 2004)

SO4 (ug/m3)

Kenansville SO4 CMAQ-J4c ASO4

Millbrook (Raleigh) Sulfate July 2004 12-hour Averages: 6am-6pm

0

2

4

6

8

10

12

14

16

18

20

184 186 188 190 192 194 196 198 200 202 204 206 208 210 212 214

Julian Day: TickMark at Midnight (July 2004)

SO4 (ug/m3)

Millbrook SO4 CMAQ-J4c ASO4

Millbrook (Raleigh) Ammonia July 2004 12-hour Averages: 6am-6pm

0

1

2

3

4

5

6

184 186 188 190 192 194 196 198 200 202 204 206 208 210 212 214

Julian Day: TickMark at Midnight (July 2004)

NH3 (ug/m3)

Millbrook NH3 CMAQ-J4c NH3

Figure 2-53. CMAQ-predicted (red symbols and lines) and 12-h measured (blue symbols and lines) NH3 and 

SO4

2- surface concentrations at high and low concentration cells in North Carolina in July 

2004. (top left) High concentration NH3 in Kenansville; (top right) high concentration SO4

2- in 

Kenansville; (bottom left) low concentration NH3 in Raleigh; (bottom right) low concentration 

SO4

2- in Raleigh. 

Free Troposphere 

Gas to 

Particle

Mixed Layer

Surface 

38m 

(−2 km)

NH3 

Emissions

Dry Deposition 

Vertical 

Diffusion 

Horizontal 

Advection 

NH3 Layer 1 Analysis 

Top of Model – 16 km 

8-15%

22-20%

69-64% 

0.04% 

 Right Number: BaseVd 

Left Number: SO2Vd 

Figure 2-54. Surface cell (layer 1) analysis of the sensitivity of NHX deposition and transport to the change 

in NH3 Vd in CMAQ for June 2002. 

 2-101
Free Troposphere 

Mixed Layer (−2 km)

Top of Model – 16 km 

NH3 

EmissionsDry Deposition 

Horizontal 

Advection 

NHX Column Analysis 

Wet Deposition 

8-15% 

0.58-0.57% 

91-84% 

Gas to Particle 

Conversion 

(unusually 

low) 

NH3 

Emissions Dry Deposition 

Right Number: BaseVd 

Left Number: SO2Vd 

Horizontal 

Advection 

NH3 Column Analysis 

Wet Deposition 

8-15% 

2.1-2.2% 

0.55-0.54% 

89-82% 

Figure 2-55. Total column analysis for NH3 (left) and NHX (right) showing modeled NH3 emissions, 

transformation, and transport throughout the mixed layer and up to the free troposphere. 

June 2002 NHx Range of Influence: BaseVd vs. SO2Vd 

Sampson County (single cell)

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

6

305478

102126150174198222246270294318342366390414438462486

Distance from Center (km)

Fraction of Emissions

Wet+Dry Dep BaseVd Advection BaseVd

Wet+Dry Dep SO2Vd Advection SO2Vd

BaseVd

−180km

SO2Vd

−400km 

Figure 2-56. Range of influence (where 50% of emitted NH3 deposits) from the high concentration 

Sampson County, NC, cell in the June 2002 CMAQ simulation of Vd sensitivities. Base case 

and sensitivity case total deposition (blue symbols and lines); base case and sensitivity case 

advection totals (red symbols and lines). 

 2-102
BaseVd

SO2Vd

Figure 2-57. Areal extent of the change in NHX range of influence as predicted by CMAQ for the Sampson 

County high concentration cell (center of range circles) in June 2002 using the base case and 

sensitivity case Vd. 

2.8.5. Policy Relevant Background Concentrations of NOX and SOX

Background concentrations of NOX and SOX used for purposes of informing decisions about 

NAAQS are referred to as PRB concentrations. PRB concentrations are those concentrations that would 

occur in the U.S. in the absence of anthropogenic emissions in continental North America, defined here as 

the U.S., Canada, and Mexico. PRB concentrations include contributions from natural sources 

everywhere in the world and from anthropogenic sources outside those three countries. Biogenic 

emissions from agricultural activities are not considered in the formation of PRB concentrations. 

Background levels so defined facilitate separation of pollution levels that can be controlled by U.S. 

regulations (or through international agreements with neighboring countries) from levels that are 

generally uncontrollable by the U.S. EPA assesses risks to human health and environmental effects from 

NO2 and SO2 levels in excess of these PRB concentrations. 

The MOZART-2 global model of tropospheric chemistry (Horowitz et al., 2003) is used to 

diagnose the PRB contribution to NOX and SOX levels and to total (wet + dry) deposition. The model 

setup for the present-day simulation has been published in a series of papers from a recent model 

intercomparison (Dentener et al., 2006a, b; Shindell et al., 2006; Stevenson et al., 2006; van Noije et al., 

2006). 

 2-103
Figure 2-58. Annual mean concentrations of NO2 (ppb) in surface air over the U.S. in the present-day 

(upper panel) and policy relevant background (middle panel) MOZART-2 simulations. The 

bottom panel shows the percentage contribution of the background to the present-day 

concentrations. 

 2-104
Figure 2-59. Annual mean concentrations of SO2 (ppb) in surface air over the U.S. in the present-day 

(upper panel) and policy relevant background (middle panel) MOZART-2 simulations. The 

bottom panel shows the percentage contribution of the background to the present-day 

concentrations. 

 2-105
First, the role of PRB in contributing to NO2 and SO2 concentrations in surface air is considered. 

Figure 2-58 shows the annual mean predicted NO2 concentration in surface air in the base case simulation 

(top) and from the PRB simulation (middle panel), along with the percentage contribution of the predicted 

background to the total base case NO2 concentrations (bottom). Maximum concentrations in the base case 

simulation occurred along the Ohio River Valley and in the Los Angeles basin just as they do in reported 

measurements; see Section 2.2 above. While present-day concentrations are often >5 ppb, predicted PRB 

was <300 ppt over most of the CONUS and <100 ppt in the eastern U.S. The distribution of PRB largely 

reflects the distribution of soil NO2 emissions, with some local enhancements due to biomass burning 

such as is seen in western Montana. In the northeastern U.S., where present-day NO2 concentrations are 

highest, PRB was predicted to contribute <1% to the total concentrations. 

Background SO2 concentrations computed with MOZART-2 are orders of magnitude smaller than 

measured ambient concentrations, <10 ppt over much of the CONUS as shown in the middle panel of 

Figure 2-59. Maximum PRB SO2 concentrations are 30 ppt. In the Northwest where there are geothermal 

sources of SO2, the contribution of PRB to total SO2 is 70 to 80%. However, excepting this, PRB 

contributes <1% to present-day SO2 concentrations in surface air as shown in the bottom panel. 

The spatial pattern of NOY (defined in the model as HNO3 + NH4NO3 + NOX + HO2NO2 + 

RONO2) in wet and dry deposition is shown in Figure 2-60. The upper panel of this figure shows that 

highest values are found in the eastern U.S. in and downwind of the Ohio River Valley. The pattern of N 

deposition in the PRB simulation shown in the Figure 2-60 middle panel, however, shows maximum 

deposition centered over Texas and in the Gulf Coast region, reflecting a combination of N emissions 

from lightning in the Gulf region, biomass burning in the Southeast, and microbial activity in soils with 

maxima in central Texas and Oklahoma. The bottom panel of Figure 2-60 shows that the PRB 

contribution to N deposition is <20% over the eastern U.S., and typically <50% in the western U.S. where 

NOY deposition is already lower. 

Present-day deposition of SOX (SO2 and pSO4 is largest in the Ohio River Valley, due to coalburning power plants in that region, while background deposition is typically at least an order of 

magnitude smaller; see Figure 2-61. Over the eastern U.S., the predicted background contribution to SOX

deposition was <10%, and even smaller, <1%, where present-day SOX deposition was highest. The 

predicted contribution of PRB to S deposition was highest in the western U.S. at >20% because of the 

geothermal sources of SO2 and oxidation of DMS at the water surface of the eastern Pacific. 

Figure 2-62 shows results from MOZART-2 discussed above as compared with those from another 

tropospheric chemistry model, GEOS-Chem (Bey et al., 2001), which was previously used to diagnose 

PRB O3 concentrations (Fiore et al., 2003). In both models, the predicted surface PRB NOX

concentrations tended to mirror the distribution of soil NO emissions, which were highest in the Midwest. 

The NO emissions in GEOS-Chem were greater than those in MOZART-2 by nearly a factor of 2. This is 

largely explained by the different assumptions regarding the contribution to soil NO emissions through 

fertilizer since GEOS-Chem total soil NO emissions were actually higher than MOZART-2 at 0.07 versus 

0.11 Tg N over the U.S. in July. Even with the larger PRB soil NO emissions, however, surface NOX

concentrations in GEOS-Chem were typically <500 ppt. 

It is also instructive to consider measurements of SO2 at relatively remote monitoring sites, i.e., 

ones located in sparsely populated areas not subject to obvious local sources of pollution. Berresheim 

et al. (1993) used a type of atmospheric pressure ionization mass spectrometer (APIMS) at Cheeka Peak, 

WA (48°30’N, 124°62’W, 480 m asl), in April 1991 during a field study for DMS oxidation products: SO2

concentrations there ranged from 20 to 40 ppt. Thornton et al. (2002a) have also used an APIMS with an 

isotopically labeled internal standard to determine background SO2 levels and found 25 to 40 ppt in 

northwestern Nebraska in October 1999 at 150 m above ground using the NCAR C-130. These values are 

comparable to remote central south Pacific convective boundary layer SO2 (Thornton, 1999). 

In summary, the PRB contribution to NOX and SOX concentrations and deposition over the 

CONUS is very small, except for SO2 in areas with volcanic activity. 

 2-106
Figure 2-60. Annual mean concentrations of wet and dry deposition of NHNO3, NH4NO3, NOX, HO2NO2, and 

organic nitrates (mg N/m2/yr) in surface air over the U.S. in the present-day (upper panel) and 

policy relevant background (middle panel) MOZART-2 simulations. The bottom panel shows 

the percentage contribution of the background to the present-day concentrations. 

 2-107
Figure 2-61. Annual mean concentrations of SOX deposition (SO2 + pSO4) (mg S/m2/yr) in surface air over 

the U.S. in the present-day (upper panel) and policy relevant background (middle panel) 

MOZART-2 simulations. The bottom panel shows the percentage contribution of the 

background to the present-day concentrations. 

 2-108
Figure 2-62. July mean soil NO emissions (upper panels; 1 × 109 molecules/cm2/s) and surface PRB NOX

concentrations (lower panels; ppt) over the U.S. from MOZART-2 (left) and GEOS-Chem (right) 

model simulations in which anthropogenic O3 precursor emissions were set to zero in North 

America. 

2.9. Ambient Monitoring and Reported Concentrations of 

Relevant Nitrogen and Sulfur Species 

Observational systems supporting air quality and related assessments include routine regulatory 

networks, deposition networks, intensive field studies, remote sensing systems, sondes, aircraft 

campaigns, satellites, and focused fixed site special purpose networks. The focus in this section is on 

routinely operating North American air quality networks with limited coverage of European and 

international efforts relevant to North American assessments. 

The scope of network coverage is broad and relatively shallow, reflecting intent to describe the 

observational foundation enabling integration of spatial scales, environmental media, and pollutant 

categories. In addition to fixed-site, surface-based air quality networks, systems providing total Earth 

column and vertical gradient information meteorological programs are included. Cursory descriptions of 

recent intensive field campaigns are included in this section to foster integration of multiple observation 

platforms and air quality modeling platforms. 

 2-109
2.9.1. Routine Air Monitoring Networks in North America 

Routine ambient air concentration and deposition monitoring networks in North America provide 

more than 3000 fixed platforms measuring numerous species and chemical and physical properties. Many 

of these long-standing network systems were initialized after the 1970 CAA, subsequent CAAA, NAAQS 

reviews and National Academy of Sciences (NAS) recommendations resulting in periodic step 

enhancements to these routine networks. Examples include CASTNet and NADP addressing 

acidification; the Photochemical Assessment Monitoring Stations (PAMS) in response to persistent O3

pollution and to monitor O3 precursors including NOX; and the PM2.5 network. Table 2-14 lists the 

networks, sponsoring agencies, monitoring site densities, and their dates of operation, locations, and 

measurement parameters. 

Table 2-14. Major routine operating air monitoring networks. 

Network Lead 

Agency 

# of 

Sites Initiated Measurement Parameters Location of Information and/or Data 

STATE / LOCAL / FEDERAL NETWORKS 

NCore—National Core 

Monitoring Network 

U.S. EPA 75 2008 O3, NO/NO2/NOY, SO2, CO, 

PM2.5/ PM10–2.5, PM2.5

speciation, NH3, HNO3, surface 

meteorology 

http://www.epa.gov/ttn/ 

amtic/monstratdoc.htm 

SLAMS—State and Local 

Ambient Monitoring Stations 

U.S. EPA ~3000 1978 O3, NOX/NO2, SO2, PM2.5/PM10, 

CO, Pb 

http://www.epa.gov/ttn/airs/ 

airsaqs/aqsweb/aqswebhome.htm 

STN—PM2.5 Speciation 

Trends Network 

U.S. EPA 300 1999 PM2.5, PM2.5 speciation, major 

ions, metals 

http://www.epa.gov/ttn/airs/ 

airsaqs/aqsweb/aqswebhome.html 

PAMS—Photochemical 

Assessment Monitoring 

Network 

U.S. EPA 75 1994 O3, NOX/NOY, CO, speciated 

VOCs, carbonyls, surface 

meteorology and upper air 

http://www.epa.gov/ttn/airs/ 

airsaqs/aqsweb/aqswebhome.htm 

IMPROVE—Interagency 

Monitoring of Protected 

Visual Environments 

NPS 110 plus 

67 protocol sites 

1988 PM2.5/PM10, major ions, metals, 

light extinction, scattering 

coefficient 

http://vista.cira.colostate.edu/IMPROVE/ 

CASTNet—Clean Air Status 

and Trends Network 

U.S. EPA 80+ 1987 O3, SO2, major ions, calculated 

dry deposition, wet deposition, 

total deposition for S/N, surface 

meteorology 

http://www.epa.gov/castnet/ 

GPMP—Gaseous Pollutant 

Monitoring Network 

NPS 33 1987 O3, NOX/NO/NO2, SO2, CO, 

surface meteorology, (plus 

enhanced monitoring of CO, 

NO, NOX, NOY, and SO2 plus 

canister samples for VOC at 

three sites) 

http://www2.nature.nps.gov/air/ 

Monitoring/network.cfm#data 

POMS—Portable Ozone 

Monitoring Stations 

NPS 14 2002 O3, surface meteorology, with 

CASTNet-protocol filter pack 

(optional) SO4

2−, NO3, NH4+, 

HNO3, SO2

http://www2.nature.nps.gov/ 

air/studies/portO3.cfm 

Passive Ozone Sampler 

Monitoring Program 

NPS 43 1995 O3 dose (weekly) http://www2.nature.nps.gov/ 

air/Studies/Passives.cfm 

 2-110
Network Lead 

Agency 

# of 

Sites Initiated Measurement Parameters Location of Information and/or Data 

NADP/NTN—National 

Atmospheric Deposition 

Program / National Trends 

Network 

USGS 200+ 1978 Major ions from precipitation 

chemistry 

http://nadp.sws.uiuc.edu/ 

NADP/MDN—National 

Atmospheric Deposition 

Program / Mercury 

Deposition Network 

None 90+ 1996 Mercury from precipitation 

chemistry 

http://nadp.sws.uiuc.edu/mdn/ 

AIRMoN—National Atmospheric Deposition Program / 

Atmospheric Integrated Research Monitoring Network 

NOAA 8 1984 Major ions from precipitation 

chemistry 

http://nadp.sws.uiuc.edu/AIRMoN/ 

IADN—Integrated 

Atmospheric Deposition 

Network 

U.S. EPA 20 1990 PAHs, PCBs, and organochlorine compounds are measured 

in air and precipitation samples 

http://www.epa.gov/glnpo/monitoring/air 

NAPS—National Air Pollution 

Surveillance Network 

Canada 152+ 1969 SO2, CO, O3, NO, NO2, NOX, 

VOCs, SVOCs, PM10, PM2.5, 

TSP, metals 

http://www.etcentre.org/NAPS/ 

CAPMoN—Canadian Air and 

Precipitation Monitoring 

Network 

Canada 29 2002 O3, NO, NO2, NOY mass, PM2.5

speciation, major ions for 

particles and trace gases, 

precipitation chemistry for major 

ions 

http://www.msc.ec.gc.ca/ cap 

mon/index_e.cfm 

Mexican Metropolitan Air 

Quality Network 

Mexico 93 ??? O3, NOX, CO, SO2, PM10, TSP See (Ceccotti and Messick, 1997) 

http://www.cec.org/pubs_docs/-

publications/index.cfm?varlan=english

AIR TOXICS MONITORING NETWORKS 

NATTS—National Air Toxics 

Trends Station 

U.S. EPA 23 2005 VOCs, Carbonyls, PM10 metals, 

Hg 

http://www.epa.gov/ttn/airs/airsaqs/ 

aqsweb/aqswebhome.htm 

State/Local Air Toxics 

Monitoring 

U.S. EPA 250+ 1987 VOCs, Carbonyls, PM10 metals, 

Hg 

http://www.epa.gov/ttn/airs/airsaqs/ 

aqsweb/aqswebhome.htm 

NDAMN—National Dioxin Air 

Monitoring Network 

U.S. EPA 34 1998– 

2005 

CDDs, CDFs, dioxin-like PCBs http://cfpub2.epa.gov/ncea/cfm/ 

recordisplay.cfm?deid=22423 

TRIBAL MONITORING NETWORKS 

Tribal Monitoring U.S. EPA 120+ 1995 O3, NOX/NO2, SO2, PM2.5/ PM10, 

PAN, NH3, PM2.5, PM10 and 

coarse fraction CO, Pb 

http://www.epa.gov/ttn/airs/ 

airsaqs/aqsweb/aqswebhome.htm 

INDUSTRY/RESEARCH NETWORKS 

New Source Permit 

Monitoring 

None variable variable O3, NOX/NO2, SO2, PM2.5/PM10, 

CO Pb 

Contact specific industrial facilities 

HRM Network—Houston 

Regional Monitoring Network 

None 9 1980 O3, NOX, PM2.5/PM10, CO, SO2, 

Pb, VOCs, surface meteorology

http://hrm.radian.com/ houston/ 

how/index.htm 

 2-111
Network Lead 

Agency 

# of 

Sites Initiated Measurement Parameters Location of Information and/or Data 

ARIES / SEARCH—Aerosol 

Research Inhalation 

Epidemiology Study / 

Southeastern Aerosol 

Research and Characterization Study experiment 

None 8 1992 O3, NO/NO2/NOY, SO2, CO, 

PM2.5/PM10, PM2.5 speciation, 

major ions, NH3, HNO3, 

scattering coefficient, surface 

meteorology 

http://www.atmosphericresearch.com/studies/ SEARCH/index.html 

SOS - SERON—Southern 

Oxidant Study – Southeastern Regional Oxidant 

Networks 

U.S. EPA ~40 1990 O3, NO, NOY, VOCs, CO, 

surface meteorology 

http://www.ncsu.edu/sos/pubs/ 

sos3/State_of_SOS_3.pdf 

Two important ambient air networks focused on environmental welfare effects were established in 

the mid-1980s. IMPROVE with >100 sites in National Parks and other remote locations is used primarily 

to assess visibility impairment, but has provided a reliable long-term record of particulate mass and major 

speciation components and served as a model for the later deployment of STN; see Figure 2-63. STN 

(now part of CSN) has provided an urban complement to characterize aerosol composition; see Figure 

2-64. Additional, minor networks identified in Figure 2-63 include those of the state and local air agencies 

deployed since the mid-1980s measuring a variety of aerosol- and gas-phase, hazardous air pollutants 

(HAPs) at ~200 locations, and a modest National Air Toxics Trends (NATTS) network of 23 sites; see the 

list and brief descriptions in Table 2-14 above. 

Figure 2-63. Aggregate map of most routine U.S. monitoring stations. 

 2-112
CASTNet was established in the early 1990s to track changes in dry deposition of major inorganic 

ions and gaseous precursors associated with the CAA Title IV reductions in SO2 and NOX or S and N. 

Complementing ongoing precipitation measurements from NADP, CASTNet (see Figure 2-63) has 

provided a valuable source of model evaluation data for many of the large regional scale applications 

since the 1990s. 

Source: 2006 U.S. EPA Air Quality Trends Report (www.epa.gov/air/airtrends/index.html). 

Figure 2-64. Trends in regional chemical composition of PM2.5 aerosols based on urban speciation sites 

and averaged over the entire 2006 sampling period. 

Figure 2-65. Original 3−tiered NCore design (left) and proposed site locations for Level 2 multiple pollutant 

sites. 

 2-113
Deployment of the PAMS and the PM2.5 networks from the early 1990s through 2002 markedly 

enhanced the spatial, temporal, and compositional attributes of gases and aerosols. 

A new multiple pollutant-monitoring network, NCore was begun in 2006. When implemented in 

2009, NCore will provide a minimum of 75 Level-2 sites (Figure 2-65) in most major urban areas and 

important transport corridor and background locations. NCore will include a variety of trace gas and 

aerosol mass and speciation measurements which are intended to support multiple data user needs 

including air quality model evaluation and long-term epidemiological studies. 

2.9.1.1. Pollutant Categories 

Inorganic Gas-phase Species 

Most U.S. sites measuring NOX and SO2 are incorporated within the State and Local Air 

Monitoring Systems (SLAMS) networks. Most of the SLAMS sites are located in populated urban 

locations with a variety of monitor siting requirements typically intended to site for high concentration 

locations resulting in an emphasis on center-city locations for NOX and proximity to major power 

generating facilities for SO2. 

Measurements of NOY, HNO3, and NH3 are useful in a variety of ways important for assessing 

NOX and SOX environmental pollution effects; some of these ways include: evaluation of emissions 

inventories; inputs to and sources of evaluation for numerical and observation–based models; and 

establishing baseline N budgets for watershed and field accountability assessments. NOY, HNO3, and 

NH3, together with true NO2 and pNH4, are significant components of the total N budget but remain 

poorly characterized at the national scale. In largest part, a lack of reliable, cost effective continuous 

measurement methods has hindered deployment of instruments for HNO3 and NH3 as described in 

Section 2.7. In the U.S., the Southeastern Aerosol Research and Characterization Study (SEARCH) 

network of eight monitoring sites is the only source of routine, continuous ambient air measurements of 

NOY together with NH3 and HNO3; see discussions in Blanchard and Hidy (2003) and Zhang et al. 

(2006). CASTNet recently deployed a network of inexpensive passive NH3 samplers which have promise 

for characterizing broad spatial patterns, with extended averaging times beyond 24 h. 

Particulate Matter Mass 

Nearly 1500 PM2.5 gravimetric sites were established before 2000 to determine nonattainment 

status of counties throughout the U.S. following the 1997 promulgation of the PM2.5 particulate matter 

standard. The network has evolved to add over 500 continuous PM2.5 monitors and a reduction of 24-h 

gravimetric samplers below 1000 sites (see Figure 2-66) that support air quality forecasting and public 

notification of adverse air quality using the Air Quality Index (AQI), a generalized indicator of exposure 

concern linked to the NAAQS (http://www.epa.gov/airnow/). While this expansion of continuous PM2.5

sites adds spatial coverage of highly temporally resolved information, the mix of instrument types 

compromises data harmonization across sites and geographic areas with different operational 

characteristics. 

 2-114
Figure 2-66. Maps illustrating coverage of PM2.5 FRM and FEM and O3 network (left); and PM2.5 continuous 

samplers (right). 

Particulate Matter Speciation 

The speciation networks typically collect a 24-h sample once every 3 or 6 days. CASTNet provides 

weekly averaged measurements of major ions including SO4

2−

, NO3

−

, calcium (Ca2+), Na+, potassium 

(K+

), NH4

+

, and magnesium (Mg2+) integrated over all aerosol sizes by means of open-face filter packs. 

Daily, 24-h speciated samples is limited to fewer than five sites in the U.S. and Canada. Similarly, a small 

and variable number of sites, fewer than 10 in most years, provide near-continuous speciation data, 

usually limited to some combination of pSO4, pNO3, and EC and OC. In addition, the 22 NATTS sites 

include aetholometers measuring semi-continuous light absorption, often used as a surrogate for EC. 

The U.S. EPA PM Supersites Program (Wittig and Solomon, 2006) provided highly time-resolved 

aerosol measurements at eight cities in the U.S. for a mix of time periods between 1999 and 2004. 

Depending on location and time period, a number of different instrument configurations were deployed 

ranging from additional spatial coverage of standard speciation sites to systems capturing near-continuous 

size-distributed chemical composition profiles. 

The SEARCH program, funded since 1998 by EPRI and Southern Company, has provided 

continuous, semi-continuous and integrated data on a wide variety of species from eight highly 

instrumented paired research sites in the southeastern U.S. in the states of AL, FL, GA, and MS; see 

descriptions in Hansen et al. (2003) for additional details. At present, the suite of measurements made at 

all sites includes: 24-h PM2.5 filter samples, analyzed for mass, ions (SO4

2-, NO3−

, NH4

+

), OC, EC, BC, 

and elements as measured by XRF; 24-h PM coarse mass, ions, and XRF elements; 24-h gaseous NH3; 

continuous (minute-to-hourly) PM2.5 mass, OC, EC, NH4

+

, NO3

−

, and SO4

2−

; light-scattering and light 

absorption; continuous gaseous O3, NO2, NOY, NO3

−

, CO, and SO2; and continuous, 10-m meteorological 

parameters: wind speed, wind direction, temperature, RH, solar radiation, barometric pressure and 

precipitation. 

Precipitation-based Networks 

Precipitation chemistry is the primary measured link between atmospheric, terrestrial, and aquatic 

systems. NADP oversees a network of more than 250 sites (see Figure 2-67) where most of the major ions 

key to aquatic chemistry addressing acidification and eutrophication effects are measured. The NADP 

includes the Mercury Deposition Network (MDN) of ~90 sites and seven Atmospheric Integrated 

Research Monitoring Network (AIRMoN) sites providing greater temporal resolution. 

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2.9.2. Intensive Field Campaigns 

Intensive field campaigns of relatively short duration supplement routine, longer-term monitoring 

networks by enhancing spatial, temporal, and compositional distribution of atmospheric species to better 

elucidate physical and chemical processes relevant to the fate, transport, and removal of secondarily 

formed gases and aerosols. Typically, these campaigns utilize some combination of aircraft studies, high 

time resolved instrumentation and advanced analytical methods (in-situ and laboratory) to complement 

routine ground-based measurements. 

Figure 2-67. Routinely operating North American precipitation and surface water networks. Upper left, 

Canadian Air and Precipitation Monitoring Network (CAPMoN); Upper right, Integrated 

Atmospheric Monitoring Deposition Network (IADN); Bottom, National Atmospheric 

Deposition Program (NADP) with Time/LTM surface chemistry sites. 

 2-116
Table 2-15. Air monitoring networks/campaigns for non-routine special intensive studies conducted since 

the mid-1990s. 

Lead 

Agency1

# of 

Sites Initiated Measurement Parameters 

Location of Information 

and/or Data Notes 

Texas 17 2006 O3, NOX, NOY, SO2, 

haze, visibility, CO, 

VOC, solar radiation, surface meteorology, upper air 

http://www.utexas.edu/research/ 

ceer/texaqsII/PDF/12-12-04_ 

Projected_SurfaceSites_tbl.pdf

Researchers from universities, state and federal agencies, private industry, and local governments are joining forces to conduct a major field study to address air 

quality issues in the eastern half of Texas. The study, 

planned for a period extending from Apr 2005 through 

Oct 2006, will examine regional O3 formation, transport 

of O3 and O3 precursors, meteorological and chemical 

modeling, issues related to O3 formation by highly 

reactive emissions, and PM formation. It is anticipated 

that the information from the study will be the scientific 

basis used for developing State Implementation Plans 

(SIPs) for O3 (with concentrations averaged over 8 h), 

regional haze, and, if necessary, for fine PM (PM < 2.5 

μm in diameter, PM2.5). 

NOAA 1 ship, 

2 

aircraft 

2006 O3, NO, NO2, NOY, 

VOCs, CO2, CO, 

SO2, HNO3, NH3, 

other reactive 

pollutants, aerosols, 

meteorological 

parameters and 

upper air 

http://esrl.noaa.gov/csd/2006/ For TexAQS 2006, the NOAA air quality component will 

investigate, through airborne and sea-based measurements, the sources, and processes that are responsible for photochemical pollution and regional haze during the summertime in Texas. The focus of the study 

will be the transport of O3 and O3 precursors within the 

state and the impact of the long-range transport of O3

or its precursors. 

NOAA 3 

aircraft 

2006 O3, NO, NO2, NOY, 

VOCs, CO2, CO, 

SO2, HNO3, NH3, 

other reactive 

pollutants, aerosols, 

meteorological 

parameters, altitude 

— NOAA aircraft 

http://cloud1.arc.nasa.gov/intex-b/ The export of air pollutants from urban to regional and 

global environments is a major concern because of 

wide-ranging potential consequences for human 

health, cultivated and natural ecosystems, visibility 

degradation, weather modification, changes in radiative forcing, and tropospheric oxidizing capacity. During 

the spring of 2006, a highly integrated atmospheric 

field experiment was performed over and around North 

America. The Megacity Initiative: Local and Global 

Research Observations (MILAGRO), 

http://www.eol.ucar.edu/projects/milagro/ , resulted 

through a highly coordinated collaboration between 

NSF (through MIRAGE-Mex), DOE (through MAXMex), NASA (through INTEX-B) and a variety of research institution in the U.S. and Mexico and involved 

ground and air borne activities centered on Mexico 

City, Mexico during March 2006. MILAGRO goals were 

greatly facilitated and enhanced by a number of 

concurrent and coordinated national and international 

field campaigns and global satellite observations. 

1EPA— Environmental Protection Agency; NASA — National Aeronautics and Space Administration; NOAA — National Oceanic and Atmospheric Administration; NPS — National Park 

Service\NSF — National Science Foundation; UCSD — University of California San Diego (Scripts Institution of Oceanography) 2This study is part of the Central California Air Quality Studies (CCAQS) which comprise the California Regional Particulate Air Quality Study (CRPAQS) and the Central California 

Ozone Study (CCOS). CCAQS is a multi-year effort of meteorological and air quality monitoring, emission inventory development, data analysis, and air quality simulation modeling. 

Prior studies in California included: Southern California Ozone Study (SCOS97) — 1997; Integrated Monitoring Study (IMS95) — 1995; San Joaquin Valley Air Quality Study (SJVAQS) 

— 1990; SARMAP Ozone Study — 1990; Southern California Air Quality Study (SCAQS) — 1987. 

3Historically, there have been many other field studies in the 1960s - 1990s that are not reflected in this table that involve both fixed monitoring sites and aircraft; well known examples 

include Regional Air Pollution Study (RAPS), Large Power Plant Effluent Study (LAPPES), Northeast Corridor Regional Modeling Program (NECRMP), Northeast Regional Oxidant 

Study (NEROS), Persistent Elevated Pollutant Episode (PEPE), and Lake Michigan Ozone Study (LMOS). 

 2-117
There has been a long history of intensive field campaigns starting with the RAPS in the 1970s, 

which formed the basis for evaluating the early photochemical gridded Eulerian airshed models used in 

acid deposition and O3 assessments. Landmark campaigns in the U.S. through the 1980s and 1990s such 

as the SCAQS (Lawson, 1990), the SJAQS/Atmospheric Utility Signatures, Predictions, and Experiments 

(AUSPEX) (Roth et al. 1988) and the Southern Oxidant Study (SOS) (Cowling and Furiness, 2001). Over 

the last decade there have been a series of field campaigns focusing on characterization of surface level 

aerosols through the PM Supersites program (Solomon and Hopke, 2008). While the early campaigns 

focused on urban environments, the Eulerian Model Evaluation Field Study (EMEFS) and SOS during the 

early 1990s shifted focus toward regional spatial scales. In addition to addressing urban areas of concern 

such as Houston, TX, and Los Angeles, CA, more recent campaigns have extended spatial scales beyond 

regional studies to address oceanic transport and a variety of air pollution issues across the Northern 

Hemisphere, recognizing the importance of far-ranging source regions and continental-scale atmospheric 

processes. Some of these campaigns included local and regional studies for the northeast and southeast 

U.S., portions of Texas, and central and southern California. A variety of federal and state entities have 

served as lead agencies for these studies. Table 2-15 provides a listing of studies conducted since the mid1990s. 

A synthesis of key findings from major field campaigns conducted over the last two decades would 

elevate exposure of these programs to a wider audience potentially generating support to enhance and 

sustain atmospheric process and model evaluation studies, which are important complements to routine 

ground-based and satellite observation platforms. While the NARSTO database 

(http://eosweb.larc.nasa.gov/PRODOCS/narsto/table_narsto.html) provides access to raw data for various 

field campaigns, coverage of campaigns beyond North America must be acquired from other sources. The 

National Aeronautics and Space Agency (NASA)’s Atmospheric Data Science Center 

(http://eosweb.larc.nasa.gov/) also provides access to some of the more recent field campaigns. 

2.9.3. Satellite-Based Air Quality Observing Systems 

An extensive array of satellite-based systems (see Table 2-16 and Table 2-17) with the capability of 

measuring atmospheric column total species has been established by U.S. and European Satellite 

programs lead by NASA and the National Oceanic and Atmospheric Administration (NOAA) in the U.S. 

and the European Space Agency (ESA). A suite of satellites including Aqua, Aura, CALIPSO, OCO, 

Glory, as well as NOAA-17, NOAA-18 and NPOESS, have either been launched since about the year 

2000 or have other near-term proposed launch dates. Collectively, the remote sensing platforms include 

techniques for measuring columns and/or profiles of aerosol optical depth (AOD), O3, CO, CO2, CH4, 

SO2, NO2, chlorinated fluorocarbon compounds (CFCs), other pollutants, and atmospheric parameters 

such as temperature and H2O content. Most of these satellites have a near-polar orbit allowing for two 

passes per day over a given location. When taken together, a group of six satellites (Aqua, Aura, 

CALIPSO, OCO, as well as CloudSat and PARASOL), coined the A-Train, is being configured to fly in a 

formation that crosses the equator a few minutes apart at around 1330 local time to give a comprehensive 

picture of earth weather, climate, and atmospheric conditions. 

Satellite imagery offers the potential to cover broad spatial areas; however, an understanding of 

their spatial, temporal and measurement limitations is necessary to determine how these systems 

complement ground based networks and support air quality management assessments. 

 2-118
Table 2-16. Satellite-based air quality observing systems.1,4

Instrument Satellite 

Platform3

Lead 

Agency Initiated Measurement Parameters Orbit & Hor. 

Resolution 

Location of Information 

and/or Data 

OLS (Operational 

Linescan System) 

DMSP satellites DOD 1962? Identify fires and smoke plume Polar Imagery 

only 

http://www.af.mil/factsheets/-

factsheet.asp?fsID=94 

BUV (Backscatter 

Ultraviolet Spectrometer) 

Nimbus 4 NASA 1970–1980 O3, CO2, SO2 Sun 

synchronous 

http://nssdc.gsfc.nasa.gov/da

tabase/MasterCatalog?sc=19

70-025A 

SBUV (Solar Backscatter 

Ultraviolet Spectrometer) 

Nimbus 7 NASA 1978–1993 O3, SO2 Polar http://jwocky.gsfc.nasa.gov/-

n7toms/nimbus7tech.html 

TOMS (Total Ozone 

Mapping Spectrometer) 

Nimbus 7 Meteor 

3 Earth-Probe 

NASA 1978–1993

1991–1994

1996 

O3, SO2, Aerosols Polar ~100 

km 

http://toms.gsfc.nasa.gov/-

fltmodel/spacecr.html 

LIMS (Limb Infrared 

Monitor of the 

Stratosphere 

Nimbus 7 NASA 1978–1979 O3, HNO3, NO2, Polar http://lims.gats-inc.com/-

about_lims.html 

ATMOS (Atmospheric 

Trace Molecule 

Spectroscopy) 

Spacelab 3 

ATLAS — 1,2,3 

NASA 1985, 1992, 

1993, 1994 

O3, CFCl3, CF2Cl2, ClONO2, 

HCl, HF, CO, CH4, HCN, HNO3, 

NO, NO2, N2O5, Aerosols 

http://remus.jpl.nasa.gov/-

atmos/sl3.html 

CLAES (Cryogenic Limb 

Array Etalon 

Spectrometer) 

UARS NASA 1991–1993 O3, CFCl3, CF2Cl2 ClONO2, CH4, 

HNO3, NO, NO2, N2O, N2O5, 

Aerosols 

http://umpgal.gsfc.nasa.gov/ 

HALOE (Halogen 

Occultation Experiment) 

UARS NASA 1991–2005 O3, HCl, HF, CH4, NO, NO2, 

Aerosols 

http://umpgal.gsfc.nasa.gov/ 

ISAMS (Improved 

Stratospheric and 

Mesospheric Sounder) 

UARS NASA 1991–1992 O3, CO, CH4, NO2, N2O, N2O5

Aerosols 

http://umpgal.gsfc.nasa.gov/ 

MLS (Microwave Limb 

Sounder) 

UARS NASA 1991–1999 O3, ClO, CH3CN, HNO3, SO2 http://umpgal.gsfc.nasa.gov/ 

GOES Imager (Geostationary Operational 

Environmental Satellites) 

GOES-10 GOES12 NOAA 1994 Fire products for WF_ABBA 

(imagery) and GASP (aerosol 

optical depth) 

Geostationary http://www.nesdis.noaa.gov/ 

GOES Sounder 

(Geostationary 

Operational 

Environmental Satellites) 

GOES-10 GOES12 NOAA 1994 Total column O3 Geostationary http://cimss.ssec.wisc.edu/-

goes/goesmain.html#sndrinfo

AVHRR (Advanced Very 

High Resolution 

Radiometer) 

NOAA-15 NOAA16 NOAA-172 NOAA 1998 Aerosol optical depth, particle 

size information and vegetation/drought index products 

related to air quality through 

fires 

Polar 

4 km 

http://noaasis.noaa.gov/-

NOAASIS/ml/avhrr.html 

SBUV/2 (Solar 

Backscattered Ultraviolet 

Radiometer Model 2) 

NOAA-16 

NOAA-172 

NOAA 2000 Total and profile O3 from surface 

to top of atmosphere in ~5 km 

thick Umkehr layers 

Polar http://www2.ncdc.noaa.gov/-

docs/podug/html/c4/sec4–

4.htm 

 2-119
Instrument Satellite 

Platform3

Lead 

Agency Initiated Measurement Parameters Orbit & Hor. Resolution 

Location of Information 

and/or Data 

MOPITT (Measurement of 

Pollution in the Troposphere) 

EOS Terra NASA 1999 CO, CH4 Polar 

22 × 22 km2

http://www.eos.ucar.edu/mopi

tt/ 

MISR (Multi-angle 

Imaging 

SpectroRadiomenter) 

EOS Terra NASA 1999 Aerosol properties and plume 

height information near the 

vicinity of fires 

Polar ~1 km http://wwwmisr.jpl.nasa.gov/mission/-

introduction/welcome.html 

MODIS (Moderate 

Resolution Imaging 

Spectroradiometer) 

EOS Terra 

EOS Aqua 

NASA 1999 

2002 

O3, aerosol optical depth, particle size information, fine 

particle fraction, and forest fires 

Polar 

1 km 

http://modarch.gsfc.nasa.gov/

index.php 

AIRS (Atmospheric 

Infrared Sounder) 

EOS Aqua NASA 2002 Total column O3, surface 

temperature, temperature and 

moisture vertical profiles, (plus 

under development are CO and 

CO2 total column, O3 vertical 

distribution, and CH4

distribution) 

Polar 

50 km 

http://www-airs.jpl.nasa.gov/ 

HIRDLS (High Resolution 

Dynamics Limb Sounder) 

EOS Aura NASA 2004 O3, CFCl3, CF2Cl2, ClONO2, 

CH4, HNO3, NO2, N2O, N2O5, 

Aerosols Polar: http://aura.gsfc.nasa. 

gov/index.html 

MLS (Microwave Limb 

Sounder) 

EOS Aura NASA 2004 O3, BrO, ClO, HOCl, HCl, CO, 

HCN, CH3CN, HNO3, N2O, OH, 

HO2, SO2

Polar http://aura.gsfc.nasa.gov/inde

x.html 

OMI (Ozone Monitoring 

Instrument) 

EOS Aura NASA 2004 O3, BrO, OClO, HCHO, NO2, 

SO2 and aerosol 

Polar 12 × 24 km2

http://aura.gsfc.nasa.gov/inde

x.html 

TES (Total Emission 

Spectrometer) 

EOS Aura NASA 2004 O3, NOY, CO, SO2, CH4 Polar 26 × 42 

km 

http://aura.gsfc.nasa.gov/inde

x.html 

CALIPSO (Cloud-Aerosol 

Lidar & Infrared Pathfinder Satellite Observations) 

CALIPSO NASA 2005 AOD, backscatter, extinction Polar 0.3 × 

0.3 km2

http://wwwcalipso.larc.nasa.gov/about/ 

OMPS Ozone Mapping 

and Profiling 

Suite NPOESS 

Preparatory 

Project 

NOAA 2006 Total column and vertical profile 

O3 data 

Polar http://www.ipo.noaa.gov/-

Projects/npp.html 

VIIRS (Visible Infrared 

Imaging Radiometer 

Suite) 

NPOESS - 

Preparatory 

Project 

NOAA 2006 AOD Polar http://www.ipo.noaa.gov/-

Projects/npp.html 

Orbiting Carbon 

Observatory 

OCO NASA 2008 CO2 Polar http://oco.jpl.nasa.gov/ 

APS & TIM (Aerosol 

Polarimetry Sensor & 

Total Irradiance Monitor) 

Glory NASA 2008 BC soot, other aerosols, total 

solar irradiance, cloud images 

Sun- synchronous, circular 

Low Earth 

Orbit 

http://glory.gsfc.nasa.gov/ 

1Non-U.S. satellite systems are not included in table at this time. 

2As of 3/15/06 the operational satellite platforms may need to include NOAA-18. 

3CALIPSO — Cloud-Aerosol Lidar & Infrared Pathfinder Satellite Observations 

DMSP— Defense Meteorological Satellite Program. EOS — Earth Observing System. GOES — Geostationary Operational Environmental Satellites 

NOAA — National Oceanic and Atmospheric Administration|. NPOESS — National Polar-orbiting Operational Environmental Satellite System. OCO — Orbiting Carbon Observatory 

UARS — Upper Atmosphere Research Satellite. 4See the following table for additional information on NASA satellites, instrument systems, pollutants measured, and data availability: 

 2-120
Table 2-17. Key atmospheric chemistry and dynamics data sets at the NASA Goddard DAAC. 

Missions Nimbus 4 Nimbus 7 

Nimbus 7 

Meteor 3 

ADEOS 1 

Earth-Probe

Nimbus 7 

Spacelab 3,

ATLAS 1, 

2, 3 

UARS ERS-2 Terra Aqua Aqua Aura 

Instruments BUV SBUV TOMS LIMS ATMOS CLAES HALOE SAMS MLS GOME MODIS AIRS OMI HIRDLS MLS TES* 

Data Period Apr 

‘70– 

May 

‘77 

Nov 

‘78– 

May 

‘93 

Nov 

‘78–

Present

Oct 

‘78– 

May 

‘79 

'85, '92, 

'93, '94 

Oct 

‘91– 

May ‘93

Oct ‘91– 

Present 

Sep ‘91– 

Jul ‘92 

Sep 

‘91–

Jul 

‘99 

April 

‘95–

Present

Mar 

‘00–

Present

Sep 

‘02–

Present 

Jul ‘04–

Present

Jul ‘04–

Present 

Jul ‘04-

Present 

Jul ‘04–

Present

Spectral 

Region 

255–

380 nm

255–

340 nm

309–

360 

312–

380 nm

6.2–

15 μm

2.98–15 μm 3.5–

12.7 μm

2.43−

10.25 μm

4.6–

16.6 μm 

63, 

183, 

205 

GHz

240–

790 nm

0.4–

14 μm 

0.4–1.1,

3.74–

15.4 μm

270–

500 nm 

6.12–

17.76 μm

118, 190,

240, 640 

GHz, 2.5 

THz 

3.2–

15.4 μm

Bands 13 13 6 6 16 9 8 8 3 3072 36 2382 1560 22 5 12 

O3 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

BrO ● ● ●

CFCl3 ● ● ●

CF2Cl2 ● ● ●

ClO ● ●

OClO ● ●

ClONO2 ● ● ●

HOCl ●

HCl ● ● ●

HF ● ●

HCHO ● ●

CO ● ● ● ● ●

CH4 ● ● ● ● ● ● ●

CH3CN ●

HCN ● ●

HNO3 ● ● ● ● ● ● ●

NO ● ● ●

NO2 ● ● ● ● ● ● ● ● ●

N2O ● ● ● ● ●

N2O5 ● ● ● ●

OH ●

H2O/ 

Humidity 

● ● ● ● ● ● ● ● ● ● ● ●

SO2 ● ● ● ● ● ● ●

Aerosols ● ● ● ● ● ● ● ●

Cloud ● ● ● ● ● ● ●

Temperature ● ● ● ● ● ● ● ● ● ●

Geopotential 

Height 

● ● ● ● ●

Reflectance ● ● ● ● ● ●

Please note that the table above does not contain parameters from all sensors and products. Also available from the GES DAAC are many more Atmospheric and Earth Sciences data 

products from AIRS, AMSU-A, HSB, MODIS, SeaWiFS, OCTS, CZCS, TRMM (PR, TMI, VIRS), TOVS Pathfinder, Data Assimilation Model (GEOS-1, GEOS-DAS, CPC/ACDB), UARS 

(HRDI, WINDII, SOLSTICE, SUSIM, PEM), SORCE, several Field Campaigns, and Interdisciplinary data sets consisting of 70 geophysical Earth Sciences parameters. TOMS & SBUV 

reprocessed data (version-8) are now available on DVD-ROM. The MLS and OMI-Aura products and Visualization tools are now available from GES DIC. 

Source: Aura instrument 'TES' is archived at the NASA Langley Atmospheric Sciences Data Center (http://eosweb.larc.nasa.gov/) http://disc.gsfc.nasa.gov/. 

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2.9.3.1. Satellite Coverages 

The near polar orbiting tracks of most satellites performing trace gas measurements provides wide 

spatial coverage of horizontal resolution on the order of 10 to 50 km, but delivers only twice-daily 

snapshots of a particular species. Consequently, temporal patterns of pollutants as well as a timeintegrated measure of pollutant concentrations cannot be delineated explicitly through satellite 

measurements alone. The geostationary satellite platforms such as the GOES systems of NOAA do 

provide near-continuous coverage of physical parameters for weather tracking and forecasting purposes. 

Polar orbiting satellites typically provide horizontal spatial resolution between 10 and 100 km, depending 

on the angle of a particular swath segment. Spatial resolution less than 10 km is possible with 

geostationary platforms. 

Characterization of elevated pollutants delivered by satellite systems complements of our ground 

based in-situ measurement networks – especially considering that a considerable fraction of pollutant 

mass resides well above Earth’s surface. With few exceptions, satellite data typically represents a total 

atmospheric column estimate. For certain important trace gases (e.g., NO2, SO2, CH2O) and aerosols, 

most mass resides in the boundary layer of the lower troposphere, enabling associations linking column 

data to surface concentrations or emissions fields. For example, reasonable correlations, especially in the 

eastern U.S., have been developed between concentrations from ground level PM2.5 stations and AOD 

from NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the Aqua and Terra 

satellites (Engel-Cox et al., 2004); see the example in Figure 2-68. The Infusing Satellite Data into 

Environmental Applications (IDEA; http://idea.ssec.wisc.edu/) site provides daily displays and 

interpretations of MODIS and surface air quality data. The Cloud-Aerosol Lidar and Infrared Pathfinder 

Satellite Observation (CALIPSO) satellite (discussed below) provides some ability to resolve aerosol 

vertical gradients. 

2.9.3.2. Measurement Issues 

Most satellite air quality observations are based on spectroscopic techniques typically using 

reflected solar radiation as a broad source of UV-through-IR electromagnetic radiation (LIDAR aboard 

CALIPSO does utilize an active laser as the radiation source). While the science of satellite based 

measurements of trace gases and aerosols is relatively mature, interferences related to surface reflections, 

cloud attenuation and overlapping spectra of nearby species require adequate filtering and accounting for 

in processing remote signals. For example, aerosol events episodes associated with clouds often are 

screened out in developing in applications involving AOD characterizations through MODIS. 

Correlations between AOD and surface aerosols generally are better in the eastern U.S. relative to the 

West due to excessive surface light scattering from relatively barren land surfaces. 

2.9.4. European Air Monitoring Networks 

Extensive air monitoring networks have also been implemented in Europe. In addition to the 

programs discussed above, many European-based programs are served by centralized organization 

structures linked to international efforts such as Convention on Long Range Transport of Air Pollution 

(LRTAP) (http://www.unece.org/env/lrtap/) and the underlying technical assessment body, the Cooperative Programme for Monitoring and Evaluation of the Long-range Transmission of Air Pollutants in 

Europe (EMEP). The Global Atmospheric Watch (GAW) program (http://www.wmo.int/pages/prog/-

arep/gaw/gaw_home_en.html) under the World Meteorological Organization (WMO) provides quality 

assurance guidelines and data access to an important body of air quality measurements relevant to 

assessing intercontinental pollution transport and climate forcing phenomena. The Norwegian Institute for 

 2-122
Air Research (NILU) (http://www.nilu.no/index.cfm?ac=topics&folder_id=4572&lan_id=3), maintains a 

database for much of the European based networks. 

Source: Engel-Cox et al. (2004). 

Figure 2-68. Correlation surfaces between MODIS AOD and hourly PM2.5 surface sites from AprilSeptember 2002. 

Table 2-18 includes combined contributions from all countries ranging from a few sites to tens of 

sites per country. Measurements for a variety of air pollutants are addressed including O3, heavy metals, 

persistent organic pollutants (POPs), PM, VOCs, and deposition from acidifying and eutrophying 

compounds. 

 2-123
Table 2-18. International and European air monitoring programs. 

Network Lead Agency Number 

of Sites Initiated Measurement Parameters Location of Information / Data 

EMEP – 

Co-operative Programmed for 

Monitoring and Evaluation of 

the Long-range Transmission 

of Air Pollutants in Europe 

(encompasses networks for 

~37 European countries and 

organizations) 

UNECE 270 1977 Acidifying / Eutrophying Compounds 

(precipitation): SO4

2−, NO3, NH4, trace 

elements, pH, acidity 

(A): SO2, NO2, HNO3, NH3, PM10, PM2.5, 

major ions 

O3 Heavy Metals precipitation, major 

ions, PM2.5, PM10, Hg, wet deposition 

POPs precipitation, air, deposition PM, 

PM2.5, PM10, EC, OC, TC, BC VOC HCs, 

Carbonyls 

http://www.nilu.no/projects/ 

ccc/emepdata.html 

EUROTRAC—The European 

Experiment on the Transport 

and Transformation of 

Environmentally Relevant 

Trace Constituents over 

Europe 

International 

Executive 

Committee 

(European 

Countries) 

??? 1986 EUROTRAC programs performed 

analyses utilizing data from existing or 

specially designed monitoring networks 

to: 

1. elucidate the chemistry and transport 

of O3 and other photo-oxidants in the 

troposphere, e.g., TOR—30 O3 stations 

and ALPTRAC—15 snow-monitoring 

sites 

2. identify processes leading to the 

formation of acidity in the atmosphere, 

particularly those involving aerosols and 

clouds. 

3. understand uptake and release of 

atmospheric trace substances by the 

biosphere. 

http://www.gsf.de/eurotrac/ 

index_what_is.html 

EUROTRAC-2 — The 

EUREKA project on the 

transport and chemical 

transformation of trace 

constituents in the 

troposphere over Europe; 

second phase. Subprojects: 

– AEROSOL 

– BIATEX-2 

– CAPMAN 

– CMD 

– EXPORT-E2 

– GENEMIS 

– GLOREAM 

– LOOP 

– MEPOP 

– PROCLOUD 

– SATURN 

– TOR-2 

– TRAP45 

– TROPOSAT 

International 

Scientific 

Secretariat 

(European 

Countries and 

EU) 

??? 1996 EUROTRAC-2 programs performed 

analyses utilizing data from existing 

monitoring networks to: support the 

further development of abatement 

strategies within Europe by providing an 

improved scientific basis for the 

quantification of source-receptor 

relationships for photo-oxidants and 

acidifying substances. 

http://www.gsf.de/eurotrac/ 

index_what_is.html 

 2-124
2.9.5. Ambient Concentrations of Relevant N Compounds 

2.9.5.1. NO and NO2

Species concentrations described in this section were taken from two types of networks described 

in Section 2.9.1 above: the mostly urban networks designed and maintained for NAAQS attainment 

demonstrations; and the mostly rural and remote networks designed and operated to comply with a range 

of requirements for protection of landscapes and views. 

Figure 2-69 shows the distribution of monitoring sites for ambient-level, continuous NO2 across the 

U.S. Data for ambient NO2 are missing or are collected at very few sites over large areas of the U.S. Few 

cities have more than two monitors and several large cities, including Seattle, WA, have none. Note that 

the number of NO2 monitors has been decreasing in the U.S. as ambient average concentrations have 

fallen to far below the level of the NAAQS. There were, for example, 375 NO2 monitors identified in 

mid-2006, but only 280 in November 2007. 

Figure 2-69. Location of ambient-level NO2 monitors for NAAQS compliance in 2007. Shaded states have 

NO2 monitors; unshaded states have none. 

Criteria for siting ambient monitors for NAAQS pollutants are given in the SLAMS / NAMS / 

PAMS Network Review Guidance (U.S. EPA, 1998). As might be expected, criteria for siting monitors 

differ by pollutant. NO2 monitors are meant to be representative of several scales: middle, or several city 

blocks, 300 to 500 m; neighborhood, or 0.5 to 4 km; and urban, or 4 to 50 km. Middle- and 

neighborhood-scale monitors are used to determine highest concentrations and source effects, while 

neighborhood- and urban-scale monitors are used for monitoring population exposures. As can be seen, 

there is considerable overlap between monitoring objectives and scales of representativeness. The 

distance of neighborhood- and urban-scale monitor inlets from roadways increases with traffic volume 

and can vary from 10 to 250 m away from roadways as traffic volume increases. Where the distance of an 

inlet to a road is shorter than the value in this range for the indicated traffic volume on that road, that 

monitor is classified as middle scale. Vertically, the inlets to NO2 monitors can be set at a height from 2 to 

15 m. 

Figure 2-70 shows box plots of ambient concentrations of NO2 measured at all monitoring sites 

located within MSAs or urbanized areas in the U.S. from 2003 through 2005. As can be seen, mean NO2

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concentrations are ~15 ppb for averaging periods ranging from a day to a year, with an interquartile range 

(IQR) of 10 to 25 ppb. However, the average of the daily 1-h maximum NO2 concentration over this 3 

year period is ~30 ppb. These values are about twice as high as the 24-h average. The highest maximum 

hourly concentration, ~200 ppb, found during the period of 2003 to 2005 was more than a factor of 10 

greater than the overall mean 24-h concentrations. The ratio of the 99th percentile concentration to the 

mean ranges from 2.1 for the 1-year averages, to 3.5 for the 1-h averages. 

Figure 2-70. Ambient concentrations of NO2 measured at all monitoring sites located within Metropolitan 

Statistical Areas (MSAs) in the U.S. from 2003 through 2005. * max; • mean 

Because ambient NO2 monitoring data are so sparse across the U.S., and are particularly so in rural 

areas, it would not be appropriate to use these data for constructing a complete coverage map of NO2

concentrations. The short τ of NO2 with respect to conversion to NOZ species and the concentrated nature 

of NO2 emissions result in steep gradients and low concentrations away from major sources that are not 

adequately captured by the existing monitoring networks. For this reason, model predictions might be 

more useful for showing large-scale features in the distribution of NO2 and could be used in conjunction 

with the values shown in Figure 2-71 to provide a more complete picture of the variability of NO2 across 

the U.S. Monthly average NO2 concentrations for January and July 2002 calculated using U.S. EPA’s 

CMAQ model are shown in Figure 2-72. (A description of the capabilities of CMAQ and other threedimensional CTMs is given in Section 2.8) The high variation in NO2 concentrations of at least a factor of 

10 is apparent in these model estimates. As expected, the highest NO2 concentrations are seen in large 

urban regions, such as the northeast corridor, and lowest values are found in sparsely populated regions 

located mainly in the West. NO2 concentrations tend to be higher in January than in July. 

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Figure 2-71. Monthly average NO2 concentrations (ppb) for January 2002 (left panel) and July 2002 (right 

panel) calculated by CMAQ (36 X 36 km horizontal resolution). 

Trends in NO2 concentrations across the U.S. from 1990 to 2006 are shown in Figure 2-72. The 

white line shows the mean values and the upper and lower borders of the shaded areas represent the 10th 

and 90th percentile values. Information on trends at individual local air monitoring sites can be found at 

www.epa.gov/airtrends/nitrogen.html. 

Source: www.epa.gov/airtrends/nitrogen.html. 

Figure 2-72. Nationwide trend in NO2 concentrations. The white line shows the mean values, and the 

upper and lower borders of the green (shaded) areas represent the 10th and 90th percentile 

values. The current NAAQS of 0.53 ppm is shown with the dotted line. 

Concentrations were substantially higher during earlier years in selected locations and contributed 

in those years to the brown clouds observed in many cities. Residents in Chattanooga, TN, for example, 

were exposed more than 30 years ago to high levels of NO2 from a munitions plant (Shy and Love, 1980). 

Annual mean NO2 concentrations there declined from ~102 ppb in 1968 to ~51 ppb in 1972. There was a 

strike at the munitions plant in 1973 and levels declined to ~32 ppb. With the implementation of control 

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strategies, values dropped further. In 1988, the annual mean NO2 concentration varied from ~20 ppb in 

Dallas, TX and Minneapolis, MN to 61 ppb in Los Angeles, CA. However, New York City, with the 

second-highest annual mean concentration in the U.S. in 1988, the mean NO2 concentration was 41 ppb. 

The month-to-month variability in 24-h average NO2 concentrations at two sites in Atlanta, GA, is 

shown in Figure 2-73. (Similar plots of variability at other individual sites in selected urban areas are 

shown in Figure 2-74 through Figure 2-81; these cities were chosen to represent regions with large 

populations and, hence, large emissions from on-road vehicles and combustion for energy production, the 

two largest sources of NO and NO2. 

Figure 2-73. Time series of 24-h average NO2 concentrations at individual monitoring sites in Atlanta, GA 

from 2003 through 2005. A natural spline function (with 9 degrees of freedom) was fit and 

overlaid to the data (dark solid line). 

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Source: U.S. EPA AQS, 2007 

Figure 2-74. Time series of 24-h average NO2 concentrations at individual monitoring sites in New York 

City from 2003 through 2005. A natural spline function (with 9 degrees of freedom) was fit and 

overlaid to the data (dark solid line). 

 

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Figure 2-75. Time series of 24-h average NO2 concentrations at individual monitoring sites in Chicago, IL 

from 2003 through 2005. A natural spline function (with 9 degrees of freedom) was fit and 

overlaid to the data (dark solid line). 

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Figure 2-76. Time series of 24-h average NO2 concentrations at individual monitoring sites in Baton Rouge, 

LA from 2003 through 2005. A natural spline function (with 9 degrees of freedom) was fit and 

overlaid to the data (dark solid line). 

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Figure 2-77. Time series of 24-h average NO2 concentrations at individual monitoring sites in Houston, TX 

from 2003 through 2005. A natural spline function (with 9 degrees of freedom) was fit and 

overlaid to the data (dark solid line). 

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Figure 2-78. Time series of 24-h average NO2 concentrations at individual monitoring sites in Los Angeles, 

CA from 2003 through 2005. A natural spline function (with 9 degrees of freedom) was fit and 

overlaid to the data (dark solid line). 

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Figure 2-79. Time series of 24-h average NO2 concentrations at individual monitoring sites in Los Angeles, 

CA from 2003 through 2005. A natural spline function (with 9 degrees of freedom) was fit and 

overlaid to the data (dark solid line). 

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Figure 2-80. Time series of 24-h average NO2 concentrations at individual monitoring sites in Riverside, CA 

from 2003 through 2005. A natural spline function (with 9 degrees of freedom) was fit and 

overlaid to the data (dark solid line). 

Strong seasonal variability exists in NO2 concentrations in the data shown above. Higher 

concentrations are found during winter, consistent with the generally lower PBL depths in winter. Lower 

concentrations are found during summer, consistent with deeper PBLs and increased rates of 

photochemical oxidation of NO2 to NOZ. Note also the day-to-day variability in NO2 concentration, which 

also tends to be larger during the winter. There appears to be a somewhat regular pattern for the other 

southern cities examined with their winter maxima and summer minima. 

Monthly maxima tend to be found from late winter to early spring in Chicago, IL, and New York, 

NY, with minima occurring from summer through fall. However, in Los Angeles and Riverside, CA, 

monthly maxima tend to occur from fall through early winter, with minima occurring from spring through 

early summer. Mean and peak NO2 concentrations during winter can be up to a factor of 2 greater than 

those during the summer at sites in Los Angeles. 

The diel variability in NO2 concentrations at the same two sites in the Atlanta metropolitan area 

shown in Figure 2-73 is illustrated in Figure 2-82. As can be seen from these figures, NO2 typically 

exhibits daily maxima during the morning rush hours, although they can occur at other times of day. In 

addition, there are differences between weekdays and weekends. At both Atlanta sites, NO2

concentrations are generally lower and the diel cycles more compressed on weekends than on weekdays. 

The diel variability of NO2 at these sites is typical of that observed at other urban sites. Monitor siting 

plays a role in determining diel variability in the sense that monitors located farther from traffic will 

sometimes measure lower concentrations and show a flatter overall distribution of data compared to 

monitors located closer to traffic. 

 2-135
Source: U.S. EPA (2006a) 

Figure 2-81. Time series of 24-h average NO2 concentrations at individual monitoring sites in Riverside, CA 

from 2003 through 2005. A natural spline function (with 9 degrees of freedom) was fit and 

overlaid to the data (dark solid line). 

 2-136
Figure 2-82. Mean hourly NO2 concentrations on weekdays and weekends measured at two sites in Atlanta, 

GA. (A) and (B) refer to a suburban site, and (C) and (D) refer to a site classified as urban and 

city center. 

2.9.5.2. NOY and NOZ

Data for individual NOY species are much less abundant than for NOX or for total NOY. Data for 

several individual NOY species are collected typically only as part of research field studies, like SOS, 

Texas Air Quality Study, and others. As a result, this information is not available for a large number of 

areas in the U.S. 

PANs 

At warm temperatures, the concentration of PAN forms a photochemical steady state with its 

radical precursors on a timescale of roughly 30 min. This steady-state value increases with the ambient 

concentration of O3 (Sillman et al., 1990). O3 and PAN may show different seasonal cycles because they 

are affected differently by temperature. Ambient O3 increases with temperature, driven in part by the 

photochemistry of PAN. The atmospheric τ of PAN decreases rapidly with increasing temperature due to 

thermal decomposition. Based on the above, the ratio of O3 to PAN is expected to show seasonal changes, 

with highest ratios in summer, although there is no evidence from measurements. Measured ambient 

concentrations show a strong nonlinear association between PAN and O3 (Pippin et al., 2001; Roberts 

et al., 1998) (Figure 2-83). Moreover, uncertainty in the relationship between O3 and PAN grows as the 

level of PAN increases. 

 2-137
Source: Roberts et al. (1998). Reprinted with permission. 

Figure 2-83. Measured O3 (ppb by volume) versus PAN (ppt by volume) in Tennessee, including (a) aircraft 

measurements, and (b, c, and d) suburban sites near Nashville. 

Measurements and models show that PANs in the U.S. includes major contributions from both 

anthropogenic and biogenic VOC precursors (Horowitz et al., 1998; Roberts et al., 1998). Measurements 

in Nashville during the 1999 summertime SOS showed PPN and MPAN amounting to 14% and 25% of 

PANs, respectively (Roberts et al., 2002). Measurements during the TexAQS 2000 study in Houston 

indicated PAN concentrations of up to 6.5 ppb (Roberts et al., 2003). PAN measurements in southern 

California during the SCOS97–NARSTO study indicated peak concentrations of 5–10 ppb, which can be 

contrasted to values of 60–70 ppb measured in 1960 (Grosjean, 2003). Vertical profiles measured from 

aircraft over the U.S. and off the Pacific coasts typically show PAN concentrations above the boundary 

layer of only a few hundred ppt, although there have been significant enhancements associated with longrange transport of pollution plumes from Asia (Kotchenruther et al., 2001; Roberts et al., 2004). 

Observed ratios of PAN to NO2 as a function of NOX at a site at Silwood Park, Ascot, Berkshire, 

UK are shown in Figure 2-84 (U.K AQEG, 2004). As can be seen there is a very strong inverse relation 

between the ratio and the NOX concentration, indicating photochemical oxidation of NOX has occurred in 

aged air masses and that PAN can make a significant contribution to measurements of NO2 especially at 

low levels of NO2. It should be noted that these ratios will likely differ from those found in the U.S. 

because of differences in the composition of precursor emissions, the higher solar zenith angles found in 

the UK compared to the U.S., and different climactic conditions. Nevertheless, these results indicate the 

potential importance of interference from these compounds in measurements of NO2. 

 2-138
Source: U.K. AQEG (2004). 

Figure 2-84. Ratios of PAN to NO2 observed at Silwood Park, Ascot, Berkshire, U.K. from July 24 to August 

12, 1999. Each data point represents a measurement averaged over 30 min. 

HNO2

Measurements of HNO2 in urban areas are extremely limited; however, data from Stutz et al. 

(2004) and Wang and Lu (2006) indicate that levels of HNO2 are <1 ppb even under heavily polluted 

conditions, with the highest levels found during the night and just after dawn and the lowest values found 

in the afternoon. However, data collected in the U.K. (Lammel and Cape, 1996; U.K. AQEG, 2004) and in 

the U.S. (Kirchstetter and Harley, 1996) indicate that HNO2-to-NOX ratios could be of the order of ~5% in 

motor vehicle emissions. These results indicate that HNO2 levels in traffic could be comparable to those 

of NO2. Several field studies conducted at ground level (Hayden et al., 2003, near Boulder, CO) and 

aircraft flights (Singh et al., 2007, over eastern North America), have found much higher NOZ

concentrations than NOX concentrations in relatively unpolluted rural air, some of which could be 

attributed to PAN. 

The ratio of HNO2 to NO2 as a function of NOX measured at a curbside site in a street canyon in 

London, UK is shown in Figure 2-85, where HNO2 is labeled HONO. The ratio is highly variable, ranging 

from about 0.01 to 0.1, with a mean ~0.05. As NO2 constitutes several percent of motor vehicle emissions 

of NOX, the above implies that emissions of HNO2 represent a few tenths of a percent of mobile NOX

emissions. A similar range of ratios has been observed at other urban sites in the United Kingdom 

(Lammel and Cape, 1996). 

 2-139
Source: UK AQEG (2004). 

Figure 2-85. Ratios of HNO2 to NO2 observed in a street canyon (Marylebone Road) in London, U.K. from 11 

a.m. to midnight during October 1999. Data points reflect 15–min average concentrations of 

HONO and NO2. 

HNO3 and NO3

–

Data for concentrations of HNO3 and NO3

– in urban areas in the U.S. are sparse. The most 

geospatially intensive set of data for any HNO3 were taken as part of the Children’s Health Study for 

which gas-phase HNO3 was measured at 12 sites in southern California from 1994 through 2001 (Alcorn 

and Lurmann, 2004). Two week average concentrations ranged from <1 ppb to >10 ppb, with the highest 

HNO3 concentrations and highest ratio of HNO3 to NO2, ~0.2, was found downwind from central Los 

Angeles in the San Bernardino area during summer, as one would expect for this more oxidized N 

product. 

HNO3 data have also been reported from the SEARCH network of four pairs (eight total sites) of 

urban and rural sites in the southeastern U.S using increasingly sophisticated methods since 1998; see 

Zhang et al. (2006). Concentrations of HNO3 in this area have ranged from <1 ppb to >10 ppb. 

Maps of ambient concentrations from CASTNet data for rural and remote areas are available 

below. The CASTNet ambient concentration maps were produced with ArcInfo using an inverse distance 

weighting (IDW) interpolation technique. Using IDW, the surface is most influenced by the nearest point 

values and less so by more distant points. CASTNet sites within 400 km of each grid point were used in 

this calculation. As noted above, thin data coverage complicates interpretation of these maps and renders 

them most useful as heuristic guides to large areas of possible differences. Strict quantitative values 

should not be imputed to areas away from the measurement sites. 

Ranges of years in the chart represent 3 year averages. For example, 2004–2006 is the average 

concentration of 2004, 2005 and 2006, as calculated from gridded output for each of the years. The three 

annual grids from the 3 year period were averaged to derive the mean concentration of the 3 year period. 

Only sites meeting completeness criteria for at least two of the three years of the averaging period were 

included. 

Figure 2-86 shows annual average concentrations for gas-phase HNO3 from CASTNet for the years 

2004 through 2006. Because HNO3 is produced mostly as a secondary product from emitted NO, the 

regions of higher concentrations HNO3 are geographically similar to those of high concentration NO and 

NO2: the northeast corridor and southern California. 

 2-140
Source: CASTNet, USEPA/CAMD 7/30/07 

Figure 2-86. Annual average gas-phase HNO3 concentrations, 2004–2006. White areas on the map are 

areas where monitoring sites are absent and no information is available. 

Because they have the same precursor reactants in NO and NO2, elevated O3 concentrations are 

often associated with elevated HNO3 concentrations. However, HNO3 can be produced in significant 

quantities in winter, even when O3 concentrations are low. The ratio between O3 and HNO3 also shows 

great variation in air pollution events, with NOX-saturated environments having much lower ratios of O3

to HNO3 (Ryerson et al., 2001). pNO3 is formed primarily by combining NO3 supplied by HNO3 and NH3

and may be limited by the availability of either reactant. pNO3 is expected to correlate loosely with O3, 

whereas NH3 is not expected to correlate with O3. 

Thus, annual average pNO3 can account for several ppb of NOY, with higher values in the West. 

There is strong seasonal variation, which is especially pronounced in western areas where there is 

extensive wood burning in the winter resulting in a larger fractional contribution of local sources. Areas in 

the East where there are topographic barriers might be expected to show higher fractional contributions 

from local sources than other eastern areas that are influenced by regionally dispersed sources. Figure 

2-87 shows a map of annual average NO3

–

 concentrations in the years 2004 to 2006 produced from 

CASTNet measurements of ambient concentrations. This maps indicates at least qualitatively that 

maximum NO3

–

 concentrations are found in areas of maximum NO and NO2 emissions. 

 2-141
Source: CASTNet, USEPA/CAMD 7/30/07 

Figure 2-87. Annual average gas-phase NO3 concentrations, 2004–2006. White areas on the map are areas 

where monitoring sites are absent and no information is available. 

2.9.5.3. Nitro-PAHs 

Nitro-PAHs are widespread and found even in high altitude, relatively unpolluted environments 

(Schauer et al., 2004). However, there are differences in composition and concentration profiles both 

within and between monitoring sites (rural vs. urban) as well as between and within urban areas (Albinet 

et al., 2006; Naumova et al., 2003; Söderström et al., 2005), with some differences in relative abundances 

of nitro- and oxo-PAHs also reported. Source attribution has remained largely qualitative with respect to 

concentrations or mutagenicity (Eide et al., 2002). The spatial and temporal concentration pattern for the 

nitro-PAHs may differ from that of the parent compounds because concentrations of the latter are 

dominated by direct emission from local combustion sources. These emissions results in higher 

concentrations during atmospheric conditions more typical of wintertime when mixing heights tend to be 

low. The concentrations of secondary nitro-PAHs are elevated under conditions that favor hydroxyl and 

NO3 radical formation, i.e., during conditions more typical of summertime, and are enhanced downwind 

of areas of high emission density of parent PAHs and show diurnal variation (Fraser et al., 1998; Kameda 

et al., 2004; Reisen and Arey, 2005). Nitro-napthalene concentrations in Los Angeles, CA varied between 

about 0.15 to almost 0.30 ng/m3

 compared to 760 to 1500 ng/m3 for napthalene. Corresponding values for 

Riverside, CA were 0.012 to more than 0.30 ng/m3

 for nitro-napthalene and 100 to 500 ng/m3 for 

napthalene. Nitro-pyrene concentrations in LA varied between approximately 0.020 to 0.060 ng/m3

compared to 3.3 to 6.9 ng/m3

 pyrene, whereas corresponding values for Riverside were 0.012 to 

0.025 ng/m3

 and 0.9 to 2.7 ng/m3. 

2.9.5.4. Ammonia 

Section 2.7.1.5 above established that a successful real-time continuous monitoring technique for 

ambient NH3 has not been identified; and, of equal importance, Section 2.4 above described the severely 

limiting unknowns related to NH3 emissions on national and local scales. With these important 

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 2-143 

information gaps, estimates of NH3 concentrations at any scale for the U.S. must be constructed and 

interpreted with caution. It is possible, for example, to rank NH3 concentrations by land use types from 

the few special field campaigns where it was measured as Walker et al. (2004) did for agricultural, nonagricultural, and urban types; see Table 2-19. This table shows the enormous range in NH3 concentrations 

by season and land use type, from a low of 0.02 μg/m3

 in summer over alpine tundra on Niwot Ridge, 

CO, (Rattray and Sievering, 2001) to 11.0 μg/m3 over fertilized lands in Wekerom in the central 

Netherlands (Buijsman et al., 1998). 

Table 2-19. Ambient NH3 concentrations summarized by study. 

Concentration (μgm3) Land Use Comment 

3.0 Agricultural Low NH3 emissions 

11.0 Agricultural Moderate NH3 emissions a

10.48 Agricultural Fall b

0.65-1.2 Agricultural Spring c

0.26 Agricultural Winter c

0.34 Non-agricultural High elevation, summer and fall d

0.02 Non-agricultural High elevation, summer e

0.62-1.47 Non-agricultural High elevation, summer f

0.29 Non-agricultural High elevation, summer g

0.22 Non-agricultural Coastal, summer g

0.21 Non-agricultural Forest, summer g

0.16 Non-agricultural Forest h

0.21 Non-agricultural Wetland, summer g

0.23 Non-agricultural Wetland, summer g

0.63 Non-agricultural Desert, summer g

4.75 Non-agricultural Grassland, summer g

0.38-1.49 Urban Pittsburgh, PA; summer i

0.63-0.72 Urban Research Triangle Park, NC; fall j

1.18 Urban Vinton, VA; summer k

a Buijsman et al. (1998). 

b McCulloch et al. (1998). 

c Pryor et al. (2001). 

d Tarnay et al. (2001). 

e Rattray and Sievering (2001) 

f Aneja et al. (2000). 

g Langford et al. (1992). 

h Lefer et al. (1999) 

I McCurdy et al. (1999). 

j Sickles et al. (1990). 

k Leaderer et al. (1999) 

A preliminary draft U.S. national-scale, county-level NH3 map was created by the U.S. EPA using 

emissions data from the widely-used Carnegie Mellon University (CMU) NH3 emissions model, 

version 3. See Pinder et al., (2007) for a description of the model and application information. The map 

included an empirical relationship between emissions and ambient concentration derived from field 

measurements over five different land use types in North Carolina, covering the range of expected NH3
 2-144

emissions densities. In the CMU emissions model used for this analysis, fertilized soils were included but 

NH3 emissions from natural (non-fertilized soils) were not because of overwhelmingly large uncertainties 

in their emissions factors. Emissions from mobile sources are included in this analysis, although 

uncertainties in those values are also large; see Section 2.4. above. Emissions from a small number of 

counties having very low emissions rates were set to missing because their extremely low values 

invalidated determination of densities in those counties. 

The CMU-derived county-level NH3 emissions map is shown in Figure 2-88. Differences in the 

techniques for estimating NH3 emissions and the very large uncertainties in NH3 emissions factors and 

totals complicate direct, fine-scale comparisons to other NH3 emissions maps. However, both the 

magnitude and areal extent of Walker’s emissions map compare very favorably to the U.S. EPA NEI 

database NH3 emissions map shown in Section 2.4 above. 

Source: J. Walker, USEPA/ORD/NRMRL 

Figure 2-88 County-scale NH3 emissions densities from the CMU inventory model. 

The empirical relationship between NH3 emissions and ambient NH3 concentration is shown in 

Figure 2-89. 
 2-145

Source : J Walker, U.S. EPA/ORD/NRMRL 

Figure 2-89. Ambient NH3 concentration as a function of county scale NH3 emissions. 

The map of estimated ambient NH3 concentration from the CMU emissions model and empirical 

relationship illustrated above is shown in Figure 2-90. 

Source: J. Walker, USEPA/ORD/NRMRL 

Figure 2-90. Estimated county-scale ambient NH3 concentrations. 
 2-146

2.9.5.5. NH4NO3

The IMPROVE network is the premier source of data about the spatial and temporal patterns of 

rural and remote pNO3 in the U.S. Data about urban pNO3 and other particles comes primarily from the 

CSN, which includes STN and others (Jayanty, 2003). Although IMPROVE began and still functions 

primarily to characterize visibility impairments in protected areas, the network has been expanded several 

times since its inception to include coverage of remote areas in central and western states of the U.S. to 

better understand regional components of particulate pollution. Except where noted, information in this 

section was derived from the IMPROVE IV Report (DeBell, 2006) with data displays created with data 

and tools available at http://vista.cira.colostate.edu/views/. Much of these data and conclusions are also to 

appear in Pitchford et al. (2008). 

IMPROVE monitors the major fine particle components pSO4, pNO3, crustal EC and OC, and 

coarse mass concentration computed as the concentration difference PM10 minus PM2.5. An implicit 

assumption is that most of the pNO3 is present as NH4NO3 in the PM2.5 size range. One component of the 

Big Bend Regional Aerosol and Visibility Observational (BRAVO) Study, conducted at Big Bend 

National Park, TX, in the summer and fall of 1999, entailed use of detailed measurements of aerosol 

chemical composition, size distribution, water growth, and optical properties to characterize the aerosol 

and assess the relationships among aerosol physical, chemical, and optical properties (Schichtel, et al., 

2004). Fine pNO3 accounted for <5% of the mass concentration in these samples and was present mostly 

as NaNO3. Approximately 67% of the pNO3, again inferred to be NaNO3, was found in the coarse mode 

where it comprised ~8% of the mass concentration. 

However, depending on the acidity of the particles, which in turn depends strongly on their SO4

2−

and NH4

+

 contents, higher pNO3 concentrations could be found in coarse mode particles (PM10-2.5) than in 

PM2.5 samples. The average pNO3 content of PM2.5 and PM10 is typically ~1% in the eastern U.S. and 

15.7% and 4.5%, respectively, in the western U.S. (U.S. EPA, 1996a). These values suggest that most of 

the pNO3 was in the PM2.5 size fraction in the studies conducted in the western U.S., but pNO3 in the 

studies in the eastern U.S. was mainly in the PM10-2.5 size fraction. 

A year-long special study of PM10 speciation was conducted at nine IMPROVE remote area 

monitoring sites during 2003 and 2004 to provide additional information about the geographic and 

seasonal variations in coarse particle composition; see Malm et al. (2007). The same sampling and 

analytical procedures were used for the PM10 samples as are routinely used on the IMPROVE PM2.5

samples. IMPROVE PM10 speciation study did not include NH4

+

 analysis, so pNO3 was assumed again to 

be NH4NO3. As expected, crustal minerals were the largest contributors to PM10 mass overall at ~60%, 

and organic particles contributed ~25%. On average, NO3

–

 was the third largest contributor to PM10 mass 

at ~8% on average for the nine monitoring sites. The sites with the highest coarse pNO3 concentrations 

were the two in California, San Gorgonio, 0.74 μg/m3

 and Sequoia, 0.69 μg/m3, where PM2.5 pNO3 were 

also high on average, 2.66 μg/m3

 and 2.14 μg/m3 respectively. Brigantine, a coastal site in New Jersey had 

the highest fraction of total PM10 pNO3 at 36%. The authors speculated that Brigantine’s pNO3 was likely 

NaNO3, the result of HNO3 reactions with sea-salt NaCl. The nine-site average fraction of total coarse 

pNO3 was 26%. 

Figure 2-91 shows maps of remote NH4NO3 for two years selected to demonstrate the additional 

information available after expansion of IMPROVE into the central U.S. The locations of monitoring sites 

supplying the data shown as color contours are shown as dot on the maps. As above, concentration 

contour maps carry the caution that their isolines are provided merely to guide visual similarities among 

sites reporting similar values, and should not be read to suggest any quantitative spatial interpolation 

where sites do not exist. These plots, showing the so-called Midwest pNO3 bulge illustrate why that 

caution is always warranted. 
 2-147

Source: http://vista.cira.colostate.edu/views 

Figure 2-91. IMPROVE network measured annual averaged NH4NO3 concentration for 2000 (left) and for 

2004 (right). 

Before 2001, no IMPROVE or any other regular remote-area aerosol speciation monitoring sites 

existed in the central states between northern Minnesota and Michigan to the north, and Arkansas and 

Kentucky to the south. The lack of monitoring over such a large region in the center of the country hid the 

largest regional distribution of particulate matter dominated by NH4NO3, previously thought to be a 

phenomenon isolated to California. However, with fewer than 6 years of complete data for this region, 

insufficient information exists to test for long-term trends. 

Combining IMPROVE and CSN data makes possible comparison of urban pNO3 to surrounding 

remote-area regional values. These are shown as paired color contour maps for IMPROVE and 

IMPROVE plus CSN in Figure 2-92. U.S. EPA (2004) used the pairing of IMPROVE and CSN 

monitoring sites at 13 selected areas to separate local and regional contributions to the major contributors 

of PM2.5, as shown for pNO3 in Figure 2-93. 

Source: http://vista.cira.colostate.edu/views 

Figure 2-92. IMPROVE and CSN (labeled STN) monitored mean NH4NO3 concentrations for 2000 through 

2004. 
Source: U.S. EPA (2004). 

Figure 2-93. Regional and local contributions to annual average PM2.5 by pNO3 for select urban areas 

based on paired IMPROVE and CSN monitoring sites. 

Urban pNO3 concentrations in western states are, in general, more than a factor of 2 greater than 

the remote-area regional concentrations. For the Central Valley of California and Los Angeles areas, the 

urban excess NH4NO3 exceed regional concentrations by amounts ranging from 2 μg/m3

 to 12 μg/m3. In 

the region of the recently identified Midwest pNO3 bulge (see Figures 2-91 and 2-92), the urban 

concentrations were less than twice the background concentrations for an annual urban excess of about 

1 μg/m3

. Northeast and southeast of the Midwest NO3 bulge, annual urban pNO3 were ~1 μg/m3 or less 

above the remote-area regional concentrations, with warmer southern locations tending to have smaller 

concentrations of both regional and urban excess pNO3. 

Holland et al. (1999) developed NOX emissions trends from 1989 to 1995 and compared them to 

corresponding trends in total NO3 (pNO3 + gas-phase HNO3) for the states in the U.S. between Louisiana 

and Minnesota and east of that line based on data from 34 rural CASTNet dry deposition monitoring sites. 

They reported a decrease in total NO3 median values of ~8% associated with a decrease of 5.4% in nonbiogenic NOX emissions. Because of the form of total NO3 assumed in this analysis, it is not possible to 

determine whether this change is larger for HNO3 or pNO3. For situations with limited gas-phase NH3 or 

with elevated temperatures, it may be assumed that the trend in total NO3 is principally in HNO3 with no 

net change in pNO3. Where NH3 concentrations are substantially in excess of those required to neutralize 

pSO4 and where temperatures are lower, this trend may be assumed to be reversed. 

Potential causes of the Midwest pNO3 bulge can be examined through comparison of the pNO3

areal extent to that of NOX and NH3 emissions. Figure 2-94 shows a map of the annual average pNO3

concentrations (top) with a map of NH3 emissions (bottom). The spatial extent of NH3 emissions in the 

 2-148
Midwest is strikingly similar to that of pNO3 concentrations, each having regional maxima centered on 

Iowa. NO and NO2 emissions are high over a broad region of the country associated with the larger 

population densities and greater numbers of fossil fueled EGUs to the east of the Midwest pNO3 bulge. 

While both NH3 and HNO3 are needed to form NH4NO3, the maps suggest the Midwest NO3 bulge is due 

primarily to the abundance of free NH3, defined as the amount beyond that required to neutralize the 

acidic pSO4. By contrast, the region east of the Midwest NO3 bulge might be expected to have excess 

HNO3 given greater emissions of NO and NO2, but apparently has a deficiency of free NH3. The few 

eastern monitoring sites with locally high pNO3 (near southern Pennsylvania) are located within a small 

region of high density animal agricultural sites identified as a high NH3 emissions region in Figure 2-94. 

Note that California’s South Coast and Central Valley have both high NH3 and NO and NO2 emissions, 

explaining its own high pNO3 concentrations. 

Figure 2-94. Maps of spatial patterns for average annual pNO3 measurements (top), and for NH3 emissions 

for April 2002 from the WRAP emissions inventory (bottom). 

Importantly, although the Midwest pNO3 bulge was not apparent before measurements were made 

in its region, air quality modeling using national-scale emissions data predicted it in 1996 in CMAQ 

applications for the U.S. EPA Western Regional Air Partnership (http://www.wrapair.org). Figure 2-95 

shows the CMAQ-predicted average pNO3 concentration for the month of January 1996 (left panel) and 

the model-predicted sensitivities of pNO3 to a 50% decrease in NH3 emissions (right panel). Decreases of 

~3 μg/m3

 in the heart of the Midwest NO3 bulge and in its areal extent were predicted as a result of the 

NH3 emissions decrease. 

 2-149
Source: U.S. EPA / WRAP. 

Figure 2-95. CMAQ simulation of January monthly averaged pNO3 concentration using 1996 emissions 

(left), and for a 50% decrease in NH3 emissions (right). 

Figure 2-96. Particulate NO3 source attribution by region using CAMx modeling for six western remote area 

monitoring sites Top left to right Olympic NP, WA; Yellowstone NP, WY; Badlands NP, SD; 

bottom left to right San Gorgonio (W), CA; Grand Canyon NP, AZ; and Salt Creek (W), NM. 

WRAP includes North Dakota, South Dakota, Wyoming, Colorado, New Mexico and all states 

further west. CENRAP includes all states east of WRAP and west of the Mississippi River 

including Minnesota. Eastern U.S. includes all states east of CENRAP. The Pacific Offshore 

extends 300km to the west of California, Oregon, and Washington. Outside Domain refers to 

the modeling domain, which extend hundreds of kilometers into the Pacific and Atlantic 

Oceans and from Hudson Bay Canada to just north of Mexico City. 

Several example monitoring locations distributed across the northern and southern portions of the 

eastern U.S. have been selected to illustrate the attribution results from air quality simulation modeling by 

source region and source type for pNO3. The modeling for this work was done with the Comprehensive 

Air Model with extensions (CAMx); see http://camx.com for model code and descriptions. They include 

Olympic National Park (NP), WA; Yellowstone NP, WY; and Badlands NP, SD across the north, and San 

 2-150
Gorgonio Wilderness (W), CA; Grand Canyon NP, AZ and Salt Creek W, NM across the south. Pie 

diagrams of pNO3 attribution results by source region for each of these sites are shown in Figure 2-96. 

Based on these sites, 25% or less of the pNO3 in remote areas of the Pacific coastal states is from outside 

of the U.S. (Pacific Offshore and Outside of the Domain). The Outside of the Domain values are derived 

by simulating the fate of the boundary condition concentrations, which for the WRAP air quality 

modeling were obtained using output from the GEOS-CHEM global air quality model (Fiore et al., 2003). 

By comparison, pNO3 at these western sites is much more from domestic regional emission 

sources, with ~60 to ~80% being from emissions within the WRAP region. For the west coast sites, ~25% 

of pNO3 is from a combination of Pacific Offshore emissions (i.e. marine shipping) and Outside Domain 

regions. Canadian emissions are responsible for ~10 to 30% of pNO3, but Mexican emissions do not 

contribute appreciably to pNO3 for the three southern sites. Motor vehicles are the largest contributing 

NO + NO2 source category responsible for pNO3 for these six WRAP sites, with a combination of point, 

area, and wildfire source categories also contributing from ~10 to 50% of the WRAP regional emissions. 

2.9.6. Ambient Concentrations of Relevant Sulfur Compounds 

2.9.6.1. SO2 and SO4

2– Near Urban Areas 

SO2 data collected from the SLAMS and NAMS networks show that the decline in SO2 emissions 

from EGUs has improved air quality. There has not been a single monitored exceedance of the SO2 annual 

ambient air quality standard in the U.S. since 2000 (U.S. EPA, 2006a). EPA’s trends data 

(www.epa.gov/airtrends) reveal that the national composite average SO2 annual mean ambient 

concentration decreased by 48% from 1990 to 2005, with the largest single-year reduction coming in 

1994–1995 (U.S. EPA, 2006a). 

In 2007, there were ~500 SO2 monitors reporting values to the U.S. EPA Air Quality System 

database (AQS). Trace level SO2 monitoring is currently required at the approximately 75 proposed 

NCore sites, as noted in CFR 40 Part 58 Appendices C and D. Continued operation of existing SLAMS 

for SO2 using FRM or FEM is required until discontinuation is approved by the U.S. EPA Regional 

Administrator. Where SLAMS SO2 monitoring is required, at least one of the sites must be a maximum 

concentration site for that area. 

Figures 2-97 through 2-102 illustrate geospatial locations of monitors for SO2, NO2, CO, PM10, 

PM2.5, and O3 in 2005. These locations, sited in several cities in six states, were selected as relevant for 

SO2 environmental effects to complement measurements from rural and remote CASTNet sites and to be 

near large sources of SO2. For each state, map A of each figure shows locations of each monitor for all six 

pollutants; map B of each figure shows only the SO2 monitor locations. Totals for each monitor type are 

included. These figures demonstrate the important point that not all SO2 monitors in any Consolidated 

Metropolitan Statistical Area (CMSA) are co-located with monitors for other pollutants. Two examples 

are given below. 

Table 2-20 lists the totals for all criteria air pollutant monitors (except Pb) in California, as well as 

the subset of these monitors in San Diego County. At each of the four sites where SO2 was measured, 

NO2, CO, PM10, PM2.5, and O3 were also measured, with the exception of PM2.5 at one site (AQS ID 

060732007) in Otay Mesa, CA. Table 2-21 lists the totals for all criteria air pollutant monitors (except Pb) 

in Ohio, as well as the subset of in Cuyahoga County. 

In Cuyahoga County, OH, PM10 and PM2.5 were measured at all four sites where SO2 was also 

measured in 2005, but O3 and CO were not measured at any of those four sites; NO2 was only measured at 

one site (AQS ID 39050060) near Cleveland’s city center and ~0.5 km from the intersection of Interstate 

Highways 77 and 90. 

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Table 2-20. Number of monitors in California and San Diego County, 2005. 

SO2 NO2 O3 CO PM10 PM2.5

California (all) 35 105 176 86 177 97 

San Diego County 4 9 10 6 7 7 

Table 2-21. Number of monitors in Ohio and Cuyahoga County, 2005. 

SO2 NO2 O3 CO PM10 PM2.5

Ohio (all) 31 4 49 15 49 49 

Cuyahoga County 4 2 3 4 6 7 

The regional distribution of SO2 and SO4

2−

 concentrations through the CONUS is shown in Table 

2-22. As for the country on the whole, in and around most individual CMSAs, the trends are also toward 

lower SO2 levels. Table 2-23 shows that many annual and even 1-h mean concentrations for the years 

2003 through 2005 were consistently at or below the operating LOD of ~3 ppb for the standard sensitivity 

UV fluorescence SO2 monitors deployed in the regulatory networks. The aggregate mean value over all 3 

years and all monitoring sites in and around the CMSAs was just above the LOD at ~4 ppb, and was 

identical to the 1-h and 24-h means. 

The maximum 1-h concentration observed at some sites in and around some CMSAs exceeded the 

mean by a large margin, with maximum 1-h values of >600 ppb. However, the 50th percentile maximum 

value outside CMSAs, 5 ppb, was only slightly greater than the 1-h, 24-h, and annual mean value, 4 ppb. 

The 50th percentile maximum value inside CMSAs, 7 ppb, was 75% greater than these longer-term 

averages, reflecting heterogeneity in source strength and location. In addition, even with 1-h max values 

of >600 ppb, the maximum annualized mean value for all CMSAs was still <16 ppb, which is below the 

current annual primary SO2 NAAQS. 

The strong west-to-east increasing gradient in SO2 emissions described above is well replicated in 

the observed concentrations in individual CMSAs. For example, Table 2-24 shows the mean annual 

concentrations from 2003–2005 for the 12 CMSAs with four or more SO2 regulatory monitors. Values 

ranged from a reported low of ~1 ppb in Riverside, CA, and San Francisco, CA, to a high of ~12 ppb in 

Pittsburgh, PA, and Steubenville, OH, in the highest SO2 source region. 

 2-152
Figure 2-97. Criteria pollutant monitor locations (A) and SO2 monitor locations (B), California, 2005. 

Shaded counties have at least one monitor. Map A shows locations of each monitor for all six 

criteria pollutants. Map B shows only the SO2 monitor locations. 

 2-153
Figure 2-98. Criteria pollutant monitor locations (A) and SO2 monitor locations (B), Ohio, 2005. Shaded 

counties have at least one monitor. Map A shows locations of each monitor for all six criteria 

pollutants. Map B shows only the SO2 monitor locations. 

 2-154
Figure 2-99. Criteria pollutant monitor locations (A) and SO2 monitor locations (B), Arizona, 2005. Shaded 

counties have at least one monitor. Map A shows locations of each monitor for all six criteria 

pollutants. Map B shows only the SO2 monitor locations. 

 2-155
Figure 2-100. Criteria pollutant monitor locations (A) and SO2 monitor locations (B), Pennsylvania, 2005. 

Shaded counties have at least one monitor. Map A shows locations of each monitor for all six 

criteria pollutants. Map B shows only the SO2 monitor locations. 

 2-156
Figure 2-101. Criteria pollutant monitor locations (A) and SO2 monitor locations (B), New York, 2005. 

Shaded counties have at least one monitor. Map A shows locations of each monitor for all six 

criteria pollutants. Map B shows only the SO2 monitor locations. 

 2-157
Figure 2-102. Criteria pollutant monitor locations (A) and SO2 monitor locations (B), Massachusetts, 2005. 

Shaded counties have at least one monitor. Map A shows locations of each monitor for all six 

criteria pollutants. Map B shows only the SO2 monitor locations. 

 2-158
Table 2-22. Regional distribution of SO2 and SO4

2− ambient concentrations, averaged for 2003−2005. 

Concentration 

Region 

SO2 (ppb) SO4

2− (μg/m3) 

Mid-Atlantic 3.3 4.5 

Midwest 2.3 3.8 

Northeast 1.2 2.5 

Southeast 1.3 4.1 

Table 2-23. Distributions of temporal averaging of SO2 concentrations inside and outside CMSAs. 

Percentiles Averaging Time 

Monitor Locations n Mean 

1 5 10 25 30 50 70 75 90 95 99 

Max 

1-h MAX CONCENTRATION 

Inside CMSAs 332405 13 1 1 1 3 4 7 13 16 30 45 92 714 

Outside CMSAs 53417 13 1 1 1 1 2 5 10 13 31 51 116 636 

1-h AVG CONCENTRATION 

Inside CMSAs 7408145 4 1 1 1 1 1 2 4 5 10 15 34 714 

Outside CMSAs 1197179 4 1 1 1 1 1 2 3 3 7 13 36 636 

24-h AVG CONCENTRATION 

Inside CMSAs 327918 4 1 1 1 1 2 3 5 6 10 13 23 148 

Outside CMSAs 52871 4 1 1 1 1 1 2 3 4 8 12 25 123 

ANNUAL AVG CONCENTRATION 

Inside CMSAs 898 4 1 1 1 1 2 4 5 6 8 10 12 15 

Outside CMSAs 143 4 1 1 1 1 2 3 4 5 8 9 13 14 

AGGREGATE 3-YR AVG CONCENTRATION, 2003−2005 

Inside CMSAs 283 4 1 1 1 2 3 3 5 5 8 10 12 14 

Outside CMSAs 42 4 1 1 1 2 2 3 4 5 8 9 13 13 

* Values are ppb. ** CMSA = Consolidate.d Metropolitan Statistical Area 

The Pearson correlation coefficients (r) for multiple monitors in these CMSAs were generally very 

low for all cities, especially at the lower end of the observed concentration ranges, and even negative at 

the very lowest levels on the West Coast; see Table 2-24. This reflects strong heterogeneity in SO2

 2-159
 2-160

ambient concentrations even within any one CMSA. At higher concentrations, the r-values were also 

higher. In some CMSAs, this heterogeneity may result from meteorological effects, whereby a generally 

well-mixed subsiding air mass containing one or more SO2 plumes with relatively high concentration 

would be more uniformly spread than faster-moving plumes with lower concentrations. However, 

instrument error may also play a role, because the highest r-values, i.e., those >0.7, correspond to the 

highest SO2 concentrations, i.e., >6 and >10 ppb. Since the lowest SO2 concentrations are at or below the 

operating LOD, and demonstrate the lowest /correlation across monitors that share at least some air mass 

characteristics most of the year, the unbiased instrument error in this range may be confounding 

interpretation of any possible correlation. This could be because the same actual ambient value would be 

reported by different monitors (with different error profiles) in the CMSA as different values in this 

lowest concentration range. 

To better characterize the extent and spatiotemporal variance of SO2 concentrations within each of 

the CMSAs having four or more SO2 monitors, the means, minima, and maxima were computed from 

daily mean data across all available monitors for each month for the years 2003 through 2005. Because 

many of these CMSAs with SO2 monitors also reported SO4

2−

, it is possible to compute the degree of 

correlation between SO2, the emitted species, and SO4

2−

, the most prominent oxidized product from SO2. 

SO4

2−

 values, however, while averaged over all available data at each site are generally available at their 

monitoring sites on a schedule of only 1 in 3 days or 1 in 6 days. Furthermore, SO2 and SO4

2−

 monitors 

are not all collocated throughout the CMSAs. For each of the five example CMSAs in Figures 2-103 

through 2-107, monthly aggregated values are depicted from daily means of: (a) the monthly mean, 

minimum, and maximum SO2 concentrations; (b) the monthly mean, minimum and maximum SO4

2−

concentrations; and (c) a scatterplot of SO2 versus SO4

2−

 concentrations. 

Table 2-24. Range of mean annual SO2 concentrations and Pearson correlation coefficients in urban areas 

having at least four regulatory monitors, 2003–2005. 

CMSA (# Monitors) Mean SO2 Concentration (ppb) Pearson Correlation Coefficient 

Philadelphia, PA (10) 3.6 – 5.9 0.37 – 0.84 

Washington, DC (5) 3.2 – 6.5 0.30 – 0.68 

Jacksonville, FL (5) 1.7 – 3.4 −0.03 – 0.51 

Tampa, FL (8) 2.0 – 4.6 −0.02 – 0.18 

Pittsburgh, PA (10) 6.8 – 12 0.07 – 0.77 

Steubenville, OH (13) 8.6 – 14 0.11 – 0.88 

Chicago, IL (9) 2.4 – 6.7 0.04 – 0.45 

Salt Lake City, UT (5) 2.2 – 4.1 0.01 – 0.25 

Phoenix, AZ (4) 1.6 – 2.8 −0.01 – 0.48 

San Francisco, CA (7) 1.4 – 2.8 −0.03 – 0.60 

Riverside, CA (4) 1.3 – 3.2 −0.06 – 0.15 

Los Angeles, CA (5) 1.4 – 4.9 −0.16 – 0.31 
 2-161 

Moving across the CONUS from highest to lowest SO2 concentrations, first consider Steubenville, 

OH (Figure 2-103), where the area of highest SO2 concentrations of all 12 CMSAs with more than four 

monitors, all monthly mean SO2 concentrations (a) were substantially <30 ppb, though max daily means 

in some months were often >60 ppb, or even >90 ppb. SO4

2−

 data (b) at Steubenville were insufficient to 

make meaningful comparisons, though the 12 months of available SO4

2−

 data suggest no correlation with 

SO2 (c). 

Next, consider Philadelphia, PA (Figure 2-104). SO2 in Philadelphia, PA (a) is present at roughly 

one-half the monthly mean concentrations in Steubenville, OH, and demonstrates a strong seasonality 

with SO2 concentrations peaking in winter. By contrast, SO4

2−

 concentrations in Philadelphia peak in the 

three summer seasons, with pronounced wintertime minima. This seasonal anticorrelation still contains 

considerable monthly scatter, however. 

Los Angeles, CA (Figure 2-105) presents a special case, since its size and power requirements 

place a larger number of SO2 emitters near it than would otherwise be expected on the West Coast. 

Concentrations of SO2 demonstrate weak seasonality in these 3 years, with summertime means of ~3 to 4 

ppb, and maxima generally higher than wintertime ones, though the highest means and maxima occur 

during the winter of 2004–2005. SO4

2−

 at Los Angeles shows stronger seasonality, most likely because the 

longer summer days of sunny weather allow for additional oxidation of SO2 to SO4

2−

 than would be 

available in winter. Weak seasonal effects in SO2 likely explain the complete lack of correlation between 

SO2 and SO4

2−

 here. 
Figure 2-103. Steubenville, OH, 2003–2005. (a) Monthly mean, minimum, and maximum SO2 concentrations. 

(b) Monthly mean, minimum, and maximum SO4

2− concentrations. (c) Monthly mean SO42−

concentrations as a function of SO2 concentrations. 

 2-162
Figure 2-104. Philadelphia, 2003–2005. (a) Monthly mean, minimum, and maximum SO2 concentrations. (b) 

Monthly mean, minimum, and maximum SO4

2− concentrations. (c) Monthly mean SO42−

concentrations as a function of SO2 concentrations. 

 2-163
Figure 2-105. Los Angeles, 2003–2005. (a) Monthly mean, minimum, and maximum SO2 concentrations. (b) 

Monthly mean, minimum, and maximum SO4

2− concentrations. (c) Monthly mean SO42−

concentrations as a function of SO2 concentrations. 

 2-164
Figure 2-106. Riverside, CA, 2003–2005. (a) Monthly mean, minimum, and maximum SO2 concentrations. (b) 

Monthly mean, minimum, and maximum SO4

2− concentrations. (c) Monthly mean SO42−

concentrations as a function of SO2 concentrations. 

 2-165
Figure 2-107. Phoenix, 2003–2005. (a) Monthly mean, minimum, and maximum SO2 concentrations. (b) 

Monthly mean, minimum, and maximum SO4

2− concentrations. (c) Monthly mean SO42−

concentrations as a function of SO2 concentrations. 

The Riverside, CA CMSA (Figure 2-106) presents the strongest example among the 12 examined 

for this study of correlation between SO2 and SO4

2−

, though even here the R2 value is merely 0.3. 

Seasonal peaks are obvious in summertime for SO2 and SO4

2−

, both at roughly one-half the ambient 

concentrations seen in Los Angeles. This is very likely due to Riverside’s geographic location just 

downwind of the regionally large electric generating utility sources near Los Angeles and the prevailing 

westerly winds in summer. Again, as with Los Angeles, the summertime peaks in SO4

2−

 are most likely 

due to the combination of peaking SO2 and favorable meteorological conditions allowing more complete 

oxidation. 

 2-166
Phoenix, AZ was the CMSA with the lowest monthly mean SO2 and SO4

2−

 concentrations 

examined here (Figure 2-107). In Phoenix, nearly all monthly mean SO2 values were at or below the 

regulatory monitors’ operating LOD of ~3 ppb. SO4

2−

 concentrations were equivalently low, roughly onehalf the concentrations seen in Riverside, CA, for example. The monthly mean data show strong 

summertime peaks for even these very low-level SO4

2−

 observations, which, at ~1 to 3 μg/m3, were 

generally one-half of those in Philadelphia. This suggests some seasonality in SO2, though anticorrelated 

with SO4

2−

; however, the trend is very weak, as the correlation scatterplot shows. 

2.9.6.2. SO2 and SO4

2− in Rural and Remote Areas 

The mean annual concentrations of SO2 and SO4

2−

 from CASTNet’s long-term monitoring sites can 

be compared using two 3-year periods, 1989–1991 and 2003−2005, shown in Figure 2-108 for SO2 and 

Figure 2-109 for SO4

2−

. 

From 1989 through 1991 the highest ambient mean concentrations of SO2 and SO4

2−

 were observed 

in western Pennsylvania and along the Ohio River Valley: >20 μg/m3

 (~8 ppb) SO2 and >15 μg/m3 SO4

2−

. 

As with SO2, in the years since the Acid Rain Program controls were enacted, both the magnitude of 

SO4

2−

 concentrations and their areal extent have significantly decreased, with the largest decreases again 

along the Ohio River Valley. 

IMPROVE monitors the major fine particle components including SO4

2−

, NO3, crustal, elemental, 

and organic carbon plus coarse mass concentration defined as PM10 minus PM2.5. An implicit assumption 

is that most of the pSO4 is present as (NH4)2SO4. Much of the information contained below is based on 

particulate elemental S used to infer the (NH4)2SO4 levels. A discussion of IMPROVE S-to-pSO4 history 

and trends is available (Eldred, 2001). As with data from IMPROVE used above for pNO3, and except 

where noted, information in this section was derived form the IMPROVE IV Report (DeBell, 2006) with 

data displays created with data and tools available at http://vista.cira.colostate.edu/views/. Much of these 

data and conclusions are also to appear in Pitchford et al., 2008.

In contrast to results seen for pNO3, additional monitoring sites in the U.S. Midwestern states 

produced no surprises for (NH4)2SO4 concentration or areal extent. Figure 2-111 shows highest 

(NH4)2SO4 concentrations in the mid-Atlantic and upper southern U.S. states where annual concentrations 

ranged from ~3 μg/m3

 to ~6 μg/m3. The (NH4)2SO4 concentrations in most western U.S. states was <~1 

μg/m3

. 

Combining IMPROVE and CSN data makes possible the comparison of urban pSO4 to surrounding 

remote-area regional values. These are shown as paired color contours maps for IMPROVE and 

IMPROVE plus CSN in Figure 2-112. U.S. EPA (2004) used the pairing of IMPROVE and CSN 

monitoring sites at 13 selected areas to separate local and regional contributions to the major contributors 

of PM2.5, as shown for pSO4 in Figure 2-113. 

As shown in Figures 2-112 and 2-113, annual-averaged urban pSO4 concentrations were in general 

not significantly greater than the regional values, with urban excess pSO4 generally <~0.5 μg/m3

. 

Exceptions to the general case appear in Texas and Louisiana where urban excess pSO4 concentrations 

were >1μg/m3

. Urban contributions were a larger fraction of the total pSO4 in western U.S. states because 

the regional levels were much lower there than in eastern ones. The small area decrease in pSO4 evident 

on these maps between the two eastern high concentrations regions may not be real, but cannot be verified 

without speciation monitoring sites in southern Ohio, Kentucky, West Virginia, and Virginia. U.S. EPA 

(2004) estimates of the contribution of local sources to pSO4 were made for the same cities included for 

pNO3 shown above; see Figure 2-93. The east-west divide in both concentration and the regional 

contribution to pSO4 is strongly apparent here. 

 2-167
Source: U.S. EPA CASTNet 

Figure 2-108. Annual mean ambient SO2 concentration, 1989 through 1991 (top), and for 2003 through 2005 

(bottom). White areas on the maps are areas where monitoring sites are absent and no 

information is available. 

 2-168
Source: CASTNet 

Figure 2-109. Annual mean ambient SO4

2− concentration, 1989 through 1991 (top), and 2003 through 2005 

(bottom). White areas on the maps are areas where monitoring sites are absent and no 

information is available. 

 2-169
Source: http://vista.cira.colostate.edu/views 

Figure 2-110. IMPROVE network measured annual averaged pSO4 concentration for 2000 (top) and for 2004 

(bottom). Note difference in scale. 

 2-170
Source: http://vista.cira.colostate.edu/views 

Figure 2-111. IMPROVE mean (NH4)2SO4

2− concentrations for 2000 through 2004. 

Source: http://vista.cira.colostate.edu/views 

Figure 2-112. IMPROVE and CSN (labeled STN) monitored mean (NH4)2SO4

2− concentrations for 2000 

through 2004. 

 2-171
Source: U.S. EPA (2004). 

Figure 2-113. Regional and local contributions to annual average PM2.5 by pSO4 for select urban areas 

based on paired IMPROVE and CSN monitoring sites. 

Source attribution of the pSO4 contribution to haze at Big Bend NP, TX was a primary motivation 

for the BRAVO Study. Schichtel, et al. (2005) showed that during the four-month field monitoring study 

(July through October, 1999) SO2 emissions sources in the U.S. and Mexico were responsible for ~55% 

and ~38% of the pSO4 respectively. Among U.S. source regions, Texas was responsible for ~16%, eastern 

U.S. ~30%, and the western U.S. ~9%. A large coal fired power plant, the Carbón facility in Mexico just 

south of Eagle Pass, TX was responsible for about ~19%, making it the largest single contributor. 

Modeling for a three-day pollution episode in September 1996 in the California South Coast Air 

Basin (SCAB) and for another episode in January 1996 in the San Joaquin Valley (SJV) by Ying and 

Kleeman (2006) has shown that ~80% of pSO4 for both regions is derived from upwind sources, with 

most of the remaining local contributions associated with diesel and high-S fuel combustion. Kleeman, 

et al. (1999) used a combination of measurements and modeling to show that the upwind pSO4 source 

region for the SCAB was over the Pacific Ocean, and this was confirmed by measurements on Santa 

Catalina Island. Moreover, these particles subsequently grew with accumulation of additional secondary 

aerosol material, principally NH3NO3, as they traversed the SCAB. Most the HNO3 that forms pNO3 in 

the SCAB is from diesel and gasoline combustion (~63%), while much of the NH3 is from agricultural 

sources (~40%) and catalyst equipped gasoline combustion (~16%) and upwind sources (~18%). In the 

SJV most of the HNO3 that forms pNO3 is from upwind sources (~57%) with diesel and gasoline 

combustion contributing most of the rest (30%), while much of the NH3 is from upwind sources (~39%) 

and a combination of area, soil and fertilizer sources (~52%). 

 2-172
Source: Xu et al. (2006). Reprinted with permission. 

Figure 2-114. Contributions of the Pacific Coast area to the (NH4)2SO4 (μg/m3) at 84 remote-area monitoring 

sites in western U.S. based on trajectory regression. Dots denote locations of the IMPROVE 

aerosol monitoring sites. 

Using a regression analysis to find the dependence of pSO4 concentration measured over a 3 year 

period (2000–2002) at 84 western IMPROVE monitoring sites on the modeled transport trajectories to the 

sites for each sample period, Xu et al. (2006) were able to infer the source regions that supplied pSO4 in 

the western U.S. Among the source regions included in this analysis is the near-coastal Pacific Ocean (i.e. 

a 300 km zone off the coast of California, Oregon, and Washington states). Up to 50% of the pSO4

measured at southern California monitoring sites is associated with this source region. As shown in Figure 

2-114 the zone of impact from this source region includes large regions of California, Arizona, and 

Nevada, possibly owing to the high S-content fuel used in marine shipping and port emissions. 

The SO4

2−

 attribution results of the WRAP air quality modeling (available from http://wrapair.org) 

are largely in line with these empirical results, finding that the Pacific Offshore source region contributed 

somewhat smaller amounts than reported by Xu et al., (2006) with concentrations at the highest affected 

site in California of ~45% compared to 50% by the regression analyses and even greater differences for 

more distant monitoring sites. 

 2-173
(a)

Figure 2-115. pSO4 source attribution by region using CAMx modeling for six western remote area 

monitoring sites. Top left to right Olympic NP, WA; Yellowstone NP, WY; Badlands NP, SD; 

bottom left to right San Gorgonio (W), CA; Grand Canyon NP, AZ; and Salt Creek (W), NM. 

WRAP includes North Dakota, South Dakota, Wyoming, Colorado, New Mexico and all states 

further west. CENRAP includes all states east of WRAP and west of the Mississippi River 

including Minnesota. Eastern U.S. includes all states east of CENRAP. The Pacific Offshore 

extends 300 km to the west of California, Oregon, and Washington. Outside Domain refers to 

the modeling domain, which extend hundreds of kilometers into the Pacific and Atlantic 

Oceans and from Hudson Bay Canada to just north of Mexico City. 

Several example monitoring locations distributed across the northern and southern portions of the 

western U.S. have been selected to illustrate the attribution results from the WRAP air quality simulation 

modeling by source region and source type for pSO4. They include Olympic NP, WA; Yellowstone NP, 

WY; and Badlands NP, SD across the north, and San Gorgonio Wilderness (W), CA; Grand Canyon NP, 

AZ and Salt Creek W, NM across the south. Pie diagrams of the pSO4 attribution results by source region 

for each of these sites are shown in Figure 2-115. Based on these sites, >50% of the pSO4 in remote areas 

of the Pacific coastal states is from outside the U.S., Pacific Offshore and Outside of the Domain. The 

Outside of the Domain values are derived by simulating the fate of the boundary condition concentrations, 

which for the WRAP air quality modeling were obtained using output from the GEOS-CHEM global air 

quality model (Fiore et al., 2003). The pSO4 fraction from the region labeled Outside of Domain is 

approximately uniform throughout the western U.S. with site-to-site variation in the fraction mostly 

caused by the variations in the total SO4

2−

 concentration. The more northerly sites have effects from 

Canadian emissions, while the southern sites have effects from Mexican emissions. Half of the Salt 

Creek, NM, pSO4 is from the domestic source emissions further to the east (CENRAP and eastern U.S.), 

which also contribute ~20% to Badland pSO4 concentrations. A breakout of the emission sources from 

within the WRAP region by source type (not shown) has most of the emissions from point sources, with 

the combination of motor vehicle, area and wildfire emissions contributing from a few percent at the 

furthest eastern sites to roughly half at San Gorgonio. 

 2-174
2.10. Deposition of Nitrogen and Sulfur Species Across the 

Landscape 

As established in Sections 2.2 and 2.3 above, total emissions of NOX and SOX have decreased 

substantially in the last 35 years. Between 1970 and 2005, NOX emissions fell from 26.9 million tons per 

year (Mt/yr) to 19 Mt/yr, and SO2 emissions fell from 31.2 to 15 Mt/yr. These decreases in emissions led 

to correlative decreases in N and S concentrations, described just above in Section 2.9, and in atmospheric 

deposition of N and S species across the landscape; see the trends summarized in Figure 2-116. 

Importantly, however, these very recent decreases in deposition still leave current deposition amounts, 

which are a factor of 2 greater than in pre-industrial times for NO3

–

 and NH4

+

, and a factor of 5 greater for 

SO4

2−

, according to modeling experiments by Luo et al. (2007). 

Figure 2-116. Trends, 1990–2005 in S (left) and N (right) deposition for 34 sites in the eastern U.S. 

Deposition maps were developed by CASTNet to show the composition of dry deposition for N 

and S. The maps are labeled with inferred total deposition at each site, and a pie chart showing the 

relative proportion of wet and dry deposition or the chemical components of the deposition. Wet 

deposition is estimated from the interpolated concentration as measured by NADP multiplied by the 

measured rainfall at the site. Dry deposition is inferred from the measured ambient air concentrations of 

the chemical multiplied by the dry deposition rate obtained from an inferential model of linked resistances 

to derive species and location-dependent Vd. (See the CASTNet QAPP for more information on methods 

of computing their deposition totals). Note that NH3 is not included in these total N estimates because it is 

not currently measured in these networks. 

Data in this section are presented to show deposition across the landscape; finer-scale data and 

maps of sensitive and vulnerable regions and ecosystems are presented in other sections. Data presented 

in the maps and charts represent 3 year averages. For example, “89–91” is the average deposition of 1989, 

1990, and 1991 for a given site. Only sites having valid total deposition for at least two of the three years 

are shown and in some instances sites only met this criterion for one of the two reporting periods. 

Because of differences like these, direct site-by-site comparisons are not possible everywhere. 

2-175 
Figure 2-117. Total average yearly wet and dry inorganic N deposition, excepting NH3, for 2004–2006 (top) 

and 1989–1991 (bottom). 

2-176 
Figure 2-118. Total average yearly inorganic N deposition by species, excepting NH3, for 2004–2006 (top) 

and 1989–1991 (bottom). 

2.10.1. Nitrogen 

For the years 2004–2006, mean N deposition was greatest in the Ohio River Valley, specifically in 

the states of Indiana and Ohio, with values as high as 9.2 and 9.6 kg N/ha/yr, respectively; see Figure 

2-117. Recent work by Elliott et al. (2007) using δ15N to trace deposition totals and isolate them to point 

to mobile source type shows that for 33 NADP/NTN sites in the East and Upper Midwest, spatial 

distributions of δ15N concentrations were strongly correlated with NOX emissions from point sources, and 

 2-177 
that wet NO3

–

 deposition at the 33 sites considered was strongly associated with NOX emissions from the 

surrounding point sources. N deposition was lower in other parts of the East, including the Southeast and 

in northern New England. In the central U.S., Kansas and Oklahoma reported the highest deposition, 7.0 

and 6.5 kg N/ha/yr, respectively. N deposition was generally much lower in the western U.S., where it 

was highest in urban areas in southern California and Denver, CO, 4.8 and 3.3 kg N/ha/yr, respectively. It 

should be noted, however, that large portions of the U.S. west of the Mississippi River are poorly covered 

by the current deposition monitoring networks as the location icons on these maps make clear. Hence, the 

actual degree of heterogeneity and magnitude of real deposition in much of the West is largely unknown. 

Because NOX emissions decreased by ~25% between 1990 and 2005, recent N deposition is lower 

compared with average deposition for the years 1989 to 1991. For 1989 to 1991, several recording 

stations in the Ohio River basin reported average annual deposition rates in excess of 10 kg N/ha/yr. Data 

are lacking, however, for much of the central and western U.S. and little can be said for changes between 

the two reporting periods in these areas for the reasons given above. The greatest mass of N deposition 

primarily occurred as wet NO3

–

 and NH4

+

, followed in importance by dry HNO3, dry NH4

+

, and dry NO3

–

; 

see Figure 2-118. Although most deposition for both reporting periods occurred as wet deposition, there 

were some exceptions, including parts of California where N deposition was primarily dry. 

Figure 2-119 and Figure 2-120 show maps of wet deposition from NADP’s data and IDW 

interpolation technique. pNO3 concentration and wet deposition amounts track dry deposition in locations 

where the two monitoring networks overlap. Thus pNO3 ambient concentration and deposition are highest 

in the upper Ohio River Valley, excepting the large reported pNO3 deposition, in excess of 6 kg N/ha/yr 

where NADP has a substantial number of monitors but CASTNet does not; see the description of the 

Midwest NO3 bulge in Section 2.9.6.2. 

Source: NADP, USEPA, CAMD 7/19/07 

Figure 2-119. NO3

– concentration in NADP wet deposition samples, 2004–2006. 

 2-178 
Source: NADP, USEPA, CAMD 7/19/07 

Figure 2-120. Average NO3

− concentration in NADP wet deposition samples, 2004–2006. 

2.10.1.1. Example of NO2 and HNO3 Deposition and Flux Data from Harvard Forest 

Harvard Forest is a rural site in central Massachusetts, where ambient NOX, NOY, and other 

pollutant concentrations and fluxes of total NOY have been measured since 1990 (Munger et al., 1996). 

An intensive study in 2000 used a TDLAS to measure NO2 and HNO3. Absolute concentrations of 

HNO3 were measured; and the flux inferred was based on the dry deposition inferential method that uses 

momentum flux measurements to compute a Vd and derives an inferred flux (Hicks et al., 1987; Wesely 

and Hicks, 1977). Direct eddy covariance calculations for HNO3 were not possible because the 

atmospheric variations were attenuated by interaction with the inlet walls despite very short residence 

time and use of fluorinated silane coatings to make the inlet walls more hydrophobic. NO response was 

adequate to allow both concentration and eddy covariance flux determination. Simultaneously, NO and 

NOY eddy covariance fluxes were determined with two separate O3 CL detectors, one equipped with a H2–

gold catalyst at the inlet to convert all oxidized N compounds to NO. Additionally, the measurements 

include concentration gradients for NO, NO2, and O3 over several annual cycles to examine their vertical 

profiles in the forest canopy. 

Overall, the results showed typical NO2 concentrations of 1 ppb under clean-air conditions and 

mean concentrations up to 3 ppb at night and 1 ppb during daytime for polluted conditions. Net positive 

fluxes (emission) of NO2 were evident in the daytime and negative fluxes (deposition) were observed at 

night (Figure 2-121). NO fluxes were negative during the daytime and near zero at night. 

 2-179 
Source: Horii et al. (2004). Reprinted with permission. 

Figure 2-121. Diel cycles of median concentrations (upper panels) and fluxes (lower panels) for the 

northwest clean sector, left panels) and southwest (polluted sector, right panels) wind sectors 

at Harvard Forest, April-November, 2000, for NO, NO2, and O3/10. NO and O3 were sampled at 

a height of 29 m, and NO2 at 22 m. Vertical bars indicate 25th and 27th quartiles for NO and 

NO2 measurements. NO2 concentration and nighttime deposition are enhanced under 

southwesterly conditions, as are O3 and the morning NO maximum. 

 2-180 
Source: Horii (2002). 

Figure 2-122. Simple NOX photochemical canopy model outputs. Left panel, concentrations of NO (dashed) 

and NO2 (solid); right, fluxes of NO (dashed) and NO2 (solid). Symbols indicate measurement 

heights for NO (29 m) and NO2 (22 m) at Harvard Forest. The model solves the continuity 

equation for NO concentration at 200 levels, d/dz(-Kc[dNO/dz]) = PNO−LNO, where PNO = 

[NO]/t1, LNO = [NO]/t2, and zero net deposition or emission of NOX is allowed. NOX (NO + NO2) 

is normalized to 1 ppb. t1 = 70 s in this example. Due to the measurement height difference, 

observed upward NO2 flux due to photochemical cycling alone should be substantially larger 

than observed downward NO flux attributable to the same process. 

In part, the opposite NO and NO2 fluxes are simply consequences of variable NO-to-NO2 ratio 

distributions responding to vertical gradients in light intensity and O3 concentration, which resulted in no 

net flux of NOX (Gao et al., 1993). In the Harvard Forest situation, the NO and NO2 measurements were 

not at the same height above the canopy, and the resulting differences derive at least in part from the 

gradient in flux magnitude between the two inlets (Figure 2-122). 

 2-181 
Source: Horii et al. (2004). Reprinted with permission. 

Figure 2-123. Hourly (dots) and median nightly (pluses) NO2 flux vs. concentration, with results of least 

squares fit on the hourly data (curve). 

At night, when NO concentrations are near zero due to titration by ambient O3 there is not a flux of 

NO to offset NO2 fluxes. Nighttime data consistently show NO2 deposition (Figure 2-123), which 

increases with increasing NO2 concentrations. Concentrations above 10 ppb were rare at this site, but the 

few high NO2 observations suggest a nonlinear dependence on concentration. The data fit a model with 

Vd of −0.08 plus an enhancement term that was second order in NO2 concentration. The presence of a 

second order term implies that NO2 deposition rates to vegetation in polluted urban sites nearer to the 

prominent sources of NO2 could be considerably larger than what was observed at this rural site. 

After accounting for the time of the NO-NO2 null cycle during the measurement-sampling period, 

the net NOX flux can be derived. Overall, there was a net deposition of NOX during the night and 

essentially zero flux in the day, with large variability in the magnitude and sign of individual flux 

observations. For the periods with NO2 concentrations >2 ppb, deposition was always observed. These 

canopy-scale field observations are consistent with a finite compensation point for NO2 in the canopy that 

offsets foliar uptake or even reverses it when concentrations are especially low. At concentrations above 

the compensation point, NOX is absorbed by the canopy. Examination of concentration profiles 

corroborates the flux measurements (Figure 2-124). During daytime for low-NOX conditions, there was a 

local maximum in the concentration profile near the top of the canopy where O3 has a local minimum, 

which is consistent with foliar emission or light-dependent production of NOX in the upper canopy. 

Depletion was evident for both NOX and O3 near the forest floor. Air reaching the ground has passed 

 2-182 
through the canopy where uptake is efficient and the vertical exchange rates near the ground are slow. At 

night, the profiles generally decreased with decreasing height above the ground, showing only uptake. At 

higher concentrations, the daytime NOX concentrations were nearly constant through the canopy; no 

emission was evident from the sunlit leaves. 

 

Source: Horii et al. (2004). Reprinted with permission. 

Figure 2-124. Averaged profiles at Harvard Forest give some evidence of some NO2 input near the canopy 

top from light-mediated ambient reactions, or emission from open stomates. 

Figure 2-125 compares observed fluxes of all the observed species. The measured NOX and 

estimated PAN fluxes were small relative to the observed total NOY flux. In clean air, HNO3 accounted 

for nearly all the NOY flux and the sum of all measured species ws about equal to the NOY concentration. 

However, in polluted conditions, unmeasured species were up to 25% of the NOY, and HNO3 fluxes 

cannot account for all the total NOY flux observed. These unmeasured NOY species likely are 

hydroxyalkyl nitrates and similar compounds rapidly deposited to surfaces but not routinely measured; 

see the descriptions of measurement techniques and challenges in Section 2.3. The deposition of HNO3

and multifunctional RONO2 were the largest elements of the measured N dry deposition budget. 

 2-183 
Two key areas of remaining uncertainty were the production of HNO2 over vegetation and the role 

of very reactive biogenic VOCs. HNO2 is important because its photolysis is a source of OH radicals, and 

its formation may represent an unrecognized mechanism to regenerate photochemically active NOX from 

NO3 that had been considered terminally removed from the atmosphere; see the discussion in Section 2.3 

above on the atmospheric chemistry of NOX and the role of oxidized N compounds in atmospheric N 

transport. 

Source: Horii et al. (2006). 

Figure 2-125. Summer (June-August) 2000 median concentrations (upper panels), fractions of NOY (middle 

panels), and fluxes (lower panels) of NOY and component species separated by wind direction 

(northwest on the left and southwest on the right). Vertical lines in the flux panels show 25th 

and 75th quartiles of F(NOY) and F(HNO3); negative fluxes represent deposition; F(NOX) is 

derived from eddy covariance F(NO) and F(NO2) measurements (corrected for photochemical 

cycling), F(HNO2) is inferred, and F(NOY) was measured by eddy covariance. The sum of NOX, 

HNO3, and PAN accounts for all of the NOY concentration and flux for northwesterly 

(unpolluted background) flows, whereas up to 50% of NOY and F (NOY) under southwesterly 

flows are in the form of Nr species whose fluxes are not measured or estimated here. 

 2-184 
2.10.2. Sulfur 

For the most recent 3-year reporting period available (2004 to 2006), mean S deposition was 

greatest in the eastern U.S. east of the Mississippi River, with the highest deposition of 21.3 kg S/ha/yr in 

the Ohio River Valley; see Figure 2-126. Most recording stations throughout the Ohio River Valley report 

3-year total S deposition averages >10 kg S/ha/yr and many other stations in the East report deposition >5 

kg S/ha/yr. Data are sparse for the central and west U.S. However, where available, data indicate lower 

values than in most of the East, ranging from 4.1 to 5.3 kg S/ha/yr. Total S deposition in the U.S. west of 

the 100th meridian is lower, with all recording stations reporting <2 kg S/ha/yr and many reporting <1.0 

kg S/ha/yr. These values can also be compared to S deposition totals from 1989–1991 in Figure 2-126. 

Station-by-station comparisons between averaging periods are difficult because some stations do not have 

sufficient data to report a mean for the sampling period. There are, however, clear regional decreases in S 

deposition across the country. S deposition for 1989–1991 (the earliest 3-year reporting period available) 

is almost uniformly greater than for the most recent 3-year average (2004–2006). Deposition since 1989–

1991 has declined throughout the Ohio River basin from a previous high of 25.4 kg S/ha/yr, and in New 

England, and the Mid-Atlantic regions, consistent with the ~48% decrease in SO2 emissions nationwide 

between 1990 and 2005. Very little coverage for western and central U.S. was available for the 1989–

2001 reporting period, but sites with data show a similar decrease. Figure 2-127 shows that for both the 

1989–1991 and 2004–2006 reporting periods, S was primarily deposited as wet SO4

2−

 followed by a 

smaller proportion as dry SO2 and a much smaller proportion as dry SO4

2−

. 

 2-185 
Figure 2-126. Total average yearly wet and dry S deposition for 2004–2006 (top) and 1989–1991 (bottom). 

 2-186 
Figure 2-127. Total average yearly S deposition by species for 2004–2006 (top) and 1989–1991 (bottom). 

 2-187 
2.11. Summary 

2.11.1. Emissions and Atmospheric Concentrations 

Total anthropogenic NO and NO2 emissions in the U.S. in 2002 were 23.19 Tg. Combustion 

chemistry at EGUs contributed ~22% of this total, and transportation-related sources contributed ~56%. 

Ambient annual NOX concentrations have decreased ~35% in the period 1990–2005 to current annual 

average concentrations of ~15 ppb. 

Biogenic NOX sources are substantially smaller than anthropogenic ones and include biomass 

burning, lightning, and soils. The NO and N2O emitted from soils as intermediate products from 

denitrification can evolve either naturally or as stimulated by addition of N-containing fertilizers to crops 

and other soil management practices. N2O, another member of the oxides of N family of compounds, is 

also a contributor to total U.S. GHG emissions: ~6.5% on a Tg CO2e basis in 2005, and its U.S. emissions 

decreased ~3% in the period 1990–2005, though there remains considerable interannual variation in this 

value. 

Concentrations of NO2 in the CONUS from biogenic sources in the U.S., Canada, and Mexico and 

from all sources elsewhere in the world are defined as policy-relevant background concentrations. On an 

annual average basis, these concentrations are calculated to be <300 ppt over most of the CONUS and 

<100 ppt in the eastern U.S. where NO emissions are greatest. The 24-h ambient NO2 levels in CMSAs 

where most of the regulatory monitors are located and where most anthropogenic emissions originate 

were, on average, <20 ppb with a 99% percentile value <50 ppb for the years 2003–2005. Annual-average 

NO2 concentrations over the CONUS are calculated to be <5 ppb for nearly all urban and rural and remote 

sites. 

On a national scale, energy production at EGUs accounted for ~66% of total SO2 emissions in the 

U.S. in 2001–2002; ~5% of total SO2 is emitted by transportation-related sources, with on-road vehicles 

accounting for ~40% of the transportation fraction and off-road diesel and marine traffic together 

accounting for the remainder. As with NOX, emissions of SOX have been significantly reduced in recent 

years: ambient annual SOX concentrations have decreased ~50% in the period 1990–2005 and now stand 

at ~4 ppb for both aggregate annual and 24-h average concentrations nationwide. 

Annual-average policy-relevant background SO2 concentrations in the U.S. are <10 ppt over most 

of the CONUS, or <1% of observed SO2 concentrations everywhere except areas in the Pacific Northwest 

where geogenic SO2 sources are particularly strong. 

NH3 emissions are chiefly from livestock and from soils as stimulated by addition of N-containing 

fertilizers to crops and other soil management practices. Confined animal feeding operations and other 

intensified agricultural production methods over a period of many decades have resulted in greatly 

increased volumes of animal wastes high in N; 30 to 70% of these wastes may be emitted as NH3. These 

increases in NH3 emissions, and the consequent increases in ambient NH3 concentrations and NH4+ 

concentration and deposition, are highly correlated geospatially with the local and regional increases in 

agricultural intensity. However, estimates of total NH3 emissions on national and sub-national scales 

range widely owing to three complex issues: the high spatial and temporal variability in NH3 emissions; 

the high uncertainty in the magnitude of those emissions; and the lack of real-time, reliable, ambient NH3

monitoring techniques. Nonetheless, U.S. national NH3 emissions totals have been calculated, taking into 

account these three drivers of uncertainty; for 2001-2002 the national NH3 emissions total from the NEI 

and as corrected by methods described in Section 2.5 was 4.08 Tg/yr. 

 2-188 
2.11.2. Field Sampling and Analysis 

The coverage of the networks is very thin over large expanses of the interior U.S. and especially so 

west of the 100th meridian. This lack of monitored sites increases the likelihood that significant exposure 

from N and S deposition is now occurring at current atmospheric concentrations where no measurements 

are available, as predicted in numerical experiments with large-scale, first-principles models of 

atmospheric chemistry and physics and deposition, and as measured at some few special experimental 

sites. 

The instrumentation deployed at present in the routine monitoring networks for determination of 

gas-phase NO2 and SO2 concentrations is likely adequate for determining compliance with the current 

NAAQS. But in application for determining environmental effects, all these methods have important 

limitations, which make them inadequate for fully characterizing the state of the atmosphere at present, 

correctly representing the complex heterogeneity of N and S deposition across the landscape, and for 

realistically apportioning the contributions of reduced and oxidized forms of atmospheric N and S in 

driving observed biological effects at a national scale. 

For example, routine NO2 measurements by CL are contaminated by unknown and varying 

concentrations of higher-order oxidized N species, including gas-phase HNO3, important in itself for N 

deposition to the biosphere and also as a precursor to pNO3. Moreover, dry deposition of NO, NO2, and 

PANs is not at present estimated in dry deposition networks, but could account for as much as 30% of 

total dry oxidized N deposition in areas near strong NOX sources. This would include estuaries and other 

wetlands near large urban areas. 

As concerns SO2, the present-day ambient annual average SO2 concentrations are very near or even 

below the operating LOD of most of the FRM monitors in the largest regulatory network. This produces 

irresolvable uncertainty in these data, which may be important for environmental effects from S 

compounds since they result in some cases from exposure at these current low concentrations. 

Routine field sampling techniques for NH3 are at present limited to integrated values from several 

days to one week because higher frequency semi-continuous methods are not yet sufficiently robust to 

deploy for routine operation in national networks although passive NH3 samplers show excellent 

potential. Estimates for the contribution of NH3 to the total N deposition budget range as high as 30% of 

total N, and are perhaps the dominant source of reduced N. Moreover, routine national-scale sampling and 

analysis for particulate-phase NO3

−

, SO4

2–, and NH4+

 are subject to positive and negative errors, chiefly 

from the loss or production of constituent species on the surface of the filter used for the long timeintegrated measurement. 

The aggregate effect of these uncertainties and errors very likely is to underestimate total N and S 

atmospheric deposition and subsequent biological exposures. 

2.11.3. Nitrogen and Sulfur Deposition 

Increasing trends in urbanization, agricultural intensity, and industrial expansion during the 

previous 100 years have produced a nearly 10-fold increase in N deposited from the atmosphere. NOX, 

chiefly from fossil fuel combustion, often dominates total N pollution in the U.S. and comprises ~50 to 

75% of the total N atmospheric deposition. 

For the period 2004–2006, the routine monitoring networks report that the mean N deposition in 

the U.S. was greatest in the Ohio River Valley, specifically in Indiana and Ohio, with values as high as 9.2 

and 9.6 kg N/ha/yr, respectively. N deposition was lower in other parts of the east, including the southeast 

and northern New England. In the central U.S., Kansas and Oklahoma reported the highest deposition: 7.0 

and 6.5 kg N/ha/yr, respectively. 

 2-189 
Table 2-25. Regional changes in wet and dry N and S atmospheric concentrations and deposition, 1989-

1991 and 2003-2005. 

Measurement Unit Region 

Average

1989–

1991 

Average 

2003–2005 

Percent 

Change* 

Wet SO4

2− deposition kg/ha Mid-Atlantic 

Midwest 

Northeast 

Southeast 

27 

23 

23 

18 

20 

16 

14 

15 

-24 

-32 

-36 

-19 

Wet SO4

2− concentration mg/L Mid-Atlantic 

Midwest 

Northeast 

Southeast 

2.4 

2.3 

1.9 

1.3 

1.6 

1.6 

1.1 

1.1 

-33 

-30 

-40 

-21 

Ambient SO2 concentration μg/m3 Mid-Atlantic 

Midwest 

Northeast 

Southeast 

13 

10 

6.8 

5.2 

8.4 

5.8 

3.1 

3.4 

-34 

-44 

-54 

-35 

Ambient SO4

2− concentration μg/m3 Mid-Atlantic 

Midwest 

Northeast 

Southeast 

6.4 

5.6 

3.9 

5.4 

4.5 

3.8 

2.5 

4.1 

-30 

-33 

-36 

-24 

Wet inorganic N deposition kg/ha Mid-Atlantic 

Midwest 

Northeast 

Southeast 

5.9 

6.0 

5.3 

4.3 

5.5 

5.5 

4.1 

4.4 

-8 

-8 

-23 

+2 

Wet NO3 concentration mg/L Mid-Atlantic 

Midwest 

Northeast 

Southeast 

1.5 

1.4 

1.3 

0.8 

1.0 

1.2 

0.9 

0.7 

-29 

-14 

-33 

-9 

Ambient NO3

– concentration μg/m3 Mid-Atlantic 

Midwest 

Northeast 

Southeast 

0.9 

2.1 

0.4 

0.6 

1.0 

1.8 

0.5 

0.7 

+5 

-14 

+20 

+17 

Total ambient NO3

– concentration (NO3– + HNO3) μg/m3 Mid-Atlantic 

Midwest 

Northeast 

Southeast 

3.5 

4.0 

2.0 

2.2 

3.0 

3.5 

1.7 

2.1 

-14 

-12 

-13 

-5 

* Percent change is estimated from raw measurement data, not rounded; some of the measurement data used to calculate percentages may be at or below detection limits. 

Source: CASTNet and the National Atmospheric Deposition Program / National Trends Network (NADP/NTN) 

N deposition estimated from measurements primarily occurred in the form of wet NO3

–

 and NH4

+

followed with decreasing amounts of dry HNO3, dry NH4

+

, and dry NO3

–

. Although deposition in most 

areas of the U.S. occurred in wet form, there were some exceptions, including parts of California where N 

deposition was primarily dry. Data are very sparse for the central U.S. between the 100th meridian and the 

 2-190 
 2-191 

Mississippi River; but where available, N deposition values there were lower than in most of the eastern 

U.S., ranging from 4.1 to 5.3 kg N/ha/yr. 

Estimates of total N loadings to estuaries, or to other large-scale elements in the landscape, are 

computed using the measurements of wet and dry N deposition (as reported above) where these are 

available, and then can be interpolated with or without a set of air quality model predictions to determine 

the relative contribution from the atmosphere of various species of reduced and oxidized N. Measurement 

and modeling experiments like these have shown that atmospheric inputs of Nr directly to the surface of 

some coastal waters are essentially equal to or greater than those contained in riverine flow in the absence 

of deposition and may contribute from 20 to >50% of external N loadings to these systems. For example, 

atmospheric N inputs to the northeast Atlantic coast of the U.S., the southeast Atlantic coast of the U.S., 

and the eastern Gulf of Mexico have been estimated to be 11, 5.6, and 5.6 kg N/ha/yr, respectively. More 

specifically and at finer spatial scales, atmospheric N loads to great waters and estuaries in the U.S. have 

been estimated to range from 2 to 8% for Guadalupe Bay, TX, on the lowest end, up to ~72% for the St. 

Catherine-Sapelo estuary at the highest end (Castro et al., 2003). At Chesapeake Bay, atmospheric N is 

estimated to contribute up to 30% of total N and 14% of the NH4 loadings to the Bay. 

For the period 2004–2006, mean S deposition in the U.S. was greatest east of the Mississippi River 

with the highest deposition amount, 21.3 kg S/ha/yr, in the Ohio River Valley where most recording 

stations reported 3 year averages >10 kg S/ha/yr. Numerous other stations in the East reported S 

deposition >5 kg S/ha/yr. Total S deposition in the U.S. west of the 100th meridian was relatively low, 

with all recording stations reporting <2 kg S/ha/yr and many reporting <1 kg S/ha/yr. 

S was primarily deposited in the form of wet SO4

2−

 followed in decreasing order by a smaller 

proportion of dry SO2 and a much smaller proportion of deposition as dry SO4

2−

. However, these annual S 

data in the western U.S., like those for N deposition, are derived from measurements in networks with 

many fewer nodes in the West than in the East and so cannot represent all subregions in the West. 

Table 2-25 lists separate concentration and deposition totals for wet and dry N and S species in 4 

sub-regions of the U.S. as annual averages for the years 1989–1991 and 2003–2005 as a summary of the 

foregoing data. Note that the U.S. West region is not present in this table owing to the dearth of annualscale measured concentration and deposition data at a sufficient number of sites to compare to those in the 

eastern and Midwestern regions. Data on concentrations and deposition for individual years are available 

at some sites in the West, as described for those specific cases in Sections 2.9 and 2.10 above. In 

summary, measured N deposition in the West ranges from lows in the Pacific Northwest of between 1 and 

2 kg N/ha/yr (N) (http://nadp.sws.uiuc.edu) to highs in the Sierra Nevada and San Bernardino Mountains 

in California of 20 to 40 kg N/ha/yr (total N) (Fenn et al., 2002 and 2003; Bytnerowicz et al., 2002), and 

may be higher in particular locations for some sub-annual seasons. 

 
Chapter 3. Ecological and 

Other Welfare Effects 

This chapter is organized into four sections. The introduction (Section 3.1) frames the organizing 

principle of this chapter and several basic concepts of ecology. A discussion of acidification is presented 

in Section 3.2. N enrichment is discussed in Section 3.3. Lastly, other welfare effects are presented in 

Section 3.4, including interactions between S deposition and Hg methylation and direct gas-phase injury 

to vegetation. 

3.1. Introduction to Ecological Concepts 

3.1.1. Critical Loads as an Organizing Principle for Ecological Effects 

of Atmospheric Deposition 

This chapter uses the critical loads concept as an organizing principle. The components that are 

necessary to develop a critical load provide a conceptual framework for linking atmospheric pollutants to 

ecological endpoints that indicate impairment. The generally accepted definition of a critical load of 

atmospheric pollutant deposition emerged from a pair of international workshops held in the late 1980s . 

The workshop participants defined a critical load as: 

“A quantitative estimate of an exposure to one or more pollutants below 

which significant harmful effects on specified sensitive elements of the 

environment do not occur according to present knowledge.” 

The development of a quantitative critical load estimate requires a number of steps. An illustrative 

example of the eight general steps is shown in Table 3-1. A more detailed description of these steps is 

given in Annex D. 

This chapter presents information with a focus on the following questions: 

 What is the disturbance? 

 What receptors are affected? 

 What indicator organisms are (or previously were) present and observable? 

 What chemical indicators are changing and can be measured? 

 What atmospheric pollutant is driving the changes in the chemical indicators? 

It is important to recognize that there is no single “definitive” critical load for a natural resource. 

Critical loads estimates reflect the current state-of-knowledge and policy priorities. Changes in scientific 

understanding may include, for example, new dose-response relationships; better resource maps and 

inventories; larger survey datasets; continuing time-series monitoring; and improved numerical models. 

Changes in the policy elements may include: new mandates for resource protection; focus on new 

pollutants; and inclusion of perceived new threats that may exacerbate the pollutant effects (e.g., climate 

change). 

 3-1
Table 3-1. An example of the matrix of information that must be considered in the definition and 

calculation of critical loads (see discussion in text). Note that multiple alternative biological 

indicators, critical biological responses, chemical indicators, and critical chemical limits 

could be used. 

This procedure can result in calculation of multiple critical loads for a given pollutant at a single 

location. The multiple solutions derive from the nested sequence of disturbances, receptors, and biological 

indicators that must be considered for a given pollutant. Multiple critical load values may also arise from 

an inability to agree on a single definition of “significant harm.” Calculation of critical loads for multiple 

definitions of “harm” may be deemed useful in subsequent discussions of the analysis and in the decisionmaking steps that may follow critical load calculation. 

Finally, there is the inescapable heterogeneity of all natural environments. Consider soils, for 

instance. The high spatial variability of soils almost guarantees that for any reasonably sized soil-based 

“receptor” that might be defined in a critical load analysis, there will be a continuum of critical load 

values for any indicator chosen. The range of this continuum of values may be narrow enough to be 

ignored; nevertheless, there is an a priori expectation in any critical load analysis that multiple values (or 

a range of values) will result from the analysis. Given the heterogeneity of ecosystems affected by N and 

S deposition, examples of published critical load values for a variety of endpoints and locations in the 

U.S. are presented here (see Section 3.3.7). 

3.1.2. Ecosystem Scale, Function, and Structure 

Information presented in this ISA was collected at multiple scales, ranging from the physiology of 

a given species to population, community, and ecosystem-level investigations. For this assessment, 

“ecosystem” is defined as a functional entity consisting of interacting groups of living organisms and their 

 3-2
abiotic (chemical and physical) environment. Ecosystems cover a hierarchy of spatial scales and can 

comprise the entire globe, biomes at the continental scale, or small, well-circumscribed systems such as a 

small pond. 

Ecosystems have both structure and function. Structure may refer to a variety of measurements 

including the species richness, abundance, community composition and biodiversity as well as landscape 

attributes. Competition among and within species and their tolerance to environmental stresses are key 

elements of survivorship. When environmental conditions shift, for example, by the presence of 

anthropogenic air pollution, these competitive relationships may change and tolerance to stress may be 

exceeded. Function refers to the suite of processes and interactions among the ecosystem components and 

their environment that involve nutrient and energy flow as well as other attributes including water 

dynamics and the flux of trace gases. Plant processes including photosynthesis, nutrient uptake, 

respiration, and C allocation, are directly related to functions of energy flow and nutrient cycling. The 

energy accumulated and stored by vegetation (via photosynthetic C capture) is available to other 

organisms. Energy moves from one organism to another through food webs, until it is ultimately released 

as heat. Nutrients and water can be recycled. Air pollution alters the function of ecosystems when 

elemental cycles or the energy flow is altered. This alteration can also be manifested in changes in the 

biotic composition of ecosystems. 

3.1.3. Ecosystem Services 

Ecosystem structure and function may be translated into ecosystem services. Ecosystem services 

identify the varied and numerous ways that ecosystems are important to human welfare. Ecosystems 

provide many goods and services that are of vital importance for the functioning of the biosphere and 

provide the basis for the delivery of tangible benefits to human society. Hassan et al. (2005) define these 

to include supporting, provisioning, regulating, and cultural services: 

 Supporting services are necessary for the production of all other ecosystem services. Some 

examples include biomass production, production of atmospheric O2, soil formation and 

retention, nutrient cycling, water cycling, and provisioning of habitat. Biodiversity is a supporting 

service that is increasingly recognized to sustain many of the goods and services that humans 

enjoy from ecosystems. These provide a basis for three higher-level categories of services. 

 Provisioning services, such as products (Gitay et al., 2001), i.e., food (including game, roots, 

seeds, nuts and other fruit, spices, fodder), fiber (including wood, textiles), and medicinal and 

cosmetic products (including aromatic plants, pigments). 

 Regulating services that are of paramount importance for human society such as (a) C 

sequestration, (b) climate and water regulation, (c) protection from natural hazards such as floods, 

avalanches, or rock-fall, (d) water and air purification, and (e) disease and pest regulation. 

 Cultural services that satisfy human spiritual and aesthetic appreciation of ecosystems and their 

components. 

3.2. Ecological Effects of Acidification 

This section describes the major effects of acidification on terrestrial and aquatic ecosystems 

resulting primarily from acidifying deposition. In this document, acidifying deposition includes gases and 

particles derived from SOX, NOX, and NHX. 

 3-3
3.2.1. Effects on Major Biogeochemical Processes 

Acidifying deposition has altered major biogeochemical processes in the U.S. by increasing the S 

(Figure 3-1) and N content of soils, accelerating SO4

2−

 and NO3

−

 leaching from soil to drainage water, 

depleting base cations (especially Ca and Mg) from soils, and increasing the mobility of Al. The extent of 

soil acidification is a critical factor that regulates virtually all acidification-related ecosystem effects from 

S and N deposition. Soil acidification occurs in response to both natural factors and acidifying deposition. 

To best integrate the effects of acidifying deposition, this assessment starts with a description of the 

effects on soils and major biogeochemical processes within ecosystems, then summarizes the chemical 

and biological effects on terrestrial, transitional, and aquatic ecosystems. More detailed information on 

acidification effects is provided in Annex B. 

Figure 3-1. Illustration of major fluxes of ions associated with S-driven acidification of drainage water. 

The upper diagram represents ion fluxes in the absence of S deposition. The two lower 

diagrams illustrate changes to these fluxes in response to S deposition on two types of soils, 

without (left) and with (right) substantial S adsorption on soil. Effects are most pronounced 

under high deposition with little adsorption, and include increased leaching of base cations 

(contributes to soil acidification), H (reduced pH), and Ali (toxic to many plant roots and 

aquatic species). Larger fluxes are represented by thicker arrows. 

 3-4
3.2.1.1. Soil Acidification 

Soil acidification is the loss of base cations plus the accumulation of acidic cations such as 

hydrogen ions (H+

) and aluminum ions (Aln+) in the soil; this happens when a proton donor is added to the 

soil. Soil acidification is a natural process. Such natural acidity is contributed by carbonic acid, organic 

acids, and plant cation uptake (Charles, 1991; Turner et al., 1991). However, the donor can also be a 

mineral acid, such as HNO3 and H2SO4, the common components of acid rain that result from NOX and 

SOX air pollution. Decreases in soil pH attributable to acidifying deposition have been documented in the 

U.S. (Bailey et al., 2005; Johnson et al., 1994a, b; Sullivan et al., 2006a). Effects in the eastern U.S. 

appear to have been limited mainly to the Northeast and portions of the Appalachian Mountains in both 

hardwood and coniferous forests. Soil acidification has also likely occurred in localized areas of mixed 

conifer forest and chaparral vegetation in, and near, the Los Angeles Basin, in response to locally high 

levels of atmospheric dry N deposition (Fenn et al., 2003a). 

To evaluate soil acidification, the soil must be considered in terms of the surface organic layer (the 

primary rooting zone), of which the Oa horizon (or in some studies the O horizon, which combines the Oe 

and Oa horizons) is an important component (See Figure 3-2). In addition, the mineral soil including the 

A and/or B horizon, which lie below the Oa horizon and are primarily comprised of mineral matter, must 

be considered. 

Acidifying deposition can have a direct effect on soil pH. However, net uptake of nutrient cations 

by vegetation can also generate acidity within the soil, and a considerable amount of natural organic 

acidity is produced in the Oa horizon through the partial decomposition of organic matter. This process 

can decrease the pH of soil water in the Oa horizon well below the lowest pH values measured in 

acidifying deposition (Krug et al., 1985; Lawrence et al., 1995). Oa-horizon soils under coniferous 

vegetation are strongly acidified by organic acids and are unlikely to have experienced a lowering of pH 

as a result of acidifying deposition (Johnson and Fernandez, 1992; Lawrence et al., 1995). Soils 

influenced by the growth of hardwood species tend to have surface horizons that are less acidic naturally 

and are, therefore, more susceptible to decreased pH in the Oa horizon from acidifying deposition. By 

taking up larger amounts of Ca from the soil, hardwoods can acidify lower soil horizons more than 

conifers even though they enrich surface horizons with Ca via litterfall (Alban, 1982). 

 3-5
Source: http://soils.usda.gov/education/resources/ 

Figure 3-2. Diagram illustrates soil horizons commonly found. Each main horizon is denoted by a capital 

letter O) Organic matter: Litter layer of plant residues in relatively undecomposed form. A) 

Surface soil: Layer of mineral soil with most organic matter accumulation and soil life. This 

layer is depleted of iron, clay, aluminum, organic compounds, and other soluble constituents. 

B) Subsoil: This layer accumulates iron, clay, aluminum and organic compounds. 

C) Substratum: Layer of unconsolidated soil parent material. This layer may accumulate the 

more soluble compounds that bypass the “B” horizon. 

Several studies document declines in soil pH within the Oa/A horizons and the upper B horizon in 

sensitive regions of the U.S. over the past several decades (Bailey et al., 2005; Drohan and Sharpe, 1997; 

Johnson et al., 1994a, b; Warby et al. (2009). These declines have been attributed at least partly to 

acidifying deposition (Bailey et al., 2005). 

In summary, soil acidification is a natural process, which is often exacerbated by acidifying 

deposition. Natural acidification is particularly pronounced in coniferous forests. Acidifying deposition 

can contribute to soil acidification, with consequent effects on the availability of nutrient cations in soil 

and the chemistry of drainage water that flows from soil into streams and lakes. Despite recent decreases 

in acidifying deposition and some improvement in surface water acid-base status, there are widespread 

observations of ongoing soil acidification such as decreases in soil exchangeable base cations (Bailey 

et al., 2005; Sullivan et al., 2006a, b; Warby et al., 2009) (See Section 3.2.1.4). More detailed descriptions 

of soil acidification are provided by Binkley and Richter (1987) and van Breemen et al. (1983). 

 3-6
3.2.1.2. Sulfur Accumulation and SO4

2− Leaching 

Most acidification-related consequences of atmospheric S and N deposition in the U.S. are caused 

by SO4

2−

 (Driscoll et al., 2001b; Sullivan, 2000). The mobility within the watershed of SO4

2−

, derived 

from atmospheric S deposition, is a key factor governing many aspects of soil and water acidification at 

locations in the U.S. that are affected by acidifying deposition. 

Upon deposition to the Earth’s surface, S may be assimilated by vegetation or microbes, 

accumulate in the soil or act as a mobile ion and leach out of the soil. When a given area is affected by 

acidifying deposition, S deposition levels are typically much higher than plant demand for S and 

consequently almost all deposited S is transported to the soil where it may accumulate and is available for 

leaching as SO4

2−

. SO4

2−

 acts as a mobile anion at many locations in the U.S. that receive high levels of S 

deposition, notably the glaciated Northeast and Upper Midwest, where much of the deposited S leaches 

through soils into streams and lakes. SO4

2−

 leaching leads to most of the ecological effects from 

atmospheric S deposition because it is accompanied by leaching of cations, and this contributes to 

acidification of soil, soil water, and surface water. 

Over time, sustained SO4

2−

 leaching and associated soil acidification contributes to pronounced 

changes in soils in some areas. When S is transported from soils to surface waters in the form of SO4

2−

, an 

equivalent amount of cations, or countercharge, is also transported. When the countercharge is provided 

by base cations, the base saturation of the soil is reduced as the acidity of the soil water is neutralized. 

However, this process acidifies the soil, thereby decreasing the soil’s capacity to neutralize additional 

acidity deposited from the atmosphere and prevent acidification of soil water, and by connection, surface 

water. As the base cations become depleted, the countercharge provided by acidic cations (H+

 and 

inorganic Al) increases, sometimes resulting in toxic conditions for plant roots and aquatic organisms 

(Charles, 1991; Turner et al., 1991). 

In the U.S., there are some regional trends of soil accumulation, retention, and leaching of S that 

are discussed below. 

Southeast 

Accumulation of atmospherically deposited S in soil has resulted from anion adsorption and 

incorporation of S into organic matter through biological assimilation. Such retention of S can 

temporarily reduce SO4

2−

 leaching and cause a delay in ecosystem recovery in response to decreases in S 

deposition, as some accumulated S is slowly released from the soil into drainage water. S adsorption on 

soil is especially pronounced in the southeastern U.S. Under continued loading of S deposition, it is 

expected that many southeastern watersheds will exhibit a gradual decrease in the extent of S adsorption 

in the future. This will likely contribute to further acidification of some streams, even under substantially 

reduced levels of S deposition (Elwood et al., 1991; Sullivan et al., 2004; Turner et al., 1991). 

Northeast 

In the Northeast, the accumulation of a portion of the historic legacy of atmospheric S deposition in 

soil was demonstrated by a positive relationship between wet deposition of SO4

2−

 and concentrations of 

total S in the forest floor of 12 red spruce stands (Driscoll et al., 2001a). However, net loss of S from soils 

now appears to be occurring in a number of northeastern watersheds in response to decreased levels of 

atmospheric S deposition. The potential for net mineralization of stored S might affect recovery of 

drainage waters (Gbondo-Tugbawa et al., 2002; Likens et al., 2002; Novák et al., 2007). Where leaching 

of previously stored S occurs, it delays soil and surface water chemical recovery from acidification 

(Driscoll et al., 2001b). 

Weathering contributes substantial S in some watersheds (Shanley et al., 2005). Uncertainties in 

estimates of ecosystem S fluxes, such as weathering and dry deposition, and the difficulty in discerning 

the effects of net SO4

2−

 desorption and net S mineralization make it difficult to predict when S outputs in 

 3-7
the northeastern U.S. will no longer exceed inputs. Recent research results, based on experimental 

reduction of S inputs, suggest that this process will occur on a decadal timescale (Martinson et al., 2005; 

Mörth et al., 2005). The long-term role of C-bonded S adds further uncertainty because enhancement of S 

mineralization by a warming climate could also affect S retention and release from soil (Driscoll et al., 

2001a; Knights et al., 2000), 

In summary, atmospheric S deposition alters soil chemistry through the following 

mechanisms: sustained SO4

2−

 leaching and associated changes in soil chemistry, and accumulation of S in 

the soil through physical/chemical adsorption and biological assimilation. The recent evidence of net loss 

of S from soils at a number of sites in the Northeast is a likely response to recent decreases in atmospheric 

S inputs (Driscoll et al., 2001b). The gradual loss of previously accumulated S is further contributing to 

continued SO4

2−

 leaching and soil acidification. 

3.2.1.3. Nitrogen Accumulation and NO3

– Leaching 

The scope of this section is the role of N deposition in the process of acidification. This assessment 

divides the effects of N deposition into the two broad categories of acidification and N nutrient 

enrichment effects. The latter is discussed in Section 3.3. N deposition may cause acidification of 

ecosystems via three main mechanisms: excess accumulation in soils followed by increased rates of 

nitrification by microbes; change in base cation status of soils caused by NO3

−

 leaching; and increased 

growth of vegetation causing increased cation uptake. 

Nitrification and Accumulation 

Atmospherically deposited N accumulates in soil through incorporation of N into organic matter. 

Accumulation is either documented or suggested to occur across large areas of the U.S. (Aber et al., 

2003). Direct evidence for such accumulation has been found in the northeastern U.S. and in Colorado. 

Increased accumulation of N in soil is suggested, for example, by a positive correlation between 

atmospheric deposition levels and total N concentration in the Oa soil horizon at red spruce sites in New 

York, Vermont, New Hampshire, and Maine (Driscoll et al., 2001b). Mass balance studies also show soil 

N retention (e.g., Campbell et al., 2004). Further evidence that atmospheric deposition has increased the 

availability of N in soil is provided by the strong negative correlation between atmospheric N deposition 

and the C:N ratio of the Oa soil horizon across the northeastern U.S. (Aber et al., 2003). 

The nitrification process is mediated by autotrophic bacteria that derive energy by oxidizing NH4

+

to NO3

−

. Nitrification produces acidity in the form of HNO3 as a byproduct. The HNO3 produced 

contributes to the acidification of soils and surface waters. If the C:N ratio of soils falls below about 20 to 

25, nitrification is stimulated and net nitrification and associated production of acidity occurs in soils 

(Aber et al., 2003; Emmett et al., 1998). This process often results in elevated NO3

−

 concentration in soil 

waters and surface waters (Aber et al., 2003; Ross et al., 2004). Thus, data collected from streams and 

lakes can yield important information about processes that occur in the soil. N saturation refers to the 

condition when N inputs from atmospheric deposition and other sources exceed the biological 

requirements of the ecosystem. Excess N supply reduces competition between plants and heterotrophic 

microbes for NH4

+

 to the point that net nitrification occurs (Aber et al., 1998, 2003). 

Leaching 

In many upland forested areas in the U.S., a large fraction of the N received in atmospheric 

deposition is retained in soil or in plant biomass. Nevertheless, elevated NO3

−

 concentration in surface 

waters during the growing season is common and widespread in the U.S. (Charles, 1991). High 

concentrations of NO3

−

 in lakes and streams, indicative of ecosystem N saturation in most natural 

systems, have been found at a variety of locations throughout the U.S. (Stoddard, 1994; U.S. EPA, 2004). 

In general, atmospheric deposition of 8 to 10 kg N/ha/yr or more, results in NO3

−

 leaching to surface 

 3-8
waters in the eastern U.S. Lower N deposition levels (<5 kg N/ha/yr) may lead to NO3

−

 leaching in the 

mountainous West because of colder temperatures, shorter growing season, little soil development, 

extensive exposed bedrock, and rapid melting of large snowpacks (Baron et al., 1994; Williams et al., 

1996a). 

NO3

−

 leaching usually contributes to the leaching of base cations from soils to surface waters. 

Although concentrations of NO3

−

 are typically less than SO4

2−

 in drainage waters in most ecosystems in 

the U.S., concentrations of NO3

−

 in some streams are high enough to suggest a substantial role for NO3

−

 in 

base cation loss from soil, particularly during periods of high soil-water NO3

−

 flux during the nongrowing season (Cook et al., 1994; Van Miegroet et al., 1992). 

The relationship between atmospheric N deposition and NO3

−

 leaching from forest ecosystems is 

often modified by land-use history, current land-use, land disturbance, tropospheric O3 levels, and 

climate. The N retention capacity of soils is highly dependent on land-use history and its effects on N 

cycling and pool sizes. For example, the removal of trees reduces the amount of N in the watershed and 

enhances the demand of vegetative regrowth for added N. This effect results in little or no NO3

−

 leaching 

and can last for decades to more than a century (Goodale et al., 2000). NO3

−

 leaching is also affected by 

current land use (U.S. EPA, 2006e). In the northeastern U.S., concentrations of N in streams of upland 

forested watersheds tend to be considerably lower than in streams draining watersheds with other land 

uses (Aber and Driscoll, 1997; Aber et al., 2003). Perhaps the most noteworthy effect of urban land use on 

processes of nutrient enrichment from N deposition concerns the transport of NO3

−

 to N-limited estuarine 

and near-coastal waters. This topic is discussed in Section 3.3.2.4. In agricultural, and especially in 

forested areas, it is generally expected that most atmospherically deposited N is taken up by terrestrial 

vegetation. This is usually not the case in urban landscapes, although it is sometimes possible. Due to the 

relatively large impervious surface area in the urban landscape (e.g., buildings, roads, parking lots), a 

higher percentage of precipitation is routed directly to surface waters, with less opportunity for vegetative 

uptake of deposited N. 

Climatic factors also play an important role in determining the extent of NO3

−

 leaching. In 

particular, temperature and moisture have large effects on N cycling and NO3

−

 leaching. Murdoch et al. 

(1998) found that, for at least one site, annual mean NO3

−

 concentrations in stream water were not related 

to annual wet N deposition, but rather, were positively correlated with mean annual air temperature. This 

pattern was likely due partly to the fact that microbial processes responsible for NO3

−

 production are very 

sensitive to temperature. Fluctuations in microbial immobilization and mineralization in response to 

climatic variability affect NO3

−

 losses to drainage waters. 

Long-term data sets also suggest that climate may affect patterns of NO3

−

 loss. Many of the original 

(sampled periodically since the early 1980s) long-term monitoring lakes in the Adirondack Mountains 

showed increased NO3

−

 leaching from terrestrial ecosystems throughout the 1980s, which was followed 

by a decline during the 1990s (Driscoll et al., 2003b, d). Decreasing stream NO3

−

 concentrations during 

the 1990s were also observed in the Catskill Mountains and in New Hampshire (Driscoll et al., 2003b). 

There was not a substantial change in N emissions or deposition in the Northeast region over that period. 

Climatic factors, increases in atmospheric CO2, and interactions with increasing availability of DOC have 

been proposed as possible contributing factors for regional decreases in NO3

−

 in drainage water during the 

1990s, but the driver of this decadal scale pattern remains under investigation. Snowmelt and rain-onsnow, along with soil freezing, can influence N cycling in cold climates (Campbell et al., 2005; Eimers 

et al., 2007; Park et al., 2003). 

3.2.1.4. Base-Cation Leaching 

Acidifying deposition has been shown to be an important factor causing decreases in 

concentrations of exchangeable base cations in soil. Loss of base cations from soil is a natural process. 

Under conditions of low atmospheric deposition of S and N the limited mobility of anions associated with 

naturally derived acidity (organic acids and carbonic acid) controls the rate of base cation leaching. Inputs 

 3-9
of S and N in acidifying deposition enhance inputs of strong acid anions that can accelerate natural rates 

of base-cation leaching (Cronan et al., 1978; Lawrence et al., 1999). 

Leaching of base cations from watershed soils to surface waters is a mechanism that depletes 

essential plant nutrients from soil and limits the extent of surface water acidification in response to 

acidifying deposition. When SO4

2−

 and NO3

−

 leaching occur in equal magnitude to base cation leaching, 

the drainage water is not acidified. However, in the process of neutralizing the acidity of drainage water, 

base cation release from soil causes depletion of the base saturation of the soil. Soil base saturation 

expresses the concentration of exchangeable bases (Ca, Mg, potassium [K], sodium [Na]) as a percent of 

the total cation exchange capacity (which includes exchangeable H+

 and inorganic Al). Under conditions 

of low soil base saturation (<20%) and elevated concentrations of strong acid anions, Al is mobilized 

from soil to drainage water (Cronan and Schofield, 1990), with potentially harmful consequences for 

sensitive terrestrial plants and aquatic organisms throughout the food web. 

In the 1990s, data were published supporting the occurrence of base cation depletion from soils in 

the U.S. (Lawrence and Huntington, 1999; Lawrence et al., 1995; 1997), although decreases in 

exchangeable Ca concentrations had earlier been identified in European soils through repeated sampling. 

Recent data revealed that decreases in concentrations of exchangeable base cations and base saturation in 

the Oa and B soil horizons have occurred over the past several decades in the eastern U.S. and most 

studies attribute this change to the effects of acidifying deposition. For example, a soil re-sampling study 

in the U.S. was conducted in northwestern Pennsylvania by Bailey et al. (2005). This study showed that 

between 1967 and 1997 pronounced decreases, attributed largely to acidifying deposition, were measured 

in exchangeable Ca and Mg concentrations in Oa/A horizons and throughout the B horizon (See also 

comments by Johnson [2005]). Data compiled by Sullivan et al. (2006a, b) suggested decreases in base 

saturation of B-horizon soils in the Adirondack Mountains between the mid-1980s and 2003. In another 

re-sampling study in the northeastern U.S. Warby et al. (2009) observed large decreases in exchangeable 

Ca and base saturation in the Oa horizons between 1984 and 2001, attributed to acidifying deposition. The 

greatest decreases in exchangeable Ca and base saturation were observed in the regions of central New 

England and Maine, where depletion of base cations was less apparent in 1984. Depletion of base cations 

contributes to soil acidification and influences the ability of watershed soils to support acid-sensitive 

vegetation and to neutralize acidity in future acidifying deposition. Both plant uptake of cations, for 

example via forest regrowth subsequent to logging or land use conversion, and acidifying deposition can 

acidify soils (Johnson and Todd, 1990; Richter and Markewitz, 2001; Trettin et al., 1999). 

Upslope decreases in exchangeable soil base cation concentrations were found to be positively 

correlated with higher S deposition in the Catskill Mountains (Lawrence et al., 1999). Furthermore, 

declines in soil exchangeable pools of base cations have been documented in New Hampshire (Likens 

et al., 1996) and Norway (Kirchner and Lydersen, 1995). In summary, leaching of base cations associated 

with acidifying deposition is occurring in sensitive regions in the U.S. Base cation loss increases the 

sensitivity of the watershed to further acidifying deposition. Watersheds that were capable of fully 

neutralizing a particular level of acidifying deposition in the past may no longer be capable of fully 

neutralizing that level today or at some time in the future because of the cumulative effect of acidifying 

deposition on soil base saturation. Where the availability of exchangeable base cations is limited, the 

leaching of potentially toxic inorganic Al into soil and surface waters can result. 

3.2.1.5. Aluminum Leaching 

If soil base saturation is 20 to 25%, or lower, acidifying deposition can mobilize inorganic Al, 

which can lead to the leaching of this potentially toxic form of Al into soil waters and surface waters 

(Cronan and Schofield, 1990; Reuss and Johnson, 1985). This is an extremely important effect of 

acidifying deposition because some forms of inorganic monomeric Al, including Al3+ and various 

hydroxide species, are toxic to tree roots, fish, algae, and aquatic invertebrates (see Section 3.2.3). In fact, 

fish mortality in response to surface water acidification is usually attributable to Al toxicity. Increased 

 3-10
concentrations of exchangeable inorganic Al in the mineral soil have been identified through repeated 

sampling in the U.S. and Europe over periods ranging from 17 years to 41 years in studies by Billet et al. 

(1990), Falkengren-Grerup and Eriksson (1990), Bailey et al. (2005), and Lawrence et al. (1995). In areas 

of Europe with excessively high acidic deposition levels, evidence of Al depletion in the mineral soil has 

also been found (Lapenis et al., 2004; Mulder et al., 1989), but Al depletion has not been documented in 

the U.S. 

Acidifying deposition is an important cause of increased mobilization of inorganic Al from soils to 

streams and lakes (Turner et al., 1991). Acidifying deposition introduces mineral acidity associated with 

anions that are more mobile than those from organic matter. If the release of base cations from the soil is 

insufficient to neutralize the inputs of sulfuric and nitric acid, then Al that had previously been deposited 

by normal soil development in the upper mineral soil is mobilized. Al may also be mobilized by organic 

acids. However, acidifying deposition mobilizes Al in inorganic forms, and in doing so increases the 

amount of exchangeable inorganic Al within the B horizon and results in transport of inorganic Al into 

soil waters and surface waters (Driscoll and Bisogni, 1984; Driscoll et al., 1985). Inorganic Al is 

minimally soluble at pH about 6.0, but solubility increases steeply at pH values below about 5.5. This 

distinction between organic and inorganic forms of Al is important because organic Al is not toxic, 

whereas inorganic Al is toxic to a variety of plants and aquatic organisms (Baker and Schofield, 1982; 

Baldigo and Murdoch, 1997; Joslin and Wolfe, 1988, 1989)(Section 3.2.3.1). Discussions in this 

document of inorganic Al in solution refer to dissolved, rather than particulate or colloidal, forms. These 

dissolved inorganic Al species are often collectively called inorganic monomeric Al (Driscoll, 1984). 

Recovery of soil chemistry will require a decrease in exchangeable Al concentrations and Al 

leaching. Once acidified, it is unlikely that soil exchangeable Al concentrations will decline again unless 

soils are limed. Therefore, it is unclear what length of time would be required to decrease soil 

exchangeable Al concentrations to levels characteristic of unpolluted systems. Furthermore, in most cases 

it is unclear whether exchangeable Al concentrations are continuing to increase, remaining stable, or 

decreasing. Predictions of trends in exchangeable Al concentrations remain uncertain because of our 

incomplete understanding of mechanisms through which mineral matter and organic matter interact to 

control dissolved Al concentrations. Possible changes in the dynamics of soil organic matter that could be 

expected from climate change add further uncertainty to predictions of future change in exchangeable Al 

concentrations in soils. 

In summary, the natural downward movement and deposition of Al within the upper soil profile is 

altered by acidifying deposition if the release of base cations is insufficient to buffer atmospheric inputs 

of acidity. Rather than be deposited as an alumino-organic complex, Al mobilized by acidifying 

deposition tends to remain in solution in inorganic forms that can be transported out of the soil and into 

surface waters. Depletion of exchangeable base cations generally precedes the mobilization of inorganic 

Al; therefore, as base cation concentrations in drainage water decrease, inorganic Al concentrations may 

increase. Increases in concentrations of inorganic Al have been documented at several locations in basecation depleted soils in the U.S. and Europe. In soils with base saturation values less than about 15 to 

20%, the ratio of exchangeable Ca to Al is typically low in upper mineral soils (Lawrence et al., 2005). 

3.2.1.6. Episodic Acidification 

The status of surface water chemistry can be examined and reported as chronic condition or 

episodic condition. Chronic condition refers to annual average conditions, which are often represented as 

summer and fall chemistry for lakes and as spring baseflow chemistry for streams. Episodic condition 

refers to conditions during rainstorms or snowmelt when proportionately more drainage water is routed 

through upper soil horizons, which tend to provide less neutralization of atmospheric acidity as compared 

with deeper soil horizons. Surface water chemistry exhibits lower pH and acid neutralizing capacity 

(ANC) during episodes than during baseflow conditions. 

 3-11
One of the most significant effects of acidifying deposition on surface water chemistry is the shortterm change in chemistry termed “episodic acidification.” While natural processes contribute to seasonal 

and short-term increases in the acidity of surface waters, research from several regions in the U.S. 

indicates that acidifying deposition likely has substantially increased the magnitude, frequency, and 

biological effects of episodic acidification events. Many streams that exhibit chemical conditions during 

base flow (relatively stable flows that occur between storms) that is suitable for aquatic biota, are subject 

to occasional episodic acidification with adverse consequences. During such episodes, both stream flow 

and water chemistry can change markedly (Figure 3-3). Episodic acidification can cause declines in pH 

and ANC, and most significantly, increases in inorganic Al concentrations in stream waters of the 

Northeast, Pennsylvania, and in the central Appalachian Mountain region (Charles, 1991). Episodic 

decreases in pH and ANC have been documented throughout the country (Wigington et al., 1990). 

Episodes are generally accompanied by changes in at least two or more of the following chemical 

parameters: ANC, pH, base cations, SO4

2−

, NO3

−

, Aln+, organic acid anions, and DOC (Sullivan, 2000). 

The U.S. EPA’s Episodic Response Project (ERP) confirmed the chemical and biological effects of 

episodic pH depressions in lakes and streams in parts of the U.S. (Wigington et al., 1993). The ERP 

illustrated that episodic processes are mostly natural, that SO4

2−

 and especially NO3

−

 attributable to 

atmospheric deposition play important roles in the episodic acidification of some surface waters, and that 

the chemical response that has the greatest effect on biota is increased Ali concentration. Similar findings 

had been reported elsewhere, especially in Europe, but the ERP helped to clarify the extent, causes, and 

magnitude of episodic acidification in portions of the U.S. (Sullivan, 2000a). 

Aquatic biota vary greatly in their sensitivity to episodic decreases in pH and increases in inorganic 

Al in waters having low Ca concentration. Baker et al. (1990b) concluded that episodes are most likely to 

affect biota if the episode occurs in waters with pre-episode pH above 5.5 and minimum pH during the 

episode of less than 5.0. The most thorough characterization of episodic variation in stream chemistry in 

the U.S. was conducted through the ERP, in which 13 low-order streams (watershed areas less than 

24 km2

) in the Adirondack and Catskill regions of New York and the Appalachian Plateau in Pennsylvania 

were monitored from 1988 to 1990 (Wigington et al., 1996). About 10% of the acid episodes involved 

decreases in ANC of up to 200 μeq/L, decreases in pH of up to one unit, and increases in concentrations 

of inorganic Al of up to 15 μM (Wigington et al., 1996). Results showed that acid episodes reduced the 

size of fish populations and eliminated acid-sensitive species if median high-flow pH was less than 5.2 

and inorganic Al concentration exceeded 3.7 μM, despite the relatively short duration of episodes (Baker 

et al., 1996). 

Results from the ERP demonstrated that episodic acidification can have long-term adverse effects 

on fish populations. Streams with suitable chemistry during low flow, but low pH and high inorganic Al 

levels during high flow, had substantially lower numbers and biomass of brook trout than were found in 

non-acidic streams (Wigington et al., 1996). 

In many regions, the most severe acidification of surface waters generally occurs during spring 

snowmelt (Charles, 1991). Stoddard et al. (2003) found that, on average, the difference between spring 

and summer ANC during baseflow in New England, the Adirondacks, and the Northern Appalachian 

Plateau was about 30 μeq/L during the period 1990 to 2000 (see Figure 3-4). This implies that lakes and 

streams in these regions would need to recover to chronic ANC values above 30 μeq/L before they could, 

on average, be expected to not experience acidic episodes (Stoddard et al., 2003). However, the estimate 

of 30 μeq/L is certain to be low because the comparison was made with non-episodic sampling in spring, 

expressed as average spring ANC. ANC measured during episodic spring events would be expected to be 

lower than average ANC during spring. 

 3-12
Source: Driscoll et al. (2001b). Reprinted with permission. 

Figure 3-3. Results of an in situ bioassay during a period of episodic acidification in Buck Creek, 

Adirondack Mountains, in spring 1990. (a) discharge, (b) acid neutralizing capacity, (c) pH, (d) 

concentration of inorganic monomeric aluminum, and (e) cumulative percentage of mortality 

of brook trout over time. 

 3-13
. 

Source: Stoddard et al. (2003) 

Figure 3-4. Relationship between mean summer acid neutralizing capacity (ANC) and the mean of 

minimum spring ANC values at long-term monitoring lake and stream sites in New England, 

the Adirondacks, and the Northern Appalachian Plateau. 

The most important factor governing watershed sensitivity to episodic acidification is the pathway 

followed by snowmelt water and storm-flow water through the watershed. The routing of water as it flows 

through a watershed determines the degree of contact with acidifying or neutralizing materials and 

therefore influences (along with soils and bedrock characteristics) the amount of episodic acidification 

that occurs. In any given watershed, surface water ANC may vary in time depending upon the proportion 

of the flow that has contact with ANC supplying substrate; in general, the more subsurface contact, the 

higher the surface water ANC (Turner et al., 1991). This pattern can be attributed in part to higher base 

saturation and (in some watersheds) greater SO4

2−

 adsorption capacity in subsurface soils. It may also 

relate to the accumulation in the upper soil horizons of acidic material derived from atmospheric 

deposition and decay processes (Lynch and Corbett, 1989; Turner et al., 1991). 

Streams having acidic episodes show significantly higher fish mortality and other aquatic 

community changes as compared with streams in which ANC remains above 0 μeq/L (Wigington et al., 

1993). Results from in situ bioassay studies from across the eastern U.S. show that acidic episodes (with 

associated low pH and elevated inorganic Al concentrations, and high streamwater discharge) caused 

rapid fish mortality under some conditions (Baker et al., 1996; Bulger et al., 1999; Driscoll et al., 2001b). 

For example, streams with suitable conditions during low flow, but moderate-to-severe episodic 

acidification during high flow, had higher fish mortality in bioassays, higher net downstream movement 

of brook trout during events, and lower brook trout abundance and biomass compared to streams that did 

not experience appreciable episodic acidification. These episodically affected streams lacked the more 

acid-sensitive fish species (blacknose dace and sculpin). Movement of trout into refugia (areas with 

 3-14
higher pH and lower inorganic Al) during episodes only partially mitigated the adverse effects of episodes 

(Baker et al., 1996). 

Consideration of episodic acidification greatly increases the extent and degree of estimated effects 

for acidifying deposition on surface waters. In the Northeast, inclusion of episodically acidified water 

bodies in regional assessments substantially increases estimates of the extent of surface water 

acidification. For example, baseflow samples collected from 1991 to 1994 through the U.S. EPA 

Temporally Integrated Monitoring of Ecosystems (TIME) Program indicated that 10% of the 1,812 lakes 

larger than 1 ha surface area in the Adirondack region could be considered chronically acidic (fall index 

ANC values less than 0 μeq/L). However, an additional 31% of these lakes had fall index ANC values 

less than 50 μeq/L and were, therefore, estimated to be susceptible to episodic acidification (Driscoll 

et al., 2001b). 

Lawrence (2002) estimated the extent of episodically acidified stream reaches in a Catskill, NY 

watershed (area = 85 km2

) using an index site at the base of the watershed that became episodically 

acidified at high flows. Upstream sites with a lower base flow ANC than the index site at the same date 

and time were found to have a high likelihood of becoming episodically acidified. Base flow sampling of 

122 upstream sites indicated that approximately 16% of the total upstream reaches had chronic ANC less 

than 10 μeq/L, but that 66% of the stream reaches had episodic ANC less than 10 μeq/L. 

In the Southeast, a recent study by Deviney et al. (2006) within Shenandoah National Park, 

Virginia used hourly ANC predictions over short time periods to compute recurrence intervals of annual 

water-year minimum ANC values for periods of 6, 24, 72, and 168 h. They extrapolated the results to the 

rest of the Shenandoah National Park catchments using catchment geology and topography to stratify 

watershed response patterns. On the basis of the models, they concluded that a large number of 

Shenandoah National Park streams had 6- to 168-h periods of low ANC values, which may stress resident 

fish populations (Deviney et al., 2006). Specifically, on the basis of a 4 year recurrence interval, 

approximately 23% of the land area (44% of the catchments) can be expected to have conditions that are 

classified with respect to brook trout response categories (Bulger et al., 1999) as indeterminate (ANC 20 

to 50), episodically acidic (ANC 0 to 20) or chronically acidic (ANC less than 0) for 72 continuous hours. 

Many catchments were predicted to have successive years of ANC values sufficiently low as to 

potentially extirpate some aquatic species (Deviney et al., 2006). The authors of the study reported that 

smaller catchments are more vulnerable to episodic acidification than larger catchments underlain by the 

same bedrock. Results from a study of six intensively monitored sites in the Park demonstrated a clear 

pattern of larger episodic ANC depressions in streams having higher median ANC than in streams with 

lower ANC. However, streams with low median ANC typically experienced decreases that resulted in 

minimum ANC values associated with toxicity to biota. These low ANC conditions were more likely to 

occur in streams underlain by siliclastic bedrock than in those with granitic or basaltic bedrock. 

In the West, episodic acidification is an especially important issue for surface waters throughout 

high-elevation areas. Where soils are sparse, as in alpine regions, most snowpack N is flushed to surface 

waters early in the snowmelt period. Even though there is evidence through use of isotopic tracers that 

much of the N was cycled microbially, snowpack N has been reported to cause temporary acidification of 

alpine streams (Campbell et al., 2002; Williams and Tonnessen, 2000). Snowmelt-related temporary 

acidification of alpine lakes and streams and associated effects have been reported in the Rocky 

Mountains (Brooks et al., 1996; Williams et al., 1996b) and Sierra Nevada (Johannessen and Henriksen, 

1978; Stoddard, 1995). 

There have been no studies in the U.S. to determine if either the severity or frequency of episodic 

acidification has lessened in response to recent decreases in acidifying deposition over the past three 

decades. In a study of two streams in Nova Scotia (Laudon et al., 2002) noticeable trends in ANC during 

different phases of storm hydrographs from 1983 to 1998 were generally not detected other than during 

the peak-flow phase of one stream (an increase of 0.87 μeq/L). 

In summary, the vast majority of water chemistry data for acid-sensitive lakes and streams in the 

U.S. were collected at low stream flow. It is well known, however, that water chemistry changes with 

season and with weather. Altered water chemistry is most stressful to aquatic biota (lowest pH and ANC; 

 3-15
highest inorganic Al concentration) during high flow following snowmelt and rainstorms. During such 

conditions, stream chemistry can be toxic to species that thrive under chemical conditions more typical of 

base flow. The U.S. EPA ERP and other more localized studies have quantified the effects of episodes on 

fish. Episodes are driven by hydrological processes, but the acidification that occurs is largely a result of 

acidifying deposition, especially in cases where inorganic Al has been mobilized. Consideration of such 

variability in water chemistry is critical for accurate assessment of the extent, magnitude, and biological 

effects of surface water acidification. The biological effects of changes in surface water chemistry are 

discussed in greater detail in Section 3.2.3.3. 

3.2.2. Terrestrial Ecosystems 

The changes in major biogeochemical processes and soil conditions described above contribute to a 

series of effects on terrestrial ecosystems. These changes are manifest in both chemical and biological 

effects that can include reduced soil base saturation, altered key element ratios, changes in plant 

productivity, reduced stress tolerance of sensitive plant species, and in some cases, increased mortality of 

canopy trees. Specific chemical indicators of change can be used to assess sensitivity to, and effects from, 

acidifying deposition. In the U.S., terrestrial effects of acidification are best described for forested 

ecosystems, with supportive information on other plant communities, including shrubs and lichens. 

3.2.2.1. Chemical Effects 

There are several chemical indicators that provide useful information about the acid-base status of 

soils and its influence on terrestrial vegetation. These include soil base saturation; Ca:Al ratio; and C:N 

ratio (see Table 3-2). Each chemical indicator provides insight into the level to which the ecosystem has 

acidified and may be susceptible to associated biological effects. These chemical indicators may also be 

used to monitor the extent of acidification or recovery that occurs in forest ecosystems as deposition rates 

of S and N change. As such, several chemical indicators and possible effect thresholds have been 

developed and applied in conjunction with efforts to estimate critical loads. The critical loads approach is 

discussed in more detail in Section 3.1.1. 

Table 3-2. Examples of chemical indicators of effects from acidifying deposition to terrestrial 

ecosystems. 

Examples of Chemical 

Indicators 

Example Possible Effect 

Threshold References 

Soil base saturation 10–20% Lawrence et al. (2005); Driscoll et al. (2001b); Cronan et al. 

(1990) 

Soil solution Ca:Al ratio 1.0 Cronan and Grigal (1995) 

Soil C:N ratio 20–25 Aber et al. (2003) 

 3-16
Soil Base Saturation 

In soils with a base saturation less than about 15 to 20%, exchange ion chemistry is dominated by 

Al (Reuss, 1983). Under this condition, responses to sulfuric and nitric acid inputs largely involve the 

release and mobilization of inorganic Al through cation exchange. This is the form of Al that interferes 

with uptake of Ca by plant roots and is also toxic to many forms of aquatic biota (Baker et al., 1990b; 

Cronan and Grigal, 1995). 

The soil O horizon tends to have a much higher base saturation than the underlying mineral soil, 

despite having lower pH due to organic acidity. The base saturation of the B horizon is in a Spodosol can 

be sensitive to base cation depletion from leaching by SO4

2−

 and NO3

−

, and is therefore useful for 

assessing base status with regard to acidifying deposition. Little direct work has been done to relate soil 

base saturation to forest health, but Cronan and Grigal (1995) determined that base saturation values 

below about 15% in the B horizon of forests in the northeastern U.S. could lead to effects from Al stress. 

Lawrence et al. (2005) also observed pronounced decreases in diameter growth of Norway spruce in 

northwestern Russia, where base saturation decreased from 30% to 20% in the upper 10 cm of the B 

horizon over a period of 37 years. 

Base saturation values less than 10% predominate in the soil B horizon in the areas in the U.S. 

where soil and surface water acidification from acidifying deposition have been most pronounced, 

including conifer and hardwood forests in the Adirondack Mountains (Sullivan et al., 2006a), red spruce 

forests throughout the Northeast (David and Lawrence, 1996), hardwood forests in the Allegheny Plateau 

(Bailey et al., 2004), and conifer and hardwood forests in the southern Appalachian Mountains (Sullivan 

et al., 2003). In a study of sugar maple decline throughout the Northeast, Bailey et al. (2004) found 

threshold relationships between base cation availability in the upper B soil horizon and sugar maple 

mortality at Ca saturation less than 2%, and Mg saturation less than 0.5% (Bailey et al., 2004). The 

authors concluded that base saturation varied as a function of topography, geologic parent material, and 

acidifying deposition. 

Aluminum Concentration in Soil Solution: Calcium to Aluminum Ratio 

Al may be toxic to tree roots. Plants affected by high Al concentration in soil solution often have 

reduced root growth, which restricts the ability of the plant to take up water and nutrients, especially Ca 

(Parker et al., 1989) (Figure 3-5). Ca is well known as an ameliorant for Al toxicity to roots in soil 

solution, as well as to fish in a stream. However, because inorganic Al tends to be increasingly mobilized 

as soil Ca is depleted, elevated concentrations of inorganic Al tend to occur with low levels of Ca in 

surface waters. Mg, and to a lesser extent Na and K, have also been associated with reduced Al toxicity. 

Dissolved Al concentrations in soil solution at spruce-fir study sites in the southern Appalachian 

Mountains frequently exceed 50 μM and sometimes exceed 100 μM (Eagar et al., 1996; Johnson et al., 

1991; Joslin and Wolfe, 1992a). All studies reviewed by Eagar et al. (1996) showed a strong correlation 

between Al concentrations and NO3

−

 concentrations in soil solution. They surmised that the occurrence of 

periodic large pulses of NO3

−

 in solution were important in determining Al chemistry in the soils of 

southern Appalachian Mountain spruce-fir forests. 

The negative effect of Al mobilization on Ca uptake by tree roots was proposed by Shortle and 

Smith (1988). Substantial evidence of this relationship has accumulated over the past two decades 

through field studies (Kobe et al., 2002; McLaughlin and Tjoelker, 1992; Minocha et al., 1997; Schlegel 

et al., 1992; Shortle et al., 1997) and laboratory studies (see review by Cronan and Grigal, 1995; Sverdrup 

and Warfvinge, 1993). Based on these studies, it is clear that high inorganic Al concentration in soil water 

can be toxic to plant roots. The toxic response is often related to the concentration of inorganic Al relative 

to the concentration of Ca, expressed as the molar ratio of Ca to inorganic Al in soil solution. As a result, 

considerable effort has been focused on determining a threshold value for the ratio of Ca to Al that could 

be used to identify soil conditions that put trees under physiological stress. 

From an exhaustive literature review, Cronan and Grigal (1995) estimated that there was a 50% risk 

of adverse effects on tree growth if the molar ratio of Ca to Al in soil solution was as low as 1.0. They 

 3-17
estimated that there was a 100% risk for adverse effects on growth at a molar ratio value below 0.2 in soil 

solution. 

 

Source: (Fenn et al., 2006). Reprinted with permission. 

Figure 3-5. Diagram based on Fenn et al. (2006) shows indicators of forest physiological function, growth 

and structure that are linked to biogeochemical cycles through processes that control rates of 

Ca supply. Calcium affects plant physiological processes that influence growth rates and the 

capacity of plants to resist environmental stresses such as extremes of temperature, drought, 

insects, and diseases. Therefore, acidifying deposition, which can deplete soil Ca or interfere 

with Ca uptake through mobilization of soil Al, is a concern for maintenance of forest health. 

The information available to define levels of risk for the Ca:Al ratio is complicated by differences 

in natural soil conditions and differences between tree species. As a result of these complications, the risk 

levels for the ratio defined in laboratory experiments have not necessarily been successfully applied to 

field conditions. For example, Johnson et al. (1994a, b) reported Ca:Al ratios above 1.0 through most of 4 

 3-18
years in the Oa and B horizons of a high-elevation red spruce stand experiencing high mortality. In the 

3−yr study of De Witt et al. (2001), Al additions lowered molar Ca to inorganic Al ratios in soil solutions 

of a Norway spruce stand below 0.5, but the authors found no response other than reduced Mg 

concentrations in needles in the third year, which was a possible precursor to damage. 

In summary, a molar ratio of Ca to Al in soil solution can be used as a general index that suggests 

an increasing probability of stress to forest ecosystems as the ratio decreases. The ratio value of 1.0 is 

proposed as a general damage threshold, but cannot be interpreted as a universally applicable threshold in 

all natural systems. Tree species vary widely in their sensitivity to Al stress (See Annex Table B-20). In 

addition, Al concentrations in soil solution often exhibit pronounced spatial and temporal variability that 

is difficult to relate to root activity. Finally, the form of Al present in solution plays an important role in 

determining toxicity. For example, organically complexed Al, which predominates in upper, organic-rich 

soil horizons, is essentially nontoxic (Baker and Schofield, 1982; Cronan and Grigal, 1995). 

Soil N: Carbon to N Ratio 

Mechanisms of retention and release of N in forest ecosystems are not fully understood, but the 

adverse effects of nitrification and associated acidification and cation leaching have been consistently 

shown to occur only in soils with a C:N ratio below about 20 to 25 (Aber et al., 2003; Ross et al., 2004). 

This observation makes the C:N ratio especially useful because N mineralization and nitrification rates 

are difficult to measure directly under natural conditions. All available measurement approaches disturb 

the soil and often cause artificially high rates. Therefore, field measurement provides a relative index 

rather than a realistic quantitative rate (Ross et al., 2004). Approaches for measuring N mineralization and 

nitrification also are subject to high degrees of variability, both temporally (hourly to seasonal) and 

spatially (down to the sub meter level). Measurements of total OC and N however, are less variable in 

space and time and are therefore more straightforward to document than N mineralization and nitrification 

rates. Also, ratios of C to N in the forest floor are inversely related to acidifying deposition levels, 

although the relationship is stronger for hardwood stands than conifer stands (Aber et al., 2003). In 

summary, these factors make the C:N ratio a reliable and relatively straightforward measure for 

identifying forest ecosystems that may be experiencing soil acidification and base leaching as a result of 

N input and increased nitrification. 

DOC leaching 

Over the past two decades, increased DOC concentrations in soils and surface waters were widely 

reported across North America and Europe (Findlay 2005; Evans et al., 2006; Roulet and Moore 2006; 

Monteith et al., 2007). It is very likely DOC levels will continue to rise, leading to an increase in C export 

from the relative stable pool (soil) to the labile pool (riverine and marine) (Roulet and Moore 2006; 

Monteith et al., 2007). Although there have been arguments about the mechanism responsible for 

producing the high DOC concentration, changes in S and N atmospheric deposition both appears to play 

key roles in this trend (Driscoll et al., 2003c; Findlay 2005; Evans et al., 2006; Roulet and Moore 2006; 

Monteith et al., 2007). Through an assessment of time series data from 522 remote lakes and streams in 

North American and Europe, Monteith et al. (2007) found that DOC concentrations had increased in 

proportion to the rates at which atmospheric S and sea salt deposition declined. Numerous laboratory and 

field studies showed that the solubility of SOM increases with decreasing soil acidity (Kalbitz et al., 

2000). Declining S deposition may increase soil DOC concentration by increasing soil pH. Decreasing S 

deposition may also result in lower ionic strength by decreasing the concentrations of a suite of 

multivalent ions, such as SO4

2- and aluminum. The coagulation of DOC decreases under low ionic 

strength, leading to higher DOC export rates. (Monteith et al., 2007). 

Chronically high N deposition has been proposed as another significant factor that influences DOC 

production and transportation. Increases in DOC concentrations were often observed in both experimental 

sites which received chronic N addition (Pregitzer et al., 2004; Sinsabaugh et al., 2004; Adams et al., 

2005), and natural ecosystems which experienced high rates of N deposition, such as Hudson River and 

 3-19
several European peatlands (Findlay 2005; Bragazza et al., 2006). Although N deposition contributes to 

the acidification of soils, it may increase DOC export by increasing litter input and decreasing DOC 

degradability (Roulet and Moore 2006). However, the mechanism behind the association between N 

deposition and DOC production is not yet well understood (Pregitzer et al., 2004; DeForest et al., 2005; 

Findlay 2005). 

3.2.2.2. Summary of Biogeochemistry and Chemical Effects 

The evidence is sufficient to infer a causal relationship between acidifying deposition and changes in 

biogeochemistry related to terrestrial ecosystems. The strongest evidence for a causal relationship comes 

from studies of forested ecosystems, with supportive information on other plant communities, including 

shrubs and lichens; grasslands are likely less sensitive to acidification than forests. Soil acidification 

occurs in response to inputs of sulfuric acid (H2SO4) and nitric acid (HNO3); the effect can be neutralized 

by weathering or base cation exchange. Soil acidification is a natural process, but is often accelerated by 

acidifying deposition. Acidifying deposition is important in decreasing concentrations of exchangeable 

base cations in soils. Despite recent decreases in acidifying deposition, there are widespread observations 

of ongoing soil acidification such as decreases in soil exchangeable base cations (Bailey et al., 2005, 

Sullivan et al. 2006a, 2006b, Warby et al., 2009). The limited mobility of anions associated with naturally 

derived acidity (organic acids and carbonic acid) controls the rate of base cation leaching from soil under 

conditions of low atmospheric deposition of S and N. Because inputs of S and N in acidifying deposition 

provide anions that are more mobile in the soil environment than anions of naturally derived acids, these 

mineral acid anions can accelerate natural rates of base-cation leaching. 

Nitrification is mediated by autotrophic bacteria that derive energy by oxidizing NH4

+

 to NO3

−

. 

Nitrification produces acidity in the form of HNO3 as a byproduct. The HNO3 produced contributes to the 

acidification of soils and surface waters. 

There are three useful indicators of chemical changes and acidification effects on terrestrial 

ecosystems, showing consistency and coherence among multiple studies: soil base saturation, Al 

concentration in soil water, and soil C:N ratio. 

 Soil base saturation is the concentration of exchangeable bases as a percent of the total soil cation 

exchange capacity. Once base saturation decreases to a critical level (approximately 15–20%), 

inputs of H2SO4 and HNO3 result in exchange of inorganic Al. 

 Inorganic Al is toxic to some tree roots. Plants affected by high inorganic Al concentrations in 

soil solution often have reduced root growth, which restricts the ability of the plant to take up 

water and nutrients, especially calcium (Ca) (Parker et al., 1989). 

 The C:N ratio of soil is used to indicate alterations to the N biogeochemical cycle. If the C:N ratio 

of soils falls below about 20 to 25, nitrification is stimulated resulting in net nitrification and 

increased acidity. 

3.2.2.3. Biological Effects 

Acidifying deposition can affect terrestrial ecosystems via direct effects on plant foliage and 

indirect effects associated with changes in soil chemistry. Biological effects of acidification on terrestrial 

ecosystems are generally attributable to Al toxicity and decreased ability of plant roots to take up base 

cations (especially Ca) and water from the soil (Cronan and Grigal, 1995). Acidifying deposition to acidsensitive soils can cause soil acidification, increased mobilization of inorganic Al from soil to drainage 

water, and depletion of the pool of exchangeable base cations in the soil. Effects on the soil and direct 

effects of acidifying deposition on foliage can influence the response of plants to climatic stresses such as 

 3-20
drought and cold temperature. They can also influence the sensitivity of plants to other stresses, including 

insect pests and disease (Joslin et al., 1992). 

The combined effects of acidifying deposition and other stressors on terrestrial vegetation are 

typically measured using indices such as percent dieback of canopy trees, dead tree basal area (as a 

percent), crown vigor index, and fine twig dieback (see Table 3-3). Each of these variables has a rating 

system used to quantify forest condition and relate the variables to foliar and soil nutrient concentrations. 

Table 3-3. Example biological effects indicators in terrestrial ecosystems. 

Indicator Species Example of Health Indices References 

Red spruce Percent dieback of canopy trees Shortle et al. (1997) 

DeHayes et al. (1999) 

Sugar maple Basal area dead sugar maple (as %) 

Crown vigor index 

Fine twig dieback 

Bailey et al. (1999) 

Drohan and DeWalle (2002) 

The effects of acidifying deposition on the health, vigor, and productivity of terrestrial ecosystems 

in the U.S. are best documented in spruce-fir and northern hardwood forests of the eastern U.S. Some 

information is also available for individual species such as red spruce, sugar maple, and some species of 

lichen. In the western U.S., the health of Ponderosa pine and Jeffrey pine has been affected by air 

pollution, but such effects have largely been attributed to ozone exposure, not acidifying deposition. 

Health, Vigor, and Reproduction of Tree Species in Forests 

Both coniferous and deciduous forests throughout the eastern U.S. are experiencing gradual losses 

of base cation nutrients from the soil due to accelerated leaching from acidifying deposition. This change 

in base cation nutrient availability may reduce the quality of forest nutrition over the long term. Evidence 

suggests that red spruce and sugar maple in some areas in the eastern U.S. have experienced declining 

health as a consequence of acidifying deposition. Existing information regarding the effects of acidifying 

deposition on these two forest tree species is summarized below and reference is made to specific health 

indicators where such information is available. 

Red Spruce 

Red spruce (Picea rubens) is a conifer that occurs mainly in the northeastern U.S. and at scattered 

high-elevation sites in the Appalachian Mountains (see Figure 3-6). Red spruce dieback or decline has 

been observed across high elevation landscapes of the northeastern, and to a lesser extent, southeastern 

U.S. At high elevations in the Adirondack and Green Mountains, more than 50% of the canopy red spruce 

trees died during the 1970s and 1980s. In the White Mountains, about 25% of the canopy spruce died 

during that same period (DeHayes et al., 1999). Dieback of red spruce has also been observed in mixed 

hardwood-conifer stands at relatively low elevations in the western Adirondack Mountains, an area that 

receives high inputs of acidifying deposition (Shortle et al., 1997); acidifying deposition has been 

implicated as a causal factor (DeHayes et al., 1999). The frequency of freezing injury to red spruce 

needles has increased over the past 40 years, a period that coincided with increased emissions of S and N 

oxides and increased acidifying deposition (DeHayes et al., 1999). 

From the 1940s to 1970s, red spruce growth also declined at high elevation in the southeastern U.S. 

(Cook and Zedaker, 1992; Eagar et al., 1996; McLaughlin et al., 1987} as emissions of both NOX and SO2

 3-21
increased to maxima of about 25 and 30 million tons/yr, respectively. The growth decline in Great Smoky 

Mountains National Park in North Carolina and Tennessee started earlier at higher elevations (around the 

1940s and 1950s) and was steeper, while the growth decline developed at lower elevation sites 20 years 

later. After the 1980s, red spruce growth increased substantially at both the higher- and lower-elevation 

sites, corresponding to a decrease in SO2 emissions in the U.S. (to about 20 million tons/yr by 2000), 

while NO emissions held fairly steady (at about 25 million tons/yr). Annual emissions of S plus NOX

explained about 43% of the variability in red spruce tree ring growth between 1940 and 1998. Climatic 

variability accounted for about 8% of the growth variation for that period. At low elevation, changes in 

radial growth could be explained by climatic variables only, and there was no correlation with national S 

plus nitrogen oxide emissions trends. Recent reductions in S oxide emissions may have changed growth 

trajectories (Webster et al., 2004). 

Source: Little (1971) 

Figure 3-6. Distribution of red spruce (rose) and sugar maple (green) in the eastern U.S. These two tree 

species have experienced adverse effects in portions of their ranges that have been attributed 

to acidification from acidifying deposition. Tree distribution data were obtained from Little’s 

Atlas. 

The observed dieback in red spruce has been linked, in part, to reduced cold tolerance of red spruce 

needles, caused by acidifying deposition. Results of controlled exposure studies showed that acidic mist 

or cloud water reduced the cold tolerance of current-year red spruce needles by 3 to 10 oC (DeHayes 

et al., 1999). There is a significant positive association between cold tolerance and foliar Ca in trees that 

exhibit foliar Ca deficiency. The membrane-associated pool of Ca, although a relatively small fraction of 

the total foliar Ca pool, strongly influences the response of cells to changing environmental conditions. 

The plant plasma membrane plays an important role in mediating cold acclimation and low-temperature 

injury (U.S. EPA, 2004). The studies of DeHayes et al. (1999) suggested that direct acidifying deposition 

on red spruce needles preferentially removes membrane-associated Ca. More recently, a link has been 

 3-22
established between availability of soil Ca and winter injury (Hawley et al., 2006) based on an 

experimental addition of Ca at the Hubbard Brook Experimental Forest, New Hampshire. This study 

demonstrated that Ca depletion from soil was associated with winter injury of red spruce foliage during 

2003 when winter injury was unusually high throughout the region (see Figure 3-7). 

Source : Hawley et al. (2006). Reprinted with permission. 

Figure 3-7. Mean (+ standard error bars) of current-year red spruce needle winter injury in reference and 

calcium-addition watersheds and among crown classes, expressed as foliar injury (A) and 

bud mortality (B). Watershed means were either not significantly different (ns) or statistically 

different at p <0.05 (*) or p <0.01 (**) based on nested analyses of variance. 

In summary, the weight of evidence suggests that changes in soil chemistry have contributed to 

high mortality rates and decreasing growth trends of red spruce trees in some areas over the past three 

decades (Sullivan et al., 2002). In forests where this has occurred, which are mainly located at high 

elevation, changes in red spruce growth rates are attributable, at least in part, to base cation deficiencies 

related to decreased availability of Ca and increased availability of Al as a result of acidifying deposition 

 3-23
effects on soils. Important factors appear to include depletion of base cations in upper soil horizons by 

acidifying deposition, Al toxicity to tree roots, and accelerated leaching of base cations from foliage as a 

consequence of acidifying deposition. Recent studies also show improvements in red spruce growth with 

decreasing emissions of SO2 in the U.S. (Webster et al., 2004). 

Sugar Maple 

Sugar maple (Acer saccharum) is the deciduous tree species of the northeastern U.S. that is most 

commonly associated with adverse acidification-related effects of S and N deposition, though other base 

cation accumulating hardwoods may also be at risk (Driscoll et al., 2001b). Sugar maple is distributed 

throughout the northeastern U.S. and central Appalachian Mountain region as a component of the 

northern hardwood forest Figure 3-6. 

A conceptual view of the interactions of acidifying deposition and other stressors in sugar maple 

decline is provided in Figure 3-8. Several studies, mainly in Pennsylvania, have hypothesized that sugar 

maple decline is linked to the occurrence of relatively high levels of acidifying deposition and base-poor 

soils (Bailey et al., 2004; Hallett et al., 2006; Horsley et al., 2000; Moore and Ouimet, 2006; St. Clair et 

al., 2005). 

Acidifying deposition may be contributing to episodic dieback of sugar maple in the Northeast 

through depletion of nutrient cations from marginal soils (Figure 3-8). Horsley et al. (2000) found that 

dieback at 19 sites in northwestern and north central Pennsylvania and southwestern New York was 

correlated with combined stress from defoliation and soil deficiencies of Mg and Ca. Dieback occurred 

predominately on ridgetops and on upper slopes, where soil base cation availability was much lower than 

occurred in the deeper soils found on middle and lower slopes (Bailey et al., 2004). A long-term decrease 

in soil pH since 1960 (0.78 pH unit decrease in the O horizon, and 0.23 pH unit decrease in the A horizon) 

in Pennsylvania hardwood forests has been documented, along with decreases in soil Ca and Mg 

concentrations. Declining sugar maples were shown to be deficient in foliar Ca and Mg (Drohan and 

Sharpe, 1997). More recent research has strengthened understanding of the role of cation nutrition in 

sugar maple health at a regional scale across a broad range of conditions (Hallett et al., 2006). 

Drohan et al. (2002) investigated differences in soil conditions in declining versus non-declining 

sugar maple plots in northern Pennsylvania from the U.S. Department of Agriculture (USDA) Forest 

Service’s Forest Inventory and Analysis (FIA) program. Soils in plots with declining sugar maple tended 

to have lower base cation concentrations and pH, and Ca:Al ratio less than 1. Regressions between foliar 

and soil chemistry showed that foliar nutrition was highly correlated with the chemistry of the upper 50 

cm of soil (Drohan et al., 2002). 

Juice et al. (2006) added Ca to watershed 1 (W1) at HBEF in October 1999 sufficient to raise the 

pH of the Oie soil horizon from 3.8 to 5.0 and the Oa horizon from 3.9 to 4.2. Subsequently, they 

measured the response of sugar maples to the Ca fertilization. Foliar Ca of canopy sugar maples increased 

markedly and foliar Mn declined. By 2005, crown condition was much healthier then in the untreated 

reference watershed (W6). The density of sugar maple seedlings increased significantly following high 

seed production in 2000 and 2002. In addition, sugar maple germinants were 50% larger on W1 and 

mycorrhizal colonization of seedlings was much higher in the treated watershed (22.47% of root length) 

as compared with the reference watershed (4.4%) (Juice et al., 2006). 

In general, evidence indicates that acidifying deposition in combination with other stressors is a 

likely contributor to the decline of sugar maple trees that occur at higher elevation, on geologies 

dominated by sandstone or other base-poor substrate, and that have base-poor soils having high 

percentages of rock fragments (Drohan et al., 2002). Such site conditions are representative of the kinds 

of conditions expected to be most susceptible to adverse effects of acidifying deposition because of 

probable low initial base cation pools and high base cation leaching losses. 

 3-24
Source: Hallett et al. (2006). Reprinted with permission. 

Figure 3-8. Conceptual diagram outlining the current understanding of sugar maple decline. Positive and 

negative signs indicate the nature of the correlative relationship between variables. 

Other Forest Ecosystems 

Loss of base cations, specifically Ca2+, has also been implicated in increased susceptibility of 

flowering dogwood (Cornus florida) to its most destructive disease, dogwood anthracnose (Figure 3-9). 

Flowering dogwood is a dominant understory species of hardwood forests in the eastern U.S. 

(Holzmueller et al., 2006), with important ecosystem functions as a food source for numerous species of 

animals, and as a large contributor to available Ca in forest litter. It is also recognized as a significant 

cultural and aesthetic resource throughout its range. Since dogwood anthracnose, a mostly fatal disease, 

was first reported in 1976 in New York State, it has spread over a large portion of the species’ range, 

generally resulting in mortality greater than 90% in affected stands. Pacific dogwood (Cornus nutallii) is 

similarly affected, but because its abundance within its range was much lower before the disease first 

appeared, the effect has received less notice. Susceptibility to the disease, and disease severity in stands, 

appear dependent on several factors, including acid deposition and various edaphic characteristics and 

meteorological conditions. 

 3-25
Source: Holzmueller et al. (2006). Reprinted with permission. 

Figure 3-9. Native range of flowering dogwood (Cornus florida) (dk. gray) and the documented range of 

dogwood anthracnose in the eastern U.S. (red). 2002 data from the U.S. Forest Service. 

In 1990 and 1991, Britton et al. (1996) exposed 200 potted dogwood plants to simulated acid rain 

(SAR) at 4 levels of pH between 2.5 and 5.5. The plants were then placed among natural stands showing 

symptoms of the disease. In both years, there was a fourfold increase in percentage of leaf area affected 

from the plants treated with SAR at a pH of 5.5 to those treated with SAR at a pH of 2.5. In 1992 and 

1993, four combinations of SAR with a pH of 2.5 or 5.5 were applied separately to the foliage and the soil 

before inoculation. The percent of leaf area affected was approximately two to four times greater for 

plants grown in soil treated with acidic SAR, regardless of foliar treatment, suggesting that the worsening 

of anthracnose damage by acid deposition occurs mostly through soil effects. 

In a study of the effects of Ca, K, and Mg on dogwood density in forest stands, and on resistance to 

anthracnose in containerized dogwood plants, (Holzmueller et al., 2007) found a strong relation between 

soil available cations, particularly Ca2+, and dogwood density in Great Smoky Mountains National Park, 

where dogwood anthracnose has resulted in significant damage. The mortality of potted dogwood plants 

fertilized with solutions varying in Ca2+, K+

, or Mg2+ concentration, and exposed to anthracnose, was both 

greatest and most rapid when Ca2+ was deficient, but not when K+

 and Mg2+ were deficient. 

Data on the possible effects of S and N deposition on the acid-base characteristics of forests in the 

U.S., other than the spruce-fir and northern hardwood forest ecosystems, are limited. Ponderosa pine 

(Pinus ponderosa) seedlings exposed to acidic precipitation (pH 5.3, 4.4, 3.5 of 1:1 NHO3:H2SO4) 

showed no significant changes in growth (Temple et al., 1992). Fenn et al. (2003a) reported that 

deposition of 20 to 35 kg N/ha/yr contributed to increased NO3

−

 leaching and soil acidity and decreased 

base saturation in southern California forest ecosystems, but they did not report quantitative measures of 

growth. Baron et al. (2000) showed that small differences in the N deposition between the east (3 to 5 kg 

N/ha/yr) and the west (1 to 2 kg N/ha/yr) side of the Rocky Mountains were associated with significant 

declines in foliar Mg levels and increased foliar N:Mg and N:Ca ratios in old-growth stands of 

Engelmann spruce (Picea engelmanii). It is not known if such changes in nutrient ratios affect the health 

or growth of these forests. 

 3-26
Despite the evidence for effects of acidifying deposition on the health and vigor of some terrestrial 

plant communities, few studies are available that have directly documented species loss, reduced 

biodiversity, or adverse effects on threatened and endangered species. A notable exception is the effect of 

acidifying deposition on lichen abundance and diversity within forest communities (See discussion 

below). In eastern North America and central Europe, areas that receive relatively high levels of 

acidifying deposition and high atmospheric concentrations of SO2, N oxides, and reduced N have 

experienced noticeable reductions in cyanolichen abundance on both coniferous and deciduous trees 

(Richardson and Cameron, 2004). Effects on lichen species biodiversity are also likely (McCune, 1988; 

van Haluwyn and van Herk, 2002). In London, epiphyte diversity, including a majority of the lichen taxa, 

declined in areas where NO exceeded 40 μg/m3

 and total N oxides exceeded 70 μg/m3. 

Health and Biodiversity of Other Plant Communities 

Shrubs 

Forest trees are not the only vascular plants that are potentially sensitive to acidifying deposition. 

Available data suggest that it is possible, or perhaps likely, that a variety of shrub and herbaceous species 

are sensitive to base cation depletion and/or Al toxicity. However, conclusive evidence is generally 

lacking. 

Research in Europe has illustrated a shift from shrub to grass dominance in heathlands in response 

to acidifying deposition. However, such effects are probably more related to the nutrient enrichment 

effects of N deposition than to the acidification effects of S and N deposition. (See further discussion in 

Section 3.3.3.1.) In summary, whereas some evidence suggests that effects on shrubs and perhaps 

herbaceous plants are possible, data in the U.S. are insufficient to support the use of shrub or herbaceous 

plant species as indicators of the acidification-related effects of acidifying deposition at this time. 

Lichens 

Typically, lichens and bryophytes are among the first components of the terrestrial ecosystem to be 

affected by acidifying deposition. Vulnerability of lichens to increased N input is generally greater than 

that of vascular plants (Fremstad et al., 2005). Even in the Pacific Northwest, which receives uniformly 

low levels of N deposition, changes from acid-sensitive and N-sensitive to pollution-tolerant and 

nitrophillic lichen taxa are occurring in some areas (Fenn et al., 2003a). Lichens remaining in areas 

affected by acidifying deposition were found by Davies et al. (2007) to contain almost exclusively the 

families Candelariaceae, Physciaceae, and Teloschistaceae. 

Effects of SO2 exposure on lichens includes reduced photosynthesis and respiration, damage to the 

algal component of the lichen, leakage of electrolytes, inhibition of N fixation, reduced K absorption, and 

structural changes (Farmer et al., 1992; Fields, 1988). In response to reductions after the 1970s in SO2

exposure and acidifying deposition in London, lichen diversity increased dramatically (Hawksworth, 

2002). However, the recovery of lichens in response to reduced S and N inputs is inconsistent. 

Improvement for bryophytes has been reported to occur in 1 year by Power et al. (2006) and Mitchell 

et al. (2004), 5 years by Gordon et al. (2001), and 49 years by Strengbom et al. (2001). 

Scott (1989a, 1989b) concluded that the S:N exposure ratio was as important as pH in causing toxic 

effects on lichens, based on experiments on Cladina rangiferina and C. stellaris. Thus, it is not clear to 

what extent acidity may be the principal stressor under high levels of air pollution exposure. The toxicity 

of SO2 to several lichen species is greater under acidic conditions than under neutral conditions. The 

effects of excess N deposition to lichen communities are discussed in Section 3.3.5.1. 

 3-27
Grasslands 

Due to structural differences and their lower canopy, grasslands are thought to be less sensitive to 

acidification than woodlands (Blake et al., 1999; Kochy and Wilson, 2001). Among grasslands, those with 

calcareous soils will be less sensitive than those with acidic soils (Bobbink et al., 1998). Most literature 

on the effects of atmospheric S and N deposition on grasslands documents effects of fertilization from N 

deposition, not acidification. Such fertilization effects are discussed in Section 3.3.5.1. 

Arctic and Alpine Tundra 

The possible effects of acidifying deposition on arctic and alpine plant communities are also of 

concern. Especially important in this regard is the role of N deposition in regulating ecosystem N supply 

and plant species composition. (See further discussion of such effects in Section 3.3.5.1.) Soil 

acidification and base cation depletion in response to acidifying deposition have not been documented in 

arctic or alpine terrestrial ecosystems in the U.S. Such ecosystems are rare and spatially limited in the 

eastern U.S., where acidifying deposition levels have been high. These ecosystems are more widely 

distributed in the western U.S. and throughout much of Alaska, but acidifying deposition levels are 

generally low in these areas. Key concerns are for listed threatened or endangered species and species 

diversity. However, for most rare, threatened, or endangered herbaceous plant species, little is known 

about their relative sensitivities to acidification from atmospheric deposition inputs. Although plant 

species diversity of arctic and alpine ecosystems is highly valued, it is difficult to document changes in 

this parameter in response to acidifying deposition. 

3.2.2.4. Summary of Biological Effects 

The evidence is sufficient to infer a causal relationship between acidifying deposition and changes in 

terrestrial biota. The strongest evidence for a causal relationship comes from studies of terrestrial systems 

exposed to elevated levels of acidifying deposition that show reduced plant health, reduced plant vigor, 

and loss of terrestrial biodiversity. Consistent and coherent evidence from multiple species and studies 

shows that acidifying deposition can affect terrestrial ecosystems by causing direct effects on plant foliage 

and indirect effects associated with changes in soil chemistry. Biological effects of acidification on 

terrestrial ecosystems are generally attributable to Al toxicity and decreased ability of plant roots to take 

up base cations. There are several indicators of stress to terrestrial vegetation (see Table 3-3) including 

percent dieback of canopy trees, dead tree basal area (as a percent), crown vigor index, and fine twig 

dieback. 

Species Level 

 Changes in soil chemistry (depletion of soil base cations, Al toxicity to tree roots, leaching of 

base cations into drainage water) have contributed to high mortality rates and decreasing growth 

trends of red spruce trees (Picea rubens) in some areas of the eastern U.S. over the past three 

decades. 

 Acidifying deposition, in combination with other stressors, is a likely contributor to the decline of 

sugar maple (Acer saccharum) trees that occur at higher elevation, in some portions of the eastern 

U.S., on geologies dominated by sandstone or other base-poor substrate, and that have base-poor 

soils. 

 Lichens and bryophytes are among the first species affected by acidifying deposition in the 

terrestrial ecosystem. Effects of SO2 on lichens include reduced photosynthesis and respiration, 

damage to the algal component of lichen, leakage of electrolytes, inhibition of N fixation, reduced 

potassium (K) absorption and structural changes. 

 3-28
 Data are insufficient to draw general conclusions for other species. 

Community Level 

 Species loss and reduced biodiversity of forests, shrubs, and meadow plant communities may 

occur, but has not been clearly demonstrated in the U.S. 

3.2.3. Aquatic Ecosystems 

3.2.3.1. Chemical Effects 

The changes in major biogeochemical processes and soil conditions caused by acidifying 

deposition have significant ramifications for the water chemistry and biological functioning of associated 

surface waters. Surface water chemistry indicates the adverse effects of acidification on the biotic 

integrity of fresh water ecosystems. Because surface water chemistry integrates the sum of soil and water 

processes that occur upstream within a watershed, it also reflects the results of watershed-scale terrestrial 

effects, including N saturation, forest decline, and soil acidification (Stoddard et al., 2003). Thus, water 

chemistry integrates and reflects changes in soil and vegetative properties and biogeochemical processes. 

The effects on aquatic ecosystems can be described by changes in several chemical effects 

indicators such as SO4

2−

 concentration, NO3

−

 concentration, base cation concentration, pH, ANC, and 

inorganic Al. All of these are of interest, and each can provide useful information regarding both 

sensitivity to surface water acidification and the level of acidification that has occurred. Importantly, these 

chemical changes can occur over both long- and short-term timescales. Short-term (hours or days) 

episodic changes in water chemistry have perhaps the most significant biological effects. The 

acidification effects on aquatic biota are most commonly evaluated using either Al or pH as the primary 

chemical indicator (Table 3-4). ANC is also used because it integrates overall acid status and because 

surface water acidification models do a better job projecting ANC than pH and inorganic Al 

concentrations. However, ANC does not relate directly to the health of biota. The usefulness of ANC lies 

in the association between ANC and the surface water constituents that directly contribute to or 

ameliorate acidity-related stress, in particular pH, Ca, and inorganic Al. The base cation surplus 

(Lawrence et al., 2007) is an alternate index that integrates acid-base status. It is based on a measurement 

of ANC (calculated from the charge balance of ionic concentrations in water) and also accounts for the 

influence of natural organic acidity. 

A synoptic illustration of the national patterns of surface water alkalinity in the conterminous U.S. 

is provided in Figure 3-10. Alkalinity is the most readily available measure of the sensitivity of lakes and 

streams to acidifying deposition. Although the actual sensitivity of a water body depends on many 

watershed characteristics and processes, the low-alkalinity areas on the map indicate where sensitive 

surface waters are most likely to be found. The map is based on data from approximately 39,000 lake and 

stream sites and the associations of the data values with factors such as land use, physiography, geology, 

and soils. 

 3-29
Table 3-4. Examples of chemical indicators of effects from acidifying deposition to aquatic ecosystems. 

Chemical Indicator Examples of Potential 

Thresholds Reference 

Surface water pH 5.0–6.0 Baker et al. (1990b) 

Surface water ANC 0–50 μeq/L Bulger et al. (1999) 

Inorganic Al 2–4 μmol/L Baldigo et al. (2007); Driscoll et al. (2001b); 

Wigington et al. (1996) 

Source: Omernik et al. (1983). 

Figure 3-10. Surface water alkalinity in the conterminous U.S. Shading indicates the range of alkalinity 

within which the mean annual values of most of the surface waters of the area fall. 

Surface Water SO4

2− 

Measurements of SO4

2−

 concentration in surface water provide important information on the extent 

of cation leaching in soils and how SO4

2−

 concentrations relate to ambient levels of atmospheric S 

deposition. Assessments of acidifying deposition effects dating from the 1980s to the present have shown 

SO4

2−

 to be the primary anion in most, but not all, acid-sensitive waters in the U.S. (Driscoll and Newton, 

1985; Driscoll et al., 1988; 2001b; Webb et al., 2004). In an analysis representative of over 10,000 acidsensitive lakes in the Northeast, inorganic anions represented most negative (anionic) charge in 83% of 

the lakes, and in this group of lakes, 82% of the total negative charge was due to SO4

2−

 (Driscoll et al., 

2001b). In contrast, naturally derived organic anions represented an average of 71% of total negative 

charge in the 17% of lakes in which organic anions predominated (Driscoll et al., 2001b). 

 3-30
Atmospheric deposition of S is widely acknowledged as causing changes in concentrations of 

SO4

2−

 in surface water. No long-term data sets exist to document changes in SO4

2−

 in surface waters since 

the onset of the Industrial Revolution. One of the longest-running monitoring programs exists at the 

Hubbard Brook Experimental Forest in New Hampshire. Surface water data from this Long-Term 

Ecological Research site have been used to develop historic estimates of SO4

2−

 concentrations using the 

Photosynthesis and EvapoTranspiration-BioGeoChemical (PnET-BGC) model (Gbondo-Tugbawa et al., 

2002). Results from Hubbard Brook suggest that acidifying deposition has contributed to a nearly fourfold increase in stream SO4

2−

 concentration between 1850 and 1970 (Driscoll et al., 2001b). 

Long-term data in other regions suggest similar trends in some cases. For example, a study of 

seven streams in the Catskill region of New York, Stoddard (1991) identified increasing trends in SO4

2−

concentrations from 1952–54 to 1970 in three streams and no trend in the four other streams. 

As emissions and deposition of S have declined over approximately the last 30 years, surface water 

concentrations of SO4

2−

 have decreased in most regions in the eastern U.S. For example, Stoddard et al. 

(2003) found that surface waters monitored in the U.S. EPA Long-Term Monitoring program showed 

consistent decreases in SO4

2−

 concentrations from 1990 to 2000 in New England lakes (1.77 μeq/L/yr), 

Adirondack lakes (2.26 μeq/L/yr), Appalachian streams (2.27 μeq/L/yr) and Upper Midwest lakes 

(3.36 μeq/L/yr). The only exception to the pattern of decreasing SO4

2−

 concentration in surface waters 

during this period was for streams in the Blue Ridge Mountain region of Virginia, which showed a 

significant increase in SO4

2−

 concentrations (0.29 μeq/L/yr) during this period. The increasing trend in 

Virginia streams is presumably the result of decreased S adsorption on soils and net desorption from the 

soil in response to decreased S deposition. 

In summary, available data indicate a pattern of increasing concentrations of SO4

2−

 in surface 

waters before the year of peak S emissions in the early 1970s, followed by widespread decreasing trends 

in SO4

2−

 concentrations after the peak (with the only exception being the Blue Ridge Mountain region in 

Virginia). On this basis, continued decreases in S emissions would be expected to result in further 

decreases in SO4

2−

 concentrations in surface waters, although the rate of response is variable and some 

model results suggest that recovery may be delayed as accumulated S leaches from watersheds, even as 

emissions and deposition decline. 

Surface Water NO3

−

As described in the previous section, the acidification potential of atmospherically deposited S is 

primarily a function of the extent of SO4

2−

 anion mobility in watershed soils and drainage waters. 

Similarly, acidification of soil water and surface water from atmospheric N deposition is largely governed 

by the mobility of the NO3

−

 anion. Both oxidized and reduced N deposition can contribute to the NO3

−

flux in drainage water. Once N is deposited, processes within the N cycle, including microbial 

assimilation, plant uptake, and loss to denitrification act to limit the extent of NO3

−

 leaching. In contrast, 

processes such as mineralization, nitrification, fixation, and atmospheric deposition contribute to the NO3

−

flux and increase the likelihood that substantial leaching of NO3

−

 in drainage water will occur. Such 

leaching of NO3

−

 is required in order for N deposition to cause N saturation, surface water acidification, 

or base cation leaching and depletion. Ultimately, the balance of these processes in the N cycle will 

determine the extent to which such effects will be manifested. 

Whereas SO4

2−

 is generally considered the dominant agent of surface water acidification in most 

affected regions of the U.S., NO3

−

 plays a large role in acidification of surface waters in some regions, 

particularly during snowmelt and rainstorms. Before the mid-1980s, atmospheric deposition effects 

research in the U.S. focused almost exclusively on S. Within the 1980 to 1990 (NAPAP) research 

program, relatively little attention was paid to N research. 

Release of NO3

−

 from soil to surface waters may affect nutrient relationships and biological 

neutralization processes in aquatic ecosystems (Bukaveckas and Shaw, 1998; Kelly et al., 1987; Momen 

et al., 2006). Driscoll and Newton (1985) found that NO3

−

 concentrations in 20 lakes in the early 1980s in 

the Adirondack region of New York averaged 12% of SO4

2−

 concentrations, whereas Lovett et al. (2000) 

 3-31
found that baseflow NO3

−

 concentrations in 1994–97 were an average of 37% of SO4

2−

 concentrations in 

39 streams in the Catskill region of New York. Murdoch and Stoddard (1993) demonstrated the 

importance of NO3

−

 during high-flow conditions in Catskill streams in which concentrations periodically 

equaled or exceeded SO4

2−

 concentrations. Concentrations of NO3

−

 in most southeastern streams tend to 

be considerably less than SO4

2−

 concentrations (Webb et al., 2004). However, Cook et al. (1994) 

documented very high NO3

−

 concentrations in stream water at high elevation in the Great Smoky 

Mountains in North Carolina. 

Surface water NO3

−

 concentrations have changed over time and these trends vary by region. 

Several regions in the northeastern U.S. showed increased NO3

−

 concentrations during the 1980s. For 

example, in the Catskill Mountains of New York all 16 streams for which data were available showed 

increasing trends in NO3

−

 concentration during that period. A similar increase in NO3

−

 concentration was 

reported for Adirondack lakes in the 1980s (Stoddard et al., 1999). These increasing trends in NO3

−

concentration were initially attributed to N saturation in response to atmospheric deposition (Aber et al., 

1998). 

More recent information on NO3

−

 trends during the 1990s, when atmospheric N deposition was 

relatively stable, suggest that the relationship between atmospheric N deposition and surface water NO3

−

concentrations is complex. During the 1990s, the only significant change occurred in the two regions with 

the highest ambient surface water NO3

−

 concentrations: lakes in the Adirondack Mountains and streams in 

the Northern Appalachian Plateau (Figure 3-11). Both exhibited small but significant downward trends in 

NO3

−

 concentration during the 1990s. The long-term record of dissolved inorganic N (which is largely 

NO3

−

) concentrations at the Hubbard Brook Experimental Forest showed a similar pattern: high 

concentrations in the late 1960s and 1970s, followed by decreases to minimum values in the mid-1990s 

(Aber et al., 2002). Across New England and the Upper Midwest, where ambient surface water 

concentrations are much lower than in the Adirondack Mountains and Northern Appalachian Plateau 

(Figure 3-11), NO3

−

 concentrations in surface waters were unchanged during the 1990s. The Ridge/Blue 

Ridge province registered a small, but significant, decrease in NO3

−

 concentration during the 1990s, but 

interpretation of trends for NO3

−

 in this region was complicated by gypsy moth defoliation, which caused 

large increases in the concentration of NO3

−

 in soil water and stream water (Eshleman et al., 1998). 

Efforts to explain the complex patterns in NO3

−

 concentrations under conditions of reasonably 

stable atmospheric N deposition have focused on both terrestrial and aquatic N cycling. Goodale et al. 

(2003) reported that lower NO3

−

 concentrations measured in the 1990s at streams in New Hampshire 

could not be accounted for by differences in stream flow or forest succession, but inter-annual climate 

variation was proposed as a possible cause. In the Adirondacks, Driscoll et al. (2007a) proposed that 

increased concentrations of atmospheric CO2 may have resulted in a fertilization effect that increased N 

assimilation. Studies by Mitchell et al. (1996) and Murdoch et al. (1998) provide some evidence of 

climate effects on trends in NO3

−

 concentrations in surface waters in the Northeast. In particular, a regionwide spike in NO3

−

 concentrations followed an unusually cold December that may have disrupted soil 

N cycling processes (Mitchell et al., 1996). Murdoch et al. (1998) also found that mean annual air 

temperatures were strongly related to average annual NO3

−

 concentrations during most years in a Catskill 

watershed with elevated NO3

−

 concentrations in stream water. 

 3-32
Source: Stoddard et al. (2003) 

Figure 3-11. Summary of regional trends in surface water chemistry from 1990 to 2000 in regions covered 

by the Stoddard et al. (2003) report. 

Processes within lakes may have also played a role in the measured trends in Adirondack lakes (Ito 

et al., 2005; 2007). In a study of 30 of the 48 long-term monitoring lakes investigated by (Driscoll et al., 

2003d; 2007a) and Momen et al. (2006) found that concentrations of dissolved NO3

−

 were inversely 

correlated with concentrations of chlorophyll a (Chl a) in 11 lakes, and that Chl a was increasing in 

concentration in 9 lakes. The increase in pH observed in most of these lakes may have stimulated 

productivity so that N assimilation by plankton increased (Momen et al., 2006). 

In summary, NO3

−

 contributes to the acidity of many lakes and streams in the eastern U.S. that have 

been affected by acidifying deposition, especially during spring months and under high-flow conditions. 

Nevertheless, there is little or no apparent relationship between recent temporal trends in N deposition and 

trends in NO3

−

 concentrations in surface waters in the eastern U.S. This observation is in sharp contrast to 

observed responses for S deposition and SO4

2−

 concentrations. These results likely reflect the 

complexities of N use within terrestrial and aquatic ecosystems. Uptake of atmospherically deposited N 

by plants and microorganisms in the terrestrial environment precludes drainage water acidification and 

base cation leaching that would be caused if excess N leached as NO3

−

 from the terrestrial to aquatic 

ecosystems. While great uncertainty exists, and the timescales of N saturation may be longer than 

previously considered (e.g., centuries rather than decades), the long-term retention of N deposited in 

forested regions and consequent dampening of deposition effects on surface waters is unlikely to continue 

indefinitely (Aber et al., 2003). Moreover, spatial patterns of NO3

−

 concentrations in surface water across 

the northeastern U.S. are consistent with atmospheric N deposition values although there is considerable 

variation in these concentrations based upon watershed attributes. 

Surface Water Base Cations 

The results from several studies in the eastern U.S. suggest that base cation concentrations in 

surface waters increased during the initial phases of acidification into the 1970s. This trend reversed and 

base cations decreased in response to decreasing SO4

2−

 and NO3

−

 concentrations. For example, Likens 

 3-33
et al. (1996) evaluated trends in base cation concentrations in stream water in relation to long-term trends 

in SO4

2−

 plus NO3

−

 for the Hubbard Brook Experimental Forest. This record showed an approximately 

linear increasing relationship between concentrations of base cations and SO4

2−

 plus NO3

−

 from 1964 to 

1969, then a reversal in 1970 and a decreasing trend up to 1994. The slope of the phase with increasing 

cation concentrations was steeper than the slope for the phase with decreasing cation concentrations. 

Regional declines in base cation concentrations were measured in the Long-Term Monitoring project 

from 1990 to 2000 for lakes in New England, the Adirondack Mountains, and the Upper Midwest (Figure 

3-11). Lawrence et al. (1999) showed decreased concentrations of base cations at a rate that exceeded 

decreases in (SO4

2−

 plus NO3

−

) in Catskill Mountain streams from 1984 to 1997. Within western Virginia 

and in Shenandoah National Park, concentrations of base cations in streams did not exhibit significant 

temporal trends from 1988 to 2001, perhaps due to the influence of S adsorption to soil on stream water 

SO4

2−

 concentrations. 

In some surface waters, interpretation of the effects of, and changes in, the concentration of base 

cations and ANC is complicated by the influence of naturally occurring organic acidity. The base cation 

surplus provides an approach for distinguishing between the effects of organic acidity and acidifying 

deposition (Lawrence et al., 2007). Base cation surplus is defined as the difference between the summed 

concentrations of base cations (Ca, Mg, Na, K) and strongly acidic inorganic anions (SO4

2−

, NO3

−

, 

chloride), plus an estimate of the strongly acidic organic anions estimated from dissolved organic C and 

an assumed charge density. These strongly acidic organic anions are dissociated at low pH, and function 

essentially as mineral acid anions in terms of their effect on ANC. The calculated base cation surplus is 

similar to the calculated ANC, but explicitly accounts for strongly acidic organic acids. When the base 

cation surplus is plotted against inorganic Al concentration, a distinct threshold for Al mobilization occurs 

at a base cation surplus value that closely approximates 0, regardless of the DOC concentration (Figure 3-

12) (Lawrence et al., 2007). This threshold provides an unambiguous reference point for evaluating the 

effects of acidifying deposition on mobilization of inorganic Al. To date, this calculated variable has only 

been used in one large-scale assessment of acidifying deposition effects on surface waters (Lawrence 

et al., 2007). 

In summary, decreases in base cation concentrations in surface water in the eastern U.S. over the 

past two to three decades are ubiquitous and are closely tied to trends in SO4

2−

 concentrations. In most 

regions, rates of decrease for base cations have been similar to those for SO4

2−

 plus NO3

−

, with the 

exception of streams in western Virginia and in the Shenandoah National Park, which are affected by 

decreases in SO4

2−

adsorption in soils. Decreasing trends of base cation concentrations do not necessarily 

indicate further acidification or recovery of surface waters, but may indicate either lower base cation 

leaching rates in soils or depletion of base cations from the soil system. 

Surface Water pH 

Surface water pH is a commonly used as an indicator of acidification. In addition, pH correlates 

with other biologically important components of surface water acid-base chemistry, including ANC, 

inorganic Al, Ca concentration, and organic acidity. Low pH can have direct toxic effects on aquatic 

species (Driscoll et al., 2001b). Threshold pH levels for adverse biological effects have been summarized 

for a variety of aquatic organisms (Baker et al., 1990b; Haines and Baker, 1986). Common reference 

values for pH, below which adverse biological effects are anticipated, are 6.0, 5.5, and 5.0 (Driscoll et al., 

2001b). Only the most acid tolerant fish species can survive below pH 5.0, and Kretser et al. (1989) found 

that half the total number of fish species that occur in the Adirondack region were present in lakes with 

pH less than 6.0. A pH value of 6.0 is often considered the level below which biota are at risk from 

acidification (Driscoll et al., 2001b). The effects of low pH are specific to the study organism and depend 

also upon the concentrations of other chemicals in the water, notably inorganic Al and Ca. Speciesspecific effects are discussed in more detail in Section 3.2.3.3. 

Long-term past changes in surface water pH have been inferred for lakes in the Adirondacks 

through paleolimnological studies (Charles et al., 1989; Cumming et al., 1992; 1994; Sullivan et al., 

 3-34
1990). These studies of algal remains in lake sediments for regionally representative Adirondack lakes 

suggested that about 25 to 35% of the Adirondack lakes that are larger than 4 ha have acidified since 

preindustrial time. An estimated 80% of the Adirondack lakes that had ambient pH less than 5.2 in the 

mid-1980s were inferred to have experienced declines in pH and ANC since the previous century. About 

30 to 45% of the lakes with ambient pH between 5.2 and 6.0 have also acidified. The results suggest that 

the low-ANC lakes of the southwestern Adirondacks acidified more compared to other lakes in the 

Adirondacks since preindustrial time. 

Source: Lawrence et al. (2007) 

Figure 3-12. Concentration of inorganic Al in Adirondack streams as a function of the calculated base 

cation surplus. 

Additional information regarding long-term changes in surface water pH has been gained through 

site-specific dynamic modeling. For example, by applying the PnET-BGC model to the long-term stream 

chemistry record at the Hubbard Brook Experimental Forest, (Gbondo-Tugbawa et al., 2002) estimated 

that past stream pH (circa 1850) was probably about 6.3, compared with values just above 5.0 in 2000 

(Driscoll et al., 2007c). 

 3-35
In recent decades, measurements of pH have been routinely collected in surface waters in the U.S. 

where effects of acidifying deposition have been monitored, but there has been a long-standing reliance 

on titrated ANC as the primary chemical measurement for evaluation of surface water acidification. 

Overall, between 1980 and 2000 most studies reported slight increases in surface water pH, including 

lakes in the Adirondack Mountains (rate variable) (Driscoll et al., 2007a) and southern New England 

(0.002 pH units per year) (Warby et al., 2005), and streams in the Catskill/Poconos region of New York 

and Pennsylvania (0.008 pH units per year) (Warby et al., 2005). 

Through frequent monitoring from 1990 to 2000, Stoddard et al. (2003) found a decrease in 

hydrogen ion (0.19 μeq/L/yr) that was similar to the rate of change observed in the same Adirondack 

lakes by Driscoll et al. (2007a) from 1992 to 2004 (0.18 μeq/L/yr). Stoddard et al. (2003) also reported an 

increase in the hydrogen ion concentration of Appalachian streams (0.08 μeq/L/yr) and Upper Midwest 

lakes (0.01 μeq/L/yr); no trends were found in New England lakes in this study (see Figure 3-11). 

In summary, increasing trends in pH (decreasing hydrogen ion concentration) in surface waters in 

the northeastern U.S. were common through the 1990s up to 2004, but many exceptions occur, and 

overall, the rates of change have been small. Driscoll et al. (2001a, 2001b; 2007c) attributed the limited 

pH recovery of lakes in acid-sensitive regions to three factors: (1) the levels of acid-neutralizing base 

cations in surface waters have decreased markedly because of the depletion of available base cations from 

the soil, and to a lesser extent, a reduction in atmospheric inputs of base cations; (2) as forests mature, 

their requirements for N decrease, and they are expected to increasingly lose NO3

−

 as forests develop; and 

(3) sulfur has accumulated in the soil under previous conditions of high atmospheric S deposition and is 

now being gradually released to surface water as SO4

2−

, even though S deposition has decreased. 

Surface Water ANC 

The most widely used measure of surface-water acidification is ANC, which is often determined by 

Gran titration (titrated ANC). This measurement is the primary chemical indicator for assessing past 

effects of acidifying deposition, and the recovery expected from decreasing atmospheric deposition 

(Bulger et al., 2000; Stoddard et al., 2003). Titrated ANC is useful because it reflects the ANC of the 

complete chemical system, which is typically decreased by acidic deposition in acid-sensitive landscapes. 

Surface water pH is a common alternative to ANC as an indicator of acidification. However, at pH values 

above about 6.0, pH is not a good indicator of either sensitivity to acidification or level of effect. In 

addition, pH measurements (especially at these higher values) are sensitive to levels of dissolved CO2 in 

the water. In contrast, ANC is more stable and it reflects sensitivity and effects of acidification in a linear 

fashion across the full range of ANC values. Therefore, ANC is the preferred indicator variable for 

surface water acidification. Both titrated and calculated ANC values are commonly determined in studies 

aimed at resource characterization or long-term monitoring. 

Bulger et al. (1999) defined ANC response categories for brook trout in Virginia as less than zero 

(chronic damage likely), 0 to 20 μeq/L (episodic damage likely), 20 to 50 μeq/L (likelihood of damage 

not determined), and greater than 50 μeq/L (brook trout not sensitive). Baker et al. (1990b) used ANC 

cutoffs of 0, 50, and 200 μeq/L for reporting on national lake and stream population estimates. ANC less 

than 0 μeq/L is of significance because waters at or below this level have limited capacity to neutralize 

acid inputs. Surface waters with ANC <50 μeq/L have been termed “extremely acid sensitive” (Schindler, 

1988), are prone to episodic acidification in some regions (DeWalle et al., 1987; Eshleman, 1988), and 

may be susceptible to future chronic acidification at current or increased rates of acidifying deposition. 

In assessing changes in surface water ANC, it is important to distinguish between acidic waters and 

acidified waters. “Acidic” describes a condition that can be measured (i.e., Gran ANC less than or equal 

to 0). It may be due either to the effects of acidifying deposition, or to other causes such as the presence 

of organic acidity or the oxidation of chemically reduced S-containing minerals in the watershed. 

“Acidified” refers to the consequences of the process of acidification (a decrease in ANC observed 

through time). It does not require that the water body be acidic, and does not imply a particular cause for 

 3-36
the change in chemistry. The term “anthropogenically acidified” implies that human activity was 

responsible for the increase in acidity that occurred. 

Some of the most detailed studies of ANC have been conducted in the Adirondack Mountains. 

Model simulations suggested that none of the lakes in the Adirondack target lake population identified by 

the U.S. EPA (EMAP) were chronically acidic or had ANC less than 20 μeq/L under preindustrial 

conditions, but that by 1980 there were hundreds of such lakes (Table 3-5). Many lakes were estimated to 

have had preindustrial ANC below 50 μeq/L, but this estimate more than doubled by 1990. Based on 

MAGIC model outputs extrapolated to the regional population of Adirondack lakes larger than 1 ha that 

currently have ANC below 200 μeq/L, maximum past acidification occurred by about 1980 or 1990, with 

median ANC of the lake population of about 61 μeq/L (reduced from a median of 92 μeq/L estimated for 

the preindustrial period). Changes in ANC produced an increase in not only the percentage of lakes that 

were chronically acidic, but also in those that were deemed likely to experience episodic acidification and 

its associated short-term changes in water chemistry (Sullivan et al., 2008). 

Table 3-5. Estimates of change in number and proportion of acidic surface waters in acid-sensitive 

regions of the North and East, based on applying current rates of change in Gran ANC to past 

estimates of population characteristics from probability surveys. 

Results of Regional Survey Results of Monitoring during 1990s 

Region Population 

Size 

Number 

Acidic1

% 

Acidic2

Time Period 

of Estimate

Rate of 

ANC 

change3

Estimated 

Number Acidic in 

2000 

% Acidic in 

2000 

% Change in 

Number of 

Acidic 

Systems 

New England 6,834 lakes 386 lakes 5.6% 1991–94 +0.3 374 lakes 5.5% −2% 

Adirondacks 1830 lakes 238 lakes 13.0% 1991–94 +0.8 149 lakes 8.1% −38% 

N. Appalachians 42,426 km 5,014 km 11.8% 1993−94 +0.7 3,600 km 8.5% −28% 

Ridge/Blue Ridge 32,687 km 1,634 km 5.0% 1987 −0.0 1,634 km 5.0% 0% 

Upper Midwest 8,574 lakes 251 lakes 2.9% 1984 +1.0 80 lakes 0.9% −68% 

1 Number of lakes/length of streams with Gran ANC <0 in past probability survey by the U.S. EPA (data collected at “Time Period of Estimate,” in column 5). 2 Percent of population (from Column 2) with Gran ANC <0 in past probability survey (data collected at “Time Period of Estimate,” in column 5). 3 Based on regional trends presented in the Stoddard et al. (2003) report, in μeq/L/yr, for the 1990s. 

In other regions, responses to reduced levels of acidifying deposition required by the CAA and 

other emissions control legislation were reported by Stoddard et al. (2003). They found tendencies during 

the 1990s toward increasing surface water Gran ANC in all of the glaciated regions of the eastern U.S. 

(i.e., New England, Adirondacks, and Northern Appalachian Plateau) and Upper Midwest, and decreasing 

Gran ANC in the Ridge/Blue Ridge province. Changes in ANC were relatively modest compared with 

observed reductions in SO4

2−

 concentrations in surface waters. The regional increases in the Adirondacks, 

Northern Appalachian Plateau, and Upper Midwest were statistically significant (Table 3-6). Median 

increases of about +1 μeq/L/yr in the Northern Appalachian Plateau, Adirondacks, and Upper Midwest 

represent significant trends towards ecological recovery from acidification (Stoddard et al., 2003). 

Estimated change in the number of acidic surface waters decreased during the 1990s in all regions 

investigated by Stoddard et al. (2003), except the Ridge and Blue Ridge Provinces in the midAppalachian Mountains (Table 3-5). For other regions, the change in number of acidic systems ranged 

from −2% in New England to −68% in the Upper Midwest. 

 3-37
Table 3-6. Regional trend results for long-term monitoring lakes and streams for the period 1990 through 

2000. 

Region SO4

2−

(μeq/L/yr) 

NO3

−

(μeq/L/yr) 

Base 

Cations 

[Ca + Mg] 

(μeq/L/yr) 

Gran ANC 

(μeq/L/yr) 

Hydrogen 

(μeq/L/yr) 

DOC 

(mg/L/yr) 

Aluminum 

(μg/L/yr) 

New England Lakes −1.77** +0.01ns −1.48** +0.11ns −0.01ns +0.03* +0.09ns 

Adirondack Lakes −2.26** −0.47** −2.29** +1.03** −0.19** +0.06** −1.12** 

Appalachian Streams −2.27* −1.37** −3.40** +0.79* −0.08* +0.03ns +0.56ns 

Upper Midwest Lakes −3.36** +0.02ns −1.42** +1.07** −0.01* +0.06** −0.06ns 

Ridge/Blue Ridge Streams +0.29** −0.07** −0.01ns −0.07ns +0.01ns NA NA 

Values are median slopes for the group of sites in each region. Regional trend not significant (p >0.05) * p <0.05 ** p <0.01. NA = insufficient data. 

In summary, ANC is the most widely used measure of acid sensitivity, acidification, and chemical 

recovery of surface waters in response to changes in acidifying deposition. Lake and stream ANC values 

decreased throughout much of the 20th century in a large number of acid-sensitive lakes and streams 

throughout the eastern U.S. This effect has been well documented in monitoring programs, paleolimnological studies, and model simulations. Since about 1990 the ANC of many affected lakes and streams 

have shown some increase, but such increases have been relatively modest. 

Surface Water Aluminum 

The concentration of inorganic Al in surface waters is an especially useful indicator of acidifying 

deposition effects because (1) it is widely toxic, and (2) it generally does not leach from the terrestrial 

soils to surface waters in the absence of acidifying deposition (Driscoll et al., 1988; Lawrence et al., 

2007) with exceptions such as acid mine drainage and relatively rare geologic deposits. Lawrence et al. 

(2005) showed that strong organic acid anions can contribute to the mobilization of inorganic Al in 

combination with SO4

2−

 and NO3

−

, but in the absence of geologic S, the presence of inorganic Al in 

surface waters is an ambiguous indication of acidifying deposition effects. 

Considerable work was done to define pH sensitivity ranges for a wide variety of aquatic 

organisms, but when pH values fall below approximately 5.5, inorganic Al generally becomes the greater 

health risk to biota. Although organically complexed Al (organic Al) can occur in surface waters as a 

result of natural soil and hydrologic processes, this form of Al is not harmful to aquatic life (Gensemer 

and Playle, 1999). Inorganic Al, however, has been found to be toxic to plant and animal species 

throughout the food web (Gensemer and Playle, 1999). 

Earlier studies demonstrated reduced growth and survival of various species of fish (Baker and 

Schofield, 1982; Baker et al., 1996) at inorganic Al concentrations between approximately 2 and 

7.5 μmol/L. Most recently, 20% mortality of young-of-the year brook trout was documented in situ during 

a 30–day period with a median inorganic Al concentration of 2 μmol/L (Baldigo et al., 2007). This study 

estimated that 90% mortality would occur over 30 days with a median inorganic Al concentration of 

4.0 μmol/L. 

The development of methods to fractionate Al into organic and inorganic forms (Driscoll, 1984; 

Sullivan et al., 1986) resulted in collection of a considerable amount of data on Al concentrations in 

surface waters in the 1980s. However, most of this sampling was done either once or for a limited period 

of time (Cronan et al., 1990; Driscoll and Newton, 1985; Driscoll et al., 1987b; Lawrence et al., 1987). 

Available long-term trend information for inorganic Al is limited. In Adirondack lakes, inorganic Al 

 3-38
concentrations decreased slightly (e.g., by 0.02 μM/yr to 0.18 μM/yr) (Driscoll et al., 2007a) or remained 

unchanged between 1982 and 2004 (Driscoll et al., 2007a; Stoddard et al., 2003). There was no trend in 

inorganic Al for this period in New England lakes, Appalachian streams, or Midwest lakes. Monthly 

stream chemistry monitoring at the Hubbard Brook Experimental Forest showed decreases in inorganic Al 

concentrations at four locations along the reference stream for the experimental forest from 1982 to 2000, 

but no trends at two other locations along this stream (Palmer et al., 2004). 

Most recently, Lawrence et al. (in press) found that 49 of 195 streams (25%) in the western 

Adirondack region had inorganic Al concentrations above 2.0 μM during August base flow. Although 

there is not a clear benchmark value above which inorganic Al is toxic to aquatic biota, 2 μM is generally 

recognized as a reasonable threshold for biological effects at a variety of trophic levels (Baldigo et al., 

2007; Driscoll et al., 2001b). 

In summary, inorganic Al is an important chemical indicator of the effects of acidifying deposition 

on surface water. It has well-documented effects on aquatic biota at specific thresholds. Limited data 

suggest that acid-sensitive regions of the northeastern U.S. have elevated inorganic Al concentrations 

which have been induced by years of acidifying deposition and which pose a threat to aquatic life. 

Concentrations have decreased slightly in some surface waters in the northeastern U.S. during the last two 

decades in response to decreased levels of acidifying deposition. 

Quantification of Acidification 

Changes in the acid-base status of soils and drainage waters operate on different time scales. Most 

temperate forest soils have high exchangeable acidity, and relatively small changes in the acidity of 

precipitation input would not be expected to have a large effect on soil acidity (Krug and Frink, 1983; 

Turner et al., 1990). Therefore, projected recovery of soil acid-base chemistry in response to future 

decreases in acidifying deposition is expected to be limited (Gbondo-Tugbawa and Driscoll, 2002; 

Sullivan et al., 2006b). In contrast, changes in the chemistry of drainage water in response to changes in 

acidifying deposition can occur more rapidly. This is because drainage water can become acidified by the 

leaching of a mobile acid anion such as SO4

2−

 or NO3

−

 even if the acidity of the soil is not measurably 

affected (Reuss and Johnson, 1986; Seip, 1980; Turner et al., 1990). In areas (including the northeastern 

U.S.) where S adsorption on soils is minimal, and therefore where SO4

2−

 is highly mobile, changes in S 

deposition input have been shown to cause changes in the ANC and base cation concentrations in lakes 

and streams over a time period of years to decades (c.f., Driscoll et al., 2003). 

One way to quantify acidification dose-response relationships is to calculate the changes in various 

ionic constituents in solution that occur in response to changes in mineral acid anion (SO4

2−

 and NO3

−

) 

concentrations due to changes in acidifying deposition input. As SO4

2−

 + NO3

−

 concentration increases or 

decreases in solution, equivalent changes must also occur in the concentrations of other anions (i.e., 

bicarbonate [HCO3

−

], organic acid anion [RCOO−]) or cations (i.e., H+, Aln+, sum of base cations [SBC]) 

to maintain the charge balance. Typically, the largest counteracting change is in SBC. It is generally 

assumed that most of the base cation change is due to Ca2+ and Mg2+. In acid-sensitive waters, additional 

changes can occur in ANC (which can be expressed as [HCO3

−

 -H+]), Aln+, and/or RCOO−) (Husar and 

Sullivan, 1991). Henriksen (1984) presented evidence for Norwegian lakes, suggesting that base cation 

release accounted for up to a maximum of 40% of the added mineral acid anions. This proportional 

change in base cations relative to SO4

2−

 or [SO4

2−

 + NO3

−

] is called the F-factor: 

Δ ]NOSO[

ΔSBC F

3

2

4

− − + =

Subsequently, diatom reconstructions for Adirondack lakes suggested higher F-factors, generally 

ranging from 0.4 to greater than 1.0 (Sullivan et al., 1990). 

Sullivan and Eilers (1994) compiled available data on proportional changes in SBC, ANC, and 

Aln+, relative to the observed or estimated change in [SO4

2−

 + NO3

−

]. Their analysis included: 1) measured 

 3-39
short-term ( <20 year) changes in drainage water chemistry in response to ambient or experimental 

increases or decreases in S deposition loading, 2) results of space-for-time substitution analyses, 3) results 

of diatom inferences of past lake chemistry, and 4) MAGIC model hindcast and forecast simulations. 

Results indicated a wide range in the estimated proportional changes in SBC as a percent of change in 

[SO4

2−

 + NO3

−

]. Estimated F-factors generally ranged from about 0.5 to 1.0, although some watersheds in 

Norway and in the western U.S. showed F-factors as low as about 0.25. The estimated proportional 

change in ANC was typically less than 0.3, and change in Aln+ was smaller. Quantitative data were not 

available for the organic acid anion response, but this response is also expected to generally be relatively 

small. More recent PnET-BGC simulations for Adirondack lakes were in close agreement with the diatom 

results in Figure 3-13 (Zhai et al., 2008), ranging from about 0.4 to 1.0. 

0.0

0.5

1.0

1.5

F= -10-5* ANC2+0.005*ANC+0.52

 R2

=0.62

ANC (μeq/L)

F-factor

-50 0 50 100 150 200 250

Source: Zhai et al. (2008) 

Figure 3-13. F-factors calculated from PnET-BGC model results for the period 1850 to 1980 as a function of 

simulated ANC in 1980 for 44 EMAP lakes in the Adirondack region of New York. 

Measured, modeled, and inferred changes in surface water chemistry in areas that have experienced 

relatively short-term (less than three decades) changes in acid deposition loading are available from many 

sources. Process model hindcasts and paleolimnological reconstructions of pre-industrial surface water 

chemistry provide insight into the extent to which individual lakes and streams have acidified over the 

longer term (Sullivan et al., 2006b). Evaluation of acidification effects from assessment of current 

conditions is generally not helpful. This is because lakes and streams vary with respect to their expected 

chemistry (i.e., ANC or pH) in the absence of acidifying deposition. In many regions, pre-disturbance lake 

and stream ANC values below 50 μeq/L and pH below 6.0 appear to have been common; in some cases, 

ANC below 0 and pH below 5.0 also occurred (Sullivan et al., 2006b). The common occurrence of lakes 

and streams that were naturally low in ANC and pH before the advent of acidifying deposition is further 

complicated by the fact that disturbances other than air pollution have contributed to further changes in 

acid-base status, including both ANC and pH increases and decreases. Such disturbances have included 

 3-40
logging, fire, forest regrowth, erosion, road-building, forest insect infestation and disease, and other landdisturbing activities and events. Current surface water chemistry is a complex function of inherent 

sensitivity (which to some degree was reflected in pre-disturbance chemistry), levels of acidifying 

deposition (historic and current), and the effects of other disturbances. 

Dose-response functions may in some cases be similar from water body to water body within a 

defined region. For example, model simulations conducted for the NAPAP Integrated Assessment 

(NAPAP 1991) reported by Sullivan et al. (1992) found that, although substantial variability was found in 

projected future change in ANC among the modeled Adirondack watersheds, there was a highly 

consistent relationship between median change in acidifying deposition and projected median change in 

ANC over 50 years. Each 1 kg S/ha/yr change in future S deposition caused approximately a 3.5 μeq/L 

change in simulated lakewater ANC (see Figure 3-14). Results of MAGIC model hindcast simulations 

suggested that all of the Adirondack lakes modeled for NAPAP (1991) had acidified (decreased in pH or 

ANC) since pre-industrial times. The median and range of estimated changes in ANC were -46 μeq/L and 

-31 to -84 respectively. None of the lakes were inferred to have been acidic (ANC ≤ 0) in pre-industrial 

times. The minimum simulated pre-industrial values were pH 5.4 and ANC = 30 μeq/L (Sullivan and 

Eilers, 1994). 

Historical changes in Adirondack lakewater chemistry inferred from measurements in diatoms 

suggested somewhat more conservative estimates of historical acidification. From these data, Sullivan 

(1990) concluded: 

 the “median” Adirondack lake had not acidified; 

 acidification was generally limited to lakes that had ambient ANC during the 1980s less than 

about 50 μeq/L (or pH less than about 6.0); 

 approximately 15% of the Adirondack lakes were inferred to have acidified by more than 0.28 pH 

units; 

 the median historical acidification (expressed as Δ ANC – Δ Ali) of lakes that were acidic 

(ANC <0) at the time of sampling was -37 μeq/L; 

 approximately 3% of the Adirondack lakes were acidic in pre-industrial times, compared to 14% 

in the 1980s. 

 3-41
Source: Sullivan et al. (1992) 

Figure 3-14. Median and range of projected change in ANC (μeq/L) of Adirondack lakes for 50-year MAGIC 

simulations versus median future change in sulfur deposition (kg S/ha/yr) for each deposition 

scenario. Points on each line correspond to -50%, -30%, -20%, 0%, +20%, +30% change from 

current deposition. 

These paleolimnological estimates suggested that Adirondack lakes that were acidic in the 1980s 

had decreased a median of about 4 μeq/L in [ANCG – Ali] for each kg S/ha/yr change in S deposition. This 

was slightly more than one-half of the median historical rate of acidification projected by MAGIC 

(7 μeq/L of calculated ANC for each kg S/ha/yr) for acidic Adirondack lakes (Sullivan et al., 1992). 

In comparing estimates, derived from different approaches, of past and future changes in 

Adirondack lakewater chemistry in response to acidifying deposition, it is important to consider several 

factors (Sullivan et al., 1992): 

 Chemical changes estimated from paleolimnology or monitoring data incorporate all influences 

on the acid-base chemistry of lakewater, including land use, disturbances, and climatic 

differences. Model estimates commonly include only postulated or estimated changes in 

acidifying deposition as having influenced the lakewater chemistry. Because watershed 

disturbances generally cause an increase in surface water ANC, they may partially explain the 

more conservative estimates of diatom-inferred acidification compared with MAGIC model 

estimates of acidification. 

 The use of a process-based model for hindcasting requires assumptions regarding historical 

deposition of all major ions. In addition to uncertainties regarding historical sulfur deposition 

levels, base cation deposition has also likely changed by an unknown amount, and the degree to 

which sulfur and base cation deposition have been coupled is unclear (Chen and Gomez, 1989; 

Driscoll et al., 1989). 

 3-42
 Organic acids may have exerted a greater influence on lakewater pH during pre-industrial times 

than they do currently because DOC and organic acid anion concentrations may have decreased 

in response to increased organic acid protonation and increased concentrations of Al (Almer 

et al., 1974; Davis et al., 1985; Kingston and Birks, 1990; Krug and Frink, 1983). Data sets from 

different points in time are often not directly comparable because of differences in ANC 

definition or pH measurement. For example, the calculated ANC used by MAGIC differs from 

titrated ANC (ANCG) used to calibrate paleolimnological transfer functions and reported in 

surveys. The differences are due to the partially counteracting influences of Al and organic acids 

on ANCG and their omission from calculated ANC. These differences can be appreciable for 

acidic and low-ANC waters (Sullivan et al., 1989). 

There is not a clear definable relationship between atmospheric S deposition and ecological effects. 

A given amount of S deposition can cause a wide range of ecological responses, from no ecological effect 

to varying levels of adverse ecological effect. These can include changes in community composition, loss 

of sensitive species, reduced biological diversity, and altered ecosystem functions. Such effects can occur 

in both aquatic and terrestrial ecosystems. The observed wide range of responses within and among 

regions is attributable to varying ecosystem sensitivity. Some soils (notably in many watersheds in the 

southeastern U.S.) have the capacity to adsorb substantial quantities of S, with essentially no acidification 

of drainage water. Nevertheless, there is a finite limit to this S adsorption capacity, and under continual 

high S deposition loading, the adsorptive capacity of soil will eventually become depleted. 

In addition to differences in S adsorption capacity of soils, watersheds also differ in their sensitivity 

to acidification effects as a consequence of differing sensitivity of the species that make up the local 

biological community. Some species of fish, aquatic insects, and mollusks, for example, are highly 

sensitive to adverse effects from low pH and high inorganic Al concentrations; others are less sensitive. 

Finally, watersheds differ in the size of the soil base cation pool available to neutralize deposited mineral 

acidity. Some watersheds have sufficient quantities of base cations in their soils such that drainage waters 

will remain well buffered, even under relatively high S deposition loads, for many decades or longer. 

Other watersheds had relatively low base cation supply during preindustrial times due to low weathering 

rates of the underlying geology, and the base cation supply may have been further depleted by past 

acidifying deposition. 

As a consequence of these, and other, differences in sensitivity to S inputs, watersheds differ in the 

extent to which they acidify in response to a given amount of S deposition and they also differ in the 

extent to which that acidification translates to biological effects. Thus, one cannot specify a level of S 

deposition that would be likely to cause adverse effects across the landscape. Sensitivity differs from 

watershed to watershed. 

Despite these differences in watershed sensitivity to acidification, it is possible to place bounds on 

the amount of acidification that has occurred in response to a given change in S deposition. Such 

quantitative estimates of acidification have been derived using watershed models of acidification 

response. Modeling results summarized in Table 3-7 illustrate a wide range in the model estimates of past 

acidification of acid-sensitive lakes and streams in the eastern U.S. 

 3-43
Table 3-7. Model projections of surface water SO4

2- and associated ANC, shown as changes between 

dates, for Adirondack and Shenandoah streams. 

Region Water 

Bodies Dates Model Pollution Scenario 

Change in 

Median Surface 

Water SO4

2-

(μeq/L) 

Change in 

Median Surface 

Water ANC 

(μeq/L) 

Reference 

HINDCASTS 

Adirondacks, 

NY 

38 lakes 1850 to 

2003 

PnETBGC  +72.9 -39.9 Zhai et al. 

(2008) 

Adirondacks, 

NY 

37 lakes 1850 to 

1984 

PnETBGC 

+107 -77.8 Chen et al. 

(2005) 

Adirondacks, MAGIC +77.8 -38.3 

NY 

44 potentially 

acid-sensitive 

lakes 

1850 to 

1990 

PnETBGC  +57.3 -29.5 

Sullivan et al. 

(2006b) 

FORWARD PROJECTIONS 

constant 

deposition 

+13 -11.6 

mild reduction -21 +6.2 

medium 

reduction 

-23 +7.2 

strong 

reduction 

-40 +24.2 

Shenandoah 

NP, VA 

5 streams on 

siliciclastic 

bedrock 

1990 to 

2040 

MAGIC 

very strong 

reduction 

-44 +27.2 

Sullivan et al. 

(2008) 

constant 

deposition 

+22 -8 

mild reduction +11 -5 

medium 

reduction 

+11 -5 

strong 

reduction 

+3 -2 

Shenandoah 

NP, VA 

4 streams on 

granitic 

bedrock 

1990 to 

2040 

MAGIC 

very strong 

reduction 

+2 -2 

Sullivan et al. 

(2008) 

constant 

deposition 

+33 -5 

mild reduction +12 0 

medium 

reduction 

+11 +1 

Shenandoah 

NP, VA 

5 streams on 

basaltic 

bedrock 

1990 to 

2040 

MAGIC 

strong 

reduction 

-4 +5 

Sullivan et al. 

(2008) 

 3-44
Region Water 

Bodies Dates Model Pollution Scenario 

Change in 

Median Surface 

Water SO4

2-

(μeq/L) 

Change in 

Median Surface 

Water ANC 

(μeq/L) 

Reference 

very strong 

reduction 

-9 +6 

current and 

expected 

controls 

-42.4 +5.89 

moderate 

emission 

controls 

-58.9 +18.6 

Adirondacks, 

NY 

44 potentially 

acid-sensitive 

lakes 

1990 to 

2050 

MAGIC 

aggressive 

emission 

controls 

-64.6 +22.6 

Sullivan et al. 

(2006b) 

current and 

expected 

controls 

-18 -3.7 

moderate 

emission 

controls 

-32.2 +1.8 

Adirondacks, 

NY 

44 potentially 

acid-sensitive 

lakes 

1990 to 

2050 

PnETBCG 

aggressive 

emission 

controls 

-38.3 +9.3 

Sullivan et al. 

(2006b) 

3.2.3.2. Summary of Biogeochemistry and Chemical Effects 

The evidence is sufficient to infer a causal relationship between acidifying deposition and changes in 

biogeochemistry related to aquatic ecosystems. The strongest evidence for a causal relationship comes 

from studies of changes in surface water chemistry including concentrations of SO4

2−

, NO3

−

, inorganic Al, 

and Ca, surface water pH, sum of base cations, ANC, and base cation surplus. Surface water chemistry 

integrates the sum of upstream soil and water processes and reflects the results of watershed-scale 

terrestrial effects of S and N deposition including, N saturation, forest decline, and soil acidification 

(Stoddard et al., 2003). In many cases, surface water chemistry indicates the effects of acidification on 

biotic species and communities found in fresh water ecosystems. 

Surface water chemistry can be examined and reported as chronic chemistry or episodic chemistry. 

Chronic chemistry refers to annual average conditions, which are often represented as summer and fall 

chemistry for lakes and as spring baseflow chemistry for streams. Episodic chemistry refers to conditions 

during rainstorms or snowmelt when proportionately more drainage water is routed through upper soil 

horizons, which tend to provide less neutralizing of atmospheric acidity as compared with deeper soil 

horizons. Surface water chemistry has lower pH and ANC during storm runoff or snowmelt than during 

baseflow conditions. One of the most important effects of acidifying deposition on surface water 

chemistry is the short-term change in chemistry that is termed “episodic acidification.” Some streams may 

have chronic or average chemistry that is suitable for aquatic biota, but be subject to occasional episodic 

acidification with lethal consequences. Episodic declines in pH and ANC are nearly ubiquitous in 

 3-45
drainage waters throughout the eastern U.S. caused partly by acidifying deposition and partly by natural 

processes. 

Acidification effects on aquatic biota are often evaluated using measures of either inorganic Al or 

pH. ANC is also used because it is an indicator of acid base status (although ANC does not relate directly 

to the health of biota). The usefulness of ANC lies in the association between ANC and the surface water 

constituents that directly contribute to or ameliorate acidity-related stress, in particular pH, Ca, and 

inorganic Al. 

SO4

2−, NO3−, and Base Cations 

Changes in water chemistry resulting from acidifying deposition typically include changes in 

SO4

2−

, NO3

−

, and base cation concentrations. Each plays an important role in the acid-base chemistry of 

water, but none are directly toxic at concentrations commonly encountered in natural waters. 

 SO4

2−

 is the primary inorganic anion found in most acid sensitive waters. Continued decreases in 

S emissions should cause further decreases in SO4

2−

 concentrations in surface waters. However, 

the rate of decrease in surface water SO4

2– concentrations may be delayed as accumulated S 

leaches from watershed soils in some regions of the country, especially the Blue Ridge 

Mountains. 

 The importance of NO3

−

 as an agent of acidification varies by region, but it is particularly 

important during periods of high hydrologic flow from soils to streams such as those that occur 

during snowmelt and rain events. The relationship between N deposition and surface water NO3

−

concentration is complex and involves the terrestrial and aquatic cycling of N and other elements. 

NO3

−

 contributes to the acidity of many lakes and streams in the eastern U.S., but there is no 

apparent relationship between recent trends in N deposition and trends in NO3

−

 concentrations in 

these surface waters (in contrast to observed responses for S deposition and SO4

2−

concentrations). This suggests that the time scales of N saturation may be longer than previously 

considered (e.g., centuries rather than decades). Nevertheless, long-term retention of N deposited 

in forested regions and consequent dampening of deposition effects on surface waters is unlikely 

to continue indefinitely (Aber et al., 2003). 

 Decreases in base cation concentrations in eastern U.S. surface waters over the past two to three 

decades are ubiquitous and are closely tied to trends in SO4

2−

 concentrations. Rates of base cation 

depletion have been similar to those for SO4

2−

 plus NO3

−

 in most areas (Shenandoah National 

Park is a notable exception). Decreasing trends in base cation concentrations do not necessarily 

indicate further acidification or recovery of surface waters, but may indicate either lower base 

cation leaching rates in soils or depletion of base cations from the soil system. 

pH, Acid Neutralizing Capacity, and Aluminum 

Acidification of surface water causes changes in pH, ANC, and inorganic aluminum concentration. 

Low pH and high inorganic Al concentration can be directly toxic to aquatic biota. 

 The pH of freshwater streams and lakes is a common measure used to link acidification to 

adverse effects on aquatic biota. Decreases in pH below values of 6.0 typically result in species 

loss of benthic invertebrates, plankton species, and fish. A number of synoptic surveys indicated 

loss of species diversity and absence of several fish species in the pH range of 5.0 to 5.5. If pH 

decreases to lower values, there is a greater likelihood that more aquatic species could be lost 

without replacement, resulting in decreased richness and diversity. (See the following discussion 

on biota). 

 3-46
 ANC reflects the difference between base cations and anions of strong acids in solution and is the 

most widely used measure of acid sensitivity, acidification, and chemical recovery of surface 

waters in response to changes in acidifying deposition. Acidic waters are defined as those having 

ANC equal to or below zero. Waters with ANC of <50 μeq/L are considered “extremely acid 

sensitive” (Schindler, 1988) and are vulnerable to episodic acidification (DeWalle et al., 1987; 

Eshleman, 1988). Lake and stream ANC values decreased throughout much of the 20th century in 

a large number of acid-sensitive lakes and streams throughout the eastern U.S. Since about 1990, 

the ANC of many affected lakes and streams has increased slightly. The number of acidic surface 

waters has decreased in some areas of the Northeast, but not in the mid-Appalachian Mountains. 

 Dissolved inorganic Al is an important chemical indicator of the effects of acidifying deposition 

on surface water because it is toxic to aquatic life and generally does not leach from soils in the 

absence of acidification. When pH falls below approximately 5.5, inorganic Al generally 

becomes a greater health risk to biota. Limited data suggest that acid-sensitive regions of the 

northeastern U.S. have elevated inorganic Al concentrations in surface waters induced by years of 

acidifying deposition, posing a threat to aquatic life. Concentrations have decreased slightly in 

some surface waters in the northeastern U.S. during the last two decades in response to decreased 

levels of acidifying deposition. 

3.2.3.3. Biological Effects 

Aquatic effects of acidification have been well studied in the U.S. and elsewhere at various trophic 

levels. These studies indicate that aquatic biota have been affected by acidification at virtually all levels of 

the food web in acid sensitive aquatic ecosystems. Effects have been most clearly documented for fish, 

aquatic insects, other invertebrates, and algae. 

Biological effects are primarily attributable to a combination of low pH and high inorganic Al 

concentration. Such conditions occur more frequently during rainfall and snowmelt that cause high flows 

of water and less commonly during low-flow conditions, except where chronic acidity conditions are 

severe. Biological effects of episodes include reduced fish condition factor, changes in species 

composition, and declines in aquatic species richness across multiple taxa, ecosystems and regions. These 

conditions may also result in direct mortality as was shown from results of in situ bioassays (Van Sickle 

et al., 1996). High concentrations of Ca, and to a lesser extent other base cations, can lessen the toxicity of 

low pH and high inorganic Al concentration where they occur (Baker et al., 1990a). 

Biological effects in aquatic ecosystems can be divided into two major categories: effects on health, 

vigor, and reproductive success; and effects on biodiversity. The first category includes changes in 

biological indicators such as individual condition factor and recruitment success. The latter can be 

described by changes in species composition and taxonomic richness. 

The following sections define concepts used to measure and evaluate acidification-related effects 

on aquatic biota. Measures are presented of changes in health, vigor, and reproductive success; and 

biodiversity for fish. Finally, the general effects literature is summarized for phytoplankton, zooplankton, 

benthic invertebrates, amphibians, and fish-eating birds. Specific reference is made to the biological 

indicators outlined above where such information exists. 

Measures of Health, Vigor, and Reproductive Success 

There are few measures of the effects of acidification on the health, vigor, and reproductive success 

of aquatic species. Condition factor is one measure of sublethal acidification stress that has been used to 

quantify effects of acidification on an individual fish. Condition factor is an index that describes the 

relationship between fish weight and length. Expressed as fish weight/length3

, multiplied by a scaling 

constant, this index reflects potential depletion of stored energy (Dennis and Bulger, 1995; Everhart and 

Youngs, 1981; Goede and Barton, 1990). Condition factor is interpreted as depletion of energy resources 

 3-47
such as stored liver glycogen and body fat in response to increased stress at sublethal levels (Goede and 

Barton, 1990). Fish with higher condition factor are more robust than fish having low condition factor. 

Field studies have shown lower condition factor in fish found in more acidic streams (Dennis and Bulger, 

1995). 

Measures of Biodiversity 

Species composition refers to the mix of species that are present in a particular ecosystem. 

Acidification alters species composition in aquatic ecosystems. There are a number of species common to 

many oligotrophic waters that are sensitive to acidifying deposition and that cannot survive, compete, or 

reproduce in acidic waters. In response to small to moderate changes in acidity, acid-sensitive species are 

often replaced by other more acid-tolerant species, resulting in changes in community composition, but 

little or no change in total community abundance or biomass. The extent of alteration of surface water 

biological community composition increases as surface waters become more acidic. There is also a 

common pattern of lower community diversity with increased acidification. 

One important tool that aids in the determination of effects on species composition is the ASI 

developed by (Baker et al., 1990b). This index uses fish bioassay survival data to predict the probability 

of fish survival expressed as a percent mortality. Separate ASI models were developed for tolerant, 

intermediate, and sensitive fish species. 

Taxonomic richness is a metric that is commonly used to quantify the effects of an environmental 

stress on biota. It can be applied at various taxonomic levels. For example, the number of fish species 

present in a lake or stream can be used as an index of acidification (Bulger et al., 1999). Similarly, 

acidification effects on aquatic insects can be evaluated on the basis of the number of families or genera 

of mayflies (order Ephemeroptera) (Sullivan et al., 2003). In the latter cases, the mayfly order was 

selected for study because it includes a number of genera and species having varying degrees of 

sensitivity to acidification. 

Decreases in ANC and pH and increases in inorganic Al concentration have been shown to 

contribute to declines in species richness and abundance of zooplankton, macroinvertebrates, and fish 

(Keller and Gunn, 1995; Schindler et al., 1985). Species richness is positively correlated with pH and 

ANC (Baker et al., 1990b; Rago and Wiener, 1986) primarily because of the elimination of acid-sensitive 

species at lower pH and ANC (Schindler et al., 1985). Interpretation of species richness can be difficult 

because more species tend to occur in larger lakes and streams as compared with smaller ones, 

irrespective of acidity (Sullivan et al., 2003). Nevertheless, decreases in species richness have been 

observed for all major trophic levels of aquatic organisms (Baker et al., 1990b), even after adjusting for 

lake size (Frenette et al., 1986; Harvey and Lee, 1982; Matuszek and Beggs, 1988; Rago and Wiener, 

1986; Schofield and Driscoll, 1987). 

Health, Vigor, and Reproductive Success of Fish 

Fish populations in acidified streams and lakes of Europe and North America have declined, and 

some have been eliminated as a result of atmospheric deposition of acids and the resulting changes in 

water quality (Baker et al., 1990b). A variety of water chemistry variables, including inorganic Al, DOC, 

and Ca, along with the timing and magnitude of episodic fluctuations in toxic acid and inorganic Al 

concentrations, are related to the degree to which surface water acidification influences fish survival in 

natural systems (Baker et al., 1990b; Baldigo and Murdoch, 1997; Gagen et al., 1993; Siminon et al., 

1993; Van Sickle et al., 1996). 

The effects of acidification on the health, vigor, and reproductive success are manifested through a 

range of physiological effects on individual life stages and fish species. The primary mechanism for the 

toxic effects of low pH and elevated inorganic Al on fish involves disruption of normal ion regulation at 

the gill surface, resulting in increased rates of ion loss and inhibition of ion uptake (Bergman et al., 1988; 

Leivestad, 1982; McWilliams and Potts, 1978; Wood and McDonald, 1987). Additional effects might 

include disruption of Ca metabolism (Gunn and Noakes, 1987; Peterson and Martin-Robichaud, 1986; 

 3-48
Reader et al., 1988); and decreased hatching success (Gunn and Noakes, 1987; Haya and Waiwood, 1981; 

Peterson et al., 1980; Reader et al., 1988; Runn et al., 1977; Waiwood and Haya, 1983). 

There is marked variability among species, and among life stages within species, in the specific 

levels of pH and inorganic Al that produce measurable responses. In general, early life stages are more 

sensitive to acidic conditions than the young-of-the-year, yearlings, and adults (Baker and Schofield, 

1985; Baker et al., 1990b; Johnson et al., 1987). Also, small fish, especially swim-up fry, are probably 

less mobile and less able to avoid exposure to adverse chemical conditions than the relatively larger adults 

(Baker et al., 1996). Here, effects are described by life stage. Several studies have shown that the earliest 

reproductive stages are highly sensitive to low pH. The processes of oogenesis and fertilization in fish are 

especially sensitive (Havas et al., 1995; Muniz, 1991), most likely due to adverse effects on the female 

spawner. For instance, Beamish (1976) reported that reduced serum and plasma Ca in female fish in 

acidified Canadian lakes caused a higher probability of failure in producing viable eggs. Depletion of Ca 

from bone and increased numbers of females with unshed eggs have also been linked to acidification at 

this life stage (cf. Muniz, 1991; Rosseland, 1986). 

After fertilization, the embryo seems to be susceptible to acidic waters throughout the whole period 

of development, although periods shortly after fertilization and before hatching seem to be most critical 

(Rosseland, 1986). The susceptibility of the embryo can be the result of direct exposure to elevated H+

concentrations and to the toxic effects of inorganic Al at intermediate pH-values. Low pH in the 

surrounding water also results in pH-depression inside the egg, leading to either prolongation of hatching 

or to reduced hatching success (Rosseland, 1986). Eggs lying in gravel on stream and lake beds are, to 

some extent, protected from exposure to rapid changes in pH (Gunn and Keller, 1984b; Lacroix, 1985). 

Nevertheless, they can experience high mortality during periods of acid runoff, such as snowmelt (Gunn 

and Keller, 1984a). Yellowstone cutthroat trout (O. c. bouveri) were exposed to 7–day pH depressions by 

Farag et al. (1993). Of the four life stages studied, eggs were most sensitive to low pH. Eggs exposed for 

seven days to pH 5.0 test water showed a statistically significant reduction in survival compared with 

eggs exposed for seven days to pH 6.5 test water. Survival of alevin and swim-up larvae was significantly 

reduced from near 100% at pH 6.5 to near 0% at pH 4.5. Intermediate pH values (6.0, 5.5) in all cases 

showed reduced survival compared with the control (6.5), but not by statistically significant amounts 

(p >0.05). 

Emergent alevins show susceptibility to the adverse effects of inorganic Al and H+

 that increases 

with age (Baker and Schofield, 1982; Wood and McDonald, 1982). Rosseland (1986) indicated that this 

increasing sensitivity results from changes that take place in the respiratory system. Shortly after 

hatching, alevins still respire through their skin but gradually gills become the primary organ of gas and 

ion exchange. Gills are the locus for interference of hydrogen ion and inorganic Al with iono-regulatory 

exchange. 

Woodward et al. (1989) exposed cutthroat trout (Oncorhynchus clarki) from the Snake River in 

Wyoming to pH depressions from pH 4.5 to 6.5 in the laboratory and found that reductions in pH from 6.5 

to 6.0 in low-Ca water (70 μeq/L) did not affect survival, but did reduce growth of swim-up larvae. The 

eggs, alevin, and swim-up larval stages showed significantly higher mortality at pH 4.5 than at pH 6.5. 

Mortality was also higher at pH 5.0 than at pH 6.5, but only statistically higher for eggs. The authors 

concluded that the threshold for effects of acidity on greenback cutthroat trout in the absence of inorganic 

Al was pH 5.0 (Woodward, 1991). 

In juvenile, young-of-year and adult fish there is an energy cost in maintaining physiological 

homeostasis; the calories used to respond to stress are a part of the fish's total energy budget and are 

unavailable for other functions, such as growth and reproduction (Schreck, 1981, 1982; Wedemeyer et al., 

1990). Observed differences in condition factor may occur because maintenance of internal chemistry in 

the more acidic streams would require energy that otherwise would be available for growth and weight 

gain (Dennis and Bulger, 1999; Sullivan et al., 2003). The energy costs to fish for active ionoosmoregulation can be substantial (Bulger, 1986; Farmer and Beamish, 1969). 

Prominent physiological disturbances to fish exposed to acid waters are iono- and osmoregulatory 

failure, acid-base regulatory failure, and respiratory and circulatory failure. Most of these effects can be 

 3-49
directly attributed to effects on gill function or structure. The acute toxicity of low pH in acidic waters 

results in the loss of Ca from important binding sites in the gill epithelium, which reduces the ability of 

the gill to control membrane permeability (Exley and Phillips, 1988; Havas, 1986; McDonald, 1983). Al 

has been shown to accumulate on the gill surface when fish are exposed to water having high inorganic Al 

concentration. 

Cumulative sublethal physiological effects can be expressed by changes in condition factor. 

Condition factor has been developed and applied mainly for blacknose dace. This fish species is widely 

distributed in Appalachian Mountain streams and is moderately tolerant of low pH and ANC, relative to 

other fish species in the region. However, the condition factor concept is probably applicable to other 

species as well. Condition factor may be a useful metric for many species in aquatic ecosystems that are 

only marginally affected by acidification. Bulger et al. (1999) observed a positive relationship between 

dace condition factor and pH in streams in Shenandoah National Park. Dennis and Bulger (1995) found a 

reduction in the condition factor for blacknose dace in waters near pH 6.0. The four populations with the 

lowest condition factor had mean habitat pH values within or below the range of critical pH values at 

which Baker and Christensen (1991) estimated that negative population effects are likely for the species. 

The mean condition factor of fish from the study stream with the lowest ANC was about 20% lower than 

that of the fish in best condition. In addition to effects on blacknose dace, condition factor, reduced 

growth rates have been also attributed to acid stress in a number of other fish species, including Atlantic 

salmon (Salmo salar), Chinook salmon (Oncorhynchus tshawytscha), lake trout (Salvelinus namaycush), 

rainbow trout (Oncorhynchus mykiss), brook trout, brown trout (Salmo trutta), and arctic char (Salvelinus 

alpines) (Baker et al., 1990b). 

In summary, some studies have been conducted on changes in the health, vigor, and reproductive 

success of fish exposed to water having low pH and high inorganic Al concentration. Blacknose dace 

have been most thoroughly studied regarding the sublethal effects of acidity on fish condition. Effects 

tend to vary by life stage; early life stages tend to be particularly sensitive. Adverse effects often involve 

disruption of gill function, partly due to Al toxicity. 

Fish Biodiversity 

Biodiversity loss is a predictable consequence of acidification and there are abundant examples of 

this in North America and Europe, mostly focused on fish (Bulger et al., 2000). Population-level fish 

response to acidification is primarily through recruitment failure, a result of increased mortality of early 

life stages or indirect effects through the food chain (loss of prey species). Changes in inorganic Al, pH, 

and Ca most likely have the greatest influence on fish community structure. These changes in water 

chemistry can alter species composition and species richness, both of which are components of 

biodiversity. 

By 1990, it was well established that changes in pH in the range of 4.0 to 6.5 could cause 

significant adverse biological effects on fish community composition. As described above, the toxicity of 

low pH was, in most cases, the result of impaired body salt regulation. Decreased water pH inhibited the 

active uptake of Na+

 and Cl− and stimulated the passive loss of these ions from the bloodstream (Baker 

et al., 1990b). Species vary in terms of their sensitivity to such disruptions of physiological condition. 

The response of fish to pH, ANC, and inorganic Al is not uniform across species. A number of 

synoptic surveys indicated loss of species diversity and absence of several fish species in the pH range of 

5.0 to 5.5. If pH is lower, there is a greater likelihood that more fish species could be lost without 

replacement, resulting in decreased richness and diversity. In general, populations of salmonids are not 

found at pH levels less than 5.0, and smallmouth bass (Micropterus dolomieu) populations are usually not 

found at pH values less than about 5.2 to 5.5. Bioassay experiments using brook trout eggs and fry have 

demonstrated greater mortality in chronically acidic stream water as compared to water having higher 

ANC. 

The ASI is an index of acidification that uses fish bioassay survival data fitted to a regression 

model of exposure to water chemistry (pH, Al, and Ca) to predict the probability of fish survival. 

 3-50
Approximate ASI reference levels were reported by (Baker et al., 1990b) for various fish species, based 

on logistic regression of fish presence as a function of the sensitive, intermediate, and tolerant ASI values 

for brown bullhead (Ameiurus nebulosus), brook trout, lake trout, and common shiner (Luxilus cornutus). 

Fish species richness is an important indicator of acidification response, in part because the public 

tends to place relatively high value on fisheries. As discussed in the previous section, lakes and streams 

having pH below about 5.0 or ANC below about 0 generally do not support fish. There is often a positive 

relationship between pH and number of fish species, at least for pH values between about 5.0 and 6.5, or 

ANC values between about 0 and 50 to 100 μeq/L (Bulger et al., 1999; Cosby et al., 2006; Sullivan et al., 

2006a). Such observed relationships are complicated, however, by the tendency for smaller lakes and 

streams, having smaller watersheds, to also support fewer fish species, irrespective of acid-base 

chemistry. This pattern may be due to a decrease in the number of available niches as stream or lake size 

decreases. Nevertheless, fish species richness is relatively easily determined and is one of the most useful 

indicators of biological effects of surface water acidification. 

Some of the most in-depth studies of the effects of acid stress on fish species richness have been 

conducted in the streams in Shenandoah National Park, Virginia and the lakes in the Adirondack 

Mountains, New York. These regions are examined in detail below. However, note that effects on fish 

species richness have also been documented in acid-sensitive streams of the Catskill Mountains of 

southeastern New York (Stoddard and Murdoch, 1991) and the Appalachian Mountains from 

Pennsylvania to Tennessee and South Carolina ((SAMAB), USDA 1996; Bulger et al., 1999; 2000). 

The Shenandoah National Park Fish in Sensitive Habitats (FISH) Project evaluated the effects of 

streamwater acidification on fish communities in streams in Shenandoah National Park (Bulger et al., 

1995; Dennis and Bulger, 1995; Dennis et al., 1995; MacAvoy and Bulger, 1995). A statistically robust 

relationship between stream ANC and fish species richness was documented. Numbers of fish species 

were compared among 13 Shenandoah National Park streams spanning a range of pH and ANC 

conditions. There was a highly significant (p <0.0001) relationship between stream acid-base status 

(during the 7–year period of record) and fish species richness among the 13 streams. The streams with the 

lowest ANC hosted the fewest species (Figure 3-15). The 3−year FISH study of stream acidification 

demonstrated negative effects on fish from both chronic and episodic acidification (Bulger et al., 1999). 

Bulger et al. (1999) concluded that the most important cause of the observed decline in species richness 

with decreasing ANC was acid stress from acidification. However, an additional causal factor may have 

been a decrease in the number of available aquatic niches when moving from downstream locations 

(which are seldom low in pH and ANC) to upstream locations (which are often low in pH and ANC in 

this region)(Sullivan et al., 2003). 

South of Shenandoah National Park, the effects of surface water acidification on fish species 

richness have been studied in some detail in the St. Marys River in Virginia. Fish species richness was 

closely associated with surface water acid-base chemistry. The number of fish species in the St. Marys 

River within the wilderness declined from 12 in 1976 to 4 in 1998. Three of the four species present in 

1998 (brook trout, blacknose dace, fantail darter [Etheostoma flabellare] are tolerant of low pH and are 

typically the only fish species present in streams having similar levels of acidity in nearby Shenandoah 

National Park (Bulger et al., 1999). 

 3-51
Dynamic water chemistry model projections have been combined with biological dose-response 

relationships to estimate declines in fish species richness with acidification. A relationship derived from 

the Shenandoah National Park data was used by Sullivan et al. (2003), along with stream ANC values 

predicted by the MAGIC model to provide estimates of the expected number of fish species in each of the 

modeled streams for the past, present and future chemical conditions simulated for each stream. Results 

suggest that historical loss of species had been greatest in the streams located on the most sensitive 

geological class (siliciclastic bedrock; 1.6 species lost), with fewer lost species on granitic bedrock and 

basaltic bedrock (average of 0.4 species lost). 

Source: Redrawn from Bulger et al. (1999) 

Figure 3-15. Number of fish species as a function of mean stream ANC among 13 streams in Shenandoah 

National Park, Virginia. Values of ANC are means based on quarterly measurements, 1987–

1994. The regression analysis showed a highly significant relationship (p <0.0001) between 

mean stream ANC and number of fish species. Streams having ANC consistently <50 μeq/L 

had three or fewer species. 

In the Adirondack Mountains, lakewater acidification and the associated elevated concentrations of 

inorganic Al have adversely affected fish populations and communities in sensitive areas (Baker and 

Schofield, 1982; Baker et al., 1990a; Johnson et al., 1987; Schofield and Driscoll, 1987; Siminon et al., 

1993). Of the 53 fish species recorded in Adirondack lakes by the Adirondack Lakes Survey Corporation, 

about half (26 species) were absent from lakes with pH below 6.0. Among the absent species were several 

important recreational species (Baker et al., 1990a), plus ecologically important minnows that serve as 

forage for sport fish. Fully 346 of 1,469 lakes surveyed supported no fish at all at the time of the survey. 

These lakes were significantly lower in pH, dissolved Ca, and ANC, and had higher concentrations of 

inorganic Al than lakes hosting one or more species of fish (Gallagher and Baker, 1990). Among lakes 

with fish, there was an unambiguous relationship between the number of fish species and lake pH, 

ranging from about one species per lake for lakes having pH less than 4.5 to about six species per lake for 

lakes having pH higher than 6.5 (Baker et al., 1990a; Driscoll et al., 2001b). 

High-elevation lakes are more likely to be fishless than larger lakes at low elevation (Gallagher and 

Baker, 1990). This observation has been attributed to the fact that high elevation lakes tend to have poor 

access for fish immigration, poor fish spawning substrate, or low pH, or they may be susceptible to 

 3-52
periodic winter kills. Small, high-elevation Adirondack lakes with fish also had significantly higher pH 

compared with fishless high-elevation lakes; acidity is likely to play an important role in the absences of 

fish from such lakes (Driscoll et al., 2001a). 

Sullivan et al. (2006b) developed a relationship between fish species richness and ANC class for 

Adirondack lakes. Under chronically acidic conditions (summer index or annual average ANC <0 μeq/L), 

Adirondack lakes are generally without fish. There was a marked increase in mean species richness with 

increases in ANC up to values of approximately 50 to 100 μeq/L (Figure 3-16). The asymptote for the fish 

species equation was 5.7 species. This analysis suggests that there could be loss of fish species with 

decreases in ANC below approximately 50 to 100 μeq/L. The response functions from Shenandoah 

National Park (Figure 3-15) and the Adirondack Mountains (Figure 3-16) are generally similar at low 

ANC values, below about 100 μeq/L. Fish species richness was somewhat higher in Shenandoah National 

Park at higher ANC values. The reasons for this difference are not known. 

Source: Sullivan et al. (2006b). 

Figure 3-16. Number of fish species per lake versus acidity status, expressed as ANC, for Adirondack 

lakes. The data are presented as mean (filled circles) and range (bars) of species richness 

within 10 μeq/L ANC categories, based on data collected by the Adirondack Lakes Survey 

Corporation. 

The absence of fish from a given lake or stream in an area that experiences surface water 

acidification does not necessarily imply that acidification is responsible for the absence of fish. For 

example, results of fisheries research in the Adirondacks have indicated that many Adirondack lakes 

always had marginal spawning habitat for brook trout (Schofield, 1993). However, multivariate regression 

of the presence or absence of brook trout in Adirondack waters produced a ranking of factors that 

appeared to influence the presence of brook trout when biological factors (stocking, presence of 

associated species, presence of competitors) were excluded from the analysis. Among contributing 

factors, including silica (Si), ANC, DOC, substrate type, and distance to the nearest road, pH ranked first 

as a predictor of brook trout presence. The results of this analysis supported the conclusion that 1990 

levels of pH and related variables restricted the distribution of fish in some Adirondack lakes. 

 3-53
In summary, acidic conditions characterized by low pH, low ANC, and high inorganic Al exert 

considerable influence on the fish species composition of sensitive surface waters, particularly in the 

eastern U.S. Low pH and ANC, and high inorganic Al concentrations, contribute to loss of the most acidsensitive fish species. Species richness is a common indicator used to reflect the effects of water 

acidification on aquatic biota. This index is most often applied to fish. Few or no fish species are found in 

lakes and streams that have very low ANC (near zero) and low pH (near 5.0). The number of fish species 

generally increases at higher ANC and pH values. This relationship is complicated, to some extent, by the 

tendency of smaller lakes and streams (which are more likely to have low ANC and pH) to host fewer fish 

species, regardless of acid-base chemistry. Nevertheless, available data strongly suggest that acid stress is 

a major factor governing the observed relationship between fish species richness and surface water 

acidity. 

3.2.3.4. Summary of Biological Effects 

The evidence is sufficient to infer a causal relationship between acidifying deposition and changes in 

aquatic biota. The strongest evidence for a causal relationship comes from studies of aquatic systems 

exposed to elevated levels of acidifying deposition that support fewer species of fishes, 

macroinvertebrates, and diatoms. Although there are few studies of the response of higher trophic levels 

to pH changes resulting from acidifying deposition, piscivorous birds are known to be affected by 

acidifying deposition (see Table 3-9). Consistent and coherent evidence from multiple species and studies 

shows that acidification can result in the loss of acid-sensitive species, and more species are lost with 

greater acidification. Biological effects are linked to changes in water chemistry including ANC, pH, and 

inorganic Al. Decreases in ANC and pH and increases in inorganic Al concentration contribute to declines 

in taxonomic richness of zooplankton, macroinvertebrates, and fish. Chemical changes can occur over 

both long- and short-term time scales, with additional effects on biological systems. Short-term (hours or 

days) episodic changes in water chemistry can have biological effects, including reduced fish condition 

factor, changes in species composition, and declines in aquatic species richness across multiple taxa, 

ecosystems and regions. 

Species 

 High levels of acidification (to pH values below 5) virtually eliminate all mayflies, crustaceans, 

and mollusks from some streams. 

 In general, populations of salmonid fish are not found at pH levels less than 5.0, and smallmouth 

bass (Micropterus dolomieu) populations are usually not found at pH values less than 5.5 to 5.2. 

 Twenty percent mortality of young-of-the year brook trout was documented during a 30–day 

period with a median inorganic Al concentration of 2 μmol/L (Baldigo et al., 2007). It was 

estimated that 90% mortality would occur over 30 days with a median inorganic Al concentration 

of 4.0 μmol/L. 

Community 

Community-level effects were observed in two well-studied ares, the Adirondacks and the 

Shenandoah National Park, where taxonomic richness is lower in lakes and streams having low ANC and 

pH. 

 Decreases in pH and increases in inorganic Al concentrations have reduced the species richness 

of plankton, invertebrates, and fish in acid-affected surface waters. 

 3-54
 Invertebrate taxa that are most sensitive to acidification include mayflies, amphipods, snails, and 

clams. 

 In the Adirondacks, a positive relationship exists between the pH and ANC in lakes and the 

number of fish species present in those lakes. A number of synoptic surveys indicated suggested 

loss of species diversity and absence of several sensitive fish species in the pH range of 5.0 to 6.0. 

 In Shenandoah National Park streams, the fish species richness decreased with decreasing stream 

ANC. On average, richness is lower by one fish species for every 21 μeq/L decrease in ANC. 

 Short-term episodes of acidification are particularly harmful to aquatic biota. Early life stages are 

more sensitive to acidic conditions than the young-of-the-year, yearlings, and adults. Episodes are 

most likely to affect biota if the water had pre-episode pH above 5.5 and minimum pH during the 

episode of less than 5.0. Episodic acidification can have long-term adverse effects on fish 

populations. 

3.2.4. Most Sensitive and Most Affected Ecosystems and Regions 

3.2.4.1. Characteristics of Sensitive Ecosystems 

The principal factor governing the sensitivity of terrestrial and aquatic ecosystems to acidification 

from S and N deposition is geology (particularly surficial geology). Geologic formations having low base 

cation supply generally underlie the watersheds of acid-sensitive lakes and streams. Bedrock geology has 

been used in numerous acidification studies (Bricker and Rice, 1989; Stauffer, 1990; Stauffer and 

Wittchen, 1991; Vertucci and Eilers, 1993; Sullivan et al., 2007b). Other factors contribute to the 

sensitivity of soils and surface waters to acidifying deposition, including topography, soil chemistry, land 

use, and hydrologic flowpath. 

Several studies have confirmed the importance of geology in regulating terrestrial and aquatic 

ecosystem sensitivity to acidification, and highlighted other key factors responsible for terrestrial and 

aquatic sensitivity to acidifying deposition throughout the southeastern U.S. Sensitive terrestrial 

ecosystems include high-elevation spruce-fir forests dominated by relatively nonreactive bedrock in 

which base cation production via weathering is limited (Elwood et al., 1991). Soils in such areas tend to 

have thick organic horizons, high organic matter content in the mineral horizons, and low pH (Joslin 

et al., 1992). Because of the largely nonreactive bedrock, base-poor litter and organic acid anions 

produced by the conifers, high precipitation, and high leaching rates, soil base saturation in these highelevation forests tends to be below about 10% and the soil cation exchange complex is generally 

dominated by Al (Eagar et al., 1996; Johnson and Fernandez, 199). 

Galloway (1996) further attributed forest soil sensitivity to acidification in the southeastern U.S. to 

atmospheric deposition level, soil age, weathering rate, and S adsorption capacity. Moncoulon et al. 

(2004) suggested that forest ecosystem sensitivity to acidification varies mainly with weathering rate. In a 

review of 241 ecosystem types in France (classified by pedologic and geologic characteristics), the 

ecosystems most susceptible to acidification were those with low weathering rates and thus limited 

buffering capacity (Moncoulon et al., 2004). 

In hardwood forests, species nutrient needs, soil conditions, and additional stressors work together 

to determine sensitivity to acidifying deposition. Stand age and successional stage also can affect the 

susceptibility of hardwood forests to acidification effects. In northeastern hardwood forests, older stands 

exhibit greater potential for Ca depletion in response to acidifying deposition than younger stands. Thus, 

with the successional change from pin cherry (Prunus pensylvanica), striped maple (Acer pensylvanicum), 

white ash (Fraxinus americana), yellow birch and white birch (Betula papyrifera) in younger stands to 

 3-55
beech and red maple in older stands, there is an increase in sensitivity to acidification (Hamburg et al., 

2003). 

Land use influences watershed sensitivity to acidification mainly through disturbance and 

consequent exposure of S-bearing minerals to oxidation, loss of base cations through erosion and timber 

harvesting, and change in N status of the forest through timber management. Each of these types of 

activity can influence the relative availability of mobile mineral acid anions (SO4

2−

, NO3

−

) in soil solution 

and base cations (Ca, Mg, K, Na) on the soil ion exchange sites and in drainage water. 

The movement of water through the soils into a lake or stream, and the interchange between 

drainage water and the soils and sediments, strongly regulate the type and degree of watershed response 

to acidic inputs (Sullivan, 2000a). Surface waters in the same setting can have different sensitivities to 

acidification depending on the relative contributions of near-surface drainage water and deeper 

groundwater (Chen et al., 1984; Driscoll et al., 1991; Eilers et al., 1983). 

Movement of a strong acid anion, such as SO4

2− or NO3−

, through an acidic soil can mobilize H+

and Al3+ because these cations are available on soil exchange sites. There is no time lag in this exchange 

reaction and it is instantly reversible if input of strong acid anions is ceased (Turner et al., 1991). It is 

necessary, however, for appreciable mobilization of H+

 and Al3+ that the soil be acidic, either naturally or 

because of soil acidification from acidifying deposition. 

In summary, lakes and streams in the U.S. that are sensitive to episodic and chronic acidification in 

response to SOX, and to a lesser extent NOX, deposition tend to occur at relatively high elevation in areas 

that have base-poor bedrock, high relief, and shallow soils. For example, in the Southern Appalachian 

region (Sullivan et al., 2002a, 2007b) determined that underlying bedrock geology dominated by 

sandstone or related rock types and elevations greater than 1000 m (3250 ft) could be used to identify 

landscapes in the region most likely to contain acidic streams. 

3.2.4.2. Extent and Distribution of Sensitive Ecosystems 

Surface Waters 

Several regions of the U.S. contain appreciable numbers of lakes and streams with low ANC (less 

than about 50 μeq/L), including portions of the Northeast (especially New England, the Adirondacks, and 

the Catskill Mountains), Southeast (the Appalachian Mountains and northern Florida), Upper Midwest, 

and western U.S. (Charles, 1991). The Adirondack and Appalachian Mountains, and to a lesser extent the 

Upper Midwest, include many acidified surface waters that have been affected by acidifying deposition. 

Portions of northern Florida also contain many acidic and low-ANC lakes and streams, although the role 

of acidifying deposition in these areas is less clear. The western U.S. contains many of the surface waters 

most susceptible to potential acidification effects, but with the exception of the Los Angeles Basin and 

surrounding areas, the levels of acidifying deposition in the West are low in most areas, acidic surface 

waters are rare, and the extent of chronic surface water acidification that has occurred to date has likely 

been very limited. 

Several national assessments were conducted to estimate the distribution and extent of surface 

water acidity in the U.S. During summer baseflow of 2004, the U.S. EPA conducted a National Wadeable 

Stream Assessment (WSA) survey of 1,392 randomly selected sites across the conterminous 48 U.S. to 

assess the ecological condition of wadeable streams (U.S. EPA, 2006d). Because this sampling was 

conducted during baseflow in the summer (which exhibits the least acidic conditions of the year), only the 

most chronically acidified streams were identified as acidic. Therefore, the extent of potential seasonal 

acidification was underestimated by this approach (Lawrence et al., 2008). Overall, less than 1% of the 

1,020,000 km of stream in the target population (based on the 1:100,000–scale U.S. Geological Survey 

(USGS) map blue line network) was acidic due to acidifying deposition. No acidic streams were observed 

in the Mountainous West, Xeric West, Upper Midwest, Northern Plains, Southern Plains, or Temperate 

Plains ecoregions. Acidic streams attributable to acidifying deposition were found in the Northern 

Appalachians (2.8% of 96,100 km of stream), and the Southern Appalachians (1.8% of 287,000 km). Very 

 3-56
low ANC (0–25 μeq/L) streams likely exposed to episodic acidification were found in the Northern 

Appalachians (2.7% of 96,100 km of stream), the Coastal Plain (6.3% of 119,000 km), and the 

Mountainous West (0.6% of 204,000 km). 

Even though the WSA had over 1,300 sample sites, it was still a very coarse sample of the nation’s 

streams with respect to acidifying deposition effects, which are only observed in spatially restricted 

subpopulations. More precise survey estimates of the effects of surface water acidification were made in 

the National Surface Water Survey (NSWS) in the mid 1980s. By statistically selecting representative 

lakes and streams in each surveyed region, the NSWS estimated chemical conditions of 28,300 lakes and 

56,000 stream reaches (Baker et al., 1990b). The NSWS concluded that 4.2% of lakes larger than 4 ha and 

2.7% of stream segments in the acid-sensitive regions of the eastern U.S. were acidic. The NSWS 

documented the status and extent of surface water acid-base chemistry during probability surveys of lakes 

and streams conducted from 1984 through 1988 in the major acid sensitive regions of the U.S. (Kaufmann 

et al., 1988; Landers et al., 1987; Linthurst et al., 1986a). 

The stream component of the NSWS, the National Stream Survey (NSS), was focused in the 

northern and southern Appalachians and Coastal Plain of the eastern U.S. (Kaufmann et al., 1991). The 

NSS included 500 stream reaches selected from 1:250,000 scale USGS topographic maps using a 

systematic, randomized sample. Study reaches were sampled at both the upstream and downstream end of 

each selected reach. Population estimates were made for chemistry at both reach ends and for stream 

length by interpolating chemical results between reach ends. 

Overall, out of the estimated 57,000 stream reaches in the NSS, after excluding streams acidic due 

to acid mine drainage, 6.2% of the upstream and 2.3% of the downstream reach ends were acidic during 

spring baseflow (Kaufmann et al., 1991). After interpolation, this corresponded to 2.7% of the 

201,000 km of stream in the study region. In acidic and low-ANC NSS reaches, ANC usually increased 

with downstream distance. Acidic (ANC ≤ 0) streams were located in the highlands of the Mid-Atlantic 

Region (southern New York to southern Virginia, 2320 km), in coastal lowlands of the Mid-Atlantic 

(2530 km), and in Florida (461 km). Acidic streams were rare (less than 1%) in the highlands of the 

Southeast and Piedmont. Inorganic monomeric Al concentrations were highest in acidic streams of the 

Mid-Atlantic Highlands, where over 70% of the acidic streams had inorganic Al greater than 3.7 μM 

(100 μg/L), a concentration above which deleterious biological effects have frequently been reported. 

Anion composition of the NSS stream samples was examined to evaluate the most probable 

sources of stream acidity in acidic and low-ANC sites (Baker et al., 1991b; Herlihy et al., 1991). Acidic 

streams that had minimal organic influence (organic anions constituted less than 10% of total anions), and 

SO4

2−

 and NO3

−

 concentrations indicative of evaporative concentration of atmospheric deposition, were 

classified as acidic due to acidifying deposition. These acidic streams were located in small (<30 km2

) 

forested watersheds in the Mid-Atlantic Highlands (an estimated 1980 km of stream length) and in the 

Mid-Atlantic Coastal Plain (1250 km). Acidic streams affected primarily by acidifying deposition but also 

influenced by naturally occurring organic anions accounted for another 1210 km of acidic stream length 

and were mainly located in the New Jersey Pine Barrens, plateau tops in the Mid-Atlantic and Southeast 

Highlands, and the Florida Panhandle. The total length of streams that were identified as acidic due to 

acid mine drainage in the NSS (4590 km) was about the same as the total length of acidic streams likely 

affected by acidifying deposition (4455 km). Acidic streams whose acid anion composition was 

dominated by organics were mainly located in Florida and the Mid-Atlantic Coastal Plain. In Florida, 

most of the acidic streams were organic-dominated, whereas about half of the acidic streams in the MidAtlantic Coastal Plain were organic-dominated. Organic-dominated acidic streams were not observed in 

the Mid-Atlantic or Southeast Highlands. 

Stoddard et al. (2003) presented a map of acid-sensitive regions of the eastern U.S. where lakes and 

streams occur that are likely to be affected by acidifying deposition (Figure 3-17). The map shows 

considerable overlap with the areas of high interest identified by Baker et al. (1990b). Surface waters in 

most other regions of the U.S. are not sensitive to the effects of acidification due largely to the nature of 

the local geology (Stoddard et al., 2003). An exception is the region surrounding the Los Angeles Basin, 

 3-57
which receives high N deposition ( >20 kg N/ha/yr in some areas) and includes streams with very high 

NO3

−

 concentrations ( >50 μeq/L; Bytnerowicz and Fenn, 1996; Fenn and Poth, 1999, 2001). 

Source: Stoddard et al. (2003). 

Figure 3-17. Regions of the northern and eastern U.S. that contain appreciable numbers of lakes and 

streams that are sensitive to acidification from acidifying deposition. 

In addition to the large water chemistry databases developed by the U.S. EPA, which help to 

identify the spatial distribution of acid-sensitive and acid-affected surface waters in the U.S., there are 

also some important supplemental regional databases in New England, the Adirondacks, the midAppalachian region, the Florida Panhandle, the Upper Midwest, and the western U.S. Results from these 

studies are summarized in the following paragraphs. 

New England 

For the New England region, results from the U.S. EPA TIME program indicate that 5.6% of the 

regional lake population (386 lakes) in New England exhibited ANC <0 μeq/L during the period 1991 to 

1994. This result is similar to the EMAP findings, which indicate that 5% of lakes in New England had 

ANC values less than 0 μeq/L. The EMAP survey was a probability based survey representative of lakes 

with surface area greater than 1 ha (1,812 lakes). The survey was conducted during low-flow summer 

conditions, and the results therefore likely reflect the highest ANC values for the year. The EMAP 

analysis also estimated that an additional 10% of the population had low ANC values, between 0 and 

50 μeq/L, and were probably sensitive to episodic acidification (Driscoll et al., 2001b). 

Adirondacks 

A study by Driscoll, et al. (2001b) used EMAP data from 1991 to 1994 to evaluate the extent of 

acidic lakes in the Adirondacks for that period. Results from the survey indicate that 10% of the 

population of Adirondack lakes were chronically acidic (ANC values of less than 0) and 31% were 

 3-58
sensitive to episodic acidification (ANC values between 0 and 50) during the study period (Driscoll et al., 

2001b). 

The Adirondack Lake Survey Corporation conducted a comprehensive survey of Adirondack lakes 

greater than 0.2 ha in surface area between 1984 and 1987 (Baker et al., 1990a). Of the 1,489 lakes 

surveyed, 24% had summer pH values below 5.0, 27% were chronically acidic (ANC <0) and an 

additional 21% were probably susceptible to episodic acidification (ANC between 0 and 50; Driscoll 

et al., 2007a). 

Mid-Appalachian Region 

A compilation of survey data from the mid-Appalachians yields a consistent picture of the acidbase status of streams. In the subpopulation of upland forested streams, which comprises about half of the 

total stream population in the mid-Appalachian area, data from various local surveys showed that 5% to 

20% of the streams were acidic and about 25 to 50% had ANC <50 μeq/L (Herlihy et al., 1993). NSS 

estimates for the whole region showed that there were 2330 km of acidic streams and 7500 km of streams 

with ANC <50 μeq/L. In these forested reaches, 12% of the upstream reach ends were acidic and 17% had 

pH ≤ 5.5. SO4

2−

 from atmospheric deposition was the dominant source of acid anions in acidic midAppalachian streams. 

Cosby et al. (2006) provided a detailed characterization of streamwater acid-base chemistry in 

Shenandoah National Park, Virginia, which has been the most thoroughly studied area within the midAppalachian Mountain region with respect to acidification from acidifying deposition. Based on MAGIC 

model simulations and extrapolation using landscape characteristics, Cosby et al. (2006) developed maps 

showing the distribution of streamwater conditions in the Park for the preindustrial past, current 

conditions, and anticipated future conditions. 

Florida Panhandle 

According to the U.S. EPA eastern lakes survey conducted in 1984, 75% of the Florida Panhandle 

lakes were acidic at that time, as were 26% of the lakes in the northern peninsula. Most of the acidic lakes 

were clear water (DOC <400 μM) seepage lakes in which the dominant acid anions were chloride and 

SO4

2−

. Most of the acidic and low-ANC lakes were located in the Panhandle and north central lake 

districts. Acidic streams were located in the Panhandle, were mildly acidic (mean pH 5.0), and extremely 

dilute, with very low sea salt-corrected sum of base cations (mean 21 μeq/L) and sea salt-corrected SO4

2−

concentrations (mean 16 μeq/L). One-fourth of these acidic Panhandle streams were organic-dominated 

but the remaining sites all had DOC <2 mg/L. Inorganic monomeric Al concentrations in these acidic 

streams were very low (mean 11 μg/L). In these low DOC, low ANC Panhandle streams, it was suggested 

that the degree of SO4

2−

 and NO3

−

 retention in soil was an important control on streamwater ANC (Baker 

et al., 1990b). 

Upper Midwest 

Based on the eastern lakes survey, the Upper Midwest has a large population of lakes having ANC 

≤ 200 μeq/L, (Linthurst et al., 1986a; 1986b); only 6% of the lakes had ANC ≤ 50 μeq/L. Groundwater 

recharge lakes (those having Si concentration less than 1 mg/L, indicating little groundwater input) 

constituted 71% of the seepage lakes in the Upper Midwest, and were more frequently low in pH and 

ANC. Five percent were acidic and 9% had pH ≤ 5.5. Nearly 90% of Upper Midwestern lakes that had 

ANC ≤ 50 μeq/L were in the groundwater recharge category (Baker et al., 1991b). Such lakes tend to be 

susceptible to acidification from acidifying deposition. 

Acidic lakes in the Upper Midwest are primarily small, shallow, seepage lakes that have low 

concentrations of base cations and Al and moderate SO4

2−

 concentrations. Organic anions, estimated by 

both the Oliver et al. (1983) method and the anion deficit, tend to be less than half the measured SO4

2−

 3-59
concentrations in the acidic lakes (Eilers et al., 1988), but much higher in many of the drainage lakes that 

are less sensitive to acidification from acidifying deposition. 

West 

Landers et al. (1987) identified subregions in the West with acid-sensitive lakes, based on results of 

the U.S. EPA Western Lakes Survey. The surface water chemistry data for the West indicate that the Sierra 

Nevada and Cascade Mountains constitute the mountain ranges with the greatest number of sensitive lake 

resources. Surface waters in this region are among the most poorly buffered surface waters in the U.S. 

(Landers et al., 1987; Melack and Stoddard, 1991). The hydrologic cycle is dominated by the annual 

accumulation and melting of a dilute, mildly acidic snowpack. 

Many Cascade and Rocky Mountain lakes are highly sensitive to potential acidifying deposition 

effects (Nelson, 1991; Turk and Spahr, 1991). It does not appear that chronic acidification has occurred to 

any significant degree, although episodic acidification has been reported for lakes in the Colorado Front 

Range (Williams and Tonnessen, 2000). 

Along the eastern edge of the Continental Divide in Colorado and southeastern Wyoming, 

Musselman et al. (1996) conducted a synoptic survey of surface water chemistry in the mountainous areas 

that are exposed to relatively high (by western standards) deposition of N. A total of 267 high-elevation 

lakes situated in watersheds having a high percentage of exposed bedrock or glaciated landscape were 

selected for sampling. None of the lakes were chronically acidic (ANC <0), although several had 

ANC <10 μeq/L, and more than 10% of the lakes had ANC <50 μeq/L. 

Forest Ecosystems 

No systematic national survey of terrestrial ecosystems in the U.S. has been conducted to 

determine the extent and distribution of terrestrial ecosystem sensitivity to acidifying deposition. The 

scarcity of information on sensitive terrestrial ecosystems is due in part to sparse soils data. In general, 

forest ecosystems of the Adirondack Mountains of New York, Green Mountains of Vermont, White 

Mountains of New Hampshire, the Allegheny Plateau of Pennsylvania, and high-elevation forests in the 

southern Appalachians are considered to be the regions most sensitive to terrestrial acidification effects 

from acidifying deposition. 

One national and a few regional efforts have been undertaken to characterize forest sensitivity to 

acidifying deposition using a critical loads approach. In this context, acid-sensitive soils are those which 

contain low levels of exchangeable base cations and low base saturation. On a broad national scale, 

McNulty et al. (2007) used a simple mass balance equation and available national databases to estimate 

forest soil critical acidic loads (for wet and dry deposition of S and N) and exceedances for forest soils. 

Exceedances are pollutant loads that are greater than the estimated critical load for that location. They 

found that approximately 15% of forest soils in the U.S. receive acidifying deposition that exceeds the 

estimated critical load of wet and dry deposition of S and N by more than 250 eq ha/yr (McNulty et al., 

2007). The areas where exceedances reach this level could be considered to represent those areas that are 

likely most sensitive to continued high levels of acidifying deposition. Thus, there is not a national survey 

of soil sensitivity to acidification, but there are approaches available with which to identify areas likely to 

include sensitive soils. 

Note that the McNulty et al. (2007) paper represents the beginning of an iterative process to 

identify more precise critical loads for terrestrial acidity. The authors note that the actual area in 

exceedance of the forest soil critical acid load may be higher than the mapped estimates for several 

reasons (McNulty et al., 2007). First, their estimated total deposition did not include cloud deposition. 

Second, base cation deposition to near-coastal areas was not corrected for marine aerosol contributions. 

Third, the 1–km squared grid size of the mapping resulted in averaging of soil and deposition data, which 

removed extreme values from the analysis (McNulty et al., 2007). The authors take care to describe their 

results as “preliminary” and note that a more systematic analysis of model-predicted and measured forest 

 3-60
soil critical acid load exceedance is needed before this approach can be used as a tool for identifying areas 

of potential forest health concern (McNulty et al., 2007). For these reasons, and because of the significant 

uncertainty associated with many of the large national databases used in the analysis, the appropriate use 

of this information is not for the actual determination of critical loads at specific locations or for 

predictions of forest health effects, but rather for increased understanding of relative differences in forest 

soil sensitivity at a national scale. In general, the Northeast, the Southern Appalachians, parts of Florida 

and the Upper Midwest have the highest proportion of soils that exceed the estimated critical acid loads 

by at least 250 eq ha/yr and therefore could be termed vulnerable. Where the exceedances are highest, 

forest soils are likely most sensitive to continued effects from acidifying deposition. 

At a regional scale, Pardo et al. (2007) calculated critical loads of S and N deposition to forests in 

Great Smoky Mountains National Park (GSMNP) based on available data. A simple mass balance model 

and the Very Simple Dynamic model (VSD) were used to calculate a critical load for acidity (N+S) and N 

nutrient. The authors concluded that current deposition exceeded the critical load at all four sites 

evaluated (2 high elevation spruce-fir sites, a mid-high elevation beech site, and a lower elevation mixed 

hardwood site). The exceedance for S + N deposition ranged from 150 eq/ha/yr for the low elevation 

mixed hardwood site to 2300 eq/ha/yr at the upper spruce-fir site. The maximum acceptable deposition of 

N ranged from 200 eq/ha/yr (3 kg N/ha/yr) for the low elevation mixed hardwood site to 500 eq/ha/yr 

(7 kg N/ha/yr) at the upper spruce-fir site. 

Another approach to identification of sensitive forest lands is to map the distribution of tree species 

thought to be most sensitive to adverse effects. The effects of acidifying deposition are particularly well 

documented for red spruce trees (Cronan and Grigal, 1995; Johnson and Lindberg, 1992; Johnson et al., 

1994b; Joslin et al., 1992) that occur in the northeastern U.S. and southern Appalachian Mountains 

(Figure 3-6 shows the distribution). In the Northeast, red spruce grows at elevations from near sea level to 

about 1,400 m. In the Appalachian Mountains, spruce-fir forests are generally found at relatively high 

elevation, for example above about 1400 m in the southern portion of the range (SAMAB, 1996). 

Northern hardwood forests have also been identified as forest resources experiencing air pollution effects. 

Effects are best documented for sugar maples, which are broadly distributed across the northern 

hardwood forests in the northeastern U.S. (Figure 3-6 shows the distribution). The areas where sugar 

maples appear to be at greatest risk are along ridges and where this species occurs on nutrient-poor soils. 

Model Simulations 

In the eight-state Southern Appalachian Mountains region, Sullivan et al. (2005) modeled future 

effects of atmospheric S and N deposition on aquatic resources. Modeling was conducted with the 

MAGIC model for 40 to 50 sites within each of three physiographic provinces, stratified by stream water 

ANC class. The model runs were based on three emissions control strategies (A2, B1, and B3). A2 is the 

base case that represents best estimates for air emission controls under regulations for which 

implementation strategies were relatively certain at the time of the study (about the year 2000). This A2 

strategy includes the acid rain controls under Title IV of the 1990 Amendments to the CAA, the 1-h ozone 

(O3) standard, NOX reductions required under the U.S. EPA call for revised State Implementation Plans 

(SIPs), and several highway vehicle and fuel reductions. The B1 and B3 strategies assumed progressively 

larger emissions reductions, targeted only to the eight states in the southern Appalachian Mountains 

region, but covering all emissions sectors. 

The results for the portion of the region south of Virginia and West Virginia suggest that the 

percentages of streams having ANC below zero and below 20 μeq/L will actually increase through the 

year 2040 under all except the most restrictive emissions control strategies (Sullivan et al., 2005). Most 

simulated changes in stream water ANC from 1995 to 2040 were rather modest, given the very large 

estimates of future decrease in S deposition. Few modeled streams showed projected change in ANC of 

more than about 20 μeq/L (Sullivan et al., 2005). Some of the largest changes were simulated for some of 

the streams that were most acidic in 1995. For such streams, however, even relatively large increases in 

 3-61
ANC would still result in stream water having negative ANC, and therefore little biological improvements 

would be expected from the simulated improvement in chemistry (Sullivan et al., 2005). 

Sullivan et al. (2002b) used the NuCM model to evaluate potential changes in soil chemistry in 

response to acidifying deposition in the southern Appalachian Mountains. The results suggest that sprucefir forests in the region are likely to experience decreased Ca:Al ratios in soil solution under virtually all 

strategies of reduced future acidifying deposition considered. This result was partly because SO4

2−

adsorption in soils is likely to decline, even with dramatically reduced S deposition. In addition, many 

spruce-fir forests in the region are N-saturated, and continued N deposition at moderate or high levels 

would be expected to contribute to elevated NO3−

 concentrations in soil water, which could further 

enhance base cation leaching and mobilization of Al from soils to soil solution. 

In the Adirondacks, model results produced by several studies suggest that the trend of increasing 

lakewater ANC for the most acid-sensitive lakes might not continue in coming decades. These results are 

discussed above in the Adirondack case study. 

In a regional application of PnET-BGC, Chen and Driscoll (2005) analyzed 60 DDRP (Direct 

Delayed Response Project) lake watersheds within northern New England under three future emissions 

reduction scenarios. Most of the lakes had surface water ANC values greater than 50 μeq/L in 1984, and 

were therefore not considered chronically acidic. The authors reported that ANC was projected to increase 

under all three scenarios, with greater rates of recovery occurring with deeper emissions reductions. Soil 

improvements were slow and modest under all scenarios. Simulations suggested that 80% of the northern 

New England sites and 60% of the Maine sites will have soil base saturation below 20% in 2050 (Chen 

and Driscoll, 2005). They concluded that the decreases in SO4

2−

 and NO3

−

 concentrations in surface water 

were coupled with nearly stoichiometric decreases in base cation concentrations. Simulated improvements 

in ANC in response to reduced acidifying deposition were minor. Therefore, while further declines in 

atmospheric deposition in S and N will bring some improvements, most ecosystems in the study were not 

expected to recover to background conditions by 2050. 

Bulger et al. (2000) developed model-based projections using the MAGIC model to evaluate the 

potential effect of reductions in S deposition of 40% and 70% from 1991 levels using data from streams 

in and near Shenandoah National Park. Projections were based on four brook trout stream categories: 

Suitable, ANC >50 μeq/L; Indeterminate, ANC 20 to 50 μeq/L; Marginal, ANC 0 to 20 μeq/L; and 

Unsuitable, ANC <0 μeq/L. Three scenarios of future acidifying deposition were modeled: constant 

deposition at 1991 levels, 40% reduction from 1991 deposition levels, and 70% reduction from 1991 

deposition levels. Based on observed 1991 ANC values, approximately 30% of all trout streams in 

Virginia were marginal or unsuitable for brook trout because they were either episodically (24%) or 

chronically (6%) acidic. In addition, another 20% of the streams were classified as indeterminate, and 

brook trout in these streams may or may not have been affected. Based on the model simulations, 82% of 

these streams would not have been acidic before the onset of acidifying deposition and would likely have 

been suitable for brook trout. 

The model projections suggested that neither the 40% nor the 70% reductions in acidifying 

deposition would increase the number of streams that were suitable for brook trout above the ambient 

50%. In fact, the results suggested that a 70% reduction in deposition would be needed in the long term 

just to maintain the number of streams that were considered suitable for brook trout. Because of the length 

of time required to restore buffering capacity in watershed soils, most of the marginal or unsuitable 

streams were expected to remain marginal or unsuitable for the foreseeable future. 

Results of modeling studies for lakes and streams in the Adirondack Mountains and in Shenandoah 

National Park are presented in the case study sections of this report. 

3.2.4.3. Levels of Deposition at Which Effects are Manifested 

The effects of S and N deposition are manifested at a range of deposition levels, depending on the 

inherent sensitivity of the natural resources, as described in the previous sections, and the historical 

 3-62
deposition loading. The intersection among current deposition loading, historic loading, and sensitivity 

defines the ecological vulnerability to the adverse effects of acidification. Few studies in the U.S. have 

defined deposition levels that are associated with effects over large areas. 

Some degree of surface water acidification, and perhaps also of soil acidification, can occur at very 

low levels of S deposition (only a few kg S/ha/yr). These highly sensitive areas are characterized by very 

low levels of exchangeable base cations and soil base saturation. Water pathways and soil depth can also 

limit the capacity of these areas to neutralize acidifying deposition. These areas provide limited 

neutralization of acidic drainage water. 

Effects levels for N deposition can be established based on changes to stream and soil chemistry 

that signal alteration of nutrient cycling, causing NO3

−

 leaching. Analyses have been conducted in the 

northeastern U.S. and Europe to examine the relationships between N deposition and NO3

−

 leaching to 

surface waters. The relationship between measured wet deposition of N and streamwater output of NO3

−

was evaluated by Driscoll et al. (1989) for sites in North America (mostly eastern areas), and augmented 

by Stoddard (1994). The resulting data showed a pattern of N leaching at wet inputs greater than 

approximately 5.6 kg N/ha/yr. Aber et al. (2003) concluded that loss of NO3

−

 to surface waters during the 

growing season in forested watersheds often occurs above a threshold of total (wet plus dry) atmospheric 

N deposition of about 8 to 10 kg N/ha/yr. 

The effects of N addition on forests have been shown to be wide-ranging. Additions of 25 kg 

N/ha/yr to spruce plots in Vermont (ambient bulk deposition 5.4 kg N/ha/yr), in which net nitrification did 

not occur before treatment, triggered net nitrification in the second year of treatment (McNulty et al., 

1996). Similar results were seen in Colorado, where additions of 25 kg N/ha/yr to old-growth spruce plots 

in Loch Vale watershed (ambient bulk deposition 4 to 5 kg N/ha/yr) doubled N mineralization rates and 

stimulated nitrification. In marked contrast to these results, concentrations of NO3

−

 plus NH4

+

 were not 

detected until the seventh year in hardwood plots in Harvard Forest, Massachusetts, which received 

additions of 150 kg N/ha/yr (Magill et al., 2004). Concentrations of NO3

−

 plus NH4

+

 in hardwood plots 

receiving 50 kg N/ha/yr were not yet detectable in the 15th year of treatment. 

Many of the changes in plant species composition, species diversity, and nitrification and 

mineralization rates in response to atmospheric N deposition are associated with nutrient N fertilization, 

rather than acidification. They are discussed in more detail in Section 3.3. 

Chemical Response 

As discussed in Section 3.2.1.6., surface water chemistry has responded to changes in emissions 

and deposition of S over the past two to three decades and most recently also decreases in N. Monitoring 

data collected within the U.S. EPA Long-Term Monitoring (LTM) and TIME projects, as well as other 

monitoring programs, has been key to understanding chemical responses. See discussion of major 

monitoring programs in Annex B. Surface water chemistry monitoring data generated through TIME and 

LTM (Stoddard et al., 2003) suggest that the following important changes in lake and stream chemistry 

have occurred over the past one to two decades in the eastern U.S.: 

 SO4

2−

 concentration has decreased as a percentage of total ion concentration in surface waters. 

 ANC has increased modestly in three of the five regions studied. 

 DOC and associated natural organic acidity increased, perhaps toward more natural predisturbance concentrations, as surface water acidity contributed from acidifying deposition has 

decreased. 

 Inorganic Al concentrations appear to have decreased slightly in some sensitive aquatic systems. 

The significant decreases in surface water SO4

2−

 concentration, which have been observed in many 

areas, have not necessarily brought large changes in the acidity of lakes and streams. For example, the 

decline in Adirondack lakewater SO4

2−

 during the 1980s was charge-balanced by a nearly equivalent 

decrease in concentrations of base cations in many of the low-ANC lakes, and this limited the increase in 

 3-63
ANC and pH that occurred in response to lower SO4

2−

 concentrations. Overall, improvements in 

lakewater acid-base chemistry since 1990 have been measurable but modest. Similar patterns have been 

observed in most other regions. There are currently no data in the U.S. that indicate increases in soil pH 

associated with recent declines in acidifying deposition levels. 

Declines in SO2 and NOX emissions have brought about measurable improvements in streamwater 

chemistry in sensitive regions of the U.S. since 1990. However, model forecasts suggest that a reversal in 

chemical recovery could occur in many sensitive ecosystems under current emissions and deposition 

levels and that further reductions beyond those required by the 1990 Amendments to the CAA may be 

needed to prevent continued adverse effects and to support biological recovery of terrestrial and aquatic 

ecosystems (see discussion in Section 3.2.4.5.). 

Biological Response 

Biological recovery can occur only if chemical recovery is sufficient to allow survival and 

reproduction of acid-sensitive plants and animals. The time required for biological recovery is uncertain. 

For terrestrial ecosystems, it may be decades after soil chemistry is restored because of the long life of 

many plant species and the complex interactions of soil, roots, microbes, and soil biota. For aquatic 

systems, research suggests that stream macroinvertebrate populations may recover relatively rapidly 

(within approximately 3 years), whereas lake populations of zooplankton recover more slowly (Gunn and 

Mills, 1998). 

Table 3-8 contains a general summary of pH levels at which biological changes are typically 

manifested. Nevertheless, for aquatic ecosystems, there is currently no theoretical basis on which to 

predict the pathway and timing of biological recovery. Biological recovery of previously acidified surface 

waters can lag behind chemical recovery because of such factors as limits on dispersal and 

recolonization; barriers imposed by water drainage patterns (Jackson and Harvey, 1995); the influence 

of predation (McNicol et al., 1995); and other environmental stressors (Gunn et al., 1995; Havas et al., 

1995; Jackson and Harvey, 1995; McNicol et al., 1995; Yan et al., 1996a, 1996b). Full biological recovery 

may take decades from the onset of chemical recovery. The results of biological recovery research from 

the Sudbury region of Canada and several experimental lakes is summarized below. 

Table 3-8. General summary of biological changes anticipated with surface water acidification, 

expressed as a decrease in surface water pH. 

pH 

Decrease General Biological Effects 

6.5 to 6.0 Small decrease in species richness of plankton and benthic invertebrate communities resulting from the loss of a few highly 

acid-sensitive species, but no measurable change in total community abundance or production. 

Some adverse effects (decreased reproductive success) may occur for highly acid-sensitive fish species (e.g., fathead 

minnow, striped bass). 

6.0 to 5.5 Loss of sensitive species of minnows and dace, such as fathead minnow and blacknose dace; in some waters, decreased 

reproductive success of lake trout and walleye, which are important sport fish species in some areas. 

Visual accumulation of filamentous green algae in the near-shore zone of many lakes and in some streams. 

Distinct decrease in species richness and change in species composition of plankton and benthic invertebrate communities, 

although little if any change in total community abundance or production. 

Loss of some common invertebrate species from zooplankton and benthic communities, including many species of snails, 

clams, mayflies, and amphipods, and some crayfish. 

 3-64
5.5 to 5.0 Loss of several important sport fish species, including lake trout, walleye, rainbow trout, and smallmouth bass, as well as 

additional nongame species such as creek chub. 

Further increase in the extent and abundance of filamentous green algae in lake near-shore areas and streams. 

Continued shift in species composition and decline in species richness of plankton, periphyton, and benthic invertebrate 

communities; decreases in total abundance and biomass of benthic invertebrates and zooplankton may occur in some waters. 

Loss of several additional invertebrate species common in surface waters, including all snails, most species of clams, and 

many species of mayflies, stoneflies, and other benthic invertebrates. 

Inhibition of nitrification. 

5.0 to 4.5 Loss of most fish species, including most important sport fish species such as brook trout and Atlantic salmon. A few fish 

species are able to survive and reproduce in water below pH 4.5 (e.g., central mud minnow, yellow perch, and in some waters, 

largemouth bass). 

Measurable decline in the whole-system rates of decomposition of some forms of organic matter, potentially resulting in 

decreased rates of nutrient cycling. 

Substantial decrease in number of species of plankton and benthic invertebrates and further decline in species richness of 

plankton and periphyton communities; measurable decrease in total community biomass of plankton and benthic invertebrates 

of most waters. 

Loss of additional species of plankton and benthic invertebrate species, including all clams and many insects and crustaceans. 

Reproductive failure of some acid-sensitive species of amphibians, such as spotted salamanders, Jefferson salamanders, and 

the leopard frog. 

The Sudbury region of Ontario, Canada has been important for studying both the chemical and 

biological effects of S deposition. Mining and smelting of copper-nickel ore began in the 1880s. By the 

1950s and 1960s, SO2 emissions from the mining and smelting operations peaked at over 5,000 tons/day 

and extensive acidification of nearby surface waters was documented (Beamish and Harvey, 1972). 

Emissions of SO2 then decreased during the 1970s to less than one-third of the peak values. S emission 

reductions resulted in improved water quality in many lakes (Keller and Pitblado, 1986; Keller et al., 

1986), and some fisheries recovery was also documented (Gunn and Keller, 1990; Keller and Yan, 1991). 

Griffiths and Keller (1992) found changes in the occurrence and abundance of benthic invertebrates that 

were consistent with a direct effect of reduced lakewater acidity. A more recent assessment of recovery of 

ecosystems in Canada provided further evidence of biological recovery, but also showed that the spatial 

extent of recovery was limited to lakes that had been severely acidified by the Sudbury smelter (Jeffries 

et al., 2003). Research at Sudbury clearly documented that chemical recovery of lakes was possible upon 

reduced emissions and deposition of S, and that biological recovery, involving multiple trophic levels, 

could follow. Major findings of the research at Sudbury and elsewhere are summarized below. 

Phytoplankton 

Studies of phytoplankton recovery from experimental acidification indicate that there is an increase 

in phytoplankton species richness and diversity as pH increases. In Lake 223 in the Experimental Lakes 

area of Ontario, there was little increase in phytoplankton diversity as pH changed from 5.0 to 5.8 but a 

strong recovery of diversity at pH above 6 (Findlay and Kasian, 1996). In Lake 302S, profound change 

began at pH 5.5; phytoplankton assemblages at pH below 5.5 resembled acidified lakes. 

 3-65
Zooplankton 

Zooplankton recovery in response to experimental de-acidification has been reported for lakes in 

Ontario, Canada and Minnesota. Zooplankton recovery in experimentally acidified Lake 223 as pH 

returned back to 6.1 was reported by Malley and Chang (1995). Species diversity that had been reduced 

during the acidification phase had partially returned to pre-acidification levels. Rotifers had recovered less 

than crustaceans. 

One decade after cessation of the experimental acidification of Little Rock Lake in Wisconsin, 

recovery of the zooplankton community was complete (Frost et al., 2006). Recovery did not follow the 

same trajectory as the initial acidification, however, indicating a substantial hysteresis in zooplankton 

community recovery. About 40% of the zooplankton species in the lake exhibited a lag of 1 to 6 years to 

recover to levels that occurred in the neutral reference basin. 

Benthic Invertebrates 

There has been some research conducted on the recovery of benthic invertebrate communities in 

surface waters exhibiting chemical recovery from acidification. In Scotland, Soulsby et al. (1995) 

reported an increase in acid-sensitive mayflies in some streams that showed recent ANC increases. 

However, no increases in invertebrates were observed in the most acidic streams despite observed 

increases in ANC. They suggested that further reductions in acidic deposition and sufficient time for 

reversal of soil acidification may be required before aquatic biotic recovery can occur. The extent to 

which benthic invertebrates in streams in the U.S. may have recovered in response to any recent increases 

that may have occurred in stream ANC and pH is not known. 

Fish 

Fish populations have recovered in acidified lakes when the pH and ANC have been increased 

through liming or reduction of acidifying deposition (Beggs and Gunn, 1986; Dillon et al., 1986; Gunn 

et al., 1988; Hultberg and Andersson, 1982; Keller and Pitblado, 1986; Kelso and Jeffries, 1988; Raddum 

et al., 1986). The timing of fish recovery is uncertain and probably depends heavily on dispersion. 

Stocking could accelerate fish population recovery (Driscoll et al., 2001a). Limitations on dispersal and 

recolonization can hamper biological recovery from acidification. 

Continued periodic episodic acidification might hamper biological recovery of a lake or stream that 

is experiencing improvement in chronic chemistry. If fish move into refugia during episodes of low pH 

and then return, behavioral avoidance would reduce the overall effect of episodic acidification on fish 

populations. If fish move out of the stream system in response to acidic episodes, as suggested by Baker 

et al. (1996), and do not return or return in smaller numbers, then the population level effects of episodic 

acidification would be greater than predicted based on mortality tests alone. 

 3-66
Table 3-9. Studies that either did or did not yield evidence that acidifying deposition affected certain 

species of birds. 

Species 

Diet/ 

Foraging 

YES NO 

Breeding 

Distribution 

YES NO 

Reproductive 

Measures 

YES NO 

Reference 

Common loon x x x x x Alvo et al. (1988); Blair (1990 ); Blancher and McNicol, 

(1991); DesGranges and Houde (1989); Wayland and 

McNicol (1990); Parker (1988) 

Arctic loon x Eriksson (1987) 

Common 

merganser 

x x McNicol et al. (1987) 

Belted 

kingfisher 

x Goriup (1989) 

Osprey x x x Eriksson (1983, 1986) 

Black duck x x xb DesGranges and Darveau (1985); Harasmis and Chu (1987); 

Hunter (1986); Rattner (1987) 

Common 

goldeneye 

xb DesGranges and Darveau (1985); McNicol et al. (1987) 

Ring-necked 

duck 

x x McAuley and Longcore (1988a); McAuley and Longcore 

(1988b) 

Eurasian 

dipper 

x x x Ormerod et al. (1985, 1986); Ormerod and Tyler (1987) 

Eastern 

kingbird 

x x Glooschenko et al. (1986) 

Tree swallow x x x Blancher and McNicol (1988, 1991); St. Louis et al. (1990) 

b Effect was beneficial 

Baker et al. (1990a) used field-based models to test the potential for biological recovery. For each 

species considered, the current presence or absence of the species was analyzed as a function of the water 

quality variables associated with acidification (e.g., pH, Al, Ca, ANC, and DOC) using maximum 

likelihood logistic regression (Reckhow et al., 1987). The results from the various models were compared 

to their prediction of the change in the number of Adirondack lakes with unsuitable acid-base chemistry, 

given a 50% decrease or a 30% increase in S deposition relative to the existing conditions at the time of 

the eastern lakes survey (1984). Most of the models provided similar results and suggest that a 30% 

increase in S deposition would increase the unsuitable fish habitat in Adirondack DDRP lakes by 15% to 

28% for brook trout, lake trout, and common shiner. A 50% decrease in S deposition was projected to 

increase suitable habitat by 8% to 18%. 

Waterfowl 

Few studies have been conducted on the recovery of higher trophic level organisms such as birds 

(Table 3-9). However, breeding distribution for the common goldeneye (Bucephala clangula), an 

insectivorous duck, may be affected by changes in acidifying deposition (Longcore and Gill, 1993). 

 3-67
Similarly, reduced prey diversity and quantity have been observed to create feeding problems for nesting 

pairs of loons on low-pH lakes in the Adirondacks (Parker, 1988). 

Logistic regression modeling with measured pH and species occurrence data for acid-sensitive 

lakes in the Algoma region of Ontario showed that the occurrences of fish, common loons, and common 

mergansers were positively related to lake water pH (McNicol, 2002). Predictions of common loon and 

merganser recovery for this area were made using the Waterfowl Acidification Response Modeling 

System (WARMS) under varying S emissions control scenarios targeted for 2010 (McNicol, 2002). The 

number of lakes projected to be suitable for supporting breeding pairs and broods increased with Lake pH 

and stricter emissions controls (McNicol, 2002). Marginal improvements in fish-eating bird habitat were 

predicted to occur by 2010, with more significant improvements expected under hypothetical S emissions 

reductions of 50% and 75% for lakes with pH below 6.5 (McNicol, 2002). Fundamental to the predicted 

improvement of these fish-eating bird populations is the expected increase in food availability with lake 

pH recovery. 

3.2.4.4. Acidification Case Study #1: Adirondack Region of New York 

In this and the following section, case studies are presented for two of the most thoroughly studied 

regions of the U.S. that are known to have been affected by acidification from atmospheric S and N 

deposition. Studies in these regions have focused on both chemical and biological effects, and have 

included extensive model simulations of past acidification and projections of the likelihood of future 

recovery as deposition levels decline. The Adirondack Mountain region is perhaps the most thoroughly 

studied region in the world with respect to surface water acidification. Large numbers of Adirondack 

lakes have been acidified over the past century, and many of those now show signs of chemical recovery. 

Shenandoah National Park contains many acidified and acid-sensitive streams. Sensitivity in this region is 

strongly controlled by geology and the extent to which deposited S is adsorbed to soils. These two case 

studies are intended to be illustrative of the types of research that has been conducted and what that 

research has revealed. 

General Description of Region 

The Adirondack Mountains are in northeastern New York State and are densely forested, have 

abundant surface waters, and have 46 peaks that extend up to 1600 m in elevation. The Adirondack Park 

has long been a nationally important recreation area for fishing, hiking, boating, and other outdoor 

activities. 

The Adirondacks, particularly the southwestern Adirondacks, are sensitive to acidifying deposition 

because they receive high precipitation, have shallow base-poor soils, and are underlain by igneous 

bedrock with low weathering rates . The Adirondacks are among the most severely acid-affected regions 

in North America (Driscoll et al., 2003b; Landers et al., 1988) and have long been used as an indicator of 

the response of forest and aquatic ecosystems to U.S. policy on atmospheric emissions of SO2 and NOX

(GAO, 2000; NAPAP, 1998; U.S. EPA, 1995a). 

Rates of Acidifying Deposition 

Current rates of wet deposition of S and N in the western Adirondacks remain among the highest in 

the nation. Spatial patterns in wet deposition of S and N from 1988 to 1999 were developed by Ito et al. 

(2002), using data from 24 precipitation and 4 wet deposition monitoring stations. Results from this effort 

suggest that wet S deposition ranged from 2.3 to 12.9 kg S/ha/yr and wet NO3-N deposition rates ranged 

from 1.7 to 5.1 kg N/ha/yr (Ito et al., 2002) (Figure 3-18). In general, deposition rates are highest in the 

southwestern Adirondacks and decrease to the northeast. Rates of dry deposition are less well known, but 

probably constitute 25% to 50% or more of total wet deposition (Sullivan et al., 2006b). 

 3-68
Deposition trends have changed with the implementation of federal and state emissions control 

regulations. For example, by 1990 average wet S deposition in the Adirondack region had declined by 

approximately 30% from its peak in the 1970s (Sullivan et al., 1990). Deposition of S has continued to 

decline (Figure 3-19) and (Figure 3-20) in response to implementation of the CAAA of 1990. Until 

recently, wet N deposition had been fairly consistent over the previous two decades. N deposition now 

appears to be decreasing (http://nadp.sws.uiuc.edu/). 

Source: Ito et al. (2002). Reprinted with permission. 

Figure 3-18. Spatial patterns in predicted wet SO4

2− and NO3− deposition in the Adirondack Park during the 

period 1988 to 1999. 

Soil Retention and Leaching of Sulfur and Nitrogen 

As discussed in Section 3.2.4.4, acidifying deposition has resulted in the accumulation of S and N 

in Adirondack soils. Although input-output budgets for S developed in the 1980s suggested that the 

amount of S exported was approximately equal to the S inputs from atmospheric deposition, more recent 

studies show that watershed loss of SO4

2−

 now exceeds atmospheric S deposition inputs (Driscoll et al., 

1998). This pattern suggests that decades of atmospheric S deposition have resulted in the accumulation 

of S in forest soils. With recent declines in atmospheric S deposition and a possible warming-induced 

enhancement of S mineralization from soil organic matter, previously retained S is gradually being 

 3-69
released to surface waters (Driscoll et al., 1998). This release of SO4

2−

 from soils could contribute to a 

delay in the recovery of surface waters in response to SO2 emissions controls. 

N dynamics are quite different from those of S. Because N is a growth-limiting nutrient for many 

forest plants, retention in forest ecosystems under low levels of air pollution is generally high and NO3

−

loss to streams is relatively low (Aber et al., 2003). However, recent research suggests that N has 

accumulated in soils over time in the Adirondacks and that some forests have exhibited increasing 

retention of N inputs (Driscoll et al., 2003a, c). The result has been increased leaching of NO3

−

 to surface 

waters. The extent and degree of leaching appear to be linked to climatic variation, land-use history, and 

vegetation type (see Section 3.2.1.3). 

Source: Sullivan et al. (2006b). 

Figure 3-19. Measured wet deposition of sulfur at the Huntington Forest NADP/NTN monitoring station. 

The leaching of both SO4

2−

 and NO3

−

 into drainage water has contributed to the displacement of 

cations from soil, acidification of surface waters (Driscoll et al., 2001b), and the associated chemical and 

biological effects discussed below. 

Soil Acidification and Base Cation Depletion 

Atmospherically deposited hydrogen ions can directly affect soil pH. Net uptake of nutrient cations 

by vegetation can also generate acidity within the soil, and a considerable amount of natural organic 

acidity is produced in the Oa horizon through the partial decomposition of organic matter and uptake of 

nutrient cations. In the only repeated soil sampling in the U.S. in which the original sampling predated 

acidifying deposition, (Johnson et al., 1994b) found significantly higher soil pH values in 1930 than in 

1984 in the Oa horizon of Adirondack soils that had an initial pH of 4.0–5.5, but no decrease in pH in 

soils with an initial pH of <4.0. (Johnson et al., 1994b) also documented a decrease in exchangeable Ca 

concentrations in both the O (combined Oa and Oe horizons) and B horizons from 1930 to 1984. The 

decrease in soil pH and Ca concentrations was attributed to a combination of acidifying deposition and 

changing vegetation dynamics. 

 3-70
Source: Sullivan et al. (2006b). 

Figure 3-20. Estimated time series of S deposition at one example watershed in the southwestern 

Adirondack Mountains. Table used by Sullivan et al. (2006a) as input to the MAGIC model for 

projecting past and future changes in lakewater chemistry attributable to acidifying 

deposition. Future deposition estimates were based on three emissions control scenarios 

(Base Case [solid line], Moderate Additional Controls [dotted line], Aggressive Additional 

Controls [dashed line]). 

In a statistically based regional assessment of changes in soil-exchange chemistry, Sullivan et al. 

(2006a) found that base saturation and exchangeable Ca concentrations in the Adirondack region 

appeared to have decreased in the B horizon between the mid 1980s and 2003 in watersheds of lakes with 

acid-neutralizing capacity less than 200 μeq/L. Although this study did not involve repeated sampling of 

the same sites, the comparison could be made on a regional basis because the sampling locations were 

selected randomly in both the mid 1980s and in 2003, and a large and similar number of sites were 

included in both samplings. 

Effects of Acidifying Deposition on Adirondack Surface Water Chemistry 

The Adirondack Lake Survey Corporation conducted a comprehensive survey of Adirondack lakes 

greater than 0.2 ha in surface area between 1984 and 1987 (Baker et al., 1990a). Of the 1,489 lakes 

surveyed, 24% had summer pH values below 5.0, 27% were chronically acidic (ANC <0) and an 

additional 21% were probably susceptible to episodic acidification (ANC between 0 and 50; Driscoll 

et al., 2007a). 

In addition to low pH and ANC, many acidic surface waters in the Adirondacks are characterized 

by high concentrations of inorganic Al. For example, a study of 12 sub-basins in the watershed of the 

North Branch of the Moose River by (Driscoll et al., 1987a) determined that the concentration of 

inorganic Al in lakewater was higher in lakes having pH below 6.0. Recently, Lawrence et al. (2007) 

determined that 66% of 188 streams sampled in the western Adirondack region during snowmelt in 2004 

had measurable concentrations of inorganic Al, an indicator of acidification by acidifying deposition. 

Historical changes in lakewater chemistry from the mid-1800s to recent times have been estimated 

for the Adirondacks using paleolimnological techniques. Fossil remains of diatoms and chrysophytes in 

 3-71
sediment cores have been used to reconstruct chemical histories. The PIRLA I and II projects 

(Paleoecological Investigation of Recent Lake Acidification) used the remains of diatoms preserved in 

lake sediments to estimate historical changes in lakewater chemistry across the Adirondack region. The 

PIRLA-II project focused on lakes that are 4 ha or larger that represented a subpopulation of 675 

Adirondack lakes. The results from these analyses suggest that nearly all lakes with estimated 

preindustrial pH less than 6.0 had acidified between 0.3 and 1.0 pH units during the 20th century. Based 

on an analysis of data from Cumming et al. (1992) and (Baker et al., 1990a), low-pH lakes were 

uncommon or rare in the preindustrial Adirondacks; the number of lakes with pH less than 5.5 had at least 

doubled by the mid 1980s and the number with pH less than 5.0 had increased by 5 to 10 times. 

The PIRLA results are generally consistent with projections from model hindcasts. (Sullivan et al., 

2006b) modeled past changes in the acid-base chemistry of 70 Adirondack lake watersheds. These 

included 44 that were statistically selected to be representative of the approximately 1,320 Adirondack 

lake watersheds that have lakes larger than 1 ha and deeper than 1 m and that have ANC ≤ 200 μeq/L. 

Model hindcasts were constructed using both the MAGIC and PnET-BGC models. Based on MAGIC 

model outputs, maximum past acidification occurred by about 1980 or 1990, with a median ANC for the 

study population of about 61 μeq/L (reduced from a median of 92 μeq/L estimated for the preindustrial 

period). By 1990, 10% of the population target lakes had decreased in ANC to below -16 μeq/L and 25% 

had ANC <28 μeq/L. The model simulations coupled with population-level extrapolations suggest that 

none of the target lakes were chronically acidic (had ANC <0 μeq/L) under preindustrial conditions, but 

that by 1980 there were about 204 chronically acidic Adirondack lakes. 

PnET-BGC model simulations generated output that was generally similar to results provided by 

MAGIC model simulations. Results from PnET-BGC suggest that none of the lakes in the Adirondack 

population had preindustrial ANC below 20 μeq/L. By 1990, there were 289 lakes having 

ANC <20 μeq/L and 217 chronically acidic (ANC ≤ 0 μeq/L) lakes according to PnET-BGC simulations. 

There were 202 lakes in the population simulated to have had preindustrial ANC below 50 μeq/L, and this 

number increased 2.8 times by 1980 under the PnET-BGC simulations. 

Zhai et al. (2008) reported PnET-BGC hindcasts for the 44 EMAP lakes. They report that simulated 

median values of pH, ANC, and soil percent base saturation were 6.63, 67.7 μeq/L, and 12.3%, 

respectively, in 1850 compared to current measured values of 5.95, 27.8 μeq/L, and 7.9%. They also 

calculated F factors for the PnET-BGC model projections of historical acidification. The F-factor 

(Henriksen, 1984; Husar and Sullivan, 1991) reflects the proportion of the increase in lakewater SO4

2−

plus NO3

−

 concentration that is charge balanced by an equivalent increase in base cation concentrations. 

The remaining proportion (1–F) is attributed to increase in the potentially toxic cations, hydrogen ions 

and inorganic Al. Based on PnET-BGC hindcast simulations, F-factors for the EMAP lakes ranged from 

0.3 to slightly over 1.0, with a mean value of 0.7 (Figure 3-13). The F-factor increased with ambient 

lakewater ANC. These results are in close agreement with paleolimnological analyses reported by 

Sullivan et al. (1990), which showed historic F-factors for Adirondack lakes ranging from about 0.5 to 

above 1.0. 

While there were differences for some variables (i.e., MAGIC showed greater soil acidification) 

and clear lake-to-lake differences, Sullivan et al. (2006b) observed that the population-level projections 

with both models represent robust indicators of the extent and magnitude of changes in lake chemistry 

associated with historical inputs of acidic deposition. The differences observed in the nature of 

acidification between the two models may be linked to model structural differences. In MAGIC, soil 

water is assumed to be in equilibrium with an aluminum trihydroxide mineral at all times (Cosby et al., 

1985a, b). Inputs of strong acids, therefore, drive the mobilization of Al. This Al strongly displaces 

exchangeable base cations from the soil exchange complex. In PnET-BGC, the soilphase is in equilibrium 

only when solutions are oversaturated with the aluminum trihydroxide solid phase. As a result, there is 

less Al available in PnET-BGC to displace exchangeable soil base cations and greater acidification of the 

solution is realized. Backward and forward projections of population-level estimates of lake water and 

soil chemistry for the Adirondacks are presented as cumulative distribution functions in Figure 3-21. 

 3-72
Source: Sullivan et al. (2006b) 

Figure 3-21. Cumulative distribution functions of selected major ions (μeq/L), calculated ANC of lakewater 

(μeq/L), and B horizon soil % base saturation for the MAGIC and PnET-BGC models. Results 

are shown for 1850, 1990, and 2100. 

 3-73
Biological Effects 

The Adirondack region has a rich aquatic biota dataset from which to examine relationships among 

lake water chemistry and species abundance, composition, and richness. In general, there tends to be a 

negative relationship in Adirondack lakes between pH, ANC, and inorganic Al chemical variables and the 

diversity and abundance of fish (Baker and Laflen, 1983; Baker et al., 1990a; Havens et al., 1993) (Figure 

3-16), phytoplankton, and zooplankton (Confer et al., 1983; Siegfried et al., 1989) (Table 3-10). 

Table 3-10. Observed relationships between zooplankton species richness and lakewater ANC 

in the Adirondack Mountains. 

Taxonomic Group Equation R2 p 

Total zooplankton Richness = 15.65 + 0.089 ANC 0.46 0.001 

Crustaceans Richness = 6.35 + 0.028 ANC 0.47 0.001 

Rotifers Richness = 9.04 + 0.053 ANC 0.30 0.001 

Large Cladocerans Richness = 1.95 + 0.017 ANC 0.41 0.001 

Source: Sullivan et al. (2006b). 

Through the Adirondack Lakes Survey, 1,469 lakes were sampled between 1984 and 1987, 

representing 80% of the estimated population of Adirondack lakes larger than 1 ha in area (Whittier et al., 

2002). The goal of the survey was to characterize the biological, physical, and chemical characteristics of 

the lakes and evaluate the relationships between fish communities and water chemistry. The major results 

were reported by (Baker et al., 1990a). Key findings are: 

 Seventy-six percent of the lakes had fish; 24% (346 lakes) were fishless. 

 The most common fish caught were native acid-tolerant species: brown bullhead, brook trout, and 

white sucker. 

 As pH decreases, fish diversity also decreases. The average number of fish species declines from 

six fish species in lakes with pH higher than 6.5 to two or fewer fish species in lakes with pH of 

5.0 or less. 

 As pH decreases, the number of fishless lakes increases. Few lakes with pH of 5.5 or higher are 

fishless. Below pH 5.0, approximately 75% of the lakes are fishless. 

Researchers in the Adirondacks were among the first in the U.S. to demonstrate that fish mortality 

increases during acid episodes, which are common to lakes and streams in the Adirondacks during spring 

runoff. (Driscoll et al., 1987b) documented surface water chemistry changes associated with periods of 

high flow. They found that pH and ANC decreased substantially during hydrological episodes and 

inorganic Al concentrations commonly exceeded thresholds harmful to fish. These relationships were 

further documented by the Episodic Response Project as shown in the example for Bald Mountain Brook 

in the Adirondacks (Wigington et al., 1996). Work by Van Sickle et al. (1996) and others linked these 

chemical changes to fish mortality in small streams. They determined that blacknose dace were highly 

sensitive to low pH and could not tolerate inorganic Al concentrations above about 3.7 μM for extended 

periods of time. After 6 days of exposure to high inorganic Al, dace mortality increased rapidly to nearly 

 3-74
100% (Van Sickle et al., 1996). Brook trout were less sensitive, but still showed high mortality during 

many acid episodes. 

Several efforts have been made to link changes in fish populations with historical changes in water 

chemistry associated with acidifying deposition. Among the most widely cited is the work of (Baker et al., 

1990a; 1996). They analyzed 988 Adirondack Lake Survey lakes for which data existed for the period 

before 1970 and for the 1980s. Of the 2,824 fish populations confirmed by pre-1970 surveys, 30% had 

apparently been lost by the 1980s (Baker et al., 1990a). An estimated 23% of the fish population losses 

were related to acidifying deposition. This relationship was strengthened by evidence from the PIRLA 

projects. In the 32 lakes that had both historic fish data and paleolimnological chemical reconstructions, 

the lakes that had acidified the most or that were originally the most acidic were the same ones that were 

judged to have lost fish populations (Baker et al., 1996). 

Recent Trends in Surface Water Chemistry and Projections of Future Change 

Several studies have been conducted to analyze trends in lake chemistry in the Adirondacks. 

(Driscoll et al., 2003c) evaluated changes from 1982 to 2000 in the original 16 Adirondack LTM lakes 

and from 1992 to 2000 in the complete set of 48 Adirondack LTM lakes. They found that nearly all study 

lakes showed marked decreases in SO4

2−

 concentration over the period of record and several lakes showed 

declines in NO3

−

 concentration. Data for one example monitoring lake are given in Figure 3-22. They 

found that 7 of the 16 original monitoring lakes showed a statistically significant increase in ANC (Figure 

3-22), with a mean rate of increase of 0.78 μeq/L/yr (Driscoll et al., 2003c). Twenty-nine of the group of 

48 lakes showed increasing ANC trends from 1992 to 2000 with a mean rate of increase of 1.60 μeq/L/yr 

(Driscoll et al., 2003b). The authors attributed this recent increase in ANC to declines in both SO4

2−

 and 

NO3

−

 concentrations (Driscoll et al., 2003c). 

Despite these recent improvements in lake water chemistry in the Adirondack Long-Term 

Monitoring lakes, 34 of the 48 lakes still had mean ANC values less than 50 μeq/L in 2000, including 10 

lakes with ANC less than 0 μeq/L. Thus, current chemistry data suggest that most of these lakes exhibit 

chemical conditions that continue to pose a risk to aquatic biota. Model projections of future acid-base 

chemistry of lakes in the Adirondack Mountains under three scenarios of future atmospheric emissions 

controls were presented by (Sullivan et al., 2006a) to evaluate the extent to which lakes might be expected 

to continue to increase in ANC in the future. Estimated levels of S deposition at one representative 

watershed are shown in Figure 3-20 for the hindcast period and in the future under the three emissions 

control scenarios. Model simulations for 44 statistically selected Adirondack lakes using the MAGIC and 

PnET-BGC models were extrapolated to the regional lake population. Cumulative distribution frequencies 

of ANC response projected by MAGIC are shown in Figure 3-24 for the past (1850), peak acidification 

period (approximately 1990), and future (2100). Results for the future are given for each of the scenarios. 

Forecasting results suggested that the ongoing trend of increasing lakewater ANC for the most 

acid-sensitive lakes would not continue under future emissions and deposition levels anticipated as of 

2003 (Base Case Scenario). The numbers of Adirondack lakes having ANC below 20 and below 50 μeq/L 

were projected to increase between 2000 and 2100 under that scenario, and the number of chronically 

acidic Adirondack lakes (i.e., ANC less than 0) was projected to stabilize at the level reached in 2000. 

Thi3s projected partial reversal of chemical recovery of acid-sensitive lakes was due to a continuing 

decline in the simulated pool of exchangeable base cations in watershed soils. Simulations suggested that 

re-acidification might be prevented with further reductions in emissions and deposition. 

Chen and Driscoll (2004) applied the PnET-BGC model to DDRP lake watersheds in the 

Adirondacks. The model was applied to three future emissions scenarios: base case, moderate emissions 

reductions, and aggressive emissions reductions. A case study for Indian Lake in the Adirondacks 

illustrated that larger reductions in deposition caused greater decreases in SO4

2−

 and base cation 

concentrations in lake water and greater recovery in pH and ANC. Within the full population of lakewatersheds, some showed decreasing ANC and pH values from 1990 to 2050 even under the moderate 

and aggressive reduction scenarios. By 2050 to 2100, however, nearly all lakes were simulated to 

 3-75
experience increasing ANC and pH. The modeled soil base saturation increased very slowly over the 

modeled time period compared to changes in surface water chemistry. For 95% of the lake-watersheds 

studied, simulated soil base saturation remained below 20% in 2100 under all emissions scenarios. 

Source: Stoddard et al. (2003) 

Figure 3-22. Time series data for SO4

2−, NO3−, base cations [Ca plus Mg], Gran ANC, pH, and DOC in one 

example of long-term monitoring in Darts Lake in the Adirondack Park. Shaded box indicates 

time period of analyses reported by Stoddard et al. (2003). 

 3-76
Source: Driscoll et al. (2003b) 

Figure 3-23. Mean rates of change in solute concentration in 16 lakes of the Adirondack Long-Term 

Monitoring (ALTM) program from 1982 to 2000. Minimum, mean, and maximum changes in 

concentrations and number of lakes showing significant trends are shown. All values are 

in μeq/L/yr, except for concentrations of inorganic monomeric aluminum (Ali), which are 

expressed in μM/yr. 

Multipollutant Interaction: Biological Mercury Hotspots in the Adirondacks 

The Adirondacks has been identified as a region at risk from the combined effects of acidifying 

deposition and Hg deposition (Driscoll et al., 2007a). The relationship between atmospheric deposition of 

S and enhanced Hg methylation is discussed in Section 3.4. In general, the solubility of Hg increases with 

increasing sulfide concentrations in anoxic waters through complexation reactions, potentially increasing 

the pool of Hg available for methylation (Benoit et al., 2003; Driscoll et al., 2007a). Evers et al. (2007) 

identified a biological Hg hotspot in the western Adirondacks based on Hg concentrations in yellow perch 

and common loons. Mean yellow perch Hg concentrations in the Adirondack hotspot were 1.5 to 2.5 

times higher than the U.S. EPA and U.S. Food and Drug Administration’s reference dose used for fish 

consumption advisories (Evers et al., 2007). The authors hypothesized that the occurrence of the 

biological hotspot was due in part to the combination of high Hg deposition and sensitive water 

chemistry, such as low ANC and pH, which is associated with both natural acidity and the long-term 

effects of acidifying deposition (Evers et al., 2007). Driscoll et al. (2007a) concluded that watersheds 

sensitive to Hg deposition tend to be forested, have an abundance of wetlands, contain shallow hydrologic 

flow paths and low nutrient concentrations, and are affected by acidifying deposition. 

 3-77
Source: Sullivan et al. (2006b). 

Figure 3-24. Simulated cumulative frequency distributions of lakewater ANC at three dates for the 

population of Adirondack lakes, based on MAGIC model simulations reported by Sullivan 

et al., 2006b. Conditions for the year 2100 are presented for three emissions control 

scenarios: Base Case, Moderate Additional Controls, and Aggressive Additional Controls. 

(See Figure 3-15) 

3.2.4.5. Acidification Case Study #2: Shenandoah National Park, Virginia 

Shenandoah National Park is located along the crest of the Blue Ridge Mountains in Virginia. Air 

pollution within Shenandoah National Park, including S and N deposition and O3 concentration, is higher 

than in most other national parks in the U.S. Measured wet S deposition in the park has ranged from 8 to 

10 kg S/ha/yr in the early 1980s to near 6 kg S/ha/yr since 2000 (Figure 3-25). Dry S deposition may be 

nearly as high as wet deposition (Sullivan et al., 2003). Most acidification effects in the park have been 

linked with S deposition. 

The sensitivity of streams in the park to acidification from acidifying deposition is determined 

mainly by the types of rocks found beneath the stream and the characteristics of the watershed soils that 

surround it. If the underlying geology is Si-based (siliclastic lithology), the soil and water in the 

watershed generally have poor ability to neutralize acids deposited from the atmosphere. About one-third 

of the streams in the park are located on this type of geology. Model estimates using the MAGIC model 

suggest that such streams have typically lost most of their natural ANC, largely in response to a century of 

industrial emissions and acidifying deposition. As a consequence, stream pH values in many streams are 

low, especially during winter and spring. Before human-caused air pollution, most streams in Shenandoah 

National Park probably had pH above about 6. Many park streams on siliclastic lithology currently have 

pH as low as about 5 (Cosby et al., 2006; Sullivan et al., 2003). Other predominant lithologies in the park 

include granite-based (granitic) lithologies typically characterized by intermediate ANC streams, and 

 3-78
basalt-based (basaltic) lithologies typically characterized by relatively high stream ANC (Cosby et al., 

2001; Sullivan et al., 2004, 2007, 2008). 

The effects of acidifying deposition on Shenandoah National Park streams have been studied for 

over 25 years by the Shenandoah Watershed Study, the longest-running watershed study program in any 

of the national parks (Cosby et al., 2006; see http://swas.evsc.virginia.edu). This program has determined 

that the high rate of atmospheric deposition of S, combined with naturally low contributions from some 

rock types of Ca and other base cations (that serve to neutralize acidity), are the most important causes of 

low streamwater ANC in many park streams. Some park streams can also become temporarily acidic for 

short periods (hours to days) during rainstorms or snowmelt. 

Source: Sullivan et al. (2003) 

Figure 3-25. Wet sulfur deposition for the period of record at the Big Meadows NADP/NTN monitoring 

station in Shenandoah National Park. 

The acidification of streams in the Park is linked to effects that are occurring in the watershed soils. 

Over time, the ability of soils to adsorb S, thereby effectively negating sulfur’s potential to acidify water, 

is decreasing due to the long term accumulation of SO4

2−

 on soil adsorption sites in response to a legacy 

of acidifying deposition. In addition, the amount of stored Ca and Mg in the soil is gradually declining in 

response to acidifying deposition. Therefore, streams are expected to acidify more in the future than they 

have so far, relative to the amount of acidifying deposition received. This prognosis is consistent with 

recent analysis of national lake and stream response to reductions in air pollution emissions (Stoddard 

et al., 2003). Unlike a number of other regions of the country, streams in the region that includes 

Shenandoah National Park are generally not recovering from acidification. 

A great deal of research has been conducted in the park on the effects of S and N deposition on soil 

and water acidification. This park was a major site of early research on acidification processes (Galloway 

et al., 1983). This early work provided much of the foundation for development of the MAGIC model 

(Cosby et al., 1985c), which has been the most widely used dynamic watershed acid-base chemistry 

model worldwide for the past two decades. 

Although research on many aspects of acidification effects science has been conducted in the park, 

it has been particularly noteworthy for studies on episodic acidification; biological effects of stream 

acidification; and dynamic modeling of acidification, recovery, and critical loads. Research within 

Shenandoah National Park on each of these topics is discussed below. 

 3-79
Episodic Acidification 

A number of studies of episodic acidification have been conducted in streams within Shenandoah 

National Park. Eshleman and Hyer (2000) estimated the contribution of each major ion to observed 

episodic ANC depressions in Paine Run, Staunton River, and Piney River during a 3−year period. During 

the study, 33 discrete storms were sampled and water chemistry values were compared between 

antecedent baseflow and the point of minimum measured ANC (near peak discharge). The relative 

contribution of each ion to the ANC depressions was estimated using the method of Molot et al. (1989), 

which normalized the change in ion concentration by the overall change in ANC during the episode. At 

the low-ANC (~0 μeq/L) Paine Run site on siliciclastic bedrock, increases in NO3

−

 and SO4

2−

, and to a 

lesser extent organic acid anions, were the primary causes of episodic acidification. Increases in base 

cations tended to compensate for most of the increases in acid anion concentration. ANC declined by 3 to 

21 μeq/L (median 7 μeq/L) during the episodes studied. 

At the intermediate-ANC (~60 to 120 μeq/L) Staunton River site on granitic bedrock, increases in 

SO4

2−

 and organic acid anions, and to a lesser extent NO3

−

, were the primary causes of episodic 

acidification. Base cation increases compensated these changes to a large degree, and ANC declined by 2 

to 68 μeq/L during the episodes (median decrease in ANC was 21 μeq/L). 

At the high-ANC (~150 to 200 μeq/L) Piney River site on basaltic (69%) and granitic (31%) 

bedrock, base cation concentrations declined during episodes (in contrast with the other two sites where 

base cation concentrations increased). SO4

2−

 and NO3

−

 concentrations usually increased. The change in 

ANC during the episodes studied ranged from 9 to 163 μeq/L (median 57 μeq/L) (Eshleman and Hyer, 

2000). Changes in base cation concentrations during episodes contributed to changes in the ANC of Paine 

Run, had little effect in Staunton River, and contributed to decreases in ANC in Piney River. 

The most acidic conditions in Shenandoah National Park streams occur during high-flow periods, 

in conjunction with storm or snowmelt runoff. There are several different mechanisms of episodic 

acidification in operation in these streams, depending at least in part on the bedrock geology of the stream 

watershed. The relative importance of the major processes that contribute to episodic acidification varies 

among the streams, in part as a function of baseflow streamwater ANC which is largely controlled by 

bedrock geology. S-driven acidification was an important contributor to episodic loss of ANC at all three 

study sites, probably because S adsorption by soils occurs to a lesser extent during high-flow periods. 

This is due, at least in part, to diminished contact between drainage water and potentially adsorbing soil 

surfaces along the shallow flow paths. Dilution of base cation concentrations during episodes, which is an 

acidifying process, was most important at the high-ANC site. 

Thus, episodic acidification of streams in Shenandoah National Park can be attributed to a number 

of causes, including dilution of base cations and increased concentrations of sulfuric, nitric, and organic 

acids (Eshleman et al., 1995; Hyer et al., 1995). For streams having low pre-episode ANC, episodic 

decreases in pH and ANC and increases in toxic Al concentrations can have adverse effects on fish 

populations. However, not all of the causes of episodic acidification are related to acidifying deposition. 

Base-cation dilution and increase in organic acid anions during high-flow conditions are natural 

processes. At least for streams in the Shenandoah National Park. the contribution of N, indicated by 

increased NO3

−

 concentrations, evidently has been related to forest defoliation by the gypsy moth 

(Eshleman et al., 1998; Webb et al., 1995). Significant contributions of H2SO4, indicated by increased 

SO4

2−

 concentrations during episodes in some streams, is an effect of atmospheric deposition and the 

dynamics of S adsorption on soils (Eshleman and Hyer, 2000). 

A recent study by Deviney et al. (2006) used hourly ANC predictions over short time periods to 

compute recurrence intervals of annual water-year minimum ANC values for periods of 6, 24, 72, and 

168 h. They extrapolated the results to the rest of the catchments using catchment geology and 

topography. On the basis of the models, they concluded that many streams in the park have 6- to 168-h 

periods of low ANC values, which may stress resident fish populations (Deviney et al., 2006). 

Specifically, on the basis of a 4–year recurrence interval, approximately 23% of the land area (44% of the 

catchments) can be expected to have conditions for 72 continuous hours that are indeterminate with 

 3-80
respect to brook trout suitability (ANC 20 to 50), episodically acidic (ANC 0 to 20), or chronically acidic 

(ANC less than 0). Many catchments were predicted to have successive years of low-ANC values 

potentially sufficient to extirpate some species (Deviney et al., 2006). The authors of the study reported 

that smaller catchments are more vulnerable to adverse effects of episodic acidification than larger 

catchments underlain by the same bedrock. Catchments with similar topography and size are more 

vulnerable if underlain by less basaltic and carbonate bedrock. 

Biological Effects of Acidification 

A robust relationship between acid-base status of streams and fish species richness was 

documented in Shenandoah National Park in the 3−year Fish in Sensitive Habitats (FISH) study (Bulger 

et al., 1999). Numbers of fish species were compared among 13 streams spanning a range of pH and ANC 

conditions. There was a highly significant (p <0.0001) relationship between stream acid-base status 

(during the 7 year period of record) and fish species richness among the 13 streams. The streams with the 

lowest ANC hosted the fewest species (Figure 3-15). This study demonstrated biological differences in 

low- versus high-ANC streams, including species richness, population density, condition factor, age, size, 

and field bioassay survival. Of particular note was that both episodic and chronic mortality occurred in 

young brook trout exposed in a low-ANC stream, but not in a high-ANC stream (MacAvoy and Bulger, 

1995), and that blacknose dace (Rhinichthys atratulus) in low-ANC streams were in poor condition 

relative to blacknose dace in higher-ANC streams (Dennis et al., 1995; Dennis and Bulger, 1995). 

Bulger et al. (1999) observed a positive relationship between condition factor and pH in streams in 

Shenandoah National (Figure 3-26). Dennis and Bulger (1995) also found a reduction in condition factor 

for blacknose dace in waters near pH 6.0. The four populations depicted in (Figure 3-26) with the lowest 

condition factor had mean habitat pH values within or below the range of critical pH values at which 

Baker and Christensen (1991) estimated that negative population effects for blacknose dace are likely for 

the species. The mean condition factor of fish from the study stream with the lowest ANC was about 20% 

lower than that of the fish in best condition. Comparisons with the work of Schofield and Driscoll (1987) 

and Baker et al. (1990b) suggest that pH values in the low-pH streams are also near or below the limit of 

occurrence for blacknose dace populations in the Adirondack region of New York (Sullivan et al., 2003). 

MacAvoy and Bulger (1995) used multiple bioassays over 3 years in one of the low-ANC streams 

as part of the FISH project to determine the effect of stream baseflow and acid episode stream chemistry 

on the survival of brook trout eggs and fry. Simultaneous bioassays took place in mid- and higher-ANC 

reference streams. Acidic episodes, with associated low pH and elevated inorganic Al concentrations and 

high streamwater discharge, caused rapid fish mortality in the low-ANC stream, while the test fish in the 

higher-ANC stream survived (Bulger et al., 1999). 

 3-81
Source: Bulger et al. (1999). 

Figure 3-26. Length-adjusted condition factor (K), a measure of body size in blacknose dace (Rhinichthys 

atratulus) compared with mean stream pH among 11 populations (n = 442) in Shenandoah 

National Park. Values of pH are means based on quarterly measurements, 1991–1994; K was 

measured in 1994. The regression analysis showed a highly significant relationship 

(p <0.0001) between mean stream pH and body size, such that fish from acidified streams 

were less robust than fish from circumneutral streams. 

Modeling of Acidification, Recovery, and Critical Loads 

Dynamic models have been used in Shenandoah National Park to help determine whether the 

changes in surface water chemistry that have occurred over the past one to two decades will continue and 

whether they will reach levels needed to support biological recovery. The most commonly used models 

are described in Annex B and details of these analyses are discussed below. In general, model forecasts 

indicated that under base case conditions (those expected under existing or anticipated emissions controls) 

surface water ANC in the southern Appalachians (and in parts of the Adirondacks) would be likely to 

decline in the future. In terms of soil chemistry, projected future improvements in both regions appear to 

be slow and in most cases do not reach a base saturation of 20% or more within the next 100 years. 

MAGIC model simulations for streams in Shenandoah National Park by Sullivan et al. (2003) 

suggested that acidifying deposition would have to be decreased substantially to improve and maintain 

acid-sensitive streams at levels of ANC that would be expected to protect against ecological harm. In 

addition, it took a long time for these streams to acidify in the past; because of complexities related to soil 

conditions, it would take even longer for them to recover in the future. To protect against chronic acidity 

in the year 2100, with associated probable lethal effects on brook trout, the authors predicted that 

S deposition to the most geologically sensitive siliciclastic lithology watersheds in the park would have to 

be kept below about 9 kg S/ha/yr for the next 100 years (Sullivan et al., 2007a). Before the Industrial 

Revolution, most streamwater in the Park had ANC higher than about 50 μeq/L. To promote ANC 

recovery to 50 μeq/L in the future, to protect against general ecological harm, S deposition to Si-based 

(siliciclastic) watersheds in the park would have to be kept below about 6 kg S/ha/yr. Finally, the authors 

 3-82
predicted that some watersheds will likely not recover streamwater ANC to values above 50 μeq/L over 

the next century even if S deposition is reduced to zero (Sullivan et al., 2007a). 

Simulation and mapping of watershed responses to historical changes in acidifying deposition 

(from preindustrial to current) by Cosby et al. (2006) suggest that large areas of Shenandoah National 

Park have suffered deterioration of both soil and stream conditions. The changes in soil condition have 

been relatively modest up to the present time, with areas in the southern district of the park moving from 

classification of “moderate concern” (watershed average mineral soil percent base saturation 10% to 20%; 

the historical baseline) to “elevated concern” (average mineral soil percent base saturation 5% to 10%) as 

a result of leaching of base cations from the soils in response to S deposition. Simulation results indicated 

that deterioration in stream conditions has been more severe than for soil conditions, with large areas in 

the southern district and some smaller areas in the central and northern districts moving from “moderate 

concern” (average stream ANC 50 to 100 μeq/L) to “elevated concern” (average stream ANC 0 to 

50 μeq/L). Neither soil nor stream conditions have shown any improvement from 1980 to the present in 

response to the decline in acidifying deposition over the last 25 years. 

Simulation and mapping of watershed responses to predicted future changes in acidifying 

deposition by Cosby et al. (2006) were developed following U.S. EPA methods for preparation of 

emissions inventory inputs into air quality modeling for policy analysis and rule making purposes. These 

alternate emissions scenarios were based on existing emission control regulations and several proposed 

alternatives. The model output suggested that the responses of soil conditions to changes in S deposition 

are expected to be relatively slow. In the short term (by the year 2020), neither improvement nor further 

deterioration is likely to be observed in soil condition regardless of the future deposition scenario 

considered. However, model results suggested that constant deposition at 1990 levels would produce 

worsening soil conditions in the park by the year 2100 with the development of areas of “acute concern” 

(average percent soil base saturation below 5%) in the southern district. Although the scenarios of 

possible reduced future deposition did not produce worsening soil conditions, neither did they indicate 

any improvement in soil condition, even in the long term. 

Simulated responses of stream conditions were more rapid than those of soils. In the short term (by 

the year 2020), constant deposition at 1990 levels would likely produce further deterioration in stream 

condition. The scenarios of future deposition reductions failed to reverse the deterioration of stream 

condition that has occurred during the last century. In the long term (by year 2100), the effects of the 

deposition reduction scenarios begin to diverge. The moderate S deposition reduction scenario (69% 

reduction from 1990 values) did not produce improvement in stream chemistry relative to current 

conditions. The larger deposition reduction scenario (75%), by contrast, produced modest improvements 

in stream chemistry by 2100. However, even the relatively large S deposition reductions of this scenario 

did not result in a simulated return of stream conditions to the preindustrial state. 

To develop projections of probable past and future responses of aquatic biota to changing S 

deposition in Shenandoah National Park, the MAGIC model was coupled by Sullivan et al. (2003) with 

several empirical models that linked biological response to past and future model projections of water 

quality. Unlike MAGIC, which is a geochemical, process-based model, the biological effects estimates 

were based on observed empirical relationships rooted in correlation and expressed as linear relationships. 

Correlation does not necessarily imply causality, but an observed pattern of covariation between variables 

does provide a quantitative context for extrapolation. In this case, the projections did not require 

extrapolation beyond the observed ranges of observations, and therefore the projections were statistically 

robust. To the extent that the observed empirical relationships used in the coupled models do in fact 

reflect the effects of acid stress on aquatic biota, the projections were also biologically robust. 

Dynamic water chemistry model projections were combined with biological dose-response 

relationships to estimate declines in fish species richness with acidification. A relationship derived from 

the data in Figure 3-15 was used by Sullivan et al. (2003) with stream ANC values predicted by the 

MAGIC model to provide estimates of the expected number of fish species in each of the modeled 

streams for the past, present and future chemical conditions simulated for each stream. The coupled 

geochemical and biological model predictions were evaluated by comparing the predicted species 

 3-83
richness in each of the 13 streams with the observed number of species that occur in each stream. The 

agreement between predicted and observed species numbers was good, with a root mean squared error in 

predicted number of species across the 13 streams of 1.2 species. The average error was 0.3 species, 

indicating that the coupled models were unbiased in their predictions. Model reconstructions of past 

species richness in the streams suggested that historical loss of species had been greatest in the streams 

located on the most sensitive geological class (siliciclastic). The average number of species lost from 

streams on the three bedrock types examined was estimated as 1.6 species on siliciclastic bedrock, 0.4 

species on granitic bedrock, and 0.4 species on basaltic bedrock. In the case of the siliciclastic streams, 

the projected past changes were much larger than the average error and root mean squared error of the 

coupled models, suggesting that the projections were reasonably robust. 

3.2.5. Ecosystem Services 

The evidence reviewed in this ISA supports that acidification of ecosystems is primarily driven by 

NOX, NHX and SOX. Ecosystem services, as defined by Hassan et al. (2005), are broadly grouped into 

four main categories (see Section 3.1.3). The specific effects of ecosystem acidification may be grouped 

into these categories as follows. 

 Supporting: altered nutrient cycling, decreased biodiversity, decline of productivity 

 Provisioning: decline in the richness, abundance, and/or health of fish, other aquatic species and 

some terrestrial trees 

 Regulating: decline in water and soil quality 

 Cultural: decline in forest aesthetics, fishing, ecotourism and cultural heritage values related to 

ecosystem integrity and biodiversity 

There are few publications that directly evaluate the ecological effects of acidification in terms of 

valuation. Even though acidification is well documented in the scientific literature, the lack of valuation 

studies related to them limits full assessments of terrestrial damages from NOX and SOX pollution. As 

noted in the case study (see Section 3.2.4.4.), the Adirondack Park in New York is among the best 

documented of all areas affected by acidic deposition in the U.S. This park has been the subject of 

numerous valuation studies in recent decades on cultural ecosystem services (Morey and Shaw 1990; 

Mullen and Menz 1985; Englin et al., 1991; Cameron and Englin 1997; Banzhaf et al., 2006). There are 

currently no studies on the valuation of supporting or regulating ecosystem services. 

3.3. Nutrient Enrichment Effects from Nitrogen Deposition 

The ecological effects caused by atmospheric deposition of Nr are the main focus of this section. As 

discussed previously, the scope of this ISA includes assessment of all forms of N compounds that 

contribute to nutrient enrichment. The various chemical forms of N can be broadly divided into two 

groups: nonreactive (N2 gas) and Nr. Nonreactive N2 gas composes 80% of the total mass of the Earth’s 

atmosphere, but it is not biologically available until transformed into reactive forms of N. Nr includes all 

biologically and chemically active N compounds in the Earth’s atmosphere and biosphere (Galloway 

et al., 2003). The Nr group includes inorganic reduced forms (e.g., NH3 and NH4

+

), inorganic oxidized 

forms (e.g., NOX, HNO3, N2O, NO3

−

), and organic N compounds (e.g., urea, amine, proteins, nucleic 

acids) (Galloway et al., 2003). Atmospheric N deposition may be composed of numerous chemical 

species besides oxides, all of which contribute to ecosystem nutrient enrichment. The ISA evaluates the 

nutrient effects of NOX in combination with all other forms of Nr deposition for which information is 

 3-84
available. Agricultural lands are excluded from this discussion because crops are routinely fertilized with 

amounts of N (100 to 300 kg N/ha) that exceed air pollutant inputs even in the most polluted areas 

(U.S. EPA, 1993a). These high rates of fertilization can contribute to ground water NO3

−

 contamination 

and eutrophication of some surface waters, especially estuaries. However, the environmental effects of 

agricultural N fertilization is beyond the scope of this assessment. 

Organisms in their natural environment are commonly adapted to a specific regime of nutrient 

availability. Change in the availability of one important nutrient, such as N, may result in imbalance in 

ecological stoichiometry, with effects on ecosystem processes, structure and function (Sterner and Elser, 

2002). In general, ecosystems that are most vulnerable to nutrient enrichment from atmospheric N 

deposition are those that receive high levels of deposition relative to non-anthropogenic N loading, those 

that are N-limited, or those that contain species that have evolved in nutrient-poor environments. The 

most common experimental designs to quantify the effects of N deposition on ecosystems are N addition, 

N gradient, or observation correlated to changing pollution levels over time. N addition experiments often 

use NH4NO3 or (NH4)2SO4 additions to simulate the chemical species in atmospheric Nr deposition. 

Deposition gradient experiments often only measure oxidized and reduced forms of N and therefore do 

not adequately identify all components of Nr deposition. Therefore, publications addressing N additions or 

deposition gradients often do not include data on all components of Nr. 

The following discussion of N-nutrient deposition begins with the N cascade, which provides a 

conceptual foundation for discussing the effects of N on the structure and function of ecosystems. 

Subsequent sections include the effects of N deposition on: N cycling, C cycling, biogenic GHG 

emissions, biodiversity, as well as the characterization of sensitive ecosystems and regions in the U.S. 

Information is presented for ecosystems in which atmospheric deposition dominates total Nr input (i.e. 

many non-managed terrestrial ecosystems) and ecosystems in which atmospheric deposition constitutes a 

proportion of total Nr load (e.g., some wetlands and estuarine ecosystems). 

3.3.1. Reactive Nitrogen and the Nitrogen Cascade 

Nr is one of the most important nutrients in practically all ecosystems (Vitousek and Howarth, 

1991). Nr is required by all organisms because it is a major constituent of both the nucleic acids that 

determine the genetic character of all living things and the enzymes and proteins that drive the 

metabolism of every living cell (U.S. EPA, 1993a; Galloway, 1998; Galloway and Cowling, 2002). It is of 

critical importance in plant metabolism and it often governs the utilization of P, K and other nutrients. 

 3-85
Source: Galloway et al. (2003). Reprinted with permission. 

Figure 3-27. Illustration of the N cascade showing the movement of the human-produced Nr as it cycles 

through the various environmental reservoirs in the atmosphere, terrestrial ecosystems, and 

aquatic ecosystems. 

An increase in global Nr has occurred over the past century, largely due to three main causes: 

widespread cultivation of legumes, rice, and other crops that promote conversion of N2 gas to organic N 

through biological N fixation; combustion of fossil fuels, which converts both atmospheric N2 and fossil 

N to NOX; and synthetic N fertilizer production via the Haber-Bosch process, which converts nonreactive 

N2 to Nr to sustain food production and some industrial activities (Galloway and Cowling, 2002 Galloway 

et al., 2003). Food production accounts for much of the conversion from N2 to Nr, and accounts for 

geographic redistribution of N as food is shipped to meet population demands and often returned to the 

environment via waste water. 

Nr accumulates in the environment on local, regional, and global scales (Galloway, 1998; Galloway 

and Cowling, 2002; Galloway et al., 2003). This accumulation occurs in the atmosphere, soil, and water 

(Galloway and Cowling, 2002), with a multitude of effects on humans and ecosystems (Galloway, 1998; 

Rabalais, 2002; Van Egmond et al., 2002; Townsend et al., 2003). The sequence of transfers, 

transformations, and environmental effects is referred to as the “N cascade” (Galloway and Cowling, 

2002; Galloway et al., 2003)(Figure 3-27). 

In general, the results of the N cascade and the various transformations in the N cycle can be both 

beneficial and detrimental to humans and to ecosystems (Galloway and Cowling, 2002; Galloway and 

Aber, 2003). Among the most important effect of atmospheric N deposition are aquatic eutrophication, 

changes in the structure of terrestrial plant communities, disruptions in nutrient cycling, increased soil 

emissions of nitrous oxide (N2O; a potent greenhouse gas), accumulation of N compounds in the soil, 

soil-mediated effects of acidification (see Section 3.2), and increased susceptibility of plants to stress 

factors (Aber et al., 1989, 1998; Bobbink, 1998; Driscoll et al., 2003c; Fenn et al., 1998). 

 3-86
3.3.2. Nitrogen Enrichment Effects on N Cycling 

Given the complexity of the N cycle, a broadly applicable and well-tested predictive model of these 

interactions has not yet been developed. There is scientific information with which to make 

generalizations about how ecological and biogeochemical processes respond to N deposition. Significant 

scientific advancements in recent years have included refinement of theoretical foundations of nutrient 

limitation, development and improvement of analytical technologies, and improved understanding of the 

role of N in regulating or influencing the cycling of other elements, especially C (see Section 3.3.3). 

The key steps in the N cycle that are outlined in Figure 3-28. Processess within this cycle include N 

fixation, assimilation, mineralization (conversion of organic N to simple inorganic forms), nitrification 

(conversion of reduced inorganic N to oxidized inorganic N), and denitrification (the reduction of NO3

−

 to 

NO, N2O, and N2 gas by microbes under anaerobic conditions). These steps generally require biologically 

mediated transformations. Key organisms involved in transforming N from one form to another include 

plants and microbes. 

Source: Garner (1994) 

Figure 3-28. N cycle (dotted lines indicated processes altered by N saturation). 

 3-87
In addition to direct effects on the ecosystem where it is deposited, N can be exported from the 

system and cause environmental effects in adjacent ecosystem. The two principal mechanisms for N 

export or loss from ecosystems are leaching and denitrification. Leaching removes N from terrestrial or 

transitional ecosystems, but adds it to aquatic ecosystems. Thus, an export from one ecosystem becomes 

an import to another. Denitrification removes N from terrestrial, transitional, and aquatic ecosystems and 

adds it to the atmosphere (Davidson et al., 2000; Seitzinger et al., 2006). Although denitrification 

provides a pathway for removing excess N from ecosystems, incidental production of NO and N2O during 

denitrification is of concern due to the roles of NO as a precursor in the p(O3), and N2O as a potent 

greenhouse gas (see Section 3.3.4 for N deposition effects on biogenic N2O flux). Here leaching and 

denitrification are discussed in addition to other fundamentals of N cycling in terrestrial, transitional, and 

aquatic ecosystems. 

3.3.2.1. Terrestrial Ecosystems 

N-deposition affects terrestrial ecosystems throughout large areas of the U.S. N-availability to 

plants in soil is largely controlled by the process of N mineralization, or the microbial conversion from 

organic N to simple amino acids and then to inorganic forms such as NH4

+

 and NO3

−

 (Schimel and 

Bennett, 2004). The two-step, aerobic, microbial process of autotrophic nitrification converts NH4

+

 to 

NO3

−

. Nitrification is an acidifying process, releasing 2 mol hydrogen ion (H+) per mol NH4

+

 converted to 

NO3

−

 (Reuss and Johnson, 1986; see Section 3.2 and Annex B for the effects of acidifying deposition). As 

the N cycle becomes enriched through cumulative N addition, N becomes more abundant, competition 

among organisms for N decreases, net nitrification rates often increase, and NO3

−

 can leach from the 

ecosystem (Aber et al., 1989 and 2003). 

Numerous experimental 15N-addition studies have been conducted as a way of understanding how 

N cycles through terrestrial ecosystems. These studies have shown that trees typically take up only a 

small fraction of added 15N; the vast preponderance is retained in the soil (Nadelhoffer et al., 1999a; 

Providoli et al., 2005; Templer et al., 2005; Tietema et al., 1998). This pattern persists even a decade after 

15N application (Nadelhoffer et al., 2004), but these experiments have been criticized for applying 15N 

directly to the soil surface, thereby precluding direct canopy uptake of N from wet or dry deposition 

(Sievering, 1999; Sievering et al., 2000). Canopy 15N experiments are now underway, but have not yet 

been published. Comparisons of rates of N deposition in throughfall and in total deposition suggest that 

forest canopies can take up an average of 16% of total atmospheric N input (Lovett, 1992), but this 

interception can be considerably higher (up to 90%) in some N-limited forests with large epiphyte loads 

(e.g., Klopatek et al., 2006). It remains unclear how much of the N from deposition that is retained in 

vegetation is then used in photosynthetic enzymes (e.g., Bauer et al., 2004). 

N in forest ecosystems is stored primarily in the soil, and soil N often exceeds 85% of the total 

ecosystem N (Bormann et al., 1977; Cole and Rapp, 1981). Most soil N is contained in organic matter, 

typically bound in humic material or organo-mineral complexes that are resistant to microbial 

degradation. This N is not directly available for biological uptake by plants or microbes or for leaching 

loss into ground water or surface water. 

Only what is termed the mineralizable, or labile, pool of N in the soil is considered to be 

biologically active (Aber et al., 1989). Bioavailable N often controls photosynthesis and net primary 

productivity (NPP) (e.g., Field and Mooney, 1986). Plants obtain N from the soil by absorbing NH4

+

, 

NO3

−

, or simple organic N compounds through their roots, or N is taken up by symbiotic organisms (e.g., 

fungi, bacteria, cyanobacteria) in plant roots (cf. Lilleskov et al., 2001; Schimel and Bennett, 2004). Plant 

roots, nitrifying bacteria, and microbial decomposers within the soil utilize, and compete for, this 

available soil N pool. Plant uptake of N can be energetically costly, as NO3

−

 must be reduced to NH4

+

, and 

NH4

+

 fixed into amino acids before N can be used in plant processes. Some species reduce NO3

−

 in their 

leaves, taking advantage of excess energy from photosynthesis, whereas other species are restricted to the 

more energy expensive approach of reducing NO3

−

 in their roots. 

 3-88
N Saturation 

The term N-saturation refers to the condition whereby the input of N to the ecosystem exceeds the 

requirements of terrestrial biota, and consequently an elevated fraction of the incoming N leaches from 

soils to surface waters. The original description of N saturation by Aber et al. (1989) described four 

stages. It was revised by Stoddard (1994) and Aber et al. (1998) (Figure 3-29). In Stage 0, N inputs are 

low and there are strong N limitations on growth. Stage 1 is characterized by high N retention and a 

fertilization effect of added N on tree growth. Stage 2 includes the induction of nitrification and some 

NO3

−

 leaching, though growth may still be high. In Stage 3 tree growth declines, nitrification and NO3

−

loss continue to increase, but N mineralization rates begin to decline. While not all terrestrial ecosystems 

move through the stages of N saturation at the same rate or in response to the same N loading, several 

experimental N addition studies and a survey of 161 spruce-fir stands along a N deposition gradient 

support the concept of N saturation progressing from the onset of increase in net nitrification and NO3

−

leaching loss to the eventual decline in tree growth and increase in tree mortality (Aber et al., 1998). 

Source: Aber et al. (1998). Reprinted with permission. 

Figure 3-29. Schematic illustration of the response of temperate forest ecosystems to long-term, chronic N 

additions. Changes from initial hypotheses of Aber et al. (1989) include the reduction in N 

mineralization in stage 3 and the addition of foliar Ca:Al and Mg:N ratios. 

Decades of atmospheric deposition of N have increased the availability of NO3

−

 and NH4

+

 in some 

terrestrial ecosystems to levels where excess N availability results in net nitrification and associated NO3

−

leaching in drainage water. Severe symptoms of N saturation have been observed in the northern 

hardwood watersheds at Fernow Experimental Forest near Parsons, West Virginia (Peterjohn et al., 1996); 

in high-elevation, nonaggrading spruce-fir ecosystems in the Appalachian Mountains (Cook et al., 1994); 

throughout the northeastern U.S. (Aber et al., 1989; 1998); and lower-elevation eastern forests (Edwards 

and Helvey, 1991; Peterjohn et al., 1996; Adams et al., 1997, 2000). 

Mixed conifer forests and chaparral watersheds with high smog exposure in the Los Angeles Air 

Basin also are N-saturated and exhibit the highest stream water NO3

−

 concentrations documented within 

wildlands in North America (Bytnerowicz and Fenn, 1996; Fenn et al., 1998). In general, it is believed 

that deciduous forest stands in the eastern U.S. have not progressed toward N-saturation as rapidly or as 

far as coniferous stands. Deciduous forests may have a greater capacity for N retention than coniferous 

forests. In addition, deciduous forests tend to be located at lower elevation and receive lower atmospheric 

 3-89
inputs of N. Many deciduous forests have higher rates of N uptake and greater N requirement than 

coniferous forests (Aber et al., 1998). 

NO3

− Leaching 

Nitrate leaching is the process of nitrate moving from one ecosystem compartment to another. The 

nitrate may first leach from terrestrial ecosystems to soil drainage waters where it then increases surface 

water NO3

−

 concentrations and may be exported downstream. Because the pathway of NO3

−

 leaching 

crosses over multiple ecosystem types is discussed in both the terrestrial, wetland and aquatic sections of 

the ISA. Two of the primary indicators of N enrichment in forested watersheds are the leaching of NO3

−

in soil drainage waters and the export of NO3

−

 in stream water, especially during the growing season 

(Stoddard, 1994). The concentration of NO3

−

 in surface water provides an indication of the extent to 

which N deposited atmospherically or otherwise leaches from the terrestrial ecosystem. 

In most upland forested areas in the U.S., most N received in atmospheric deposition is retained in 

soil (Nadelhoffer et al., 1999a). Several different data compilations indicate that 80% to 100% of N 

deposition is retained or denitrified within terrestrial ecosystems that receive less than about 10 N/ha/yr 

(Aber et al., 2003; Dise and Wright, 1995; Kristensen et al., 2004; MacDonald et al., 2002; Sullivan, 

2000a). In general, because much of the atmospherically deposited N is retained within the terrestrial 

ecosystem or denitrified during export, a relatively small fraction of this N reaches downstream estuaries 

(Alexander et al., 2002; Castro et al., 2001; Seitzinger et al., 2002; van Breemen et al., 2002). 

Despite retention of most atmospheric N deposition within the terrestrial environment, N-related 

adverse effects on aquatic life do occur (Driscoll et al., 2003d). For example, although 70% to 88% of 

atmospheric N deposition was retained in the Catskill Mountains watersheds in upstate New York, fish 

populations could not be sustained because high NO3

−

 concentrations in stream water during high flows 

caused the concentrations of inorganic Al to reach toxic levels (Lawrence et al., 1999; see Section 3.2). 

Source: Aber et al. (2003). Reprinted with permission. 

Figure 3-30. Surface water NO3

− concentrations as a function of N deposition at the base of each 

watershed in summer and spring. N deposition to the whole watershed may be 2 to 6 kg 

N/ha/yr greater than at the base. 

 3-90
In the northeastern U.S., an analysis by Aber et al. (2003) of data collected during the mid- to late 

1990s from lakes and streams suggested that nearly all N deposition is retained or denitrified in 

northeastern watersheds that receive less than about 8 to 10 kg N/ha/yr. An analysis of N deposition to 

forestland in the northeastern U.S. based on Ollinger et al. (1993) suggested that approximately 36% of 

the forests in the region received 8 kg N/ha/yr or more and may therefore be susceptible to elevated NO3

−

leaching (Driscoll et al., 2003d). Aber et al. (2003) further found that surface water NO3

−

 concentrations 

exceeded 1 μeq/L in watersheds receiving about 9 to 13 kg N/ha/yr of atmospheric N deposition (Figure 

3-30). The lakes and streams found to have high NO3

−

 concentration were those receiving N deposition 

above this range, but responses were variable among those receiving high N deposition. Above this range, 

mean NO3

−

 export increased linearly with increasing deposition at a rate of 0.85 kg NO3

−

 kg N/ha/yr for 

every 1 kg N/ha/yr increase in deposition, although there was considerable variability in N retention 

among watersheds at higher rates of deposition (Figure 3-31) (Aber et al., 2003). 

Source: Aber et al. (2003). Reprinted with permission. 

Figure 3-31. a) N export in stream water as a function of N deposition at the base of sampled watersheds. 

N export is represented by the equation NBexp = 0.85 Ndep – 5.8; r2 = 0.56; p <0.001. (b) 

Watershed N retention decreases as N deposition at the base of the watersheds increases (N 

retention = −0.07 N deposition + 1.44; r2 = 0.50). 

 3-91
In the West, a study of mixed conifer forests of the Sierra Nevada and San Bernardino mountains 

reported that the deposition load that causes the onset of NO3

−

 leaching is 17 kg N/ha/yr. Several studies 

in the Rocky Mountains indicate that the onset of NO3

−

 leaching in alpine catchments occurs at 

approximately 10 kg N/ha/yr (Baron et al., 1994; Williams and Tonnesen 2000). 

In other studies, the isotopic signature of 18O in streamwater NO3

−

 indicates that only a small 

percentage of the incoming NO3

−

 from atmospheric deposition leached directly to drainage waters (e.g., 

(Spoelstra et al., 2001; Burns and Kendall, 2002; Pardo et al., 2004). The rest of the NO3

−

 that leached 

from the terrestrial ecosystem was cycled by biota in soils or streams before being exported. That cycled 

N may have originated in atmospheric deposition, but its origin was not identified. 

In general, field experiments have shown that NO3

−

 leaching can be induced by chronic addition of 

N (Kahl et al., 1993, 1999; Norton et al., 1999; Edwards et al., 2002a; Peterjohn et al., 1996) (See Table 

3-11). Several N-exclusion studies in Europe demonstrated that decreases in N deposition produced 

immediate reductions in NO3

−

 leaching from forest stands (Gundersen et al., 1998; Quist et al., 1999). At 

a regional scale, the leaching transport of N from terrestrial to freshwater systems has important 

implications beyond its impact on upland lakes and streams, because N exports can ultimately also 

contribute to the eutrophication of coastal ecosystems (Howarth et al., 1996; Driscoll et al., 2003d). 

Denitrification 

The role of denitrification in terrestrial ecosystems is important to understand the fraction of the 

atmospheric deposition that is returned to the atmosphere and therefore does not have direct effects on 

terrestrial and aquatic ecosystems. Denitrification has been difficult to measure directly in most 

ecosystems, due to the difficulty of measuring small changes in N2 and to the great degree of spatial and 

temporal heterogeneity inherent in the denitrification process (Davidson and Seitzinger, 2006). Additional 

information on measurement techniques is available in Annex C. 

Denitrification is accomplished by facultative anaerobic denitrifying bacteria, and occurs only 

under anaerobic conditions in the presence of sufficient NO3

−

 and organic C. Hence, most terrestrial 

denitrification occurs in “hotspots,” that is, in sporadically wet places or times or in anaerobic soil 

microsites (McClain et al., 2003; Seitzinger et al., 2006). The high organic matter content of terrestrial 

soils provides an ample supply of C. Therefore, the factors that typically limit the rate of denitrification in 

terrestrial ecosystems are the NO3

−

 supply and the occurrence of anaerobic conditions (See Annex C for 

additional studies and Section 3.3.4 for an analysis of N deposition effects on N2O flux from terrestrial 

and wetland ecosystems). 

Using a simple model of the fate of global N inputs to terrestrial ecosystems, Seitzinger et al. 

(2006) estimated that denitrification in terrestrial soils removed 46% (124 Tg/yr) of global N inputs from 

all sources (N deposition, fertilizer, and N fixation). Half of this denitrification (66 Tg/yr) was estimated 

to have occurred in agricultural systems. However, this model assumed that all N entering terrestrial 

systems was leached as NO3

−

 if it was not taken up by plants. Hence, the model overestimated the 

potential for denitrification by the extent to which N accumulated in soils or ground water (Seitzinger 

et al., 2006). 

Foliar N Concentration 

The concentration of N in plant foliage, especially in forest trees, can provide an indicator of 

nutrient enrichment (McNeil et al., 2007) (Table 3-11). This indicator may be especially relevant because 

there is a potential to acquire regional-scale data on foliar N through remote sensing techniques. This 

allows rapid assessment of N status across large land areas. The N content in tissue of some plant species 

varies in proportion to N inputs (Baddeley et al., 1994; Hyvarinen and Crittenden, 1998; Pitcairn et al., 

2003). Similarly, species typical of nutrient-poor environments tend to accumulate the amino acid 

arginine in plant tissue (Van Dijk and Roelofs, 1988). Therefore arginine concentration varies in 

proportion to N inputs. Foliar N and foliar arginine concentrations both provide good indices of N 

deposition effects. 

 3-92
There are reported interactions between N content and insects. Increased N content has been shown 

to increase palatability to defoliating insects and therefore increasing the extent of defoliation (Nordin 

et al., 1998; Forkner and Hunter, 2000). 

Soil Carbon-to-Nitrogen Ratio 

The N and C cycles are tightly coupled in forest soils. For example, NO3

−

 leaching has been 

correlated with forest floor C:N. Nitrification and NO3

−

 leaching rates are generally low on sites having 

soil C:N ratios above about 22 to 25 (Lovett et al., 2002; Ross et al., 2004). The C:N ratio of the forest 

floor can be changed by N deposition over time, although it is difficult to detect a change over time 

against the background of spatial heterogeneity (Galloway et al., 2003). The forest floor C:N ratio has 

been used as an indicator of ecosystem N status in mature coniferous forests (Table 3-11). 

Table 3-11. Summary of biogeochemical indicators of N deposition to terrestrial ecosystems. 

Region/Country Endpoint Observations Forest Type/ Species Reference 

White Mountains 

New Hampshire 

Soil C:N Ratio 

Nitrification 

Observation: field relationships between soil C:N 

ratio, canopy lignin:N ratio, and high spectral resolution remote sensing data was used to predict 

spatial patterns in C:N. Remote-sensed data were 

obtained from NASA’s Airborne Visible and Infrared Imaging Spectrometer (AVIRIS) instrument. 

Preliminary regional estimates of soil C:N ratio 

suggested that 63% of the land area in the region 

had C:N below 22, which was suggested as a 

critical threshold for the onset of nitrification. Below C:N = 22, increasing, but variable, rates of 

nitrification were found. 

Broadleaf and mixed conifer/ sugar 

maple (Acer saccharum), red maple 

(Acer rubrum), American beech 

(Fagus grandifolia), yellow birch 

(Betula alleghaniensis), paper birch 

(Betula papyrifera), red spruce 

(Picea rubens), balsam fir (Abies 

balsamea), eastern hemlock (Tsuga 

canadensis) 

Ollinger et al. 

(2002) 

Europe Soil C:N Ratio 

NO3

− Leaching 

Observation and Modeling: data from 160 sites 

across Europe was used to determine that NO3

−

leaching when soil C:N ratio is above 30. NO3

−

leaching occurs with foliar N <13 mg N/g. The 

responses of soil solution nitrate concentration to 

changes in N input are more pronounced in broadleaf than in coniferous forests, because in European forests broadleaf species grow on the more 

fertile soils. 

Broadleaf and mixed conifer/ three 

broadleaf species (Quercus robur, 

Quercus petraea, Fagus sylvatica) 

and six conifer species (Picea abies, 

Picea sitchensis, Abies alba, Pinus 

sylvestris, Pinus nigra, Pseudotsuga 

menziesii) 

Kristensen 

et al. (2004) 

Northeast U.S. Soil C:N Ratio 

Nitrification 

surface water 

[NO3

−] 

foliar [N ion] 

Observation: In a compilation of soil C:N and 

nitrification data from 250 plots across an N 

deposition gradient of 3.3 – 12.7 kg N/ha/yr 

showed a statistically significant but weak correlations between both soil C:N and nitrification to 

annual N deposition rate. Across plots, nitrification 

increased sharply as C:N ratio (by mass) decreased below about 22. Significant foliar chemistry alterations only were attributable to effects of 

climate and elevation. Strong relationship between 

surface water [NO3

–] and flux across N deposition 

gradient. 

Hardwood and conifer forests; Red 

Spruce (Picea rubens), Sugar maple 

(Acer saccharum) 

Aber et al. 

(2003) 

 3-93
Region/Country Endpoint Observations Forest Type/ Species Reference 

Continental Divide, 

Colorado 

Soil C:N Foliar 

C:N 

Mineralization 

Soil %N 

Foliar N:Mg 

Foliar N:P 

Deposition Gradient: Comparison of Englemann 

spruce forest stands on the east (3 to 5 kg 

N/ha/yr) and west (1 to 2 kg N/ha/yr) slopes of the 

Continental Divide in Colorado. The east slope 

receives higher levels of N deposition due to proximal agricultural and urban areas. East slope sites 

showed (1) lower soil organic horizon C:N ratio, 

(2) lower foliar C:N ratio, (3) higher potential net 

mineralization, and (4) higher percent N, N:Mg 

ratio, and N:P ratio in foliage. These results suggested that even moderate levels of N deposition 

input can cause measurable changes in spruce 

forest biogeochemistry. 

Englemann spruce (Picea 

englemannii) dominated mesic 

spruce-fir forests; Vaccinium spp. 

Understory, subalpine fir (Abies 

lasiocarpa) and lodgepole pine 

(PInus contorta) 

Rueth and 

Baron (2002) 

Adirondacks, New 

York 

Foliar [N] Deposition Gradient: Eight of nine major canopy 

tree species had increased foliar N in response to 

a gradient of roughly 4 to 8 kg N/ha/yr for wet 

deposition (total N deposition gradient of 5 to 10 N 

ha/yr). Species specific differences were strongly 

related to two functional traits that arise from 

within-leaf allocations of N resources: leaf mass 

per area and shade 

Broadleaf and mixed conifer/ paper 

birch (Betula papyrifera), yellow 

birch (Betula alleghaniensis), red 

maple (Acer rubrum), sugar maple 

(Acer saccharum), American beech 

(Fagus grandifolia), white pine 

(Pinus strobus), eastern hemlock 

(Tsuga canadensis), balsam fir 

(Abies balsamea), red spruce (Picea 

rubens) 

McNeil et al. 

(2007) 

Scotland Foliar [N] & 

[arginine] 

Deposition Gradient: Total tissue N and arginine 

concentrations were closely correlated with both 

atmospheric NH3 concentration and estimated N 

deposition (r2 >l.97 and >0.78, respectively), and 

this affected species composition in a gradient of 

nitrophilous plant species closer to the N source 

replacing the more N sensitive species 

Mixed woodland; plantation of Pinus 

sylvestris; Fagus sylvatica; various 

mosses, ferns, forbs 

Pitcairn et al. 

(2003) 

Vermont Nitrification 

Basal area 

growth 

Mineralization 

Foliar %N 

Field Addition: Additions of 25 kg N/ha/yr to spruce 

plots (ambient bulk deposition 5.4 kg N/ha/yr), in 

which net nitrification did not occur before treatment, triggered net nitrification in the second year 

of treatment, whereas nitrification was not triggered until the third year in plots receiving 19.8 kg 

N/ha/yr. Positive correlation between forest floor % 

N, nitrification potential and foliar %N increased 

with N addition 

High elevation spruce-fir stand; Red 

spruce (Picea rubens), maple (Acer 

spp.) and birch (Betula spp.) 

McNulty et al. 

(1996) 

Colorado Mineralization 

Nitrification 

Foliar [N] Organic Soil [N] 

and C:N 

Field Addition: Additions of 25 kg N/ha/yr to plots 

in Loch Vale watershed (ambient bulk deposition 

~3.2–5.5 kg N/ha/yr) doubled N mineralization 

rates and stimulated nitrification, while the addition 

of the same amount to plots receiving ambient 

bulk deposition of ~1.7 kg N/ha/yr in Fraser 

Experimental Forest elicited no microbial response 

but significantly increased foliar and organic soil 

horizon N, and decrease soil C:N. 

Old-growth spruce-fir; Engelmann 

spruce (Picea engelmanni) Subalpine fir (Abies lasiocarpa) Lodgepole pine (Pinus contorta) Vaccinium 

spp. 

Rueth et al. 

(2003) 

 3-94
Region/Country Endpoint Observations Forest Type/ Species Reference 

Harvard Forest, MA Soil [NO3

−] 

Soil [NH4+] 

Field Addition: Elevated concentrations of NO3

−

plus NH4

+ in soil water observed after 1 year of 

150 kg N/ha/yr doses, and after 5 years of 50 kg 

N/ha/yr doses. In plots that received additions of 

150 kg N/ha/yr, elevated concentrations were detected on the seventh year. In plots receiving 

50 kg N/ha/yr, elevated soil concentrations were 

not observed after 15 years of treatment. 

Red pine plantation and mixed 

hardwood/ Pinus resinosa 

Magill et al. 

(2004) 

Bear Brook Watershed, ME NO3

− Leaching Field Addition: Ammonium sulfate ([NH4]2SO4) 

fertilization of a forested watershed resulted in 

long-term increases in NO3

– concentration in 

stream water and high annual export of N. The 

annual retention of N decreased from 96 % to 

81%, with a cumulative retention of 82% of N inputs, mostly in soil. The export of N from the reference watershed has declined from 178 to 23 kg 

N/ha/yr during the treatment period. 

Mixed hard and soft wood Kahl et al., 

(1999); Norton 

et al. (1999) 

Tunk Mountain 

Watershed, ME 

NO3

− Leaching Observation: Changes in watershed processing of 

nitrogen may influence acid-base status of surface 

waters. The very low concentrations of NH4+ and 

NO3

− in lake waters in Maine (Kahl et al., 1991) 

suggest that changes in N-status have not played 

a major role in changes in ANC. However, N loss 

to surface waters is well documented in Adirondack surface waters (Driscoll et al., 1991), coincident with the decline in ANC. The Wild River data 

also showed increased NO3

– concentrations during 1964-83. Different responses in soil reactions 

among sites could account for the different response of ANC to changing deposition patterns. 

Mixed hard and soft wood (Kahl et al., 

1993) 

Fernow Experimental Forest, WV NO3

− Leaching 

Ca Leaching 

Observation: The percentage of conductivity attributable to NO increased similar to concentration. In 

contrast, the percentage of conductivity attributable to Ca2+ decreased slightly overtime. The Ca2+

is believed to be pairing with the NO as the NO is 

leaching through the soil. While nitrification in mature stands can be strongly inhibited, limited nitrification, especially in forest gaps, and high 

anthropogenic inputs of NO3

− probably were primary sources of leached NO3

− Preferential 

adsorption of SO4

2-, rather than NO3−, on soil 

colloids is given as an explanation for the lack of 

retention of NO3

− in the soil system and subsequent leaching to the stream. 

Mixed hardwoods/ principally northern red oak (Quercus rubra L.), 

American beech, red maple (A. 

rubrum L.), sugar maple, sweet 

birch (B. lenta L.), and black cherry 

(Prunus serotina) 

Edwards and 

Helvey (1991) 

Fernow Experimental Forest, WV NO3

– leaching Observation: N saturation observed. Progressive 

increases in streamwater NO3

– and Ca concentrations were measured at the Fernow Experimental 

Forest in the 1970s and 1980s. This watershed 

has received higher N deposition (average 

throughfall input of 22 kg N/ha/yr of N in the 

1980s) than is typical for low-elevation areas of 

the eastern U.S., however (Eagar et al., 1996), 

and this may help to explain the observed N 

saturation. 

Mixed hardwoods Peterjohn 

(1996) 

 3-95
Region/Country Endpoint Observations Forest Type/ Species Reference 

Fernow Experimental Forest, WV NO3

– leaching Field Addition: Annual experimental additions of 40 

kg S/ha/yr and 35 kg N/ha/yr as ammonium sulfate 

fertilizer were applied to a 34 ha watershed with a 

25-year-old stand of central Appalachian hardwoods. An adjacent watershed served as the control. After 5 years of treatment (total additions of 

275 kg S/ha and 220 kg N/ha), stream water NO3

−, 

Ca2+, Mg2+ concentrations and export increased. 

Soil solution concentrations provide evidence that 

the treatment watershed is nitrogen-saturated, 

which was unexpected for such a young stand. 

Mixed hardwoods Adams et al. 

(1997; 2000) 

Fernow Experimental Forest, WV NO3

− Leaching Field Addition: Three times per year N was added 

as (NH4)2SO4

2- to watersheds. In the spring and 

autumn 7.1 and 8.1 kg N/ha/yr were added, and in 

the summer 21.3 and 24 kg N/ha/yr were added. 

Anion adsorption on WS3 apparently delayed 

increases in SO4

2– leaching, but resulted in enhanced early leaching losses of Cl and NO3

–. 

Leaching of Ca and Mg was strongly tied to NO3

–

and SO4

2– leaching. F-factors for WS3 baseflow 

and peakflow indicated that the catch ment was 

insensitive to acid neutralizing capacity reductions 

both before and during treatment, although NO3

–

played a large role in reducing the treatment period F-factor. 

Mixed hardwoods Edwards et al. 

(2002b) 

Greater Los Angeles area, CA N Saturation Observation: plant communities exposed to air 

pollution received sufficiently high levels of atmospheric N deposition to be N saturated. Symptoms 

of N saturation were evident in mixed conifer or 

chaparral sites receiving atmospheric deposition of 

20 to 25 kg N/ha/yr or higher. In the more highly 

polluted study site (1) accumulation of NO3

– in 

foliage of plants (2) accumulation of NO3

– in soil 

(3) NO and N2O emissions higher (4) higher N 

mineralization (5) low foliar and soil C:N, high 

foliar N:P. 

Chaparral and mixed conifer forest; 

Ponderosa pine (Pinus ponderosa), 

Jeffrey pine (P. jeffreyi), white fir 

(Abies concolor), California black 

oak (Quercus kelloggii ), iincense 

cedar (Calocedrus decurrens), 

sugar pine (Pinus lambertiana), 

bracken fern (Pteridium aquilinum 

var. pubescens Underw.) 

Fenn et al. 

(1996) 

Sierra Nevada and 

San Bernardino, CA 

NO3− Leaching Critical load for increased NO3

− leaching 

calculated as 17 kg N/ha/yr and the empirical 

critical load for adverse effects on lichen 

communities is 3.1 kg N/ha/yr. Much of this study 

area is far above such levels of N deposition. 

Mixed conifer forests; Ponderosa 

pine (Pinus ponderosa ) Jeffrey pine

(P. jeffreyi.); white fir (Abies concolor);- California black oak (Quercus kelloggii), incense cedar (Calocedrus decurrens, the lichen Letharia vulpina 

Fenn et al. 

(2008) 

San Bernardino 

Mountains, CA 

N Saturation Deposition Gradient: over the range of 12.1 to 

31.7 kg N/ha/yr, the site nearest to urban area 

(Los Angeles) received much more N deposition, 

as well as other pollutants (i.e. S deposition), and 

received much more fog, coinciding with much 

more wet deposition of N in that site. Ecosystem 

was N saturated, as evidenced by high streamwater NO3

– concentration, 151 and 65 μeq/L at upper 

and lower ends, respectively, of Devil Canyon 

West Fork 

Mixed conifer forest; Ponderosa 

pine (Pinus ponderosa); Jeffrey pine 

(P. jeffreyi); white fir (Abies concolor); California black oak (Quercus 

kelloggii ); incense cedar (Calocedrus decurrens) 

Fenn et al. 

(2000) 

 3-96
Region/Country Endpoint Observations Forest Type/ Species Reference 

San Bernardino 

Mountains, CA 

DIN Export Deposition Gradient: over the range of 11to 40kg 

N/ha/yr , dissolved inorganic N (DIN) export was 

scale dependent, with highest export occurring in 

watersheds of ~150/ha. Differences attributed to 

temporal asynchrony between N availability and 

biological demand 

Mixed forest- chaparral, hardwood, 

coniferous; White alder (Alnus 

rhombifolia); California Bay Laurel 

(Umbellularia californica); Scrub oak 

(Quercus dumosa); Coast live oak 

(Quercus agrifolia) 

Meixner and 

Fenn (2004) 

Rocky Mountain 

Alpine Catchments 

N Retention Results from several studies suggest that the capacity of Rocky Mountain alpine catchments to 

sequester N is exceeded at input levels of about 

10 kg N/ha/yr 

Rocky Mountain alpine Baron et al. 

(1994); Williams and 

Tonnessen 

(2000) 

Disturbance and stand age effects on N retention 

The varying degree of N assimilation, leaching and microbial transformation often reflect 

differences in N status among treatment sites. These variations have most often been attributed to 

disturbance history, dating back a century or more (Goodale and Aber, 2001). Sites which have undergone 

disturbances that cause loss of soil N, such as logging, fire, and agriculture, tend to be most effective at 

retaining atmospheric and experimental inputs of N. Fire causes substantial N losses from ecosystems 

(see Table 3-12). Timber harvest contributes to nutrient removal from the ecosystem via biomass export 

and acceleration of leaching losses (Bormann et al., 1968; Mann et al., 1988). In particular, logging 

contributes to loss of N and Ca2+ from the soil (Tritton et al., 1987; Latty et al., 2004). N retention 

capability often decreases with stand age, which suggests that older forests are more susceptible than 

younger forests to becoming N-saturated (Hedin et al., 1995). Aber et al. (1998) surmised that land use 

history may be more important than cumulative atmospheric deposition of N in determining the N status 

of a forest ecosystem. See Annex C for a more detailed discussion of how disturbance affects N cycling. 

Table 3-12. Effects of fire on nutrient concentrations in forests in Nevada and California. 

Region/Country Endpoint Observations Grassland Type/ Species Reference

Lake Tahoe Basin, 

Nevada 

Nutrient concentration in runoff Field Measurement: Compared runoff from fixed 

plots within wildfire-burned and unburned areas in 

both summer and winter seasons. Wildfire increased the frequency and magnitude of elevated 

nutrient in discharge runoff for all 3 parameters 

studied: NO3

− N, ammonium nitrogen, phosphate 

P. The mobilization of nutrients was increased 

due to wildfire, but the lack of O horizon material 

(surface organic layer of mineral soils) after burning may ultimately reduce discharge concentrations over time 

Jeffery pine, white fir, sugar pine, 

Sierra chinquapin, currant, and snow 

brush, bitterbrush 

Soils: Cagwin series 

Miller (2006)

 3-97
Region/Country Endpoint Observations Grassland Type/ Species Reference

Lake Tahoe, Nevada Leaching, N 

concentrations in 

forest floor and 

soil 

Field Measurement: Fire and post-fire erosion 

caused large and statistically significant losses of 

C, N, P, S, Ca, and Mg from the forest floor; Before the burn, there were no significant differences in leaching, but during the first winter after 

the fire, soil solution concentrations of NH4

+, 

NO3

−, ortho-P, and (especially) SO42− were elevated in the burned area, and resin lysimeters 

showed significant increases in the leaching of 

NH4

+ and mineral N. The leaching losses of mineral N were much smaller than the losses from 

the forest floor and A11 horizons. The major 

short-term effects of wildfire were on leaching, 

whereas the major long-term effect was the loss 

of N from the forest floor and soil during the fire. 

Sierra Nevada mixed conifer forest: 

Jeffrey pine (Pinus jeffreyi), white fir 

(Abies concolor), sugar pine (Pinus 

lambertiana) and incense-cedar 

(Calocedrus decurrens). Understory 

vegetation: green leaf manzanita 

(Arctostaphylos patula), snowbrush 

(Ceanothus velutinus). 

Soils: Cagwin series: coarse, loamy 

sand 

Murphy 

et al. (2006) 

Sierra Nevada, 

California 

Forest floor and 

nutrient content, 

soil chemical 

properties, and 

soil leaching 

Field Experiment: investigated the effect of forest 

thinning treatments and prescribed burning on C, 

N, ortho-P, and SO4

2− in the forest floor organic 

layer and surface soil mineral horizons. The study 

included a prescribed fire and three timber 

harvest treatments: whole-tree thinning (WT) cutto-length thinning (CTL), and no harvest (CONT). 

There were no statistically significant effects of 

burning on soil C, N, C:N ratio, Bray-extractable 

P, exchangeable Ca2+, K+, or Mg2+. Burning had 

no significant effect on soil solution pH, ortho-P, 

SO4

2–, NO3−, or NH4+ as measured by ceramic 

cup lysimeters and no effect on the cumulative 

leaching of ortho-P, NO3

–, or NH4+ as measured 

by resin lysimeters. Prescribed fire had little 

impact on total and soluble nutrients in the upper 

mineral soil layer. Loss of N capital from the 

forest floor appears to be the major effect of 

prescribed burning. 

Jeffery pine (Pinus jeffreyi) forest Murphy 

et al. (2006) 

Sierra Nevada, 

California 

Nutrient budget of 

C, N, Ca, P, K, S, 

Mg 

Field Measurement/Modeling: effects of fire, postfire salvage logging, and revegetation on nutrient 

budgets were estimated for a site that burned in a 

wildfire in 1981. 2 decades after the fire, the 

shrub ecosystem contained less C and more N 

than the adjacent forest ecosystem. C was exported in biomass during salvage logging and will 

not be recovered until forest vegetation occupies 

the site again. Most N was lost via volatilization 

during the fire rather than in post-fire salvage 

logging (assuming that foliage and O horizons 

were combusted). Comparison of the pre-fire and 

present day N showed the lost N was rapidly 

replenished in O horizons and mineral soils, 

probably due to N-fixation by snowbush. No 

differences in ecosystem P, K, or S contents or in 

soil extractable P or S between the shrub and 

forested plots. K+, Ca2+, and Mg2+ were greater in 

shrub than in adjacent forested soils. The large 

increase in Ca resulted from either the release of 

Ca from non-exchangeable forms in the soil or 

the rapid uptake and recycling of Ca by post-fire 

vegetation. 

110–130 year old Jeffery pine (Pinus 

jeffreyi) 

Johnson 

et al. (2005) 

 3-98
Region/Country Endpoint Observations Grassland Type/ Species Reference

Little Valley, Nevada C and N loss Field Measurement/ Modeling: On an ecosystem 

level, the fire consumed approximately equal 

percentages of C and N (12 and 9%, respectively), but a greater proportion of aboveground N 

(71%) than C (21%). Salvage logging was the 

major factor of C lost, and C lost will not be replenished until forest vegetation is reestablished. 

N2 fixation by Ceanothus velutinus in the post-fire 

shrub vegetation appears to have more than 

made up for N lost by gasification in the fire over 

the first 16 year, and may result in long-term 

increases in C stocks once forest vegetation 

takes over the site. N loss from the fire equaled 

>1,000 years of atmospheric N deposition and 

>10,000 years of N leaching at current rates. 

Calculations of C and N losses from theoretical 

wildfires in the IFS sites show similar patterns to 

those in Little Valley. Calculated losses of N in 

most of the IFS sites would equal many centuries 

of leaching. Conceptual models of biogeochemical cycling in forests need to include episodic 

events such as fire. 

Jeffery pine (Pinus jeffreyi) Mesic 

forests in the Integrated Forest 

Study (IFS). 

Johnson 

et al. (2004) 

N. Lake Tahoe, 

Nevada; 

Truckee, California 

(Tahoe National 

Forest); 

Glenbrook, Nevada 

(Lake Tahoe Basin) 

C and N loss Field measurement: The quantities of C and N 

volatilized from the forest floor by prescription fire 

in the Sierra Nevada were measured at three 

sites: Marlene, Sawtooth and Spooner. C losses 

calculated by the weight method were 6.12, 7.39, 

and 17.8 mg C/ha at the Sawtooth, Marlene, and 

Spooner sites. N losses calculated by the weight 

method were 56.2, 60.8, and 362 kg N/ha, at the 

Sawtooth, Marlene, and Spooner sites, respectively. N volatilization during prescribed fire is the 

dominant mechanism of N loss from these systems. 

Marlene: Jeffery pine, white fir, 

snowbrush, squawcarpet, greenleaf 

manzanita, pinemat manzanita, 

Soils: Cagwin series 

Sawtooth: Jeffery and Ponderosa 

pine, Soils: Kyburz series 

Spooner: mixed confer, red fir, white 

fir, snowbrush and manzanita, 

Soils: Tahoma series 

Caldwell 

et al. (2002) 

3.3.2.2. Wetland Ecosystems 

N dynamics in wetland ecosystems vary in time, with type of wetland and with environmental 

factors, especially water availability (Howarth et al., 1996). A wetland can act as a source, sink, or 

transformer of atmospherically deposited N (Devito et al., 1989) and these functions can vary with season 

and with hydrological conditions. Vegetation type, physiography, local hydrology, and climate all play 

significant roles in determining source and sink N dynamics in wetlands (Devito et al., 1989; Koerselman 

et al., 1993; Arheimer and Wittgren, 1994; Mitchell et al., 1996). 

N Fixation and Mineralization 

N fixation and mineralization are two mechanisms by which N becomes available for plants. It is 

documented that ecosystems may derive substantial amounts of new N inputs via N2-fixation (Hurd et al., 

2001). N mineralization has been shown to increase with N addition, and this can cause an increase in 

wetland N export to adjacent surface water (Groffman, 1994). Drought has been shown to inhibit 

mineralization and nitrification in soils leading to a decrease in NO3

−

 concentration (Foster et al., 1992). 

However, drying may stimulate mineralization upon re-wetting (Kieft et al., 1987). A laboratory study 

 3-99
showed that within 24 h of re-wetting, extractable NO3

−

 concentration in dried peat increased 

approximately 7-fold as compared to continuously moist peat (Watmough et al., 2004). 

NO3

− Leaching 

Leaching losses from wetlands are rarely considered separately from leaching losses from upland 

terrestrial environments and the wet environments that occur in upland catchments. That is, when 

leaching losses from terrestrial ecosystems are quantified based on stream exports, estimated leaching 

losses implicitly include the net loss of NO3

−

 from both terrestrial ecosystems and adjacent wetlands. 

Leaching losses of NO3

−

 in water derived directly from wetlands are often small because of NO3

−

 removal 

by denitrification. However, hydrologic flowpaths that deliver water to streams by bypassing wetland 

soils can deliver substantial quantities of NO3

−

-rich water from terrestrial uplands. 

Denitrification 

Transitional ecosystems can remove significant quantities of NO3

−

 from water because they 

represent a convergence of conditions of NO3

−

, O2, and C that are requisite for denitrification. 

Denitrification is frequently optimized when NO3

−

 from more oxic upland areas passes through wet, often 

C-rich and anoxic wetlands. In some cases NO3

−

 concentration was found to be a better predictor of 

denitrification rates than soil moisture (Groffman, 1994), and there is evidence that in some cases 

denitrification is limited by C and NO3

−

 supply in wetlands (Ashby et al., 1998). 

Denitrification has been studied in riparian zone ecosystems (Lowrance, 1992; Pinay et al., 1993; 

Watts and Seizinger, 2001; Hefting et al., 2003) and seems to be related to C availability. Generally, 

riparian soils that are both rich in organic matter and anaerobic have high denitrification potential. Where 

riparian soils are aerobic, however, nitrification, rather than denitrification, can be the dominant process 

(Stevens et al., 1997). 

Table 3-13. Summary of N cycling studies for wetlands. 

Region/Country Endpoint Observations 

Wetland 

Type / 

Species 

Reference 

Canada Soil [N] Deposition Gradient: N deposition ranged from 2.7 to 8.1 kg 

N/ha/yr across 23 ombrotrophic peatlands. Soil [N] (g/m2/yr) 

increased linearly with deposition (y[wet deposition] =3.50(soil N) 

+0.64; r2=0.29, p<0.001) 

Peatland Moore et al. 

(2004) 

Adirondack 

Mountains, New York 

N sources 

used by 

vegetation 

Isotopic tracer: The study estimated N2 fixation by speckled alder 

in five wetlands by the 15N natural abundance method and by 

acetylene reduction using a flow-through system. The study of 

alder-dominated wetlands showed that alder derived >85% of leaf 

N from N-fixation at an estimated rate of 43 kg N/ha/yr. 

Conclusion: speckled alder in wetlands of northern New York State 

relies heavily on N2 fixation to meet N demands, and symbiotic N2

fixation in speckled alders adds substantial amounts of N to alderdominated wetlands in the Adirondack Mountains. These additions 

may be important for watershed N budgets, where alderdominated wetlands occupy a large proportion of watershed area 

Alnus 

incana 

Hurd et al. 

(2001) 

Rhode Island Denitrification Observation: the highest rates of denitrification (4 to 135 kg 

N/ha/yr) were observed in very poorly drained soils on nutrient-rich 

parent material, with lower rates (1.2 to 5.3 kg N/ha/yr) in soils that 

were better-drained or less nutrient-rich. 

Groffman 

(1994) 

 3-100
 3-101

Region/Country Endpoint Observations 

Wetland 

Type / 

Species 

Reference 

Catskill Mountain, 

New York 

Denitrification Field Addition: Higher rates of denitrification per unit area 

associated with soils with higher organic matter content and waterfilled pore spaces. Instantaneous NO3

− concentration did not 

correlate with denitrification rate, suggesting that the rates of NO3

−

supply through microbial production or hydrologic transport were 

more important than in situ NO3

− concentration. Denitrification was 

most stimulated by amendments with glucose alone or glucose 

plus NO3

−, suggesting limitation by labile C and NO3− supply. 

Ashby et al. 

(1998) 

Adirondack 

Mountains, New York, 

Archer Creek 

Watershed 

Denitrification Ecological Gradient: Changes in stream N were measured in one 

riparian wetland and one beaver meadow: Strong effects of 

peatlands on local N concentrations, but little effect of peatlands 

on adjacent stream chemistry, since peatland ground water 

contributed little to streamflow. 

Peatlands McHale 

et al. (2004) 

Smith Creek, 

Michigan 

Denitrification Field Additions/Measurement: Over a two-year period >1400 

individual samples of subsurface waters were analyzed. Both 

spatial patterns of water chemistry and additions of labile C to 

demonstrate that the supply of degradable C from shallow 

flowpaths limited rates of NO3

− removal via denitrification in nearstream zones. Thus, the immediate near-stream region may be 

especially important for determining the landscape-level function 

of many riparian wetlands. 

Riparian 

wetlands 

Hedin et al. 

(1998) 

N deposition may stimulate biogenic emissions if the N supply is limiting the rate of denitrification 

in wetland soils (Hayden and Ross, 2005). Previous studies suggest that elevated N inputs to wetlands 

will often increase the rate of denitrification (Dierberg and Brezonik, 1983; Broderick et al., 1988; 

Cooper, 1990). This process increases the emission of nitrous oxide, a greenhouse gas, to the atmosphere 

(see Section 3.3.4). 

In a review of the effects of riparian zones on NO3

−

 removal from ground water, Hill (1996) 

concluded that there are large losses of NO3

−

 to denitrification within riparian zones. However, there are 

important limitations to the generalization that riparian wetlands prevent the leaching of NO3

−

 to streams. 

Not all water entering streams passes directly through adjacent riparian zones, and denitrification in deep 

subsurface flowpaths is often limited by the supply of labile C. In addition, not all streamwater passes 

through riparian zones, and large amounts of water may follow flowpaths beneath organic-rich riparian 

zones, allowing significant transport of NO3

−

 to streams (McHale et al., 2004). The supply of degradable 

C from shallow flowpaths has been shown to limit rates of NO3

−

 removal via denitrification in nearstream zones (Hedin et al., 1998). 

In summary, wetland soils can be hotspots of NO3

−

 removal by denitrification in anoxic sites rich in 

NO3

−

 and labile C, but denitrification rates can be limited by suboptimal conditions of any single 

biogeochemical factor, and deep water flowpaths can bypass wetland denitrification altogether (see 

Table 3-13). 

3.3.2.3. Freshwater Aquatic Ecosystems 

As previously noted in Section 3.3.2.1., a large fraction of atmospheric N deposition is retained in 

most terrestrial ecosystems. Nevertheless, the fraction that does leach to streams can make a substantial 

contribution to total N inputs of downstream rivers and estuaries, especially in the eastern U.S. (Driscoll 

et al., 2003d). 
NO3

− Leaching 

After NO3

−

 leaches from terrestrial ecosystems it arrives in adjacent aquatic systems and may then 

be transported downstream. The following discussion is intended to build on the information introduced 

in Section 3.3.2.1 by addressing elevated NO3

−

 concentration in surface water; and NO3

−

 leaching 

downstream. The concentration of NO3

−

 in surface water can serve as a chemical indicator of N input in 

excess of ecosystem requirements, and is relevance to acidification and eutrophication effects on surface 

water. 

The relationship between wet deposition of N and streamwater output of NO3

−

 was evaluated by 

Driscoll et al. (1989) for sites in North America (mostly eastern areas), and augmented by Stoddard 

(1994). The data showed a pattern of N leaching at wet inputs greater than approximately 5.6 kg N/ha/yr, 

which probably corresponds with a total N deposition input of about 8 to 10 kg N/ha/yr. In the Northeast, 

a survey of 230 lakes and streams documented NO3

−

 concentrations ranging from less than 2 up to 

42 μeq/L, with the highest median values occurring in the Adirondacks (Aber et al., 2003) (Figure 3-32). 

Source: Aber et al. (2003). Reprinted with permission. 

Figure 3-32. Mean annual NO3

− concentrations in 230 lakes and streams across the northeastern U.S. Inset 

indicates the median, quartile, and 90% range of mean annual NO3

− in the Adirondacks (ADK), 

the Catskills (CAT), Vermont (VT), New Hampshire (NH), and Maine (ME). 

In the western U.S., NO3

−

 concentrations of freshwater ecosystems have been shown to increase 

with proximity to urban areas. Results from the Western Lake Survey (WLS) conducted by the U.S. EPA 

 3-102
(Eilers et al., 1987), document increased N concentrations in high elevation lakes adjacent to and 

downwind of urban centers (Fenn et al., 2003a), such as those found in the Sierra Nevada and Colorado 

Front Range (see Figure 3-33). For example, NO3

−

 concentrations in streamwater during the growing 

season in the Sierra Nevada were reported to range from 4 to 19 μeq/L (Fenn et al., 2003b). 

Concentrations above 10 μeq/L are generally considered high. 

An interesting example from the Colorado Front Range indicates that lakes on the eastern and 

western slopes can experience significantly different levels of NO3

−

. A survey of 44 lakes east and west of 

the Continental Divide indicated that lakes on the western side of the Continental Divide averaged 

6.6 μeq/L of NO3

−

, whereas lakes on the eastern side of the divide averaged 10.5 μeq/L of NO3

−

concentration. NO3

−

 concentrations above 15 μeq/L have commonly been measured in lakes on the 

eastern slope of the Front Range, suggesting some degree of N saturation (Baron, 1992), and extreme 

values as high as 40 μeq/L have also been reported (Campbell et al., 2000). Williams et al. (1996a) 

concluded that N-saturation is occurring throughout high-elevation catchments of the Colorado Front 

Range. Many lakes in the Colorado Front Range have chronic NO3

−

 concentrations greater than 10 μeq/L 

and concentrations during snowmelt are frequently much higher, due at least in part to leaching from 

tundra, exposed bedrock, and talus areas. 

In the Unita Mountains of Utah and the Bighorn Mountains of central Wyoming, 19% of the lakes 

included within the Western Lakes Survey had NO3

−

 concentrations greater than 10 μeq/L. This pattern 

suggests that N deposition in these areas may have exceeded the capability of these lakes to assimilate N. 

It is unknown if these concentrations of NO3

−

 represent effects from anthropogenic sources or if this 

constituted a natural condition associated with inhibited NO3

−

 assimilation in cold alpine environments. 

Source: Fenn (2003a). Reprinted with permission. 

Figure 3-33. NO3

− concentrations in high-elevation lakes in western North America. Circles represent cities 

with a population greater than 100,000. 

 3-103
Dissolved Inorganic Nitrogen 

Dissolved inorganic nitrogen (DIN) concentrations in surface waters have been directly linked to 

wet deposition. Bergstrom and Jansson (2006) evaluated the relationship between wet-deposition and 

DIN concentration in 4296 lakes across Canada, Europe and the U.S. They calculated a significant 

correlation showing that DIN concentration increased with increasing N wet-deposition (log (y)= 

1.34log(x) - 1.55, r2

 = 0.70, p <0.001). 

Denitrification 

Denitrification in freshwater aquatic ecosystems has been studied in small streams most 

intensively, though some work has been done at larger scales. N is cycled rapidly within streams, 

especially small streams with large relative areas for contact with benthic surfaces and hyporheic zones. 

For example, Peterson et al. (2001) found that 15N-NH4

+

 added to streams of various sizes was taken up 

most rapidly in the smallest streams, and that these headwater streams exported less than 50% of their 

added NH4

+

. Nevertheless, the long-term fate of this removed or transformed and recycled N is more 

difficult to assess. Mulholland et al. (2004) found that addition of 15N-NO3

−

 to a headwater stream at 

Walker Branch, TN, indicated a mean uptake length of 35 m under ambient conditions. The uptake length 

extended three-fold (i.e., reduced uptake) under a modest fertilization treatment, which employed NO3

−

addition of approximately 500 μg N/L. Direct measurements of denitrification of added 15N indicated that 

denitrification accounted for 16% of the NO3

−

 loss under the ambient treatment, and only 1% of NO3

−

uptake under the fertilized treatment. Nearly all of the denitrification occurred as reduction to N2 gas 

rather than to N2O. 

Hyporheic losses of NO3

−

 to denitrification may be largely controlled by supplies of labile 

dissolved organic carbon (DOC). Bernhardt and Likens (2002) found that adding 6 mg/L of DOC as 

acetate to a small stream at Hubbard Brook, NH, reduced stream NO3

−

 concentrations from ~5 

to <1 μmol/L. In experimental mesocosms designed to mimic hyporheic flowpaths of a small river in the 

Catskill Mountains, NY, Sobczak et al. (2003) found that adding just 0.5 to 1.0 mg/L DOC from leaf litter 

resulted in the net consumption of nearly all of the 40 μmol/L NO3

−

 in solution. Acetylene block 

measurements indicated that most NO3

−

 loss was due to microbial assimilation rather than denitrification, 

consistent with the isotopic tracer results of Mulholland et al. (2004). 

At large spatial scales, water residence time is the variable most frequently identified as a controller 

of N loss from aquatic ecosystems examined. Examples include lakes of various sizes (Howarth et al., 

1996) and large river basins spanning the northeastern U.S. (Seitzinger et al., 2002). Compiling N loss 

data sets from a wide range of aquatic ecosystems, Seitzinger et al. (2006) found that water residence time 

alone explained 56% of the variance in rates of N loss across lakes, rivers, estuaries, and continental 

shelves, from fast-flowing river reaches (residence time of hour) with 0% to 15% N loss to century-scale 

turnover lakes that eventually incur 80% to 100% N loss. 

N Transport Downstream: Urbanization and Determination of N Sources 

The transport of N via rivers and streams represents an important source of N to downstream 

ecosystems. The transport and loss of N is determined by the net balance of delivery of N by direct 

atmospheric deposition and from upland terrestrial and associated transitional ecosystem sources, minus 

the uptake and gaseous loss of that N during transport. Alexander et al. (2002) showed ~70% of the N in 

headwater streams is from N deposition and the net transport of N from headwater streams is between 40–

65% of the total N flux to lower order streams. Numerous studies have illustrated correlations between 

water quality or ecological conditions and various measures of the extent of urbanization, such as human 

population density or percent impervious surface (Hachmoller et al., 1991; Charbonneau and Kondolf, 

1993; Johnson et al., 1997; Thorne et al., 2000; Alberti et al., 2007); see additional discussion of 

urbanization in Annex C). In many higher order streams and estuaries, atmospheric N combines with 

fertilizer N in agricultural areas and with N from wastewater treatment facilities in urban areas, and the 

 3-104
role of atmospheric deposition in residential and urban ecosystems is rarely addressed (see Section 3.3.2.4 

for additional discussion of inputs to estuaries). 

Table 3-14. Summary of studies on the effects of N deposition on freshwater aquatic ecosystems. 

Region/ 

Country Endpoint Observations Ecosystem Type /

Species Reference 

Eastern U.S. [NO3

−] 

leaching 

Observation: NO3

− leaching observed at 5.6 kg N/ha/yr wet-deposition, 

which is equal to 8-10 kg N/ha/yr total N deposition. 

streams Driscoll et al. 

(1989); Stoddard (1994) 

Western U.S. surface 

water [NO3

−] 

Observation: surface water [NO3

−] is elevated downwind of urban areas in 

the West. 

lakes Fenn et al. 

(2003b) 

Colorado 

Front Range, 

U.S. 

surface 

water [NO3

−] 

Deposition gradient: higher levels of deposition on the eastern slope 

caused elevated lake water [NO3

−] 

lake Baron (1992) 

New England N sources in 

rivers and 

streams 

Modeling: Application of the statistical model SPARROW (SPAtially Referenced Regression On Watershed attributes) showed that first-order headwaters contributed 65%, 55%, and 40% of the N flux to 2nd, 4th, and 

higher-ordered catchments, respectively. Atmospheric deposition accounted for almost 70% of the total simulated N load to these headwater 

streams. 

Rivers Alexander 

et al. (2002; 

2007) 

Neuse 

Estuary, NC 

water [N] 

N sources 

Observation: Trends in N and P concentrations from 1998 to 2002 could be 

explained mainly by a combination of climate, management policies, and 

urban/agricultural development. Nutrient loading reductions did occur in 

response to imposed management practices in the watershed, but they 

were affected by increases in human and livestock population in the watershed. Thus, goals for estuarine and coastal nutrient loading reduction must 

consider the influence of within-watershed development 

Watershed/ estuary Burkholder 

et al. (2006) 

Chesapeake 

Bay system 

Watershed N 

sources 

Modeling: The Choptank tributary of the Chesapeake Bay had become 

eutrophic over the last 50-100 years. Systematic monitoring of nutrient 

inputs began in 1970, and there have been 2- to 5-fold increases in 

nitrogen (N) and P inputs during 1970–2004 due to sewage discharges, 

fertilizer applications, atmospheric deposition, and changes in land use. 

Hydrochemical modeling and land-use yield coefficients suggest that 

current input rates are 4-20 times higher for N and P than under forested 

conditions existing 350 year ago. The Choptank watershed (1756 km2) is 

dominated by agricultural land use (62%), with only 5% urban 

development. O2 concentration in bottom waters of the Patuxent estuary is 

consistently below 3 mg/L in summer; O2 levels have been steadily 

decreasing in the Choptank estuary over the past two decades and now 

approach 3 mg/L in wet years 

Watershed/ estuary Fisher et al. 

(1998; 2006) 

Chesapeake 

Bay system 

Watershed N 

sources 

Modeling: The Patuxent watershed (2260 km2) is dominated by forest 

(64%), with significant urban land use coverage (16%) and less intensive 

agricultural development (20%). Sewage is a major cause of nutrient enrichment. The low N: P of sewage inputs to the Patuxent results in an Nlimited, P-saturated system, whereas the Choptank is primarily limited by 

N, but with P limitation of phytoplankton during spring river flows. Reduced 

eutrophication in dry years suggests that both estuaries will respond to 

significant decreases in nutrients 

Watershed/estuary Fisher et al. 

(1998; 2006) 

 3-105
In lowland areas, other terrestrial sources of N, such as fertilizer, livestock waste, septic effluent, 

and wastewater treatment plant outflow, often become much more important than in upland areas. In 

lowland areas, it is difficult to determine the percent of atmospheric N that leaches to drainage water 

because there are other ill-defined sources of N to drainage waters. In Table 3-14 studies are summarized 

that evaluate how atmospheric deposition of N to the estuary surfaces and to the terrestrial watershed 

interact with the other anthropogenic sources of N to make up the total anthropogenic N load to the 

system. 

3.3.2.4. Estuarine and Coastal Marine Ecosystems 

Estuaries and coastal marine environments tend to be N-limited, and many currently receive high 

levels of N input from human activities (Howarth et al., 1996; Vitousek and Howarth, 1991). The nature 

and extent of the impacts on estuarine and coastal environments is related, in part, to the export of N from 

upland systems to coastal environments, as discussed in previous sections. Denitrification is the primary 

mechanism of N output from the estuary and back to the atmosphere (See Annex C). Important 

environmental effects of NCAM include increased algal blooms, depletion of dissolved O2 in bottom 

waters, and reduction in fisheries and sea grass habitats (Boynton et al., 1995; Howarth et al., 1996; Paerl, 

1995, 1997; Valiela and Costa, 1988; Valiela et al., 1990). The general process of estuarine eutrophication 

is depicted in Figure 3-34. 

Source: Bricker et al. (2007) 

Figure 3-34. A conceptualization of the relationship between overall eutrophic conditions, associated 

eutrophic symptoms, and influencing factors (N loads and susceptibility). Overall eutrophic 

condition was assessed for estuaries throughout the U.S. 

There is broad scientific consensus that N-driven eutrophication of shallow estuaries in the U.S. 

has increased over the past several decades and that environmental degradation of coastal ecosystems is 

now a widespread occurrence (Paerl et al., 2001b). For example, the frequency of phytoplankton blooms 

and the extent and severity of hypoxia have increased in the Chesapeake Bay (Officer et al., 1984) 

Pamlico estuary in North Carolina (Paerl et al., 1998), and along the continental shelf adjacent to the 

Mississippi and Atchafalaya River discharges to the Gulf of Mexico (Eadie et al., 1994). A recent national 

 3-106
assessment of eutrophic conditions in estuaries found that 65% of the assessed systems had moderate to 

high overall eutrophic conditions (Bricker et al., 2007). Estuaries with high overall eutrophic conditions 

were generally those that received the greatest N loads from all sources, including atmospheric and landbased sources (Bricker et al., 2007). The relative importance of the various N sources varies from estuary 

to estuary. Atmospheric sources are proportionately more important to estuaries that exhibit large surface 

area relative to watershed drainage area, and in those estuaries that drain watersheds dominated by natural 

ecosystems rather than agricultural or urban lands (Boyer et al., 2002). 

Assessing the contribution of atmospheric N deposition to total N loading 

The importance of atmospheric deposition as a cause of estuary eutrophication is determined by the 

relative contribution of atmospheric versus non-atmospheric sources of N input. Anthropogenic sources of 

N to estuarine and coastal ecosystems include atmospheric deposition, wastewater discharge, agricultural 

runoff, and urban runoff. Valigura et al. (2000) estimated that direct atmospheric deposition to the estuary 

surface generally constitutes at least 20% of the total N load for estuaries that occupy more than 20% of 

their watershed. The U.S. EPA (2000b) estimated that between 10% and 40% of the total N input to 

estuaries in the U.S. is typically derived from atmospheric deposition 

(http://www.epa.gov/ttn/oarpg/t3/reports/head_2kf.pdf). 

Estimates of the relative contribution of each major source have been developed by using the 

Watershed Assessment Tool for Evaluating Reduction Scenarios for Nitrogen (WATERS-N) model 

(Castro and Driscoll, 2002). Driscoll et al. (2003a) estimated annual net anthropogenic N inputs to eight 

large watersheds in the Northeast for the year 1997 (see Figure 3-35). Input values of total atmospheric 

plus non-atmospheric anthropogenic N ranged from 14 kg N/ha/yr in the watershed of Casco Bay in 

Maine to 68 kg N/ha/yr in the watershed of Massachusetts Bay (Driscoll et al., 2003a). In all eight 

watersheds, net import of N in food for humans (input into estuaries as wastewater) was the largest 

anthropogenic input. Atmospheric deposition was estimated to be the second largest anthropogenic N 

input, ranging from 5 to 10 kg N/ha/yr, or 11% to 36% of the total inputs, with four watersheds ranging 

from 34% to 36% (Driscoll et al., 2003a) (Table 3-34). These results are broadly consistent with estimates 

by Boyer et al. (2002), who used a similar N budgeting approach for 16 large northeastern U.S. river 

basins and reported that N deposition contributes approximately 31% of the total N load to large river 

basins, although this fraction varies regionally (Boyer et al., 2002). Boyer et al. (2002) considered only 

the portions of each basin above USGS gauging stations, which often occurred above large population 

centers. Hence, the Driscoll et al. (2003a) budgets included regions with greater human food consumption 

than those considered by (Boyer et al., 2002). 

Castro and Driscoll (2002) studied 10 estuaries along the U.S. east coast and found total 

atmospheric N inputs (watershed runoff plus direct deposition to the surface of estuary) accounted for 

15-42% of the total N inputs. Simulated reductions of atmospheric N deposition by 25% and 50% of 

current deposition rates reduced the contribution made by atmospheric N deposition to the total N loads 

by 1–6% and 2–11%, respectively. Overall, results from the simulated reductions suggested that 

considerable reductions ( >25%) in atmospheric N deposition were needed to significantly reduce the 

contribution made by atmospheric N deposition to the total N loads. In a later study, Driscoll et al. 

(2003a) estimated that the implementation of aggressive controls on both mobile N emissions sources and 

electric utilities would produce an estimated reduction in estuarine loading in Casco Bay, ME of 13% 

(Driscoll et al., 2003a). 

 3-107
Source: Driscoll (2003a) 

Figure 3-35. Estimated anthropogenic N inputs to the estuaries of the northeastern U.S., in kg N/ha/yr. 

One of the challenges in determining the contribution of atmospheric N deposition to estuaries is 

estimating the inputs from upstream river basins. Smith et al. (1997) applied the SPAtially Referenced 

Regressions on Watershed Attributes (SPARROW) model to streamwater chemistry data from the 

National Stream Quality Accounting Network to evaluate NO3

−

 leaching in large river basins. Leaching 

losses of N in large river systems provide important sources of N to estuaries and coastal marine waters. 

Smith et al. (1997) concluded that much of the U.S. probably exports less than 5 kg N/ha/yr, but that N 

export in watersheds of the northeastern U.S. is probably higher. For the watersheds that export more than 

10 kg N/ha/yr, Smith et al. (1997) concluded that fertilizer was the largest source of N (48%), followed by 

atmospheric deposition (18%). In this analysis, fertilizer used for human food production was considered 

to be the ultimate source of N contributed to waterways through waste water treatment plants. If the 

analysis of N sources to estuarine water is restricted to only nonpoint sources of N, atmospheric 

deposition is often considered to be the largest individual source (Howarth, 2007; Howarth et al., 1996; 

Jaworski et al., 1997; NRC, 2000; Smith et al., 1997). 

Overall, these estimates of the relative importance of atmospheric deposition, compared to nonatmospheric sources of N, typically involve many assumptions regarding dry deposition, riverine fluxes, 

and the relationship between human populations and wastewater inputs. Thus, such estimates entail 

considerable uncertainty. It is clear, however, that the relative contribution of atmospheric deposition to 

total N loading varies with the atmospheric N deposition level, land use, watershed and estuary areas, and 

hydrological and morphological estuarine characteristics. It is also clear that atmospheric deposition is 

generally an important contributor to the overall N load that stimulates eutrophication of estuaries in the 

eastern U.S. (Boyer et al., 2002; Driscoll et al., 2003c; Paerl et al., 2001b). See Annex C and Section 2.X 

for an additional discussion of estuarine N budgets. 

 3-108
3.3.2.5. Summary of N Effects on Biogeochemical Cycling of N and Associated 

Chemical Indicators 

Terrestrial Ecosystems 

The evidence is sufficient to infer a causal relationship between N deposition and the alteration of 

biogeochemical cycling of N in terrestrial ecosystems. This is supported by numerous observational, 

deposition gradient and field addition experiments. The main source of new N to non-managed terrestrial 

ecosystems is atmospheric deposition. N deposition disrupts the nutrient balance of ecosystem. The 

chemical indicators that are typically measured are summarized in Table 3-11 and include NO3

−

 leaching, 

C:N ratio, N mineralization, nitrification, denitrification, foliar N and soil water NO3

−

, and NH4

+

. Values 

for these indicators that represent a threshold for the onset of a related biogeochemical or biological effect 

are also summarized. Note that N saturation does not need to occur to cause adverse effects on terrestrial 

ecosystems. However, in some regions N saturation is a plausible mechanism of net nitrification and 

associated NO3

−

 leaching in drainage water. Leaching of NO3

−

 from forest soils to stream water can 

acidify downstream waters and deplete soils of nutrient base cations, especially Ca and Mg (see Section 

3.2). 

In northeastern watersheds, Aber et al. (2003) suggested that nearly all N deposition is retained or 

denitrified in areas that receive less than about 8 to 10 kg N/ha/yr. Aber et al. (2003) further found that 

surface water NO3

−

 concentrations exceeded 1 μeq/L in watersheds receiving about 9 to 13 kg N/ha/yr of 

atmospheric N deposition (Figure 3.30). The lakes and streams found to have high NO3

−

 concentration 

were those receiving N deposition above this range, but responses were variable among those receiving 

high N deposition. Above this range, mean NO3

−

 export increased linearly with increasing deposition at a 

rate of 0.85 kg NO3

−

 kg N/ha/yr for every 1 kg N/ha/yr increase in deposition, although there was 

considerable variability in N retention among watersheds at higher rates of deposition (Aber et al., 2003). 

In the West, a study of mixed conifer forests of the Sierra Nevada and San Bernardino mountains in 

California reported that the deposition load that causes the onset of NO3

−

 leaching is 17 kg N/ha/yr. 

Several studies in the Rocky Mountains indicate that the onset of NO3

−

 leaching in alpine catchments 

occurs at approximately 10 kg N/ha/yr (Baron et al., 1994; Williams and Tonnesen 2000). 

Wetlands 

The evidence is sufficient to infer a casual relationship between N deposition and the alteration of 

biogeochemical cycling of N in wetlands. N deposition contributes to total N load in wetlands that may also 

receive N from terrestrial run-off. The chemical indicators that are typically measured include NO3

−

leaching, N mineralization, and denitrification. N dynamics in wetland ecosystems are variable in time 

and with type of wetland and environmental factors, especially water availability (Howarth et al., 1996). 

A wetland can act as a source, sink, or transformer of atmospherically deposited N (Devito et al., 1989) 

and these functions can vary with season and with hydrological conditions. Vegetation type, 

physiography, local hydrology, and climate all play significant roles in determining source/sink N 

dynamics in wetlands (Arheimer and Wittgren, 1994; Devito et al., 1989; Koerselman et al., 1993; 

Mitchell et al., 1996). 

N mineralization has been shown to increase with N addition, and this can cause an increase in 

wetland N export to adjacent surface water (Groffman, 1994). In general, leaching losses of NO3

−

 in water 

derived directly from wetlands are often small because of NO3

−

 removal by denitrification. Previous 

studies suggest that elevated N inputs to wetlands will often increase the rate of denitrification (Dierberg 

and Brezonik, 1983; Broderick et al., 1988; Cooper, 1990). This process limits the available N supply to 

soils and drainage waters; but increases the biogenic emission of greenhouse gasses to the atmosphere 

(see Section 3.3.4). Denitrification appears to be negligible in wetland environments that are typically 

nutrient (including N) poor, such as some bogs and fens (Morris, 1991). 

 3-109
Freshwater aquatic 

The evidence is sufficient to infer a casual relationship between N deposition and the alteration of 

biogeochemical cycling of N in freshwater ecosystems. N deposition is the main source of N to headwater 

streams, lower order streams and high elevation lakes. The predominant chemical indicators are NO3

−

concentration in surface waters and DIN. Elevated surface water NO3

−

 concentrations indicate N 

saturation of the terrestrial ecosystem and occur in both the eastern and western U.S. In the East, 8-10 kg 

N/ha/yr can cause increase NO3

−

 concentrations (Driscoll et al., 1989; Stoddard 1994). In the West, 

elevated NO3

−

 concentrations have been observed down wind of urban centers (Fenn et al., 2003a) and on 

slopes of the Colorado Front Range that are effected by elevated deposition from urban and agricultural 

sources (Baron et al., 1992) 

Estuaries and coastal marine 

The evidence is sufficient to infer a casual relationship between N deposition and the biogeochemical 

cycling of N in estuaries and coastal marine waters. The contribution of atmospheric N deposition to total 

N load is calculated for some estuaries and can be greater than 40%. Atmospheric deposition N is not the 

sole source of N loading to estuaries and it is unknown if atmospheric deposition alone is sufficient to 

cause eutrophication. In general, estuaries tend to be N-limited, and many currently receive high levels of 

N input from human activities to cause eutrophication (Vitousek and Howarth, 1991; Howarth et al., 

1996, Elser et al., 2007). The most widespread chemical indicators of eutrophication are submerged 

aquatic vegetation, Chl a, algal blooms, macroalgae and dissolved O2. 

3.3.3.

3.3.3.1.

N Deposition Effects on Productivity and C Budgets 

The addition of N from an exogenous source will alter the productivity of N-limited ecosystems. 

In a meta-analysis including terrestrial, freshwater and marine ecosystems, Elsner et al. (2007) found 

there were similar patterns of N and P limitation among ecosystem types. This finding is in contrast with 

the existing paradigm that N-limitation dominates in terrestrial ecosystems and P-limitation dominates in 

freshwater ecosystems. Marine ecosystems tend to be N-limited; however P-limitation can play an 

important role. It is important to distinguish between effects on primary productivity and effects on C 

sequestration. N addition to a given ecosystem may increase primary productivity; however this often 

does not translate into greater C sequestration because C lost from the ecosystem by respiration may 

offset the C gained by production. These topics are discussed for a range of ecosystem types in the 

following section. 

Terrestrial Ecosystems 

The following section discusses the mechanisms by which atmospheric N deposition alters C 

cycling in terrestrial ecosystems (see Figure 3-36). Although predicted values of atmospheric [CO2] in the 

future may alter the interaction between N and terrestrial C cycling (Hyvönen et al., 2007; Norby, 1998; 

Schindler and Bayley, 1993) this topic is beyond the scope this review. Available studies include N 

deposition gradients, N addition studies, modeling and time-trend analyses. Few studies have isolated the 

effect of chronic N deposition on plant growth and ecosystem C balances. In those that have, it is difficult 

to disentangle the effects of climate, disease and land use from N deposition effects. There are numerous 

field studies of N addition; however there are some difficulties in interpreting the results regarding 

chronic N deposition effects on ecosystems. Some studies add N in a large pulse at one time, a technique 

that does not simulate chronic N loads of smaller amounts that are characteristic of atmospheric 

deposition. Ecosystem growth response to a pulse of 100 kg N/ha at one time may not be same as 10 kg 

 3-110
N/ha/yr for 10 years, although the net load is the same. Additionally, there are few long-term addition 

studies therefore limiting the evaluation of changes in ecosystem response over decadal time scales. 

Carbon accumulation in terrestrial ecosystems occurs above and below ground. C cycling is a 

complex process that can be quantified into ecosystem C budgets on the basis of net ecosystem 

productivity (NEP), defined as gross primary productivity (GPP) after subtracting the ecosystem 

respiration (vegetative + heterotrophic respiration). Factors that may increase terrestrial CO2 sinks on a 

regional scale are increased NPP, and decreased respiration of CO2 from leaf or soil processes. These two 

mechanisms may be altered by atmospheric deposition of N, tropospheric ozone exposure, increased CO2

concentrations, land-use change and factors associated with climate warming (Beedlow et al., 2004; 

Caspersen et al., 2000; Melillo et al., 2002; Myneni et al., 1997; Ollinger et al., 2002; Nowak et al., 2004; 

Schimel et al., 2001). This adds to the uncertainty regarding the sources and sinks in the terrestrial 

biosphere (Houghton, 2003). It should be noted that it is not known whether present terrestrial C 

sequestration can be sustained, in view of limits of forest re-growth, nutrient availability and uncertainty 

about changes in the frequency of disturbances such as fire (Schimel et al., 2001; Scholes and Noble, 

2001). 

Figure 3-36. Interactions between the C and N cycles. 

Forests 

C Allocation Interactions with Stressors 

Addition of N is believed to decrease resistance to drought stress. This occurs because plants 

balance their resources to maximize above-ground processes, such as light and C capture, with belowground processes, such as capture of water and other nutrients, including N (Sterner and Elser, 2002). N 

 3-111
addition often causes trees to allocate less photosynthate to roots than shoots (Minnich et al., 1995). 

Because shoot growth is more enhanced than root growth, the water supply from the roots can become 

insufficient during periods of drought to support water loss via transpiration (Fangmeier et al., 1994a; 

Krupa, 2003). Smaller root systems also cause greater susceptibility to wind-throw. For example, in 

Switzerland, the amount of trees uprooting during a strong storm event was significantly correlated with 

base saturation and N concentration in the leaves (positively) in beech trees (Braun et al., 2003). Across 

Europe, soil acidification and soil N content since the 1980s have accentuated the storm sensitivity due to 

changes in root architecture, including more superficial roots and loss of root ramification (Braun et al., 

2003; Godbold et al., 1988; Puhe, 2003; Nilsson et al., 2004). 

Deposition of N is also believed to reduce frost hardiness of plants (Dueck et al., 1990), which may 

play a role in red spruce die back (see Section 3.2). This is likely because the addition of N prolongs the 

growth phase of the plants during autumn and delays winter hardiness. This can cause detrimental effects 

if the first frost occurs early in the autumn period (Cape et al., 1991). Plant shoots also appear to be more 

susceptible to pathogenic fungal infection under high N status or changed nutrient balance such as an 

increase in the ratio of N to K+

 (Ylimartimo, 1991; Krupa, 2003). As opposed to shoot diseases, addition 

of N has been found to reduce mycorrhizal fungus colonization of roots (see more detailed discussion in 

Section 3.3.5.1). 

Above-Ground Processes 

Nitrogen availability often limits rates of net primary production in temperate terrestrial 

ecosystems (Vitousek and Howarth, 1991; LeBauer and Treseder, 2008); therefore there is an implicit link 

between the C and N cycles (Figure 3-36). Over 50% of N taken up by plants is used for photosynthetic 

enzymes. Rates of photosynthesis and net primary productivity (NPP) typically correlate with metrics of 

N availability such as leaf N content and net N mineralization rate (Field and Mooney, 1986; Reich et al., 

1997a, 1997b; Smith et al., 2002). A meta-analysis of 126 N addition experiments evaluated N limitation 

of NPP in terrestrial ecosystems by evaluating above-ground plant growth in fertilized to control plots 

(LeBauer and Treseder, 2008). The results showed that most ecosystems are N limited with an average 

29% growth response to N. The response ratio was significant within temperate forests, tropical forests, 

temperate grasslands, tropical grasslands, wetlands, and tundra, but not deserts (LeBauer and Treseder, 

2008). 

There is substantial evidence that N additions to trees cause increased leaf-level photosynthetic 

rates. However, the potential for N deposition to increase above-ground C biomass is limited for reasons 

related to the biogeochemical cycling of N (see more detailed discussion in Section 3.3.2.1). Briefly, C:N 

stoichiometry of the forest ecosystem compartments determines the C response to N deposition. Only a 

small portion of added N is taken up by vegetation, thus only a small portion of N contributes to C 

capture by trees (Nadelhoffer et al., 1999). A recent study reported that tree biomass (e.g., foliage, woody 

tissue, and fine roots) accumulated 7 to 16% of N additions (Nadelhoffer et al., 2004). N may be 

immobilized in the soil, leached out before biological assimilation, or, upon the addition of N, some other 

factor may become limiting to growth (e.g., water or other nutrients). Even though only a portion of N 

deposition is incorporated into vegetation, the general result of additional N is an increase in leaves, 

wood, and root biomass (Nilsson and Wiklund, 1995). The biological endpoints typically measured to 

evaluated plant growth in listed in Table 3-15. 

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Table 3-15. Summary of N effects on forest carbon cycling. 

Region/Country Endpoint Observations Forest type/ 

species Reference 

Europe Growth 

(biomass) 

Modeling: Large regions of forests were used as empirical data 

from which to build a model that forest biomass is accumulating, 

largely in response to increases in forest area and improved 

management, but other possible mechanisms of growth 

enhancement (including N) cannot be ruled out 

Forests from 

Austria, Finland, 

Sweden, France, 

Germany, and 

Switzerland 

Kauppi 

et al. 

(1992); 

Spiecker 

et al. (1996) 

Norway Growth (tree 

ring increments) 

Deposition gradient: A comprehensive analyses of regional 

forest growth trends analyzed tree increment cores from more 

than 31,000 plots. In this study, growth increased during the 

1960s and 1970s and then declined in the 1990s, especially in 

southern regions exposed to the highest rates of N deposition 

(Figure 3-37) 

Boreal forest 

(Picea abies and 

Pinxus sylvestris) 

Nellemann 

and 

Thomsen 

(2001) 

Sweden Growth (stem 

volume) 

Field Addition: chronic fertilization at 30 kg N/ha/yr continued to 

stimulate stemwood production even after 30 years, whereas a 

higher application (90 kg N/ha/yr) decreased stem volume 

growth, and an intermediate application (60 kg N/ha/yr) had little 

positive or negative effect relative to the control plots 

Boreal forest, 

Scots pine forest 

Högberg 

et al. (2006) 

Bear Brook, ME, 

U.S. 

Growth (basal 

area) 

Field Addition: basal area increment of sugar maple was 

enhanced 13 to 104% by addition of 25 kg N/ha/yr as ([NH4]

2SO4), whereas red spruce was not significantly affected. 

Sugar maple and 

red spruce 

Elvir et al. 

(2003) 

Fernow 

Experimental 

Forest, WV, U.S. 

Growth Field Addition: The application of 35 kg N/ha/yr as (NH4) 2SO4

enhanced growth of Black cherry (Prunus serotina) and yellow 

poplar (Liriodendron tulipifera) during the first 7 years, but led to 

reduced growth of these species relative to control trees in years 

9 through 12, with no change in red maple (Acer rubrum) or 

sweet birch (Betula lenta) 

Black cherry, 

yellow poplar, red 

maple, sweet 

birch 

DeWalle 

et al. (2006) 

Harvard Forest, 

MA, U.S. 

Mortality Field Addition: chronic N addition levels of 50 and 150 kg N/ha/yr 

for 15 years caused a 31% and 54% decrease, respectively, in 

red pine growth. As red pine has died, striped maple 

(Acer pensylvanicum), black cherry, and black birch (Betula 

lenta) have increased their contributions to annual litterfall 

production. 

Red pine, striped 

maple, black 

cherry, black 

birch 

Magill et al. 

(2004) 

Northeastern U.S. Live basal area Field Addition: In a high-elevation red spruce-balsam fir (Abies 

balsamea) forest in the, N fertilization over 14 years led to a 

decrease in live basal area (LBA) with increasing N additions. In 

control plots, LBA increased by 9% over the course of the study, 

while LBA decreased by 18% and 40% in plots treated, 

respectively, with 15.7 kg N/ha/yr and 31.4 kg N/ha/yr. 

Red sprucebalsam fir McNulty 

et al. (2005) 

Harvard Forest, 

MA, U.S. 

Growth 

mortality 

root production 

Field Addition: Control, low and high N additions of NH4NO3 (0, 

50 and 150 kg N/ha/yr) were made over 15 years (1988-2003) to 

a pine plantation and mixed hardwood stand. Ambient deposition 

was calculated to be 8 kg N/ha/yr. N addition stimulated 

productivity, although the drought in 1995 induced significant 

mortality in small red maple trees. Fine root biomass was 

slightly, but not significantly, lower in highly fertilized stands 

relative to controls in both red pine and oak/maple ecosystems. 

Stem mortality of Pinius resinosa increased with N addition 

Plantation: Pinus 

resinosa 

Hardwood stand: 

Quercus velutina, 

Q. rubra, Betula 

lenta, Acer 

rubrum, Fagus 

grandiflora, 

Prunus serotina

Magill et al. 

(2004) 

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Region/Country Endpoint Observations Forest type/ 

species Reference 

Harvard Forest, 

MA 

Growth 

Photosynthesis 

Needle age 

Field Addition: Ambient deposition to the site was 8 kg N/ha/yr. N 

was applied as a concentrated solution of NH4NO3 divided into 6 

equal monthly doses (May to Sept) totaling 50 (low N) and 

150(high N) 8 kg N/ha from 1989-1999. Elevated nitrogen 

additions caused decreased photosynthesis and decreased 

needle age 

Red pine (Pinus 

resinosa) 

Bauer et al. 

(2004) 

Ysselsteyn, The 

Netherlands 

Growth 

Root 

production 

Field Addition and deposition exclusion: improvements in wood 

accumulation rate, root production, and mycorrhizal associations 

occurred when a “clean roof” was installed at the site receiving 

the highest rate of N deposition (>40 kg N/ha/yr). Decreased 

production of fine roots may predispose N-fertilized plants to be 

more sensitive to intermittent drought, as well as to nutrient 

depletion exacerbated by acid deposition. 

Coniferous- Picea 

abies, Picea 

sitchesis, 

Pseudotsuga 

menziesii, Pinus 

sylvestris

Emmett 

et al. (1998) 

Boxman 

et al. 

(1998b) 

Southern CA, U.S. N- saturationreduced soil 

base saturation, and lack 

of a growth 

response 

Observational: Areas of chaparral and mixed conifer forests that 

receive very high levels of dry N deposition in southern 

California have experienced significant environmental change 

over the past several decades 

Chaparral and 

mixed conifer 

Fenn et al., 

(1996, 

2003a) 

California Growth 

(productivity) 

and mortality 

Observational: high inputs of Nr appear to exhibit decreases in 

productivity and increases in mortality (Fenn et al., 1998). 

Conifer forests Fenn et al. 

(1998) 

California Litter 

accumulation, 

above-ground 

woody 

biomass, fire 

susceptibility 

Field Addition: N fertilization has been shown to cause increased 

litter accumulation and C storage in above-ground woody 

biomass, which in turn may lead to increased susceptibility to 

more severe fires. 

Ponderosa pine Fenn et al. 

(2003a) 

California Growth Increased N deposition caused increased growth for Jeffrey 

(Pinus jeffreyi) and Ponderosa pine (Pinus ponderosa) stands, 

Mixed conifers Takemoto 

et al. (2001) 

North Carolina 

and Virginia 

Growth (basal 

area), foliar 

chemistry, 

nitrification and 

mineralization 

Deposition Gradient: Results from a study of 46 forest plots on 

six sites in North Carolina and Virginia dominated by American 

beech, sugar maple, and yellow birch suggested that N 

deposition is associated with changes in basal area, foliar 

chemistry, and nitrification and mineralization rates. Growth 

rates for the three tree species were similar at the lowest rates 

of N deposition, and then diverged as N deposition increased, 

with growth of yellow birch and American beech decreasing at 

the high N deposition loads. These differential growth rates have 

the potential to affect forest structure and biodiversity 

American beech, 

sugar maple, and 

yellow birch 

Boggs et al. 

(2005) 

Fernow 

Experimental 

Forest, WV, U.S. 

Stem growth 

Foliar N 

dynamics 

Field Addition: sites fertilized annually with 35 kg N/ha (7.1 in the 

spring and 21.2 in the fall) of (NH4)2SO4 over 11 years (1989-

2000). Responses among three species differed for foliar [N] 

either showing no change or a decrease in the fertilized plots. 

For all three species N/P ratio initially increased with a 

subsequent decrease. Over a two year increment (1999-2001) 

stem diameter growth was 37% lower for all three species in the 

fertilized plots. 

Acer rubrum 

Liriodendron 

tulipifera 

Prunus serotina

May et al. 

(2005) 

California, 

Sequoia National 

Park 

Growth Field Addition: Aspen (Populus tremuloides) have been reported 

to show positive growth effects from fertilization at N deposition 

rates as low as 10 kg N/ha/yr 

Aspen Bytnerowicz 

et al. (2002) 

 3-114
Region/Country Endpoint Observations Forest type/ 

species Reference 

Michigan ANPP and 

surface soil 

organic matter 

Field Addition: Chronic N fertilization (30 kg N/ha/yr applied as 

NaNO3) for ten years (1994 to 2004) caused significant 

increases in ANPP for 3 out of the 10 years. There was a 

significant effect of year, however no consistent increase or 

decrease through time. After ten years of addition woody 

biomass and surface soil organic matter (0–10 cm) were both 

significantly greater for the treatment than control. 

Pregitzer 

et al. (2008) 

Acer saccharum 

(sugar maple)

Source: Nellemann and Thomsen (2001). Reprinted with permission. 

Figure 3-37. Mean 5-year radial increment from 31,606 core samples from Picea abies during the period 

1945 to 1996 for three atmospheric N deposition zones (high, medium, and low wet Ndeposition in 1990), respectively. Note that the decline in radial increment after 1975 

corresponds with the peak in exceedances for critical loads for the same areas. The increase 

and subsequent decline from 1965–1996 is significant (p <0.01) using Kruskal-Wallis analysis 

with Dunn’s tests. S.E.s are all below 5% or 1–3.5 mm increment. 

Experimental N additions to forest ecosystems have elicited positive growth responses in some, but 

certainly not all, organisms (Emmett, 1999; Elvir et al., 2003; DeWalle et al., 2006; Högberg et al., 2006). 

Forest growth enhancement, can potentially exacerbate other nutrient deficiencies, such as Ca, Mg, or K. 

Multiple long-term experiments have demonstrated transient growth increases followed by increased 

mortality, especially at higher rates of fertilization (Elvir et al., 2003; Högberg et al., 2006; Magill et al., 

2004; McNulty et al., 2005). An additional line of evidence comes from the experimental N removal 

studies: removal of N and S from throughfall increased tree growth in Europe (Beier et al., 1995; Boxman 

et al., 1998). 

Decreased growth and increased mortality have more commonly been observed in high-elevation 

coniferous stands than in lower elevation hardwood forests, and these differences have been partially 

attributed to higher inputs of N at higher elevation and to response characteristics of coniferous, as 

opposed to deciduous, trees (Aber et al., 1998). Conifer forests that receive high inputs of N appear to 

exhibit decreases in productivity and increases in mortality (Fenn et al., 1998). For example, fertilization 

experiments at Mount Ascutney, VT suggested that N saturation may lead to the replacement of slow-

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growing spruce-fir forest stands by fast-growing deciduous forests that cycle N more rapidly (McNulty 

et al., 1996; 2005). 

Below-Ground Processes 

Soils contain the largest near-surface reservoir of terrestrial C. More than 50% of C captured 

annually by plants may be allocated below ground (Kubiske and Godbold, 2001). Therefore, 

understanding the factors that control soil C storage and turnover is essential for understanding the C 

cycle and sequestration. Although there remains considerable uncertainty in the potential response of soil 

C to increases in Nr additions (Neff et al., 2002), a meta-analysis by Johnson and Curtis (2001) suggested 

that N fertilization caused an 18% increase in soil carbon content. 

There is also evidence of a relationship between N deposition and root production. Nadelhoffer 

(2000) stated that N deposition functions to decrease forest fine-root biomass but to stimulate fine-root 

turnover and production. However, very high levels of N ( >100 kg N/ha/yr) decreased root life span of 

Pinus ponderosa (Johnson et al., 2000). 

Litter fall is usually the dominant source of soil organic C and a substantial source of organic N. 

Decomposition of litter fall is often facilitated by heterotrophic bacteria and mycorrhizae. The quantity of 

litter has been shown to increase with elevated N deposition (Schulze et al., 2000), with the result of 

increased microbial metabolism in soil. It is also well demonstrated that increased N availability reduces 

the ratio of C:N in leaf tissue. In turn, lower C:N in leaf litter has been shown to cause faster initial rates 

of decomposition (Melillo et al., 1982), however higher N litter can actually decompose more slowly in 

the long-term (Berg, 2000). N bound by leaf organic matter is released over a shorter period of time under 

higher decomposition rates, this leads to lower N retention by the soil (De Vries et al., 2006). A 10 year 

experiment that investigated decomposition in 21 sites from 7 biomes found net N release from leaf litter 

is predominantly driven by the initial N concentration and mass remaining regardless of climate, edaphic 

conditions, or biota (Parton et al., 2007). A recent meta-analysis by Knorr et al. (2005) indicated that, as 

expected, litter decomposition was stimulated by additional N deposition, however only at sites with low 

ambient N deposition rates (<5 kg N/ha/yr). Additional N deposition reduced decomposition at sites with 

moderate levels of N deposition (5 to 10 kg N/ha/yr). 

Soil respiration is the dominant means by which plant-assimilated C is returned to the atmosphere 

as CO2. Changes in the magnitude of soil CO2 efflux due to changes in environmental conditions will 

likely influence the global atmospheric CO2 budget (Schlesinger and Andrews, 2000). Overall, the effects 

of N addition on soil respiration are mixed; reductions at high levels of N (Lu et al., 1998; Bowden et al., 

2004), no effect (Vose et al., 1995), and increases (Griffin et al., 1997; Mikan et al., 2000) all have been 

observed. At the Harvard Forest LTER Site Chronic Nitrogen Amendment Study, N additions increased 

soil respiration for hardwood stand, but not for pine stand during the first year of fertilization. However, 

continued N additions over a decade caused a 40% decrease in soil respiration for both stands and that 

was attributed mostly to a decrease in microbial respiration (Bowden et al., 2004). 

Long-Term N Addition 

There are few long-term experiments (10+ years) on N addition effects on the C cycle. Pregitzer 

et al. (2008) found that 30 kg N/ha/yr applied as NaNO3 for ten years caused significant increases in 

ANPP for 3 out of the 10 years. There was a statistically significant effect of year, however no consistent 

increase or decrease through time. After ten years of addition, woody biomass and surface soil organic 

matter (0–10 cm) were both significantly greater for the treatment than control. May et al. (2005) found 

that 35 kg N/ha/yr applied as (NH4)2SO4 over 11 years (1989-2000) caused different foliar [N] among 

three species either showing no change or a decrease in the fertilized versus control plots. For all three 

species N/P ratio initially increased with a subsequent decrease. Over a two year increment (1999-2001) 

stem diameter growth was 37% lower for all three species in the fertilized plots. At Harvard forest 

average atmospheric deposition of nitrogen was 8 kg N/ha/yr. N was applied as a concentrated solution of 

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NH4NO3 for treatments that totaled 50 kg N/ha/yr (low N) and 150 kg N/ha/yr (high N) from 1989-1999. 

Bauer et al. (2004) found that the nitrogen additions caused decreased photosynthesis and decreased 

needle age. At the same site Magill et al. (2004) found that fine root biomass was slightly, but not 

significantly, lower in highly fertilized stands relative to controls in both red pine and oak/maple 

ecosystems. Stem mortality of Pinius resinosa increased with N addition. 

Regional Trends in NEP and NEE 

An analysis of >100 young and mature forest stands from around the world indicated that annual 

values of CO2 exchange varied from approximately −100 to 250 g C/m2

/yr for boreal forests and 250 to 

700 g C/m2/yr for temperate forests (Malhi et al., 1999). Net ecosystem exchange (NEE), defined as the 

difference between NPP and heterotrophic respiration, was positive when the forest was a sink that took 

up CO2. Townsend et al. (1996) and Holland et al. (1997) modeled the impact of NOY and NHX

deposition on ecosystem C budgets by combining estimates of emissions with three dimensional transport 

models. They used spatially explicit estimates of N inputs and climate data as drivers for a process-based 

biogeochemical model to simulate ecosystem C dynamics globally. Their simulations predicted that 

CO2-C uptake due to NOY deposition on land surfaces ranged from 0.3 to 1.4 Pg C uptake/yr (Townsend 

et al., 1996; Holland et al., 1997). The model allowed for variations in the degree of ecosystem N 

retention. The highest C uptake was calculated when trees were assumed to uptake 80% of N inputs, 

which is a likely overestimation because field studies suggested trees only took up a small portion (7–

16%) of N deposition (Nadelhoffer et al., 1999). 

Satellite observations of canopy greenness over the last 20 years across North America suggest 

enhancement of NEP in some regions, corresponding to observed changes in climate and forest 

management. Few such changes were observed in the northeastern U.S., where rates of N deposition are 

relatively high (Hicke et al., 2002). In another study, evaluation of tree growth rates in five states 

(Minnesota, Michigan, Virginia, North Carolina, and Florida) found little evidence for growth 

enhancement due to any factor examined, including N deposition, CO2 fertilization, or climate change 

(Caspersen et al., 2000). Potential effects of N deposition on boreal forests of North America are of 

concern in part due to the large size of this terrestrial biome. Climate warming and N deposition may 

increase NPP and C sequestration in the boreal forest, but may also stimulate decomposition of soil 

organic matter, potentially leading to a net loss of C from the ecosystem (Kirschbaum, 1994; Mäkipää 

et al., 1999). 

A recent European study suggested that N deposition increased forest growth (Magnani et al., 

2007). Magnani et al. (2007) reported a strong correlation between estimated average long-term NEP and 

estimated 1990 N wet-deposition (Holland et al., 2005) for 20 forest stands mostly in western Europe and 

the conterminous U.S. The authors reported that when confounding effects of disturbance were factored 

out, carbon sequestration was found to be increased by moderate N deposition (estimated up to 9.8 kg 

N/ha/yr). However, this study did not evaluate forest stands that receive higher levels of N deposition that 

may be showing negative symptoms of N saturation. Several responses to this study have been critical of 

the methods and conclusions have been published (De Schrijver et al., 2008; De Vries et al., 2008; Sutton 

et al., 2008b). For example, Sutton et al. (2008b) re-analyzed the data from Magnani et al. (2007) and 

concluded the NEP response to N response reported by Magnani et al. (2008) was implausibly high 

(725 kg C/kg wet-deposited N). Magnani (2008) responded by calculating NEP sensitivity to total N 

deposition of approximately 175–225 kg C/kg N deposition and noted that one of the main effects of 

increased N availability is an increased allocation to woody tissues (with a high C:N ratio of up to 500:1). 

After considering the uncertainties in wet and dry N deposition and climate variability, Sutton et al. 

(2008b) reported the estimated NEP response to N deposition was 68 kg C/ deposited N. Sutton et al. 

(2008b) concluded that N deposition remains an important driver of NEP, but did not find support that the 

NEP was overwhelmingly driven by N deposition. 

The U.S. EPA conducted a meta-analysis of 17 observations from 9 studies in U.S. forests to 

examine the effect of N fertilization on forest ecosystem C content (EC). Here EC was defined as the sum 

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of C content of vegetation, forest floor and soil (Johnson et al., 2006). To avoid possible confounded 

variability caused by site conditions, this meta-analysis only included studies of which control and 

treatment sites experienced the same climatic, soil and vegetation conditions. Studies on N nutrient effects 

along a deposition gradient, such as Magnani et al. (2007), were not included. The U.S. EPA metaanalysis revealed that while there was a great deal of variation in response, overall N addition increased 

EC by 6% for U.S. forest ecosystems (see Figure 3-38). Different from Magnani et al. (2007), this study 

did not find any correlation between the amount of N addition and the response magnitudes of EC. 

It is uncertain if short term C accumulation may lead to long term C sequestration. N fertilization 

could reduce the capacity of ecosystem to sequester decay resistant soil C. Giardina et al. (2004) found 

that although N fertilization significantly increased plant production, the C flux moving to mineral soil 

was reduced by 22% in a humid tropical forest in Hawaii. Mycorrhizal biomass comprises a substantial 

carbon pool – represent up to 15% of soil organic matter in some ecosystems (Vogt et al., 1982). A metaanalysis by Treseder (2004) suggested that mycorrhizal abundance decreased 15% under N fertilization. 

response ratio

0.8 0.9 1.0 1.1 1.2 1.3

obervation numbers

0

1

2

3

17 I I

coniferous

deciduous

mean response ratio

Figure 3-38. Effects of N addition on forest ecosystem C content. The bars show the distribution of the 

number of studies categorized by vegetation type. The dot with error bars shows the overall 

mean response ratio with 95% CI. 

Arctic Tundra 

Arctic tundra is adapted to cold temperature, short growing season, high soil moisture, and 

periodically low soil O2 level. In general, arctic tundra plants respond to reduced N availability by 

changing the allocation of biomass to favor root growth (Bloom et al., 1985) or changing the efficiency 

with which N is used or stored (Chapin, 1980). 

Mack et al. (2004) examined C and N pools in a long-term fertilization experiment at the arctic 

Long-Term Ecological Research site near Toolik Lake, AK. Fertilized plots in moist acidic tundra 

received 10 g N and 5 g N/m2

/yr from 1981 to 2000. This is approximately 5 to 8 times the annual soil N 

uptake requirement for above-ground production in the ecosystem. Two decades of fertilization shifted 

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community composition from graminoid tundra dominated by the tussock-forming sedge, Eriophorum 

vaginatum, to shrub tundra dominated by Betula nana (Shaver et al., 2001). Consequently, this greatly 

increased above-ground NPP, but had a larger effect on decomposition than on plant production, resulting 

in a net loss of almost 2,000 g C/m2

 from this ecosystem over 20 year (p <0.04). Carbon storage increased 

above ground because of the accumulation of woody shrub biomass and litter, but this was offset by a 

larger decrease of C in below-ground pools due to a pronounced decrease in the C contained in deep 

organic (>5 cm depth) and upper mineral soil layers. This study clearly showed that increased nutrient 

availability enhanced decomposition of below-ground C pools in deep soil layers more than it increased 

primary production, leading to a substantial net loss of C from this ecosystem. 

The key process responsible for the C loss was identified as increased deep soil C decomposition in 

response to increased nutrient availability. The authors noted that increasing temperatures may amplify 

these effects and further stimulate C losses from high-latitude systems. The amount of N released due to a 

3 to 7 oC increase in mean annual temperature (MAT) is likely to range in magnitude from 7 to 9.4 g 

N/m2

/yr, respectively (Mack et al., 2004). This will cause species shifts in the vegetation community from 

tussock to increased shrub abundance and lead to decreased ecosystem C storage. Finally, the decreased 

soil moisture and increased depth of thaw that accompany temperature rise are predicted to have a 

positive effect on decomposition (Shaver et al., 2001), releasing more CO2. 

Grasslands 

Below-Ground Factors 

An investigation by Neff et al. (2002) of long-term effects (10+ years) of N deposition (10 kg 

N/ha/yr) in a dry meadow ecosystem indicated that N additions significantly accelerated decomposition of 

soil C fractions with decadal turnover times while further stabilizing soil C compounds in mineralassociated fractions with multi-decadal to century lifetimes. Despite these changes in the dynamics of 

different soil pools, no significant changes in bulk soil C were observed, highlighting a limitation of the 

single-pool approach for investigating soil C responses to changing environmental conditions (Neff et al., 

2002). The authors noted that it remains to be seen if the effects that were caused by relatively high, 

decadal-term fertilizer additions are similar to those which would arise from lower, longer-term additions 

of N to natural ecosystems from atmospheric deposition. 

Interactions with Fire 

Several lines of evidence suggest that N deposition may be contributing to greater fuel loads and 

thus altering the fire cycle in a variety of ecosystem types (Fenn et al., 2003a). Invasive grasses, which 

can be favored by high N deposition, promote a rapid fire cycle in many locations (D'Antonio and 

Vitousek, 1992). The increased productivity of flammable understory grasses increases the spread of fire 

and has been hypothesized as one mechanism for the recent conversion of coastal sage scrub (CSS) to 

grassland in California (Minnich and Dezzani, 1998). 

High grass biomass has also been associated with increased fire frequency in the Mohave Desert 

(Brooks, 1999; Brooks and Esque, 2002; Brooks et al., 2004). This effect is most pronounced at higher 

elevation, probably because the increased precipitation at higher elevation contributes to greater grass 

productivity. Increased N supply at lower elevation in arid lands can only increase productivity to the 

point at which moisture limitation prevents additional growth. Fire was relatively rare in the Mojave 

Desert until the past two decades, but now fire occurs frequently in areas that have experienced invasion 

of exotic grasses (Brooks, 1999). 

 3-119
Wetlands 

Above-Ground Processes 

The 1993 NOX AQCD showed that N applications, ranging from 7 to 3120 kg N/ha/yr, stimulated 

standing biomass production by 6–413% (U.S. EPA 1993a). However, the magnitude of the changes in 

primary production depended on soil N availability and limitation of other nutrients. The degree of N 

limitation to growth is varied among wetlands across the U.S. (Bedford et al., 1999). 

The genus Sphagnum dominates ombrotrophic bogs and some nutrient poor fens in the Northern 

U.S. and Canada. These mosses efficiently capture atmospheric deposition with retention rates between 

50–90%, much of the variation due to the depth of the water table (Bedford et al., 1999). Studies 

conducted on 4 species of Sphagnum in Maine (2 to 4 kg N/ha/yr ambient deposition) and New York (10 

to 13 kg N/ha/yr ambient deposition) document that higher N deposition resulted in higher tissue N 

concentrations and greater NPP (Aldous, 2002), but lower bulk density (Aldous, 2002). A study of 

Sphagnum fuscum in six Canadian peatlands showed a weak, although significant, negative correlation 

between NPP and N deposition when deposition levels were greater than 3 kg N/ha/yr (y = 150 – 3.4(x); 

r

2

=0.01, p = 0.04) (Vitt et al., 2003). A study of 23 ombrotrophic peatlands in Canada with deposition 

levels ranging from 2.7 to 8.1 kg N/ha/yr showed peat accumulation increases linearly with N deposition 

(y = 2.84(x) +0.67, r2

 = 0.32, p <0.001), however in recent years this rate has begun to slow indicating 

limited capacity for N to stimulate accumulation (Moore et al., 2004). 

Primary production of plant species from intertidal wetlands typically increases with N addition, 

however most studies apply fertilizer treatments that are several orders of magnitude larger than 

atmospheric deposition (Mendelssohn, 1979; Wigand et al., 2003; Tyler et al., 2007; Darby and Turner, 

2008). N loads brought by tidal water and ground water (565–668 kg N/ha/yr) are much larger than N 

depositing directly to the surface of coastal marshes, which suggested that direct N deposition may have 

limited impacts on this ecosystem (Morris, 1991). On the other hand, indirect atmospheric deposition that 

is N deposited to the watershed and transported via surface or ground water, could be the major sources of 

the total N load to coastal marshes. For example, model calculation suggested that the contribution from 

the atmosphere (36 million kg N/yr) was about 21–30% of the total N loading (170 million kg N/yr) in 

Chesapeake Bay waters (U.S. EPA, 2000b). Therefore 30% of the N delivered to wetlands via estuarine 

tides would originate from atmospheric deposition. Future studies are needed to determine the role of 

indirect atmospheric N deposition on the nutrient budget of intertidal wetlands. 

Below-Ground Processes 

Bragazza et al. (2006) investigated the decomposition rates of recently formed litter peat samples 

collected in nine European countries under a natural gradient of atmospheric N deposition from 2 to 

20 kg N/ha/yr. They found enhanced decomposition rates for material accumulated under higher 

atmospheric N supplies resulted in higher CO2 emissions and dissolved organic carbon release. The 

increased N availability favored microbial decomposition by removing N constraints on microbial 

metabolism and through a chemical amelioration of litter peat quality with a positive feedback on 

microbial enzymatic activity. In a follow-up study, Bragazza and Freeman (2007) evaluated whether there 

was a relationship between N deposition and a decay-inhibiting polyphenol in the Sphagnum tissue. They 

found that as N deposition level increased the polyphenol concentration decreased. This observation 

implies the lower concentration of decay-inhibiting polyphenols would lead to accelerated peat 

decomposition. Although some uncertainty remains about whether Sphagnum will continue to dominate 

litter peat, these findings indicated that even without such changes, increased N deposition poses a serious 

risk to the valuable peatland C sinks. 

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Reduced vs. Oxidized N 

The form of added N may regulate wetland response to N deposition. Experimental applications of 

NO3

−

 appear to have been less effective at stimulating wetland plant productivity than applications of 

NH4

+

 (U.S. EPA, 1993a). This may reflect higher rates of denitrification in response to the added NO3

−

, 

suggesting the importance of competition between plants and microbes for bioavailable N. Plants appear 

to compete more successfully for NH4

+

 and microbes to compete more successfully for NO3

−

. An 

important caveat expressed by U.S. EPA (1993a), however, was that the results of relatively short-term N 

fertilization experiments are not necessarily good predictors of long-term wetland community responses 

to increased N inputs. 

NEE of Grassland, Tundra and Wetlands 

In the meta-analysis of 16 observations from 9 publications on the relationship between N addition 

and C sequestration of non-forest ecosystems, N addition had no significant effect on NEE of non-forest 

ecosystems (Figure 3-39). N limitation to NPP is globally distributed and therefore plant productivity is 

normally enhanced by N addition. A meta-analysis by Lebauer and Treseder (2008) indicated that N 

fertilization increased aboveground NPP (ANPP) in all non-forest ecosystems except for desert. However, 

N addition also has been observed to stimulate ecosystem C loss. For example, N fertilization stimulated 

soil organic carbon decomposition in arctic tundra. Increasing N deposition led to higher C loss in 

temperate peatlands (See Section 3.3.3.1). In an agricultural experiment site, Khan et al. (2007) observed 

that 40 to 50 years N fertilization resulted in a net decline in soil C despite massive residue C 

incorporation. This meta-analysis indicated that N addition had no significant impact on C sequestration 

in non forest ecosystems, which may be due to C gain via NPP was exceeded by C loss via heterotrophic 

respiration. 

response ratio

 observation numbers

0

1

2

3

16 I I

grassland

tundra

wetland

0.4 0.6 0.8 1.0 1.2 1.4 1.6

mean response ratio

Figure 3-39. Effects of N addition on NEE of non-forest ecosystems. The bars show the distribution of the 

number of studies categorized by vegetation type. The dot with error bars shows the overall 

mean response ratio with 95% CI. 

 3-121
3.3.3.2. Freshwater Aquatic 

The biogeochemical cycles of N, P and C are linked in freshwater ecosystems (Figure 3-40), 

therefore N additions alter the balance of all three cycles. In N-limited aquatic systems, atmospheric 

inputs of N increase productivity and alter biological communities, especially phytoplankton. N 

deposition effects on productivity are discussed in this Section. The results of numerous publications 

addressing the experimental additions of N are tabulated in Annex C. Evidence that altered productivity 

leads to altered community structure is discussed in Section 3.3.5. 

Generally, the dose-response data for aquatic organisms such as those cited below are expressed in 

concentration units, as mg/L or μmol/L of N, for example. Such concentration data cannot be directly 

related to ecosystem exposure (i.e. deposition), which is generally expressed in such units as kg N/ha. 

This is because N deposition can result in widely varying concentrations of N compounds (especially 

NO3

−

) in water. For convenience, a concentration of 1 mg/L of N (as, for example, in the case of NO3

−

-N 

or NH4

+

-N) is equal to 71.4 μmol/L or 71.4 μeq/L of NO3

−

 or NH4

+

. 

Figure 3-40. N cycle in freshwater ecosystem. 

N-Limitation 

A freshwater lake or stream must be N-limited to be sensitive to N-mediated eutrophication. There 

are many examples of fresh waters that are N-limited or N and P co-limited (e.g., Elser et al., 1990; Fenn 

et al., 2003a; Tank and Dodds, 2003; Bergström et al., 2005; Baron, 2006; Bergström and Jansson, 2006). 

In a meta-analysis that included 653 datasets, Elser et al. (2008) found that N-limitation occurred as 

frequently as P-limitation in freshwater ecosystems. Recently, a comprehensive study of available data 

from the northern hemisphere surveys of lake along gradients of N deposition show increased inorganic N 

concentration and productivity to be correlated with atmospheric N deposition (Bergström and Jansson, 

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2006). The results are unequivocal evidence of N limitation in lakes with low ambient inputs of N, and 

increased N concentrations in lakes receiving N solely from atmospheric N deposition (Bergström and 

Jansson, 2006). These authors suggested that most lakes in the northern hemisphere may have originally 

been N-limited, and that atmospheric N deposition has changed the balance of N and P in lakes so that Plimitation is generally observed today. If this is correct, the role of atmospheric N deposition as an 

influence on aquatic primary production may have been underestimated throughout the entire history of 

limnology. 

Recent research (e.g., Wolfe et al., 2001, 2003, 2006; Lafrancois et al., 2003; Das et al., 2005; 

Saros et al., 2005) has provided additional evidence indicating that N deposition has played an important 

role in influencing the productivity of oligotrophic, high-elevation lakes in the western U.S. and Canada, 

and the Canadian arctic. There is evidence suggesting historical N-limitation of some lakes based on 

paleolimnological studies conducted in mountainous regions of the western U.S. that have been the 

recipient of elevated levels of N, but not S, deposition over background values (see Section 3.3.4). 

Interactions between N and P loading are discussed in Annex C. 

Productivity investigations have included gradient studies in which the relationship between lake N 

concentration and primary productivity (reported as Chl a, NPP, or an index such as the lake chemistry 

ratio of dissolved inorganic N [DIN] to total P, DIN:TP) was surveyed and correlated with atmospheric N 

deposition. Productivity studies have also included lake and stream bioassays in which N was added to 

waters in field or laboratory to measure the response. The most common, and easiest to document, 

indicators of change in algal productivity are measures of the concentration of Chl a and water clarity. 

However, clarity is also strongly influenced by erosional inputs of fine sediment to the lake or stream 

system. Chl a concentration is generally more directly tied to algal productivity than is water clarity. 

Phytoplankton Biomass 

Studies have shown an increase in lake phytoplankton biomass with increasing N deposition in 

several regions, including the Snowy Range in Wyoming (Lafrancois et al., 2003), the Sierra Nevada 

Mountains in California (Sickman et al., 2003), and across Europe (Bergström and Jansson, 2006). 

Gradient studies of undisturbed northern temperate, mountain, or boreal lakes that receive low levels of 

atmospheric N deposition found strong relationships between N-limitation and productivity where N 

deposition was low, and P and N+P limitations where N deposition was higher (Fenn et al., 2003a; 

Bergström et al., 2005; Bergström and Jansson, 2006). 

Bergström and Jansson (2006) concluded the eutrophication caused by inorganic N deposition 

indicates that phytoplankton biomass in a majority of lakes in the northern hemisphere is limited by N in 

their natural state. Chemical data from 3,907 lakes and phytoplankton biomass data from 225 lakes from 

Swedish monitoring programs showed a clear north-south gradient of increasing lake concentrations and 

algal productivity related to the pattern of increasing N deposition input (Bergström et al., 2005). The 

lowest productivity was found at sites where wet N deposition was about 1.3 kg N/ha/yr; increasing 

productivity occurred at greater than 2.2 kg N/ha/yr (Bergström et al., 2005). Although these lakes are all 

in Sweden, the study size and the strong correlation between productivity and atmospheric N deposition 

makes the results likely relevant to North American audiences. 

Experiments conducted with mesocosms in lakes where NO3

−

 was below the detection level found 

a strong response in phytoplankton biomass with additions of N (bringing concentrations to ~1.0 mg N/L) 

and even stronger responses to additions of N plus P, but not P alone (Lafrancois et al., 2004). The reverse 

was found in Colorado Front Range lakes with ambient NO3

−

 concentrations of ~1.0 mg/L: productivity 

increased with additions of N plus P or P only, but not NO3

−

 alone (Lafrancois et al., 2004). 

A meta-analysis of enrichment bioassays in 62 freshwater lakes of North America, including many 

of the studies described above, found algal growth enhancement from N amendments to be common in 

slightly less than half the studies (Elser et al., 1990). There was a mean increase in phytoplankton biomass 

of 79% in response to N enrichment (average of 46.3 μeq/L N) (Elser et al., 1990). This meta-analysis 

was recently repeated with a much large data set and similar results (Elser et al., 2007). Freshwater 

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enrichment bioassay studies from 990 separate studies worldwide were gleaned from the literature. The 

natural log (ln)-transformed response ratio (RRX), a frequently used effect metric in ecological metaanalysis, was equal at about 0.3 for N and P experiments with stream benthos (periphyton) bioassays, and 

approximately equal at about 0.2 for lake phytoplankton. There was a stronger response to P than N in 

lake benthos studies, but the RRX for N was still about 0.3, showing that many sites increased 

productivity when fertilized with N alone (Elser et al., 2007). Additional studies are discussed in Table 

3-16. 

Table 3-16. Summary of additional evidence of N effects on productivity of freshwater ecosystems. 

Region Endpoint Observation Ecosystem 

Type Reference 

Lake Tahoe, CA Primary 

productivity 

Water clarity 

Long-term (28 years) measurements showed that primary 

productivity has doubled, while water clarity has declined, 

mostly as a result of atmospheric N deposition 

Lake Goldman 

(1988); Jassby 

(1994) 

Alaska Primary production 

Fungal biomass 

Decomposition 

rates 

Benthic 

macroinvertebrate 

N amendment experiments with 6.4 μM N to elicited 

responses throughout the ecosystem, including enhanced 

primary production, enhanced fungal biomass and elevated 

leaf litter decomposition rates, and a fourfold to sevenfold 

greater benthic macroinvertebrate abundance 

Small arctic 

streams 

Benstead et al. 

(2005) 

Rocky Mountain 

Lakes, Colorado 

Growth In situ mesocosm: incubations the growth of the diatom A. 

formosa has been stimulated with N amendments during from 

6.4 to 1616 μM N 

Diatoms McKnight 

et al., (1990) 

Yellowstone 

National Park, 

Wyoming 

Growth In situ incubations in large lakes: stimulated F. crotonensi. 

This publication did not reveal how much N was added to the 

incubations 

Lakes Interlandi and 

Kilham, (1998) 

Central Rocky 

Mountains of 

North America 

Growth The N requirements for A. formosa and F. crotonensis were 

determined to be 0.041 μM and 0.006 μM, respectively, and 

higher concentrations stimulated growth 

Alpine lakes Michel et al. 

(2006) 

Chlorophyll a

The most widely used index of biological change in response to nutrient addition is measurement 

of Chl a concentration in water. Surveys and fertilization experiments show increased inorganic N 

concentration and aquatic ecosystem productivity (as indicated by Chl a concentration) to be strongly 

related. For example, a series of in situ meso- and microcosm N amendment experiments more than 30 

years ago showed increases in lake algal productivity. Lake 226S in Ontario’s Experimental Lake Area 

(ELA) showed doubling of average epilimnetic Chl a over five years of fertilization. However, because 

the response to P fertilization was much greater, the effects of N received less attention (Schindler, 1980). 

Other ELD lakes that had relatively low N to P concentration ratios experienced 3 to 10 times greater 

increases in Chl a than Lake 226S (Schindler, 1980). 

Similar experiments at Castle Lake, California, the Snowy Range of southern Wyoming, and 

Alaskan arctic foothill lakes yielded measurable increases in Chl a, primary productivity and algal 

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detritus with N amendments (Axler and Reuter, 1996; Levine and Whalen, 2001; Nydick et al., 2003; 

Lafrancois et al., 2004; Nydick et al., 2004b). 

Periphyton Biomass 

N effects have been observed in periphyton, which grows on rocks or sediment in lakes and 

streams where there is sufficient light for photosynthesis. No studies have reported resource requirements 

for periphyton, although several papers described stimulated growth with N amendments from 

ecosystems throughout the U.S. (Annex C), including streams in Alaska, Arizona, Iowa, Texas, 

Minnesota, Missouri, and lakes in California, Colorado, and Massachusetts. Growth stimulation occurred 

with N additions ranging from 8 to 50 μM/L, or with exposure to 0.5 M N concentrations on agar 

substrate (Bushong and Bachmann, 1989; Allen and Hershey, 1996; Wold and Hershey, 1999; Smith and 

Lee, 2006). Additional lake bioassay experiments that enriched the water column down into the sediments 

found enhancement of periphyton growth on bioassay container walls in experiments in California, 

Wyoming, and Massachusetts (Axler and Reuter, 1996; Nydick et al., 2004a; Smith and Lee, 2006). 

Strong N limitation of benthic algae has also been inferred in streams of Arizona (Grimm and Fisher, 

1986), California (Hill and Knight, 1988), Missouri (Lohman et al., 1991), and Montana (Lohman and 

Priscu, 1992; Smith and Nicholas, 1999). 

Trophic Status Indices 

Nutritional responses of aquatic ecosystems to atmospheric N deposition are heavily dependent on 

surface water P concentrations. Thus, chemical ratios of N to P can be very useful in evaluating 

eutrophication potential. A series of papers, described below, has been published exploring nutrient 

limitations and offering indices that describe the trophic state of freshwaters. Valuable insights have been 

gained from several indices, including total N to total P (TN:TP), dissolved inorganic N to total P 

(DIN:TP), dissolved inorganic N to total dissolved P (DIN:TDP), dissolved inorganic N to soluble 

reactive P (DIN:SRP), and dissolved inorganic N to the ratio of Chl a to total P (DIN:[Chl a:TP]). While 

there are publications that compare the effectiveness of some of these indices, it appears that different 

indices are useful for different purposes; we make no attempt to favor one over another. 

Algal growth was reported to be limited at DIN:TP values between 5 and 20 (Schindler, 1980; 

Grimm and Fisher, 1986; Morris and Lewis, 1988; Downing and McCauley, 1992; Bergström and 

Jansson, 2006). When DIN:TP ratios are greater than reference values, growth stimulation, N and P 

colimitation, or P limitation commonly occurs (Sickman et al., 2003). In a Swedish lake survey, Nlimitation occurred in lakes where the DIN:TP ratio was less than 7 (concentrations <33 μM N/L). Colimitation of both N and P were found in lakes with DIN:TP ratio between 8 and 10, and P-limitation at 

DIN:TP values greater than 10. This corresponds roughly to N concentrations of 45 to 80 μM N/L for colimited lakes, and concentrations >80 μM N/L for P-limited lakes (Bergström et al., 2005). Other 

thresholds for N-limitation were reported in the literature to occur at DIN:SRP ratios <4 (Lohman and 

Priscu, 1992) and <10 (Wold and Hershey, 1999). 

Bergström et al. (2005) reported a new index, (DIN:[Chl a:TP]) to indicate the eutrophication of 

lakes from N deposition. The choice of DIN/[Chl a:TP] was based on whole lake experiments in Sweden 

(Jansson et al., 2001) and permits the assessment of a possible eutrophication effect of N deposition 

independent of differences in P input between lakes in different regions. These researchers found that the 

mean Chl a:TP ratios increased more than three times from low N to high N deposition areas, indicating 

that N deposition contributed to eutrophication. 

3.3.3.3. Estuarine and Marine 

In coastal marine ecosystems, the nutrients most commonly associated with phytoplankton growth 

are N, P, and Si (see Annex C for interactions between hydrology and nutrient cycling). Interactions 

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among the supplies of these nutrients can affect phytoplankton species composition in ways that might 

effect ecosystem function (Paerl et al., 2001a; Riegman, 1992). The relative proportions of these nutrients 

are important determinants of primary production, food web structure, and energy flow through the 

ecosystem (Dortch and Whitledge, 1992; Justic et al., 1995a; Justic et al., 1995b; Turner et al., 1998). 

There is a strong scientific consensus that N is the principal cause of coastal eutrophication in the U.S. 

(NRC, 2000) On average, human activity has likely contributed to a sixfold increase in the N flux to the 

coastal waters of the U.S., and N now represents the most significant coastal pollution problem (Howarth 

et al., 2002b; Howarth and Marino, 2006). Atmospheric deposition is responsible for a portion of the N 

input (Howarth et al., 2002a). 

Ecological effects of accelerated estuarine eutrophication and climatic perturbations such as 

droughts, floods, and hurricanes are often observed most closely at the level of the primary producers. 

Phytoplankton can be divided into functional groups that reflect ecological change, for example: 

chlorophytes, cryptophytes, cyanobacteria, diatoms, and dinoflagellates (Pinckney et al., 2001). 

The relative abundances of these groups can be determined using photopigment indicators which 

can be easily measured in the laboratory (Paerl et al., 2003). Changes in phytoplankton community 

composition, which may affect food web interactions, can have important effects on nutrient cycling. For 

example, if the growth of phytoplankton species that are more readily grazed by zooplankton (i.e., 

diatoms) is favored, trophic transfer will occur in the water column from diatoms to fish and nutrient 

export will take place as fish move to the ocean. However, if the phytoplankton that are favored by 

nutrient addition and disturbance are not readily grazed (i.e., cyanobacteria and dinoflagellates), trophic 

transfer will be poor. In that case, more unconsumed algal biomass will settle to the bottom where it can 

contribute to O2 consumption and associated hypoxia (Paerl et al., 2003). 

To evaluate the impacts of eutrophication, five biological indicators were used in the recent 

national assessment of estuary trophic condition: Chl a, macroalgae, dissolved O2, nuisance/toxic algal 

blooms, and submerged aquatic vegetation (SAV) (Bricker et al., 2007) (Figure 3-41). Each of these 

indicators is discussed below and/or within the biodiversity section of this document (see Section 3.3.5.4). 

N-Limitation 

Estuaries and coastal waters tend to be N-limited and are therefore inherently sensitive to increased 

N loading (D'Elia et al., 1986; Howarth and Marino, 2006). There is a scientific consensus that N-driven 

eutrophication of shallow estuaries has increased over the past several decades and that environmental 

degradation of coastal ecosystems is now a widespread occurrence (Paerl et al., 2001a). 

For example, the frequency of phytoplankton blooms and the extent and severity of hypoxia have 

increased in the Chesapeake Bay (Officer et al., 1984) and Pamlico estuaries in North Carolina (Paerl 

et al., 1998) and along the continental shelf adjacent to the Mississippi and Atchafalaya rivers discharges 

to the Gulf of Mexico (Eadie et al., 1994). It is partly because many estuaries and near-coastal marine 

waters are degraded by N enrichment that they are highly sensitive to potential adverse impacts from N 

addition from atmospheric deposition. 

N enrichment of marine and estuarine waters can alter the ratios among nutrients and affect overall 

nutrient limitation. The sensitivity of estuarine and coastal marine waters to eutrophication from 

atmospheric N deposition depends on the supply of, and relative availability of, N and P. At upstream 

freshwater locations in Chesapeake Bay, P is often the limiting nutrient (Larson and Moore, 1985). At the 

transition between fresh water and salt water, N and P may be co-limiting, whereas the saltwater 

environments of the outer bay are usually N-limited (Fisher et al., 1998; Rudek et al., 1991). Nutrient 

limitation varies in space and over time, in response to changes in discharge and temperature that interact 

with estuarine morphology and hydrology (Paerl et al., 2006). 

 3-126
Source: Bricker et al. (2007)) 

Figure 3-41. Description of the eutrophic symptoms included in the national estuary condition 

assessment. 

The data for 92 worldwide coastal marine sites analyzed by Smith (2006), for which measurements 

of both total N and total P were available, illustrated that about half of the sites had total nitrogen 

(TN):total phosphorus (TP) above the Redfield ratio, which is commonly used to evaluate nutrient 

limitation in freshwater (TN:TP = 16). As was emphasized in earlier work on nutrient limitation in fresh 

waters by Redfield (1958) and Reiners (1986), elemental stoichiometry is a fundamental property of life 

that probably stems from the shared phylogenetic histories of marine and freshwater autotrophs (Smith, 

2006; Sterner and Elser, 2002). 

In general, the scientific community is at an early stage in development of an understanding of the 

effects of anthropogenic activities on the stoichiometry of nutrient loading to estuaries and marine waters 

(Dodds, 2006; Turner, 2002). Changes in nutrient stoichiometry in estuarine and marine ecosystems could 

alter algal assemblages and cascade to higher trophic levels (Frost et al., 2002). 

 3-127
 

Source: Bricker et al. (2007) 

Figure 3-42. A high Chl a rating was observed in a large number of the nation’s estuaries. White squares 

indicate that data were not available for a particular estuary. 

Chlorophyll a 

Chl a concentration in estuarine or marine water is an indicator of total phytoplankton biomass. It 

can signal an early stage of water quality degradation related to nutrient loading. High concentration of 

Chl a suggests that algal biomass is sufficiently high that it might contribute to low dissolved O2

concentration due to increased decomposition of dead algae. In the national estuary condition assessment, 

high Chl a concentration was the most widespread documented symptom of eutrophication (Bricker et al., 

2007) (see Figure 3-42). Half of the estuaries for which there were available data exhibited high Chl a

concentration (Bricker et al., 2007). 

San Francisco Bay, California is an example of an estuary that has experienced considerable 

increases in Chl a concentrations in recent years. Phytoplankton biomass in much of the bay has increased 

by more than 5% per year from 1993 to 2004. During this time, modeled primary production has doubled 

and nutrient loading is identified as one of eight possible causes (Cloern et al., 2006). 

Macroalgal Abundance 

Macroalgae are generally referred to collectively as seaweed. Macroalgal blooms can contribute to 

loss of important SAV by blocking the penetration of sunlight into the water column. Although 

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macroalgal data for estuaries in the U.S. were generally sparse, the national estuary condition assessment 

reported that conditions were moderate or high for 33 of the estuaries evaluated (Bricker et al., 2007). 

Dissolved O2

The decomposition of organic matter associated with increased algal abundance consumes 

dissolved O2 and can reduce dissolved O2 concentrations in eutrophic waters to levels that cannot support 

aquatic life. Decreased dissolved O2 can lead to development of hypoxic or anoxic zones that are 

inhospitable to fish and other life forms. Perhaps the most important environmental effect of N input to 

coastal waters is the development of hypoxia. The largest zone of hypoxic coastal water in the U.S. has 

been documented in the northern Gulf of Mexico on the Louisiana-Texas continental shelf. During 

midsummer, this hypoxic zone has regularly been larger than 16,000 km2

 (Rabalais, 1998). The timing, 

duration, and spatial extent of hypoxia in this case are related mostly to the nutrient flux from the 

Mississippi River (Justic et al., 1993, 1997; Lohrenz et al., 1997; Paerl et al., 2001a; Rabalais et al., 

1996). 

Source: Bricker et al. (2007)) 

Figure 3-43. Frequency of hypoxia in Long Island Sound, 1994 to 2002. 

Although impacts on dissolved O2 can be quite severe in the areas where they are manifested, the 

national assessment reports that the severity of dissolved O2 impacts related to eutrophication are 

relatively limited in many of the systems assessed (Bricker et al., 2007). In the shallow estuary of Long 

Island Sound, the existence of extended periods of low dissolved O2 is a notable problem, and 

atmospheric deposition is considered to comprise a significant fraction of the total N loading. Dissolved 

O2 levels below 3 mg/L are common, and levels below 2 mg/L also occur. During some years, portions of 

the Long Island Sound bottom waters become anoxic ( <1 mg/L; see Figure 3-43). 

Nuisance/Toxic Algal Blooms 

Nuisance or toxic algal blooms reflect the proliferation of a toxic or nuisance algal species that 

negatively affects natural resources or humans. Such blooms can release toxins that kill fish and shellfish 

and pose a risk to human health. Unlike the other indicators of estuarine eutrophication, the role of 

nutrients in stimulating toxic algal blooms is less clear. Of the 81 estuary systems for which data were 

available, 26 exhibited a moderate or high symptom expression for nuisance or toxic algae (Bricker et al., 

2007). 

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3.3.3.4. Summary of Nitrogen Effects on Carbon Cycling 

The evidence is sufficient to infer a casual relationship between N deposition and alteration to the 

biogeochemical cycling of C in terrestrial, wetland, freshwater and marine ecosystems. N deposition causes 

alteration to the C cycle in forest ecosystems. Experimental N addition studies show a range of responses 

in terms of mortality and productivity. In general, moderate to high additions of N lead to either no 

significant change in growth rates or transient growth increases followed by increased mortality, 

especially at higher rates of fertilization (See Table 3-15). Although there remains considerable 

uncertainty in the potential response of soil C to increases in Nr additions (Neff et al., 2002), a metaanalysis by Johnson and Curtis (2001) suggested that N fertilization caused an 18% increase in soil 

carbon content. The effects of N addition on soil respiration are mixed; reductions at high levels of N (Lu 

et al., 1998), no effect (Vose et al., 1995) and increases (Griffin et al., 1997; Mikan et al., 2000) all have 

been observed. 

Region trends in NEP have been documented through models based on observational gradient 

studies. Magnani et al. (2007) recently reported a strong correlation between estimated average long-term 

NEP (NEPav) and estimated 1990 wet N deposition (up to 9.8 kg N/ha/yr) for 20 forest stands mostly in 

Western Europe and the conterminous U.S. Sutton et al. (2008b) critiqued the methods of Magnani et al. 

(2007). After considering the uncertainties in N deposition and climate variability, Sutton et al. (2008b) 

reported the estimated NEP response to N deposition was 68 kg C/wet-deposited N. Sutton et al. (2008b) 

agreed with Magnani et al. (2007) that N deposition remains an important driver of NEPav, but did not 

support the NEPav was overwhelmingly driven by N deposition. The ISA staff conducted a meta-analysis 

to examine the effect of N fertilization on forest ecosystem C content, defined as the sum of C content of 

vegetation, forest floor and soil (Johnson et al., 2006) and found that N addition increased ecosystem C 

by 6%. In summary, it remains unclear to what extent N deposition at current levels could potentially 

increase C sequestration in forest ecosystems in the U.S. 

N addition causes alterations to the C cycle in Tundra. Mack et al. (2004) examined C and N pools 

in a long-term fertilization experiment at the arctic Long-Term Ecological Research site near Toolik Lake, 

AK. This study showed that increased nutrient availability enhanced decomposition of below-ground C 

pools in deep soil layers more than it increased primary production, leading to a substantial net loss of C 

from this ecosystem. 

N deposition causes alteration to the C cycle in freshwater wetlands. In Sphagnum-dominated 

ombrotrophic bogs, higher N deposition resulted in higher tissue N concentrations and greater NPP 

(Aldous, 2002), but lower bulk density (Aldous, 2002). A study of 23 ombrotrophic peatlands in Canada 

with deposition levels ranging from 2.7 to 8.1 kg N/ha/yr showed peat accumulation increases linearly 

with N deposition (y = 2.84(x) + 0.67, r2

 = 0.32, p <0.001), however in recent years this rate has begun to 

slow indicating limited capacity for N to stimulate accumulation (Moore et al., 2004). Soil respiration has 

been studied in European countries under a natural gradient of atmospheric N deposition from 2 to 20 kg 

N/ha/yr. They found enhanced decomposition rates for material accumulated under higher atmospheric N 

supplies resulted in higher CO2 emissions from soil. Primary production of plant species from intertidal 

wetlands typically increases with N addition, however most studies apply fertilizer treatments that are 

several orders of magnitude larger than atmospheric deposition (Darby and Turner, 2008; Mendelssohn, 

1979; Tyler et al., 2007; Wigand et al., 2003). 

N deposition causes alteration to the C cycle in freshwater aquatic ecosystems. The productivity of 

many freshwater ecosystems is currently limited by the availability of N (Elser et al., 2007). European 

and North American lakes may have been N-limited before human-caused disturbance, and remote lakes 

may have remained N-limited until slight increases in atmospheric N deposition brought about an increase 

in phytoplankton and periphyton biomass and induced P limitation. Numerous studies investigated the 

relationship between lake N concentration and primary productivity (reported as Chl a, NPP, or an index 

such as the lake chemistry ratio of dissolved inorganic N [DIN] to total P, DIN:TP) and atmospheric N 

deposition. N addition experiments of lake and stream bioassays in which N was added to waters in field 

or laboratory to measure the response. Gradient studies of undisturbed northern temperate, mountain, or 

 3-130
boreal lakes that receive low levels of atmospheric N deposition found strong relationships between Nlimitation and productivity where N deposition was low, and P and N+P limitations where N deposition 

was higher (Bergström et al., 2005; Bergström and Jansson, 2006; Fenn et al., 2003a). 

N deposition causes alteration to the C cycle in near coastal marine ecosystems. As previously 

mentioned, N deposition is not the sole source of N loading to estuaries and its contribution varies across 

the U.S. Estuaries and coastal waters tend to be N-limited and are therefore sensitive to increased 

atmospheric N loading (Elser et al., 2007; D'Elia et al., 1986; Howarth and Marino, 2006). This is at least 

partly because denitrification by microbes found in estuarine and marine sediments releases much of the 

added N inputs back into the atmosphere (Vitousek et al., 1997). However, other limiting factors occur in 

some locations and during some seasons. Levels of N limitations are affected by seasonal patterns. Nlimited conditions are likely to be found during the peak of annual productivity in the summer. 

Numerous studies evaluate the relationship between N loading, eutrophication and ecological 

endpoints including Chl-a, macroalgal abundance, dissolved O2, nuisance/toxic and algal blooms In the 

national estuary condition assessment, high Chl a concentration was the most widespread documented 

symptom of eutrophication (Bricker et al., 2007) (see Figure 3-42). 

3.3.4. Biogenic Trace Gases: Nitrous Oxide, Methane, Nitric Oxide and 

VOCs 

Methane (CH4) and nitrous oxide (N2O) are greenhouse gases (GHGs) contributing to global 

warming. Although atmospheric concentrations of CH4 (1774 ppb) and N2O (319 ppb) are much lower 

than CO2 (379 ppm), their global warming potential is 23 and 296 times that of CO2, respectively. Human 

activities have dramatically increased atmospheric concentration of CH4 by 48% and N2O by 18% since 

pre-industrial times (IPCC 2007). The continuing increase of those GHGs concentrations have been 

shown to threaten human and ecosystem health. Anthropogenic N deposition to natural ecosystem is a 

primary component of global change. Additional N input not only changes the global N cycle, but also 

has profound effects on biogeochemical processes associated with GHGs emission (Vitousek et al., 1997; 

Dalal et al., 2003; Bodelier and Laanbroek 2004). In the following section, the effects of N addition on 

CH4 and N2O emissions were reviewed and quantitatively synthesized by meta-analysis. Further details 

on this meta-analysis including study site, ecosystem type, N addition level, chemical form of N, 

experimental conditions, relationship between N addition and CH4 flux, are in included Annex C. 

3.3.4.1. Methane 

Atmospheric CH4 originates mainly (70–80%) from biogenic sources (Le Mer and Roger, 2001). 

Methane is produced in anaerobic environment by methanogenic bacteria during decomposition of 

organic matter. Once produced in soil, CH4 can then be released to the atmosphere or oxidized by 

methanotrophic bacteria in the aerobic zone (Le Mer and Roger, 2001). Methane production and 

oxidation processes occur simultaneously in most ecosystems. Wetland soils are generally CH4 sources, 

accounting for about 20% of global CH4 emission (see Annex C for a more detailed discussion of 

methane in wetlands). Non-flooded upland soils are the most important biological sink for CH4, 

consuming about 6% of the atmospheric CH4 (Le Mer and Roger, 2001). Numerous studies have 

demonstrated that N is an important regulatory factor for both CH4 production and oxidation (Bodelier 

and Laanbroek, 2004). 

The U.S. EPA conducted a meta-analysis, including 61 observations from 27 publications, to 

evaluate the relationship between N addition and CH4 flux. Details on those publications, including study 

site, ecosystem type, N addition level, chemical form of N, experimental condition, are given in Annex C. 

The impact of N addition on CH4 source and sink strength were estimated by CH4 emission and CH4

 3-131
uptake respectively. The result suggested that N addition significantly increased CH4 emission by 115% 

for grasslands and wetlands (Figure 3-44). This response ratio did not differ among vegetation type, N 

addition level, chemical form of N and experiment condition. observation numbers

0

1

2

3

14 I I

grassland

wetland

mean response ratio

12345 >

response ratio

Figure 3-44. Effects of N addition on biogenic CH4 emission. The bars show the distribution of the number 

of studies categorized by ecosystem type. The dot with error bars shows the overall mean 

response ratio with 95% CI. 

Overall, N addition significantly reduced CH4 uptake by 38% (Figure 3-45). Ecosystem type, N 

form and experiment condition influenced the degree of CH4 uptake response to N addition (Figure 3-46). 

Methane uptake was reduced for all ecosystems, but this inhibition was significant only for coniferous 

and deciduous forest, with a reduction of 28% and 45%, respectively (Figure 3-46). 

All forms of N fertilizer except urea were shown to reduce CH4 uptake (Figure 3-46). Several 

possible mechanisms have been proposed to explain the inhibition in CH4 oxidation by N addition. 

Besides the oxidation of CH4, methane monooxygenase (MMO) can convert NH4

+

 to NO3

−

, and NH4

+

therefore usually inhibits CH4 oxidation by competing for MMO (Bodelier and Laanbroek, 2004). 

Methanotrophic bacteria are sensitive to osmotic stress induced by salts. Inhibition of CH4 uptake by 

nitrogenous salts (e.g., KNO3, NH4Cl, and NH4NO3) and non-nitrogenous salts (e.g., K2SO4, KCl and 

NaCl) has been observed in field and laboratory studies (Bodelier and Laanbroek, 2004; King and 

Schnell, 1998). Other mechanisms, such as toxicity of nitrite (NO2

−

) produced by nitrification or 

denitrification processes, may also involve in the inhibition of CH4 oxidation (Schnell and King, 1994). 

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response ratio 

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4

 observation numbers

0

2

4

6

8

47 I I

heathland

tundra

wetland

mean response ratio

coniferous

deciduous

tropical forest

grassland

>

Figure 3-45. Effects of N addition on biological CH4 uptake. The bars show the distribution of the number 

of studies categorized by ecosystem type. The dot with error bars shows the overall mean 

response ratio with 95% CI. 

The mean response ratio of CH4 uptake from laboratory incubation studies was significantly lower 

than that from field studies (Figure 3-46). This difference could be due to that the spatially and chemically 

heterogeneous field conditions resulted in large experimental errors (Crill et al., 1994; Gulledge and 

Schimel, 2000; Weitz et al., 1999). Also laboratory microcosms were characterized by a closed and 

incomplete N cycle, where N loss by leaching was very small and no plant competed for N with soil 

microbes. Therefore, N addition may result in stronger impacts on microbial processes under laboratory 

condition than under field condition 

Several laboratory incubation studies found that CH4 uptake rates decreased with increasing N 

input (Schnell and King 1994; King and Schnell 1998). This meta-analysis did not find significant 

correlation between the amount of N addition and the response ratio of CH4 production/consumption 

(Figure 3-46). The lack of dose response relationship is probably because CH4 production is influenced by 

multiple interactions of soil N content, soil moisture, pH and temperature (Le Mer and Roger, 2001), and 

varies greatly over small spatial and temporal scales (IPCC, 2001). 

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response ratio

0.2 0.4 0.6 0.8 1.0 1.2 1.4

coniferous (18)

deciduous (10)

grassland (7)

wetland (6)

tropical forest (2)

heathland (3)

NH4NO3 (27)

NH4

+

 (8)

urea (5)

NO3

-

 (5)

field (36)

incubation (11)

vegetation

N form

experimental condition

Figure 3-46. Effects of N addition on biological CH4 uptake. The data are expressed as the mean response 

ratio with 95% confident intervals. The numbers of studies included are indicated in 

parentheses. 

3.3.4.2. Nitrous Oxide 

Biogenic sources are the dominating contributors (>90%) to atmospheric N2O. Terrestrial soil is the 

largest source of atmospheric N2O, accounting for 60% of global emissions (IPCC, 2001). Nitrous oxide 

production in soil is mainly governed by microbial nitrification and denitrification (Dalal et al., 2003). 

The contribution of each process to the total N2O production varies with environmental conditions. 

Denitrifying bacteria reduce NO3

−

 or NO2

−

 into N2O or N2 under anaerobic condition. In submerged soils 

such as wetland soil, denitrification should be the dominant contributing process to N2O emission 

(Conrad, 1996). Increasing NO3

−

 input generally increases denitrification rate under suitable condition of 

temperature and organic C supply. High soil NO3

−

 concentrations also inhibit N2O reduction to N2 and 

result in high N2O/N2 ratio (Dalal et al., 2003). Under aerobic environment, autotrophic nitrifying bacteria 

obtain energy by reducing NH4

+

. Nitrous oxide is an intermediate product of the oxidation of NH4

+

 to 

NO2

−

 or decomposition of NO2

−

. The increase in N2O emission following NH4

+

 addition has been 

observed in many lab and field experiments (Aerts and Toet, 1997; Aerts and de Caluwe, 1999; Keller 

et al., 2005). 

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The U.S. EPA conducted a meta-analysis on the effects of N addition on N2O emissions from nonagricultural ecosystems, including 99 observations from 30 publications. N addition normally increased 

N2O emissions, but some studies also observed N2O emission was decreased by N addition (Ambus and 

Robertson, 2006; Ambus et al., 2006; Borken et al., 2002; Curtis et al., 2006; Skiba et al., 1999). 

Although some natural ecosystems can be an N2O sink (Chapuis-Lardy et al., 2007), very limited 

publications assessed the impact of N addition on N2O uptake. Thus, only changes in N2O production 

were estimated in this meta-analysis. Overall, the results of the meta-analysis indicated that N addition 

increased N2O emission by 215% (Figure 3-47). The response of N2O emissions was influenced by 

ecosystem type, the form and the amount of the N addition (Figure 3-48). 

response ratio 

 observation numbers

0

2

4

6

8

47 I I

mean response ratio

coniferous

deciduous

tropical forest

grassland

heathland

tundra

wetland

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 >

Figure 3-47. Effects of N addition on biogenic N2O emission. The bars show the distribution of the number 

of studies categorized by vegetation type. The dot with error bars shows the overall mean 

response ratio with 95% CI. 

Compared to other ecosystems, tropical forest emitted more N2O under N enrichment condition 

(+735%) (Figure 3-48). This greater response may be because tropical forests are often P limited rather 

than N limited (IPCC, 2001). Hall and Matson (1999) measured N2O emissions after adding N fertilizer in 

two tropical rainforests in Hawaii. They found that N2O emissions from P-limited sites was 54 times 

greater in the short term N addition experiment and 8 times greater in the chronic N addition experiment 

compared to that from N-limited sites. The P-limited soil had higher inorganic N concentration than the 

N-limited soil (Hall and Matson, 1999) which increased N availability to the nitrifying and denitrifying 

bacteria. However, climatic conditions, especially temperature and precipitation, can also be important 

factors driving N2O emissions from tropical forest ecosystem. 

NO3 caused a higher stimulation (+494%) o N2O emissions than NH4

+ did (+95%) (Figure 3-48), 

which was consistent with the previous field studies (Keller et al., 1988; Russow et al., 2008; Wolf and 

Russow, 2000). By adding 15N labeled NO3

−

 and NH4

+ to soil, Russow et al. (2008) found that N2O was 

mainly emitted by denitrification and the contribution of denitrification to the total N2O production 

increased from 54% in soil with normal SOM content to 76% in soil with high SOM. 

The Intergovernmental Panel on Climate Change (IPCC) issued a guideline for national GHGs 

inventories of biogenic N2O (IPCC, 2000). In this guideline, the default N2O emission factor is 1.25% for 

 3-135
N fertilizer applied to agricultural fields (i.e., 1.25% of the amount of N applied to a field will be emitted 

to the atmosphere as N2O). Several studies have questioned the validity of this emission factor. Some 

studies suggested a much lower emission factor, such as 0.25% for rice paddy field (Yan et al., 2003) and 

0.02% for semi-arid regions (Barton et al., 2008), while others found the amount of N2O emission was not 

clearly related to the amount of N addition (Akiyama et al., 2005; Barnard et al., 2005; FAO/IFA, 2001). 

The U.S. EPA compiled ambient N2O emission data from 36 studies and did not find any correlation 

between N2O emission and the level of N deposition (Figure 3-49). In this meta-analysis, although the 

mean response ratio increased with the amount of N addition, the differences among the three levels (<75, 

75–150 and >150 kg N/ha/yr) were not significant (Figure 3-48). The weak correlation is probably due to 

that the effect of N addition on N2O emission is affected by many other biotic and abiotic factors such as 

fertilizer type, vegetation, temperature, soil drainage etc. (Dalal et al., 2003). 

response ratio

coniferous (34)

deciduous (17)

grassland (22)

wetland (19)

tropical forest (11)

heathland (3)

NH4NO3 (47)

NH4

+

 (19)

NO3

-

 (21)

urea (9)

<75 (30)

75-150 (19)

>150 (24)

vegetation

N form

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 60

N addition level

Figure 3-48. Effects of N addition on biogenic N2O emission. The data are expressed as the mean 

response ratio with 95% confident intervals. The numbers of studies included are indicated in 

parentheses. 

 3-136
N deposition (kg N ha-1 yr-1)

0 10 20 30 40 50 60

N O emission (kg N ha 2 -1 yr-1)

0

2

4

6

8

coniferous forest

deciduous forest

heathland 

 tropical forest 

Figure 3-49. The relationship between N2O emission and N deposition. The data were compiled from 36 

independent studies. 

3.3.4.3. Nitric Oxide and VOCs 

N saturation caused by chronic N input often leads to increased soil NO emission (Hall and Matson 

1999; Venterea et al., 2003; Venterea et al., 2004; Kitzler et al., 2006). Although nitrification and 

denitrification both contribute to NO production, many studies have found that NO flux increased in a 

manner that was consistent with nitrification rate increases, suggesting that NO emission is more 

triggered by nitrification activity rather than by denitrification activity (Wolf and Russow 2000; Venterea 

et al., 2004). 

Monthly NO monitoring at Harvard Forest showed that NO emission increased at coniferous plots 

treated with 50 and 150 kg N/ha/yr and hardwood plots treated with 150 kg N/ha/yr (Venterea et al., 

2003). NO emission accounted for 3.0-3.7% of N inputs to the high N plots and 8.3% of inputs to the 

coniferous low N plots. At two paired watersheds subjected to elevated N input, 35.5 kg N/ha/yr at 

Fernow Experimental Forest in Western Virginia and 25.2 kg N/ha/yr at the Bear Brook Watershed in 

Maine, NO emissions at the N amended watersheds (0.61-6.8 μg NO-N /m2

/h ) were higher than the 

reference watersheds (0.21-1.4 μg NO-N /m2

/h). Mean NO fluxes at both watersheds were positively 

correlated with mean soil NO3

–

 concentrations (Venterea et al., 2004). Similarly, (Pilegaard et al., 2006) 

found that NO emission from 7 coniferous forests in Europe were highly correlated with the rates of Ndeposition (NO flux (μg N/m2

/h) = 25.52 ×N deposition (g N/m2/yr) -13.93; r2=0.82). 

The response of NO production rates to N deposition may vary significantly between vegetation 

types and climatic regimes. The measurements of 15 forest sites across Europe showed that coniferous 

forest emitted more NO (31.0±15.6 μg NO-N/m2

/h) than deciduous forest (4.1±1.9μg NO-N/m2/h), 

although the average N deposition to coniferous forests (16 ± 5 kg N/ha/yr) was similar to deciduous 

forests (15±2 kg N/ha/yr) (Pilegaard et al., 2006). Several studies, such as ButterbachBahl et al. (1997) 

and Venterea et al. (2003), also found that NO emissions from coniferous forest were generally higher 

than from deciduous forest. This difference is probably due to that the characteristics of coniferous forest 

floor, such as low soil moisture and low pH, favour nitrification and thus NO emission (Pilegaard et al., 

2006). 

 3-137
In addition to vegetation type, climate and soil nutrient conditions also influence NO flux under N 

enrichment. Natural forests in tropic region are generally considered P limited, contrasting with N-limited 

temperate forests (Vitousek et al., 1997). After adding N (50 kg N/ha/yr) to three tropical rainforests (N 

limited, NP-limited and P limited) in Hawaii, Hall and Matson (2003) found that all N addition plots 

emitting more NO than control plots and responses of NO production to N addition increased in the order 

of: P limited sites >NP-limited sites >N limited sites. Hall and Matson (2003) work suggested that 

P-limited and N-limited ecosystems responded differently to N deposition. Rather than retaining N in 

ecosystems, anthropogenic N addition may immediately increase N trace gas loss if N availability 

exceeds biological demand. 

3.3.4.4. Volatile Organic Compounds (VOCs) 

Volatile organic compounds (VOCs) comprise a wide range of chemical compounds including 

hydrocarbons, halocarbons and oxygenates. Methane is one of the most important VOCs and the effect of 

N addition on CH4 emission was discussed in Section 3.3.4.1. The available information for non-methane 

VOCs emissions is much sparser than that for CH4, with only a few studies measuring isoprene emissions 

under N enrichment. Isoprene is the primary non-methane hydrocarbon emitted from temperate deciduous 

forest and tropical forest (Funk et al., 2006). Both pot and field studies found that there was a strong 

positive correlation between isoprene emission rates and leaf N concentrations (Litvak et al., 1996; Funk 

et al., 2006). N addition could significantly increase plant isoprene emissions by increasing leaf N 

concentrations (Harley et al., 1994; Litvak et al., 1996). 

3.3.4.5. Summary of N Effects on Biogenic Trace Gases 

Integrating 160 observations across 57 independent studies, this meta-analysis suggested that N 

addition tended to increase CH4 emission, reduce CH4 uptake and increase N2O emission. Overall, N 

deposition may result in higher CH4 and N2O concentrations in atmosphere and exacerbate global 

warming, but these responses can also be influenced by many environmental factors, such as vegetation 

type, N form, and climate. 

The evidence is sufficient to infer a casual relationship between N deposition and the alteration of 

biogeochemical flux of CH4 in terrestrial and wetland ecosystems. N addition ranging from 10 to 560 kg 

N/ha/yr reduced CH4 uptake by 38% across all ecosystems (Figure 3-45), but this inhibition was 

significant only for coniferous and deciduous forest, with a reduction of 28% and 45%, respectively 

(Figure 3-46). Wetlands are generally net sources of CH4, but some wetlands can be net sinks depending 

on environmental conditions such as drainage and vegetation (Crill et al., 1994; Saarnio et al., 2003). The 

meta-analysis indicated that N addition, ranging from 30 to 240 kg N/ha/yr, increased CH4 production by 

115% from the source wetlands (Figure 3-44), but had no significant effect on CH4 uptake of the sink 

wetlands (Figure 3-46). 

The evidence is sufficient to infer a casual relationship between N deposition and the alteration of 

biogeochemical flux of N2O in terrestrial and wetland ecosystems. Overall, the results of the meta-analysis 

discussed in Section 3.3.4 indicated that N addition ranging from 10 to 560 kg N/ha/yr. increased N2O 

emission by 215% in terrestrial ecosystems (Figure 3-47). The response of N2O emission to N for 

coniferous forest, deciduous forest and grasslands was significant (Figure 3-48). In the meta-analysis of 

19 observations from studies that evaluated the effects of N additions ranging from 15.4 to 300 N kg 

N/ha/yr was shown to increase the production of N2O by 207% in wetlands (Figure 3-48). 

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3.3.5. Species Composition, Species Richness and Biodiversity 

A common response to environmental stress is the tendency for the more sensitive species to 

decrease in abundance, or to be eliminated, while the more tolerant species increase in abundance 

(Woodwell, 1970). Species composition and species richness, as well as impacts on rare or threatened 

species, indicate changes to biodiversity. The ecological consequences of changing species composition, 

richness and/or biodiversity can be profound. Selective removal of certain species can result in an 

impairment of ecosystem function, change in community structure and food web dynamics, and decrease 

in species richness and diversity. Such changes in species composition can occur in response to N 

addition to terrestrial, aquatic, and transitional ecosystems. 

Weiss et al. (2006) presented an overview of potential biodiversity loss from N enrichment. A 

survey by Stohlgren et al. (1999) of variables that contribute to species richness and invisibility of sites 

found negative relationships between soil N, and species richness and numbers of nonnative plant species. 

The implication of this work is that N fertilization alters competitive interactions that may cause native 

species to be lost, with subsequent decrease in species richness. 

Alteration of plant productivity and growth by N deposition (see Section 3.3.3) causes a cascading 

effect on the competitive interactions among species. Atmospheric deposition of N is expected to benefit 

those species that are best able to take advantage of the increased nutrient availability. Other species may 

experience decreased growth, reproduction and population size, because they are out-competed by species 

that are more successful under conditions of enhanced N availability. Numerous studies evaluate 

ecosystem response to levels of N addition that far exceed the range of N deposition levels in the U.S. 

This assessment focuses on the information most relevant to the review of the NAAQS, therefore research 

conducted at N loading levels that greatly exceed current conditions ( >150 kg N/ha/yr) are excluded from 

the discussion. 

3.3.5.1. Terrestrial Ecosystem Biodiversity 

Atmospheric inputs of N can alleviate deficiencies and increase growth of some plants at the 

expense of others. Thus, N deposition can alter competitive relationships among terrestrial plant species 

and therefore alter species composition and diversity (Ellenberg, 1987; Kenk and Fischer, 1988; 

U.S. EPA, 1993a). Wholesale shifts in species composition are easier to detect in short-lived ecosystems 

such as annual grasslands, in the forest understory, or mycorrhizal associations than for long-lived forest 

trees where changes are evident on a decadal, or longer, time scale. Note species shifts and ecosystem 

changes can occur even if the ecosystem does not exhibit signs of N saturation. 

Forests 

Trees 

There is very little information on the effect of N deposition on the biodiversity of overstory trees 

in the U.S. However, the altered growth rates caused by N enrichment have the potential to affect forest 

structure and biodiversity. The life span of many trees is 100 years or more, therefore observation of how 

growth rates effect biodiversity within established forests are difficult to observe on a decadal time scale. 

N deposition has been observed to cause tree invasion into grasslands, also called forest 

encroachment. A study of the northern edge of the Great Plains (southern Canada), showed that increasing 

N deposition over a range of 8 to 22 kg N/ha/yr to aspen-dominated forest and boreal forest caused an 

increase forest expansion into the grasslands (Kochy and Wilson, 2001). The following mechanisms have 

been document to facilitate forest encroachment. Due to their height, trees and shrubs can intercept more 

airborne particulate N than grasses and they should therefore benefit most from N deposition (Kellman 

and Carty, 1986; Binkley, 1995). Fertilization also increases the water-use efficiency of woody invaders 

 3-139
(Bert et al., 1997) and this may enable them colonize temperate grasslands on dry, coarsely textured soils. 

Accelerated N cycling following deposition (Berendse, 1994; Carreiro et al., 2000; Högbom and 

Högberg, 1991) decreases competition for N and increases competition for light (Wilson and Tillman, 

1991), and may give further advantage to tall or fast growing trees (Aerts et al., 1999). Overall, therefore, 

increased deposition rates may result in a self-maintaining positive feedback that allows trees to establish 

in grasslands (Wilson et al., 1998). 

Understory Herbaceous Plants and Shrubs 

Studies in Europe have generally been based on natural gradients, whereas findings in the U.S. 

have mostly been based on experimental N addition. The effects of increasing N deposition on herbaceous 

plants were reviewed by (Gilliam et al., 2006b). Reported effects include species shifts towards 

nitrophilous and more acid-tolerant plant species along a deposition gradient from 6 to 20 kg N/ha/yr in 

Swedish oak forests; a decline in abundance and cover of ericaceous shrubs along a deposition gradient 

from <3 to >12 kg N/ha/yr in the boreal forest in Sweden; and a decline in herbaceous cover under 

hardwoods following 3 years of N additions applied as (NH4)2SO4 at rates ranging from 14 to 28 kg 

N/ha/yr. The decline in herbaceous cover in the latter study was attributed to increased shading by ferns, 

and the effect was more pronounced at sites that experienced lower ambient atmospheric N inputs. 

Van Breemen and Van Dijk (1988) noted that over the previous several decades of N deposition the 

composition of plants in forest herb layers in The Netherlands had shifted toward species commonly 

found in N-rich areas. Brunet et al. (1998) and Falkengren-Grerup (1998) reported the effects of excessive 

N deposition on mixed-oak forest vegetation along a depositional gradient. Results of this study suggest 

that N deposition had affected non-woody vegetation directly by increased N availability and, indirectly, 

by accelerating soil acidity. Time series studies indicated that 20 of the 30 non-woody plant species that 

were associated most closely with high N deposition had increased in abundance in those areas in Europe 

that received high N deposition. 

Mixed results have been reported in other studies. Research at Fernow Experimental Forest, West 

Virginia, indicated that application of 35 kg N/ha/yr applied as (NH4)2SO4 for 6 years had no significant 

impact on the herbaceous layer in an Appalachian hardwood forest (Gilliam et al., 2006a). Fernow has 

been the recipient of high levels of N deposition for decades, raising the possibility that the herbaceous 

layer responded long ago to changes in N availability. 

Mycorrhizal and Microbial Diversity 

Mycorrhizal and microbial biodiversity can also be affected by N enrichment. Relationships among 

plant roots, mycorrhizal fungi, and microbes are critical for N cycling and for the growth and health of 

plants. Mycorrhizal fungal diversity has been shown to be associated with above-ground plant 

biodiversity and ecosystem productivity (Wall and Moore, 1999) and to be adversely affected by 

increased N availability (Egerton-Warburton and Allen, 2000). The loss of mycorrhizal function has been 

hypothesized as a key process contributing to reduced N uptake by vegetation and increased NO3

−

mobility from soil into drainage water under conditions of high N supply (U.S. EPA, 2004). 

Progressive decline in ectomycorrhizal fungal species richness in Alaskan coniferous forest (white 

spruce [Picea glauca] dominant) occurred along a local N deposition gradient, from 1 to 20 kg N/ha/yr, 

downwind from an industrial complex (Lilleskov et al., 2002). Ectomycorrhizal fungal communities are 

important in tree nutrition, and ectomycorrhizal fungal trees tend to be dominant in N-limited forest 

ecosystems. 

N fertilization at rates of 54 and 170 kg N/ha/yr (as NH4NO3) led to a decline in ectomycorrhizal 

fungal diversity and species composition in an oak savanna at Cedar Creek Natural History Area in 

Minnesota (Avis et al., 2003). In the reference plots, five species collectively accounted for more than 

40% cover versus four plant species in the lower N addition plots. In the higher N addition plots, a single 

plant species accounted for more than 40% cover. 

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Compton et al. (2004) investigated the effects of 11 years of experimental N addition on forest soil 

microbial ecology. Experimental N addition decreased the C content of microbial biomass in the O 

horizon of both experimental forest stands, based on chloroform fumigation-extraction. In addition, the 

use of N-containing substrates by microbes appeared to be reduced by N addition in the pine stand, but 

not in the hardwood stand. In addition, the use of N-containing substrates by microbes appeared to be 

reduced by N addition in the pine stand, but not in the hardwood stand. The mechanisms responsible for 

such changes are not clear (Arnebrandt et al., 1990; Compton et al., 2004). It is possible that added N has 

both direct (nutrient) and indirect (soil chemistry, organic matter quality, and quantity) effects on 

microbial ecology. Effects can be manifested on mycorrhizal fruiting body abundance, hyphal networks, 

and community composition (Frey et al., 2004; Lilleskov et al., 2002). 

Grasslands 

Reduced biodiversity in response to N deposition is reported for grasslands in the U.S. and Europe. 

Clark and Tilman (2008) recently evaluated the effects of chronic N deposition over 23 years in 

Minnesota prairie-like successional grasslands and in a native savanna grassland, each originally 

dominated by a species-rich mixture of native C4 grasses and forbs (Cedar Creek Long Term Ecological 

Research Site, Minnesota). Experimental N addition ranged from 10 to 95 kg N/ha/yr above ambient 

atmospheric N deposition (6 kg N/ha/yr). The N addition rate reduced plant species numbers by 17% 

relative to controls receiving ambient N deposition. Moreover, species numbers were reduced more per 

unit of added N at lower addition rates and relative species number was reduced at all addition levels. 

This suggests that chronic but low-level N deposition may decrease diversity below the lowest addition 

levels tested (the critical load was calculated as 5.3 kg N/ha/yr with an inverse prediction interval of 1.3–

9.8 kg N/ha/yr). A second experiment showed that a decade after cessation of N addition, relative plant 

species number, although not species abundances, had recovered, demonstrating that some effects of N 

addition are reversible. 

Change in species composition in response to N deposition has been observed regardless of soil 

type in European grasslands. Such effects have been found in calcareous, neutral, and acidic 

environments, species-rich heaths, and montane-subalpine grasslands (Bobbink et al., 1992b; 1998; 

Bobbink, 1998; Stevens et al., 2004). A transect of 68 acid grasslands across Great Britain, covering the 

lower range of ambient annual N deposition (5 to 35 kg N/ha/yr), indicates that long-term, chronic N 

deposition significantly reduced plant species richness. Species richness declined as a linear function of 

the rate of inorganic N deposition, with a reduction of one species per 4–m2

 quadrant for every 

2.5 kg N/ha/yr of chronic N deposition. 

Grasslands are well known to respond to increased N availability through changes in growth rates 

of both native and exotic species. Under high N supply, exotic grasses often out-compete other species, 

and cause changes in plant community composition (Lowe et al., 2002). A summary of studies, which 

have shown altered plant community composition, or growth rates of plant species that have implications 

for community composition is in Table 3-17. Increased availability of N to grasses can also affect 

herbivores that feed on grasses by altering food quality, quantity, and phenology, and perhaps by changing 

the relationships between herbivores and their predators (Throop and Lerdau, 2004). 

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Table 3-17. Summary of N effects on grassland biodiversity. 

Region/Country Endpoint Observations Grassland Type/ Species Reference

San Francisco Bay Area, 

CA 

Bay 

Checkerspot 

Populations 

Observation: Serpentinitic soils sustain native 

grasses that support populations of the 

endangered Bay Checkerspot butterfly. Several 

lines of evidence indicate that dry N deposition is 

responsible for grass invasion and subsequent 

decline of the butterfly population. However, this 

relationship is uncertain. 

Serpentine Grasslands 

Bay Checkerspot Butterfly 

(Euphydryas Editha Bayensis) 

Weiss (1999)

Colorado Growth Greenhouse experiment: they tested the 

response of grassland species to increased N 

availability (0, 10, 40, 70, or 100 kg N/ha/yr) over 

75 days. All of the grass species exhibited 

increased growth with increased N availability. 

Native species did not consistently grow better at 

low N levels than the exotic species. Two of the 

exotic grasses exhibited the greatest increase in 

growth, while another of the exotics exhibited the 

smallest increase in growth. 

Two North American Native 

Species (Blue Grama And 

Western Wheatgrass) and Four 

Exotic Species (Cheatgrass, 

Leafy Spurge, Canada Thistle, 

and Russian Knapweed) 

Lowe et al. 

(2002) 

Plant Research 

Laboratory, University of 

Illinois at Chicago 

Growth and 

Tissue Quality 

Field Addition: N additions in (0.1, 1, 3 mmol N 

addition for 80 days), caused species-specific 

growth and plant tissue quality changes. C3

grasses (Elymus virginicus L., E. Canadensis L.) 

showed a greater positive growth response to N 

additions than C4 grasses (Andropogon geradii 

Vitmanm, Schizachyrium scoparium Michx.) and 

forbs (Solidago nemoralis Ait., S. rigida L.). 

Species with smaller initial biomass exhibited the 

greatest increase in biomass, with a sevenfold to 

eightfold increase in S. nemoralis and E. 

canadensis and only a threefold increase in S. 

rigida. 

Tallgrass Prairie Lane and 

BassiriRad 

(2002) 

Jasper Ridge Biological 

Preserve in California 

Species 

Richness and 

Diversity 

Field Addition: 70 kg N/ha/yr over three years led 

to decreased to a decline in total species richness, 

species diversity decreased by 5% and all three 

N-fixing forbs disappeared. 

Nine Annual Species: Avena 

Barbata, Bromus Hordeaceus, 

Lolium Multiflorum, Avena 

Fatua, Bromus Diandrus, 

Anagallis Arvensis, Geranium 

Dissectum Erodium Botrys, 

Vicia Sativa, and one Biennial 

Species, Crepis Vesicaria 

Zavaleta 

et al. (2003) 

Lund University, Lund, 

Southern Sweden 

Growth Greenhouse experiment: Sand-solution 

experiments studying how growth was effected by 

N concentrations of 50, 250 and 1250 μM in a 

simulated acid forest soil solution, similar to 

naturally occurring soil solutions is Southern 

Sweden. 46% of grasses displayed a significantly 

greater biomass at 250 than at 50 μM N as 

compared with only 7% for the herbs. Some 

species attained their highest biomass at 1250 μM 

N and others at 50 μM N. Grasses grew better 

than herbs in response to experimental addition of 

N. At the highest experimental N deposition rates, 

growth was limited for most species by the supply 

of nutrients other than N. 

15 Herb and 13 Grass Species FalkengrenGrerup 

(1998) 

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Region/Country Endpoint Observations Grassland Type/ Species Reference

Minnesota, Cedar Creek 

Natural History Area 

Species 

Composition 

Field Addition: N enrichment over a 12 year 

period on 162 plots using a N addition gradient 

from 0 to 30 g N/m2/yr. Plots initially dominated by 

native warm-season grasses shifted to lowdiversity mixes of species dominated by coolseason grasses at all but the lowest rates of N 

addition. Grasslands with high N retention and C 

storage rates were the most vulnerable to loss of 

species and major shifts in N cycling in response 

to experimental N enrichment. 

Three N-Limited Minnesota 

Grasslands with Varying 

Successional Age, Species 

Composition, and Total Soil C 

Wedin and 

Tilman 

(1996) 

San Francisco Bay Area, 

CA 

Species 

Composition 

Observational: N deposition levels of 10 to 15 kg 

N/ha/yr, exotic nitrophilous grasses have been 

reported to have displaced native grass species, 

likely due to greater N availability from deposition 

and from the cessation of grazing, which 

previously exported N out of the system 

Grasslands Fenn et al. 

(2003a) 

Jasper Ridge Biological 

Preserve in California 

Npp Field Addition: 70 kg N/ha/yr applied as 

(CaNO3)2 increased NPP by 30% 

Grasslands Shaw et al. 

(2003) 

Jasper Ridge Biological 

Preserve in California 

Herbivory, Leaf 

Tissue N And 

Growth Rates 

Field Addition: 70 kg N/ha/yr applied as 

(CaNO3)2 caused altered herbivory by gastropod 

which differed by species (↓, ↑ and no change), 5 

out of 6 species had increased leaf tissue, 5 out of 

6 species had increased growth rates 

Grasslands Cleland et al. 

(2006) 

Field Addition: 120 kg N/ha/yr applied as 

NH4NO3 pellets had a significant positive growth 

effect on annual dicot biomass but no significant 

growth effect on annual grass biomass. 

Michigan old field Biomass Grasslands Huberty 

et al. (1998) 

Vegetative and 

Seed Biomass 

and Decreased 

Root: Shoot 

Ratios 

Field Additions and Greenhouse experiment: N 

addition to common ragweed (Ambrosia 

artemisifolia) led to increased vegetative and seed 

biomass and decreased root: shoot ratios. N 

deposition may indirectly affect biomass 

production and allocation through affecting insect 

herbivory. The particularly strong influence of both 

herbivory and N deposition on A. artemisiifolia

reproduction suggests potential population and 

community-level consequences. 

Ambrosia Artemisiifolia

(Common Ragweed) and two of 

its Insect Herbivores; a Leaf 

Beetle, Ophraella Communa 

Lesage (Coleoptera: 

Chrysomelidae), and an Aphid, 

Uroleucon Tuataiae Olive 

(Hemiptera: Aphididae) 

Throop 

(2005) 

Brookhaven Nat. Lab., 

Long Island, New York 

and the Sciences 

Greenhouse Facility, 

SUNY, Stony Brook, NY 

Not all studies have shown an effect of N addition on species richness or diversity. In old 

agricultural fields in Michigan, increased N deposition changed neither the successional timing nor the 

gain or loss of species numbers (Huberty et al., 1998). A lack of response in species richness may have 

been due to application of mid-growing season fertilization in the experimental design. Huberty and 

colleagues (1998) suggested that N additions may change the dominance structure instead of the species 

composition, of these successional old-field communities. Other studies in Michigan on successional 

grasslands showed no response to N application of 10 kg N/m2

/yr, equivalent to about 2.5 times current 

deposition rates (Ambus and Robertson, 2006). 

Arid and Semi-Arid Land Ecosystems 

Some arid and semi-arid ecosystems in the southwestern U.S. are considered sensitive to N 

enrichment effects and receive high levels of atmospheric N deposition. However, water is generally more 

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limiting than N in these systems. Nevertheless, enhanced N may play a role in the observed invasion of 

some exotic plant species and associated changes in ecosystem function, especially where water supply is 

adequate. 

In semi-arid ecosystems, results from several N fertilization experiments showed increased 

biomass of nonnative plant species over native species; decreased soil moisture under some conditions; 

and increased fire risk where dense mats of grasses replaced shrub cover (See Table 3-18). 

Much of the arid land data are from the coastal sage scrub (CSS) communities of southern 

California, down-wind of the Los Angeles Basin, where dry N deposition is very high. The CSS 

community in California has been declining in land area and in shrub density over about the past 60 years 

and is being replaced in many areas by Mediterranean annual grasses (Padgett and Allen, 1999; Padgett 

et al., 1999). N deposition is considered a possible cause or contributor to this ecosystem alteration. More 

than 30 kg N/ha/yr of atmospheric N is deposited to portions of the Los Angeles Air Basin (Bytnerowicz 

and Fenn, 1996). The CSS community is of particular interest because about 200 sensitive plant species 

and several federally listed threatened or endangered animal species are found in the area. 

Native shrub and forb seedlings in the CSS community are unable to compete with dense stands of 

exotic grasses, and thus are gradually replaced by the grasses, especially following disturbances such as 

fire (Cione et al., 2002; Eliason and Allen, 1997; Yoshida and Allen, 2001). Biodiversity impacts have 

also been documented for microbial communities in coastal sage scrub ecosystems. It has been 

hypothesized that the decline in coastal sage shrub species could be linked to the decline of the arbuscular 

mycorrhizal community (Egerton-Warburton and Allen, 2000). 

Table 3-18. Summary of N effects on arid and semi-arid ecosystems. 

Region/Country Endpoint Observations Ecosystem Type/ 

species Reference 

Southeast Idaho Species composition and 

cover 

Field Addition: 6 or 12 kg N/ha/yr applied as NH4NO3 for 6 

years (in addition to ambient inputs of 1.3 to 1.4 kg N/ha/yr) 

resulted in a decrease in soil moisture caused by shifts in 

plants species composition and cover. However, there were 

no effects on perennial grass cover in response to experimental N additions 

Sagebrush (Artemisia 

tridentata) steppe ecosystem 

Inouye (2006) 

Southern California Plant community Observation: Dry N deposition is above 30 kg N/ha/yr in 

some places. Native shrub and forb seedlings in this plant 

community are unable to compete with dense stands of 

exotic grasses, and thus are gradually replaced by the 

grasses, especially following disturbances such as fire 

Coastal sage scrub Eliason and 

Allen, (1997); 

Yoshida and 

Allen (2001); 

Cione et al. 

(2002) 

Southern California Plant community Greenhouse and deposition gradient experiments: N 

deposition is considered a possible cause or contributor to 

declining shrub density over about the past 60 years and is 

being replaced in many areas by Mediterranean annual 

grasses 

Coastal sage scrub Allen et al. 

(1998); Padgett 

and Allen (1999); 

Padgett et al. 

(1999) 

 3-144
Region/Country Endpoint Observations Ecosystem Type/ 

species Reference 

Southern California Response of 

native and 

nonnative 

plants. 

Greenhouse Experiment: N (as 5.4 g/L NH4Cl or 10g/L 

KNO3) was added to obtain soil N concentrations of 2, 20, 

40, and 80 μg/g. The grasses demonstrated a 1.5– to 2.5–

fold growth increase when soil N levels increased from 20 to 

40 μg/g. To achieve a comparable growth increase, shrubs 

required higher soil N levels (between 20 and 80 μg/g). 

These lab experiments agree with observations in the field, 

where exotic grasses, especially once established, have 

replaced native shrubs under elevated N deposition. 

Coastal sage scrub Padgett and Allen 

(1999) 

Southern California Plant root-toshoot growth 

ratios 

Greenhouse Experiment: Changes in plant root-to-shoot 

growth ratios were observed in the plant community, which is 

composed largely of the drought-resistant deciduous shrubs 

Artemisia californica, Encelia farinosa, and Eriogonum fasciculatum. 

Coastal sage scrub Padgett and Allen 

(1999); Padgett 

et al. (1999) 

Southern California Response of 

native and 

nonnative 

plants. 

Observation: More than 30 kg N/ha/yr of atmospheric N is 

deposited to this ecosystem in portions of the Los Angeles 

Air Basin). Decreases in the diversity of native plants paralleled increases in exotic grass biomass. 

Coastal sage scrub Bytnerowicz and 

Fenn (1996) 

Colorado Plateau Response of 

native and 

nonnative 

plants. 

Field Addition: For 2 years, plots were treated with 0, 10, 

20, or 40 kg N/ha/yr as a KNO3 solution. Galleta (Hilaria 

jamesii) and Indian ricegrass (Oryzopis hymenoides) showed 

no increase in leaf photosynthesis or tiller size, but ricegrass 

showed a 50% increase in tiller density in the second year at 

the 20 and 40 kg N/ha/yr application levels. For both species, the increased N application hastened the onset of water 

stress. Unexpectedly, a non-native species, Russian thistle 

(Salsola iberica) showed a rapid growth response to the 

highest fertilization rate in the first summer, when rainfall was 

above average. The authors suggested that the timing and 

amount of N deposition could facilitate noxious weed invasion and thus change community composition. 

Arid grassland Schwinning et al. 

(2005) 

Joshua Tree National Park, California Response of 

native and 

nonnative 

plants. 

Deposition Gradient: 18 locations, chosen to cover the 

dominant vegetation types (Creosote Bush Scrub, Joshua 

Tree Woodland, Pinyon Juniper Woodland), were sampled 

for atmospheric concentrations of NO, NO2, NH3, and soil 

[N]. The relationship between reactive atmospheric N concentrations and soil N were consistent in most sites. 

Observations along the N gradient did not reveal a clear relationship between non-native grass cover and soil N 

concentration up to 20 μg/g. 

Arid grassland Allen et al. (2007)

Joshua Tree National Park, California Response of 

native and 

nonnative 

plants. 

Field Addition: Was applied at levels of 5 and 30 kg N/ha/yr 

at four sites over a 2 year period. Low-elevation sites were 

dominated by creosote bush scrub and higher-elevation sites 

by pinyon-juniper woodland. Non-native grass biomass increased significantly at three of four treatment sites that 

received 30 kg N/ha/yr, but not at the sites that received 

5 kg N/ha/yr. A soil N concentration of 23 μg/g was conservatively considered the low threshold for significant plant N 

response based on this fertilization study. 

Arid grassland Allen et al. (2007)

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Region/Country Endpoint Observations Ecosystem Type/ 

species Reference 

Observation: Vegetative changes stimulated by nutrient 

enrichment from N deposition may affect the frequency and 

severity of subsequent disturbance. Several lines of evidence suggest that N deposition may be contributing to 

greater fuel loads, thus altering the fire cycle in a variety of 

ecosystem types. Invasive grasses, which can be favored by 

high N deposition, promote a rapid fire cycle in many locations. 

Western U.S. Fire cycle Arid grassland Fenn et al. 

(2003a) 

San Francisco Bay 

area 

Response of 

native and 

nonnative 

plants 

Observation: N deposition levels of 10 to 15 kg N/ha/yr, 

exotic nitrophilous grasses have displaced native grass 

species, likely due to greater N availability from deposition 

and from the cessation of grazing, which previousy exported 

N out of the system. Since this change in species composition, populations of the rare and threatened bay checkerspot 

butterfly (Euphydryas editha bayensis) have declined greatly. 

It has been hypothesized that the response of the butterfly 

has been due to the vegetative changes. 

Grasslands dominated 

by exotic annuals such 

as wild oat (Avena 

fatua), brome (Bromus 

mollis), and ryegrass 

(Lolium multiflorum). 

Fenn et al. 

(2003a) 

Mycorrhizal and Microbial Diversity 

It has been hypothesized that the decline in coastal sage shrub species in California could be linked 

to the decline of the arbuscular mycorrhizal community (Egerton-Warburton and Allen, 2000). The authors discerned a shift in arbuscular mycorrhizal community composition with decreased species richness 

and diversity along a deposition gradient (2 to 57 μg N/g as soil NO3

−

). These shifts in mycorrhizal fungal 

communities may facilitate replacement of native plant communities by Mediterranean annual grasslands. 

Larger-spored fungal species (Scutellospora and Gigaspora) have decreased in number due to a failure to 

sporulate, with a concomitant proliferation of small-spored species. This pattern suggests selective pressure favoring the smaller spored species of fungi (Egerton-Warburton and Allen, 2000), and that N enrichment of the soil might alter the arbuscular mycorrhizal species composition and diversity. 

Desert Ecosystems 

Some desert ecosystems in the southwestern U.S. are considered sensitive to N enrichment effects 

and receive high levels of atmospheric N deposition. However, water is generally more limiting than N in 

these systems. Nevertheless, N deposition can stimulate plant growth and cause the observed invasion of 

some exotic plant species and associated changes in ecosystem function, especially where water supply is 

adequate. Most evidence is from field additions of N, levels ranging from 10–100 kg N/ha/yr (Table 3-

19). 

Fertilization experiments in the Mojave Desert showed that increased levels of N deposition could 

favor the establishment of nonnative species where the non-natives are already prevalent (Brooks, 2003). 

There is also evidence that N deposition decreases the growth of desert legumes (Baez et al., 2007). A link 

between N deposition and decrease in legumes has been found across other North American sites (Suding 

et al., 2005). The effect on legumes may be attributable because legumes, which are N fixers, often compete better under low N supply. 

There is evidence from the desert ecosystems that N accumulates during periods of drought, and 

that more N is immobilized during periods of high precipitation (Stursova et al., 2006). Thus, where water 

and N appear to be co-limiting factors, the observed pattern of higher rates of N deposition during months 

with higher precipitation may result in a stronger fertilization effect than if N deposition were independ-

 3-146
ent of precipitation (Hooper and Johnson, 1999; Asner et al., 2001; Knapp and Smith, 2001; McLain and 

Martens, 2006). 

Table 3-19. Summary of N effects on desert ecosystems. 

Region/Country Endpoint Observations 

Grassland 

type/ species 

Reference 

Chihuahuan Desert Growth 

response of 

native 

species 

Field Addition: 20 kg N/ha/yr addition in one season showed 

blue gramma (Bouteloua gracilis) was favored over black 

gramma (Bouteloua eriopoda), the current dominant species 

blue 

gramma and 

black 

gramma 

Baez et al. 

(2007) 

Jornada Basin, New 

Mexico 

Growth 

response of 

native 

species 

Field Addition: Black grama and another dominate species, 

creosote bush (Larrea tridentate) did not significantly increase 

biomass after experimental additions of 25 kg N/ha/yr, but did 

after additions of 100 kg N/ha/yr 

black grama 

and creosote bush 

Ettershank 

et al. (1978); 

Fisher et al., 

(1998) 

Chihuahuan Desert, Growth 

response of 

native 

species 

Field Addition: Additions of 100 kg N/ha/yr over about a 

decade, resulted in percent soil N that was 15–61% higher, 

extractable NO3– that was 25–175% higher, and extractable NH4

that was 247–1721% higher compared to control plots (Stursova 

et al., 2006). The resultant biologic effects were a 30% increase 

in cover of warm season grasses and a 52% reduction in cover 

of legumes (Baez et al., 2007). 

Grasses 

and 

legumes 

Stursova 

et al., (2006); 

Baez et al. 

(2007) 

Mojave Desert Native vs. 

non-native 

Field Addition: At application rates of 32 kg N/ha/yr over 2 

years, both density and biomass of non-native plants increased 

(54% increased biomass), while native species biomass 

declined by about 39%. Plant responses were influenced by 

rainfall events rather than by average annual rainfall, with the 

annual plants thriving in a year when high rainfall events 

triggered germination. 

Grasses Brooks 

(2003) 

Great Basin Desert, 

sites near Mono 

Lake, CA 

Growth and 

seed 

viability 

Field Addition: Sarcobatus vermiculatus, a desert shrub found 

demonstrated a twofold to threefold increase in stem growth, a 

2.5 to 4 fold increase in viable seed production, and a 17% to 

35% increase in leaf N with N additions. N was applied in March 

and November as NH4NO3, at a cumulative addition rate of 

233.6 g N per plant. 

Drenovsky 

and Richards 

(2005) 

Sarcobatus 

vermiculatu 

Field Addition: The shrub Larrea tridentata showed no 

increased growth response to N additions (at 10 and 40 kg 

N/ha/yr CaNO3) but did respond to increased water 

Mojave desert Growth 

response 

Barker et al. 

(2006) 

Larrea 

tridentata 

Observation: Invasive annuals showed a greater response to 

elevated N than native species, and have recently invaded. 

Though their invasion is correlated with greater N deposition, no 

causation has been established. 

Mojave and 

Sonoran deserts. 

Native vs. 

non-native 

Fenn et al. 

(2003a) 

Lichens 

Lichens are frequently used as indicators of air pollution and atmospheric deposition levels (see 

Annexes A and C for an additional discussion). In addition to being good subjects for biomonitoring, they 

 3-147
constitute important components of the forest ecosystem by contributing to biodiversity, regulating 

nutrient and hydrological cycles, and providing habitat elements for wildlife (McCune and Geiser, 1997). 

Little is known about the mechanisms that control growth and resource partitioning in lichens, 

which are complex symbiotic systems comprised of a fungus (mycobiont) and a green alga and/or 

cyanobacterium (photobionts) (Palmqvist, 2000; Sundberg et al., 2001). Organic compounds required for 

growth are produced via photobiont photosynthesis. Production is strongly coupled with N utilization. 

The non-photosynthetic fungus comprises much of the lichen biomass and requires N for protein 

synthesis, nucleic acids and fungal cell wall (chitin) synthesis (Palmqvist et al., 1998). Carbon and N 

uptake must be balanced for coordinated development of lichen thalli (Sundberg et al., 2001). 

Lichens can be classified on the basis of their response to atmospheric pollution. Nitrophytic 

lichens occur in areas that receive high atmospheric N deposition; acidophytic lichens are prevalent in 

areas that receive low N input (Rouss 1999)(Gaio-Oliveira et al., 2005; van Herk, 2001). Lichens differ 

with respect to N requirements. Many lichens that have a cyanobacterial photobiont are N-fixing, whereas 

those with a green algal photobiont are dependent on atmospheric deposition for their N supply. 

N-fixing lichen species are particularly affected by N deposition (Dahlman et al., 2002). 

Cyanobacteria have been shown to grow on either NO3

−

 or NH4

+

 sources when administered at non-toxic 

concentrations. More rapid growth was observed with NH4

+ fertilization as compared with NO3−

fertilization (Rückert and Giani, 2004). Ammonium is more easily assimilated; both NO3 and nitrite must 

first be reduced to NH4

+

 before assimilation (Rückert and Giani, 2004). 

Lichens with a green algal photobiont are solely dependent on atmospheric deposition as a source 

of N. However, a buildup of N within the thallus can lead to toxicity. Lichens exhibit varying degrees of 

sensitivity to increasing N deposition, owing to diverse mechanisms of responding to high N supply by 

reducing N uptake or assimilating N into non-toxic forms such as arginine (Dahlman et al., 2003; GaioOliveira et al., 2005). 

Lichens that contain a cyanobacterial photobiont appear to be more sensitive to adverse effects 

from atmospheric N deposition than most other lichens (Hallingback and Kellner, 1992; Hallingbäck, 

1991). In Sweden, the proportion of cyanobacterial lichens that has disappeared or is threatened is three 

times as large as the corresponding proportion of lichens having green algal photobionts (Hallingbäck, 

1991). Low pH may be the most important effect of air pollution on Peltigera aphthosa in Sweden. 

Nevertheless, there is some indication that NH4

+ in combination with SO42−

 is more detrimental than low 

pH per se (Hallingback and Kellner, 1992). The decline of lichens containing cyanobacteria in parts of 

northern Europe has been associated with N deposition in the range of 5 to 10 kg N/ha/yr (Bobbink et al., 

1998). In fact, epiphytic cyanobacteria-containing lichens may be among the most sensitive species in 

humid forested ecosystems to atmospheric N deposition (Bobbink et al., 1998; Hallingbaeck, 1991). 

Epiphytic macro lichens (those that grow attached to trees or other plants) exhibit different 

sensitivities to atmospheric pollutants, with some species being adversely impacted at air pollution levels 

that may not be considered high relative to other sensitive receptors. Particularly sensitive genera include 

Alectoria, Bryoria, Ramalina, Lobaria, Pseudocyphellaria, Nephroma, and Usnea (Blett et al., 2003; 

McCune and Geiser, 1997). 

Community composition of epiphytic lichens in the U.S. can be altered by relatively small 

increases in N deposition (Fenn, 2003a). Most epiphytic lichens meet their nutritional requirements from 

atmospheric deposition and can store N in excess of their nutritional needs (van Herk, 1999). Early work 

in the San Bernardino Mountains, CA indicated that lichen cover was inversely related to estimated 

oxidant doses (Sigal and Nash, 1983). In recent analysis it has been determined that up to 50% of lichen 

species that occurred in the region in the early 1900s have disappeared, with a disproportionate number of 

locally extinct species being epiphytic cyanolichens (Fenn et al., 2003a; Nash and Sigal, 1999). The 

calculated critical load for lichen communities in mixed conifer forests in California is 3.1 kg N/ha/yr 

(Fenn et al., 2008). 

The Pacific Northwest retains widespread populations of pollution-sensitive lichens (Fenn, 2003a). 

However, in urban areas, intensive agricultural zones, and downwind of major urban and industrial 

centers in the Pacific Northwest, there are few air pollution-sensitive lichen species, such as epiphytic 

 3-148
cyanolichens, and high N concentrations have been measured in lichen tissue (Fenn, 2003a). With N 

enrichment, especially around urban and agricultural areas, there is a shift towards weedy, nitrophilous 

lichen species (Fenn, 2003a). Replacement of sensitive lichens by nitrophilous species has undesirable 

ecological consequences. In late-successional, naturally N-limited forests of the Coast Range and western 

Cascade Mountains, for example, epiphytic cyanolichens make important contributions to mineral cycling 

and soil fertility (Antoine, 2001, 2004; Pike, 1978; Sollins et al., 1980), and together with other large, 

pollution-sensitive macrolichens, are an integral part of the food web for mammals, insects, and birds 

(McCune and Geiser, 1997). Sensitive lichen species appear to be negatively affected by N inputs as low 

as 3 to 8 kg N/ha/yr (Fenn, 2003a). (A summary of additional experiments on lichens is given in 

Table 3-20.) 

Table 3-20. Summary of N effects on lichens. 

Region/ 

Country Endpoint Observations 

Ecosystem 

Type / 

Species 

Reference 

Netherlands Species 

richness 

Deposition Gradient: Van Dobben, et al. (2001) recorded epiphytic lichen 

presence, tree bark chemical composition, and atmospheric 

concentrations of SO2, NO2 and NH3 at 123 sites along depositional 

gradients. Relationships between atmospheric and bark chemistry and the 

composition of the lichen vegetation were evaluated (ter Braak and Wiertz, 

1994). Results showed nearly all lichen species investigated were 

negatively affected by exposures to SO2 and NO2, collectively decreasing 

lichen species richness. Of somewhat less importance were the ecological 

factors such as bark pH, host tree species and tree diameter. 

Epiphytic 

lichens 

Van 

Dobben, 

et al. (2001) 

ter Braak 

and Wiertz 

(1994) 

Scotland and 

northern 

England 

Community 

composition 

Deposition Gradient: The authors suggested that the empirical critical 

load of N deposition for protection of community composition of lichens 

and bryophytes was in the range of 11 to 18 kg N/ha/yr 

Atlantic oak 

woods 

Mitchell 

et al. (2005) 

Umea, 

Vasterbotten, 

Sweden 

NH4+ vs. 

NO3 uptake 

rate 

Isotopic Tracer: In a study of 15N uptake in 14 lichen associations (for 

simplicity, designated as “species”), found that NH4

+ uptake was 

significantly greater, and to a higher extent passive, relative to amino acid 

or NO3

– sources of N. Differences were also observed in NO3– uptake, 

depending on photobiont group; cyanobacterial lichens had a lower NO3

–

uptake rate than green algal lichens. Morphology and microhabitat were 

not found to be associated with N uptake 

Cyanobacterial 

lichens and 

green algal 

lichens 

Dahlman 

et al. (2004) 

Sweden NH4

+ vs. 

NO3 uptake 

The assimilation and allocation of externally added N was investigated for 

two N-fixing tripartite (possessing both green algal and cyanobacterial 

phytobionts) lichen species, Peltigera aphthosa and Nephroma articum. N 

uptake ranged from 2 to 27 percent of the 5 kg N/ha/yr that was applied 

during the experiment over a 3 month period. Atmospheric deposition in 

this part of Sweden (~5 kg N/ha/yr was about one-fourth the total experimental N application rate. NH4

+ was absorbed to a greater extent than was 

NO3

–. In general, 15N levels of NH4+ treated thalli were about four times 

higher than for NO3

– treated thalli. To some extent, this may reflect the 

increased energy requirements of NO3

– reduction as compared with NH4+ 

assimilation (Raven et al., 1992) and/or the adsorption of positively 

charged NH4

+ on the negatively charged functional groups present on 

hyphal cell walls. 

Peltigera 

aphthosa and 

Nephroma 

articum

Dahlman 

et al. 

(2002), 

Sweden NH4

+ vs. NO3

uptake 

NH4

+to be the preferred N source for the green algal foliose lichen 

Plathismatia glauca, followed by glutamine and then NO3

–. This species 

responded to increased N availability by increasing growth rate and C 

assimilation capacity through increased investment in the photobiont cells 

Plathismatia 

glauca, a 

foliose lichen 

Palmqvist 

and 

Dahlman 

(2006) 

 3-149
Region/ 

Country Endpoint Observations 

Ecosystem 

Type / 

Species 

Reference 

Pampulha 

reservoir 

(Belo 

Horizonte, 

Brazil) 

NH4

+ vs. 

NO3 growth 

Cyanobacteria have been shown to grow on either NO3

– or NH4+ sources 

when administered at non-toxic concentrations. More rapid growth was 

observed with NH4

+ fertilization as compared with NO3– fertilization. 

Ammonium is more easily assimilated; both NO3 and nitrite must first be 

reduced to NH4

+ before assimilation 

Cyanobacteria von Rückert 

and Giani 

(2004) 

Sweden NH4

+ in 

combination 

with SO4

2–

Low pH may be the most important effect of air pollution on Peltigera 

aphthosa in Sweden. Nevertheless, there is some indication that NH4

+ in 

combination with SO4

2– is more detrimental than low pH per se 

(Hallingbäck and Kellner, 1992). 

Hallingbäck 

and Kellner 

(1992) 

Peltigera 

aphthosa 

Santo 

Antonio, 

near Serra 

de Aire 

Candeeiros 

Natural Park, 

central 

Portugal 

NH4

+ uptake 

Physiological 

responses 

Comparison of the physiological responses of two lichens to increased N 

supply. Uptake was quantified using 15N labeled NH4

+. Cholrophyll a and 

ergonsterol were used as indirect markers of algal and fugal activity, 

respectively. The acidophytic lichen Evernia prunastri showed greater N 

uptake from NH4

+ than the nitrophytic lichen Xanthoria parietina. In the 

acidophytic lichen, but not the nitrophytic lichen, ergosterol concentrations 

decreased with increasing N uptake, and an increase in the NH4

+ pool was 

also observed at the highest N doses (216 kg N/ha/yr, applied as nine 

applications over a 2-month period). These differences can partially 

explain the higher tolerance of X. parietina to high N deposition. 

Evernia 

prunastri and 

Xanthoria 

parietina

GaioOliveira 

et al. (2005) 

Alpine 

Herbaceous plants in alpine communities are considered very sensitive to changes in N deposition. 

A combination of short growing season, strong seasonal variation in moisture and temperature, shallow 

and poorly developed soils, steep terrain, sparse vegetation, and low rates of primary productivity 

generally limit the N uptake and retention capacity of herbaceous plant species in alpine ecosystems 

(Burns, 2004; Fisk et al., 1998). Alpine herbaceous plants are generally considered N-limited and changes 

in alpine plant productivity and species composition have been noted in response to increased N inputs 

(Bowman et al., 2006; Vitousek et al., 1997) (See Table 3-21). 

Research on N enrichment effects on alpine and subalpine ecosystems in the Western U.S. has 

mainly been limited to studies at the Loch Vale Watershed in Rocky Mountain National Park and the 

Niwot Ridge LTER site, both located east of the Continental Divide in Colorado (see review by Burns, 

2004). Changes in alpine plant species composition on Niwot Ridge have included increased cover of the 

plant species that tend to be most responsive to N fertilization in some of the long-term monitoring plots 

(Fenn, 2003a; Korb and Ranker, 2001). These changes are likely due to response to changes in N 

deposition. However, the influences of climatic change, particularly changes in precipitation (Williams 

et al., 1996b) and pocket gopher disturbance (Sherrod and Seastedt, 2001) could not be ruled out as 

contributors to vegetation change (Fenn, 2003a). Other environmental factors also affect the species 

make-up of alpine ecosystems, but long-term experimental fertilization plots demonstrate a clear response 

of alpine flora to N, including shifts toward graminoid plants that shade smaller flowering species, and 

accompanying changes in soil N cycling (Bowman et al., 2006). 

 3-150
Table 3-21. Summary of N effects on alpine ecosystems. 

Region/Country Endpoint Observations Ecosystem Type / 

Species Reference 

Niwot Ridge, CO 

and Southern 

Wyoming 

Community 

shift 

Field Addition: 25 kg N/ha/yr added during 

summer caused a community shift towards 

greater dominance of hairgrass (Deschampsia 

sp.) in wet alpine meadows, but the increase in 

plant biomass (+67%) and plant N content 

(+107%) following N fertilization was higher in 

graminoid-dominated dry meadows than in forbdominated wet meadows (+53% plant biomass, 

+64% standing N crop, respectively) 

Wet and dry alpine meadow, 

alpine tundra, talus, alpine 

and subalpine forest—

Englemann spruce, 

Bristlecone pine, surface 

waters, algae, amphibians 

Bowman et 

al. (1995); 

Burns (2004) 

Field Addition: Showed that 4 years of N 

addition to alpine vegetation at rates ranging 

between 100 and 200 kg N/ha/yr (depending on 

the year) caused marginal increases in alpine 

plant foliage productivity but reduced species 

richness. 

Niwot Ridge, CO Plant foliage 

productivity 

species 

richness. 

Wet and dry alpine tundrasedge Kobresia myosuroides. 

Acomastylis rossii, 

Polygonum viviparurn 

Trifolium. A.rossii and 

Deschumnpsia caespitosa. D. 

caespitosa, Caltha 

leptosepala, Sibbaldia 

procurnbens and Trifolium 

parryi.

Seastedt and 

Vaccaro 

(2001) 

Field Addition: Additions of 20, 40, and 60 kg 

N/ha/yr (on top of ambient N deposition near 5 

kg N/ha/yr) over an 8 year period to a dry alpine 

meadow led to a change in plant species 

composition, an increase in species diversity and 

plant biomass, and an increase in tissue N 

concentration at all treatment levels within 3 

years of application. Much of the response was 

due to increased cover and total biomass of 

sedges (Carex spp.). There was a significant 

decrease in Kobresia biomass with increasing N 

input. Vegetation composition appeared to 

respond at lower N input levels than those that 

caused measurable changes in soil inorganic N 

content. Changes in an individual species (Carex 

rupestris) were estimated to occur at deposition 

levels near 4 kg N/ha/yr. Changes in the plant 

community, based on the first axis of a detrended 

correspondence analysis, were estimated to 

occur at deposition levels near 10 kg N/ha/yr. In 

contrast, increases in NO3

− leaching, soil solution 

NO3

− concentration, and net nitrification occurred 

at levels above 20 kg N/ha/yr. The authors 

concluded that changes in vegetation 

composition preceded detectable changes in soil 

indicators of ecosystem response to N 

deposition. 

Niwot Ridge, CO Species 

composition 

species 

diversity plant 

biomass tissue 

[N] 

Dry alpine meadow Bowman 

et al. (2006) 

 3-151
Mycorrhizae across ecosystems 

Microbial biodiversity can also be affected by N enrichment. Interactions between atmospherically 

deposited N and terrestrial vegetation frequently occur in the rhizosphere. The rhizosphere includes the 

soil that surrounds and is influenced by plant roots (Wall and Moore, 1999). Relationships among plant 

roots, mycorrhizal fungi, and microbes are critical for N cycling and for the growth and health of plants. 

The plant provides shelter and C; the fungi and bacteria provide access to potentially limiting nutrients, 

particularly N and P. 

A meta-analysis of the effect of N and P fertilization on mycorrhize observed a 15% decrease in 

mycorrhizal abundance due to N fertilization across 16 studies at 31 sites, covering a range of grassland, 

shrubland, temperate and boreal forest ecosystems (Treseder, 2004). Declines in mycorrhizal abundance 

were slightly higher at higher rates of N fertilization, but there was significant variation across all studies. 

The loss of mycorrhizal function has been hypothesized as a key process contributing to reduced N uptake 

by vegetation and increased NO3

−

 mobility from soil into drainage water (U.S. EPA, 2004). 

NO3

− versus NH4+ deposition 

Plants also exhibit different degrees of response to NO3

−

 versus NH4

+

 deposition. In general, fastgrowing annual species, including many agricultural crops, and fast growing pioneer trees such as birch 

(Betula spp.) prefer NO3

−

 (Pearson and Stewart, 1993). Slow-growing perennial plant species generally 

prefer NH4

+

. There are also many plant species which readily utilize both NO3

−

 and NH4

+

 (Krupa, 2003). 

These include members of the family Ericaceae (e.g., Calluna, Erica, Vaccinium), conifer trees, and 

climax species such as Quercus and Fagus (Krupa, 2003). 

3.3.5.2. Transitional Ecosystems 

Wetlands in the U.S. support over 4200 native plant species, of which 121 are federally threatened 

or endangered (http://plants.usda.gov/). Wetlands can be divided into three general categories based on 

hydrology. Hydrologic pathways are often the same pathways of N input; therefore they are useful for 

discussing the N sources and sensitivity to atmospheric N deposition. Nearly all new N comes from 

atmospheric deposition in ombrotrophic bogs because they only receive water inputs via precipitation and 

they develop where precipitation exceeds evapotranspiration and where there is some impediment to 

drainage of the surplus water (Mitsch and Gosselink, 1986). Fens, marshes and swamps are characterized 

by ground and surface water inputs that are often on the same order of magnitude as precipitation 

(Koerselman et al., 1989). Lastly, intertidal wetlands receive water from precipitation, ground/surface 

water and marine/estuarine sources. 

The balance of competition among plant species in some sensitive wetland ecosystems can be 

altered by N addition, with resulting displacement of some species by others that can utilize the excess N 

more efficiently (U.S. EPA, 1993a). The sensitivity of wetlands is particularly important given that they 

contain a disproportionately high number of rare plant species that have evolved under N-limited 

condition (Moore et al., 1989) (See Annex C). In general these include the genus Isoetes sp., of which 

three species are federally endangered; insectivorous plants like the endangered green pitcher Sarracenia 

oreophila; and the genus Sphagnum, of which there are 15 species are listed as endangered by eastern 

U.S. states. Roundleaf sundew (Drosera rotundifolia) is also susceptible to elevated atmospheric N 

deposition (Redbo-Torstensson, 1994). This plant is native to, and broadly distributed across, the U.S. and 

is federally listed as endangered in Illinois and Iowa, threatened in Tennessee, and vulnerable in New 

York (http://plants.usda.gov/). 

Freshwater wetlands 

Peatlands and bogs are among the most vulnerable transitional ecosystems to adverse nutrientenrichment effects of N deposition (Krupa, 2003). The sensitivity of peatland Sphagnum species to 

 3-152
elevated atmospheric N deposition is well documented in Europe (Berendse et al., 2001; Tomassen et al., 

2004). Sphagnum squarrosum and S. fallax have been observed to be negatively affected by 

experimentally elevated atmospheric N and S inputs in Europe (Kooijman and Bakker, 1994). The genus 

Sphagnum dominates ombrotrophic bogs and some nutrient poor fens in the northern U.S. and Canada. 

These mosses efficiently capture atmospheric deposition with retention rates between 50–90%, much of 

the variation due to the depth of the water table (Aldous, 2002). Studies conducted on 4 species of 

Sphagnum in Maine (2 to 4 kg N/ha/yr ambient deposition) and New York (10 to 13 kg N/ha/yr ambient 

deposition) document that higher N deposition resulted in higher tissue N concentrations and greater NPP, 

but lower bulk density (Aldous, 2002). A study of Sphagnum fuscum in six Canadian peatlands showed a 

weak, although significant, negative correlation between NPP and N deposition when deposition levels 

were greater than 3 kg N/ha/yr (y = 150 – 3.4x, p=0.04, r2

=0.01) (Vitt et al., 2003). A study of 23 

ombrotrophic peatlands in Canada with deposition levels ranging from 2.7 to 8.1 kg N/ha/yr showed peat 

accumulation increases linearly with N deposition (y = 2.84(x) + 0.67, r2 = 0.32, p <0.001), however in 

recent years this rate has begun to slow indicating limited capacity for N to stimulate accumulation 

(Moore et al., 2004). 

The sensitivity of peatland Sphagnum species to elevated atmospheric N deposition is well 

documented in Europe (Berendse et al., 2001; Tomassen et al., 2004). Sphagnum squarrosum and S. fallax

have been observed to be negatively affected by experimentally elevated atmospheric N and S inputs in 

Europe (Kooijman and Bakker, 1994). Roundleaf sundew (Drosera rotundifolia) is also susceptible to 

elevated atmospheric N deposition (Redbo-Torstensson, 1994). 

Sarracenia purpurea is a long lived (30–50 years) northern pitcher plant and widely distributed in 

bogs, fens and swamps across Canada and the eastern U.S. (Ellison and Gotelli, 2002). S. purpurea has 

adapted to nutrient poor environments and very sensitive to increasing N input. In a study of S. purpurea

in Vermont and Massachusetts, Ellison and Gotelli (2002) conducted a series of N enrichment 

experiments by augmenting N availability to leaves with 0, 0.1 and 1 mg N/L NH4

+ solution for one 

growing season. Population growth rates, estimated by demographic survey, were positive for 0 and 0.1 

mg N/L additions (equal to atmospheric deposition of 0–1.4 kg N/ha/yr)1

 and negative for 1 mg N/ L 

additions (equivalent to 10–14 kg N/ha/yr)1 (Gotelli and Ellison, 2006). Based on the annual demographic 

rates, a non stationary matrix model forecasted that the extinction risk within the next 100 years increased 

substantially if N deposition rate increase (1–4.7%) from the rate of 4.5–6.8 kg N/ha/yr (Gotelli and 

Ellison, 2002). 

Increasing N availability not only reduced population growth of S. purpurea, also dramatically 

altered plant morphology. S. purpurea produces carnivorous leaves (pitcher) and photosynthesis efficient 

leaves (phyllodia). N enrichment was shown to stimulate the photosynthesis rate and increase the 

production of phyllodia relative to pitcher (Ellison and Gotelli, 2002). The field N deposition simulation 

experiment (ranged from 0–35 kg N/ha/yr)1

 revealed a positive linear relationship between N deposition 

level and relative keel size (keel width/total width). This correlation was supported by the field surveys of 

26 sites across Massachusetts and Vermont (Ellison and Gotelli, 2002), and 39 sites across Canada and 

eastern U.S. (Ellison and Gotelli, 2002). The relative kneel size of northern pitcher plant increased with 

increasing NH4

+

 concentration in soil water, and may be used as bioindicator (log [NH4

+

] = -1.57 + 1.78x 

relative keel size). 

In wet heathlands in Europe, changes in plant species composition have been attributed to elevated 

atmospheric N deposition (Roem and Berendse, 2000). Diverse plant communities have been replaced by 

monospecific stands Dutch wet heathlands (Aerts and Berendse, 1988; Houdijk et al., 1993). In other 

studies, wetland species such as Calluna vulgaris can successfully compete with grasses even at relatively 

 

1

 N treatments were selected to represent annual N deposition measured at the nearest monitoring sites of National Atmospheric Deposition 

Program (NADP). The unit of N treatments reported in the publication was precipitation-weighted mean concentrations (mg N/ L), from which 

we calculated the level of deposition (kg N/ha/yr) using the equation: Deposition= Precipitation-Weighted Mean Concentrations × Annual 

Precipitation. More detailed information on nitrogen deposition is available on the NADP website: http://nadp.sws.uiuc.edu/sites/ntnmap.asp? 

 3-153
high rates of N deposition, as long as the vegetative canopies are closed (Aerts et al., 1990). However, N 

deposition causes nutrient imbalances, and increased plant shoot-to-root ratio, and therefore increases in 

the sensitivity of shrubs to drought stress, frost stress, and attack by insect pests (Heil and Diemont, 

1983). These can result in gaps in the canopy of the shrub layer, which can then be readily invaded by 

grasses that are more efficient in using the additional N and therefore gain a competitive advantage 

(Krupa, 2003). 

Riparian wetlands 

Marler et al. (2001) evaluated the potential impacts of experimentally elevated stream water 

nutrient concentrations on three riparian wetland tree species: Fremont cottonwood (Populus fremontii), 

Goodding willow (Salix gooddingii), and exotic saltcedar (Tamarix ramosissima) in the riparian zone of 

the Salt River near Phoenix, AZ. The results from this 43−day experiment showed that growth of all three 

riparian plant species responded positively to increased nutrient supply (treatment 3 and 4) (Marler et al., 

2001). The exotic and invasive salt cedar showed the greatest increases in biomass at high nutrient supply. 

Other studies have also found that exotic plant species often respond more rapidly than native vegetation 

to increased nutrient supply (Milberg et al., 1999; Paschke et al., 2000). This experiment was conducted 

to simulate impacts of wastewater effluent on riparian zones, and N additions were therefore very large 

nutrient supply to riparian systems via atmospheric N deposition in the U.S. is more typically in the range 

of treatments in this experiment that showed minimal response to N addition. 

Table 3-22. Summarized responses of coastal marshes ecosystem to N fertilization. The table includes 

studies in which the lowest fertilization treatment is below 400 kg N/ha/yr, a value at the higher 

end of the range that includes direct and indirect N deposition. 

Site Species Responses N enrichment Reference 

Walden Creek 

(NC) 

Spartina 

alterniflora 

Field Addition: (1) increased the growth of short Spartina, but 

had no effect on tall Spartina; (3) biomass production of short 

Spartina increased linearly with N addition; and (3) ammonium 

showed higher growth stimulation on short Spartina than NO3

does 

0, 280, 560, 1120 

kg N/ha/yr 

Mendelssohn 

et al. (1979) 

Field Addition: (1) decreased the density and extent of S 

patens; (2) decreased the extent of tall S. alterniflora increased 

with 

Narragansett 

Bay (RI) 

N gradient from 9 

to 3282 kg 

N/ha/yr 

Wigand et al. 

(2003) 

Spartina 

patens; 

Spartina 

alterniflora 

Field Addition: increased live above ground biomass, leaf 

area coverage, evapotranspiration 

Great 

Sippewissett 

Marsh (MA) 

396 kg N/ha/yr Howes et al. 

(1986) 

Spartina 

alterniflora 

Intertidal wetlands 

Wetland eutrophication could significantly damage the structure and function of coastal marshes. 

N enrichments were shown to facilitate the invasion of nonnative species (Tyler et al., 2007); shift the 

competition between native species (Mendelssohn, 1979; Wigand et al., 2003; Crain 2007); increase 

herbivore damage on plants (Bertness et al., 2008); stimulate evapotranspiration (Howes et al., 1986); 

change microbial community and pore water chemistry (Caffrey et al., 2007); and alter carbon allocation 

 3-154
between root and shoot (Darby and Turner, 2008). Most N fertilization experiments add levels of N orders 

of magnitude above that expected by atmospheric deposition. A summary of experiments that include 

addition levels below 400 kg N/ha/yr is given in Table 3-22. 

3.3.5.3. Freshwater Aquatic Ecosystems 

Paleolimnological studies 

The paleolimnological method of taxonomic identification of fossil diatoms in lake sediments has 

been augmented in recent years with cell counts and pigment concentrations of Chl a and chlorophyll 

derivatives, rendering inferences about trophic state from proxies preserved in sediments more robust than 

before (Das et al., 2005). Paleolimnological studies of mountain lakes that have only been disturbed by 

atmospheric deposition and climate change have reported changes in diatom species assemblages, 

increases in cell numbers, and pigment-inferred increases in whole lake primary production. These 

inferred changes have been coincident with regional surrogates for increased N deposition. Such changes 

have included increases in human population, industrial animal production, and fossil fuel combustion 

emissions (Das et al., 2005; Saros et al., 2003; Wolfe et al., 2001a, 2003). In most, but not all, of these 

studies, the observed changes in ecology were inconsistent with changes in climate and more concordant 

with effects from increased atmospheric N deposition. 

Available data suggest that the increases in total N deposition do not have to be large to elicit an 

ecological effect. For example, a hindcasting exercise determined that the change in Rocky Mountain 

National Park lake algae that occurred between 1850 and 1964 was associated with an increase in wet N 

deposition that was only about 1.5 kg N/ha (Baron, 2006). Similar changes inferred from lake sediment 

cores of the Beartooth Mountains of Wyoming also occurred at about 1.5 kg N/ha deposition (Saros et al., 

2003). Pre-industrial inorganic N deposition is estimated to have been only 0.1 to 0.7 kg N/ha based on 

measurements from remote parts of the world (Galloway et al., 1995; Holland et al., 1999). In the western 

U.S., pre-industrial, or background, inorganic N deposition was estimated by (Holland et al., 1999) to 

range from 0.4 to 0.7 kg N/ha/yr. 

Bioassay, mesocosm, and laboratory experiment 

Bioassay, mesocosm, and laboratory experiments have been conducted on algae (both 

phytoplankton and periphyton), invertebrates, amphibians, and fish, to determine effects of N on sensitive 

aquatic organisms (see Annex C). Some freshwater algae are particularly sensitive to the effects of added 

nutrient N and experience shifts in community composition and biodiversity with increased N deposition. 

For example, two species of diatom, Asterionella formosa and Fragilaria crotonensis, now dominate the 

flora of at least several alpine and montane Rocky Mountain lakes and sharp increases have occurred in 

Lake Tahoe (Baron et al., 2000; Interlandi and Kilham, 1998; Saros et al., 2003, 2005; Wolfe et al., 2001 

and 2003). The timing of this shift has varied, with changes beginning in the 1950s in the southern Rocky 

Mountains and in the 1970s or later in the central Rocky Mountains (Figure 3-50). These species are 

opportunistic algae that have been observed to respond rapidly to disturbance and slight nutrient 

enrichment in many parts of the world (See Annex C for additional discussion). 

Further evidence for the relationship between N enrichment and algal changes has been provided 

by N addition studies that include in situ mesocosm studies (Lafrancois et al., 2004; McKnight et al., 

1990; Saros et al., 2005) and in situ incubations in large lakes (Interlandi and Kilham, 1998). Differences 

in resource requirements allow some species to gain competitive advantage over others upon nutrient 

addition, causing changes in species composition (Lafrancois et al., 2004; Saros et al., 2005; Wolfe et al., 

2003). A summary of these experiments is given in Table 3-23. This is in keeping with findings of 

Interlandi and Kilham (2001), who demonstrated that maximum species diversity was maintained when 

N levels were low (<3 μM) in lakes in the Yellowstone National Park region. 

 3-155
The implication of this research is that species diversity declines with increasing availability of N. 

In studies of lake sediment diatom remains, typical oligotrophic species such as Aulacoseria perglabra, 

Cyclotella steligera, and Achnanthes spp. declined coincident with the rise in dominance of A. formosa

and F. crotonensis (Wolfe et al., 2001b, 2003). 

Source : Saros (2003). Reprinted with permission. 

Figure 3-50. Diatom assemblage sediment patterns in Emerald Lake, WY. 

Community shifts in phytoplankton other than diatoms have also been observed under conditions 

of elevated N availability (Lafrancois et al., 2004). For example, a positive correlation between the 

proportion of the phytoplankton comprised of chrysophytes and the concentration of NO3

−

 in lake water 

was found in a survey of 15 Snowy Range lakes (Lafrancois et al., 2003). Chlorophytes, like the two 

diatom species identified above, generally prefer high concentrations of N and are able to rapidly 

dominate the flora when N concentrations increase (Findlay et al., 1999). This occurs in both 

circumneutral and acidified waters (Findlay et al., 1999; Wilcox and Decosta, 1982). 

In summary, survey data and fertilization experiments demonstrate that increase in algal 

productivity, as well as species changes and reductions in biodiversity, have occurred at sensitive high 

elevation lakes in the western U.S. in response to increased availability of N. 

 3-156
Table 3-23. N effects on algal species composition and biodiversity 

Region Endpoint Observation Ecosystem 

Type Reference 

Snowy Range, 

Wyoming 

Community shifts 

in phytoplankton 

Mesocosm Experiment: correlation between the proportion of the 

phytoplankton comprised of chrysophytes and the concentration of 

NO3

− in lake water was found in a survey of 15 lakes. Chrysophytes 

were favored in lakes having lower N and cyanophytes and 

chlorophytes favored in lakes having higher N 

Lakes Lafrancois 

et al. (2003) 

Beartooth 

Mountains of 

MontanaWyoming 

Diatom 

community 

Paleo and Observation: evaluation of resource requirements for 

dominant diatom species with paleolimnological reconstructions 

and contemporary surveys of the flora of seven lakes. Results 

reinforced the likelihood that recent increases in dominant diatom 

numbers have been the result of N enrichment rather than climatic 

change 

Lakes Saros et al. 

(2005) 

Paleo: sediment cores showed increasing representation of 

mesotrophic diatoms in recent times, as compared with predevelopment conditions 

Colorado Front 

Range 

Community shifts 

in phytoplankton 

Lakes Wolfe et al., 

(2001b) 

Paleo: there has been a sharp increase in the ratio of araphidinate 

pennate to centric diatoms since about 1950 (largely due increases 

in Fragilaria crotenensis), associated with increased N loading to 

the lake. Jassby et al. (1994) showed that atmospheric deposition 

supplies most of the N to Lake Tahoe. 

Lake Tahoe, 

CA 

Community shifts 

in phytoplankton 

Lakes Goldman 

(1988) 

3.3.5.4. Estuarine and Marine Ecosystems 

In coastal ecosystems, eutrophication can cause changes in marine biodiversity and species 

composition. Phytoplankton production and community composition in estuarine and marine 

environments also respond to differences in the form of atmospheric N input. Major algal functional 

groups, including diatoms, dinoflagellates, cyanobacteria, and chlorophytes, may show different 

responses to changing mixtures of added N (Paerl et al., 2002). 

Phytoplankton 

In addition to causing increased phytoplankton biomass, as indicated by Chl a measurements (see 

Section 3.3.3.2), excess N can contribute to changes in phytoplankton species composition. High loadings 

of N and P can also increase the potential for Si limitation, with associated changes in diatoms. Such 

changes to the phytoplankton community can also affect higher trophic levels. For example, Officer and 

Ryther (1980) and Turner et al. (1998) suggested that a shift in the Si-to-N atomic ratio to less than 1 

would alter the marine food web. Specifically, the diatom-to-zooplankton-to-higher tropic level ratios 

would decrease, whereas flagellated algae (including those that often contribute to hypoxia) would 

increase (Paerl et al., 2001a). 

Changes in phytoplankton species abundances and diversity have been further documented through 

in situ bioassay experiments such as the results reported by Paerl et al. (2003) for the Neuse River Estuary 

in North Carolina. Effects were species-specific and varied dramatically depending on whether, and in 

what form, N was added. The findings illustrate the potential impacts of N additions on phytoplankton 

community structure (see Figure 3-51). 

 3-157
Changing phytoplankton community composition has numerous potential ecological ramifications, 

including modifications to the ecosystem food web and nutrient dynamics. For example, if the nutrient 

mix favors species that are not readily grazed (e.g., cyanobacteria , dinoflagellates), trophic transfer will 

be poor and relatively large amounts of unconsumed algal biomass will settle to the bottom, which could 

stimulate decomposition, O2 consumption, and the potential for hypoxia (Paerl et al., 2003). 

Reduced vs. Oxidized N 

The form of N input to coastal aquatic ecosystems has an important influence on its effects. 

Atmospheric deposition of reduced N has increased relative to oxidized N in the eastern U.S., and this 

trend is expected to continue in the future under existing emissions controls. Such patterns can influence 

marine eutrophication responses. Some studies suggest that large diatoms tend to dominate coastal waters 

when NO3

−

 is supplied (Paerl et al., 2001a; Stolte et al., 1994), whereas smaller diatom species have a 

greater preference for NH4

+

 uptake. Thus, ongoing trends of decreasing NO3

−

 deposition and increasing 

NH4

+

 deposition might lead to changes in species distributions and size distributions of phytoplankton, 

with cascading effects on trophic structure and biogeochemical cycling (Paerl et al., 2001a). 

Not all studies have found variation in algal response with the form of N applied. For example, 

Richardson et al. (2001) examined the effects of different forms of N application (NO3

−

, NH4

+

, urea) on 

the structure and function of estuarine phytoplankton communities in mesocosm experiments in the Neuse 

River Estuary, NC. Even though NH4

+

 is more readily taken up by phytoplankton in this estuary than is 

NO3

−

 (Twomey et al., 2005), the results of the Richardson et al. (2001) study suggested that 

phytoplankton community structure was determined more by the hydrodynamics of the system than by 

the form of N available for growth. 

Twomey et al. (2005) measured Neuse River Estuary phytoplankton uptake rates of NH4

+

, NO3

−

, 

and urea. Ammonium was the dominant form of N taken up, contributing about half of the total N uptake 

throughout the estuary. Uptake varied spatially; in particular NO3

−

 uptake declined from 33% of the total 

uptake in the upper estuary to 11% and 16%, respectively in the middle and lower estuary. Urea uptake 

contributed least to the total in the upper estuary (16%) but comprised 45 and 37% of the total N uptake in 

the middle and lower estuary. Therefore, N budgets based only on inorganic forms may seriously 

underestimate the total phytoplankton uptake (Twomey et al., 2005). 

Submerged Aquatic Vegetation (SAV) 

SAV provides important nursery grounds to many estuarine fish. There are few data documenting 

the long-term response of SAV in coastal ecosystems to N loading. The national assessment (Bricker 

et al., 2007) suggested that only a small fraction of the estuary systems evaluated reported high severity of 

SAV loss. Most of those that did report moderate or high loss were located in the Mid-Atlantic region. 

However, where SAV loss is a problem, the results can be severe, and there is evidence suggesting a 

correlation with increases in N loading. For example, at Waquoit Bay, Massachusetts, Valiela et al. (1990) 

reported a strong negative relationship between modeled N loading and measured eelgrass area based on 

measurements of eelgrass coverage from 1951 to 1992. 

 3-158
Source: Paerl et al. (2003) 

Figure 3-51. Microscopic counts of phytoplankton species composition in the Neuse River Estuary, NC 

following 36-h in situ bioassays to manipulate available forms of N. Treatments included a 

control (unamended estuarine water sample), all nutrients (N, P, vitamins, trace metals, and 

Si), all with urea as the N form, all with ammonium (NH4

+) as the N form, all with NO3− as the N 

form, and all with no N. Bars represent the mean density of cells present (three replicate 

counts for each treatment). 

3.3.5.5. Summary of N Effects on Species Composition, Species Richness and 

Biodiversity 

Terrestrial 

The evidence is sufficient to infer a causal relationship between N deposition and the alteration of 

species richness, species composition and biodiversity in terrestrial ecosystems. The ecological effects of 

N deposition were described for a variety of taxa and ecosystem types including: forests, grasslands, arid 

and semi-arid, deserts, lichens, alpine, and mycorrhizae. Among the most sensitive terrestrial taxa are 

lichens. Empirical evidence indicates that lichens in the U.S. are adversely affected by deposition levels 

as low as 3/ha/yr. Among the most sensitive ecosystems are Alpine ecosystems; alteration of plant cover 

of an individual species (Carex rupestris) in Alpine communities were estimated to occur at deposition 

levels near 4 kg N/ha/yr and modeling indicates that deposition levels near 10 kg N/ha/yr alter plant 

community assemblages. 

Wetland 

The evidence is sufficient to infer a causal relationship between Nr deposition and the alteration of 

species richness, species composition and biodiversity in wetland ecosystems. The effect of N deposition 

on wetland ecosystems depends on the fraction of rainfall in its total water budget. The sensitivity to N 

deposition was suggested as bogs >fens >intertidal wetlands (Morris, 1991). 

 3-159
Excess N deposition can cause shifts in wetland community composition by altering competitive 

relationships among species, which potentially leads to effects such as decreasing biodiversity, increasing 

non-native species establishment and increasing the risk of extinction for sensitive and rare species. 

Wetlands contain a high number of rare plant species (Bedford and Godwin, 2003; Moore et al., 1989; 

U.S. EPA,1993a). High levels of atmospheric N deposition increase the risk of decline and extinction of 

these species that are adapted to low N conditions. In general these include the genus Isoetes sp., of which 

three species are federally endangered; insectivorous plants like the endangered green pitcher Sarracenia 

oreophila; and the genus Sphagnum, of which there are 15 species are listed as endangered by eastern 

U.S. states. Roundleaf sundew (Drosera rotundifolia) is also susceptible to elevated atmospheric 

N deposition (Redbo-Torstensson, 1994). This plant is native to, and broadly distributed across, the U.S. 

and is federally listed as endangered in Illinois and Iowa, threatened in Tennessee, and vulnerable in New 

York (http://plants.usda.gov/). In the U.S., Sarracenia purpurea can be used as a biological indicator of 

local N deposition in some locations (Ellison and Gotelli, 2002). 

Freshwater Aquatic 

The evidence is sufficient to infer a causal relationship between Nr deposition and the alteration of 

species richness, species composition and biodiversity in freshwater aquatic ecosystems. Evidence from 

multiple lines of research and experimental approaches support this observation, including 

paleolimnological reconstructions, bioassays, mesocosm and laboratory experiments. Increased N 

deposition can cause a shift in community composition and reduce algal biodiversity. Elevated N 

deposition results in changes in algal species composition, especially in sensitive oligotrophic lakes. 

In the West, a hindcasting exercise determined that the change in Rocky Mountain National Park 

lake algae that occurred between 1850 and 1964 was associated with an increase in wet N deposition that 

was only about 1.5 kg N/ha (Baron, 2006). Similar changes inferred from lake sediment cores of the 

Beartooth Mountains of Wyoming also occurred at about 1.5 kg N/ha deposition (Saros et al., 2003). 

Some freshwater algae are particularly sensitive to added nutrient N and experience shifts in 

community composition and biodiversity with increased N deposition. For example, two species of 

diatom (a group of algae), Asterionella formosa and Fragilaria crotonensis, now dominate the flora of at 

least several alpine and montane Rocky Mountain lakes. Sharp increases have occurred in Lake Tahoe 

(Baron et al., 2000; Interlandi and Kilham, 1998; Saros et al., 2003; Saros et al., 2005; Wolfe et al., 

2001b; Wolfe et al., 2003). The timing of this shift has varied, with changes beginning in the 1950s in the 

southern Rocky Mountains and in the 1970s or later in the central Rocky Mountains. These species are 

opportunistic algae that have been observed to respond rapidly to disturbance and slight nutrient 

enrichment in many parts of the world. 

Estuarine Aquatic 

The evidence is sufficient to infer a causal relationship between N deposition and the alteration of 

species richness, species composition and biodiversity in estuarine ecosystems. Increased N deposition 

can cause shifts in community composition, reduced hypolimnetic DO, reduced biodiversity, and 

mortality of submerged aquatic vegetation. The form of deposited N can significantly affect 

phytoplankton community composition in estuarine and marine environments. Small diatoms are more 

efficient in using NO3

−

 than NH4

+

. Increasing NH4

+

 deposition relative to NO3

−

 in the eastern U.S. favors 

small diatoms at the expense of large diatoms. This alters the foundation of the food web. Submerged 

aquatic vegetation is important to the quality of estuarine ecosystem habitats because it provides habitat 

for a variety of aquatic organisms, absorbs excess nutrients, and traps sediments. Nutrient enrichment is 

the major driving factor contributing to declines in submerged aquatic vegetation coverage. The MidAtlantic region is the most heavily impacted area in terms of moderate or high loss of submerged aquatic 

vegetation due to eutrophication. Indicators to assess the eutrophic condition of estuarine and coastal 

waters are given in Figure 3-34. 

 3-160
3.3.6. N Deposition Effects on NO3

− Toxicity 

NO3

−

 in freshwater at extremely high concentrations can have direct adverse effects on many life 

stages of fish, as well as on invertebrates and amphibians. These effects occur at levels that are typically 

more than 30 times higher than those that would commonly be attributable to atmospheric deposition, and 

therefore NO3

−

 concentration has not been defined as a primary biological indicator. These effects are 

described in Annex C. 

3.3.7. Critical Loads and Other Quantified Relationships between 

Deposition Levels and Ecological Effects 

This section highlights a variety of sensitive chemical and biological receptors that have been used 

in developing critical loads for nutrient effects of N deposition on natural ecosystems. Sensitive receptors 

for effects of excess nutrient N deposition on surface water could include water chemistry, productivity, 

and the response of important taxa. Key sensitive receptors for assessing effects on soil include soil 

chemistry and soil solution chemistry. Sensitive receptors for flora include macro-lichens and vascular 

plant species that are adapted to nutrient-poor environments. In some cases, the chemical receptors may 

be easier to characterize, although they likely also reflect important biological changes that may be more 

difficult to document. Background information on critical loads is presented in Annex C. Empirical 

models of critical loads for nutrient-N have been in use in Europe for some time (UNECE, 2004). Efforts 

have begun to develop empirical relationships in the U.S. 

3.3.7.1. Empirical Critical Loads for Europe 

Within the United Nations Economic Commission for Europe (UNECE) Long Range 

Transboundary Air Pollution (LRTAP) convention, empirical procedures have been developed to set 

critical loads for atmospheric N deposition to protect against effects caused by nutrient enrichment. 

Empirical critical loads of N deposition for natural and semi-natural terrestrial and wetland ecosystems 

were first presented in a background document for the 1992 LRTAP workshop on critical loads held at 

Lökeberg, Sweden (Bobbink et al., 1992b). A number of European expert workshops have taken place to 

reach agreement among specialists regarding the impacts of N deposition on various ecosystems and 

related critical loads (Achermann and Bobbink, 2003; Bobbink et al., 1992b; Bobbink et al., 1996; 

Hornung et al., 1995; Nilsson and Grennfelt, 1988). 

Information from the period 1996–2002 on the effects of increased N deposition on the structure 

and function of natural and semi-natural ecosystems in Europe was evaluated in Bobbink et al. (2003). 

The updated N critical loads were discussed and approved by full consensus at the November 2002 expert 

meeting held under the LRTAP Convention in Berne, Switzerland, (Achermann and Bobbink, 2003). 

Values for areas with low N deposition were updated by a CLRTAP workshop on critical loads of N in 

low-deposition areas (Stockholm, Sweden, March 2007) and adopted by ICP M&M and WGE in 2007. 

The resulting values are given in Table 3-24. 

 3-161
Table 3-24 Biological indicators for the effects of elevated N deposition and related empirical critical 

loads for major ecosystem types (according to the eunis classification) occurring in Europe. 

Ecosystem Type Biological Effect Indicators Empirical Critical Load 

(kg N/ha/yr) 

GRASSLANDS AND TALL FORB HABITATS (E) 

Sub-Atlantic semi-dry calcareous 

grassland 

Increased mineralization, nitrification and N leaching; increased tall 

grasses; decreased diversity 

15–25 

Non-Mediterranean dry acid and 

neutral closed grassland 

Increase in nitrophilous graminoids, decline of typical species 10–20 

Inland dune grasslands Decrease in lichens, increase in biomass, accelerated succession 10–20 

Low and medium elevation hay 

meadows 

Increased tall grasses, decreased diversity 20–30 

Mountain hay meadows Increase in nitrophilous graminoids, changes in diversity 10–20 

Moist and wet oligotrophic 

grasslands 

Increase in tall graminoids, decreased diversity, decrease in 

bryophytes 

10–25 

Alpine and subalpine meadows Increase in nitrophilous graminoids, changes in diversity 10–15 

Moss and lichen dominated 

mountain summits 

Effects on bryophytes and lichens 5–10 

HEATHLAND HABITATS (F) 

Northern wet heaths Decreased heather dominance, transition heather to grass, decline in 

lichens and mosses 

10–20 

Dry heaths Transition from heather to grass, decline in lichens 10–20 

Arctic, alpine, and subalpine scrub 

habitats 

Decline in lichens, mosses, and evergreen shrubs 5–15 

COASTAL HABITAT (B) 

Shifting coastal dunes Increased biomass, increased N leaching 10–20 

Coastal stable dune grasslands Increase in tall grasses, decreased prostrate plants, increased N 

leaching 

10–20 

Coastal dune heaths Increase in plant production, increased N leaching, accelerated 

succession 

10–20 

Moist to wet dune slacks Increase in biomass and tall graminoids 10–25 

MIRE, BOG, AND FEN HABITATS (D) 

Raised and blanket bogs Changed species composition, N saturation of Spagnum 5–10 

Poor fens Increased sedges and vascular plant, negative effects on mosses 10–20 

Rich fens Increase in tall graminoids, decreased diversity, decrease of 

characteristic mosses 

15–35 

Mountain rich fens Increase in vascular plants, decrease in bryophytes 15–25 

 3-162
Empirical Critical Load 

(kg N/ha/yr) Ecosystem Type Biological Effect Indicators 

FOREST HABITATS (G) 

Mycorrhizae Reduced sporocarp production, reduced below ground species 10–20 

composition 

Ground vegetation Changed species composition, increased nitrophilous species; 

increased susceptibility to parasites (insects, fungi, virus) 

10–15 

Lichens and algae Increase in algae; decrease in lichens 10–15 

Source: Achermann and Bobbink (2003). Reprinted with permission. 

3.3.7.2. Empirical Critical Loads for U.S. 

Efforts have begun to develop empirical relationships in the U.S., particularly for western 

ecosystems, however there is currently no published assessment for the U.S or the continent of North 

America. Table 4-4 summarizes publications of deposition levels and related ecological effects, presenting 

critical loads when reported in the original publication. Table 4-4 includes N levels at which effects are 

manifested in terrestrial and freshwater ecosystems that have been documented through N addition and 

deposition gradient studies. Several important studies from Europe published after the assessment by 

Bobbink et al. (2003) are included in addition to some publications from Asia. Dose-response 

relationships between N and ecological indicators are given in Table 3-25. 

In terrestrial ecosystems, the reported effect levels range from 4 to 5 kg N/ha/yr for changes in the 

abundance of individual sensitive alpine plant species, to 20 kg N/ha/yr for community level changes in 

alpine plant communities. Clark and Tilman (2008) calculate the CL for the onset of reduced relative 

species number in grasslands to be 5.3 kg N/ha/yr with a 95% inverse prediction interval of 1.3–

9.8 kg N/ha/yr. A critical load of 3.1 kg N/ha/yr is considered protective of lichen communities in the 

West (Fenn et al., 2008). 

Differences in the levels at which increased nitrification and NO3

−

 leaching have been observed in 

eastern and western watersheds. For example, Rueth (2002) observed increased rates of nitrification in 

old-growth forests in Colorado at approximately 5 kg N/ha/yr, whereas Aber et al. (2003) associated the 

onset of NO3

−

 leaching in eastern forests with deposition levels of 7 to 10 kg N/ha/yr. The critical load for 

NO3

−

 leaching in western chapparal ecosystems is 17 kg N/ha/yr (Fenn et al., 2008). 

There is evidence that freshwater wetlands in the U.S. and Canada that are dominated by 

Sphagnum sp. are affected by N deposition. Most evidence documents N retention, peat accumulation and 

changes in NPP, and is not sufficient to quantify a critical load. The suggested critical load for protecting 

the population health of northern pitcher plant is 10–14 kg N/ha/yr (Gotelli and Ellison 2006). There are 

no publications suggesting critical loads for coastal wetland ecosystems. 

 3-163
Table 3-25. Summary of dose-response curves for N deposition and ecological indicators. 

Ecosystems 

Deposition 

Range 

(kg N/ha/yr) 

Effect Indicator Study Region Response Curve Reference 

N DEPOSITION 

Forest 1–75 Nutrient 

enrichment 

N leaching 65 forest sites 

across Europe 

Y = 0.48(X) − 2.17 

(r2 = 0.69; p <0.001) 

Y: N leaching (kg N/ha/yr) 

X: total inorganic N deposition 

(kg N/ha/yr) 

Dise and 

Wright (1995); 

Lawrence 

(1995, 2007) 

Forest 2–8 

*wet deposition only 

Acidification/ 

Nutrient 

enrichment 

%N in the Oa horizon 12 red spruce 

stands across 

the northeastern 

U.S. 

Y = 0.097(X) + 1.03 (r2 = 0.63) 

Y: N content in the 

Oa horizon (%) 

X: wet inorganic N deposition 

(kg N/ha/yr) 

Driscoll et al. 

(2001) 

Forest 0–50 

*throughfall N 

Acidification/ 

Nutrient 

enrichment 

Soil solution NO3

− 104 European 

monitoring site 

Conifers: Y = 0.06e0.132(X)

(r2 = 0.59, p = 0.0001) 

Deciduous: Y = 0.018e0.266(X)

(r2 = 0.65, p = 0.0001) 

Y: NO3

− concentration in soil 

water (averages Jan 1996 to 

Jan. 1998) (mg N/L) 

X: throughfall N deposition 

(averages 1993–1997) 

(kg N/ha/yr) 

Gundersen et 

al. (2006) 

Forest 0–40 

*throughfall N 

Acidification/ 

Nutrient 

enrichment 

Soil solution NO3

− 104 European 

monitoring site 

Conifers: log(Y) = 0.06(X) –

 1.2 (r2 = 0.59, p = 0.0001) 

Broadleaves: 

log (Y) = 0.12(X) - 1.8 

(r2 = 0.65, p=0.0001) 

Y: NO3

− concentration in soil 

water (averages Dec 1995 to 

Feb. 1998) (mg N/L) 

X: Throughfall N deposition 

(averages 1993 – 1997) 

(kg N/ha/yr) 

Kristensen et 

al. (2004) 

Forest 0–25 

*bulk precipitation N 

Acidification/ 

Nutrient 

enrichment 

Soil solution NO3

− 104 European 

monitoring site 

Conifers: log(Y) = 0.09(X) – 

1.3 (r2 = 0.32, p<0.0001) 

Broadleaves: 

log(Y) = 0.09(X) - 1.18 

(r2 = 0.16, p=0.02) 

Y: NO3

− concentration in soil 

water (averages Dec 1995 to 

Feb. 1998) (mg N/L) 

X: Bulk 

precipitation N deposition 

(averages 1993 – 1997) 

(kg N/ha/yr) 

Kristensen et 

al. (2006) 

 3-164
Ecosystems 

Deposition 

Range 

(kg N/ha/yr) 

Effect Indicator Study Region Response Curve Reference 

Forest Relation between 

throughfall and bulk 

precipitation N input 

104 European 

monitoring site 

Conifers: Y = 1.8(X) – 2.3 

(r2=0.65, p <0.0001) 

Broadleaves: Y=0.86(X) – 4.9 

(r2 = 0.32, p = 0.0008) 

Y: Throughfall N deposition 

(averages 1993–1997) 

(kg N/ha/yr) 

X: Bulk precipitation N 

deposition (averages 1993–

1997) (kg N/ha/yr) 

Kristensen et 

al. (2006) 

Forest 0–40 

*throughfall N 

Acidification/ 

Nutrient 

enrichment 

Foliage N 104 European 

monitoring site 

Conifers: Y = 0.14(X) + 12.7 

(r2 = 0.40 , p <0.0001) 

Y: Foliage N concentration 

(mg/g) 

X: Throughfall N deposition 

(averages 1993–1997) 

(kg N/ha/yr) 

Kristensen et 

al. (2006) 

Forest 0–40 

*throughfall N 

Acidification/ 

Nutrient 

enrichment 

C:N organic layer 104 European 

monitoring site 

Conifers: Y = -0.21(X) + 31.5 

(r2 = 0.19 ; p = 0.0002) 

Broadleaves: Y = -0.48(X) + 

32.3 (r2 = 0.14; p = 0.036) 

Y: C:N organic layer 

X: Throughfall N deposition 

(averages 1993 – 1997) 

(kg N/ha/yr) 

Kristensen et 

al. (2006) 

Spruce forest 5 to 30 kg N/ha/yr Nutrient 

enrichment 

N2O flux 

showed a significant 

and positive 

correlations between 

increasing N 

deposition and 

increasing N2O flux 

2 sites in 

Germany and 

Ireland 

Y = 4.7 + 1.4(X) 

(r2 = 0.38, p <0.001) 

Y = N2O flux rates 

(μg N2O N/m2/h) 

X= NH4

+ input by wet 

deposition (mmol/m2) 

ButterbachBahl et al. 

(1998) 

Spruce forest 5 to 30 kg N/ha/yr Nutrient 

enrichment 

NO flux 

showed a significant 

and positive 

correlations between 

increasing N 

deposition and 

increasing NO flux 

2 sites in 

Germany and 

Ireland 

Y = 14.1 + 16.7(X) 

(r2 = 0.67, p <0.001) 

Y= NO flux rates (μg NO–

N/m2/h) 

X= NH4

+ input by wet 

deposition (mmol/m2) 

ButterbachBahl et al. 

(1998) 

Spruce forest 5 to 30 kg N/ha/yr Nutrient 

enrichment 

CH4 oxidation 

rate 

showed a significant 

and negative 

correlations between 

increasing N 

deposition and 

decreasing CH4

oxidation rate 

2 sites in 

Germany and 

Ireland 

Y = -34.7 + 3.4(X) 

(r2 = 0.33, p <0.001) 

Y= CH4 oxidation rate 

(μg CH4/m2/h) 

X= NH4

+ input by wet 

deposition (mmol/m2) 

ButterbachBahl et al. 

(1998) 

 3-165
Ecosystems 

Deposition 

Range 

(kg N/ha/yr) 

Effect Indicator Study Region Response Curve Reference 

Grassland 5–35 Acidification/ 

Nutrient 

enrichment 

Species richness 68 acid 

grasslands 

across UK 

Y = 23.3 – 0.408(X) 

(r2=0.70; p <0.0001) 

Y: plant species richness 

X: total inorganic N deposition 

(kg N/ha/yr) 

Stevens et al. 

(2004) 

Lakes 1–16* 

*wet deposition only 

Nutrient 

enrichment 

water [DIN] 4296 lakes 

across USA, 

Canada and 

Europe 

log(Y) = 1.34log(X) – 1.55 

(r2 = 0.70; p <0.001) 

Y: water DIN (μg/L) 

X: wet deposition (kg N/km2/yr) 

Bergstrom and 

Jansson 

(2006) 

Lakes 1–16* 

*wet deposition only 

Nutrient 

enrichment 

Chl a: Tot-P 515 lakes 

across USA, 

Canada and 

Europe 

Log(Y) = 1.03ln(X)-1.43 

(r2 = 0.52; p <0.001) 

Y: Chl a : Tot-P 

X: wet deposition (kg N/ 

km2/yr) 

Bergstrom and 

Jansson 

(2006) 

Surface water 3.1 to 17.6 * 

* net anthropogenic 

nitrogen inputs 

(NANI) 

Nutrient 

enrichment 

Riverine exports of 

anthropogenic N 

16 watersheds 

from Maine to 

Virginia 

Y = (0.00087(Q)–0.096) 

NANI - 101 

Y: Riverine N flux (kg N 

/km2/yr) 

Q: riverine discharge 

NANI: Net anthropogenic N 

inputs (N/km2/yr) 

Howarth and 

Marino (2006) 

Surface water 4–12 Acidification/ 

Nutrient 

enrichment 

Surface water [NO3

−] 220 lakes and 

streams across 

the northeastern 

U.S. 

Summer: Y = 2.5(X) – 14.4 

(r2 = 0.30, p <0.001) 

Spring: Y = 6.7(X)-40.7 

(r2 = 0.38, p <0.001) 

Y: NO3

− concentration in water 

(μmol/L) 

X: N deposition (kg N/ha/yr) 

Aber et al. 

(2003) 

Stream water 5–13 Acidification/ 

Nutrient 

enrichment 

N export 83 lakes and 

streams across 

the northeastern 

U.S. 

Y=0.85(X) – 5.8 

(r2 = 0.56, p = 0.01) 

Y: NO3

− export (kg N/ha/yr) 

X: N deposition (kg N/ha/yr) 

Aber et al. 

(2003) 

Stream water 5–13 Acidification/ 

Nutrient 

enrichment 

Inorganic N retention 83 lakes and 

streams across 

the northeastern 

U.S. 

Y=-0.07(X) + 1.44 

(r2 = 0.50, p = 0.01) 

Y: inorganic N retentions 

(kg N/ha/yr) 

X: N deposition (kg N/ha/yr) 

Aber et al. 

(2003) 

Estuary 8–24* 

*N loads from 

atmospheric deposition, human 

waster water and 

fertilization 

application 

Nutrient 

enrichment 

Eelgrass area (ha) Waquoit Bay Y = -1.9(X) + 50.7 (r2 = 0.89) 

Y: Eelgrass area (ha) 

X: modeled N load (kg N/ha/yr) 

Driscoll et al. 

(2003) 

 3-166
Ecosystems 

Deposition 

Range 

(kg N/ha/yr) 

Effect Indicator Study Region Response Curve Reference 

Peatland 0–160 Acidification/ 

Nutrient 

enrichment 

NPP of Sphagnum 

fuscum

8 bogs in North 

America and 

Europe 

Y = 831e-0.185(X) – 48 

(r2 = 0.73, p <0.01) 

Y: difference between NPP of 

S. fuscum under augmented N 

and control treatment 

(g/m2/yr); 

X: N deposition (kg N/ha/yr) 

Vitt et al. 

(2003) 

Peatland 2–20 Nutrient 

enrichment 

Decomposition of litter 

collected from the field 

and incubated and 

constant temperature 

12 bogs from 9 

European 

countries 

Y = 0.98 + 0.21ln(X) 

(r2 = 0.75, p <0.01) four days 

incubation 

Y= 0.49 + 0.11ln(X) 

(r2 = 0.73, p <0.01) 10 days 

incubation 

Y: CO2 emission (mg/g/h) 

X: N deposition (g/m2/yr) 

Bragazza et al. 

(2006) 

Peatland 2–20 Nutrient 

enrichment 

Decomposition of litter 

collected from the field 

and incubated and 

constant temperature 

12 bogs from 9 

European 

countries 

Y = 4.3 + 2.4ln(X) 

(r2 = 0.61, p = 0.01) 

Bragazza et al. 

(2006) 

Y: DOC concentration (mg/g) 

X : N deposition (g/m2/yr) 

Peatland 2.7 to 8.1 kg N/ha/yr Nutrient 

enrichment 

Showed N 

accumulation 

increases linearly with 

N deposition 

23 ombrotrophic 

peatlands in 

Canada 

Y = 3.50(X) +0.64 

(r2 = 0.29, p <0.001) 

Moore et al. 

(2004) 

Y: N accumulation in soil 

(g/m2/yr) 

X : N wet deposition (g/m2/yr) 

3.3.8. Characterization of Sensitivity and Vulnerability 

3.3.8.1. Extent and Distribution of Sensitive and Vulnerable Ecosystems 

In general, ecosystems that are most responsive to nutrient enrichment from atmospheric N 

deposition are those that receive high levels of N loading, are N-limited, or contain species that have 

evolved in nutrient-poor environments. Species that are adapted to low N supply will often be more 

readily outcompeted by species that have higher N demand when the availability of N is increased (Aerts, 

1990; Krupa, 2003; Tilman and Wedin, 1991). As a consequence, some native species can be eliminated 

by N deposition (Ellenberg, 1985; Falkengren-Grerup, 1986, 1989; Roelofs, 1986; Stevens et al., 2004). 

Note the terms “low” and “high” are relative to the amount of bioavailable N in the ecosystem and the 

level of deposition. 

The following discussion of sensitive ecosystems is organized into three ecosystem categories: 

terrestrial, transitional, and aquatic. Case studies are intended to highlight ecosystems and/or regions 

where there are many publications documenting the effects of N deposition, thus they can provide 

sufficient data for quantitative risk assessment. 

 3-167
Source: (Fenn et al., 2003a). Reprinted with permission. 

Figure 3-52. Map of the western U.S. showing the primary geographic areas where N deposition effects 

have been reported. Eutrophication effects are more widespread and of greater importance 

than acidification effects in western North America. Areas where effects of air pollution on 

lichen communities have been reported in CA are represented by pink triangles. The plots in 

north central CO where lichen community changes were observed are exposed to emissions 

of both N and sulfur (S) from two large power plants in Craig, CO and Haydens, CO (Peterson 

et al., 2001). The areas shown in red in OR and WA (lichen communities affected by N 

deposition) are kriged data (Geiser and Neitlich, 2007). Only lakes at an elevation greater than 

1000 m and with a NO3 concentration of more than 5 μeq/L (measured in fall surveys or on an 

annual volume-weighted basis) are shown in thisfgure. Other high-elevation lakes in the West 

also had elevated NO3 concentrations, but were F excluded because N sources other than N 

deposition may have contributed to the elevated concentrations of NO3. 

Terrestrial 

Most terrestrial ecosystems are N-limited, therefore they are sensitive to perturbation caused by N 

additions (LeBauer and Treseder, 2008). Little is known about the full extent and distribution of the 

terrestrial ecosystems in the U.S. that are most sensitive to adverse impacts caused by nutrient enrichment 

 3-168
from atmospheric N deposition. Effects are most likely to occur where areas of relatively high 

atmospheric N deposition intersect with N-limited plant communities. The factors that govern the 

vulnerability of terrestrial ecosystems to nutrient enrichment from N deposition include the degree of Nlimitation, rates and form of N deposition, elevation, species composition, length of growing season, and 

soil N retention capacity. 

Regions and ecosystems in the western U.S. where N enrichment effects have been documented in 

terrestrial ecosystems are shown on Figure 3-52 (Fenn, 2003a). The alpine ecosystems of the Colorado 

Front Range (see case study), chaparral watersheds of the Sierra Nevada, lichen and vascular plant 

communities in the San Bernardino Mountains (see case study) and the Pacific Northwest, and the 

southern California coastal sage scrub community are among the most sensitive terrestrial ecosystems. 

In the eastern U.S., the degree of N saturation of the terrestrial ecosystem is often assessed in terms 

of the degree of NO3

−

 leaching from watershed soils into ground water or surface water. Stoddard (1994) 

estimated the number of surface waters at different stages of saturation across several regions in the 

eastern U.S. Of the 85 northeastern watersheds examined, 40% were in N-saturation Stage 0, 52% in 

Stage 1, and 8% in Stage 2 (stages are defined in Section 3.3.2.1). Of the northeastern sites for which 

adequate data were available for assessment, those in Stage 1 or 2 were most prevalent in the Adirondack 

and Catskill Mountains. Effects on individual plant species have not been well studied in the U.S. More is 

known about the sensitivity of particular plant communities. Based largely on results obtained in more 

extensive studies conducted in Europe, it is expected that the more sensitive terrestrial ecosystems include 

hardwood forests, alpine meadows, arid and semi-arid lands, and grassland ecosystems. 

Source: Data were obtained from the National Land Cover Data (NLCD) (2001) (http://www.mrlc.gov/) 

Figure 3-53. Location of wetlands in CONUS. 

Transitional 

About 107.7 million acres of wetlands are widely distributed in the conterminous U.S., 95 percent 

of which are freshwater wetlands and 5 percent are estuarine or marine wetlands (U.S. FWS 2005) 

(Figure 3-53). At one end of the spectrum, ombrotrophic bogs are very sensitive to N deposition because 

they receive exogenous nutrients exclusively from precipitation, and the species in them are adapted to 

 3-169
low levels of N (Bridgham et al., 1995, 1996; Shaver and Melillo 1984). Intertidal wetlands are at the 

other end of the spectrum; in these ecosystems marine/estuarine water sources generally exceed 

atmospheric inputs by one or two orders of magnitude (Morris, 1991). Data are not available with which 

to evaluate the extent to which wetlands in the U.S. have been affected by nutrient enrichment from N 

deposition. Wetlands are widely distributed, including some areas that receive moderate to high levels of 

N deposition. 

Peat-forming bog ecosystems are among the most sensitive transitional ecosystems to the effects of 

N deposition. In the conterminous U.S., peat-forming bogs are most common in areas that were glaciated, 

especially in portions of the Northeast and Upper Midwest (U.S. EPA, 1993a). In Alaska, these 

ecosystems are common in poorly drained locations throughout the state. 

Nutrient concentrations in wetland waters associated with the Great Lakes suggest that coastal 

Great Lakes wetlands are N-limited. Hill et al. (2006) found that more wetlands were N- than P-limited at 

each of the five Laurentian Great Lakes. This result is consistent with the apparent N-limitation of most 

North American marsh lands (Bedford et al., 1999). Nutrient loading to lakeshore wetlands is a concern 

throughout the lower lakes (Lakes Erie, Ontario, and the southern part of Lake Michigan) and in some 

localized areas of the upper lakes (Hill et al., 2006). Both agricultural and atmospheric sources of 

nutrients contribute to this stress. 

Coastal marsh ecosystems, unlike bog ecosystems, often receive large N inputs in tidal water, 

groundwater, and surface runoff. Atmospheric inputs to these systems are important because any N 

addition has the potential to contribute to eutrophication of coastal marshes and nearby marine and 

estuarine ecosystems (Galloway et al., 2003; Paerl, 2002). At many locations, especially along the 

Atlantic and Gulf coasts, atmospheric N inputs probably contribute to eutrophication problems in coastal 

marshes either by direct deposition to the wetland or from marine water inputs of N that originated from 

atmospheric deposition. 

Freshwater Aquatic 

Aquatic systems in which N has been observed to influence ecological processes either receive 

extremely high inputs (e.g., Dumont et al., 2005), or have very low initial N concentrations, and respond 

rapidly to additional inputs (Baron et al., 2000; Bergström and Jansson, 2006). Eutrophication effects on 

freshwater ecosystems from atmospheric deposition of N are of great concern in lakes and streams that 

have very low productivity and nutrient levels and that are located in remote areas. In more productive 

freshwaters, nutrient enrichment from N deposition usually does not stimulate productivity or community 

changes because P is more commonly the limiting nutrient. Also, in many places with even minor levels 

of human disturbance, nutrient enrichment with both N and P from non-atmospheric sources is common. 

Thus, eutrophication effects from N deposition are most likely to be manifested in undisturbed, lownutrient surface waters such as those found in the higher elevation areas of the western U.S. The most 

severe eutrophication from N deposition effects is expected downwind of major urban and agricultural 

centers. 

High concentrations of lake or streamwater NO3

−

, indicative of ecosystem saturation, have been 

found at a variety of locations throughout the U.S., including the San Bernardino and San Gabriel 

Mountains within the Los Angeles Air Basin (Fenn et al., 1996), the Front Range of Colorado (Baron 

et al., 1994; Williams et al., 1996b), the Allegheny Mountains of West Virginia (Gilliam et al., 1996), the 

Catskill Mountains of New York (Murdoch and Stoddard, 1992; Stoddard, 1994), the Adirondack 

Mountains of NY (Wigington et al., 1996), and the Great Smoky Mountains in TN (Cook et al., 1994). All 

of these regions, except CO, received more than about 10 kg N/ha/yr atmospheric deposition of N 

throughout the 1980s and 1990s. In contrast, the Front Range of Colorado receives up to about 5 kg 

N/ha/yr of total (wet plus dry) deposition (Sullivan et al., 2005), less than half of the total N deposition 

received at many of these other locations. 

 3-170
Table 3-26. Changes in aquatic ecosystems associated with elevated N loadings in the Western U.S. 

Ecological or 

Environmental 

Impact 

Location Level of Uncertainty 

Possibility of 

Broader 

Occurrence (at 

other sites) 

Reference 

EFFECTS IN AQUATIC SYSTEMS 

Elevated NO3

− in runoff; 

most severe in southern 

California and in 

chaparral catchments in 

the southwestern Sierra 

Nevada 

Transverse ranges of 

southern California; lowelevation catchments in 

the Sierra Nevada; highelevation catchments in 

the Colorado Front Range 

Well-documented 

response 

It is unclear how 

widespread this 

phenomenon is 

outside the 

ecosystems listed, 

because there is littler 

information from lowelevation systems in 

the Sierra Nevada 

and elsewhere. 

Williams et al. (1996a); 

Fenn and Poth (1999); 

Fenn et al. (2003a) 

N enrichment and shifts 

in diatom communities in 

alpine lakes 

Colorado Front Range; 

Lake Tahoe 

(California/Nevada 

border) 

Documented for two 

lakes east of the 

Continental Divide and 

Lake Tahoe 

These effects seem 

likely in other 

N-enriched lakes but 

have not been 

investigated. 

Baron et al. (2000); Wolfe 

et al. (2001b); Goldman 

(1988). 

Reduced lake water 

clarity and increased 

algal growth 

Lake Tahoe 

(California/Nevada 

border); high-elevation 

lakes throughout central 

and southern Sierra 

Nevada 

Well-documented 

response; N and P 

deposition believed to 

be important factors 

Lake Tahoe is an 

unusual case 

because of its 

renowned lake clarity; 

extent of occurrence 

elsewhere in northern 

Sierra Nevada is 

unknown. 

Jassby et al. (1994); 

Sickman et al. (2003). 

Increased NO3

−

concentrations in highelevation lakes 

Several regions, mainly 

downwind of urban 

centers 

Fairly well established 

from lake surveys, but 

more data needed for 

improved definition of 

frequency and severity 

Evidence suggests 

that urban plumes 

and agricultural 

emissions affect lake 

NO3

− levels. There is 

also evidence of 

impacts on lowelevation lakes. 

Figure 2, Sickman et al. 

(2002) 

High concentrations of NO3

−

 in surface waters in the western U.S. are not widespread. NO3

−

concentrations during the fall sampling season were low in most western lakes sampled in the Western 

Lakes Survey. Only 24 sampled lakes were found to have NO3

−

 concentrations greater than 10 μeq/L. Of 

those, 19 lakes were situated at high elevation, most above 3,000 m (Eilers et al., 1987). Other effects on 

aquatic ecosystems in the west are summarized in Table 3-26. 

There is some evidence suggesting that reductions in atmospheric N deposition could decrease the 

extent of eutrophication in at least some of the Great Lakes. It has generally been believed that the 

Laurentian Great Lakes are P-limited (Downing and McCauley, 1992; Rose and Axler, 1998; Schelske, 

1991). Water quality in the open waters of these lakes has been improving in recent years in response to 

controls on point sources of P (Nicholls et al., 2001). Work by Levine et al. (1997), however, suggested a 

more complicated pattern of response to nutrient addition for Lake Champlain. They added nutrients to in 

situ enclosures and measured indicators of P status, including alkaline phosphatase activity and 

 3-171
orthophosphate turnover time. Although P appeared to be the principal limiting nutrient during summer, N 

addition also resulted in algal growth stimulation. P sufficiency appeared to be as common as 

P deficiency. During spring, phytoplankton growth was not limited by P, N, or Si, but perhaps by light or 

temperature (Levine et al., 1997). 

Estuarine and Coastal Aquatic 

N is an essential nutrient for estuarine and marine fertility. However, excessive N contributions can 

cause habitat degradation, algal blooms, toxicity, hypoxia (reduced dissolved O2), anoxia (absence of 

dissolved O2), reduction of sea grass habitats, fish kills, and decrease in biodiversity (Boynton et al., 

1995; Howarth et al., 1996; Paerl, 1995; 1997; Valiela and Costa, 1988; Valiela et al., 1990). Each of these 

potential impacts carries ecological and economic consequences. Ecosystem services provided by 

estuaries include fish and shellfish harvest, waste assimilation, and recreational activities (Costanza et al., 

1997). 

Source: Bricker et al. (2007) 

Figure 3-54. Overall eutrophication condition on a national scale. 

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Estuaries and coastal waters tend to be N-limited and are therefore inherently sensitive to increased 

atmospheric N loading (D'Elia et al., 1986; Howarth and Marino, 2006). The national estuary condition 

assessment conducted by Bricker et al. (2007) found that the most impacted estuaries occurred in the midAtlantic region and the estuaries with the lowest symptoms of eutrophication were in the North Atlantic. 

N over-enrichment is a major environmental problem for coastal regions of the U.S., especially in the 

eastern and Gulf Coast regions. Of 138 estuaries examined by Bricker et al. (1999), 44 were identified as 

showing symptoms of nutrient over-enrichment. Estuaries are among the most biologically productive 

ecosystems on Earth and provide critical habitat for an enormous diversity of life forms, especially fish. 

Of the 23 estuaries examined in the Northeast, 61% were classified as moderately to severely degraded 

(Bricker et al., 1999). Other regions had mixtures of low, moderate, and high degree of eutrophication 

(See Figure 3-54). 

The estuaries with the greatest extent of eutrophication corresponded with conditions related to 

both the degree of N loading and the inherent sensitivity of the estuary, as influenced by morphology and 

water flushing dynamics (see Annex C for a discussion of estuarine sensitivity). The most eutrophic 

estuaries were generally those that had large watershed-to-estuarine surface area, high human population 

density, high rainfall and runoff, low dilution, and low flushing rates (Bricker et al., 2007). 

Bricker et al. (2007) evaluated the future outlook of the nations estuaries based on population 

growth and future management plans. They predicted that trophic conditions would worsen in 48 

estuaries, stay the same in 11, and improve in only 14 by the year 2020. Between 1999 and 2007, an equal 

number of estuary systems have improved their trophic status as have worsened. The assessed estuarine 

surface area with high to moderate/high eutrophic conditions have stayed roughly the same, from 72% in 

1999 (Bricker et al., 1999), to 78% in the recent assessment (Bricker et al., 2007). 

Studies linking changes in estuary nutrient status to atmospheric N deposition have been limited, 

though it is noted that many states are addressing atmospheric inputs as part of their development of Total 

Maximum Daily Load plans to address estuarine water quality impairments, including those associated 

with low dissolved O2. In an effort to evaluate the contribution of atmospheric N deposition to the future 

reduction in N loading to estuaries, Castro and Driscoll (2002) reported model calculations that suggested 

that considerable reductions (more than 25%) in atmospheric N deposition will be needed to reduce the 

contribution made by atmospheric N deposition to the total N loads to their study estuaries in the 

northeastern U.S. A simulated reduction in atmospheric deposition of 25% of ambient deposition rates 

reduced the contribution made by atmospheric deposition to the total estuarine N loads by only 1% to 6% 

(Castro and Driscoll 2002). In a later study, Driscoll et al. (2003b) estimated that reduction of both mobile 

N emissions sources and electric utilities would produce an estimated reduction in estuarine N loading in 

Casco Bay, Maine of 13% (Driscoll et al., 2003c). Casco Bay receives the lowest atmospheric and nonatmospheric N loading per unit area of watershed (4 kg N/ha/yr) of the eight estuaries in the northeastern 

U.S. evaluated by (Driscoll et al., 2003a) (Figure 3-35). 

3.3.8.2. Case Study: Alpine and Subalpine Communities of the Eastern Slope of the 

Rocky Mountains 

Some alpine plant communities occur in areas that receive moderately elevated atmospheric N 

deposition; especially those proximal to urban areas (see Annex C for a map). Because alpine plant 

species are typically adapted to low nutrient availability, they often are sensitive to effects from N 

enrichment. 

Research on N enrichment effects on alpine and subalpine ecosystems in the western U.S. has been 

limited mainly to studies at the Loch Vale Watershed in Rocky Mountain National Park and the Niwot 

Ridge Long-Term Ecosystem Research site; both located east of the Continental Divide in Colorado (see 

review by Burns 2004). Research has been conducted in this region on both the terrestrial and aquatic 

effects of nutrient enrichment. 

 3-173
Terrestrial Effects 

Changes in biomass production and NO3

−

 leaching are indicative of effects on the health and vigor 

of plants in alpine and subalpine ecosystems. Biomass production responses of alpine communities to 

increased N deposition are dependent on moisture regimes (Fisk et al., 1998) and are driven by shifts in 

species composition. In a fertilization experiment, the addition of 25 kg N/ha/yr during summer caused a 

community shift towards greater dominance of hairgrass (Deschampsia sp.) in wet alpine meadows. 

However, the increase in plant biomass (+67%) and plant N content (+107%) following N fertilization 

was higher in graminoid-dominated dry meadows than in forb-dominated wet meadows; +53% plant 

biomass, +64% standing N crop, respectively (Bowman et al., 1995; Burns, 2004). 

Alteration of plant productivity and species richness has been observed in fertilization experiments. 

Seastedt and Vaccaro (2001) showed that four years of N addition to alpine vegetation at rates ranging 

between 100 and 200 kg N/ha (depending on the year) caused marginal increases in plant foliage 

productivity but reduced species richness. In a follow-up study at Niwot Ridge additions of 20, 40, and 

60 kg N/ha/yr (on top of ambient N deposition near 5 kg N/ha/yr) over an 8 year period to a dry alpine 

meadow led to an increase in plant biomass, and an increase in tissue N concentration at all treatment 

levels within three years of application. Much of the response was due to increased cover and total 

biomass of sedges (Carex spp.). There was a significant decrease in Kobresia myosuroides with increasing 

N input. 

High elevation alpine zones exhibit a relatively low capacity to sequester atmospheric deposition of 

N because of steep slopes, shallow soils, sparse vegetation, short growing season and other factors (Baron 

et al., 1994; Williams et al., 1996b). Results from several studies suggest that the capacity of Rocky 

Mountain alpine catchments to sequester N is exceeded at deposition levels less than 10 kg N/ha/yr 

(Baron et al., 1994; Williams and Tonnessen, 2000). The changes in plant species that occur in response to 

N deposition in the alpine zone can result in further increased leaching of NO3

−

 from the soils, because 

the plant species favored by higher N supply are often associated with greater rates of N mineralization 

and nitrification than the preexisting species (Bowman et al., 1993; 2006; Steltzer and Bowman, 1998; 

Suding et al., 2005). 

Effects of N deposition to alpine terrestrial ecosystems in this region include community-level 

changes in plants, lichens, and mycorrhizae. Alpine plant communities are sensitive to changes in species 

composition in response to added N (Bowman et al., 1995; Seastedt and Vaccaro, 2001). Plant species 

composition likely responds at lower N input levels than those that cause measurable changes in soil 

inorganic N content. For example, Bowman et al. (2006) conducted a N-addition experiment in the 

Colorado Front Range with 20, 40, or 60 kg N/ha/yr (see Figure 3-55). Experimental sites were monitored 

for 8 years along with a reference site that received about 5 kg N/ha/yr total ambient deposition. Changes 

in plant species composition associated with the treatments occurred within 3 years of the initiation of the 

experiment, and were significant at all levels of N addition. 

Using changes of individual species abundance and ordination scores to evaluate critical load, the 

critical load for total N deposition was estimated for change in individual species to be 4 kg N/ha/yr and 

for overall community change to be 10 kg N/ha/yr (Bowman et al., 2006). In contrast, increases in NO3

−

leaching, soil solution inorganic NO3

−

, and net nitrification were detectable at levels above 20 kg N/ha/yr 

(Bowman et al., 2006). These results indicate that changes in plant species composition may be detectable 

at lower N deposition rates than the level at which the traditional soil indicators signal ecosystem 

responses to N deposition. This response suggests that changes in species composition are probably 

ongoing in alpine dry meadows of the Front Range of the Colorado Rocky Mountains at current 

atmospheric N deposition levels. This research also demonstrated that long-term experimental fertilization 

plots illustrate a clear response of alpine flora to N addition, including shifts toward graminoid plants that 

shade smaller flowering species, and accompanying changes in soil N cycling (Bowman et al., 2006). 

Changes in alpine plant species composition have also been documented on Niwot Ridge, where 

increased cover of plant species that are most responsive to N fertilization has occurred in some of the 

long-term monitoring plots (Fenn et al., 2003a; Korb and Ranker, 2001). These changes have probably 

 3-174
developed in response to changes in N deposition. However, the influences of climatic change, 

particularly changes in precipitation (Williams et al., 1996b) and pocket gopher disturbance (Sherrod and 

Seastedt, 2001) could not be ruled out (Fenn et al., 2003a). The altered N cycling provided the potential 

for replacement of some native plant species by more competitive, faster growing native species (Baron 

et al., 2000; Bowman, 2000; Bowman and Steltzer, 1998). 

Aquatic Effects 

Rocky Mountain National Park has been the site of research addressing the effects of N deposition 

on algal species abundance in freshwater lakes. Wolfe et al. (2001) analyzed sediments from Sky Pond 

and Lake Louise, two small alpine lakes located at more than 3300 m elevation on the east slope of the 

Colorado Front Range in Rocky Mountain National Park. Before 1900, the diatom flora was typical of 

oligotrophic Rocky Mountain lakes, dominated by such species as Aulacoseira distans, A. perglabra, 

Fragilaria pinnata, F. construens, and various Achnanthes spp. The mesotrophic planktonic species 

Astrionella formosa and Fragilaria crotonensis were present in trace frequencies, but became common 

elements of the diatom flora during the 20th century. Between 1950 and 1970, A. formosa became the 

dominant taxa in both lakes. It is known from studies in other locations as an opportunistic alga that 

responds rapidly to disturbance and nutrient enrichment (Renberg et al., 1993; Anderson et al., 1995; 

Reavie and Smol, 2001). This shift in diatom species is apparently the result of environmental 

stimulation, rather than recent colonization, as evidenced by the presence of these mesotrophic taxa in the 

older sediment record. 

Source: Bowman et al. (2006)) 

Figure 3-55. Changes in plant species composition associated with N addition treatments in an alpine dry 

meadow of the Colorado Front Range. Within 3 years of the initiation of the experiment, 

statistically significant changes in the cover of Carex rupestris occurred at all treatment 

levels. 

Additional corroborative evidence for the linkage between atmospheric N deposition and the 

observed diatom shifts in these alpine lakes is provided by the results of laboratory (Interlandi and 

Kilham, 1998) and in-lake (McKnight et al., 1990) N addition experiments. In both sets of experiments, 

growth of A. formosa and F. crotonensis was accelerated by experimental N addition. The post-1950 

period of rapid shifts in diatom species composition in Sky Pond and Lake Louise corresponded with 

intensification of agricultural practices, animal husbandry, and population growth in adjacent regions to 

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the east of Rocky Mountain National Park (Wolfe et al., 2001). Nevertheless, N deposition at that time 

was estimated to be low, probably less than 2 kg N/ha/yr (Baron, 2006). 

3.3.8.3. Case Study: Chesapeake Bay 

Chesapeake Bay is the largest estuary in the U.S. and one of the most sensitive to N inputs (Bricker 

et al., 1999; Howarth, 2007). Eutrophication effects have been pronounced in Chesapeake Bay (Howarth, 

2007) and it is perhaps the best known example in the U.S. of human activities leading to accelerated 

estuarine eutrophication and its associated negative effects. In the recent national assessment of eutrophic 

conditions in estuaries, the Chesapeake Bay stands out as a system with both physical features and N 

loading levels that make it particularly vulnerable to eutrophication (Bricker et al., 2007). 

The role of atmospheric N deposition was not considered as a factor in estuary eutrophication in 

the U.S., and was ignored until Fisher and Oppenheimer (1991) suggested that it could constitute up to 

40% of the total N inputs to the Chesapeake Bay. Recent studies have reported more conservative 

estimates (Boyer et al., 2002); however, Fisher and Oppenheimer (1991) drew attention to the potential 

for atmospheric deposition as an important contributor to the overall N budget of estuaries in the eastern 

U.S. N inputs to the Chesapeake Bay have increased substantially over the last 50 to 100 years. The 

increase is attributed to rapid acceleration of the use of chemical fertilizers in agriculture, the increasing 

human population density and associated wastewater discharge, and rising atmospheric N emissions 

within the airshed and consequent deposition within the Chesapeake Bay watershed. Atmospheric 

deposition of N is currently estimated to contribute about one-fourth of the total N loading to Chesapeake 

Bay (Boyer et al., 2002; Howarth, 2007) 

Human activities have increased the susceptibility of the Chesapeake Bay to the effects of 

atmospheric N deposition. For example, the filling in of wetlands and deforestation for agricultural and 

urban development, have reduced the ability of natural ecosystem processes to remove or trap nutrients, 

thereby further accelerating nutrient delivery to the bay. In addition, diseases and over-harvesting led to a 

dramatic decline of the once highly abundant eastern oyster, seriously reducing the natural filtering of 

algae and other organic matter from the water column. 

As a result of these changing conditions, eutrophic symptoms intensified in the Chesapeake Bay 

from the mid-1950s to the mid-1980s. The most apparent symptoms were high production of algae; 

increasingly turbid water; major declines in SAV abundance and species; and increasingly worsening 

anoxia and hypoxia (Boesch et al., 2001). The recent national estuary condition assessment (Bricker et al., 

2007) reported that Chl a, dissolved O2, nuisance/toxic algal blooms, and SAV rated “high” in 

Chesapeake Bay in terms of severity of effects associated with eutrophication. In addition, macroalgae 

and toxic algal bloom conditions have worsened since the previous national assessment in 1999 (Bricker 

et al., 1999, 2007). 

Concentrations of Chl a in the surface mixed layer have increased tenfold in the seaward regions of 

the bay and one-and-one-half- to twofold elsewhere, paralleling estimates of increased loading of N and P 

to the bay since 1945 (Harding and Perry, 1997). 

SAV began to decline as a result of nutrient enrichment during the mid-1960s, disappearing entirely 

from the Patuxent and lower Potomac Rivers. By 1980, many areas of the bay that once contained 

abundant SAV beds had none or only very small remnants left (Orth and Moore, 1984). Research 

indicated that the major driving factor in the decline of SAV was nutrient enrichment, which was causing 

excessive growth of algae in the water column and on SAV leaf blades (epiphytic algae). This algal 

growth decreased light availability to the submerged plants to the point that they could not survive (Kemp 

et al., 1983; Twilley et al., 1985). 

There is an annual cycle of O2 depletion in the Chesapeake Bay that begins as the water starts to 

warm in spring, and O2 depletion accelerates during and following the spring freshet. The spring 

accumulation of algal biomass is more than sufficient to create conditions for O2 depletion and summer 

anoxia (Malone, 1991, 1992). Hypoxia (very low dissolved O2 concentration ~ <2mg/L) and anoxia 

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(absence of dissolved O2) generally occur from May through September, with the most severe conditions 

observed in mid-summer. Seasonal hypoxia has been a feature of the Chesapeake Bay since deforestation 

during the colonial period (Cooper and Brush, 1991; Malone, 1991), but evidence suggests an increase in 

the extent of the problem in recent decades (Malone, 1991; Officer et al., 1984). Estuarine eutrophication 

is sometimes accompanied by increases in the populations of species of algae, often cyanobacteria that 

produce toxins. Such chemicals can affect people, fish, shellfish, and other organisms. Blooms of algae 

that produce toxins in Chesapeake Bay have become more extensive over approximately the past decade 

(Bricker et al., 2007). 

In 1983, the U.S. EPA, District of Columbia, and states of Virginia, Maryland, and Pennsylvania 

signed the first Chesapeake Bay Agreement, which established the Chesapeake Bay Program—a 

voluntary government partnership that directs and manages bay cleanup efforts. Scientific findings from 

the program led to the signing of the second Chesapeake Bay Agreement in 1987, in which it was agreed 

to reduce by 40% the N and P entering the Chesapeake Bay by the year 2000. Point source reductions 

have been most successful, especially for P. Between 1985 and 1996, emissions from P point sources were 

reduced by 58% and N by 15%. Nonpoint source reductions have been slower, largely because nonpoint 

sources of nutrients are more difficult to control. Nonpoint source emissions of N and P have been 

reduced by only 7% and 9%, respectively (Boesch et al., 2001). Strategies to reduce nonpoint source 

nutrients include changes such as adoption of better agricultural practices, reduction of atmospheric N 

deposition, enhancement of wetlands and other nutrient sinks, and control of urban sprawl. 

3.3.8.4. Case Study: San Bernardino 

The San Bernardino Mountains lie east of the Los Angeles Air Basin in California. Pollutants 

generated in the greater LA metropolitan area are transported 60–100 km downwind and affect midelevation forests in the San Bernardino Mountains and the San Gorgonio Class I Wilderness area. The 

primary source of air pollution is fossil fuel combustion. Approximately half of the air pollution in the LA 

air basin is generated from mobile sources including trucks, trains, cars, ships, and buses (South Coast Air 

Quality Management District). On the western end of the San Bernardino Mountains, nearly half of the N 

deposition is in reduced forms (Fenn and Poth, 2004), most of which is believed to originate from dairy 

farms in the Chino/Norco area. 

In the San Bernardino Mountains, a wide variety of N species (NO, NO2, HNO3, HNO2, NO3

−

) are 

deposited to vegetation and soil surfaces, in gaseous, wet, and dry forms. Ammonium (NH4 +) can also be 

transported moderate distances from feedlots for cattle and poultry (Bytnerowicz, 2002). Along a west to 

east gradient in the San Bernardino Mountains, throughfall N deposition goes from averaging 71 kg 

N/ha/yr at Camp Paivika (nearer to the pollutant sources), to 9 kg N/ha/yr at Barton Flats (farther from the 

sources), 45 km east of Camp Paivika (Breiner et al., 2007; Fenn et al., 2008). These throughfall 

measurements were made using ion exchange resin columns that measure total NO3

−

 and NH4

+

 deposition 

from precipitation, plus fog or dry deposition that has been scavenged by the overstory pine canopy and 

then washed through the canopy (Fenn and Poth, 2004). 

Several key ecological endpoints in the mixed conifer forests of the San Bernardino Mountains 

have been linked to anthropogenic N deposition. Because air pollution has been high since 1945 in the LA 

air basin (Lee et al., 2003), N deposition is in excess of plant and microbial demand (Fenn et al., 1996). 

The cardinal symptom of excess N is the export of high NO3

−

 levels in streamwater (see 3.3.2.1), which is 

well demonstrated for areas with N deposition above 17 kg N/ha/yr in the San Bernardino and San 

Gabriel Mountains (Breiner et al., 2007; Fenn and Poth, 1999; Fenn et al., 2008; Michalski et al., 2004; 

Riggan et al., 1985, Fenn et al., 2008). Other indicators of excess N in the ecosystem include lowered 

litter C:N and elevated emissions of NO and N2O from the soil (see 3.3.4.2), which have been observed at 

the more polluted sites in the San Bernardino Mountains. Lichen communities in the San Bernardino 

Mountains have also been dramatically changed by the disappearance of up to 50% of the species that 

occurred in the region in the early 1900s, due to N pollution (Fenn et al., 1996; Nash and Sigal 1999). A 

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disproportionate number of the locally extinct lichen species are cyanolichens. N deposition has also been 

tentatively linked to reduction in fine root biomass in Ponderosa pine (Pinus ponderosa C. Lawson) at 

three sites (Grulke et al., 1998; Fenn et al., 2008). However, ozone is also believed to contribute to 

decreased C allocation to fine roots (Grulke et al., 1998), and could be a confounding factor (see below). 

Recently, (Grulke et al., 1998) have used the linkages between N deposition and effects described above 

to calculate empirical and simulated (i.e. DayCent, Simple Mass Balance for N as a nutrient) critical loads 

for N deposition for California mixed conifer forests. 

The effects of high N deposition in the forests of San Bernardino Mountains are compounded by 

the high ozone exposures that have occurred throughout the past 65 years. For example, from west to east, 

O3 concentrations were high at Camp Paivika (80 ppb/h, averaged over 24 h, from April 15 through 

October 15, from 1993 through 1995(Grulke et al., 1998); moderately high (72 to 74 ppb/h) 5 km further 

east near Rim Forest; and moderate (62–64 ppb/h) 45 km east of Camp Paivika, at Barton Flats. Both O3

exposure and N deposition reduce foliar retention (Grulke and Balduman, 1999) and alter tissue chemistry 

of both needles and litter (Poth and Fenn, 1998). In addition, confounding factors such as drought and fire 

suppression add to the complexity of ecosystem response (Arbaugh et al., 2003, Minnich et al., 1995; 

Takemoto et al., 2001) . Extensive crown injury measurements have also been made, linking ambient O3

exposure data to chlorotic mottle and needle retention (Arbaugh et al., 1998). Ozone exposure and N 

deposition reduce carbon allocation to stems and roots (Grulke et al., 1998), further predisposing trees to 

drought stress, windthrow, root diseases, and insect infestation (Takemoto et al., 2001). Recently, Grulke 

et al. (2008) reported that various lines of phenomenological and experimental evidence indicate that N 

deposition and ozone pollution contribute to the susceptibility of forests to wildfire in the San Bernardino 

Mountains by increasing stress due to drought, weakening trees, and predisposing them to bark beetle 

infestation. Figure 3-56 shows the multiple factors contributing to susceptibility to wildfires in the San 

Bernardino Mountains. 

Source: Grulke et al. (2008 ) 

Figure 3-56. Diagram of multiple factors contributing to forest susceptibility to wildfire. 

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3.3.9. Ecosystem Services 

Ecosystem services are the benefits people obtain from ecosystems (Millenium Ecosystem 

Assessment, 2005). This concept has gained recent interest and support because it recognizes that 

ecosystems are valuable to humans and important in ways that are not generally appreciated (Daily 1997). 

It also provides a context for assessing the collective effects of human actions on a broad range of the 

goods and services upon which humans rely. 

There are no publications at this time that focus on the ecosystem services specifically affected by 

N deposition. Some valuation studies address the effects of N enrichment from multiple sources (see 

Annex F). The evidence reviewed in this ISA illustrates that N deposition affects ecosystem services in 

the following categories (defined by Hassan et al., 2005): 

 Supporting: nutrient cycling, biodiversity 

 Provisioning: forest yields, fishing yields in estuaries 

 Regulating: water quality, air quality, climate regulation (interactions with greenhouse gases CO2, 

N2O, CH4), fire frequency and intensity, disease resistance 

 Cultural: swimming, boating, recreation, biodiversity 

In general, both ecosystem structure and function play essential roles in providing goods and 

services (Table 3-27) (Daily, 1997). Ecosystem processes provide diverse benefits including absorption 

and breakdown of pollutants, cycling of nutrients, binding of soil, degradation of organic waste, 

maintenance of a balance of gases in the air, regulation of radiation balance and climate, and fixation of 

solar energy (Westman, 1977; Daily, 1997; World Resources Institute, 2000). These ecological benefits, in 

turn, provide economic benefits and values to society (Costanza et al., 1997; Pimentel et al., 1997). Goods 

such as food crops, timber, livestock, fish, and drinking water have market value. The values of 

ecosystem services such as flood-control, wildlife habitat, cycling of nutrients, and removal of air 

pollutants are more difficult to measure (Goulder and Kennedy, 1997). 

Figure 3-57. Diagram of relationships of human actions, N loading, and ecosystem services. 

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Particular concern has developed within the past decade regarding the consequences of decreasing 

biological diversity (Ayensu et al., 1999; Chapin et al., 1998; Hooper and Vitousek, 1997; Tilman, 2000; 

Wall, 1999). Human activities that decrease biodiversity also alter the complexity and stability of 

ecosystems, and change ecological processes. In response, ecosystem structure, composition and function 

can be affected (Pimm, 1984; Tilman and Downing, 1994; Tilman, 1996; Chapin et al., 1998; Levlin, 

1998; Peterson et al., 1998; Daily and Ehrlich, 1999; Wall, 1999). Biodiversity is an important 

consideration at all levels of biological organization, including species, individuals, populations, and 

ecosystems. Human-induced changes in biotic diversity and alterations in the structure and functioning of 

ecosystems are the two most dramatic ecological trends of the past century (Vitousek et al., 1997b; U.S. 

EPA, 2004). The deposition of nutrient N from the atmosphere alters ecosystem structure and function by 

altering nutrient cycling and changing biodiversity. 

How does N deposition impact ecosystem services? Figure 3-57 illustrates some of the 

relationships between human actions, N loading, and ecosystem services. Human actions that affect N 

loading influence the disturbances and physical drivers. These changes influence ecosystem structure and 

function, which in turn alter the production of ecosystem goods and services. In the bottom row, the 

impacts on human benefits or endpoints are shown. These endpoints are highlighted because they are 

most easily linked to valuation (Boyd and Banzhaf 2007). Changes in human benefits from ecosystems 

can then lead to policy changes, resulting in a feedback between these endpoints and human actions. 

Information supporting several of the benefits outlined in Figure 3-57 is developed. These relationships 

are futher discussed for some outcomes below, drawing upon the evidence presented in the preceeding 

sections. 

Human Health. Increasing N deposition has a number of ecological effects that can be linked to 

human health. Harmful algal blooms in some areas are linked to increased N loading, and these can cause 

bans in swimming as they contain neurotoxins. In addition, links between acid deposition and mercury 

mobilization are important for human health in areas where fish, birds and shellfish are consumed. 

Swimming and Recreation. Appropriate uses are identified by taking into consideration the use and 

value of the water body for public water supply, for protection of fish, shellfish, and wildlife, and for 

recreational, agricultural, industrial, and navigational purposes. Increases in N in estuaries can result in 

hypoxic zones and fish kills, with numerous negative impacts on commercial and recreation fishing and 

shellfish harvest. Harmful algal blooms (e.g., red tide) can be linked to increased N loading, and lead to 

swimming bans as they contain neurotoxins. Unpleasant odors and dead fish and wildlife can also reduce 

the value of areas for swimming, recreation and even impact property values. 

Drinking Water. Forests, wetlands and streams all store and remove nitrogen at various timescales 

via plant and microbial uptake, biogeochemical stabilization, and denitrification. This N removal is a 

valuable ecosystem service, protecting water quality. In particular, the provision of clear, cool and clean 

water as a drinking water supply is an important service provided by forested watersheds, recognized by 

many states (e.g., Ashendorff et al., 1997). Air pollution can threaten the provision of this service, by 

directly increasing nitrogen loads, some of which will enter the drinking water supply, and also by 

affecting forest health, which could in turn decrease water quality and affect other forest ecosystem 

services. N inputs can impair the ability of terrestrial and aquatic ecosystems to retain and remove N 

(Aber et al., 1989, 1998; Mulholland et al., 2008; see Section 3.3.2), which can lead to degradation of 

water quality for many uses. 

Forest Supporting and Provisioning Services. Nitrogen limits the primary production of most 

terrestrial ecosystems on earth (Vitousek and Howarth 1991; LeBauer and Treseder 2008; see Section 

3.3.3). Food and timber harvests have increased tremendously via inputs of properly timed inputs of 

nitrogen fertilizers. However, nitrogen additions to agricultural and industrial forest lands typically far 

exceed (100 to >300 kg N/ha/yr) the atmospheric deposition of N, which therefore only makes a small 

contribution to total N loading. Managed ecosystems are not within the scope of this review. Increases in 

availability of this limiting nutrient via atmospheric deposition could increase forest production over large 

non-managed areas. Some studies suggest that chronic N additions via deposition can increase production 

in forests (Holland et al., 1997; Magnani et al., 2007), but other studies show little effect on wood 

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production (e.g., Nadelhoffer et al., 1999) (see Section 3.3.3.1). Leaching of nitrate can promote cation 

losses, which in some cases create nutrient imbalances, slower growth and lessened disease and freezing 

tolerances for forest trees, particularly in sensitive areas such as high elevation forests. The net effect of 

increased N on forests in the U.S. is a key unknown. 

Table 3-27. Primary Goods and Services Provided by Ecosystems 

Ecosystem Goods Services 

Coastal 

Ecosystems 

Fish and shellfish 

Fish meat (animal feed) 

Seaweeds (for food and industrial use) 

Salt 

Genetic resources 

Moderate storm impacts (mangroves, barrier islands) 

Provide habitat and breeding areas/hatcheries/nurseries for wildlife 

(marine and terrestrial) 

Maintain biodiversity 

Dilute and treat wastes 

Provide harbors and transportations routes 

Provide human and wildlife habitat 

Provide employment 

Contribute aesthetic beauty and provide recreations 

Forest Ecosystems Timber 

Fuel wood 

Drinking and irrigation water 

Fodder 

Non timber products (vines, bamboos, 

leaves, etc.) 

Food (honey, mushrooms, fruit, and other 

edible plants; game) 

Genetic resources 

Remove air pollutants, emit O2

Cycle nutrients 

Maintain array of watershed functions (infiltration, purification, flow 

control, soil stabilization) 

Maintain biodiversity 

Sequester atmospheric carbon 

Moderate weather extremes and impacts 

Generate soil 

Provide employment 

Provide human and wildlife habitat 

Contribute aesthetic beauty and provide recreation 

Freshwater Drinking and irrigation water 

Fish 

Hydroelectricity 

Genetic resources 

Buffer water flow (control timing and volume) 

Dilute and carry away wastes 

Cycle nutrients 

Maintain biodiversity 

Provide aquatic habitat 

Provide transportation corridor 

Provide employment 

Contribute aesthetic beauty and provide recreation 

 3-181
Ecosystem Goods Services 

Grassland 

Ecosystems 

Livestock (food, game, hides, and fiber) Maintain array of watershed functions (infiltration, purification, flow 

control, and soil stabilization) Drinking and irrigation water 

Cycle nutrients Genetic resources 

Remove air pollutants and emit O2

Maintain biodiversity 

Generate soil 

Sequester atmospheric carbon 

Provide human and wildlife habitat 

Provide employment 

Contribute aesthetic beauty and provide recreations 

Source: World Resources Institute (2000). 

3.4. Other Welfare Effects 

This section includes the non-acidification effects of sulfur and direct phytotoxic effects of gasphase NOX and SOX on vegetation. Materials and structures damage caused by NOX and SOX are 

addressed in Annex E. 

3.4.1. Non-Acidification Effects of Sulfur 

As discussed in Section 3.2, a number of environmental effects are associated with S deposition, in 

particular soil and water acidification. However, S deposition also contributes to nutrient enrichment, 

toxicity, and has secondary effects on the cycling and bioavailability of Hg, a highly neurotoxic 

contaminant. High concentrations of SO2 can harm vegetation by causing foliar injury, decreasing plant 

growth, and eliminating sensitive plant species, although atmospheric concentrations of SO2 are seldom 

high enough to cause these effects on vegetation at ambient air pollution levels in the U.S. The 

biogeochemical cycling of S is closely linked with the cycling of other important elements, including C, 

N, P, Al, and Hg. Therefore, S deposition can influence the cycling of these elements in ways that 

influence nutrient availability or contaminant toxicity. In particular, current research suggests that S 

deposition influences the cycling of Hg in transitional and aquatic ecosystems by stimulating 

SO4

2−

-reducing bacteria, which are responsible for the bulk of Hg methylation, a key process that 

increases the bioavailability of Hg. 

3.4.1.1. Biological Role of Sulfur 

Effects on Plants 

S is an essential plant nutrient. Low dosages of S serve as a fertilizer, particularly for plants 

growing in S-deficient soil (Hogan et al., 1998). A certain level of foliar SO4

2−

is necessary for adequate 

plant S nutrition (Johnson and Mitchell, 1998; Marschner, 1995), and S deficiency has been shown to 

occur at foliar SO4

2−

 levels below 80 μg/g in Pinus radiata (Turner and Lambert, 1980). Nevertheless, the 

annual increment of S in vegetation is usually small compared to atmospheric deposition and leaching 

fluxes. Plants require similar levels of S and P, but S is generally available in much higher concentrations 

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in soil. Storage of S in vegetation is of minor significance in the retention or loss of S in most forests 

(Johnson and Mitchell, 1998; Mitchell et al., 1992a, 1992b). 

Atmospheric deposition is an important component of the S cycle. This is true not only in polluted 

areas where atmospheric deposition is very high, but also in areas of low S deposition. Biochemical 

relationships between S and N are involved in plant protein synthesis and metabolism. S deficiency 

reduces NO3 reductase and glutamine synthetase activity. N uptake in forests, therefore, could be loosely 

regulated by S availability, but SO4

2−

 additions in excess of needs do not necessarily lead to injury (Hogan 

et al., 1998; Turner and Lambert, 1980). Current levels of S deposition throughout much of the U.S. 

exceed the capacity of most plant communities to immobilize the deposited S (Johnson, 1984; Lindberg, 

1992). S excesses associated with acidic deposition have been found (Johnson et al., 1982a; Meiwes and 

Khanna, 1981; Shriner and Henderson, 1978). 

S deficiency in forest soil is rare, but has been reported in remote areas that receive very low levels 

of atmospheric S deposition and that have inherently low S levels in soil (Kelly and Lambert, 1972; 

Schnug, 1997; Turner et al., 1977, 1991). In such cases, atmospheric S deposition might be taken up by 

vegetation, with little SO4

2−

 leaching. Within areas of the U.S. influenced by acidic deposition, this is not 

expected to be a common phenomenon. To some extent, plant uptake of S is determined by the 

availability of N. This is because most S in plant tissue is in protein form, with a specific S:N ratio 

(Johnson et al., 1982a; Turner et al., 1977, 1991). 

S plays a critical role in agriculture, and is an essential component of fertilizers (Ceccotti and 

Messick, 1997). It is particularly important for plants growing in S-deficient soil (Hogan et al., 1998). The 

most important source of S to vegetation is SO4

2−

, which is taken up from the soil by plant roots 

(Marschner, 1995). There are few field demonstrations of foliar SO4

2−

 uptake (Krupa and Legge, 1986, 

1998, 1999; U.S. EPA, 2004). Rather, SO4

2−

 in throughfall is often enriched above levels in precipitation. 

The relative importance of the contribution of foliar leachate versus prior dry-deposited SO4

2−

 particles to 

this enrichment is difficult to quantify (Cape et al., 1992). The major factor controlling the movement of S 

from the soil into vegetation is the rate of release through microbial decomposition of S from organic to 

inorganic forms (Marschner, 1995; May et al., 1972; U.S. EPA, 1982c, 1993b). 

S deposition can also have direct effects on plants via nutrient enrichment pathways. Sulfur is an 

essential nutrient for protein synthesis in plants. Adequate S supply for sustaining plant health is 0.01% to 

0.05% in soils (Nriagu, 1978). SO4

2−

 is the dominant form of bioavailable S in soils. Plants can also 

utilize volatile S compounds such as SO2 in the atmosphere to fulfill nutrient requirements 

(Rennenberg, 1984). This S is directly available for diffusive uptake through the leaf surface to support 

plant growth (Jager and Klein, 1980), and can also become bioavailable in the soil for plant root uptake 

(Moss, 1978). However, excess S inputs via atmospheric deposition can be toxic to plants and result in 

delayed flowering, reduced growth, and mortality (Rennenberg, 1984; Roelofs, 1991; Smith, 1981; 

Smolders and Roelofs, 1996). Plants that have exhibited reduced growth due to S toxicity have also been 

observed to have reduced molybdenum (Mo) uptake and increased copper (Cu), manganese (Mn), and 

zinc (Zn) uptake (Gupta and Mehla, 1980; Munro and Gupta, 1969). The threshold level of S toxicity is 

variable among species (Mudd and Kozlowski, 1975). 

Koch et al. (1990) found that hypoxia and high levels of sulfide (>1 mM) limited wetland plant 

growth by inhibiting nutrient uptake. Sulfide toxicity to plants (e.g., Carex spp. Juncus acutiflorus, 

Galium palustre, Gramineae) has also been observed in wetland mesocosm experimentally enriched with 

SO4

2−

 (Lamers et al., 1998). Biomass regrowth was significantly reduced for these species for both 2 and 

4 mmol/L SO4

2−

 treatments (Lamers et al., 1998). Van der Welle (2007) also showed that increased SO4

2−

loading had negative effects on aquatic macrophytes (Stratiotes aloides and Elodea nuttallii), via sulfide 

toxicity. Though S. aloides was native to the study region (The Netherlands) of Van der Welle (2007), it is 

considered a noxious invasive plant in the U.S. However, E. nuttallii is native to the U.S., widely 

distributed across 33 states, and is considered threatened in Kentucky and a species of concern in 

Tennessee (http://plants.usda.gov/). Negative impacts from elevated rates of atmospherically deposited 

SO4

2−

 on this species could be of concern. It is important to note, however, that the SO4

2−

 concentrations 

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reported in these studies were much higher than would generally be expected to occur in regions of the 

U.S. exposed to elevated atmospheric S deposition. 

Fe concentrations can influence the level of sulfide toxicity in wetland sediments (Lamers et al., 

2002; Smolders et al., 2001). Free sulfide produced through SO4

2−

 reduction is able to bind with Fe, 

forming insoluble Fe sulfide (FeS). If sufficient Fe is present, this complexation can reduce or eliminate 

sulfide toxicity to plants by removing the free sulfide from solution. Van der Welle (2007) confirmed the 

role of Fe in buffering against sulfide toxicity to plants by observing no toxic effects when sufficient Fe 

was available to precipitate free sulfide. 

However, the formation of FeS can disrupt, or compete with, Fe phosphate (FePO4) complexation, 

resulting in P release and potential undesirable eutrophication effects on downstream receiving waters 

(Caraco et al., 1989; Smolders et al., 2003). Iron(III) hydroxides and iron(III) phosphates are reduced in 

anaerobic soils and highly insoluble FeS is formed, increasing phosphate (PO4

3−

) mobility and 

bioavailability in surface waters (Smolders et al., 2006). This process has been termed “internal 

eutrophication” since P is mobilized from within the system and is not contributed from an external 

source (Roelofs, 1991). Increased nutrient availability via SO4

2−

-induced P release from wetland 

sediments can result in changes in aquatic vegetation community composition. Rooted aquatic 

macrophytes can be out-competed by non-rooting floating species and filamentous algae (Smolders et al., 

2003). If Fe is available in high enough concentrations, it can prevent P release from saturated soils with 

high S loading by providing adequate Fe to bind with sulfide without releasing P (Van der Welle, 2007). 

The observation that NO3

−

 addition decreases P release in wetland enclosures provides further 

indication that S-induced P release is related to redox conditions and microbial dynamics in the soil 

profile (Lucassen et al., 2004). Sufficiently high NO3

−

 concentrations can prevent SO4

2−

 reduction, and 

subsequent interruption of Fe-P binding, by maintaining redox status above that suitable for SO4

2−

reduction (Lucassen et al., 2004). In the absence of a sufficient supply of NO3

−

 to act as a redox buffer, 

SO4

2−

 will undergo reduction and potentially trigger the internal eutrophication mechanism described 

above (Lucassen et al., 2004). It is important to note that most research on the topic of internal 

eutrophication of has occurred in Dutch peatlands that have historically experienced much larger N 

loading than those in the U.S., making it difficult to extrapolate these findings to U.S. systems. 

Effects on Methane-producing Microbes 

Increased atmospheric S deposition and its impacts on microbial community structure can also 

affect CH4 emissions from saturated soils. Early investigation into the effects of elevated pore water SO4

2−

concentrations on CH4 emissions from wetland soils involved the application of a single large dose of 

SO4

2−

 (Fowler et al., 1995). CH4 production was observed to be suppressed (40% less than the control) 

three weeks after the addition of SO4

2−

. This was followed by a 4–week recovery period, after which CH4

production had returned to pre-treatment levels. These results led to the hypothesis that large single 

addition of SO4

2−

, as applied by Fowler (1995), only stimulate SO4

2−

-reducing bacterial (SRB) activity for 

a short time. Fowler concluded that studies that more closely approximated long-term SO4

2−

 loading, as 

with atmospheric S deposition, were necessary. Dise and Verry (2001) and Gauci et al. (2002) showed that 

smaller and more numerous SO4

2−

 additions sustained CH4 emission suppression in wetland soils. These 

studies more closely approximated SO4

2−

 enrichment associated with acidic deposition. These results 

provided support to the hypothesis that continuous elevated SO4

2−

 deposition, as encountered in areas 

affected by acidic deposition, contributes to sustained suppression of CH4 emissions from wetland soils. 

Gauci et al. (2004) considered both methods of SO4

2−

 addition (a single large dose versus numerous 

small doses) in the same experiment. Rates of SO4

2−

 addition ranged between 15 and 100 kg S/ha/yr to 

wetland soils previously exposed to 4 kg S/ha/yr of atmospheric S deposition. They observed that CH4

emissions from these wetland soils were almost equally suppressed under each treatment, and that each 

treatment experienced the same CH4 emission “recovery” as found in the single-dose Fowler et al. (1995) 

study. The two main conclusions from Gauci et al. (2004) were that 15 kg S/ha/yr is either at or above the 

rate of S deposition required to achieve maximum CH4 emission suppression, and that a single large dose 

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of S has similar effects on CH4 emission suppression as do numerous smaller doses. The authors observed 

that CH4 emissions from treated soils recovered to levels that were observed from untreated soils during 

the period of plant senescence. This led to the hypothesis that SRB will out-compete CH4 producing 

bacteria under conditions of elevated S deposition and during vigorous plant growth when available C 

substrate is limited, but that root exudates and root degradation during the period of plant senescence 

provides adequate substrate to sustain both methanogenic and SRB bacteria populations. Although the 

suppression of CH4 emissions can fluctuate based upon plant growth cycles, elevated S deposition is 

considered to shift microbial community structure in favor of SRB over methanogenic bacteria, reducing 

annual CH4 emissions from saturated soils (Granberg et al., 2001). However, climate change simulations 

suggest that increased soil temperature may override the suppressive effect that elevated S deposition has 

on CH4 emissions (Gauci et al., 2004; Granberg et al., 2001). 

3.4.1.2. Cycling and Storage of Sulfur 

Terrestrial Ecosystems 

Considerable effort was devoted in the 1980s to the computation of S budgets for watersheds and 

forest plots, with the objective of evaluating S retention and release. These budgets were subject to 

complications from fluxes that could not be measured directly, such as dry deposition and weathering, but 

they generally indicated net S retention at sites south of the line of glaciation — a result attributed to net 

adsorption of SO4

2−

 (Cappellato et al., 1998; Rochelle and Church, 1987). During the 1990s, little or no 

decrease in SO4

2−

 concentration occurred in streams in the Ridge and Blue Ridge physiographic 

provinces, despite regional decreases in atmospheric deposition of S (Webb et al., 2004), and no evidence 

of S addition from mine drainage. This lack of response in stream chemistry has been generally attributed 

to a shift in S equilibrium between the adsorbed and solution phases under conditions of decreased 

atmospheric inputs of SO4

2−

. This interpretation is supported by a decrease in concentrations of adsorbed 

SO4

2−

 from 1982 to 1990 in a Piedmont soil in South Carolina that received decreasing levels of S 

deposition during this period (Markewitz et al., 1998). This same soil also experienced an increase in 

adsorbed SO4

2−

 from 1962 to 1972 (Markewitz et al., 1998). The only published S budget more recent 

than 1992 for an unglaciated site in the U.S. (Castro and Morgan, 2000) also suggested a net release of 

SO4

2−

. This upland Maryland watershed released 1.6 times more SO4

2−

 than measured in throughfall in 

1996–97. 

Numerous S budgets were compiled in the 1980s for glaciated sites, and results generally indicated 

that inputs approximately equaled outputs on an annual basis (Rochelle and Church, 1987). The 

observation of little or no S retention at glaciated sites was attributed to relatively low SO4

2−

 adsorption 

capacity in soils. Balanced S budgets in glaciated regions implied that decreases in atmospheric 

deposition of S would lead directly to decreases in SO4

2−

 leaching. The strong correlation between recent 

decreases in both atmospheric S deposition and SO4

2−

 concentrations in surface waters is widely 

recognized to be a result of this direct linkage (Stoddard et al., 2003). Nevertheless, considerable evidence 

also indicates that S inputs in glaciated ecosystems do not behave conservatively, but instead are cycled in 

part through microbial and plant biomass (Alewell and Gehre, 1999; David et al., 1987; Likens et al., 

2002). As a result, large quantities of S are stored in organic forms within the soil. David et al. (1987) 

found that annual S deposition (wet plus dry) at a site in the central Adirondack region of New York was 

about 1% of the organic S pool in the soil. Houle et al. (2001) estimated that annual S deposition at 11 

sites in North America ranged from 1% to 13% of the organic S pool in soil. 

The S cycle in forest ecosystems can be represented as a series of input, uptake, and output terms 

(Figure 3-58). Some of the fluxes illustrated in this schematic drawing can be measured in the field, 

including wet deposition, litterfall, and throughfall. Other fluxes must be calculated or estimated, which 

involves considerable uncertainty (Johnson and Mitchell, 1998). Perhaps the most important uncertainty 

concerns the amount of dry deposition, which can be substantial (Lindberg et al., 1990). 

 3-185
Source: Johnson (1984) 

Figure 3-58. Representation of the S cycle in forest ecosystems. 

Atmospheric deposition is an important part of the S cycle, including in areas that are not exposed 

to appreciable air pollution levels. In fact, although agricultural S and geologic S (especially associated 

with mining activities) can be locally important or dominant, atmospheric S inputs may constitute the 

major source of S input to many terrestrial ecosystems (Johnson and Mitchell, 1998; Probert and Asmosir, 

1983). 

Much of the organic S stored in soil is in C-bonded forms that are relatively unreactive, but can be 

oxidized by bacteria or mineralized to SO4

2−

 under oxic conditions, which are typically found in 

moderately well drained to well drained soils (Johnson and Mitchell, 1998). Carbon-bonded S in forest 

soils can be found in a variety of organic S compounds, including amino acids, sulfolipids, and sulfonic 

acids. Carbon-bonded S can also be found in humic material in the form of aliphatic and aromatic 

structures (Likens et al., 2002). Furthermore, strong correlations have been shown between levels of 

atmospheric deposition of S and concentrations of S in soil (Driscoll et al., 2001b; Novák et al., 2005). 

Long-term increases in concentrations of total S in soils that are at least partially attributable to increases 

in organic S have also been documented (Knights et al., 2000; Lapenis et al., 2004), although the study of 

Houle et al. (2001) did not find a relation between these factors. A Swedish “clean roof” study also 

provides some insight into the role of organic S in possibly delaying chemical recovery from acidification 

due to S deposition (Mörth et al., 2005). After 9 years of application of pre-industrial levels of 

 3-186
S deposition, the amount of SO4

2−

 in runoff still exceeded inputs by 30%. Most of the S in runoff was 

attributed to mineralization of organic S in the O horizon. 

Thus, research on the effects of atmospheric S deposition on soils has indicated pronounced 

changes in soils from sustained SO4

2−

 leaching, and accumulation of S through physical/chemical 

adsorption and biological assimilation. The recent evidence of net loss of S from soils at a number of sites 

is likely a response to decreased atmospheric inputs. The gradual loss of previously accumulated S 

contributes to continued SO4

2−

 leaching. Uncertainties in estimates of ecosystem fluxes such as 

weathering and dry deposition, and complications in discerning the effects of desorption from 

mineralization make it difficult to predict when S outputs will no longer exceed inputs as levels of S 

deposition continue to decline. Research based on experimental reduction of S inputs suggests that this 

process will occur on a decadal time scale (Martinson et al., 2005; Mörth et al., 2005). The long-term role 

of C-bonded S adds further uncertainty because enhancement of S mineralization by a warming climate 

could also affect S retention and release (Driscoll et al., 2001b; Knights et al., 2000). This process can be 

microbially catalyzed, and bacteria are generally more active at higher temperature. 

Transitional Ecosystems 

Transitional ecosystems exert important controls on watershed S budgets, especially in watersheds 

that contain extensive wetland development. Sulfur storage in wetland soils provides an important 

buffering system that restricts chronic SO4

2−

 leaching to surface waters. Input-output studies of bogs in 

Massachusetts (Hemond, 1980),Ontario (Urban and Bayley, 1986), and Minnesota (Urban and Eisenreich, 

1988) suggested more than 50% retention of atmospheric S inputs. However, oxidation of S that was 

previously stored in wetland soils can provide an important episodic source of SO4

2−

 to downstream 

surface waters. Thus, the presence of wetlands in a watershed can either temporarily increase or decrease 

the flux of SO4

2−

 to surface waters, and these differences are largely determined by changes in hydrology 

and redox conditions in wetland soils. Overall, wetlands act as sinks for S because of microbial SO4

2−

reduction and sequestering of reduced S as sulfide minerals and organic S. 

Changes in S flux that are controlled by processes in transitional ecosystems can have important 

effects on surface water chemistry. For example, reduction of SO4

2−

 in sediments by assimilatory and 

dissimilatory processes is an important source of acid neutralizing capacity (ANC) to lakes having long 

hydraulic residence time, and a likely source also to beaver ponds and wetlands. In-lake ANC production 

is mostly due to S retention from microbial SO4

2−

 reduction (Brezonik et al.; Schindler and Bayley, 1993; 

Turner et al., 1990b). It is unlikely, however, that the changes in S flux caused by wetlands and ponds in a 

watershed would be large enough to have any direct non-acidification effects on biota. More likely, the 

major non-acidification effects of wetland influence on S cycling relate to changes in Hg methylation in 

wetland soils. This is discussed in Section 3.4.1.4. Other changes can also occur, including enhanced 

release of N and P from wetland soils. 

Some of the organic S in wetlands can be converted to reduced S gasses, including dimethylsulfide 

and hydrogen sulfide (under acidic conditions), and released to the atmosphere. Up to 30% of the 

atmospheric deposition of S in remote areas may be derived from release of reduced S gasses from 

wetlands (Nriagu et al., 1987). Thus, wetland processes can have important effects on local atmospheric S 

deposition and trace gas emissions. 

Aquatic Ecosystems 

In aquatic ecosystems that are sensitive to acidification from atmospheric S deposition, SO4

2−

 is 

generally highly mobile within the ecosystem. Acid-sensitive streams tend to be relatively fast-flowing, 

high-gradient, low-order streams that exhibit high SO4

2−

 mobility. Acid sensitive lakes tend to be 

relatively small, headwater lakes with short hydraulic residence times (weeks to months). In such streams 

and lakes, most of the SO4

2−

 contributed by inflowing ground and surface waters is directly flushed 

through the ecosystem and emerges as outflow. 

 3-187
However, larger streams, streams that flow through series of ponds (i.e., beaver ponds) or lakes, 

and especially larger lakes, tend to have longer hydraulic residence, and provide opportunity for microbial 

S reduction in sediments. This S reduction can have important effects on the concentration of SO4

2−

 in 

drainage water, and results in the generation of ANC. The importance of sediment reactions to the acidbase chemistry of surface water depends mainly on the flux rate of material across the sediment-water 

interface and the amount of time that water remains in contact with the sediment (Baker and Brezonik, 

1988; Kelly et al., 1987; Turner et al., 1991). In some lakes having long water residence times, about half 

of the input SO4

2−

 is retained in lake sediments (Baker and Brezonik, 1988; Kelly et al., 1987). 

S is an essential nutrient for algae and planktonic bacteria. Nevertheless, S concentration in most 

lakes is well above the limiting concentration for algal productivity, and therefore biotic S uptake in the 

water column is not a quantitatively important part of the S cycle in acid-sensitive lakes (Turner et al., 

1991). 

3.4.1.3. Export of Sulfur 

Terrestrial Ecosystems 

In order for atmospherically deposited S to exert influence on drainage water, with the range of 

associated environmental effects that can occur, it must be exported as SO4

2−

 from the soil. If the 

incoming S in atmospheric deposition is retained in the vegetation or soil compartments, it will not be 

available to affect soil water or surface water downstream within the watershed. In areas of S deposition, 

almost all deposited S moves into the soil and can then be exported from the terrestrial ecosystem or 

adsorbed on soil. In most parts of the U.S., most deposited S is exported in drainage water. In much of the 

southeastern U.S., however, S adsorption on soil substantially limits S export (See discussion in 

Annex B). 

Transitional Ecosystems 

When saturated, wetland soils act as sinks for incoming S via SO4

2−

 reduction. Sulfide is produced 

through this process and sequestered in anoxic wetland sediments (Mitsch and Gosselink, 2000). 

However, it has been observed that wetlands can act as sources of SO4

2−

 to downstream drainage waters 

during storm events that follow prolonged periods of drought (Dillon and LaZerte, 1992; Devito and Hill, 

1999; Eimers and Dillon, 2002; Jeffries et al., 2002; Laudon et al., 2004; Mitchell et al., 2006). The 

mechanism has been described as follows. SO4

2−

 is produced through oxidative processes in wetland 

sediments when they are exposed to atmospheric O2 as the water table falls during periods of drought. 

This newly formed SO4

2−

 is mobile, and therefore can be flushed from the wetland into streams or lakes 

when the water table rises as more typical hydrologic conditions resume. This flush of SO4

2−

 can result in 

episodic acidification of downstream surface waters (Laudon et al., 2004) and potentially prolong the 

chemical recovery of surface water ANC as S deposition declines (Aherne et al., 2006). 

Much of the supporting research on this topic has been performed within the boreal watersheds of 

Ontario, Canada (Dillon and LaZerte, 1992; Devito and Hill, 1997; Jeffries et al., 2002; Aherne et al., 

2004; Laudon et al., 2004). A Sphagnum-conifer wetland within the Plastic Lake watershed in Ontario 

was determined to be a source of SO4

2−

 to downstream drainage waters after extended periods of 

summertime drought (Dillon and LaZerte, 1992). Comparisons of stream water chemistry were made 

between the wetland inlet and the wetland outlet, which drains a watershed consisting entirely of upland 

soils. The results showed little difference between SO4

2−

 concentrations in the wetland inlet and outlet 

during typical hydrologic conditions. However, SO4

2−

 concentrations in the outlet increased by up to a 

factor of 5 during storm events that followed extended periods of drought. This occurred during 4 

separate years. 

Most the study watersheds in the Plastic Lake region of Ontario, Canada have consistently exported 

more SO4

2−

 than was atmospherically deposited on an annual basis over an 18–year period (Eimers and 

 3-188
Dillon, 2002). This observation suggests either the existence of an internal watershed SO4

2−

 source, or an 

underestimation of S deposition. It is possible that dry deposition is underestimated (Likens et al., 1990; 

Edwards et al., 1999), but a variety of potential watershed sources of additional S have also been 

proposed in areas that are sensitive to atmospheric S deposition, including: 

 weathering of S-containing minerals (Baron et al., 1995) 

 desorption of SO4

2−

 previously adsorbed to soils when S deposition was higher (Driscoll et al., 

1995; (Mitchell et al., 1996) 

 mineralization of S previously incorporated into organic matter (Driscoll et al., 1998) 

 drought-related oxidation and release of S stored in wetlands and riparian soils (Dillon and 

LaZerte, 1992; Dillon et al., 1997) 

Underestimation of dry deposition was not considered to be a significant issue for the Canadian 

study watersheds (Eimers and Dillon, 2002). Furthermore, mineral weathering is not considered a 

significant source of SO4

2−

 in that region, due to the low S content of the bedrock (Neary et al., 1987). 

Reoxidation and mobilization of S stored in wetland sediments was considered the most likely 

explanation for the observed higher SO4

2−

 outputs for those watersheds that contain a significant 

proportion of wetland. Other mechanisms, including increased soil SO4

2−

 desorption and/or increased S 

mineralization in response to decreased S deposition inputs, may explain the SO4

2−

 input/output 

imbalance observed in watersheds containing little or no wetland area (Alewell and Gehre, 1999; Eimers 

and Dillon, 2002). Jeffries et al. (1995, 2002) determined that within the Turkey Lakes watershed in 

western Ontario wetland, reoxidation and SO4

2−

 remobilization mechanism can delay lake acidification 

recovery by as much as 6 years. 

Wetland S transformations have been incorporated into state-of-the-science modeling to better 

describe climate-induced acidification effects on lake water chemistry (Aherne et al., 2004, 2006). A 

wetland component to the MAGIC model was developed and tested for its ability to predict observed 

stream water SO4

2−

 fluxes from the Plastic Lake watershed (Aherne et al., 2004). This model was then 

used to investigate acidification recovery under two different climate scenarios: an “average climate” 

scenario consisting of long-term (most recent 20 years) monthly precipitation and runoff;and a “variable 

climate” scenario that included sequential repetition of the measured monthly precipitation and runoff for 

the preceding 20 years. The average climate scenario did not include any significant drought periods, 

whereas the variable climate scenario included several periods of summer drought. 

Model results under the average climate scenario suggested that chemical recovery of lake water 

would occur, with ANC reaching 40 μeq/L by 2020 and 50 μeq/L by 2080. However, the variable climate 

scenario projected that recovery would be greatly reduced. ANC recovery by 2080 was estimated to only 

reach 2.6 μeq/L. The authors acknowledged that reiterating the past 20 years of climate under the variable 

climate scenario was somewhat arbitrary. Nevertheless, results suggested that climate effects on the 

cycling of S can modify chemical recovery of lake water from acidification in watersheds that are 

wetland-influenced. 

Aquatic Ecosystems 

Export of S from surface waters is controlled primarily by retention in sediments through microbial 

SO4

2−

 reduction. In-stream and in-lake biological demand for S is generally a very small component of the 

S input levels in areas affected by atmospheric S deposition. Sulfur reduction can be an important process 

regulating S export from aquatic ecosystems, mainly in waters that exhibit long hydraulic retention. 

Sulfur reduction in lake and pond sediments can also be closely associated with Hg methylation. 

Therefore, the dynamics of S storage and export can influence the bioavailability of Hg to fish, 

piscivorous wildlife, and humans who consume large quantities of fish. 

 3-189
3.4.1.4. Sulfur and Methylation of Mercury 

Hg has long been established to be a potent neurological, reproductive, and developmental toxin 

that accumulates at progressively higher concentrations in higher trophic levels (biomagnification). The 

biogeochemical cycle of Hg is closely tied to that of S, and the presence of SO4

2- in wetlands and lake 

sediments is essential for entry of Hg into the food web. Hg is taken up by living organisms and 

bioaccumulates in the MeHg form. For the protection of human health, the U.S. EPA set the fish tissue 

criterion for MeHg at 0.3 μg/g. This has resulted in 2,436 fish consumption advisories for Hg in 2004, 

2,682 in 2005 and 3,080 in 2006. Forty-eight states, one territory, and two tribes have issued Hg 

advisories. Eighty percent of all fish consumption advisories have been issued, at least in part, because of 

Hg. Most of the new Hg advisories issued in 2005 and 2006 were in Wisconsin (293), Michigan (46), 

New York (36) and Minnesota (32). In 2005, American Samoa, Kansas, Oklahoma and Utah started 

issuing Hg advisories, and Iowa started in 2006. In 2006, a total of 14,177,175 lake acres and 882,963 

river miles were under advisory for Hg. As of July 2007, 23 states have issued statewide advisories for Hg 

in freshwater lakes and/or rivers. 

Examples of elevated tissue and blood Hg have also been reported in terrestrial animals, and a few 

of those studies included comparisons between aquatic and terrestrial species. In a survey of four species 

of turtles in and around a portion of river with a long-term history of industrial Hg pollution, Bergeron 

et al. (2007) observed that species differences in blood Hg level varied with the proportion of the species’ 

diet that is aquatic. The species with the smallest aquatic component to its diet still had higher blood Hg at 

contaminated sites than at non-contaminated sites. However, its blood Hg level at contaminated sites was 

approximately 90% lower than the three other species. In non contaminated parts of the river, blood Hg 

level was very low for all species, even though it was lower in the most terrestrial one than in the others. 

A survey of songbirds on the same 24 km stretch of river (Cristol et al., 2008) found elevated Hg in the 

blood of terrestrial birds in proximity to the river, but not at reference sites away from the river, or 

upstream from the contaminated portion. In their survey of northeastern freshwater avian species, Evers 

et al. (2005) observed that among birds living in ecosystems with a large aquatic component, blood, egg, 

and tissue Hg levels were much lower in insectivorous species than in piscivorous ones. Rimmer et al. 

(2005) sampled blood and feather samples of insectivorous passerines in their summer and winter habitat 

in the northeastern U.S. and southeastern Canada, and Cuba, Haiti and the Dominican Republic. They 

found elevated MeHg in birds without clear connection to aquatic food webs. However, the putative role 

of S in Hg methylation in terrestrial ecosystems has not been explored. 

SRB are the main agent of Hg methylation in transigional and aquatic environments, and changes 

in SO4

2−

 deposition have been shown to result in commensurate changes in both Hg methylation, and Hg 

levels in fish. 

Effects of Mercury in Aquatic Biota 

Adverse effects of Hg, including behavioral, reproductive, neurochemical, and hormonal effects, 

have been demonstrated in piscivorous mammals and birds (Scheuhammer et al., 2007; U.S. EPA, 

1996b), and MeHg has been shown to be the form in which Hg accumulates in tissue of fish and 

piscivorous species (Becker and Bigham, 1995; Bloom, 1992; Harris et al., 2003; Scheuhammer et al., 

2007). Exposure of fish and wildlife to Hg occurs primarily through the diet. Top predatory, especially 

piscivorous, animals feeding on aquatic food chains are at greatest risk for Hg accumulation and toxicity 

(Scheuhammer et al., 2007). Wildlife living in inland lake habitats tends to accumulate higher tissue 

concentrations of Hg than those living in coastal habitats (Evers et al., 2005; Frederick et al., 2002) . 

Available data suggest that numerous wild populations of fish, birds, and mammals experience 

MeHg exposures that are high enough to cause substantial reproductive, behavioral or health impairment. 

Reproduction is the component of response that appears to be most affected (Scheuhammer et al., 2007). 

In fish, exposure to MeHg can affect growth, reproductive ability, morphological characteristics, and 

feeding efficiency. Examples of studies documenting the effects of MeHg on fish include Friedmann et al. 

 3-190
(1996), who investigated the effects of low-level (0.137 μg Hg/g) and high-level (0.987 μg Hg/g) dietary 

Hg concentrations (as MeHg) on hatchery juvenile walleye (Stizostedion vitreum). These experimental Hg 

exposures were chosen to reflect dietary Hg concentrations commonly encountered in North American 

lakes and streams. Results showed impaired fish growth and impaired gonad development in males. Fjeld 

et al. (1998) exposed grayling (Thymallus thymallus) embryos to varying concentrations of MeHg (0.16, 

0.8, 4.0, and 20 μg Hg/L) during their first 10 days of development. This exposure resulted in body tissue 

MeHg concentrations of 0.09, 0.27, 0.63, and 3.80 μg Hg/g respectively. Morphological deformities were 

observed in fish exposed to the highest level of MeHg. Samson and Shenker (2000) also observed 

morphological disturbance in zebrafish (Danio rerio) at embryonic MeHg exposure levels of 20 and 

30 μg CH3HgCl/L. Other fish such as mummichog (Fundulus heteroclitus) and rainbow trout 

(Oncorhynchus mykiss) have also been observed to suffer teratogenic effects such as cyclopia, tail 

flexures, cardiac malformations, jaw deformities, twinning, and axial coiling from embryonic MeHg 

exposure (Samson and Shenker, 2000). Fish survival and subsequent population status can be jeopardized 

as a result of exposure to MeHg. Fathead minnows (Pimephales promelas) showed impaired feeding 

efficiency after exposure to both 6.79 and 13.57 μg HgCl2/L (Grippo and Heath, 2003). Reduced feeding 

efficiency and competitive ability was also observed in grayling exposed to 0.8 to 20 μg Hg/L as embryos 

(Fjeld et al., 1998). 

Role of Sulfur in the Biogeochemical Cycle of Mercury 

The global cycle of Hg has atmospheric, aquatic, edaphic, and biotic components. In the 

atmosphere, Hg is transported locally, regionally, and globally, depending on speciation. Both elemental 

and oxidized forms are found in soil and aquatic environments, but the oxidized form is more prevalent. 

MeHg is the form that is found in tissues (Figure 3-59). SRB are the main agent of Hg methylation in the 

environment. Although Hg methylation in watersheds has been shown to occur through other processes, 

their contribution to MeHg loads is negligible in comparison to that of SRB-mediated methylation. 

Addition of SO4

2– has been demonstrated to stimulate Hg methylation by SRB in studies spanning scales 

from the culture of isolated bacteria, to the experimental amendment of entire lakes. Those studies have 

included addition of SO4

2– at rates corresponding to observed deposition. 

Figure 3-59. Simplified cycle of mercury, showing the role of sulfur. Arrows are not proportional with 

actual rates. 

 3-191
SRB are commonly found in anoxic wetland and lake bottom sediments (Compeau and Bartha, 

1985; Gilmour and Henry, 1991; 1992). Under increased SO4

2−

 availability, their number and activity 

increase. The mechanisms for Hg methylation, as mediated by SRB, have been discussed by Choi et al. 

(1994), Ekstrom et al. (2003), and Ekstrom and Morel (2004). Abiotic mechanisms responsible for Hg 

methylation have been discussed by Weber (1993), Hintelmann and Evans (1997), and Siciliano et al. 

(2005). Studies demonstrating the response of SRB-mediated methylation to SO4

2−

 in pure cultures 

include King et al. (2000), and Benoit et al. (2001). This response has also been established in samples of 

soil and sediments (Compeau and Bartha, 1985; Gilmour et al., 1992; Harmon et al., 2004), and in 

experimental manipulations in wetlands and lakes (Benoit et al., 2003; Branfireun et al., 1999; 2001; 

Frost et al., 1999; Harmon et al., 2004; Jeremiason et al., 2006; Watras et al., 2006; Wiener et al., 2006). 

Evidence regarding the importance of SRB in MeHg production was provided by Compeau and 

Bartha (1985), and Gilmour et al. (1992), who showed that MeHg production was substantially reduced 

with addition of a known SRB inhibitor (Na2MoO4). This is also in agreement with observations of SRBmediated Hg methylation in salt marsh sediments (Compeau and Bartha, 1985). The work of Gilmour 

et al. (1992) considered anoxic lake bottom sediments, rather than wetland sediments. However, anoxia is 

also common in freshwater wetland sediments, where SO4

2−

 addition has also been observed to enhance 

Hg methylation (Branfireun et al., 1999; Harmon et al., 2004; Jeremiason et al., 2006). Accumulation of 

sulfidic forms of S in sediments, also resulting from SRB activity, has been shown to diminish the 

availability of S to SRBs, and thus net Hg methylation (Benoit et al., 1998; 1999a; 2001; Gilmour, 1998; 

King et al., 2001a). 

Source: Gilmour et al. (1992). 

. 

Figure 3-60. (A) SO4

2– and (B) MeHg concentrations as a function of time in sediment slurries made from 

Quabbin Reservoir littoral sediments. Each delta point represents the average value from 

three separate incubations and the associated standard error. 

 3-192
Gilmour et al. (1992) investigated MeHg production within anoxic sediments of a reservoir located 

in central Massachusetts. Elevated MeHg production with SO4

2−

 addition was measured in both 

experimental laboratory slurries (Gilmour et al., 1992) (Figure 3-60B) and intact sediment cores. The 

background SO4

2−

 concentration in the experimental sediment slurries was 60 μeq/L. SO4

2−

 additions of 0, 

100, 200, and 400 μeq/L were applied to these samples in the presence of 50 mg/L of Hg as HgCl2. The 

rate of production and the final concentration of MeHg increased in proportion to the initial SO4

2−

concentration. Furthermore, SO4

2−

 concentrations decreased during the experiment (Gilmour et al., 1992), 

experiment (Figure 3-60A), suggesting that SO4

2−

 reduction had occurred. MeHg production within 

isolated lake bottom sediment cores was also enhanced across a gradient of SO4

2−

 addition (3 to 1040 

μmol sodium SO4

2−

 [Na2SO4]; Figure 3-61). Sediment MeHg production was most enhanced when SO4

2−

concentration was above about 60 μeq/L, with increased production from a pre-treatment background 

MeHg concentration of 0.26 ng/g to approximately 7.0 to 8.5 ng/g. These results suggest maximum 

MeHg production at SO4

2−

 concentrations between about 200 and 400 μeq/L, although optimal conditions 

for methylation are likely to vary with other factors that influence SO4

2−

 reduction. SO4

2−

 concentrations 

in the range of these experiments (about 60 to 200 μeq/L) are often found in waters affected by S 

deposition in the U.S. 

Interacting factors 

Many studies have also shown an association between low lake water pH and high Hg 

concentrations in fish (Driscoll et al., 1994a; Grieb et al., 1990; Kamman et al., 2004; Suns and Hitchin, 

1990). Hrabik and Watras (2002) found that decreases in fish Hg concentration in an experimentally deacidified lake basin exceeded those in the reference lake basin by a factor of 2 over a 6–year period of 

experimental de-acidification. The association between low pH and high Hg accumulation in fish suggests 

a response of methylation to pH, but although SRB activity does respond to pH (Kelly et al., 2003), 

quantification of the interactive effects of pH with SO4

2−

 in the environment has only been tentative. 

Other interacting factors, mainly Fe, P, and dissolved organic matter, have been identified, but very 

incompletely quantified (Munthe, 2007; Watras and Morrison, 2008). Driscoll et al. (2007b) developed 

indicators of Hg sensitivity using two stratified, random-probability surveys of northeastern lakes 

combined with the survey data sets of Chen et al. (2005). This analysis showed that lakes with Hg levels 

above the U.S. EPA criterion of 0.3 μg/g in yellow perch had significantly higher DOC, and lower pH, 

ANC, and total P than lakes with fish Hg concentrations below 0.3 μg/g (Driscoll et al., 2007b). Based on 

the probability surveys, they calculated that about 20% of lakes in the region had total P concentrations 

above 30 μg/L and yellow perch Hg concentrations below 0.3 μg/g. In the remaining 80% of lakes, 75% 

had yellow perch Hg concentrations exceeding 0.3 μg/g when surface water DOC levels exceeded 4.0 mg 

C/L, a pH of less than 6.0, or an ANC of less than 100 μeq/L. Most Hg in the water column of freshwaters 

is bound to organic matter, either to DOC or to suspended particulate matter. Therefore, total Hg and 

MeHg concentrations are often positively correlated with DOC in lake waters (Driscoll et al., 1994a; 

Mierle and Ingram, 1991; U.S. EPA, 1996b). DOC, in turn, has an important influence on pH. Thus, 

several interrelated factors seem to affect Hg loading in tissue. For example, Driscoll (1995) found one or 

more yellow perch exceeding the 0.5 μg/g action level in 14 of 16 Adirondack study lakes despite wide 

ranges in pH (to above 7) and ANC (to above 200 μeq/L). Driscoll et al. (1994a) concluded that the most 

obvious factor regulating the concentration and availability of both total Hg and MeHg in Adirondack 

lakes is DOC. They found increased fish Hg concentrations with increasing DOC up to DOC 

concentrations of about 8 mg/L, followed by lower concentrations in the highly dystrophic Rock Pond 

(DOC = 26 mg/L). They hypothesized that DOC may bind with MeHg at very high DOC concentration, 

limiting the bioavailability of the Hg. In addition, calculations made by Driscoll et al. (1995) with the Hg 

Cycling Model suggested that increases in DOC result in increasing concentrations of Hg in biota, but 

decreases in the bioconcentration factor of Hg in fish tissue. Because the transport of Hg to Adirondack 

lakes appeared to be linked to DOC production from wetlands within the watersheds of the study lakes, 

Driscoll et al. (1995) concluded that DOC is important in regulating Hg concentrations in the lakes, and 

 3-193
ultimately the supply to fish. In a peatland experiment testing the effects of various sources of organic C, 

Mitchell et al. (2008) demonstrated that while SO4

2−

 is required for methylation of Hg, the addition of 

some sources of C greatly enhanced the process. The combinations of C and SO4

2−

 additions that 

enhanced methylation in the experiment corresponded to the combinations present in MeHg ‘hot spots’ 

within watersheds that include peatlands. 

Figure 3-61. MeHg produced in sediment cores incubated two weeks under artificial lake water containing 

3–1040 μM Na2SO4. Error bars represent standard error between two replicate cores. Data 

from cores incubated under natural water are shown individually. The average MeHg 

concentration in unamended Purgee sediments sampled in July was 0.26 ± 0.01 ng/g (n = 2). 

Several researchers have suggested that the export of Hg from terrestrial watersheds to lakes may 

be controlled in large part by the nature of watershed soils and the transport of naturally occurring organic 

acids (Engstrom et al., 1994; Meili, 1991; Mierle, 1990; Mierle and Ingram, 1991). This suggestion is 

based partly on the fact that dissolved organic matter strongly binds with Hg, and partly on the observed 

positive correlation between Hg accumulation in lake sediments and the ratio of the watershed area to the 

lake area in relatively undisturbed watersheds (r2

 = 0.91; r2 = 0.91) (Engstrom et al., 1994). Engstrom 

et al. (1994) concluded that Hg export from the terrestrial watershed to lake water may be explained by 

factors regulating the export of fulvic and humic matter and by watershed area. They based this 

conclusion on the close correlation between Hg concentration and humic matter in surface waters, the 

observation that peak concentrations of both Hg and dissolved organic matter tend to occur during periods 

of high runoff, and the experimental determination that Hg transport occurs primarily in upper soil 

horizons. 

Ecosystems Characteristics Conducive to Methylation 

S deposition is most likely to result in enhanced Hg methylation in regions that receive relatively 

high levels of atmospheric Hg and S deposition and that exhibit characteristics conducive to methylation. 

These include low ANC and low pH surface waters, with large upstream or adjoining wetlands (Chen 

et al., 2005; Scheuhammer and Blancher, 1994; Scheuhammer et al., 2007). Such sensitive ecosystems are 

prevalent in portions of the northeastern U.S. and southeastern Canada, but ecosystems with high Hg 

 3-194
methylation are present in other regions as well. Studies of Hg concentration in feathers, blood, and eggs 

of the common loon (Gavia immer) indicate decreasing concentrations from west to east in this region 

(Evers et al., 1998; 2003). This pattern is in general agreement with patterns of deposition of both Hg 

and S. 

Wetland environments have been shown to be significant areas of MeHg production and sources of 

export to downstream receiving waters (St. Louis et al., 1994). Wetland MeHg production has been 

measured at rates 26 to 79 times higher than in upland areas of a Canadian boreal forest (wetland: 1.84 to 

5.55 mg/ha/yr; upland: 0.07 mg/ha/yr) (St. Louis et al., 1994). Watersheds containing 14.0% to 16.3% 

wetland yielded 5 to 14 times more MeHg than upland catchments that lacked wetlands (St. Louis et al., 

1994). In the same region, St. Louis et al. (1996) found that all watersheds were net sinks for total Hg, but 

that watersheds containing wetlands regularly exported MeHg (St. Louis et al., 1996). However, MeHg 

export from these watersheds was not directly proportional to percent wetland coverage, indicating that 

other variables are also involved in the major processes that regulate MeHg production and export. In 

particular, the level of atmospheric Hg deposition and the acid-base chemistry of drainage water may be 

important. 

Branfireun et al. (1996) measured highest peat and pore water MeHg concentrations in wetland 

areas that exhibited characteristics of a poor fen environment (i.e., interaction with nutrient-poor ground 

water). St. Louis et al. (1996) observed that high water yield resulted in high MeHg export. Thus, the 

proportion of upland to wetland land area within a watershed was not the only control on MeHg export, 

but wetland type and annual water yield also played important roles (St. Louis et al., 1996). 

As noted by Munthe (2007), multiple hydrological, chemical, and biological characteristics of 

watersheds determine the movement of Hg between compartments. With regards to MeHg, however, the 

chemical and biological characteristics of the lake compartment may be more critical: comparing two 

remote lakes, one a seepage lake, and the other a drainage lake. Watras and Morrison (2008) found that 

although wetland MeHg export was the dominant external source of MeHg to the drainage lake, in-lake 

methylation remained four- to seven-fold greater than loading from the wetland. Likewise, Harris et al. 

(2007) demonstrated, using traceable stable isotopes of Hg in a whole ecosystem experiment, that nearly 

all of the increase in fish MeHg came from Hg deposited to the lake surface, with less than 1% of Hg 

deposited to the watershed being exported to the lake, in any form. 

Regardless, methylation of Hg occurs in anoxic sediments that contain a sufficient C source to 

support SO4

2−

-reducing bacterial activity along with an adequate supply of SO4

2−

 for SRB-mediated SO4

2−

reduction. These conditions are found in lake and pond bottom sediments (Gilmour et al., 1992), 

freshwater wetland sediments (Branfireun et al., 1999; Harmon et al., 2004; Jeremiason et al., 2006), and 

salt mash sediments (Compeau and Bartha, 1985). Such wetland systems, expected to exhibit high levels 

of Hg methylation, can be found throughout the U.S. 

In a 1998 preliminary national survey of 106 sites from 21 basins across the U.S., Krabbenhoft 

et al. (1999) examined the relations of total Hg and MeHg in water, sediment and fish, and concluded that 

wetland density was the single most important factor controlling MeHg production at the basin scale. 

Four study basins along the east coast of the U.S. had the greatest methylation efficiency, while 

nationwide, sub-basins characterized as mixed agriculture and forest cover types had the highest 

methylation efficiency. A recent study of biological Hg hotspots in the northeastern U.S. and southeastern 

Canada (Evers et al., 2007) analyzed more than 7,300 observations of Hg levels in seven species from 

three major taxonomic groups to quantify the spatial heterogeneity in tissue Hg concentrations. Using 

published effect thresholds for Hg tissue concentrations, they identified five known and nine possible 

biological Hg hotspots. They reported that two of the biological hotspots, located in the Adirondack 

Mountains of New York and south-central Nova Scotia, occur in areas with relatively low to moderate 

atmospheric Hg deposition and high landscape sensitivity, as determined by the abundant forest and 

wetland cover as well as the acidic surface water conditions (Evers et al., 2007). Using data collected by 

the Northeastern Ecosystem Research Cooperative (NERC) initiative (Evers and Clair, 2005) to examine 

the link between Hg deposition and biotic Hg, (Driscoll et al., 2007b) concluded that “forested regions 

with a prevalence of wetland and unproductive surface waters,” which are common in the northeastern 

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U.S., “promote high concentrations of Hg in freshwater biota.” In contrast, in a study of over 600 

randomly selected streams and rivers throughout the western U.S., (Peterson et al., 2007) found little 

relationship between fish tissue Hg concentrations and surface water pH, SO4

2−

, or DOC. They attributed 

the lack of a relationship to the fact that low pH (<7) and high DOC systems were rare in the West. The 

major factors controlling fish tissue Hg concentrations in western streams and rivers were fish size and 

feeding group, not water chemistry. Likewise, a 1999 survey of high altitude western lakes with both low 

Hg and low MeHg (Krabbenhoft et al., 2002) identified high pH and elevated rates of photodemethylation as the likely causes for low net methylation. Water clarity and high sunlight exposure were 

cited as sources of enhanced photo degradation of MeHg. 

Sulfur Deposition and MeHg in Fish 

As shown by Harris and Rudd (2007), the response of fish MeHg to changes in Hg deposition can 

occur on a time scale of less than a year. In their comprehensive synthesis of information on all elements 

of the connection between environmental Hg loading, and Hg in fish, Munthe (2007) concluded that 

several interacting factors are expected to affect the speed and magnitude of the changes in fish 

contamination that result from changes in Hg loading. As indicated previously, numerous studies have 

ascertained that SO4

2−

 supply is a principal driver of MeHg production, and Hrabik and Watras (2002) 

showed that decreased deposition of both Hg and SO4

2−

 are followed by MeHg decrease in fish. Drevnick 

et al. (2007), however, were able to establish an explicit linkage between S deposition and fish Hg, by 

verifying that even in the absence of change in Hg deposition, changes in S deposition alone result in 

commensurate changes in MeHg accumulation in fish. 

3.4.1.5. Summary of S and Methylation of Mercury 

The evidence is sufficient to infer a causal relationship between S deposition and increased 

methylation of Hg, in aquatic environments where the value of other factors is within adequate range for 

methylation. The main agent of Hg methylation is SO4

2−

-reducing-bacteria, and experimental evidence 

from laboratory to mesocosm scales has established that only inconsequential amounts of MeHg can be 

produced in the absence of SO4

2−

. These experimental results are highly coherent with one another, and 

with observational studies at larger scales. Changes in the amount of SO4

2−

 present have been shown to be 

followed by commensurate changes in MeHg, and mechanistic links have been established between 

variation in SO4

2− and variation in methylation of Hg. 

Quantification of the relationship between SO4

2− and methylation of Hg in natural settings has 

proved difficult because of the presence of multiple interacting factors in aquatic environments where 

SO4

2−

 and Hg are present. The amount of MeHg produced has been shown to vary with O2 content, 

temperature, pH, and supply of labile organic carbon. In some watersheds, such as high altitude lakes in 

the Western U.S., no effect of changes in SO4

2−

 deposition have been recorded on methylation of Hg,. 

This is because one or several interacting factors were not present in the amounts required for methylation 

to occur at more than inconsequential rates. Watersheds with conditions known to be conducive to Hg 

methylation can be found in the northeastern U.S. and southeastern Canada, but significant biotic Hg 

accumulation has been observed in other regions that have not been studied as extensively, and where a 

different set of conditions may exist. 

Mercury is a highly neurotoxic contaminant, and enters the food web in the methylated form. 

MeHg is then concentrated in higher trophic levels, including fish eaten by humans, with undesirable 

consequences for affected species, and for populations that consume large amounts of fish. Once MeHg is 

present, other variables influence how much of it accumulates in fish. Current evidence indicates that 

increased S deposition very likely results in MeHg accumulation in fish. 

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3.4.1.6. S Nutrient Enrichment Case Study: Interactive Effects of S and Hg in Little 

Rock Lake, WI 

Little Rock Lake is an 18–ha precipitation-dominated seepage lake located in a forested and 

undisturbed catchment of north central Wisconsin. The extensive experimental work conducted at Little 

Rock Lake was described by Hrabik and Watras (2002). The research at Little Rock Lake provides 

considerable insight into the interactions of S and Hg in the lake, and also bioaccumulation of Hg in fish 

in freshwater ecosystems. 

In 1984, the lake was divided into two basins by placing an impermeable curtain across a narrow 

lake section. One of the basins was experimentally acidified from pH 6.1 to 4.7 by mixing H2SO4 into the 

surface water over a period of 6 years (Watras and Frost, 1989). The other basin was left undisturbed to 

serve as a reference. Beginning in 1990, the treated basin was left to de-acidify naturally. 

Hg accumulation in yellow perch showed significant declines in fish in both the experimental and 

reference basins between 1994 and 2000, commensurate with declines in atmospheric deposition of Hg. 

Fish Hg concentrations in the experimental basin were 57% higher in 1994 than in 2000, whereas 

concentrations were 36% higher in the reference basin (Hrabik and Watras, 2002). The authors 

determined that half of the decrease in fish Hg concentration was attributable to lakewater de-acidification 

and the other half was associated with regional declines in atmospheric Hg deposition. In the reference 

basin, which had higher pH and exhibited a lower rate of de-acidification, 15% of the decrease in fish Hg 

concentration was due to de-acidification (Hrabik and Watras, 2002). 

These findings were consistent with the hypothesis that SO4

2−

 and newly added Hg synergistically 

contribute to enhanced bioaccumulation of Hg in fish. In subsequent analyses, Watras et al. (2006) found 

that maximum MeHg concentrations in hypolimnetic waters were directly correlated with the SO4

2−

deficit (mean epilimnetic SO4

2−

 concentration - minimum hypolimnetic SO4

2−

 concentration) and they 

observed a correlation between MeHg and lakewater SO4

2−

 concentrations. The tracking of external loads 

of Hg and S, and internal loads of Hg and MeHg suggested a tight biogeochemical connection among 

atmospheric deposition, SO4

2−

 reduction, and Hg methylation. However, these relationships did not fully 

explain the observed large inter-annual variability in MeHg accumulation. The variability appeared to be 

influenced by OC, terrestrial runoff and temperature. 

The results from the Little Rock Lake acidification experiment suggest that S deposition plays an 

important role in the accumulation and methylation of Hg in freshwater ecosystems, and that acid 

deposition and Hg deposition have a disproportionately larger effect together than either would have 

separately (Watras et al., 2006). 

3.4.2. Direct Phytotoxic Effects of Gaseous N and S on Vegetation 

This section is intended to provide a brief overview of the exposure and phytotoxic effects of 

gaseous N and S compounds on vegetation. This recognizes that the major focus of this review is the 

effect of acidifying deposition and N deposition on ecosystems. However, direct effects of gaseous N and 

S could augment the effects of deposition on vegetation and effects of gaseous N and S may occur in 

some areas. 

The effects of gaseous pollutants such as SO2, NO2, NO, HNO3 and O3 on vegetation have been 

studied since the 1950s and 1960s. Methodologies have been developed to study these effects in the lab, 

greenhouse, and in the field. The methodologies to study gaseous pollutants effects on vegetation have 

been recently reviewed in the 2006 O3 AQCD (U.S. EPA, 2006b). A thorough description of the 

methodologies used to expose vegetation to gaseous pollutants can be found in Section AX9.1 of the 2006 

O3 AQCD (U.S. EPA, 2006b) and Section 9.2 in the 1993 NO2 AQCD (U.S. EPA, 1993a). 

Uptake of gaseous pollutants in a vascular plant canopy is a complex process involving adsorption 

to surfaces (leaves, stems, and soil) and absorption into leaves. These pollutants penetrate into leaves 

 3-197
primarily in gaseous form through the stomata, although there is evidence for limited pathways via the 

cuticle. Pollutants must be transported from the bulk air to the leaf boundary layer to get to the stomata. 

Although the transport of pollutants through a boundary layer into the stomata region is known to be 

important, and even rate limiting in many cases of low wind velocity, its description has been defined 

from aeronautical concepts and usually relates to smooth surfaces that are not typical of leaf-surface 

morphology; however, it is nearly the only treatment available (Gates, 1968). Once through the boundary 

layer, the gas must enter the leaf through the stomata. The entry of gases into a leaf is dependent upon the 

physical and chemical processes of gas phase and surfaces as well as the stomatal aperture. The aperture 

of the stomata is controlled largely by the prevailing environmental conditions, such as humidity, 

temperature, and light intensity. When the stomata are closed, as occurs under dark or drought conditions, 

resistance to gas uptake is very high and the plant has a very low degree of susceptibility to injury (Figure 

3-62). The stomatal control of uptake of gaseous pollutants is described in more detail in AX9.2 of the 

2006 O3 AQCD (U.S. EPA, 2006b) and Section 9.3.1.5 of the 1993 NOX AQCD (U.S. EPA, 1993a). It 

should be noted that unlike higher plants, mosses and lichens do not have a protective cuticle barrier to 

gaseous pollutants, a major reason for their sensitivity to gaseous S and N. 

Figure 3-62. The microarchitecture of a dicot leaf. While details among species vary, the general overview 

remains the same. Light that drives photosynthesis generally falls upon the upper (adaxial 

leaf surface. CO2, SOX, NOX, and O3 gases generally enter through the stomata on the lower 

(abaxial) leaf surface, while water vapor exits through the stomata (transpiration). 

3.4.2.1. Direct Phytotoxic Effects of SO2 on Vegetation 

It has been known since the early 1900s that exposure of plants to SO2 can cause damage and death 

(Wislicenus, 1914). The large sources of SO2 were ore smelters. Sulfides in the ore were oxidized during 

smelting and resulted in large releases of SO2. Emissions from large ore smelters in the U.S. and Canada 

resulted in large areas denuded of vegetation surrounding these facilities (Swain 1949; Thomas 1951). 

Much of the damage to the vegetation was due to acute effects of high concentrations of SO2. However, as 

early as 1923 researchers recognized that SO2 might reduce plant growth without acute symptoms of 

foliar injury (Stoklasa, 1923). In the 1950s through the early 1980s, there was much research on the 

effects of lower levels of SO2 as well as the interaction with other pollutants such as O3 and NO2. Since 

 3-198
then, there has been much less research on the effects of SO2 on vegetation, especially in the U.S., due to 

the decreasing ambient concentrations of SO2. The effects of SO2 on vegetation are summarized below. 

Currently, SO2 is the only criteria pollutant with a secondary NAAQS distinct from the primary 

standard. This standard is to protect acute foliar injury resulting from SO2 exposure. The standard is a 3-h 

average of 0.50 ppm and was promulgated in 1970 to protect against acute foliar injury in vegetation. The 

last AQCD for ecological effects of SOX was completed in 1982 and concluded that controlled 

experiments and field observations supported retaining this secondary standard (U.S. EPA, 1971, 1982b, 

1982c). 

Acute foliar injury usually happens with hours of exposure, involves a rapid absorption of a toxic 

dose and involves collapse or necrosis of plant tissues. Another type of visible injury is termed chronic 

injury and is usually a result of variable SO2 exposures over the growing season. After entering the leaf, 

SO2 is converted to sulfite and bisulfite, which may be oxidized to SO4

2−

. SO4

2−

 is about 30 times less 

toxic than sulfite and bisulfite. The conversion of sulfite and bisulfite to SO4

2−

 results in net H+ production 

in the cells. Kropff (1991) proposed that the appearance of SO2-induced leaf injury was likely due to a 

disturbance of intracellular pH regulation. Kropff (1991) pointed out several studies that the pH of 

homogenates only shifted towards greater acidity when plants were lethally damaged from long-term SO2

exposures (Grill, 1971; Jager and Klein, 1977; Thomas et al., 1944). The appearance of foliar injury can 

vary significantly between species and growth conditions affecting stomatal conductance. Currently there 

is not regular monitoring for SO2 foliar injury effects in the U.S. 

Besides foliar injury, long-term lower SO2 concentrations can result in reduced photosynthesis, 

growth, and yield of plants. These effects are cumulative over the season and are often not associated with 

visible foliar injury. As with foliar injury, the effects of foliar injury vary among species and growing 

environment. The 1982 SO2 AQCD summarized the concentration-response information available at the 

time (U.S. EPA, 1982b). Effects on growth and yield of vegetation were associated with increased SO2

exposure concentration and time of exposure. However, that document concluded that more definitive 

concentration-response studies were needed before useable exposure metrics could be identified. Because 

of falling ambient SO2 concentrations and focus on O3 vegetation effects research, few studies have 

emerged to better inform a metric and levels of concern for effects of SO2 on growth and productivity of 

vegetation. 

Since the 1982 SO2 AQCD was published, several studies have investigated a number of different 

effects of SO2 effects on plants. Most recent research has been performed in areas of Europe where 

ambient SO2 concentrations are generally higher than in the U.S. A brief summary of some of the major 

studies are presented in Table 3-28. 

SO2 is considered to be the primary factor causing the death of lichens in many urban and industrial 

areas, with fruticose lichens being more susceptible to SO2 than many foliose and crustose species 

(Hutchinson et al., 1996). Damage caused to lichens in response to SO2 exposure includes reduced 

photosynthesis and respiration, damage to the algal component of the lichen, leakage of electrolytes, 

inhibition of N fixation, reduced K+

 absorption, and structural changes (Belnap et al., 1993; Farmer et al., 

1992; Hutchinson et al., 1996). Significant reductions in lichen photosynthesis have been measured at 

concentrations as low as 91 ppb over 2–4 hours (Huebert, 1985; Sanz, 1992). Damage to the algal 

component of the thallus is evidenced by its discoloration. The entire thallus dies soon after algal cells are 

damaged (Hutchinson et al., 1996). At higher levels, SO2 deactivates enzymes by chemical modification 

leading to reduced metabolic activity and loss of membrane integrity (Zeigler, 1975, Nieboer et al., 1976). 

It also binds to the central metal atoms of enzymes, adversely affecting membrane function and cell 

osmolality. In addition, SO2 competitively inhibits carbonate (HCO3) and phosphate (H2PO4) interactions 

with enzymes (Hutchinson et al., 1996). Low pH increases the toxicity of SO2 action (Farmer et al., 

1992). The toxic effects of atmospheric deposition of SO2 are lessened when lichen are attached to a 

substrate, typically bark or rock, having high pH or superior buffering capacity (Richardson and 

Cameron, 2004). Van Herk (2001) evaluated relationships between bark pH and air pollution levels as two 

significant variables affecting epiphytic lichen composition, and concluded that bark pH was the primary 

factor regulating the distribution of acidophilic species in The Netherlands. In studies of unpolluted areas, 

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differences in bark chemistry also affect the presence and distribution of epiphytes (Farmer et al., 1992). 

Indirect effects on bark pH, caused by acidification and high SO2 concentrations, also affect lichen 

distribution (Farmer et al., 1992). 

3.4.2.2. Direct Phytotoxic Effects of NO, NO2 and PAN 

It is well known that in sufficient concentrations nitric oxide (NO) and NO2 can have phytotoxic 

effects on plants through decreasing photosynthesis and inducing visible foliar injury (U.S. EPA, 1993a). 

The 1993 NOX AQCD concluded that concentrations of NO2 or NO in the atmosphere are rarely high 

enough to have phytotoxic effects on vegetation (U.S. EPA, 1993a). Since the 1993 NOX AQCD, very 

little new research has been done on these phytotoxic effects to alter this conclusion. However, it is 

known that these gases alter the N cycle in some ecosystems, especially in the western U.S., and 

contributing N saturation (Bytnerowicz and Fenn, 1996; Fenn et al., 2003a). See Section 3.3 for a 

discussion of the nutrient effects of N. 

In general, NO and NO2 enters leaves through stomata (Saxe, 1986). However, it has also been 

shown that the leaf cuticle may be an important receptor for NO2 and there is evidence of transport of NO 

and NO2 across isolated cuticles (Lendzian and Kerstiens, 1988). Several studies have demonstrated that 

plant canopies can directly assimilate N in the form of NO2, but canopy uptake of NO2 is generally small 

relative to total plant uptake (Hanson et al., 1989, Nussbaum et al., 1993; Ammann et al., 1999; 

Segschneider et al., 1993; Vallano and Sparks, 2008; von Ballmoos et al., 1993). After entering the leaves, 

NO2 dissolves in the extracellular water of the sub-stomatal cavity to form HNO2 and HNO3, which then 

dissociate to form nitrite, NO3

−

, and protons (Bytnerowicz et al., 1998b). Both cell and tonoplast 

membranes contain ATP-dependent H+

 pumps and the tonoplast pumps are strongly inhibited by NO3

−

(Bytnerowicz et al., 1998b). If extra protons are deposited in vacuoles of the plant cells during normal 

cellular regulation, then additional acidity will occur in combination with additional NO3

−

. This 

combination can cause disruptions in cellular control (Taylor and MacLean, 1970). NO3

−

 and nitrite are 

metabolized to amino acids and proteins through a series of enzymatic reactions mainly involving NO3

−

and nitrite reductases (Amundson and MacLean, 1982). The effectiveness of plants to reduce NO3

−

 and 

nitrite to amino acids and proteins determines the potential of the plant to detoxify NO and NO2

(Wellburn, 1990). Reduction of NO3

−

 takes place outside of the chloroplast while the reduction of nitrite 

is coupled with the light reactions of photosynthesis. Therefore, when leaves are exposed to NO and NO2

in the dark, highly phytotoxic levels of nitrite accumulate and may lead to greater toxicity to NO and NO2

at night (Amundson and MacLean, 1982). Exposure to NO produces both NO3

−

 and nitrite in the leaves, 

but the rate of NO3

−

 accumulation is much slower than nitrite. Thus, plants exposed to high NO could be 

at risk to elevated concentrations of nitrite (Wellburn, 1990). More detailed information on the cellular 

effects of NO and NO2 can be found in the 1993 NOX AQCD. 

The functional relationship between ambient concentrations of NO or NO2 and a specific plant 

response, such as foliar injury or growth, is complex. Factors such as inherent rates of stomatal 

conductance and detoxification mechanisms and external factors, including plant water status, light, 

temperature, humidity, and the particular pollutant exposure regime, all affect the amount of a pollutant 

needed to cause symptoms of foliar injury. Plant age and growing conditions, and experimental exposure 

techniques also vary widely among reports of experimental exposures of plants to NO2. An analysis 

conducted in the 1993 NOX AQCD of over 50 peer-reviewed reports on the effects of NO2 on foliar injury 

indicated that plants are relatively resistant to NO2, especially in comparison to foliar injury caused by 

exposure to O3 (U.S. EPA, 1993a). With few exceptions, visible injury was not reported at concentrations 

below 0.20 ppm, and these occurred when the cumulative duration of exposures extended to 100 hours or 

longer. At 0.25 ppm, increased leaf abscission was reported on navel orange trees (Citrus sinensis L), but 

only after exposures in excess of 1000 hours (Thompson et al., 1970). Green bean plants used as bioindicators of NO2 injury in Israel developed foliar injury symptoms when ambient concentrations 

 3-200
exceeded 0.5 ppm (Donagi and Goren, 1979). Only when concentrations exceeded 1 ppm did injury occur 

on most plants in less than one day (U.S. EPA, 1993a). 

Reductions in rates of photosynthesis have been recorded in experimental exposures of plants to 

both NO and NO2, but usually at concentrations significantly higher than would normally be encountered 

in ambient air. For example, Sabaratnam et al. (1988) reported that soybean (Glycine max) exposed 

7 hours/day for 5 days showed an increase in photosynthetic rates at a concentration of 0.2 ppm, but a 

reduction in net photosynthesis at a concentration of 0.5 ppm. Short-term exposures of soybean to 

0.6 ppm NO2 for 2 to 3 hours also had no effect on net photosynthesis (Carlson, 1983). Most plants appear 

to be more susceptible to NO than to NO2, as shown by Saxe (1986), who exposed a variety of 

horticultural plants raised in greenhouses (species of Hedera, Ficus, Hibiscus, Nephrolepis, and 

Dieffenbachia) to both NO and NO2. Saxe (1986) reported that reductions in net photosynthesis occurred 

at doses of NO that were 22 times less than that for NO2. However, these reductions in net photosynthesis 

required concentrations as high as 1 ppm NO for 12 hours to elicit a response in these plants. 

Hundreds of studies have been conducted on the effects of NO2 on growth and yield of plants 

mostly performed in the 1970s and 1980s. These studies varied widely in plant species, growing 

conditions, exposure equipment, concentrations, durations, exposure regimes, and environmental 

conditions during exposures. No clear dose-response relationships for exposure to NO2 and reductions in 

growth and/or yield of plants have emerged from these experiments. Readers are referred to the analysis 

of over 100 studies conducted in the 1993 NOX AQCD. A few key studies are highlighted in this section. 

Several plant species appear to be susceptible to reductions in growth by relatively low concentrations of 

NO2 (less than 0.2 ppm), particularly when exposed during low-light conditions. For example, nearly 

continuous exposure to 0.1 ppm NO2 for eight weeks significantly reduced growth of Kentucky blue grass 

(Poa pratensis L) (Ashenden, 1979; Whitmore and Mansfield, 1983). Eight species of tree seedlings were 

exposed to 0.1 ppm NO2 for six hours/day for 28 days, resulting in reduced shoot or root growth in two 

species, white ash (Fraxinus americana L.) and sweetgum (Liquidambar styraciflua L.), reduced height 

growth in two clones of loblolly pine (Pinus taeda L.), and no effects on the other species (Kress and 

Skelly, 1982). No effects of NO2 at 0.1 ppm or lower were observed on numerous other species, including 

potato (Solanum tuberosum L.), black poplar (Populus nigra L.), radish (Raphanus sativus L.), soybean, 

or peas (Pisum sativum L.) (U.S. EPA, 1993a). No effects of NO2 were observed on soybeans grown in 

field plots subjected to a series of 10 episodic exposures averaging 0.4 ppm for 2.5 or 3 hours (Irving 

et al., 1982). Numerous studies have reported negative effects on growth of a variety of plants exposed to 

0.5 ppm NO2 and above (U.S. EPA, 1993a), but these concentrations are unrealistically high relative to 

current ambient levels of NO2. 

The 1993 NOX AQCD reviewed the extensive literature on the effects of NO2 in combination with 

other gaseous air pollutants, particularly SO2 and O3, and concluded that combinations of pollutants can 

cause reductions in photosynthesis or foliar injury at concentrations lower than those associated with NO2

acting alone. However, the plant responses occur at concentrations much higher than are found in ambient 

air (U.S. EPA, 1993a). In addition, the presence of NO2 in combination studies did not produce symptoms 

different from those caused by the dominant pollutant, either SO2 or O3, so that a plant response produced 

by combinations of NO2 with other air pollutants in the field would be difficult, if not impossible, to 

distinguish from those of the other single pollutants (U.S. EPA, 1993a). 

Since the 1993 NOX AQCD was completed, most new research on NO2 exposure to vegetation has 

taken place in Europe and other areas outside the U.S. For example, foliar NO3 reductase activity was 

increased in Norway spruce (Picea abies) trees growing near a highway with average exposures of about 

0.027 ppm compared to trees growing 1300 meters away from the highway with NO2 exposures less than 

0.005 ppm (Ammann et al., 1995). This was consistent with other studies on Norway spruce in the field 

and laboratory (von Ballmoos et al., 1993; Thoene et al., 1991). Muller et al. (1996) found that the uptake 

rate of NO3

–

 by roots of Norway spruce seedlings was decreased by the exposure to 0.1 ppm of NO2 for 

48 hours. Similarly, soybean plants grown in Australia had decreased NO3 uptake by roots and reduced 

growth of plants exposed to 1.1 ppm of NO2 for 7 days (Qiao and Murray, 1998). In a Swiss study, poplar 

cuttings exposed to 0.1 ppm for of NO2 for approximately 12 weeks resulted in decreased stomatal 

 3-201
density and increased specific leaf weight, but did not result in other effects such as leaf injury or a 

change in growth (Gunthardt-Goerg et al., 1996). However, NO2 enhanced negative effects of ozone, 

including leaf injury, on these poplars when the pollutants were applied in combination (Gunthardt-Goerg 

et al., 1996). 

Peroxyacetyl nitrate (PAN) is a well-known photochemical oxidant, often co-occurring with O3

during high photochemical episodes, which has been shown to cause injury to vegetation (See reviews by 

Cape, 2003, 1997; Kleindienst, 1994 Smidt, 1994; Temple and Taylor, 1983). Acute foliar injury 

symptoms resulting from exposure to PAN are generally characterized as a glazing, bronzing, or silvering 

of the underside of the leaf surface; some sensitive plant species include spinach, Swiss chard, lettuces, 

and tomatoes. Petunias have also been characterized as sensitive to PAN exposures and have been used as 

bioindicators of in areas of Japan (Nouchi et al., 1984). Controlled experiments have also shown 

significant negative effects on the net photosynthesis and growth of petunia (Petunia hybrida L.) and 

kidney bean (Phaseolus vulgaris L.) after exposure of 30 ppb of PAN for four hours on each of three 

alternate days (Izuta et al., 1993). As mentioned previously, it is known that oxides of N, including PAN, 

could be altering the N cycle in some ecosystems, especially in the western U.S., and contributing 

N saturation (Bytnerowicz and Fenn, 1996; Fenn et al., 2003a, see Section 3.3). However, PAN is a very 

small component of N deposition in most areas of the U.S. Although PAN continues to persist as an 

important component of photochemical pollutant episodes, there is little evidence in recent years 

suggesting that PAN poses a significant risk to vegetation in the U.S. 

3.4.2.3. Direct Phytotoxic Effects of HNO3

Relatively little is known about the direct effects of HNO3 vapor on vegetation. It has been 

established that HNO3 has a very high deposition velocity compared to other pollutants and may be an 

important source of N for plants (Hanson and Lindberg, 1991; Hanson and Garten, 1992; Vose and 

Swank, 1990). This deposition could contribute to N saturation of some ecosystems close to sources of 

photochemical smog (Fenn et al., 1998). For example, in mixed conifer forests of the Los Angeles basin 

mountain ranges HNO3 has been estimated to provide 60% of all dry deposited N (Bytnerowicz et al., 

1999). 

Norby et al. (1989) reported that exposure of 75 ppb of HNO3 for one day increased nitrate 

reductase activity in red spruce foliage. In another study, foliar nitrate reductase activity was also 

increased in California black oak (Quercus kelloggi), canyon live oak (Quercus chrysolepis) and 

Pondersosa pine (Pinus ponderosa) seedlings exposed to HNO3 concentrations of 65 to 80 ppb for 24 

hours (Krywult and Bytnerowicz, 1997). Because the induction of nitrate reductase activity is a step in a 

process leading to the formation of organic N compounds (amino acids), the nitrate from HNO3 could 

function as an alternative source of N for vegetation (Calanni et al., 1999). However, in plants under 

stress, the reduction of nitrate to amino acids consumes energy needed for other metabolic processes. 

At high ambient concentrations, HNO3 can cause vegetation damage. Seedlings of Ponderosa pine 

and California black oak subjected to short-term exposures from 50–250 ppb of HNO3 vapor for 12 hours 

showed deterioration of pine needle cuticle at 50 ppb in light (Bytnerowicz et al., 1998a). Oak leaves 

appeared to be more resistant to HNO3 vapor, however, with 12-h exposures in the dark at 200 ppb 

producing damage to the epicuticular wax structure (Bytnerowicz et al., 1998a). The observed changes in 

wax chemistry caused by HNO3 and accompanying injury to the leaf cuticle (Bytnerowicz et al., 1998a) 

may predispose plants to various environmental stresses such as drought, pathogens and other air 

pollutants. Because elevated concentrations of HNO3 and ozone co-occur in photochemical smog 

(Solomon et al., 1988), synergistic interactions between the two pollutants are possible (Bytnerowicz 

et al., 1998b). However, it should be noted that the experiments described above were observed at 

relatively short-term exposures at above ambient concentrations of HNO3. Long-term effects of lower air 

concentrations that more approximate ambient HNO3 should be investigated. 

 3-202
It has been suspected that HNO3 may have caused a dramatic decline in lichen species in the Los 

Angles basin (Nash and Sigal, 1999). The suggestion was strengthened by transplant of Ramalina lichen 

species from clean air habitats (Mount Palomar and San Nicolas Island) to analogous polluted habitats in 

the Los Angeles basin and repeatedly observing death of the lichens over a few weeks in the summer 

(Boonpragob and Nash, 1991). Associated with this death was massive accumulation of H+

 and NO3

−

 by 

the lichen thalli (Boonpragob et al., 1989). Recently, Riddell et al. (2008) exposed the healthy Ramalina 

menziesii thalli to moderate (8–10 ppb) and high (10–14ppb) HNO3 in month-long fumigations and 

reported a significant decline in chlorophyll content and carbon exchange capacity compared to thalli in 

control chambers. Thalli treated with HNO3 showed visual signs of bleaching and by day 28 were clearly 

damaged and dead. The damage may have occurred through several mechanisms including acidification 

of pigments and cell membrane damage (Riddell et al., 2008). The authors concluded that Ramalina 

menziesii has an unequivocally negative response to HNO3 concentrations common to ambient summer 

conditions in the Los Angeles air basin. They believed it was very likely that HNO3 contributed to the 

disappearance of this sensitive lichen species from the Los Angeles air basin, as well as other locations 

with arid conditions with high deposition loads (Riddell et al., 2008). 

3.4.2.4. Summary of Phytotoxic Effects of Gaseous Nitrogen and Sulfur on Vegetation 

Sulfur Dioxide

The evidence is sufficient to infer a causal relationship between exposure to SO2 and injury to 

vegetation (Section 3.4.2.1). The current secondary standard for SO2

 is a 3-h average of 0.50 ppm, which 

is designed to protect against acute foliar injury in vegetation. There has been limited research on acute 

foliar injury since the 1982 PM-SOX AQCD and there is no clear evidence of acute foliar injury below the 

level of the current standard. 

Effects on growth and yield of vegetation are associated with increased SO2

 exposure concentration 

and time of exposure. The 1982 PM-SOX AQCD concluded that more definitive concentration-response 

studies were needed before useable exposure metrics could be identified. The few new studies published 

since the 1982 PM-SOX AQCD continue to report associations between exposure to SO2

 and reduced 

vegetation growth. However, most these studies have been performed outside the U.S. and at levels well 

above ambient concentrations observed in the U.S. 

Nitrogen Oxide, Nitrogen Dioxide and PAN 

The evidence is sufficient to infer a causal relationship between exposure to NO, NO2 and PAN and 

injury to vegetation (Section 3.4.2.2). It is well known that in sufficient concentrations, NO, NO2 and PAN 

can have phytotoxic effects on plants through decreasing photosynthesis and induction of visible foliar 

injury (U.S. EPA, 1993). However, the 1993 NOX AQCD concluded that concentrations of NO, NO2 and 

PAN in the atmosphere are rarely high enough to have phytotoxic effects on vegetation (U.S. EPA, 1993). 

Since the 1993 NOX AQCD, very little new research has been done on these phytotoxic effects at 

concentrations currently observed in the U.S. 

HNO3

The evidence is sufficient to infer a causal relationship between exposure to HNO3 and changes to 

vegetation (Section 3.4.2.3). Experimental exposure of HNO3 resulted in damage to the leaf cuticle of 

pine and oak seedlings which may predispose those plants to other stressors such as drought, pathogens 

and other air pollutants (Bytnerowicz et al., 1998a; Bytnerowicz et al., 1998b). However, these tree 

seedling experiments used relatively short-term exposures at concentrations well above current ambient 

conditions. In lichen studies, several lines of evidence, including transplant and controlled exposure 

studies, indicate that past and current HNO3 concentrations may be contributing to the decline in lichen 

 3-203
species in the Los Angles basin (Boonpragob and Nash, 1991; Nash and Sigal, 1999; Riddell et al., 2008). 

Current deposition of HNO3 is contributing to N saturation of some ecosystems close to sources of 

photochemical smog (Fenn et al., 1998) such as the mixed conifer forests of the Los Angeles basin 

mountain (Bytnerowicz et al., 1999). 

Table 3-28. Summary of recent studies of SO2 exposure to plants. 

Species Exposurea Endpoint(s) Results Reference

Scots pine (Pinus 

sylvestris L.); 

Norway spruce 

(Picea abies (L.) 

Karst.) 

0, 50, 100, 150, 155 ppb SO2 in growth 

chambers simulating natural weather in 

Finland in early June. The SO2 concentrations represented the range of hourly 

SO2 concentrations in the vicinity of 

industrial areas in Finland. 

Concentrations of 

carbohydrates and 

secondary components 

Exposure to SO2 (100 and 155 ppb) reduced concentrations of glucose and 

fructose and increased concentrations of 

sucrose in pine needles. By contrast, one 

spruce clone had more glucose and 

fructose and less sucrose in needles 

exposed to 100 ppb SO2, but in other 

spruces, no changes in sugar concentrations were detected in different SO2

exposures (50–155 ppb). Exposure to SO2

had no effects on concentrations of 

monoterpenes in pine or spruce needles. 

Concentration of total resin acids was 

significantly smaller in needles exposed to 

the greatest concentration of SO2 (155 

ppb), but no changes were detected in 

other exposures (50–150 ppb) in either 

tree species. Concentrations of palustric 

and neoabietic acids were affected by SO2

in needles of pine (155 ppb SO2) and clonal spruces (100 ppb SO2). Exposure to 

SO2 did not affect foliar concentration of 

total phenolics in pine and spruce seedlings. In exposure to 0, 50, 100 and 150 

ppb SO2, total phenolic concentration of 

spruce roots increased linearly with 

elevated SO2 exposure level. By contrast, 

one spruce clone had decreased 

concentrations of phenolics in roots after 

exposure to 155 ppb SO2. 

Kainulainen 

et al. (1995) 

Scots pine (Pinus 

sylvestris L.);

Norway spruce 

(Picea abies (L.) 

Karst.)

Mature trees growing at a polluted (32 

ppb SO2) and low pollution (1 ppb SO2) 

sites in Finland. In addition, seedlings 

were placed in the chambers and openfield plots in mid-Sept. 1991 and 

fumigated 8 hours daily, 5 days a week 

from 19 September to 15 Nov 1991 and 

from 19 May to 12 Oct 1992. Mean 

pollutant concentrations in the fumigated 

chambers during the 8–h exposure 

periods were 5–6 ppb SO2 and 7–8 ppb 

NO2. The mean pollutant concentrations 

not receiving the particular pollutant were 

~2 ppb SO2 and 5 ppb NO2. 

Response of needle 

S and N concentrations 

Elevated concentrations of S were found 

in mature pine and spruce trees at polluted sites. The response of mature Scots 

pine to SO2 differed from that of mature 

Norway spruce. The greater increase in 

the needle total S concentrations of pine 

suggested more abundant stomatal uptake of SO2 compared to spruce. Mature 

pine was able to assimilate SO4

2– derived 

from SO2 into organic S more effectively 

than mature spruce at the high S and N 

deposition sites, whereas both pine and 

spruce seedlings accumulated S under 

NO2+SO2 exposure. 

Manninen 

et al. (2000) 

 3-204
Species Exposurea Endpoint(s) Results Reference

Mature red spruce 

(Picea rubens Sarg.) 

Branches were fumigated in late summer 

of 1990 and 1991 in Canada. Four SO2

treatment levels (0, 100, 200, 400 ppb) 

Net photosynthesis, 

stomatal conductance, visible foliar 

injury 

Net photosynthesis and stomatal conductance were found to decrease in direct 

proportion with cumulative foliar SO2

absorption. Needle injury was observed in 

sun branches exposed to 200 and 400 

ppb of SO2 in 1990. Net photosynthesis 

was depressed by SO2 regardless of 

branch position. Foliage subjected to high 

level SO2 did not recover from SO2

damage 1 year after treatment: needles 

had fallen off twigs and twig length of new 

foliage was reduced. 

Meng et al. 

(1994) 

Scots pine (Pinus 

sylvestris L.); 

Norway spruce 

(Picea abies (L.) 

Karst.)

Open-air experiment, Finland- trees were 

exposed to F, N and S pollutants 

individually, or in mixtures, by spraying F 

and N compounds in aqueous solution 

and fumigating plants with gaseous SO2, 

for 5 months in each of 3 consecutive 

growing seasons. SO2 concentration 

among the trees varied between 35 and 

140 ppb SO2, depending on velocity and 

direction of the wind. Exact 

concentrations of SO2 were not reported. 

Visible symptoms, 

pollutant concentrations, ultrastructure 

of seedlings 

Visible injury symptoms were most pronounced in combination exposures and 

whenever F was applied. Visible symptoms correlated well with needle pollutant 

concentrations. Exposure to F increased 

needle F contents particularly when F was 

applied with SO2 or NH4NO3. This 

suggests that a reduction in N or SO2

emissions, in F polluted areas, could 

improve the condition of conifers via 

decreased accumulation of phytotoxic F in 

the needles. Norway spruce needles 

accumulated 2–10 times as much S and F 

as those of Scots pine. In both species, 

exposure to SO2 increased significantly 

the amount of cytoplasmic vacuoles, 

suggesting detoxification of excess 

sulphate or low pH. All exposures 

enhanced the accumulation of lipid bodies. 

Both visible symptoms and ultrastructural 

changes pointed to the more pronounced 

sensitivity of Norway spruce compared to 

Scots pine. 

Wulff et al. 

(1996) 

European Beech 

(Fagus sylvatica L.), 

Norway Spruce 

(Picea abies (L.) 

Karst.) European 

Silver Fir (Abies alba 

Mill.). 

Weekly concentrations of SO2, (averaging 

3–42 ppb) and O3 (10–90 ppb) was 

applied to trees in open-top chambers in 

Hohenheim, Germany, for almost five 

years. 

Shoot length, leaf 

surface area, dry 

weight 

Fumigation with SO2 alone caused 

insignificant decreases of shoot length, 

total dry weight, and needle surface of 

spruce and fir. Fir trees fumigated with 

SO2 in combination with O3 showed lower 

rates of productivity compared to filtered 

control treatments. Beech was not as 

affected by SO2 than with O3 or SO2 + O3. 

Billen et al. 

(1990) 

Norway spruce 

(Picea abies (L.) 

Weekly concentrations of SO2, (averaging 

3–42ppb) and O3 (10–90 ppb) was 

applied to trees in open-top chambers in 

Hohenheim, Germany, for almost five 

years. 

Ectomycorrhizal 

(EM) frequency, fine 

root structure, 

distribution of short 

roots 

SO2 resulted in higher percentages of 

non-mycorrhizal short root tips, and 

decreased number of living short roots. 

EM percentage decreased by 38% on SO2

exposed roots. 

Blaschke 

(1990) 

European Beech 

(Fagus sylvatica L.), 

Norway Spruce 

(Picea abies (L.) 

Karst.) European 

Silver Fir (Abies alba 

Mill.). 

Weekly concentrations of SO2, (averaging 

3–42 ppb) and O3 (10–90 ppb) was 

applied to trees in open-top chambers in 

Hohenheim, Germany, for almost five 

years 

Visible injury In Jan to Feb 1985, after long frost, SO2

treated fir showed development of tip 

necrosis, showing SO2 inhibits frost resistance. No clear visible effects were found 

due to SO2 alone on beech or spruce. 

Arndt et al. 

(1990) 

 3-205
 3-206

Species Exposurea Endpoint(s) Results Reference

European Beech 

(Fagus sylvatica L.), 

Norway Spruce 

(Picea abies (L.) 

Karst.) European 

Silver Fir (Abies alba 

Mill.). 

Root samples taken in five-year Hohenheim Long Term Experiment in Germany 

from 2 tree groups. Each group of trees 

consisted of three younger (10–year-old) 

and five older (13–year-old) trees. Weekly 

concentrations of SO2, (averaging 3–42 

ppb) and O3 (10–90 ppb) was applied to 

trees in open-top chambers. 

Fine root and mycorrhizae production

In beech seedlings, SO2 and SO2 +O3

resulted in reduced fine root production by 

35% and 55%, respectively. SO2 had no 

clear effect on fine root production in fir. 

SO2 increased fine root production in 

spruce by 31%, but significantly reduced 

relative frequency of mycorrhizae (~20). 

Wollmer and 

Kottke 

(1990) 

Norway spruce 

(Picea abies L. 

Karst.) and fir seedlings (Abies aiba 

Mill.) 

One- and two-year old seedlings exposed 

to low levels of SO2 and O3 in open-top 

chambers in five year experiment 1983 

through 1988 in Hohenheim, Germany. 

SO2 concentrations averaged weekly between 3–42 ppb and O3 concentrations 

were between 10–90 ppb. 

Visible symptoms, 

photosynthesis, 

transpiration 

The twigs did not exhibit any visible sign of 

injury due to SO2 treatments. Exposure of 

fir to SO2 alone or in combination with O3

resulted in a significant decrease in 

photosynthesis and transpiration. No 

changes either in photosynthesis or in 

transpiration were found in spruce under 

fumigation with S02 alone. 

Schweizer 

and Arndt 

(1990) 

Black sage (Salvia 

mellifera) CA sagebrush (Artemisia 

californica) Eastern 

Mojave buckwheat 

(Eriogonum 

asciculatum) CA 

brittlebush (Encelia 

californica) 

SO2 fumigation over 10 weeks, at 0, 50, 

200, and 500 ppb, California 

Number of inflorescences Decreased inflorescences were observed 

at 50 ppb SO2 for black sage, and at 200 

ppb SO2 for California sagebrush, Eastern 

Mojave buckwheat, and California 

brittlebush, with progressive declines as 

SO2 concentration increased. 

Westman 

et al. (1985) 

Timothy grass 

(Phleum pratense) 

Exposure to 120 ppb SO2 for 40 days. Leaf production, leaf 

senescence, dry 

weight (LAR), leafarea ratio, specific 

leaf area (SLA) 

Diminished leaf production and increased 

leaf senescence in seedlings exposed to 

120 ppb SO2 at 35 days. Exposure to 120 

ppb SO2 in seedlings over 40 days 

resulted in a 62% reduction in the dry 

weight of roots and 51% reduction in the 

dry weight of shoots, as well as a 

significant decline in leaf-area ratio and 

specific leaf area by the end of the 

experiment. 

Mansfield 

and Jones 

(1985) 

Mixed native prairie 

grassland 

Exposed grasses to a control (~7 ppb) 

and three elevated levels of SO2 (~21, 37, 

64 ppb) over 5 year study. 

Root and rhizome S 

concentrations, biomass, primary productivity, lichen 

cover, population 

Year-to-year S accumulation did not 

appear to occur over the 5–year course of 

the treatment, though progressive 

increases in root and rhizome S concentrations were observed seasonally. No 

significant negative effects on either 

above-ground net primary productivity or 

below-ground biomass dynamics in 

grasses were observed, except a decrease in biomass for Bromus japonicus. 

Lichen cover declined after 1 year of 

exposure at the low treatment level. 

Laurenroth 

and Milchunas, (1985) 

Faba bean (Vicia 

faba L) 

Experiment done in 3 different years 

(1986, 1986, 1988). Seasonal mean 

elevated exposures of SO2 were 58 ppb 

in 1985, 22 ppb in 1986 and 26 ppb in 

1988. Ambient concentrations were 6 ppb 

in 1985, 3 ppb in 1986 and 3 ppb in 1988.

Yield, leaf injury Exposure to elevated SO2 resulted in leaf 

injury in all three years. SO2 exposure 

reduced yield by 17% in 1985, 7% in 1986 

and 9% in 1988. 

Kropff (1990)

aConcentration, Duration (hours, days)
Chapter 4. Summary and Conclusions 

The previous chapters present the policy-relevant science pertaining to the emissions, atmospheric 

transformation and transport, deposition and ecological effects of NOX and SOX. Ecological effects are 

divided into broad categories of ecosystem types typically studied: terrestrial, wetlands, freshwater 

aquatic, and estuarine aquatic. Several NOX and SOX chemical species were considered because of their 

complex multi-phase and multi-species in both the atmosphere and the biosphere. For example, the 

atmospheric chemistry of NOX and SOX would be incomplete if only gas-phase compounds were 

considered; therefore, descriptions of current ambient concentrations and deposition amounts related to 

the particulate forms of N and S are given in Chapter 2. Similarly, the roles of other atmospheric 

pollutants including Hg, O3, NH3, and NH4

+

, and their interactions with NOX and SOX in the atmosphere 

and biosphere are also considered. 

4.1. Source to Deposition 

4.1.1. Chemical Families and Constituent Species 

NOX is the name given to the family of chemical species containing oxidized N, chief among which 

are NO, NO2, HNO3, and PAN in the gas phase. And because it has a prominent role in transporting N 

from the atmosphere to the biosphere, particulate NO3

–

 is included in this ISA as well even though it is 

not a member of the oxidized N family of species as typically defined. Some of these oxidized N species 

are directly emitted; others are formed as secondary products from the emitted species. 

Similarly, SOX is the name for the family of chemical species containing oxidized S, including SO, 

SO2, SO3, and S2O; however, of these gas-phase species, only SO2 is present in concentrations relevant 

for atmospheric chemistry and environmental exposures. In addition, and as was the case with the NOX

species, particulate SO4

2−

 is included in this ISA because of its dominant role in transferring S species 

from the atmosphere to the biosphere. Furthermore, this ISA includes extensive treatment of the reduced 

N chemical species NH3 and NH4—together given the chemical family name NHX—because NHX can 

play a crucial role controlling the transfer of total N and S to the biosphere on many levels of spatial 

extent. The most salient points from the foregoing chapters are summarized below. 

4.1.2. Transport and Transformation 

Convective processes and small-scale turbulence will both transport pollutants up and down 

throughout the PBL and the FT. Emitted NOX, SOX, NH3 and other pollutants can be transported vertically 

by convection into upper part of the mixed layer on one day, and then transported overnight in a layer of 

elevated mixing ratios like a nocturnal low-level jet or the conveyor belts that characterize flows around 

frontal systems. Once pollutants are lofted to the middle and upper troposphere, they typically have a 

much longer lifetime and, with the generally stronger winds at these altitudes, can be transported long 

distances from their source regions. The length scale of this transport is highly variable owing to chemical 

and meteorological conditions encountered along the transport path. Transport of NOX and SOX from the 

boundary layer to the upper troposphere by convection, for example, usually dilutes high surface 

concentrations and extends species lifetimes by several days. During this transport time, the emitted and 

now chemically transformed pollutants can be entrained from aloft into the convective boundary layer 

 4-1
downwind as it grows with increasing insolation. In this way, the transported, transformed pollutants can 

be mixed back down to the surface. 

CTMs are the prime tools for computing the emissions and interactions of NOX, SOX, NHX and 

other pollutants; their transport and transformation, including production of secondary aerosols; the 

evolution of particle size distributions; and the deposition of pollutants to the surface. CTMs are driven by 

calculated pollutant emissions for primary species such as NOX, SOX, NH3, and primary PM, and by 

computed meteorological fields produced by other numerical prediction models. Meteorological 

quantities such as winds and temperatures are taken from operational analyses, reanalyses, or numerical 

weather circulation models to determine both transport and emission components of the CTMs: higher 

temperatures, for example, will substantially alter the rates and chemical forms of some emitted N 

pollutants and the lofting of N and S pollutant plumes. Additional discussion of these processes and the 

general performance of CTMs is provided in Section 2.8. 

The emitted, transported, and transformed pollutants reach the earth’s surface where they can exert 

ecological effects largely through deposition. Wet and dry deposition are important removal processes for 

pollutants on all scales and so are included in all CTMs. 

Wet deposition results from the incorporation of atmospheric particles and gases into cloud droplets 

and their subsequent precipitation as rain or snow, or from the scavenging of particles and gases by 

raindrops or snowflakes as they fall. Receptor surface properties like vegetation leaf surfaces have little 

effect on wet deposition, although leaves can retain liquid and solubilized particles containing S and N. In 

terrain containing extensive vegetative canopies, any material deposited by precipitation to the upper 

stratum of foliage is likely to be intercepted by several foliar surfaces before reaching the soil. This allows 

such processes as foliar uptake, chemical transformation, and re-suspension into the atmosphere to occur. 

Dry deposition is a complicated and poorly characterized process that appears to be controlled by a 

highly varying complex of such variables as atmospheric stability, macro- and micro-surface roughness, 

particle diameter, and receptor surface characteristics. The general approach for dry deposition used in 

most CTMs is the resistance-in-series method described in Section 2.8.2. The range of particle sizes, the 

diversity of canopy surfaces, and the variety of chemical constituents in airborne particulates have made it 

difficult to estimate with precision dry particulate deposition totals for large expanses of the U.S. 

Direct and indirect wet and dry deposition to specific locations like watersheds depend on air 

pollutant concentrations in the airshed above the watershed, but the shape and extent of the airshed is 

quite different from that of the watershed owing to the transport and transformation of emitted pollutants 

described above. In a watershed, everything that falls in its area, by definition, flows into a single body of 

water. An airshed, by contrast, is a theoretical concept that defines the source area containing the 

emissions that contribute a given level, often 50 or 75%, to the deposition in a particular watershed or to a 

given waterbody. Hence, airsheds are modeled domains containing the sources estimated to contribute a 

given level of deposition from each pollutant of concern. The principal NOX airsheds and corresponding 

watersheds for several regions in the eastern U.S. are shown in Section 2.8.4. 

4.1.3. Emissions and Atmospheric Concentrations 

Total anthropogenic NO and NO2 emissions in the U.S. in 2002 were 23.19 Tg. Combustion 

chemistry at EGUs contributed ~22% of these total and transportation-related sources, ~56%. Ambient 

annual NOX concentrations have decreased ~35% in the period 1990–2005 to current annual average 

concentrations of ~15 ppb. 

Biogenic NOX sources are substantially smaller than anthropogenic ones and include biomass 

burning, lightning, and soils. The NO and N2O emitted from soils as intermediate products from 

denitrification can evolve either naturally or as stimulated by addition of N containing fertilizers to crops 

and other soil management practices. N2O, another member of the oxides of N family of compounds, is 

also a contributor to total U.S. GHG emissions: ~6.5% on a Tg CO2e basis in 2005, and its U.S. emissions 

 4-2
decreased ~3% in the period 1990–2005, though there remains considerable interannual variation in this 

value. 

Concentrations of NO2 in the CONUS from non-anthropogenic sources in the U.S. and elsewhere 

in the world are <300 ppt over most of the CONUS and <100 ppt in the eastern U.S. on an annual average 

basis. The 24-h ambient NO2 concentrations in CMSAs where most of the regulatory monitors are located 

and where most anthropogenic emissions originate were, on average, <20 ppb with a 99 percentile value 

<50 ppb for the years 2003–2005. Annual-average NO2 concentrations over the CONUS are calculated to 

be <5 ppb for nearly all urban and rural and remote sites. 

On a national scale, energy production at EGUs accounted for ~66% of total SO2 emissions in the 

U.S. in 2001-2002; ~5% of total SO2 is emitted by transportation-related sources, with on-road vehicles 

accounting for ~40 % of the transportation fraction and off-road diesel and marine traffic together 

accounting for the remainder. Ambient annual SOX concentrations have decreased ~50% in the period 

1990–2005 and now stand at ~4 ppb for both aggregate annual and 24-h average concentrations nationwide. 

Annual-average policy-relevant background SO2 concentrations in the U.S. from uncontrolled 

sources here and elsewhere in the world are <10 ppt over most of the CONUS, or <1% of observed SO2

concentrations everywhere except areas in the Pacific Northwest where geogenic SO2 sources are 

particularly strong. 

NH3 emissions are chiefly from livestock and from soils as stimulated by addition of N-containing 

fertilizers to crops and other soil management practices. Confined animal feeding operations and other 

intensified agricultural production methods over a period of many decades have resulted in greatly 

increased volumes of animal wastes high in N; 30 to 70% of these wastes may be emitted as NH3. This 

increase in NH3 emissions, and the consequent increase in NH4

+

 concentration and deposition, correlates 

well with the local and regional increases in agricultural intensity. However, there remain no reliably 

consistent estimates of national average NH3 concentrations owing to three complex issues: the high 

spatial and temporal variability in NH3 emissions; the high uncertainty in the magnitude of those 

emissions; and the lack of real-time, ambient level NH3 monitoring techniques. Nonetheless, U.S. national 

NH3 emissions totals have been calculated taking into account these three drivers of uncertainty; for 

2001-2002 that national NH3 emissions total was ~4.08 Tg/yr. 

4.1.4. Deposition 

Increasing trends in urbanization, agricultural intensity, and industrial expansion during the 

previous 100 years have produced a nearly 10-fold increase in N deposited from the atmosphere. NOX, 

chiefly from fossil fuel combustion, often dominates total N pollution in the U.S. and comprises ~50 to 

75% of the total N atmospheric deposition. 

For the period 2004–2006, the routine monitoring networks report the mean N deposition in the 

U.S. was greatest in the Ohio River Valley, specifically in the states of Indiana and Ohio, with values as 

high as 9.2 and 9.6 kg N/ha/yr, respectively. N deposition was lower in other parts of the East, including 

the Southeast and in northern New England. In the central U.S., Kansas and Oklahoma reported the 

highest deposition, 7.0 and 6.5 kg N/ha/yr, respectively. 

N deposition primarily occurred in the form of wet NO3

–

 and NH4

+

, followed with decreasing 

amounts of dry HNO3, dry NH4

+

, and dry NO3⎯. Although deposition in most areas of the U.S. occurred in 

wet form, there were some exceptions, including parts of California where N deposition was primarily 

dry. Data are very sparse for the central U.S. between the 100th meridian and the Mississippi River; but 

where available, N deposition values there are lower than most of the eastern U.S., ranging from 4.1 to 

5.3 kg N/ha/yr. 

For the period 2004–2006, mean S deposition in the U.S. was greatest east of the Mississippi River 

with the highest deposition amount, 21.3 kg S/ha/yr, in the Ohio River Valley where most recording 

 4-3
stations reported 3 year averages >10 kg S/ha/yr. Numerous other stations in the East reported S 

deposition >5 kg S/ha/yr. Total S deposition in the U.S. west of the 100th meridian is relatively low, with 

all recording stations reporting less than 2 kg S/ha/yr and many reporting less than 1 kg S/ha/yr. 

S was primarily deposited in the form of wet SO4

2−

 followed in decreasing order by a smaller 

proportion of dry SO2 and a much smaller proportion of dry SO4

2−

. However, these S data in the western 

U.S., like those for N deposition, are derived from networks with many fewer nodes in the West than in 

the East. 

4.1.5. Field Sampling and Analysis 

The instrumentation deployed at present in the routine regulatory monitoring networks for 

determination of gas-phase NO2 and SO2 concentrations is designed for determining compliance with the 

current NAAQS. But in applications for determining environmental effects, all these methods have 

important limitations which make them inadequate for fully characterizing the state of the atmosphere at 

present; for correctly representing the complex heterogeneity of N and S deposition across the landscape; 

and for realistically apportioning the contributions of reduced and oxidized forms of atmospheric N and S 

in driving observed biological effects at a national scale. 

Routine NO2 measurements by chemilumenescnce difference (the FRM) are contaminated by 

unknown and varying concentrations of higher-order oxidized N species, including gas-phase HNO3, 

important itself for N deposition to the biosphere and also as a precursor to pNO3. Moreover, dry 

deposition of NO, NO2, and PANs is not at present estimated, but could be as much as 30% of total dry 

oxidized N deposition in areas near strong NOX sources. 

The present-day ambient annual average SO2 concentrations are very near or even below the 

operating limit of detection of most of the FRM monitors in the largest regulatory network. This produces 

irresolvable uncertainty in these data which may be important for environmental effects from S 

compounds, since they result in some cases from exposures at these current low concentrations. 

Routine field sampling techniques for NH3 are at present limited to integrated values from several 

days to one week because higher frequency semi-continuous methods are not yet sufficiently robust to 

deploy in regulatory networks although passive NH3 samplers show excellent potential. Estimates for the 

contribution of NH3 to the total N deposition budget range as high as 30% of total N, and are perhaps the 

dominant source of reduced N. 

Routine, regulatory, national-scale sampling and analysis for particulate-phase NO3

−

, SO4

2–, and 

NH4

+

 are subject to positive and negative errors, chiefly from the loss or production of constituent species 

on the surface of the filter used for the long time-integrated measurement. 

The coverage of the networks is very thin over large expanses of the interior U.S. and especially so 

west of the 100th meridian. This lack of monitored sites increases the likelihood that significant N and S 

deposition is now occurring at current atmospheric concentrations where no measurements are available, 

as predicted in numerical experiments with large-scale, first-principles models of atmospheric chemistry 

and physics and deposition, and as measured at some few selected special sites. 

4.2. Acidification 

Acidifying deposition includes gases and particles derived from SOX, NOX, and NHX. The effects 

of acidifying deposition on ecosystems have been well studied over the past several decades and 

vulnerable areas have been identified for the U.S. The wealth of available data has led to the development 

of robust ecological models used for predicting soil and surface water acidification. The principal factor 

governing the sensitivity of terrestrial and aquatic ecosystems to acidifying deposition is geology. 

 4-4
Watersheds of acid-sensitive lakes and streams have geologic formations with low base cation supply. 

Other factors contribute to the sensitivity of soils and surface waters to acidifying deposition, including 

topography, vegetation, soil chemistry, land use, and hydrologic flowpath. Regional and ecosystem 

vulnerability to acidification results from sensitivity and exposure to acidifying deposition. 

4.2.1. Terrestrial 

In the 1982 PM-SOX AQCD foliar and root uptake pathways for SOX were described in detail, as 

well as the role of S as a nutrient. Though small amounts of SO2 may be beneficial, it was understood that 

large amounts and high frequency of SO2 exposure and S deposition can be detrimental in the long term. 

At that time, there were no documented observations or measurements of changes in natural terrestrial 

ecosystems that were directly attributed to acidic precipitation; however, changes still may have been 

occurring. 

The 1993 NOX AQCD documented few cases in which excessive atmospheric N deposition was 

linked to soil acidification, although the process of soil acidification was already well understood. Since 

the preparation of these assessments, direct links between NOX and SOX deposition and many adverse 

effects associated with ecosystem loading have been reported. 

4.2.1.1. Biogeochemistry and Chemical Effects 

The evidence is sufficient to infer a causal relationship between acidifying deposition and changes in 

biogeochemistry related to terrestrial ecosystems. The strongest evidence for a causal relationship comes 

from studies of forested ecosystems, with supportive information on other plant communities, including 

shrubs and lichens (Section 3.2.2.1.). Grasslands are likely less sensitive to acidification than forests. Soil 

acidification occurs in response to inputs of sulfuric acid and nitric acid; the effect can be neutralized by 

weathering or base cation exchange. Soil acidification is a natural process, but is often accelerated by 

acidifying deposition. Acidifying deposition is important in decreasing concentrations of exchangeable 

base cations in soils. Despite recent decreases in acidifying deposition, there are widespread observations 

of ongoing soil acidification such as decreases in soil exchangeable base cations. The limited mobility of 

anions associated with naturally derived acidity (organic acids and carbonic acid) controls the rate of base 

cation leaching from soil under conditions of low atmospheric deposition of S and N. Because inputs of S 

and N in acidifying deposition provide anions that are more mobile in the soil environment than anions of 

naturally derived acids, these mineral acid anions can accelerate natural rates of base-cation leaching. 

Nitrification is mediated by autotrophic bacteria that derive energy by oxidizing NH4

+

 to NO3

−

. 

Nitrification produces acidity in the form of HNO3 as a byproduct, which contributes to the acidification 

of soils and surface waters. 

There are three useful indicators of chemical changes and acidification effects on terrestrial 

ecosystems, with consistency and coherence seen among multiple studies, including soil base saturation, 

Al concentration, and C:N Ratio (see Table 4-1). 

 Soil base saturation is the concentration of exchangeable bases as a percent of the total soil cation 

exchange capacity. Once base saturation decreases to a critical level (~15-20%), inputs of H2SO4

and HNO3 result in exchange of inorganic Al. 

 Inorganic Al is toxic to some tree roots. Plants affected by high inorganic Al concentrations in 

soil solution often have reduced root growth, which restricts the ability of the plant to take up 

water and nutrients, especially calcium (Parker et al., 1989). 

 4-5
 The C:N ratio of soil is used to indicate alterations to the N biogeochemical cycle. If the ratio 

falls below about 20 to 25, nitrification is stimulated resulting in net nitrification and increased 

acidity. 

Table 4-1. Chemical indicators of acidification to terrestrial ecosystems. 

Reference Indicator 

SOIL BASE SATURATION 

Reuss (1983) If base saturation is less than 15–20%, exchange ion chemistry is dominated by inorganic Al. 

Cronan and Grigal 

(1995) 

Base saturations below about 15% in the soil B-horizon could lead to effects from Al stress. 

Lawrence et al. (2005) Base saturation decreases from 30% to 20% in the upper soil B-horizon showed decreases in diameter growth of 

Norway spruce. 

Bailey et al. (2004) At Ca saturation less than 2% and Mg saturation less than 0.5% in the upper soil B-horizon, sugar maple mortality 

was observed. 

ALUMINUM CONCENTRATIONS 

Johnson et al. (1991) 

Joslin and Wolfe (1992) 

In soils with base saturation below about 20%, base cations reserves are so low that Al exchange dominates. 

Eagar et al. (1996) 

Cronan and Grigal 

(1995) 

There is a 50% risk of negative effects on tree growth if the molar ratio of Ca to Al in soil solution was 1.0. 100% risk 

for negative effects on growth at a molar ratio below 0.2. 

Johnson et al. (1994a; 

1994b) 

Ca:Al ratios above 1.0 over the course of 4 years were found in a forest stand experiencing high mortality. 

DeWitt et al. (2001) Ca:Al ratios below 0.5 in a Norway spruce stand showed reduced Mg concentrations in needles in the third year. 

C:N RATIO 

Aber et al. (2003) 

Ross et al. (2004) 

Increased effects of nitrification occur only in soil with C:N ratio below about 20–25. 

4.2.1.2. Biological Effects 

The evidence is sufficient to infer a causal relationship between acidifying deposition and changes in 

terrestrial biota. The strongest evidence for a causal relationship comes from studies of terrestrial systems 

exposed to elevated levels of acidifying deposition that show reduced plant health, reduced plant vigor, 

and loss of terrestrial biodiversity. In multiple studies, consistent and coherent evidence shows that 

acidifying deposition can affect terrestrial ecosystems by causing direct effects on plant foliage and 

indirect effects associated with changes in soil chemistry (Section 3.2.2.3). Biological effects of 

acidification on terrestrial ecosystems are generally attributable to Al toxicity, decreased ability of plant 

roots to take up nutrient cations and elevated leaching of Ca2+ from conifer needles. There are several 

indicators of stress to terrestrial vegetation (see Table 3-3), including percent dieback of canopy trees, 

dead tree basal area (as a percent), crown vigor index, and fine twig dieback. 

 4-6
Species Level 

 Changes in soil chemistry (e.g., depletion of soil base cations, Al toxicity to tree roots, leaching 

of base cations into drainage water) have contributed to high mortality rates and decreasing 

growth trends of red spruce trees (Picea rubens) in some areas of the eastern U.S. over the past 

three decades (see Red Spruce, Section 3.2.2.3). 

 Acidifying deposition, in combination with other stressors, is a likely contributor to the decline of 

sugar maple (Acer saccharum) trees that occur at higher elevation, in some portions of the eastern 

U.S., on geologies dominated by sandstone or other base-poor substrate, and that have base-poor 

soils (see Sugar Maple, Section 3.2.2.3). 

 Lichens and bryophytes are among the first species affected by acidifying deposition in the 

terrestrial ecosystem. Effects of SO2 on lichens include reduced photosynthesis and respiration, 

damage to the algal component of lichen, leakage of electrolytes, inhibition of N fixation, reduced 

potassium absorption, and structural changes. 

 Data are insufficient to draw general conclusions for other species. 

Community Level 

 Species loss and reduced biodiversity of forests, shrubs, and meadow plant communities may 

occur in response to acidifying deposition; however, such effects are likely more related to the 

nutrient enrichment effects of N deposition. 

4.2.1.3. Regional Vulnerability and Sensitivity 

There has been no systematic national survey of terrestrial ecosystems to determine the extent and 

distribution of terrestrial ecosystem sensitivity to the effects of acidifying deposition. However, one 

preliminary national evaluation estimated that ~15% of forest ecosystems in the U.S. exceeds the 

estimated critical load of wet and dry deposition of S and N by >250 eq/ha/yr (McNulty et al., 2007). 

Forests of the Adirondack Mountains of New York, Green Mountains of Vermont, White 

Mountains of New Hampshire, the Allegheny Plateau of Pennsylvania, and high-elevation forest 

ecosystems in the southern Appalachians are the regions most sensitive to terrestrial acidification effects 

from acidifying deposition (Section 3.2.4.2). While studies show some recovery of surface waters, there 

are widespread measurements of ongoing depletion of exchangeable base cations in forest soils in the 

northeastern U.S. despite recent decreases in acidifying deposition. 

4.2.2. Aquatic 

In the 1982 PM-SOX AQCD, the evidence on acidifying deposition and its role in the acidification 

of aquatic ecosystems was assessed. The most vulnerable regions were identified, including the 

Adirondack Mountains of New York. Significant changes were reported in aquatic ecosystems with 

increasing acidity, particularly as the pH decreases below ~5.5. It was concluded that: changes in 

community structure occur at all levels in the food web; bacterial decomposition is reduced and fungi that 

feed on organic debris may become dominant in aquatic communities; organic matter accumulates 

rapidly, tying up nutrients and limiting nutrient mineralization and cycling; phytoplankton productivity 

may be reduced because of changes in nutrient cycling and increased acidity; biomass and total 

productivity of benthic macrophytes and algae may increase, in part because of increased lake 

transparency; andspecies diversity and total numbers of species of aquatic plants and animals (especially 

invertebrates and fish species) are reduced, and acid-tolerant species predominate. 

 4-7
In the 1993 NOX AQCD a much-expanded body of evidence was available on the role of N 

deposition in the acidification of aquatic ecosystems. This was especially the case with respect to episodic 

acidification, which is far more common than chronic acidification and has been well documented for 

streams and lakes in the eastern U.S. The most well known examples are in the Adirondack and Catskill 

Mountains of the Northeast, as well as in the Great Smoky Mountains of the Southeast. Instances of 

episodic acidification were also reported in the western U.S. but to a much lesser extent than in the East. 

4.2.2.1. Biogeochemistry and Chemical Effects 

The evidence is sufficient to infer a causal relationship between acidifying deposition and changes in 

biogeochemistry related to aquatic ecosystems. The strongest evidence for a causal relationship comes 

from studies of changes in surface water chemistry including concentrations of SO4

2−

, NO3

−

, sum and 

surplus of base cations, ANC, inorganic Al, Ca, and surface water pH (see Section 3.2.2.1). Surface water 

chemistry integrates the sum of upstream soil and water processes and reflects the results of watershedscale terrestrial effects of S and N deposition, including N saturation, forest decline, and soil acidification 

(Stoddard et al., 2003). In many cases, surface water chemistry indicates the effects of acidification on 

biotic species and communities found in fresh water ecosystems. 

The status of surface water chemistry can be examined and reported as chronic chemistry or 

episodic chemistry. Chronic chemistry refers to annual average conditions, which are often represented as 

summer and fall chemistry for lakes, and as spring baseflow chemistry for streams. Episodic chemistry 

refers to conditions during rainstorms or snowmelt when proportionately more drainage water is routed 

through upper soil horizons, which tend to provide less neutralizing of atmospheric acidity as compared 

with deeper soil horizons. Surface water chemistry has lower pH and ANC during storm runoff or 

snowmelt than during baseflow conditions. One of the most important effects of acidifying deposition on 

surface water chemistry is the short-term change in chemistry that is termed “episodic acidification.” 

Some streams may have chronic or average chemistry that is suitable for aquatic biota, but be subject to 

occasional episodic acidification with lethal consequences. Episodic declines in pH and ANC are nearly 

ubiquitous in drainage waters throughout the eastern U.S., caused partly by acidifying deposition and 

partly by natural processes. 

Acidification effects on aquatic biota are often evaluated using measures of either inorganic Al or 

pH. ANC is also used because it is an indicator of acid-base status (although ANC does not relate directly 

to the health of biota). The usefulness of ANC lies in the association between ANC and the surface water 

constituents that directly contribute to or ameliorate acidity-related stress, in particular pH, Ca, SO4

2−

 and 

inorganic Al. 

Sulfate, Nitrate, and Base Cations 

Changes in water chemistry resulting from acidifying deposition typically include changes in 

SO4

2−

, NO3

−

, and base cation concentrations. Each plays an important role in the acid-base chemistry of 

water; none, however, are directly toxic at concentrations commonly encountered in surface waters 

(Table 4-2). 

 Sulfate is the primary inorganic anion found in most acid-sensitive waters. Continued decreases 

in S emissions should cause further decreases in SO4

2−

 concentrations in surface waters. 

However, the rate of decrease in surface water SO4

2−

 concentrations may be delayed as 

accumulated S leaches from watershed soils in some regions of the country, especially the 

southern Appalachian Mountains. 

 4-8
 The importance of NO3

−

 as an agent of acidification varies by region, but is particularly important 

during periods of high hydrologic flow from soils to streams, such as those that occur during 

snowmelt and rain events. The relationship between N deposition and surface water NO3

−

concentration is complex and involves the terrestrial and aquatic cycling of N and other elements. 

NO3

−

 contributes to the acidity of many lakes and streams in the eastern U.S. However, there is 

no apparent relationship between recent trends in N deposition and trends in NO3

−

 concentrations 

in these surface waters (in contrast to observed responses for S deposition and SO4

2−

concentrations). This suggests that the time scales of N saturation may be longer than previously 

considered (e.g., centuries, rather than decades). Nevertheless, long-term retention of N deposited 

in forested regions and consequent dampening of deposition effects on surface waters is unlikely 

to continue (Aber et al., 2003). 

 Decreases in base cation concentrations in eastern U.S. surface waters over the past two to three 

decades are ubiquitous and are closely tied to trends in SO4

2−

 concentrations. Rates of base cation 

depletion have been similar to those for SO4

2− plus NO3−

 in most areas (Shenandoah National 

Park is a notable exception). Decreasing trends in base cation concentrations do not necessarily 

indicate further acidification or recovery of surface waters, but may indicate either lower base 

cation leaching rates in soils or depletion of base cations from the soil system. 

Acid Neutralizing Capacity, Aluminum, and pH 

Acidification of surface water causes changes in ANC, Al concentration, and pH. Low pH and high 

inorganic Al concentration can be directly toxic to aquatic biota (Section 3.2.3). 

 ANC reflects the difference between base cations and anions of strong acids in solution; it is the 

most widely used measure of acid sensitivity, acidification, and chemical recovery of surface 

waters in response to changes in acidifying deposition. Acidic waters are defined as those having 

ANC equal to or below zero. Waters with ANC of <50 μeq/L are considered “extremely acidsensitive” (Schindler, 1988), and are vulnerable to episodic acidification (DeWalle et al., 1987; 

Eshleman, 1988). Lake and stream ANC values decreased throughout much of the 20th century in 

a large number of acid-sensitive lakes and streams throughout the eastern U.S. Since ~1990, the 

ANC of many affected lakes and streams has increased slightly. The number of acidic surface 

waters has decreased in some areas of the Northeast, but not in the central and southern 

Appalachian Mountains. 

 Dissolved inorganic Al is an important chemical indicator of the effects of acidifying deposition 

on surface water because it is toxic to aquatic life and generally does not leach from soils in the 

absence of acidification. When pH falls below approximately 5.5, inorganic Al generally 

becomes a greater health risk to biota. Limited data suggest that acid-sensitive regions of the 

northeastern U.S. have elevated inorganic Al concentrations in surface waters induced by years of 

acidifying deposition, posing a threat to aquatic life. Concentrations have decreased slightly in 

some surface waters in the northeastern U.S. during the last two decades in response to decreased 

levels of acidifying deposition. 

 The pH of freshwater streams and lakes is a common measure used to link acidification to 

adverse effects on aquatic biota. Decreases in pH below values of 6.0 typically result in species 

loss of benthic invertebrates, plankton species, and fish. A number of synoptic surveys indicated 

loss of species diversity and absence of several fish species in the pH range of 5.0 to 5.5. If pH 

decreases to lower values, there is a greater likelihood that more aquatic species could be lost 

without replacement, resulting in decreased richness and diversity. 

 4-9
Table 4-2. Chemical indicators of acidification in surface water. 

Reference Indicator 

SULFATE 

Driscoll et al. 

(2001a) 

Acidifying deposition at Hubbard Brook Experimental Forest in New Hampshire contributed to a nearly four-fold increase 

in stream SO4

2− concentration between 1850 and 1970. 

Stoddard et al. 

(2003) 

Widespread decreasing trends in SO4

2− concentrations were documented by U.S. EPA during the period 1990–2000 in 

the eastern U.S. including New England lakes (1.77 μeq/L/yr), Adirondack lakes (2.26 μeq/L/yr), Appalachian streams 

(2.27 μeq/L/yr), and Upper Midwest lakes (3.36 μeq/L/yr). 

NITRATE 

Driscoll and 

Newton (1985) 

NO3

− concentrations in 20 Adirondack lakes in the early 1980 averaged 12% of SO42− concentrations. 

Lovett et al. 

(2000) 

Baseflow NO3

− concentrations in 1994–97 were an average of 37% of SO42− concentrations in 39 Catskill streams. 

Murdoch and 

Stoddard (1993) 

During high-flows in Catskill streams NO3

− concentrations periodically equaled or exceeded SO42− concentrations. 

Webb et al. 

(2004) 

Average concentrations of NO3

− in most southeastern streams tend to be considerably less than SO42− concentrations. 

Cook et al. (1994) Very high NO3

− concentrations in streamwater were documented at high elevations in the Great Smoky Mountains in 

North Carolina. 

BASE CATIONS 

Likens et al. 

(1996) 

Approximately linear increasing relationship between concentrations of base cations and SO4

2−+ NO3− concentrations in 

Hubbard Brook streams from 1964 to 1969, then a reversal in 1970 and a decreasing trend up to 1994. 

Lawrence et al. 

(1999) 

Decreasing concentrations of base cations at a rate that exceeded decreases in concentrations of SO4

2−+ NO3− in Catskill 

Mountain streams from 1984 to 1997. 

ACID NEUTRALIZING CAPACITY 

Sullivan et al. 

(2006b) 

Model simulations suggest that none of the lakes in the Adirondack target lake population were chronically acidic or had 

ANC less than 20 μeq/L under preindustrial conditions. By 1980, there were hundreds of such lakes. 

Stoddard et al. 

(2003) 

Tendencies during the 1990s toward increasing surface water Gran ANC in all glaciated regions of the eastern U.S. (i.e., 

New England, Adirondacks, Northern Appalachian Plateau) and Upper Midwest; and decreasing Gran ANC in the 

Ridge/Blue Ridge province. 

SURFACE WATER ALUMINUM 

Gensemer and 

Playle (1999) 

Found that organically complex aluminum (organic Al) can occur in surface waters as a result of natural soil and 

hydrologic processes, but this form of Al is not harmful to aquatic life. 

Gensemer and 

Playle (1999) 

Demonstrated that inorganic Al has been found to be toxic to plant and animal species throughout the food web. 

Baldigo et al. 

(2007) 

20% mortality of young-of-the-year brook trout during a 30–day period with a median inorganic Al concentration of 

2 μmol/L. 90% mortality occurs over 30 days with a median inorganic Al concentration of 4.0 μmol/L. 

Lawrence et al. 

(2007) 

49 of 195 streams (25%) in the western Adirondack region had inorganic Al concentrations above 2.0 μM during August 

base flow. 

 4-10
PH 

Haines and Baker 

(1986) 

Baker et al. 

(1990b) 

pH values for biological effects have been summarized for a variety of aquatic organisms; common threshold values for 

pH are 5.0, 5.5, and 6.0. 

Charles et al. 

(1989) 

Sullivan et al. 

(1990) 

Cumming et al. 

(1992; 1994) 

25 to 35% of the Adirondack lakes larger than 4 ha have been acidified since preindustrial time. An estimated 80% of the 

Adirondack lakes that had pH less than 5.2 in the mid-1980s were inferred to have experienced large declines in pH and 

ANC since the previous century. About 30 to 45% of the lakes with pH between 5.2 and 6.0 have also acidified. 

GbondoTugbawa et al. 

(2002) 

PnET-BGC modeling at Hubbard Brook estimated that past stream pH (circa 1850) was probably about 6.3, compared 

with just above 5.0 in 2000. 

Stoddard et al. 

(2003) 

An increase in the hydrogen ion concentration of Appalachian streams (0.08 μeq/L/yr) and Upper Midwest lakes (0.01 

μeq/L/yr) was reported. No trends were found in New England lakes or Appalachian streams in this study. 

4.2.2.2. Biological Effects 

The evidence is sufficient to infer a causal relationship between acidifying deposition and changes in 

aquatic biota. The strongest evidence for a causal relationship comes from studies of aquatic systems 

exposed to elevated levels of acidifying deposition that support fewer species of fishes, 

macroinvertebrates, and diatoms (Section 3.2.3.3). Consistent and coherent evidence from multiple 

species and studies shows that acidification can result in the loss of acid-sensitive species, and there is 

evidence of a biological gradient in effects in that more species are lost with greater acidification. 

Biological effects are linked to changes in water chemistry including ANC, inorganic Al, and pH. 

Decreases in ANC and pH and increases in inorganic Al concentration contribute to declines in taxonomic 

richness of zooplankton, macroinvertebrates, and fish. Chemical changes can occur over both long- and 

short-term time scales, with additional effects on biological systems. Short-term (hours or days) episodic 

changes in water chemistry can have biological effects, including reduced fish condition factor, changes 

in species composition, and declines in aquatic species richness across multiple taxa, ecosystems and 

regions. 

Species Level 

 High levels of acidification (to pH values below 5) virtually eliminate all mayflies, crustaceans, 

and mollusks from some streams. 

 In general, populations of salmonid fish are not found at pH levels less than 5.0, and smallmouth 

bass (Micropterus dolomieu) populations are usually not found at pH values less than 5.5 to 5.2. 

 Twenty percent mortality of young-of-year brook trout were documented during a 30-day period 

with a median inorganic Al concentration of 2 μmol/L (Baldigo et al., 2007). It was estimated that 

90% mortality would occur over 30 days with a median inorganic Al concentration of 4.0 

μmol/L. 

 4-11
Community Level 

 Community-level effects were observed in the Adirondacks and Shenandoah National Park where 

taxonomic richness is lower in lakes and streams having low ANC and pH. 

 Decreases in pH and increases in inorganic Al concentrations have reduced the species richness 

of plankton, invertebrates, and fish in acid-affected surface waters. 

 In the Adirondacks, a positive relationship exists between the pH and ANC in lakes and the 

number of fish species present in those lakes. A number of synoptic surveys indicated suggested 

loss of species diversity and absence of several sensitive fish species in the pH range of 5.0 to 6.0 

(Section 3.2.4.4). 

 In Shenandoah National Park streams, the fish species richness decreased with decreasing stream 

ANC. On average, richness is lower by one fish species for every 21 μeq/L decrease in ANC 

(Section 3.2.4.5). 

 Short-term episodes of acidification are particularly harmful to aquatic biota. Early life stages are 

more sensitive to acidic conditions than the young-of-the-year, yearlings, and adults. Episodes are 

most likely to affect biota if the water had pre-episode pH above 5.5 and minimum pH during the 

episode of less than 5.0. Episodic acidification can have long-term adverse effects on fish 

populations. 

4.2.2.3 Regional Vulnerability and Sensitivity 

The effects of acidifying deposition have been assessed by several national surveys since the 

1980s, including the National Surface Water Survey and the National Stream Survey in the mid-1980s, 

the Wadeable Streams Assessment (WSA) in 2004, the U.S. EPA Long-Term Monitoring program 

beginning in 1983, and Temporally Integrated Monitoring of Ecosystems probability surveys beginning in 

1991. These surveys indicate that acidifying deposition has acidified surface waters in the southwestern 

Adirondacks, New England uplands, low-silica eastern Upper Midwest, forested Mid-Atlantic Highlands, 

and Mid-Atlantic Coastal Plain (Section 3.2.4.2). 

In the U.S., the Northeast and Mountainous West regions contain many of the surface waters most 

sensitive to acidification. Levels of acidifying deposition in the West are low in most areas, acidic surface 

waters are rare, and the extent of chronic surface water acidification that has occurred to date has been 

very limited. However, episodic acidification does occur. In both the west and the northeast, the most 

severe acidification of surface waters generally occurs during spring snowmelt. On average, spring ANC 

values of acid-sensitive surface waters in New England, the Adirondacks, and the northern Appalachian 

Plateau were on average 30 μeq/L lower than summer values between 1990 and 2000. This implies that 

lakes and streams in these regions would need on average to recover to chronic ANC values above 

~30 μeq/L or more before they could be expected not to experience acidic episodes (Stoddard et al., 

2003). 

In 2004, the U.S. EPA conducted a national WSA survey and found that, overall, less than 1% of 

the 1,020,000 km of stream in the target population was acidic due to acidifying deposition. No acidic 

streams were observed in the Mountainous West, Xeric West, Upper Midwest, Northern Plains, Southern 

Plains, or Temperate Plains ecoregions. Streams that were acidified from acidifying deposition were 

found in the Northern Appalachians (2.8% of 96,100 km of stream), and the Southern Appalachians (1.8% 

of 287,000 km). Very low ANC (0 to 25 μeq/L) streams, likely exposed to episodic acidification, were 

found in the Northern Appalachians (2.7% of 96,100 km of stream), the Coastal Plain (6.3% of 119,000 

km), and the Mountainous West (0.6% of 204,000 km). Stream surveys were not conducted in the 

Adirondacks or New England. 

It is important to address surface water recovery in response to reduced acidifying deposition over 

the past few decades. The following summarizes recent regional trends in acidification recovery. 

 4-12
About one-quarter to one-third of the lakes and streams that were chronically acidic during summer 

in the 1980s were no longer chronically acidic in 2000. These improvements in water chemistry are 

largely attributed to decreases in S deposition. Throughout the northeastern U.S., the concentration of 

SO4

2−

 in surface waters has decreased substantially in response to decreased emissions and atmospheric 

deposition of S. Decreased SO4

2−

 concentrations of a third or more in lakes and streams have been 

commonly observed. 

Data from U.S. EPA monitoring programs show that the following important changes in lake and 

stream chemistry occurred during the 1990s in response to S and N emissions reductions: SO4

2−

concentration decreased as a percentage of total ion concentration in surface waters; ANC increased 

modestly in three of the five regions included in surface water efforts; dissolved organic carbon and 

associated natural organic acidity increased, perhaps toward more natural pre-disturbance concentrations, 

as surface water acidity contributed from acidifying deposition decreased; and inorganic, and potentially 

toxic, Al concentrations appear to have decreased slightly in some sensitive aquatic systems. 

Despite these improvements, some regions and specific locations remain sensitive to acidifying 

deposition. For example, in the Adirondacks, the current rates of N and S deposition exceed the amount 

that would allow recovery of the most acid-sensitive lakes. In the Shenandoah, past SO4

2− has 

accumulated in the soil and is slowly released from the soil into stream water, where it causes 

acidification, making parts of this region sensitive to current loading. Numerical models specifically 

calibrated to these locations and conditions suggest that the number of acidic streams will increase under 

the current deposition rates. 

4.2.3. Ecosystem Services 

Acidification of ecosystems is primarily driven by NOX, NHX and SOX. Ecosystem services, as 

defined by Hassan et al. (2005), are broadly grouped into four main categories (see Section 3.1.3). The 

specific effects of acidification on ecosystem services may include: 

 Supporting: altered nutrient cycling, decreased biodiversity, decline of productivity 

 Provisioning: decline in the richness, abundance, and/or health of fish, other aquatic species and 

some terrestrial trees 

 Regulating: decline in water and soil quality 

 Cultural: decline in forest aesthetics, fishing, ecotourism and cultural heritage values related to 

ecosystem integrity and biodiversity 

4.3. Nitrogen Nutrient Enrichment 

NOX and NHX are the main contributors to N deposition across the U.S (see Chapter 2). Given the 

complexity of the N cycle, a broadly applicable and well-tested predictive model of the ecological effects 

of N deposition is not available. However, there is substantial empirical information for specific 

ecosystems and endpoints about ecological and biogeochemical responses to N deposition. The most 

commonly used experimental designs are N addition, N deposition gradient, and observational studies 

that evaluate relationships between effects and changing pollution levels over time (see Table 4-4 and 

Table 3-25). N addition experiments often use NH4NO3 or (NH4)2SO4 additions to simulate the chemical 

species in atmospheric Nr deposition. Deposition gradient experiments often only measure oxidized and 

reduced forms of N. Therefore, publications addressing N additions or deposition often do not include 

 4-13
data on all components of Nr. Thus throughout this Section, causality determinations are made on effects 

of N deposition that incorporate evidence related to various forms of N. 

The different ecosystem types that occur across the U.S. have a broad range of sensitivity to N 

deposition. In general, N deposition to terrestrial ecosystems causes accelerated growth rates in some 

species, which may lead to altered competitive interactions among species and nutrient imbalances, 

ultimately affecting biodiversity. The onset of these effects occurs with N deposition levels as low as 3 kg 

N/ha/yr in sensitive terrestrial ecosystems. In aquatic ecosystems, N that is both leached from the soil and 

directly deposited can pollute surface water. This causes alteration of the diatom community at levels as 

low as 1.5 kg N/ha/yr in sensitive freshwater ecosystems. In estuarine and near coastal marine 

ecosystems, total N loading, of which atmospheric deposition is a contributing factor, promotes 

eutrophication. Eutrophication is a serious problem in many coastal areas of the U.S. 

Factors that govern the sensitivity of terrestrial ecosystems to nutrient enrichment from N 

deposition include the degree of N-limitation to plant growth, rates and form of N deposition, elevation, 

climate, species composition, length of growing season, and soil N retention capacity. Critical N loads are 

described for European ecosystems (see Section 3.3.7.1). The range of critical loads in Europe varies by 

ecosystem type: terrestrial ecosystems are between 5-30 kg N/ha/yr; freshwater wetlands are between 5-

35 kg N/ha/yr; inland surface waters are between 5-20 kg N/ha/yr; coastal habitats are between 20-25 kg 

N/ha/yr; and marine habitats are between 30-40 kg N/ha/yr. Less is known about the extent and 

distribution of the terrestrial ecosystems in the U.S. that are most sensitive to the effects of nutrient 

enrichment from atmospheric N deposition; however available data for quantified relationships between 

loading and ecological effects are discussed in Section 3.3.7.2 and in Table 4-4. Table 4-4 summarizes 

field studies of the ecological effects of N deposition and N addition. It includes 51 studies that were 

conducted in the U.S. and 8 that were conducted in Europe and Asia since the last UNECE critical loads 

assessment. The ecosystem types from the U.S. include coastal sage scrub, desert, sub-alpine forest, 

coniferous forest, mixed hardwood forest, chaparral, oak savanna, grassland, freshwater lakes and 

freshwater wetlands. Endpoints include lichen, mycorrhizae, herbaceous and woody vascular plant 

species, algae and multiple biogeochemical indicators. Critical loads are available for few ecosystems and 

endpoints in the U.S. 

4.3.1. Terrestrial 

The 1993 NOX AQCD concluded that N deposition may cause important effects on terrestrial 

systems, and that the effects are often due to total N loading, not just that the oxidized forms. N deposited 

to an N-deficient ecosystem is generally expected to increase growth. If N is deposited on an ecosystem 

with adequate N or saturated with N, NO3

−

 leaching is expected to occur. Much of the information 

presented in the 1993 NOX AQCD was based on results from studies of forest ecosystems. N saturation 

was known to be more common in older forests. Disturbances such as fire and harvesting would push 

ecosystems to a state more removed from a condition of N saturation. Fertilization was known to increase 

growth in N-deficient forests in the short-term, but little was known about long-term effects of N 

fertilization and the differential growth effects on various tree and herbaceous plant species. It was known 

that plants do not necessarily benefit from added N. When N increases to the point that it is no longer 

limiting, deficiencies of other nutrients can occur (Aber et al., 1989). 

A few studies documented the deleterious effects of excessive N on tree growth and grassland 

biodiversity. Alpine ecosystems were identified as particularly sensitive to N deposition. The studies 

published since the 1993 NOX AQCD generally support its conclusions confirm findings of previous 

studies, provide more information on the long-term effects of N deposition, and expand the knowledge of 

effects to include more ecosystems and species. 

 4-14
4.3.1.1. Biogeochemical Effects 

N cycling 

The evidence is sufficient to infer a causal relationship between N deposition and the alteration of 

biogeochemical cycling of N in terrestrial ecosystems (Section 3.3.2.1). This is supported by numerous 

observational, deposition gradient and field addition experiments. N deposition disrupts the nutrient 

balance of ecosystems with numerous biogeochemical effects. The chemical indicators that are typically 

measured include NO3

−

 leaching, C:N ratio, N mineralization, nitrification, denitrification, foliar N 

concentration, and soil water NO3

−

 and NH4

+

 concentrations. Note that N saturation does not need to 

occur to cause adverse effects on terrestrial ecosystems. However, in some regions N saturation is a 

plausible mechanism for net nitrification and associated NO3

−

 leaching in drainage water. Substantial 

leaching of NO3

−

 from forest soils to stream water can acidify downstream waters (see Section 3.2) and 

deplete soils of nutrient base cations, especially Ca and Mg (Likens et al., 1998). 

 Two of the primary indicators of N enrichment in forested watersheds are the leaching of NO3

−

 in 

soil drainage waters and the export of NO3

−

 in stream water, especially during the growing season 

(Stoddard, 1994). 

 There is consistent and coherent experimental evidence that NO3

−

 leaching can be induced by 

chronic addition of N (Edwards et al., 2002b; Kahl et al., 1999; Kahl et al., 1993; Peterjohn et al., 

1996, Norton et al., 1999). Several N-exclusion studies in Europe demonstrated that decreases in 

N deposition produced immediate reductions in NO3

−

 leaching from forest stands (Gundersen 

et al., 1998; Quist et al., 1999). 

 In upland forested areas in the U.S., most N received by atmospheric deposition is retained in 

soil, and lesser amounts (7–16%) are retained in plant biomass (Nadelhoffer et al., 1999a). 

Several different data compilations indicate consistent and coherent results that 80% to 100% of 

N deposition is retained or denitrified within terrestrial ecosystems that receive less than about 8–

10 kg N/ha/yr (Aber et al., 2003; Dise and Wright, 1995; Kristensen et al., 2004; MacDonald 

et al., 2002; Sullivan, 2000b). 

 In the West, mixed conifer forests and chaparral watersheds with high smog exposure in the Los 

Angeles Air Basin also are N-saturated and exhibit the highest stream water NO3

−

 concentrations 

within wildlands in North America (Bytnerowicz and Fenn, 1996; Fenn et al., 1998). In the mixed 

conifer forests of the Sierra Nevada and San Bernardino mountains, a critical load for increased 

NO3

−

 leaching has been calculated to be 17 kg N/ha/yr. Several studies in the Rocky Mountains 

indicate that the capacity of alpine catchments to sequester N is exceeded at levels greater than 5-

10 kg N/ha/yr (Baron et al., 1994; Williams and Tonnesen, 2000). 

 Aber et al. (2003) found that surface water NO3

−

 concentrations exceeded 1 μeq/L in watersheds 

receiving about 9 to 13 kg N/ha/yr of atmospheric N deposition. The lakes and streams found to 

have high NO3

−

 concentration were those receiving N deposition above this range, but responses 

were variable among those receiving high N deposition. Above this range, mean NO3

−

 export 

increased linearly with increasing deposition at a rate of 0.85 kg NO3

−

 kg N/ha/yr for every 1 kg 

N/ha/yrincrease in deposition, although there was considerable variability in N retention among 

watersheds at higher rates of deposition. 

 Activities or disturbances such as logging or fire that export large quantities of N from the site 

alter future N availability and site propensity to achieve N saturation (Chanasyk et al., 2003). 

C cycling 

The evidence is sufficient to infer a causal relationship between N deposition and the alteration of 

biogeochemical cycling of C in terrestrial ecosystems (Section 3.3.3.1). The most extensive evidence on 

 4-15
the interactions between N deposition and C cycling is available for forest ecosystems. Experimental N 

addition studies show a range of responses in terms of tree mortality and productivity. In general, 

moderate to high additions of N lead to either no significant change in growth rates or transient growth 

increases (generally at deposition rates lower than 10 kg N/ha/yr), followed by increased mortality, 

especially at higher rates of fertilization (see Section 3.3.3.1). This group of studies shows coherence in 

effects, and indicates the presence of a biological gradient in responses with increasing N deposition. 

Due to the complexity of interactions between the N and C cycling, the effects of N on C budgets 

(quantified input and output of C to the ecosystem) are variable. Regional trends in NEP of forests have 

been estimated through models based on gradient studies (Magnani et al., 2007). There have been 

critiques of the method and the magnitude of these reported effects (Sutton et al., 2008b). N addition was 

found to slightly increase ecosystem C in a meta-analysis that examined the effects of N fertilization 

ranging from 25.5 to 200 kg N/ha/yr on forest ecosystem C content (see Section 3.3.3.1). In the western 

U.S., atmospheric N deposition has been shown to cause increased litter accumulation and carbon storage 

in above-ground woody biomass, which in turn may lead to increased susceptibility to more severe fires 

(Fenn et al., 2003a). 

Less is known regarding the effects of N deposition on C budgets of non-forest ecosystems. A 

meta-analysis, including 16 observations from 9 publications, conducted to evaluate the relationship 

between N addition ranging from 16 to 320 kg N/ha/yr and C sequestration of non-forest ecosystems 

showed that N addition has no significant effect on net ecosystem exchange of non-forest ecosystems. 

N deposition also affects the patterns of C allocation because most growth occurs above ground. 

This increases the shoot-to-root ratio, which can be detrimental to the plant because of decreased 

resistance to environmental stressors, such as drought and windthrow (Braun et al., 2003; Fangmeier 

et al., 1994b; Krupa, 2003; Minnich et al., 1995). 

N2O and CH4 flux 

The evidence is sufficient to infer a causal relationship between N deposition and the alteration of 

biogeochemical flux of N2O in terrestrial ecosystems (Section 3.3.4.2). Terrestrial soil is the largest source 

of N2O, accounting for 60% of global emission (Johnson, 2002). In a meta-analysis of 80 observations of 

terrestrial ecosystems that received chemical forms of N (NH4

+

, NO3

−

, NH4NO3, and urea) and addition 

rates (10 to 562 kg N/ha/yr), N addition resulted in a two-fold increase in N2O emission. The response of 

N2O emission to N addition for coniferous forest, deciduous forest and grasslands was significant (see 

Section 3.3.4.2). 

The evidence is sufficient to infer a causal relationship between N deposition and the alteration of 

biogeochemical flux of CH4 in terrestrial ecosystems (Section 3.3.4.1). Non-flooded upland soil is the 

largest biological sink for atmospheric CH4, consuming about 6% of the atmospheric CH4 (Le Mer and 

Roger, 2001). A meta-analysis was performed on a data set of 41 observations including four forms of N 

(NH4

+

, NO3

−

, NH4NO3 and urea) and the addition rates ranging from 10 to 560 kg N/ha/yr. The results 

indicated that N addition reduced CH4 uptake, but this inhibition was significant only for coniferous and 

deciduous forests (see Section 3.3.4.1). 

4.3.1.2. Species Richness, Composition and Biodiversity 

The evidence is sufficient to infer a causal relationship between N deposition on the alteration of 

species richness, species composition and biodiversity in terrestrial ecosystems. The most sensitive 

terrestrial taxa are lichens. Empirical evidence indicates that lichens in the U.S. are adversely affected by 

deposition levels as low as 3 kg N/ha/yr. Alpine ecosystems are also sensitive to N deposition, changes in 

an individual species (Carex rupestris) were estimated to occur at deposition levels near 4 kg N/ha/yr and 

modeling indicates that deposition levels near 10 kg N/ha/yr alter plant community assemblages. A 

summary of N deposition effects are presented below, organized by ecosystem type. 

 4-16
Lichens 

There is consistent and coherent evidence indicating that lichen communities are affected by 

current levels of N deposition. Sensitive lichens are frequently used as indicators of air pollution and 

atmospheric deposition levels. In addition to being good subjects for biomonitoring, they constitute 

important components of the forest ecosystem by contributing to biodiversity, regulating nutrient and 

hydrological cycles, and providing habitat elements for wildlife (McCune and Geiser, 1997); see 

Section 3.3.5). 

Lichens that contain a cyanobacterial photobiont appear to be more sensitive to adverse effects 

from atmospheric N deposition than most other lichens (Hallingbäck, 1991; Hallingbäck and Kellner, 

1992). The decline of lichens containing cyanobacteria in parts of northern Europe has been associated 

with N deposition in the range of 5 to 10 kg N/ha/yr (Bobbink et al., 1998). In the U.S., lichen species are 

negatively affected by N inputs as low as 3 to 8 kg N/ha/yr (Fenn et al., 2003a). 

In the San Bernardino Mountains, California, up to 50% of lichen species that occurred in the 

region in the early 1900s have disappeared (Fenn et al., 2003a; Nash and Sigal, 1999). The critical load 

has been calculated for lichen communities in mixed conifer forests in California at 3.1 kg N/ha/yr (Fenn 

et al., 2008). 

The Pacific Northwest retains widespread populations of pollution-sensitive lichens (Fenn et al., 

2003a). In this area, lichen communities are beginning to show evidence of changes in response to 

increased N pollution, including decreased distribution of sensitive lichen taxa, and their replacement 

with nitrophilous species (Geiser and Neitlich, 2007). 

Alpine Plant Communities 

Consistent and coherent evidence indicates that alpine plant communities are among the most 

sensitive terrestrial communities to atmospheric N deposition. Factors that govern the sensitivity of alpine 

tundra to N deposition include low rates of primary production, short growing season, low temperature, 

and wide variation in moisture availability in the alpine environment (Bowman et al., 1993; 1994; 

Bowman and Fisk, 2001; Fisk et al., 1998). Alpine herbaceous plants are generally considered N-limited 

and changes in alpine plant productivity and species composition have been noted in response to 

increased N inputs (Vitousek et al., 1997; Bowman et al., 2006). Alpine plant communities have also 

developed under conditions of low nutrient supply, in part because soil-forming processes are poorly 

developed, and this also contributes to their N-sensitivity. 

The western U.S. contains extensive land areas that receive low levels of atmospheric N deposition, 

interspersed with hot spots of relatively higher N deposition that typically occur downwind of large 

metropolitan centers and agricultural areas. Some of these areas of higher N deposition occur at high 

elevation. Results from several studies suggest that the capacity of Rocky Mountain alpine catchments to 

sequester N is exceeded at input levels less than 10 kg N/ha/yr (Baron et al., 1994; Williams, 1999). 

Changes in an individual species (Carex rupestris) were estimated to occur at deposition levels near 

4 kg N/ha/yr. Changes in the plant community were estimated to occur at deposition levels near 10 kg 

N/ha/yr. (Bowman et al., 2006). In comparison, critical loads for alpine plant communities in Europe are 

between 5-15 kg N/ha/yr (Bobbink et al., 2003). 

Grasslands 

Consistent and coherent evidence for reduced biodiversity in response to N deposition is reported 

for grasslands in the U.S. and Europe. Clark and Tilman (2008) evaluated the effects of chronic N 

addition over 23 years in Minnesota prairie-like successional grasslands and in native savanna grassland 

and found species numbers declined at the lowest addition level (10 kg N/ha/yr added to 6 kg N/ha/yr of 

ambient deposition). The authors calculated the critical load as 5.3 kg N/ha/yr with an inverse prediction 

interval of 1.3–9.8 kg N/ha/yr. 

 4-17
Change in species composition in response to N deposition has been observed regardless of soil 

type in European grasslands. Such effects have been found in calcareous, neutral, and acidic 

environments, species-rich heaths, and montane-subalpine grasslands (Bobbink et al., 1992b; 1998; 

Stevens, 2004).In a transect of 68 acid grasslands across Great Britain, covering the lower range of 

ambient annual N deposition (5 to 35 kg N/ha/yr) chronic N deposition significantly reduced plant species 

richness. Species richness declined as a linear function of the rate of inorganic N deposition, with a 

reduction of one species per 4 m2

 quadrant for every 2.5 kg N/ha/yr of chronic N deposition. The critical 

loads for ten different types of grasslands in Europe ranged between 10 and 30 kg N/ha/yr, above which 

changes in species composition were reported (Bobbink et al., 2003) (See Section 3.3.7.1). 

In the San Francisco Bay area of California, which receives N deposition levels of 10 to 

15 kg N/ha/yr, exotic nitrophilous grasses have displaced native grass species, likely due to greater N 

availability from deposition and from the cessation of grazing, which previously exported N out of the 

system (Fenn et al., 2003b). 

Forests 

Forests include overstory trees, understory herbaceous plants and mycorrhizae. There is very little 

information on the effect of N deposition on the biodiversity of overstory trees within forests in the U.S. 

This is due to the long life span and slow growth of trees, which makes such changes difficult to detect. A 

study of the northern edge of the Great Plains (southern Canada), showed that increasing N deposition 

over a range of 8 to 22 kg N/ha/yr to aspen-dominated and boreal forests increase forest expansion into 

the grasslands (Kotchy and Wilson 2001). More is known concerning the effects of N deposition on 

understory herbs, however most of the evidence is from Europe, where alteration of species composition 

is known to occur over the gradient of N deposition ranging from 6 to 20 kg N/ha/yr for acid tolerant 

species and a decline in the cover and abundance of ericaceous shrubs along a gradient from 3 to 12 kg 

N/ha/yr (Gilliam, 2006a). Loss of mycorrhizal diversity was recorded for Alaskan coniferous forest over a 

gradient of 1 to 20 kg N/ha/yr, and studies in oak savanna ecosystems in Minnesota show N addition 

decreases mycorrhizal diversity (Avis et al., 2003). 

Arid and Semi-arid Grasslands 

Alteration to arid and semi-arid plant communities resulting from experimental N fertilization have 

been reported in the Colorado Plateau, Joshua Tree National Park in California, and the coastal sage scrub 

community (CSS) of Southern California. 

Results from several lines of evidence showed increased biomass of non-native plant species over 

native species; decreased soil moisture under some conditions; and increased fire risk where dense grasses 

replaced shrub cover. 

In some areas of the CSS of Southern California, dry N deposition may be upwards of 

30 kg N/ha/yr (Bytnerowicz and Fenn, 1996). Native shrub and forb seedlings in this plant community are 

unable to compete with dense stands of exotic grasses, and thus are gradually replaced by the grasses, 

especially following disturbances such as fire (Cione et al., 2002; Eliason and Allen, 1997; Yoshida and 

Allen, 2001). The CSS community in California has been declining in land area and in shrub density for 

the past 60 years and is being replaced in many areas by Mediterranean annual grasses (Allen et al., 1998; 

Padgett and Allen, 1999; Padgett et al., 1999}. N deposition is considered a possible cause or contributor 

to this ecosystem alteration. 

Egerton-Warburton and Allen (2000) discerned a shift in arbuscular mycorrhizal community 

composition with decreased species richness and diversity along a deposition gradient (2 to 57 μg N/g as 

soil NO3

−

). These shifts in mycorrhizal fungal communities may facilitate replacement of native plant 

communities by Mediterranean annual grasslands in CSS. 

A coherent body of evidence suggests that N deposition may be contributing to greater fuel loads 

and thus altering the fire cycle in a variety of ecosystem types (Fenn, 2003b). Invasive grasses, which can 

be favored by high N deposition, promote a rapid fire-cycle in many locations (D'Antonio and Vitousek, 

 4-18
1992). The increased productivity of flammable understory grasses increases the spread of fire and has 

been hypothesized as one mechanism for the recent conversion of CSS to grassland in California 

(Minnich and Dezzani, 1998). 

Deserts 

Consistent and coherent evidence shows that N fertilization alters desert plant communities in the 

Chihuahuan Desert, Jordana Basin, Mojave Desert, and the Great Basin. N additions stimulate plant 

growth and cause the observed invasion of some exotic plant species and associated changes in ecosystem 

function, especially where water supply is adequate. There is little evidence evaluating biological 

responses across deposition gradients. However there are numerous field experiments that evaluate N 

addition levels ranging from 10-100 kg N/ha/yr. This is within the range of deposition that occurs in the 

U.S., which may be as high as 30–90 kg N/ha/yr downwind of major urban and agricultural areas (Fenn et 

al., 2003). Increased grass biomass has also been associated with increased fire frequency in the Mojave 

Desert (Brooks, 1999; Brooks and Esque, 2002; Brooks et al., 2004). This effect is most pronounced at 

higher elevation, probably because the increased precipitation at higher elevation contributes to greater 

grass productivity. In some cases, precipitation may be a more limiting factor than N to plant growth in 

deserts. Increased N supply at lower elevation in arid lands can only increase productivity to the point at 

which moisture limitation prevents additional growth. Fire was relatively rare in the Mojave Desert until 

the past two decades, but now fire occurs frequently in areas that have experienced invasion of exotic 

grasses (Brooks, 1999). 

4.2.3. Transitional 

Anaerobic conditions of waterlogged soils in wetlands result in slow decomposition of organic 

matter and accelerated denitrification. N cycles of two types of wetland, ombrotrophic bogs and coastal 

salt marshes, were discussed in 1993 NOX AQCD. Ombrotrophic bogs are generally considered the most 

sensitive to atmospheric N deposition because they are nutrient poor, with a closed N cycle in which the 

predominant source of N is rainfall. The 1993 NOX AQCD found that the three main ecological effects of 

N deposition on wetland ecosystem are: increasing primary production; modifying microbial processes; 

and reducing biodiversity. Recent studies support and extend the conclusions in the 1993 NOX AQCD, 

especially with regard to the effects of N deposition on species diversity. 

4.3.2.1. Biogeochemical Effects 

The contribution of N deposition to total N load varies among wetland types. A wetland is more 

vulnerable to N deposition as the relative contribution of N deposition to its total N load increases. For 

example, in freshwater wetland ecosystems atmospheric deposition is the main source of N to the 

ecosystem while N deposition is a minor contributor to N load in many coastal estuarine wetlands. 

N Cycling 

The evidence is sufficient to infer a causal relationship between N deposition and the alteration of 

biogeochemical cycling of N (Section 3.3.2.2). N deposition contributes to total N load in wetlands. The 

chemical indicators that are typically measured include NO3

−

 leaching, N mineralization, and 

denitrification rates. N dynamics in wetland ecosystems are variable in time, among types of wetlands and 

environmental factors, especially water availability (Howarth et al., 1996). A wetland can act as a source, 

sink, or transformer of atmospherically deposited N (Devito et al., 1989) and these functions can vary 

with season and with hydrological conditions. Vegetation type, physiography, local hydrology, and 

 4-19
climate all play significant roles in determining source/sink N dynamics in wetlands (Arheimer and 

Wittgren, 1994; Devito et al., 1989; Koerselman et al., 1993; Mitchell et al., 1996). 

N mineralization has been shown to increase with N addition, and this can cause an increase in 

wetland N export to adjacent surface water (Groffman, 1994). In general, leaching losses of NO3

−

 in water 

derived directly from wetlands are often small because of NO3

−

 removal by denitrification. Elevated N 

inputs to wetlands will often increase the rate of denitrification (Broderick et al., 1988; Cooper, 1990; 

Dierberg and Brezonik, 1983). This decreases environmental effects associated with increased N supply 

to soils and drainage waters; however it increases the emissions of greenhouse gases (e.g., N2O) to the 

atmosphere. Denitrification appears to be negligible in wetland environments that are typically nutrient 

(including N) poor, such as some bogs and fens (Morris, 1991). 

C Cycling 

The evidence is sufficient to infer a causal relationship between N deposition and the alteration of 

biogeochemical cycling of C. A meta-analysis that included wetlands with other non-forest ecosystems 

indicated no effect of N deposition on overall net ecosystem exchange of C (See Section 3.3.3). In other 

words, any gain in C capture by photosynthesis was offset by ecosystem respiration and C leaching. There 

were not enough studies to evaluate wetlands as a separate category. There is evidence that above and 

below ground C exchange processes are affected by N deposition. In Sphagnum-dominated ombrotrophic 

bogs, higher N deposition resulted in higher tissue N concentrations and greater NPP (Aldous, 2002), but 

lower bulk density. A study of 23 ombrotrophic peatlands in Canada with deposition levels ranging from 

2.7 to 8.1 kg N/ha/yr showed peat accumulation increases linearly with N deposition; however in recent 

years this rate has begun to slow indicating limited capacity for N to stimulate accumulation (Turunen et 

al., 2004). Soil respiration has been studied in European countries under a natural gradient of atmospheric 

N deposition from 2 to 20 kg N/ha/yr. They found enhanced decomposition rates for material accumulated 

under higher atmospheric N supplies resulted in higher carbon dioxide (CO2). 

In intertidal wetlands, primary production of plant species typically increases with N addition, 

however most studies apply fertilizer treatments that are several orders of magnitude larger than 

atmospheric deposition (Mendelssohn, 1979; Darby et al., 2008; Wigand et al., 2003, Tyler et al., 2007). 

N fertilization experiments in salt marsh ecosystems show biomass stimulation from 6 to 413% with 

application rates ranging from 7 to 3120 kg N/ha/yr (U.S. EPA, 1993a). 

Increases in biomass linked to N deposition, have also increased evapotranspiration rates (Howes, 

1986). This changed the soil water balance of water and may influence the direction of plant community 

succession. Model results suggest 7 kg N/ha/yr is the threshold for an oligotrophic bog to become a 

mesotrophic bog dominated by trees, as found in the 1993 NOX AQCD. 

N2O and CH4 flux 

The evidence is sufficient to infer a causal relationship between N deposition and the alteration of N2O

flux in wetland ecosystems. Nineteen observations of the effects of different N forms (NH4

+

, NO3

−

, 

NH4NO3, and urea) and addition rates (15.4 to 300 kg N/ha/yr) on wetland N2O emissions were evaluated 

in a meta-analysis (see Section 3.3.4.2; Mendelssohn, 1979). The results indicated that N addition 

increased the production of N2O by about two-fold. 

The evidence is sufficient to infer a causal relationship between N deposition and the alteration of CH4

flux in wetland ecosystems. Wetlands are generally net sources of CH4, but some wetlands can be net sinks 

depending on environmental conditions such as drainage and vegetation (Crill et al., 1994; Saarnio et al., 

2003). A meta-analysis was performed on a data set of 17 observations to assess the effects of N additions 

on CH4 fluxes (see Section 3.3.4.1). This data set included four forms of N (NH4

+

, NO3

−

, NH4NO3 and 

urea) and the addition rates ranged from 30 to 240 N kg N/ha/yr (see Section 3.3.4.1). The results 

indicated that N addition increased CH4 production from the wetlands, but had no significant effect on 

CH4 uptake of wetlands. 

 4-20
4.3.2.2. Biological Effects 

The evidence is sufficient to infer a causal relationship between N deposition and the alteration of 

species richness, species composition and biodiversity in wetland ecosystems (Section 3.3.5.2). Wetlands 

contain a high number of rare plant species. Excess N deposition can cause shifts in wetland community 

composition by altering competitive relationships among species, which potentially leads to effects such 

as decreasing biodiversity, increasing non-native species establishment and increasing the risk of 

extinction for sensitive and rare species. 

Changes in plant species composition caused by elevated atmospheric N deposition haven been 

demonstrated in Europe. Achermann and Bobbink (2003), see Table 3-24, evaluated the empirical 

evidence linking N deposition to wetland species composition and biodiversity to develop the following 

critical loads in Europe: raised and blanket bogs = 5-10 kg N/ha/yr; poor fens = 10-20 kg N/ha/yr; rich 

fens 15-35 kg N/ha/yr; mountain rich fens 15-25 kg N/ha/yr; pioneer and low-mid salt marshes 

30-40 kg N/ha/yr. 

Some wetland species are adapted to low-N environments. High levels of atmospheric N deposition 

increase the risk of decline and extinction of those sensitive species. In general, these include the genus 

Isoetes sp., of which three species are federally endangered; insectivorous plants like the endangered 

green pitcher Sarracenia oreophila; and the genus Sphagnum, of which there are 15 species listed as 

endangered by eastern U.S. states. Roundleaf sundew (Drosera rotundifolia) is also susceptible to 

elevated atmospheric N deposition (Redbo-Torstensson, 1994). This plant is native to, and broadly 

distributed across, the U.S. and is listed as endangered in Illinois and Iowa, threatened in Tennessee, and 

vulnerable in New York . In the U.S., Sarracenia purpurea can be used as a biological indicator of local N 

deposition in some locations (Ellison and Gotelli, 2002). S. purpurea is a perennial pitcher plant native to 

Canada and the eastern U.S. that grows in nutrient-poor peatlands and is sensitive to changes in N 

availability. Based on the annual demographic rates, a non-stationary matrix model forecasted that the 

extinction risk within the next 100 years increased substantially if N deposition rate increase (1-4.7%) 

from the rate of 4.5-6.8 kg N/ha/yr (Gotelli and Ellison, 2002). 

4.3.2.3. Regional Vculnerability and Sensitivity 

Bogs are among the most sensitive wetland ecosystems to N deposition. In the U.S., peat-forming 

bogs are most common in areas that were glaciated, especially in portions of the Northeast and Upper 

Midwest (U.S. EPA, 1993a). 

N input and output rates of fens are intermediate between bogs and coastal marshes. N deposition 

could drastically change species composition, increase primary productivity and increase methane 

emission in fens (Aerts and de Caluwe, 1999; Pauli et al., 2002). 

Atmospheric N inputs contribute to eutrophication problems in coastal marshes at many locations. 

However marine inputs of N are typically higher than direct atmospheric input. Models of sources of N to 

wetland ecosystems are not yet available. 

The effect of N deposition on wetland ecosystems depends on the fraction of rainfall in its total 

water budget and the sensitivity to N deposition was suggested as bogs (70-100% rainfall) >fens (55-83% 

rainfall) >intertidal wetlands (10-20% rainfall) (Morris, 1991). 

4.3.3. Freshwater Aquatic 

The 1993 NOX AQCD concluded that productivity of fresh water is usually limited by the 

availability of phosphorus (P). However, it was noted that high inputs of P from anthropogenic sources 

could lead to N limitation. The ratio of dissolved N to total phosphorus (molar basis) was used as an 

 4-21
indicator for nutrient limitation, with values less than 2 indicating N limitation. The proportions of N 

limited lakes showed a wide regional variation: Pacific Northwest (27.7%), Upper Midwest (19%), 

Northeast (5%), and Southeast (2.5%). All sub regions of the West contain substantial numbers of N 

limited lakes. 

4.3.3.1. Biogeochemical Effects 

N Cycling 

The evidence is sufficient to infer a causal relationship between N deposition and the alteration of 

biogeochemical cycling of N in freshwater aquatic ecosystems (Section 3.3.2.3). N deposition is the main 

source of N enrichment to headwater streams, lower order streams and high elevation lakes. The chemical 

indicators that were studied included NO3

−

 and DIN concentration in surface waters as well as Chl a:total 

P ratio. Elevated surface water NO3

−

 concentrations occur in both the eastern and western U.S. Bergstrom 

and Jansson (2006) report a significant correlation between N deposition and lake biogeochemistry by 

identifying a correlation between wet deposition and [DIN] and Chl a: Total P. Recent evidence provides 

examples of lakes and streams that are limited by N and show signs of eutrophication in response to N 

addition. 

C Cycling 

The evidence is sufficient to infer a causal relationship between N deposition and the alteration of 

biogeochemical cycling of C in freshwater aquatic ecosystems (Section 3.3.3.3). If growth of the 

autotrophic community of a freshwater stream is N-limited, then N addition will stimulate C-capture via 

photosynthesis often altering the C cycle. Moreover, a freshwater lake or stream must be N-limited to be 

sensitive to N-mediated eutrophication. Elser et al. (2008) in a meta-analysis of over 600 experiments 

found that N-limitation occurs frequently in freshwater ecosystems, in contrast to the traditional 

paradigm. There are also many examples of fresh waters that are N-limited or N and P co-limited (See 

Annex C). Bergström and Jansson (2006) concluded that most lakes in the northern hemisphere are 

limited by N in their natural state. 

Numerous studies investigate the relationship between N concentration of freshwater and primary 

productivity (reported as Chl a, NPP, or an index such as the lake chemistry ratio of DIN:TP) and 

atmospheric N deposition. Typically N addition experiments of lake and stream bioassays in which N was 

added to waters in field or laboratory to measure the response are conducted. A meta-analysis of 

enrichment bioassays in 62 freshwater lakes of North America found algal growth enhancement from 

N amendments to be common in slightly less than half the studies (Elser et al., 1990). Gradient studies of 

undisturbed northern temperate, mountain, or boreal lakes, that generally receive low levels of 

atmospheric N deposition found strong relationships between N-limitation and productivity where N 

deposition was low, and P and N+P limitations where N deposition was higher (Fenn, 2003b; Bergström 

et al., 2005; Bergström and Jansson, 2006). One such study in Sweden found, the lowest productivity at 

sites where wet N deposition was about 1.3 kg N/ha/yr; increasing productivity occurred at deposition 

levels greater than 2.2 kg N/ha/yr (Bergström et al., 2005). 

4.3.3.2. Biological Effects 

The evidence is sufficient to infer a causal relationship between N deposition and the alteration of 

species richness, species composition and biodiversity in freshwater aquatic ecosystems (Section 3.3.5.3). 

Increased N deposition can cause a shift in community composition and reduce algal biodiversity, 

especially in sensitive oligotrophic lakes. 

 4-22
In the West, a hindcasting exercise determined that the change in Rocky Mountain National Park 

lake algae that occurred between 1850 and 1964 was associated with an increase in wet N deposition that 

was only about 1.5 kg N/ha (Baron, 2006). Similar changes inferred from lake sediment cores of the 

Beartooth Mountains of Wyoming also occurred at about 1.5 kg N/ha deposition (Saros et al., 2003). 

Some freshwater algae are particularly sensitive to added nutrient N and experience shifts in 

community composition and biodiversity with increased N deposition. For example, two species of 

diatom (a group of algae), Asterionella formosa and Fragilaria crotonensis, now dominate the flora of at 

least several alpine and montane Rocky Mountain lakes and sharp increases have occurred in Lake Tahoe 

(Baron et al., 2000; Interlandi and Kilham, 1998; Saros et al., 2003; Saros et al., 2005; Wolfe et al., 

2001a; Wolfe et al., 2003). The timing of this shift has varied, with changes beginning in the 1950s in the 

southern Rocky Mountains and in the 1970s or later in the central Rocky Mountains. These species are 

opportunistic algae that have been observed to respond rapidly to disturbance and slight nutrient 

enrichment in many parts of the world. 

Extremely high NO3

−

 concentrations can have direct adverse effects on fish, invertebrates and 

amphibians, but the concentrations required to elicit such effects are typically more than 30 times higher 

than those that would commonly be attributable to atmospheric deposition. For example, mortality of 

rainbow trout eggs and fry occurred after 30-day incubations in concentrations greater than 79 μg N/L; 

adverse effects on amphibians and insects occur at even higher concentrations. 

4.3.3.3. Regional Vulnerability and Sensitivity 

Eutrophication effects on freshwater ecosystems from atmospheric deposition of N are most likely 

to occur in lakes and streams that have low productivity, low nutrient levels and that are located in the 

most undisturbed areas. 

 In the western U.S., high-elevation lakes are considered the most sensitive aquatic ecosystems to 

N deposition. Some examples include the Snowy Range in Wyoming, the Sierra Nevada 

Mountains, and Lake Tahoe in California, and the Colorado Front Range. 

 The most severe eutrophication from N deposition effects are expected downwind of major urban 

and agricultural centers. 

4.3.4. Estuarine Aquatic 

The 1993 NOX AQCD concluded that the primary effect of N enrichment effect on aquatic 

ecosystems is eutrophication of estuarine and near-coastal marine waters, which results in an increase of 

algal biomass and changes in community composition. Recent studies generally support and expand upon 

the conclusions of 1993 NOX AQCD. The data for estimating the contribution of N deposition to the 

nutrient budget of aquatic ecosystems were very sparse and mainly limited to the Chesapeake Bay before 

1993. The contribution of N deposition to estuarine eutrophication is now better understood in the 

Chesapeake Bay and other estuaries. 

4.3.4.1. Biogeochemical Effects 

A recent national assessment of eutrophic conditions in estuaries found that 65% of the assessed 

systems had moderate to high overall eutrophic conditions and generally received the greatest N loads 

from all sources, including atmospheric and land-based sources (Bricker et al., 2007). Estuarine and 

coastal marine ecosystems experience a range of ecological problems associated with nutrient enrichment. 

 4-23
Because the productivity of estuarine and near shore marine ecosystems is generally limited by the 

availability of N, they are susceptible to eutrophication effect of N deposition. 

N Cycling 

The evidence is sufficient to infer a causal relationship between Nr deposition and the biogeochemical 

cycling of N (Section 3.3.2.4). The N load from atmospheric deposition is estimated to comprise 10% to 

40% of the total input of N to many coastal estuaries, and could be higher for some. Atmospheric N loads 

to great waters and estuaries in the U.S. are estimated to range from 2 to 8% for Guadalupe Bay, TX on 

the lowest end to ~72% for the St. Catherines-Sapelo estuary, GA (Castro et al., 2003) on the highest. At 

Chesapeake Bay, where N and S deposition and ecological effects have been extensively studied, total 

atmospheric deposition of atmospheric NO3 is estimated to contribute from 20% to 30% of total N and 

14% of the NH4 loadings to the Bay. 

Estimates of total N loadings to estuaries are computed using measurements of wet and dry N 

deposition where these are available and interpolated with or without the use of air quality models. Direct 

atmospheric inputs (directly to the water surface) of Nr to coastal waters are essentially equal to or greater 

than those contained in riverine flow in the absence of deposition and may contribute from 20 to >50% of 

N loadings to these systems: 11, 5.6, and 5.6 kg N/ha for the Northeast Atlantic coast of the U.S., the 

Southeast Atlantic coast of the U.S., and the U.S. Eastern Gulf of Mexico, respectively. 

It is unknown if current levels of atmospheric deposition alone are sufficient to cause 

eutrophication. In general, estuaries tend to be N-limited (Elser et al., 2008), and many currently receive 

high levels of N input from human activities to cause eutrophication (Howarth et al., 1996; Vitousek and 

Howarth, 1991). The most widespread chemical indicators of eutrophication are submerged aquatic 

vegetation, Chl a, algal blooms, macroalgae and dissolved O2. 

C Cycling 

The evidence is sufficient to infer a causal relationship between N deposition and alteration to the 

biogeochemical cycling of C (Section 3.3.3.4). Estuaries and coastal waters tend to be N-limited and are 

therefore inherently sensitive to increased atmospheric N loading (D'Elia et al., 1986; Howarth and 

Marino, 2006; Elsner et al., 2007). This is at least partly because denitrification by microbes found in 

estuarine and marine sediments releases much of the added N inputs back into the atmosphere (Vitousek 

et al., 1997). However, other limiting factors occur in some locations and during some seasons. Levels of 

N limitations are affected by seasonal patterns. N-limited conditions are likely to be found during the 

peak of annual productivity in the summer. 

Numerous studies evaluate the relationship between N loading, eutrophication and ecological 

endpoints including Chl a concentration, macroalgal abundance, dissolved O2, nuisance or toxic and algal 

blooms In the national estuary condition assessment, high Chl a concentration was the most widespread 

documented indicator of eutrophication (Bricker et al., 2007) (see Figure 3-42). 

Excess N inputs will affect the Si:N ratio in water. If the Si:N ratio decreases below about 1, the 

marine food web structure would be expected to change, with decreasing diatom-to-zooplankton-tohigher tropic level ratios and increasing abundance of flagellated algae. 

 4-24
Table 4-3. Indicators of estuarine eutrophication. 

Primary Symptom Description 

Chl a Excess N input will stimulate primary productivity and Chl a concentration 

indicator of phytoplankton biomass 

Macroalgal Abundance Macroalgal blooms were moderate or high for half of the nation’s assessed 

estuaries (Bricker et al., 2007). Macroalgal blooms can cause the loss of 

important submerged aquatic vegetation by blocking sunlight. 

Dissolved O2 Dissolved O2 concentration decreases with increasing algal abundance under 

elevated N, because microbes consume O2 as they decompose dead algae. 

Increased atmospheric N deposition could stimulate the development of hypoxic 

or anoxic zones. The northern Gulf of Mexico is the largest documented zone of 

hypoxic coastal water in U.S. 

Nuisance/Toxic Algal Blooms Excess N input can cause nuisance or toxic algal blooms, which release toxins 

in the water that can poison aquatic animals and threaten human health. About 

one third of the nation’s assessed estuary systems exhibited a moderate or high 

symptom expression for nuisance or toxic algae (Bricker et al., 2007). 

Source: Bricker et al. (2007). 

4.3.4.2. Biological Effects 

The evidence is sufficient to infer a causal relationship between N deposition and the alteration of 

species richness, species composition and biodiversity in estuarine ecosystems (Section 3.3.5.4). 

Increased N deposition can cause shifts in community composition, reduced hypolimnetic DO, reduced 

biodiversity, and mortality of submerged aquatic vegetation. The form of deposited N can significantly 

affect phytoplankton community composition in estuarine and marine environments. Small diatoms are 

more efficient in using NO3

−

 than NH4

+

. Increasing NH4

+

 deposition relative to NO3

−

 in the eastern U.S. 

favors small diatoms at the expense of large diatoms. This alters the foundation of the food web. 

Submerged aquatic vegetation is important to the quality of estuarine ecosystem habitats because it 

provides habitat for a variety of aquatic organisms, absorbs excess nutrients, and traps sediments. Nutrient 

enrichment is the major driving factor contributing to declines in submerged aquatic vegetation coverage. 

The Mid-Atlantic region is the most heavily impacted area in terms of moderate or high loss of 

submerged aquatic vegetation due to eutrophication. Indicators to assess the eutrophic condition of 

estuarine and coastal waters are given in Table 4-3. Because estuaries receive N from multiple sources, it 

is unknown if N deposition alone could cause estuary eutrophication. 

4.3.4.3. Regional Vulnerability and Sensitivity 

The most eutrophic estuaries were generally those that had large watershed-to-estuarine surface 

area, high human population density, high rainfall and runoff, low dilution, and low flushing rates 

(Bricker et al., 2007). The national estuary condition assessment conducted by Bricker et al. (2007) found 

the most eutrophic estuaries occurred in the mid-Atlantic region and the estuaries with the lowest degree 

of eutrophication were in the North Atlantic. Other regions had mixtures of low, moderate, and high 

degree of eutrophication. The regional assessment results from the report of Bricker et al. (2007) are 

summarized in Section 3.3.8. 

The Chesapeake Bay is the largest estuary in the U.S. Its watershed covers 64,299 square miles and 

the surface area of the bay and its major tributaries is 4,479 square miles (Pyzik et al., 2004). The 

 4-25
Chesapeake Bay is perhaps the best-documented case study in the U.S. of the effects of human activities 

on estuarine eutrophication. Recent studies (Boyer et al., 2002; Howarth, 2007) indicated that 

atmospheric deposition makes a substantial contribution (about 25%) to the overall N budget of 

Chesapeake Bay. Human disturbances, such as landscape changes, have exacerbated the negative impacts 

of N deposition by reducing N removal and retention in the upper watershed region. Anthropogenic N 

inputs have substantially altered the trophic condition of Chesapeake Bay over the last 50 to 100 years. 

Signs of eutrophication in the bay include high algal production, low biodiversity, and large hypoxia and 

anoxia zones. Submerged aquatic vegetation was once abundant in Chesapeake Bay, covering about 

200,000 acres along the shallows and shorelines. Increased nutrient inputs caused submerged aquatic 

vegetation declines since the mid-1960s, and had fallen to about 38,000 acres by 1984. Eutrophication has 

been implicated in declines and disappearance of striped bass (Morone saxatilis) and blue crab 

(Callinectes sapidus) in the Chesapeake Bay. 

4.3.5. Ecosystem Services 

Some valuation studies assess the effects of N enrichment from multiple sources (see Annex 

F).There are no publications at this time which focus on the ecosystem services specifically affected by N 

deposition. The evidence reviewed in this ISA illustrates that N deposition can affect ecosystem services 

in the following categories (defined by Hassan et al., 2005): 

 Supporting: nutrient cycling, biodiversity 

 Provisioning: forest yields, fishing yields in estuaries 

 Regulating: water quality, air quality, climate regulation (interactions with greenhouse gases CO2, 

N2O, CH4), fire frequency and intensity, disease resistance 

 Cultural: swimming, boating, recreation, biodiversity 

4.4. Direct Phytotoxic Effects 

4.4.1. Sulfur Dioxide 

The evidence is sufficient to infer a causal relationship between exposure to SO2 and injury to 

vegetation (Section 3.4.2.1). The current secondary standard for SO2 is a 3-h average of 0.50 ppm, which 

is designed to protect against acute foliar injury in vegetation. There has been limited research on acute 

foliar injury since the 1982 PM-SOX AQCD and there is no clear evidence of acute foliar injury below the 

level of the current standard. 

Effects on growth and yield of vegetation are associated with increased SO2 exposure concentration 

and time of exposure. The 1982 PM-SOX AQCD concluded that more definitive concentration-response 

studies were needed before useable exposure metrics could be identified. The few new studies published 

since the 1982 PM-SOX AQCD continue to report associations between exposure to SO2 and reduced 

vegetation growth. However, most these studies have been performed outside the U.S. and at levels well 

above ambient concentrations observed in the U.S. 

 4-26
4.4.2. NO, NO2 and PAN 

The evidence is sufficient to infer a causal relationship between exposure to NO, NO2 and PAN and 

injury to vegetation (Section 3.4.2.2). It is well known that in sufficient concentrations, NO, NO2 and PAN 

can have phytotoxic effects on plants through decreasing photosynthesis and induction of visible foliar 

injury (U.S. EPA, 1993a). However, the 1993 NOX AQCD concluded that concentrations of NO, NO2 and 

PAN in the atmosphere are rarely high enough to have phytotoxic effects on vegetation (U.S. EPA, 

1993a). Since the 1993 NOX AQCD, very little new research has been done on these phytotoxic effects at 

concentrations currently observed in the U.S. 

4.4.3. HNO3

The evidence is sufficient to infer a causal relationship between exposure to HNO3 and changes to 

vegetation (Section 3.4.2.3). Experimental exposure of HNO3 resulted in damage to the leaf cuticle of 

pine and oak seedlings, which may predispose those plants to other stressors such as drought, pathogens, 

and other air pollutants (Bytnerowicz et al., 1998a, b). However, these tree seedling experiments used 

relatively short-term exposures at concentrations well above current ambient conditions. In lichen studies, 

several lines of evidence, including transplant and controlled exposure studies, indicate that past and 

current HNO3 concentrations may be contributing to the decline in lichen species in the Los Angles basin 

(Boonpragob and Nash, 1991; Nash and Sigal, 1999; Riddell et al., 2008). Current deposition of HNO3 is 

contributing to N saturation of some ecosystems close to sources of photochemical smog (Fenn et al., 

1998) such as the mixed conifer forests of the Los Angeles basin mountain (Bytnerowicz et al., 1999). 

4.5. Mercury Methylation 

The evidence is sufficient to infer a causal relationship between S deposition and increased 

methylation of Hg, in aquatic environments where the value of other factors is within adequate range for 

methylation (Section 3.4.1.4). The main agent of Hg methylation is SO4

2−

-reducing-bacteria, and 

experimental evidence from laboratory to mesocosm scales has established that only inconsequential 

amounts of MeHg can be produced in the absence of SO4

2−

. These experimental results are highly 

coherent with one another, and with observational studies at larger scales. Changes in the amount of SO4

2−

present have been shown to be followed by commensurate changes in MeHg, and mechanistic links have 

been established between variation in SO4

2− and variation in methylation of Hg. 

Quantification of the relationship between SO4

2− and methylation of Hg in natural settings has 

proved difficult because of the presence of multiple interacting factors in aquatic environments where 

SO4

2−

 and Hg are present. The amount of MeHg produced has been shown to vary with O2 content, 

temperature, pH, and supply of labile organic carbon. In some watersheds, such as high altitude lakes in 

the western U.S., where no effect of changes in SO4

2−

 deposition have been recorded on methylation of 

Hg. This is because one or several interacting factors were not present in the amounts required for 

methylation to occur at more than inconsequential rates. Watersheds with conditions known to be 

conducive to Hg methylation can be found in the northeastern U.S. and southeastern Canada, but 

significant biotic Hg accumulation has been observed in other regions that have not been studied as 

extensively, and where a different set of conditions may exist. 

Hg is a highly neurotoxic contaminant, and enters the food web in the methylated form. MeHg is 

then concentrated in higher trophic levels, including fish eaten by humans, with undesirable consequences 

for affected species, and for populations that consume large amounts of fish. Once MeHg is present, other 

 4-27
variables influence how much of it accumulates in fish. Current evidence indicates that increased S 

deposition very likely results in MeHg accumulation in fish. 

Table 4-4. Summary of N deposition levels and corresponding ecological effects. 

Type of 

Ecosystem 

Ambient N 

Deposition 

 (kg N/ha/yr)1

N Additions 

(kg N/ha/yr) 

Biological and Chemical Effects Study Site Study Species Reference

U.S. 

Coastal 

sage scrub 

Level = 9.8 to 35 

kg N/ha/yr 

Species = nitrate as 

HNO3/NO3

Measure = referred to 

Padgett et al. (1999) 

60 kg N/ha/yr; as 

NH4NO3 between 

Jan. and March 

1994, 1995, 1996 

in two 

30 kg N/ha/yr 

applications 

N addition caused a shift in 

arbuscular mycorrhizal 

community composition 

with decreased species 

richness and diversity 

promoting a shift from 

shrub to grasslands 

California EgertonWarburton 

and Allen 

(2000) 

Coastal 

sage scrub 

Level = not reported 

Species = not reported 

Measure = not reported 

50 ml N solution 

(10 μg/g NH4NO3; 

Or 50 μg/g NO3

−; 

Or 50 μg/g NH4

+) 

every 2 weeks 

from March to 

May, 1997 

No evidence that decline in 

native coastal sage scrub 

and increase in exotic 

grass is due to mycorrhizal 

response to increased 

NO3

−

Southern California Artemisia californica

(native); Bromus 

madritenis spp. 

Rubens (exotic) 

Yoshida and 

Allen (2001) 

Coastal 

sage scrub 

Level = Up to 30 

kg N/ha/yr 

Species = not reported 

Measure = referred to 

Bytnerowicz et al. 

(1987); Fenn et al. 

(2003a) 

60 kg N/ha/yr; as 

NH4NO3

Soil inoculum from high N 

deposition site caused native shrub growth depression likely due to 

mycorrhizal fungi 

response. Growth of exotic 

grass may be promoted by 

soil inoculum from high N 

deposition site. 

Western Riverside County 

Multispecies 

Reserve; University of California Riverside Botanical 

Gardens 

Artemisia californica

(native); Bromus 

madritenis spp. 

Rubens (exotic) 

Siguenza 

et al. (2006) 

Desert Level = 30 kg N/ha/yr 

Species = not reported 

Measure = referred to 

Bytnerowicz et al. 1987 

Two additions of 

16 kg N/ha/yr, one 

as NH4NO3 and 

one as an NPK 

treatment 

N addition increased biomass of non-native plants 

by ~54% , decreased 

native species biomass by 

about ~39% 

Mojave desert Creosote bush (Larrea

tridentate), invasive 

grasses Bromus 

madritensis spp. 

Rubens, and Schismus spp.; and the forb 

Erodium cicutarium

Brooks 

(2003) 

 

1

 Ambient N deposition information is divided into three categories: Level = deposition rate; Species = chemical species of N that were measured; 

Measure = source of the deposition data reported (i.e. a monitoring network, reference to another publication, etc.) 

2

 Ambient N deposition information is divided into three categories: Level = deposition rate; Species = chemical species of N that were measured; 

Measure = source of the deposition data reported (i.e. a monitoring network, reference to another publication, etc.) 

 4-28
Type of 

Ecosystem 

Ambient N 

Deposition 

 (kg N/ha/yr)1

N Additions 

(kg N/ha/yr) 

Biological and Chemical Effects Study Site Study Species Reference

Desert Level = 1.71 to 2.45 kg 

N/ha/yr 

Species = NH4NO3; 

Measure = field study 

for 16 years with 

network of six funnel 

precipitation collectors 

Long term experiment: 100 kg 

N/ha/yr as granular 

NH4NO3 to 10 

plots 2x/year since 

Dec. 1995; 

Single season 

experiment: 20 

kg N/ha/yr to 40 

plots once 

Long term: increased the 

cover of warm season 

grasses and decreased the 

cover of legumes; 

Short term: increased N 

lead to significant plant 

community structure 

change, especially in blue 

and black grama grassland 

patch types 

Chihuahuan 

Desert 

Blue and black grama 

(Bouteloua gracilis

and Bouteloua eropoda, respectively) 

Baez et al. 

(2007) 

Forest 

(sub-alpine) 

Level = 3.6 and 3.5 kg 

N/ha/yr on east slope; 

1.1 kg N/ha/yr on west 

slope 

Species = not reported 

Measure = referred to 

Williams et al. (1998); 

NADP 1999; 

Stottlemyer et al. (1997) 

No addition Compared to west side, 

east side had lower C:N, 

lignin:N, and higher N:Mg, 

N:P, foliar [N], soil [N], N 

mineralization rates and 

lake water [NO3]. 

Eastern vs. 

Western slope 

of Continental 

Divide in 

Rocky 

Mountains. 

Englemann sprucesubalpine fir forests Baron et al. 

(2000) 

Forest 

(sub-alpine) 

Level = 1-2 kg N/ha/yr 

on west slope; 

3-5kg N/ha/yr on east 

slope 

Species = not reported 

Measure = unspecified 

No addition East side had decreased 

soil organic horizon C:N 

and foliar C:N, and 

increased foliar N 

concentration, foliar N:Mg, 

foliar N:P and potential net 

mineralization; Englemann 

spruce forest biogeochemistry altered 

Eastern vs. 

Western slope 

of Continental 

Divide in 

Rocky 

Mountains. 

Englemann spruce Rueth and 

Baron 

(2002) 

Forest Level = 1.2 to 23 

kg N/ha/yr-dry dep and 

0.8 to 45 kg N/ha/yr-wet 

dep 

Species = NO3

− and 

NH4

+ 

Measure = various 

citations 

No addition N saturation observed at 

25–45 kg N/ha/yr of total 

inorganic N deposition. 

Where N saturation occurred, high NO3

−

concentrations in 

streamwater, soil, leaves; 

high NO emissions; high 

foliar N:P. 

California-Los 

Angeles Air 

Basin 

Bytnerowicz 

and Fenn 

(1996) 

 

1

 Ambient N deposition information is divided into three categories: Level = deposition rate; Species = chemical species of N that were measured; 

Measure = source of the deposition data reported (i.e. a monitoring network, reference to another publication, etc.) 

 4-29
Type of 

Ecosystem 

Ambient N 

Deposition 

 (kg N/ha/yr)1

N Additions 

(kg N/ha/yr) 

Biological and Chemical Effects Study Site Study Species Reference

Forest 

(coniferous) 

Level = 2 study sites: 

CP = 18.8 kg N/ha/yr 

BF= 2.9 kg N/ha/yr 

Species = NO3

− and 

NH4

+ 

Measure = throughfall 

and fog water 

No addition Site nearest to urban area 

(Los Angeles) received 

much more N deposition, 

as well as other pollutants 

(i.e. S deposition), and 

received much more fog, 

coinciding with much more 

wet deposition of N in that 

site 

Ecosystem was N 

saturated, as evidenced by 

high streamwater NO3

−

concentration, 151 and 65 

μeq/L at upper and lower 

ends, respectively, of Devil 

Canyon West Fork 

San Bernardino Mountains, California Coniferous forest Fenn et al. 

(2000) 

Forest 

(mixedchaparral, 

hardwood, 

coniferous) 

Level = 11 to 40 

kg N/ha/yr 

Species = NO3

− and 

NH4+ 

Measure = throughfall 

(Fenn, 2002a) 

No addition DIN export was scale dependent, with highest 

export occurring in watersheds of ~150–ha. 

Differences attributed to 

temporal asynchrony between N availability and 

biological demand 

San Bernardino Mountains, CA Mixed forest- chaparral, hardwood, coniferous Meixner and 

Fenn (2004)

Forest 

(coniferous) 

Level = 8 kg N/ha/yr 

and 82 kg N/ha/yr 

Species = not reported 

Measure = referred to 

Fenn et al. (2002a) 

0, 50, 150 

kg N/ha/yr 

annually from 1996 

to 2002 

Tree mortality was 9% 

higher and beetle activity 

50 % higher for unfertilized 

trees at the high deposition 

site compared to the low 

pollution site. Tree 

mortality and beetle activity 

increased 8% and 20%, 

respectively under highest 

N fertilization rates at the 

low deposition site 

San Bernardino Mountains, CA Ponderosa pine Jones et al. 

(2004) 

 

1

 Ambient N deposition information is divided into three categories: Level = deposition rate; Species = chemical species of N that were measured; 

Measure = source of the deposition data reported (i.e. a monitoring network, reference to another publication, etc.) 

 4-30
Type of 

Ecosystem 

Ambient N 

Deposition 

 (kg N/ha/yr)1

N Additions 

(kg N/ha/yr) 

Biological and Chemical Effects Study Site Study Species Reference

Forest 

(chaparral) 

Level = 35 kg N/ha/yr 

Species = not reported 

Measure = taking mean 

of two methods (estimation methods using data 

in situ and published 

data; NADP) 

No addition Continued high export (~3 

kg N/ha/yr) of NO3

− in 

stream water (15 year) 

after prescribed burn indicates that chaparral 

ecosystem did not recover 

N-retention capabilities 

after disturbance 

San Dimas Experimental 

Forest (40 km 

NW of LA), 

San Gabriel 

Mountains, CA

Chamise 

(Adenostoma 

fasiculatum), Ceanothus spp., live oak 

(Quercus agrifolia) 

Meixner 

et al. (2006) 

Forest 

(conifer) 

Level = 1.2 to 71.1 kg 

N/ha/yr 

Species = NO3

− and 

NH4

+

Measure = throughfall 

(Fenn and Poth, 2004) 

No addition Empirical critical load for 

adverse impacts on lichen 

at 3.1 kg N/ha/yr. 

Enhanced NO3

− leaching 

calculated with N 

deposition above 17 

kg N/ha/yr. Lowered litter 

C:N and increases foliar N 

also observed at highly 

polluted sites. 

California 

mixed conifer 

forests 

Lichens, Ponderosa 

pine 

Fenn et al. 

(2008) 

Forest Level = 20 to 35 

kg N/ha/yr 

Species = NO3

− 

Measure = field studies 

and NuCM, a nutrient 

cycling model (Fenn 

et al., 1996) 

No addition Areas with higher 

deposition had increased 

NO3

− leaching, increased 

soil acidity, and decreased 

base cation saturation. 

Southern California Fenn et al. 

(2003) 

Forest Level = 3.2 to 5.5 kg 

N/ha/yr 

Species = not reported 

Measure = data from 

Loch Vale, a 

NADP/NTN monitoring 

station and Campbell 

et al. (2000) 

25 kg N/ha/yr of 

NH4NO3

N addition increased N 

concentration in foliar and 

organic soil horizon 

Fraser Experimental Forest, 

Colorado 

Spruce Rueth et al. 

(2003) 

Forest Level = 1.7 kg N/ha/yr 

Species = not reported 

Measure = referred to 

Stottlemyer and 

Troendle 1992, 

Stottlemyer et al. 1997 

25 kg N/ha/yr of 

NH4NO3

N addition doubled N 

mineralization rates and 

stimulated nitrification 

Loch Vale 

watershed, 

Colorado 

Old-growth spruce Rueth et al. 

(2003) 

 

1

 Ambient N deposition information is divided into three categories: Level = deposition rate; Species = chemical species of N that were measured; 

Measure = source of the deposition data reported (i.e. a monitoring network, reference to another publication, etc.) 

2

 Ambient N deposition information is divided into three categories: Level = deposition rate; Species = chemical species of N that were measured; 

Measure = source of the deposition data reported (i.e. a monitoring network, reference to another publication, etc.) 

 4-31
Type of 

Ecosystem 

Ambient N 

Deposition 

 (kg N/ha/yr)1

N Additions 

(kg N/ha/yr) 

Biological and Chemical Effects Study Site Study Species Reference

Forest 

(alpine) 

Level = 50 kg N/ha/yr 

Measure =referred to 

Sievering et al. (1996); 

Theodose and Bowman 

(1997); Fisk et al. 

(1998) 

200 kg N/ha/yr in 

1993 and 1994; 

No fertilizer in 

1995; 

100 kg N/ha/yr as 

(NH4)2SO4 in 1996 

and 1997 

Nutrient amendments consisted of 

a mixture a mixture 

of (NH4)2)NO3 and 

(NH4)2SO4 for the 

N plots 

N deposition increased 

plant foliage productivity 

but reduced species 

richness. The reduction of 

species is best explained 

by changes in soil 

chemistry that resulted directly or indirectly from N 

additions 

Niwot Ridge, 

Colorado 

Alpine tundra: sedge 

Kobresia 

myosuroides. 

Acomastylis rossii, 

Polygonum viviparurn 

Trifolium. More mesic 

tundra-A. rossii and 

Deschumnpsia caespitosa. Snow bedD.caespitosa, Sibbaldia rocurnbens, 

Rifolium parryi

Seastedt 

and Vaccaro 

(2001) 

Forest Level = 11.5 to 25.4 kg 

N/ha/yr 

Species = NO3

− and 

NH4

+

Measure = 16 

IMPROVE monitoring 

sites and 11 NADP/NTN 

wet dep. sites 

No addition Concentrations of N in 

lichen thallus were highest 

at eastern and westernmost sites where N 

deposition was highest, 

implicating both 

agricultural (east) and 

urban (west) sources; 

Columbia 

River Gorge, 

OR/WA 

Lichens Fenn at al. 

(2007). 

Forest 

(mapledominated 

hardwood) 

Level = 5 to 8 

kg N/ha/yr 

Species = not reported 

Measure = NADP 2006 

30 kg N/ha/yr as 

NaNO3

Arbuscular mycorrhizal 

fungal biomass, storage 

structures and lipid storage 

declined in response to N 

addition 

Northern 

Michigan 

Maple-dominated 

hardwood- Sugar 

maple (Acer saccharum) 

van Diepen 

et al. (2007) 

Forest Level = 3.3 to 12.7 kg 

N/ha/yr 

Species = not reported 

Measure = at NERC, 29 

Jan 2003, compiled 

data sets and used 

them in stats model by 

Ollinger et al., 1993, or 

used published values 

No addition At deposition levels above 

approximately 7–10 kg 

N/ha/yr, stream NO3

− concentration increase with increasing deposition 

Soil C:N and nitrification 

flux increased with N 

deposition 

N deposition did not alter 

foliar chemistry 

Northeastern 

U.S. 

Aber et al. 

(2003) 

Forest Level = 5.4 kg N/ha/yr 

Species = not reported 

Measure = McNulty and 

Aber (1993) 

15.7 to 31.4 

kg N/ha/yr as 

NH4Cl-N or 

NaNO3

−N 

Forest trees in plots receiving <20 kg N/ha/yr had 

high rate of growth initially 

followed by a decline, and 

forests trees in plots 

receiving >25 kg N/ha/yr 

showed moderate rates of 

decline. 

Vermont Red spruce McNulty 

et al. (1996) 

 

1

 Ambient N deposition information is divided into three categories: Level = deposition rate; Species = chemical species of N that were measured; 

Measure = source of the deposition data reported (i.e. a monitoring network, reference to another publication, etc.) 

 4-32
Type of 

Ecosystem 

Ambient N 

Deposition 

 (kg N/ha/yr)1

N Additions 

(kg N/ha/yr) 

Biological and Chemical Effects Study Site Study Species Reference

Forest 

(herbaceous 

layer) 

Level = 7 kg N/ha/yr 

Species = not reported 

Measure = Shepard 

et al. (1989) 

14 and 28 

kg N/ha/yr as crystalline (NH4)2SO4

N addition decreased 

herbaceous cover under 

hardwoods 

Adirondack 

Park, New 

York 

Mixed hardwood – 

American beech, 

sugar maple, and 

yellow birch 

Hurd et al. 

(1998). 

Forest 

(ectomycorrhiza) 

Level = 3.5–7.8 

kg N/ha/yr in wet 

N deposition 

Species = not reported 

Measure = NADP data 

Greenhouse study: 

3 rates of N applications (0, 35, 140 

kg N/ha). 

Ectomycorrhizal 

abundance and richness 

declined along increasing 

N deposition transect 

under pitch pine. The 

decline in richness was 

significantly correlated with 

the N deposition rate. In 

greenhouse study, pine 

seedling biomass was inversely related to N 

addition. 

New Jersey 

Pine Barrens 

Pitch pine Dighton 

et al. (2004).

Forest Level = Wet plus dry 

deposition 600 eq ha/yr 

for N and 900 

equiv./ha/yr for S 

Species: not reported 

Measure =Kahl et al. 

(1999) 

25.2 kg N/ha for 10 

years. 

After 10 years of 

treatment, basal area 

increment of sugar maple 

was enhanced 13 to 104%, 

whereas red spruce was 

not significantly affected. 

The increase in sugar 

maple radial growth was 

attributed to a fertilization 

effect from the (NH4)2SO4

treatment 

Bear Brook, 

Maine 

Sugar maple; red 

spruce 

Elvir et al. 

(2003) 

Forest Level = 17 kg N/ha/yr 

Species = not reported 

Measure = referred to 

Adams et al. (1993) 

35.5 kg N/ha/yr 

and 

40.5 kg S/ha/yr 

fertilized annually 

(NH4)2SO4

(17 years) 

N addition enhanced 

growth of black cherry and 

yellow poplar during the 

first 7 years, but reduced 

growth of these species in 

years 9 to 12, with no 

change in red maple or 

sweet birch 

Fernow 

Experimental 

Forest, West 

Virginia 

Mixed hardwood- red 

oak, red maple, tulip 

poplar, black cherry, 

sweet birch 

DeWalle 

et al. (2006) 

 

1

 Ambient N deposition information is divided into three categories: Level = deposition rate; Species = chemical species of N that were measured; 

Measure = source of the deposition data reported (i.e. a monitoring network, reference to another publication, etc.) 

 4-33
Type of 

Ecosystem 

Ambient N 

Deposition 

 (kg N/ha/yr)1

N Additions 

(kg N/ha/yr) 

Biological and Chemical Effects Study Site Study Species Reference

Forest 

(mixed 

hardwood) 

Level = 17 kg N/ha/yr 

Species = not reported 

Measure = referred to 

Adams et al. (1993) 

35 kg N/ha/yr 

(NH4)2SO4

(16 years) 

Possible declining growth 

vigor in red maple, and to 

lesser extent black cherry 

and tulip poplar. Observed 

interspecific differences in 

growth and plant nutrition 

responses suggest 

eventual changes in 

species composition under 

increasing N saturation. 

Fernow 

Experimental 

Forest, West 

Virginia 

Mixed hardwood- red 

oak, red maple, tulip 

poplar, black cherry, 

sweet birch 

May et al. 

(2005) 

Forest 

(mixed 

hardwood) 

Level = 17 kg N/ha/yr 

Species = not reported 

Measure = referred to 

Adams et al. (1993) 

35 kg N/ha/yr 

(NH4)2SO4

(12 years) 

N addition altered 

response of N-processing 

microbes to environmental 

factors, becoming less 

sensitive to seasonal 

changes in soil moisture 

and temperature 

Fernow 

ExperimentalExperimental Forest, 

West Virginia 

Mixed hardwood- red 

oak, red maple, tulip 

poplar, black cherry, 

sweet birch 

Gilliam et al. 

(2001). 

Forest 

(herbaceous 

layer) 

Level = 17 kg N/ha/yr 

Species = not reported 

Measure = referred to 

Adams et al. (1993) 

35 kg N/ha/yr 

(NH4)2SO4 (4 

years) 

Increased foliar N in overstory tree species and 

Viola rotundifolia and 

decreased foliar Ca2+ and 

Mg2+, in response to 4 

years of treatment. 

Nitrification rates were 

equally high in soils of all 

watersheds; Results 

support earlier studies that 

high amounts of ambient N 

deposition brought about N 

saturation on untreated 

watersheds at the Fernow 

Experimental Forest 

Fernow 

Experimental 

Forest, West 

Virginia 

Viola rotundifolia 

Michx 

Gilliam et al. 

(1996) 

 

1

 Ambient N deposition information is divided into three categories: Level = deposition rate; Species = chemical species of N that were measured; 

Measure = source of the deposition data reported (i.e. a monitoring network, reference to another publication, etc.) 

 4-34
Type of 

Ecosystem 

Ambient N 

Deposition 

 (kg N/ha/yr)1

N Additions 

(kg N/ha/yr) 

Biological and Chemical Effects Study Site Study Species Reference

Forest 

(herbaceous 

layer) 

Level = 17 kg N/ha/yr 

Species = not reported 

Measure = referred to 

Adams et al. (1993) 

35 kg N/ha/yr 

(NH4)2SO4 (6 

years) 

no significant impact on 

the herbaceous layer 

under hardwoods 

Fernow 

Experimental 

Forest, West 

Virginia 

Mixed hardwood Gilliam et al. 

(2006b) 

Forest Level = 6.6 to 

8 kg N/ha/yr 

Measure = regional 

extrapolation from 

NADP sites. Ollinger 

et al. (1993); and estimates from Munger 

et al. (1996). 

50 and 150kg 

N/ha/yr for 

15 years 

Mortality of red pine 

reached 56% in 15 years 

in the pine high N plot, and 

biomass accumulation has 

stopped altogether. The 

high N hardwood stand 

shows increased aboveground NPP, but excess N 

availability and a severe 

drought in 1995 contributed to mortality of 72% 

of red maple trees by 

2002. Species importance 

and litterfall patterns were 

altered in several plots 

after 1995. Roots, foliage 

and wood have diminished 

as net sinks for added N, 

re-emphasizing the role of 

soils in N retention. DIN 

was not detected in soil 

water of high N hardwood 

plot until the 15th year. 

Losses of inorganic N 

remain high in the high N 

plots (higher in pines than 

hardwoods) and low N 

plots in the pine stand also 

have measurable DIN 

losses. Foliar and fine root 

N concentrations are elevated significantly 

Harvard Forest Red pine (Pinus resinosa Ait.), black and 

red oak (Quercus 

velutina Q. rubra) 

black birch (Betula 

lenta), red maple 

(Acer rubrum), American beech (Fagus 

grandifolia) and black 

cherry (Prunus serotina) 

Magill et al. 

(2004) 

 

1

 Ambient N deposition information is divided into three categories: Level = deposition rate; Species = chemical species of N that were measured; 

Measure = source of the deposition data reported (i.e. a monitoring network, reference to another publication, etc.) 

 4-35
Type of 

Ecosystem 

Ambient N 

Deposition 

 (kg N/ha/yr)1

N Additions 

(kg N/ha/yr) 

Biological and Chemical Effects Study Site Study Species Reference

Forest 

(herbaceous 

layer) 

Level = 6.6 to 8 

kg N/ha/yr 

Measure = regional 

extrapolation from 

NADP sites. Ollinger 

et al. (1993); and estimates from Munger 

et al. (1996). 

50 and 150kg 

N/ha/yr 

Following 7 years of N 

additions, density and biomass of herb layer species 

had declined by 80% and 

90% 

Harvard Forest Understory of red pine Rainey et al. 

(1999) 

Forest 

(mixed 

hardwood 

and coniferous) 

Level = 6.6 to 8 

kg N/ha/yr 

Measure = regional 

extrapolation from 

NADP sites. Ollinger 

et al. (1993); and estimates from Munger 

et al. (1996). 

50 and 150kg 

N/ha/yr. 

Wood production 

increased (hardwood) and 

decreased (coniferous) 

Foliar N increased 25% 

(hardwood) and 67% (coniferous); NO3

– leaching increased continuously over 

6–year study in coniferous 

forest, but was unchanged 

in hardwood forest, most 

(85–99%) of added N was 

retained, primarily in 

recalcitrant soil pool. Of 

the retained N, 50–83% 

appears to be in the longterm, recalcitrant soil pool. 

Harvard Forest Red pine (Pinus resinosa Ait.) stand and 

mixed hardwood stand

Magill et al. 

(1997). 

 

1

 Ambient N deposition information is divided into three categories: Level = deposition rate; Species = chemical species of N that were measured; 

Measure = source of the deposition data reported (i.e. a monitoring network, reference to another publication, etc.) 

 4-36
Type of 

Ecosystem 

Ambient N 

Deposition 

 (kg N/ha/yr)1

N Additions 

(kg N/ha/yr) 

Biological and Chemical Effects Study Site Study Species Reference

Oak savanna Level=5.3 kg N/ha/yr 

Species = not reported 

Measure = NADP 2003 

54 , and 170 

kg N/ha/yr (16–yr 

addition of 

NH4NO3) 

N addition decreased total 

ectomycorrhizal fungal 

diversity by 50% and 

changed species 

composition. 

Minnesota Native oak savannah: 

bur oak (Quercus 

macrocarpa Michaux) 

and pin oak (Q.ellipsoidalis E.J. Hill), 

ectomycorrhizal fungi 

Avis et al. 

(2003) 

Grassland Level = 6 kg N/ha/yr 

Species = 58% NH4, 

42% NO3 

Measure = CASTN35 

for total inorganic (wet 

plus dry) N deposition; 

local wet deposition 

from the on-site NADP 

monitoring station 

10, 20, 34, 54 or 

95 kg N/ha/yr at 

3,5,5,7, and 9 

years respectively 

from 1982 to 2004 

Reduced relative number 

of species at every 

deposition level. Species 

numbers were reduced 

more per unit of added N 

at lower addition rates, 

suggesting that chronic but 

low-level N deposition may 

have a greater impact on 

diversity than previously 

thought. Chronic (23 year) 

N addition (10 kg N/ha/yr) 

reduced plant species 

numbers by 17% relative 

to controls receiving 

ambient. 

Critical load calculated at 

5.3 kg N/ha/yr with an 

inverse prediction interval 

of 1.3–9.8 kg N/ha/yr 

Cedar Creek 

Biological 

Station, Minnesota 

Species = rich mixture 

of native C4 grasses 

and forbs ( full list at 

http://www.cedarcreek.umn.edu/) 

Clark and 

Tilman 

(2008) 

Grassland Level = not reported 

Species = not reported 

Measure = not reported 

0, 54.4, and 272 

kg N/ha/yr 

NH4NO3 added 

twice a yr for 18 

years 

Most of the forbs were lost 

from the high N plots, and 

two grass species, P. 

pratensis and A. repens, 

dominated. Loss of plant 

diversity in areas of high N. 

N addition changed 

composition of soil microbial community; increased 

bacterial and decreased 

fungal fatty acid methyl 

ester activity 

Cedar Ck, 

LTER, Minnesota 

P. pratensis, A. 

repens, and 

Schizachyrium 

scoparium

Bradley et 

al. (2006) 

 

1

 Ambient N deposition information is divided into three categories: Level = deposition rate; Species = chemical species of N that were measured; 

Measure = source of the deposition data reported (i.e. a monitoring network, reference to another publication, etc.) 

 4-37
Type of 

Ecosystem 

Ambient N 

Deposition 

 (kg N/ha/yr)1

N Additions 

(kg N/ha/yr) 

Biological and Chemical Effects Study Site Study Species Reference

Grassland Level = 10–15 

kg N/ha/yr in San Jose 

grasslands; 4–6 

kg N/ha/yr at peninsula 

sites 

Species = not reported 

Measure = referred to 

Blanchard et al., (1996) 

No addition Dry N deposition from 

smog contributes to grass 

invasion. Soil N limits 

grass invasion on 

serpentinitic solids. Grazing cattle select grasses 

over forbs, and grazing 

lead to a net export of N as 

cattle are removed for 

slaughter. Decreased 

populations of the bay 

checkerspot butterfly due 

to invasion of grasses after 

cattle grazing. 

San Francisco 

Bay area, 

California 

Bay checkerspot butterfly/ serpentinitic 

grasslands 

Weiss 

(1999) 

Grassland Level = 10–15 

kg N/ha/yr 

Species = not reported 

Measure =referred to 

Weiss 1999 

No addition N deposition displaced native grass species by 

exotic nitrophilous grasses. 

Low levels of soil N 

normally limit grass 

invasion in serpentinitic 

soils, but in ungrazed 

areas with experimental N 

fertilization or high N 

deposition, the introduced 

grasses crowd out many 

native species. 

San Francisco 

Bay area, 

California 

Serpentinitic grasslands Fenn et al. 

(2003). 

Grassland Level = not reported 

Species = not reported 

Measure = not reported 

70 kg N/ha/yr divided into a liquid 

Ca(NO3)2 pulse 

with the first autumn rains and a 

time-release pellet 

application (Osmocote) in January of 

each yr for 3 

years. 

After three years, N 

addition suppressed plant 

diversity, forb production, 

and forb abundance in 

association with enhanced 

grass production 

Jasper Ridge 

Biological Preserve, California 

Avena barbata, 

Bromushordeaceus, 

Lolium multiflorum, 

Avena fatua, and Bromus diandrus, Anagallis arvensis, Geranium 

dissectum, Erodium 

botrys, Vicia sativa, 

Crepis vesicaria

Zavaleta 

et al. (2003).

 

1

 Ambient N deposition information is divided into three categories: Level = deposition rate; Species = chemical species of N that were measured; 

Measure = source of the deposition data reported (i.e. a monitoring network, reference to another publication, etc.) 

 4-38
Type of 

Ecosystem 

Ambient N 

Deposition 

 (kg N/ha/yr)1

N Additions 

(kg N/ha/yr) 

Biological and Chemical Effects Study Site Study Species Reference

Grassland 

(alpine) 

Level =6 kg N/ha/yr 

Species = not reported 

Measure = not reported 

20, 40, 60 

kg N/ha/yr for 

8 years 

N deposition caused 

changes of plant species 

composition within 3 years 

of the initiation of the 

experiment and were 

significant at all levels of N 

addition. Changes in net 

nitrification were 

detectable at levels above 

20 kg N/ha/yr. N addition 

increased NO3

− leaching 

and NO3

− concentration in 

soil water. Changes in the 

plant community, based on 

the first axis of a detrended 

correspondence analysis, 

were estimated to occur at 

deposition levels near 10 

kg N/ha/yr. 

Colorado Front 

Range 

Alpine dry meadows 

dominated by the 

sedge Kobresia myosuroides, with a mix of 

perennial forbs, 

sedges, and grasses 

making up the remainder of the community. 

Bowman 

et al. 

(2006) 

Grassland Level = 5 kg N/ha/yr 

Measure =referred to 

Sievering et al. (1996); 

Theodose and Bowman 

(1997); Fisk et al. 

(1998) 

200 kg N/ha/yr In 

1993 and 1994; 

No fertilizer in 

1995; 

100 kg N/ha/yr as 

(NH4)2SO4 in 1996 

and 1997; 

As a mixture of 

NH4NO3 and 

(NH4)2SO4

Increased plant biomass 

and tissue N concentration

Niwot Ridge, 

Colorado 

Alpine tundra: sedge 

Kobresia 

myosuroides. 

Acomastylis rossii, 

Polygonum viviparurn 

Trifolium. More mesic 

tundra-A. 

Rossii and 

Deschumnpsia caespitosa. Snow bedD. casepitosa, Sibbaldia procurnbens, 

Trifolium parryi

Seastedt 

and Vaccaro 

(2001) 

Grassland Level = not reported 

Species = not reported 

Measure = not reported 

0, 10, 40, 70, and 

100 kg N/ha/yr for 

75 days 

N addition increased 

growth. Native species 

gained more height at 

every level of N availability 

compared to exotics 

Greenhouse 

study 

Two N. American natives (Blue grama, 

western wheatgrass), 

and Four exotics, 

(cheatgrass, leafy 

spurge, Canada thistle, Russian 

knapweed) 

Lowe et al. 

(2002) 

 

1

 Ambient N deposition information is divided into three categories: Level = deposition rate; Species = chemical species of N that were measured; 

Measure = source of the deposition data reported (i.e. a monitoring network, reference to another publication, etc.) 

 4-39
Type of 

Ecosystem 

Ambient N 

Deposition 

 (kg N/ha/yr)1

N Additions 

(kg N/ha/yr) 

Biological and Chemical Effects Study Site Study Species Reference

Grassland Level = 5 kg N/ha/yr 

Measure =referred to 

Sievering et al. (1996) 

Theodose and Bowman 

(1997); Fisk et al. 

(1998) 

25 kg N/ha/yr with 

biweekly additions 

of 2 g of NH4NO3

dissolved in deionized water for 2 

years 

N addition caused a 

community shift towards 

greater dominance of 

hairgrass in wet alpine 

meadows. 

Niwot Ridge, 

Colorado 

Alpine tundra: sedge 

Kobresia 

myosuroides. 

Acomastylis rossii, 

Polygonum viviparurn 

Trifolium. More mesic 

tundra-A. 

Rossii and 

Deschumnpsia caespitosa. Snow bedD.caespitosa, Sibbaldia procurnbens, 

Trifolium 

Bowman 

et al. (1995); 

Burns, 

(2004). 

Lake (alpine) No addition By analysis of a lake sediment cores for a 400 year 

time interval, the study 

shows a typical alpine lake 

diatom flora, consisting 

mainly of small Fragilaria 

sensu lato species, dominated until approximately 

1995, at which time Fragilaria crotonensis and 

Cyclotella bodanica var. 

lemanica rapidly increased 

to 30% each of the total 

assemblage. The shifts 

appear indicative of both 

increased N loading to 

these systems as well as 

changes in thermal 

stratification patterns 

Beartooth 

Lake, Minnesota 

Diatoms: Fragilaria 

sensu lato, Fragilaria 

crotonensis, Cyclotella 

bodanica var. 

lemanica

Saros et al. 

(2003) 

Lake (alpine) Deposition values back 

to 1900 (0.5 kg N/ha/yr) 

were calculated using 

19 years (1984-2003) of 

measured values from 

Loch Vale (Colorado, 

USA; NADP site CO98) 

No addition The mean wet Ndeposition values of 1.5 kg 

N/ha/yr (1950-1964) corresponded with the 

alteration of diatom 

assemblages attributed to 

N deposition in alpine 

lakes in Rocky Mountain 

National Park. This value 

becomes the critical load 

defining the threshold for 

ecological change from eutrophication. 

Loch Vale, 

Colorado 

Diatoms Baron 

(2006) 

 

1

 Ambient N deposition information is divided into three categories: Level = deposition rate; Species = chemical species of N that were measured; 

Measure = source of the deposition data reported (i.e. a monitoring network, reference to another publication, etc.) 

 4-40
Type of 

Ecosystem 

Ambient N 

Deposition 

 (kg N/ha/yr)1

N Additions 

(kg N/ha/yr) 

Biological and Chemical Effects Study Site Study Species Reference

Lake Level= 2.5 - 3.5 

kg N/ha/yr at elevations 

greater than 2,500m; 

<2.5 kg N/ha/yr at elevations less than 

2,500m 

Measure= NADP sites 

in the five-state (WY, 

CO, UT, AZ and NM) 

region of the intermountain West for the period 

1992-1996 

Species=not reported 

No addition The deposition of inorganic 

N in wetfall increased at 

the rate of 0.32 kg N/ha/yr 

from 1984 to 1997 at the 

Niwot Ridge NADP site; 

Annual loading of NO3

–

increased with elevation; 

Current level of N 

deposition in wetfall 

decreased surface water 

pH and ANC, and resulted 

in episodic acidifycation 

and NO3

– leakage. 

Green Lakes 

Valley of the 

Colorado Front 

Range 

Williams 

and Tonnessen 

(2000) 

Lake Level = 2 kg N/ha/yr 

Measure = NAPD site 

from 1981 through 1996 

N additions in 

semi-continuous 

laboratory bioassays of mixed 

diatom assemblages, 

Increasing N shifted 

diatom species in alpine 

lakes. 

Rocky Mountain , Colorado Diatoms:Stephanodiscus minutulus, 

Stephunodiscus 

niugarue, and 

Cyclotella bodanica. 

Interlandi 

and Kilham 

(1998) 

Surface 

waters 

Level = 8–10 kg N/ha/yr 

Measure = NADP from 

1982 to 1994 

No addition Chronic N deposition resulted in increased N 

leaching. 

Eastern U.S./ 

New England 

and Adirondack lakes 

Stoddard 

(1994) 

Surface 

waters 

Level = 4.71 kg N/ha/yr 

–200% increase in NO3

−

loading from wet 

deposition over the prior 

decade, increasing from 

8 kg N/ha/yr for 1985–

1987 to 16.5 kg N/ha/yr 

1990-1992 

Species= not reported 

Measure = NADP 

No addition A shift in from an N-limited 

system to an N-saturated 

system. Many lakes having 

(NO3

–) concentrations 

greater than 10 íequiv/L. 

Increasing atmospheric 

deposition of N with 

elevation is causing a 

change from N limitation to 

P limitation in the highestelevation bristlecone pines 

Niwot 

Ridge/Green 

Lakes, Colorado Front 

Range 

Williams 

et al. (1996) 

Wetland 

(freshwater) 

Level = 10 to14 

kg N/ha/yr 

Hawley Bog: 2 

treatments of 0.1 

or 1.0 mg NH4–N/L 

every 2 weeks 

between June 1 

and Sept 30 for 

1998, 1999, and 

2000 

Negative population 

growth rate of pitcher plant

Hawley Bog 

(MA) 

Molly Bog (VA)

Sarracenia purpurea Gotelli and 

Ellison 

(2002; 

2006) 

 

1

 Ambient N deposition information is divided into three categories: Level = deposition rate; Species = chemical species of N that were measured; 

Measure = source of the deposition data reported (i.e. a monitoring network, reference to another publication, etc.) 

 4-41
Type of 

Ecosystem 

Ambient N 

Deposition 

 (kg N/ha/yr)1

N Additions 

(kg N/ha/yr) 

Biological and Chemical Effects Study Site Study Species Reference

CoastalMarine Discontinuously 

diluted N limited 

cultures, which 

were pulsed with 

NO3

−every 3 days; 

Large diatoms became 

dominant when nitrate was 

supplied as the only N 

source once in 3 days. 

Sinking rate of the nitrate 

grown population was 

higher (0.12 m/day) than 

that of the ammonium 

grown population (0 

m/day). 

Marine phytoplankton Stolte et al. 

(1994) 

Variable Variable 60–120 kg N/ha/yr 

variable range 

across 23 different 

experiments 

Soil CEC and temperature 

were the strongest contributors to multivariable 

explanation of species 

richness response to 

experimental N addition in 

23 studies throughout the 

U.S. Greater plant species 

loss was associated with 

lower soil CEC, colder 

temperature and larger 

production increases 

Variable Variable Clark et al. 

(2007) 

CHINA AND EUROPE 

Forest Level = 3 kg N/ha/yr 

Species = not reported 

Measure = not reported 

30 years of annual 

additions of 

NH4NO3 in 3 treatments: N1 and N2

(34 and 68 kg 

N/ha/yr) and 20 

years of N3 (108 

kg N/ha/yr), plus a 

control 

Stimulate stemwood 

production in all levels of N 

addition until 7 years into 

experiment; thereafter the 

second and third 

treatments (with medium 

and high N addition) 

decreesed stemwood production and the first treatment (the lowest amount of 

N addition) continued to 

increase stemwood production throughout experiment 

Northern Sweden Scots pine Högberg 

et al. (2006) 

Forest (boreal) Level = 3–12 kg N/ha/yr 

Species = not reported 

Measure = MATCH 

model of N-deposition 

for 1996; surveys in 

permanent plots in the 

National Forest 

Inventory 

No addition N deposition decreased 

the abundance and cover 

of ericaceous shrubs 

Sweden Vaccinium species 

(Ericaceae) 

Strengbom 

et al. (2003) 

 

1

 Ambient N deposition information is divided into three categories: Level = deposition rate; Species = chemical species of N that were measured; 

Measure = source of the deposition data reported (i.e. a monitoring network, reference to another publication, etc.) 

 4-42
Type of 

Ecosystem 

Ambient N 

Deposition 

 (kg N/ha/yr)1

N Additions 

(kg N/ha/yr) 

Biological and Chemical Effects Study Site Study Species Reference

Forest Level= not reported 

Species= not reported

Measure=not reported 

22 years of annual 

additions of 

NH4NO3 in four 

treatments: N0 

(control); N1 (30-

60 kg N/ha/yr), N2 

(60-120 kg 

N/ha/yr) and N3 

(90-180 kg 

N/ha/yr) 

The early increase in 

growth rates was reduced 

at the highest N level and 

partly also at the middle 

level. N retention in 

ecosystem was high at the 

lowest level of N addition, 

while considerable N lost 

from the sites at higher 

levels, indicating N saturation. 

North Sweden Scot pine Tamm et al. 

(1995) 

Forest Level: wet deposition 

from 1965-1990: 

Norrliden (S:5.5-7.2kg 

S/ha/yr; NH4-N: 1.3-1.9 

kg N/ha/yr; NH3-N: 1.6-

2.2 kg/ha/yr) 

Lisselbo (S:5.2-6.7kg 

S/ha/yr; NH4-N: 1.2-1.9 

kg N/ha/yr; NH3-N: 1.4-

1.9 kg/ha/yr) 

Species: S, NH4 and 

NH3 

Measure=not reported 

Norrliden: 60-

120kg N/ha/yr from 

1971 to 1988 

Lisseblo: 40-120kg 

N/ha/yr from 1969 

to 1988 

N normally is a growthlimiting factor in boreal 

forest, but regular N 

additions can induce boron 

and magnesium deficiency, 

and low internal concentrations in the trees, e.g., 

of potassium and phosphorus. 

Norrliden in 

northern Sweden and Lisselbo in southern Sweden 

Scot pine Tamm et al. 

(1999) 

Lake Wet DIN = 1.3–11 kg 

N/ha/yr 

Total N = 1–4 and 9–18 

kg N/ha/yr 

Species = wet DIN and 

total N 

Measure = SEPAs use 

of MATCH model 

No addition Increased lake concentrations of inorganic N caused 

P limitation in summer and 

increased eutrophication 

with increased lake algal 

productivity 

Sweden Bergström 

et al. (2005) 

Wetland Level = 0.2 – 0.3 g 

N/m2/a (2 – 3 kg 

N/ha/yr) 

Species = NH4

+ and 

NO3− 

Measure = refers to 

Ruoho-Airola et al. 1998 

Cumulative addition of 3 g N/m2/a 

(30 kg N/ha/yr) as 

NH4NO3 on six 

occasions during 

growing season 

Increased NH4NO3

affected comp. of moss 

layer, specifically 

decreasing Sphagnum 

balticum, and caused decrease in litter and an increase of a Vaccinium species 

Eastern 

Finland 

Sphagnum sp., Eriophorum vaginatum, 

Carex pauciflora, Vaccinium oxycoccos, 

Scheuchzeria palustris

Saarnio 

et al. (2003) 

 

1

 Ambient N deposition information is divided into three categories: Level = deposition rate; Species = chemical species of N that were measured; 

Measure = source of the deposition data reported (i.e. a monitoring network, reference to another publication, etc.) 

 4-43
 4-44

Type of 

Ecosystem 

Ambient N 

Deposition 

 (kg N/ha/yr)1

N Additions 

(kg N/ha/yr) 

Biological and Chemical Effects Study Site Study Species Reference

Wetland Level = not reported 

Species = not reported 

Measure = not reported 

240 kg N/ha/yr as 

NH4NO3 applied in 

field experiment 

where 3 of 6 plots 

received treatment 

every 2 weeks for 

2 years 

N addition increased 

above ground biomass and 

abundance of Deyeucia 

angustifolia, stimulated 

CO2 and CH4, and N2O 

emissions from D. angustifolia wetlands. N2O 

emissions significantly 

influenced by N addition. 

Northeast 

China 

Deyeucia angustifolia Zhang et al. 

(2007) 

Wetland Level = 2 to 20 kg 

N/ha/yr 

Species = not reported 

Measure = national 

precipitation monitoring 

programs of each 

country and refer to the 

3 years prior to peat 

sampling in experiment 

No addition Decomposition rates for 

material accumulated 

under higher atmospheric 

N supplies resulted in 

higher CO2 emissions and 

dissolved organic carbon 

release 

Nine European 

countries 

Species like Sphagnum Bragazza 

et al. (2006) 

1 Ambient N deposition information is divided into three categories: Level = deposition rate; Species = chemical species of N that were measured; 

Measure = source of the deposition data reported (i.e. a monitoring network, reference to another publication, etc.) 

 

1

 Ambient N deposition information is divided into three categories: Level = deposition rate; Species = chemical species of N that were measured; 

Measure = source of the deposition data reported (i.e. a monitoring network, reference to another publication, etc.) 
Annex A. Ecosystem Monitoring 

and Models 

A.1. Introduction 

A tremendous amount of research has been conducted in the U.S., and elsewhere, over the past 

three decades on the ways in which atmospheric deposition of sulfur (S) and nitrogen (N) affect the 

health, condition, and vitality of aquatic, transitional, and terrestrial ecosystems. Much of this work has 

focused on developing a better understanding of acidification and nutrient enrichment processes. Some of 

this work has been highly quantitative allowing researchers to determine key process rates in multiple 

ecosystem compartments. Nevertheless, quantification of overall ecosystem response requires a higher 

level of process rate aggregation. It is important to develop quantitative understanding of the extent of 

past ecosystem effects in response to atmospheric S and N deposition, the extent to which conditions will 

worsen or recover under continued or reduced deposition levels, and the sustained loads of deposition that 

would be required to prevent further ecosystem damage and to allow damaged ecosystems to recover. 

This kind of quantitative understanding cannot evolve directly out of process-based research. It requires 

development of mathematical models that encode process knowledge and link it in such a way as to produce quantitative estimates of change in resource conditions over time in response to changes in the major 

forcing functions, including atmospheric deposition, climate, and landscape disturbance. As described in 

this Annex, many such models have been developed and used to estimate past and future changes in 

ecosystem condition. Such models cannot be validated, per se, because environmental systems are never 

closed and because important processes yield conflicting, often opposing, results. Therefore, a model can 

produce the right answer for the wrong reason (Oreskes et al., 1994). Similarly, a particular process may 

not be important at a particular site where a model is tested, but assume much greater importance 

elsewhere. For these reasons, it is critical that environmental models be tested and confirmed at multiple 

locations that exhibit differing conditions and pollutant loads before they are used as the foundation for 

public policy (Sullivan, 2000a). 

Some of the best data with which to test and confirm environmental models are derived from 

long-term monitoring sites. These are locations where one or more attributes of a natural ecosystem 

compartment (i.e., surface water, soil, plants) is periodically sampled and analyzed over a long period of 

time. Such data are often especially valuable for sites, which experience rather large changes in one of the 

forcing functions (often atmospheric deposition). This enables evaluation of the extent to which the model 

accurately captures the dynamics of ecosystem response(s) that occur. Because many environmental 

attributes undergo rather substantial intra- and inter-annual variability in response to climatic variation 

and other changes, a long period of record is required before a monitoring data set can be used for 

evaluation of ecosystem response or for model confirmation. 

Long-term monitoring data provide not only data with which to test model projections, but also a 

reality check on scientific understanding of damage and recovery processes. If observed (monitored) 

changes are not in agreement with process understanding, it is possible or perhaps likely that one or more 

key processes is not well understood or well formulated in the model. 

This Annex summarizes the primary long-term monitoring sites and programs in the U.S., and the 

principal mathematical models used to simulate environmental responses to atmospheric S and N 

deposition. Quantitative data derived from the model projections and from trends analyses of the 

monitoring data provide an important part of the foundation for evaluating the past, current, and future 

effects of S and N deposition, and expected recovery as emissions levels decrease in the future. 

 A-1
A.2. Ecosystem Monitoring 

The effects of acidic and N nutrient deposition on ecosystems require long-term study. Changes in 

ecosystems often occur gradually, and sustained monitoring of key variables provides the principal record 

of change over time. Monitoring data are also useful for establishing a baseline of resource conditions and 

determining if short-term events were unusual or extreme (Lovett et al., 2007). There are limited 

monitoring programs and data to document ecosystem responses to changes in atmospheric deposition in 

the past. It is often difficult to sustain funding for ecosystem monitoring, perhaps because results are 

produced slowly and because results are seldom viewed as novel. Nevertheless, monitoring data provide 

some of the best means for evaluating the completeness of the scientific knowledge base and for testing 

how robust our projections of future conditions might be. This section describes some of the more 

important and useful monitoring programs for evaluating the effects of N and S deposition on ecosystems 

in the U.S. 

There are long-term monitoring sites scattered throughout the U.S. where samples are periodically 

collected and analyzed to determine the condition of aquatic, transitional, or terrestrial ecosystem 

elements. Some have been in operation for only a short period of time; others have continued for decades. 

None extend back far enough to have documented resource conditions before the advent of high levels of 

atmospheric S and N deposition. Some of the monitoring sites exist as an individual entity, or small 

collection of sites, often established primarily for research purposes. Despite the research focus, many of 

these long-term research sites include collection of monitoring data. Other long-term monitoring sites 

exist as part of large regional programs with a specific focus on long-term monitoring. The most 

significant individual monitoring sites and networks are discussed below. 

Lovett et al. (2007) reviewed the characteristics of successful environmental monitoring programs, 

and argued that monitoring is a fundamental part of environmental science and policy. Their analysis 

underscored the fact that environmental monitoring costs little relative to the value of the resources that it 

protects and the policy that it informs. Monitoring data also have substantial added value because they 

can be used for multiple purposes, including various research objectives. 

Ecosystems also require long-term study because most changes occur slowly. When more rapid 

change does occur, for example in response to an extreme event, a long-term record is needed to put the 

effects of the extreme event into proper context. 

A.2.1. Environmental Monitoring and Assessment Program 

The EMAP began regional surveys of the nation’s surface waters in 1991 with a survey of 

northeastern U.S. lakes. Since then, EMAP and Regional-EMAP (REMAP) surveys have been conducted 

on lakes and streams throughout the country. The objective of these EMAP surveys is to characterize 

ecological condition across populations of surface waters. EMAP surveys are probability surveys where 

sites are picked using a spatially balanced systematic randomized sample so that the results can be used to 

make estimates of regional extent of condition (e.g., number of lakes, length of stream). EMAP sampling 

typically consists of measures of aquatic biota (fish, macroinvertebrates, zooplankton, and periphyton), 

water chemistry, and physical habitat. 

Of particular interest with respect to acidic deposition effects were two EMAP surveys conducted 

in the 1990s, the Northeastern Lake Survey and the Mid-Atlantic Highlands Assessment of streams 

(MAHA). The Northeastern Lake Survey was conducted in summer from 1991 to 1994 and consisted of 

345 randomly selected lakes in the states of New York, New Jersey, Vermont, New Hampshire, Maine, 

Rhode Island, Connecticut, and Massachusetts (Whittier et al., 2002). To make more precise estimates of 

the effects of acidic deposition, the sampling grid was intensified to increase the sample site density in the 

Adirondacks and New England Uplands areas known to be susceptible to acidic deposition. The MAHA 

study was conducted on 503 stream sites from 1993 to 1995 in the states of West Virginia, Virginia, 

 A-2
Pennsylvania, Maryland, Delaware, and the Catskill Mountain region of New York (Herlihy et al., 2000). 

Sampling was done during spring baseflow. Sample sites were restricted to first through third order 

streams as depicted on the U.S. Geological Survey (USGS) 1:100,000 digital maps used in site selection. 

To make more precise estimates of the effects of acidic deposition, the sampling grid was intensified to 

increase the sample site density in the Blue Ridge, Appalachian Plateau, and Ridge section of the Valley 

and Ridge ecoregions. Results from both of these surveys were used to develop and select the sampling 

sites for the Temporally Integrated Monitoring of Ecosystems (TIME) program, which is described below. 

Source: Stoddard et al. (2003). 

Figure A-1. Location of acid-sensitive regions of the northern and eastern U.S. These are regions for 

which statistical survey data are available in the 1990s, and locations of individual TIME sites 

used in trend analysis. 

 A-3
A.2.2. Surface Water Chemistry Monitoring 

There are two surface water chemistry monitoring programs, administered by the U.S. EPA, that 

are especially important to inform the assessment of aquatic ecosystem responses to changes in 

atmospheric deposition. These are the TIME program (Stoddard et al., 2003) and the Long-term 

Monitoring (LTM) program (Ford et al., 1993; Stoddard, 1998a). These efforts focus on portions of the 

U.S. most affected by the acidifying influence of S and N deposition, including lakes in the Adirondack 

Mountains of New York and in New England, and streams in the Northern Appalachian Plateau and Blue 

Ridge in Virginia and West Virginia. Both projects are operated cooperatively with numerous 

collaborators in state agencies, academic institutions and other federal agencies. The TIME and LTM 

projects have slightly different objectives and structures, which are outlined below. Stoddard et al. (2003) 

conducted a thorough trends analysis of the TIME and LTM data. 

A.2.2.1. TIME Project 

At the core of the TIME project is the concept of probability sampling, whereby each sampling site 

is chosen statistically from a pre-defined target population. Collectively, the monitoring data collected at 

the sites are representative of the target population of lakes or streams in each study (Figure A-1). The 

target populations in these regions include lakes and streams likely to be responsive to changes in acidic 

deposition, defined in terms of acid neutralizing capacity (ANC), which represents an estimate of the 

ability of water to buffer acid. It can be either calculated (calculated ANC = sum of base cations – sum of 

mineral acid anions, where all concentrations are in μeq/L) or titrated in the laboratory (Gran ANC). 

Measurement of Gran ANC uses the Gran technique to find the inflection point in an acid-base titration of 

a water sample (Gran, 1952). In the Northeast, the TIME target population consists of lakes with a Gran 

ANC less than 100 μeq/L. In the Mid-Atlantic, the target population is upland streams with Gran ANC 

less than 100 μeq/L. In both regions, the sample sites selected for future monitoring were selected from 

the EMAP survey sites in the region (Section A.2.1) that met the TIME target population definition. 

Each lake or stream is sampled annually (in summer for lakes; in spring for streams), and results 

are extrapolated with known confidence to the target population(s) as a whole using the EMAP site 

population expansion factors or weights (Larsen and Urquhart, 1993; Larsen et al., 1994; Stoddard et al., 

1996; Urquhart et al., 1998). TIME sites were selected using the methods developed by the EMAP 

(Herlihy et al., 2000; Paulsen et al., 1991). The TIME project began sampling northeastern lakes in 1991. 

Data from 43 Adirondack lakes can be extrapolated to the target population of low-ANC lakes in that 

region. There are about 1,000 low-ANC Adirondack lakes, out of a total population of 1830 lakes with 

surface area greater than 1 ha. Data from 30 lakes (representing about 1,500 low-ANC lakes, out of a total 

population of 6,800) form the basis for TIME monitoring in New England. Probability monitoring of 

Mid-Atlantic streams began in 1993. Stoddard et al. (2003) analyzed data from 30 low-ANC streams in 

the Northern Appalachian Plateau (representing about 24,000 km of low-ANC stream length out of a total 

stream length of 42,000 km). 

The initial 1993−1995 EMAP-MAHA sample in the Mid-Atlantic was not dense enough to obtain 

enough sites in the TIME target population in the Blue Ridge and Valley and Ridge ecoregions. In 1998, 

another denser random sample was conducted in these ecoregions to identify more TIME sites. After 

pooling TIME target sites taken from both MAHA and the 1998 survey, there are now 21 TIME sites in 

the Blue Ridge and Ridge and Valley that can be used for trend detection in this aggregate ecoregion in 

the Mid-Atlantic in addition to the Northern Appalachian Plateau ecoregion. 

 A-4
A.2.2.2. Long-Term Monitoring Project 

As a complement to the statistical lake and stream sampling in TIME, the LTM project samples a 

subset of generally acid-sensitive lakes and streams that have long-term data, many dating back to the 

early 1980s (Figure A-2). These sites are sampled 3 to 15 times per year. This information is used to 

characterize how some of the most sensitive of aquatic systems in each region are responding to changing 

deposition, as well as giving information on seasonal variation in water chemistry. In most regions, a 

small number of higher-ANC (e.g., Gran ANC greater than 100 μeq/L) sites are also sampled, and help 

separate temporal changes due to acidic deposition from those attributable to other disturbances (e.g., 

climate, land use change). Because of the availability of long-term records (more than two decades) at 

many LTM sites, their trends can also be placed in a better historical context than those of the TIME sites, 

where data are only available starting in the 1990s. 

Monitored water chemistry variables include pH, ANC, major anions and cations, monomeric 

aluminum (Al), silicon (Si), specific conductance, dissolved organic carbon (DOC), and dissolved 

inorganic carbon (DIC). The field protocols, laboratory methods, and quality assurance procedures are 

specific to each team of investigators. This information is contained in the cited publications of each 

research group. The EMAP and TIME protocols and quality assurance methods are generally consistent 

with those of the LTM cooperators. Details of LTM data from each region are given below. 

Source: Stoddard et al. (2003). 

Figure A-2. Location LTM sites used in the 2003 Surface Water report. 

New England Lakes: The LTM project collects quarterly data from lakes in Maine (sampled by the 

University of Maine; (Kahl et al., 1991; Kahl et al., 1993) and Vermont (data collected by the Vermont 

Department of Environmental Conservation; Stoddard and Kellogg, 1993; Stoddard et al., 1998a). Data 

from 24 New England lakes were available for the trend analysis reported by Stoddard et al. (2003) for 

the time period 1990 to 2000. In addition to quarterly samples, a subset of these lakes have outlet samples 

 A-5
collected on a weekly basis during the snowmelt season; these data are used to characterize variation in 

spring chemistry. Most New England LTM lakes have mean Gran ANC values ranging from –20 to 

100 μeq/L; two higher ANC lakes (Gran ANC between 100 and 200 μeq/L) are also monitored. 

Adirondack Lakes: The trend analysis of Stoddard et al. (2003) included data from 48 Adirondack 

lakes, sampled monthly by the Adirondack Lake Survey Corporation (Driscoll and Van Dreason, 1993; 

Driscoll and Paostek, 1995) a subset of these lakes are sampled weekly during spring snowmelt to help 

characterize spring season variability. Sixteen of the lakes have been monitored since the early 1980s; the 

others were added to the program in the 1990s. The Adirondack LTM dataset includes seepage and 

drainage lakes, most with Gran ANC values in the range of –50 to 100 μeq/L; three lakes with Gran ANC 

between 100 μeq/L and 200 μeq/L are also monitored. 

Appalachian Plateau streams: Stream sampling in the Northern Appalachian Plateau is conducted 

about 15 times per year, with the samples spread evenly between baseflow (e.g., summer and fall) and 

high flow (e.g., spring) seasons. Data from four streams in the Catskill Mountains (collected by the U.S. 

Geological Survey; (Murdoch and Stoddard, 1993) and five streams in Pennsylvania (collected by 

Pennsylvania State University (DeWalle and Swistock, 1994a) were analyzed by Stoddard et al. (2003). 

All of the Northern Appalachian LTM streams have mean Gran ANC values in the range –25 to 50 μeq/L. 

Upper Midwest lakes: Forty lakes in the Upper Midwest were originally included in the LTM 

project, but funding in this region was terminated in 1995. The Wisconsin Department of Natural 

Resources (funded by the Wisconsin Acid Deposition Research Council, the Wisconsin Utilities 

Association, the Electric Power Research Institute and the Wisconsin Department of Natural Resources) 

has continued limited sampling of a subset of these lakes, as well as carrying out additional sampling of 

an independent subset of seepage lakes in the state. The data reported by Stoddard et al. (2003) included 

16 lakes (both drainage and seepage) sampled quarterly (Webster et al., 1993) and 22 seepage lakes 

sampled annually in the 1990s. All of the Upper Midwest LTM lakes exhibit mean Gran ANC values from 

–30 to 80 μeq/L. 

Ridge/Blue Ridge streams: Data from the Ridge and Blue Ridge provinces consist of a large number 

of streams sampled quarterly throughout the 1990s as part of the Virginia Trout Stream Sensitivity Study 

(Webb et al., 1989), and a small number of streams sampled more intensively (as in the Northern 

Appalachian Plateau). A total of 69 streams, all located in the Ridge section of the Ridge and Valley 

province, or within the Blue Ridge province, and all within the state of Virginia, had sufficient data for the 

trend analyses by Stoddard et al. (2003). The data are collected cooperatively with the University of 

Virginia and the National Park Service. Mean Gran ANC values for the Ridge and Blue Ridge data range 

from –15 to 200 μeq/L, with 7 of the 69 sites exhibiting mean Gran ANC greater than 100 μeq/L.FIA 

field personnel collect soil data during the Phase 3 field season, which begins in early June and ends in 

September. Soil samples are sent to the laboratory immediately after collection where they are stabilized 

by air drying. Laboratory analyses are conducted throughout the fall and winter following the field 

season. On-plot measurements include soil compaction and bare soil observations. Soil compaction, the 

percentage of the soil surface exhibiting evidence of soil compaction as well as the type of compaction, is 

measured by ocular estimation. The relative amount of bare soil is also estimated. Field measurements 

related to erosion and compaction estimates are made on all four subplots on the Phase 3 field plot. Soil 

samples are collected on FIA sample plots along soil sampling lines adjacent to subplots 2, 3, and 4. Soils 

are collected if the soil sampling location is in a forested condition. A total of five samples are collected 

on each plot (three forest floor, two mineral soil). The entire forest floor layer is sampled from a known 

area after measuring the thickness of the litter and duff layers at the north, south, east, and west edges of a 

12–inch diameter sampling frame. Only organic material that is <one-fourth-inch diameter is collected; 

rocks and larger woody materials are discarded before collection. 

Once the forest floor has been removed, mineral and organic soils are sampled volumetrically by 

collecting cores from two depths: 0 to 4 inches and 4 to 8 inches. The texture of each layer is estimated in 

the field and characterized as organic, loamy, clayey, sandy, or coarse sandy. Following soil sampling, the 

depth to any restrictive horizon within the top 20 inches is estimated using a soil probe. Soil samples are 

mailed to the regional laboratory for physical and chemical analysis. 

 A-6
In the lab, mineral soil samples collected from FIA plots are analyzed for a suite of physical and 

chemical properties including: 

 Bulk density, water content, and coarse fragment ( >2–mm) content 

 pH (water and 0.01 M CaCl2

) 

 Total carbon 

 Total inorganic carbon (carbonates) (pH >7.5 soils only) 

 Total N 

 Exchangeable cations (Na, K, Mg, Ca, Al, Mn) 

 Extractable sulfur and trace metals (Sr, Ba, Mn, Ni, Cu, Zn, Cd, Pb) 

 Extractable P (Bray 1 method for pH <6 soils, Olsen method for pH >6 soils) 

Forest floor and litter samples are analyzed for: 

 Bulk density and water content 

 Total carbon 

 Total N 

Soil chemical and physical properties can be highly variable in the field and are expensive to 

analyze. As a result, interpretation of soil chemical data is confounded by spatial variability within the 

plot. In addition, depending upon the soil type, both the number of samples and the methods used in 

collecting these samples may vary between plots, complicating compilation and estimation procedures. 

Finally, soil samples reflect conditions only in the forest floor and upper 20 cm of the soil. In many 

systems, the upper portion of the soil profile is likely to be more responsive to disturbance, providing a 

useful index for monitoring changes in soil properties over time. 

A.2.3. USGS Monitoring Programs 

A.2.3.1. National Water Quality Assessment Program 

The National Water Quality Assessment (NAWQA) Program was created in 1991 by the USGS to 

assess the nation’s water quality in 51 study units defined primarily by major drainage divides. These 

units comprise approximately 50% of the conterminous U.S. Its major of the program are to determine the 

condition of the nation’s streams, rivers, and ground water; whether these conditions are changing over 

time; and how these conditions are affected by natural features and human activities. 

The major priority of the NAWQA Program since its inception has been on watersheds that have 

experienced impacts from agriculture and various forms of development. The location of sites, sampling 

frequency, and types of measurements taken, all reflect this priority. Each study unit runs on a 9–year 

cycle, with approximately one-third of the study units beginning the cycle every 3 years. Each 9–year 

cycle is comprised of 3 years of intensive data collection and 6 years of low-level assessment. Three types 

of sampling sites are established within each study unit: integrator sites, indicator sites, and synoptic sites. 

Integrator sites are located on major rivers at points that drain much or the entire study unit. Indicator 

sites drain large fractions of the study unit that are representative of a particular landscape or land use 

type. Some indicator sites are also located to evaluate point sources of water pollution, and some are 

located downstream of undisturbed drainages to provide reference, or background conditions. Reference 

indicator sites are generally located too low in the drainage basin for assessment of surface water 

 A-7
acidification. Sites associated with synoptic studies are chosen for improving spatial resolution of data 

collection within the study unit. The strategies for site selection, sampling, and analysis for synoptic sites 

are issue-specific and keyed to hydrologic conditions, times, and places of specific interest for the 

targeted water quality issue. 

During the 3 year intensive sampling period, integrator and indicator sites are sampled multiple 

times, both periodically and in association with high flows. The sampling approach for synoptic studies 

varies depending on the issue of interest, but is usually done within the second or third year of the 

intensive sampling period. During the six years of the low-level assessment, sampling usually involves 

base-flow sampling of high priority integrator sites, and possibly some sampling of indicator sites. 

Water quality measurements vary among study units, but usually include pathogens, nutrients 

(including N and S), trace elements, pesticides, industrial organics, suspended sediment, salinity, 

temperature, acidity, and dissolved O2. 

NAWQA studies have resulted in over 1000 reports on an extensive list of water quality issues, 

including freshwater and marine eutrophication associated with N pollution. None deal with acidification, 

however. Program details and access to publications can be obtained at http://water.usgs.gov/nawqa/. 

A.2.3.2. Hydrologic Benchmark Network 

The Hydrologic Benchmark Network (HBN) was started in 1963 by the USGS and gradually grew 

to include 57 river gauging stations and 1 lake-stage station in 39 states by 1990. Most of the stations 

have been established at the outlet of watersheds that were virtually free of human activities, located in 

places such as in national parks and forests, wilderness areas, or nature preserves. Streamflow was 

initially monitored continuously at each station, and samples were collected every month for waterquality analyses that included concentrations of nutrients and all major ions. The frequency of water 

sampling at HBN stations was decreased to quarterly in 1986 because of budgetary restrictions. Sampling 

was discontinued in October 1997, except for a small study in the eastern U.S. that focused on the initial 

response of rivers to decreases in industrial emissions mandated by the CAAA of 1990 

(http://www.epa.gov/oar/oaq_caa.html/). 

All HBN watersheds were evaluated in 2002 to determine whether upstream development had 

made them unsuitable as reference watersheds. The 36 sites that best met the network criteria were 

selected for continued streamflow monitoring, and water sampling was reinitiated at 15 of those 36 sites. 

In 2003, 15 of the original HBN stations were equipped with refrigerated, automated samplers and 

telemetry systems that allow program coordinators to monitor stream conditions and adjust sampling 

frequency and capture unique stream conditions or special sampling needs. The automated sampling 

system is designed to collect samples through a wide range of flow conditions and to transmit data by 

satellite. About 25 water samples are collected annually at each HBN water quality station and 

refrigerated on site until retrieved by field personnel who visit the sites regularly. The most recent trends 

analysis was done by Clow and Mast (1999) to evaluate long-term trends in stream chemistry with respect 

to the CAA. The program is further described in a fact sheet that can be found at: 

http://ny.water.usgs.gov/pubs/fs/fs20053135/. 

A.2.3.3. New York City Water Quality Network 

The New York District of the U.S. Geological Survey operates a water quality network throughout 

the water supply watershed for New York City, in the Catskill Mountains region. The purpose of the 

network is to provide stream flow and water quality data at key locations within the watershed. There are 

currently 34 sites throughout the network at which stream flow data are collected, and at thirteen of those 

sites stream water quality data are also collected. The water quality network is composed of paired 

“nodes” consisting of one or more “upper nodes” that provide water quality of undeveloped, forested 

 A-8
watersheds, and “lower nodes” that provide downstream water quality data that may reflect some level of 

development within the watershed. 

Water quality sampling for this program began at the 13 sites in 1998–99. Water samples are 

collected biweekly and during high flow for approximately 6 storms per year. All water samples are 

analyzed for concentrations of nutrients and major ions. Because streams in this area are also affected by 

acidic deposition, acid-neutralizing capacity and 3 forms of Al are also measured. Further details on the 

program are available at: http://ny.cf.er.usgs.gov/nyc/unoono.cfm. 

A.2.3.4.

A.2.3.5.

Catskill Long-Term Monitoring Sites 

Within the Catskill Mountains region of New York State, stream samples are collected and stream 

flow is measured at three locations within the Neversink River basin, and at one site on Rondout Creek. 

Water samples are collected biweekly and during most storms. These sites are currently part of the 

U.S. EPA LTM program, but also are affiliated with other programs. Sampling at two of the four sites 

began in the mid 1980s, whereas sampling at the remaining two sites began in 1991. The primary purpose 

of these sites is to monitor effects of acidic deposition on stream chemistry. The full suite of analytes 

needed to assess acidic deposition effects are measured on these water samples. 

Buck Creek, New York 

Stream flow and water chemistry are monitored at three locations within Buck Creek watershed, in 

the western Adirondack Region of New York. Samples are collected biweekly and during most storms at 

each location. Sampling began in 1998 at two sites and 2001 at the third site. The full suite of analytes 

needed to assess acidic deposition effects is measured on these water samples. Measurements of ANC and 

pH were also collected at one site weekly for the period of 1991 to 2001 (Lawrence et al., 2004). Recent 

data from Buck Creek are presented in Lawrence et al. (2007). Buck Creek is the only stream within the 

acidified region of the Adirondacks where base flow and storm samples are collected in conjunction with 

flow monitoring. 

A.2.4. NSF Long-Term Ecological Research Network 

The Long-Term Ecological Research (LTER) program constitutes a loose network of 26 sites 

(Table A-1), funded by the National Science Foundation (NSF). There is increasing concern over such 

globally significant problems as loss of biodiversity, climate change, destruction of forests, depletion of 

stratospheric ozone, regional air and water pollution, and soil erosion. The research conducted at the 

various LTER sites has examined and continues to examine aspects of these problems and provides 

scientific information which has been invaluable in the formation of public policy. Site locations and 

research activities are summarized in (Table A-1). A few of the sites that have been used most extensively 

for evaluation of long-term effects of N and sulfur deposition are discussed in greater detail below. 

 A-9
Table A-1. LTER site locations and basic site description information. 

Site Landsat WRS Institutional 

Affiliations 

Principal Biome/ 

Main Communities Research Topics 

H.J. 

Andrews 

Experimental 

Forest 

(AND) 44.2, 

−122.2 

Path 46 Row 29; 

Lat/Long: 44°14'N/ 

122°11'W 

Oregon State University; 

USDA Forest Service 

Pacific Northwest 

Research Station 

Temperate coniferous forest. 

Douglas-fir/western hemlock/ 

western red cedar; true fir and 

mountain hemlock; streams 

Successional changes in 

ecosystems; forest-stream 

interactions; population dynamics of 

forest stands; patterns and rates of 

decomposition; disturbance regimes 

in forest landscapes 

Arctic 

Tundra 

(ARC) 68.6, 

−149.6 

Path 73, Row 12; 

Lat/Long: 68°38'N/ 

149°34'W 

The Ecosystem Center, 

Marine Biological 

Laboratory; Universities 

of Alaska, 

Massachusetts, 

Minnesota, Cincinnati, 

and Kansas; Clarkson 

University 

Arctic tundra, lakes, streams. 

Tussock tundra; heath tundra; 

riverine willows; oligotrophic 

lakes; headwater streams 

Research topics: Movement of 

nutrients from land to stream to 

lake; changes due to anthropogenic 

influences; controls of ecological 

processes by nutrients and by 

predation 

Baltimore 

Ecosystem 

Study (BES) 

39.1, −76.3 

Path 15, Row 33; 

Lat/Long 38°54" 04' 

(N), 76°52" 04' (W) 

Institute of Ecosystem 

Studies; USDA Forest 

Service, Johns Hopkins 

University; University of 

Maryland; Baltimore 

County and College 

Park; University of North 

Carolina; Parks and 

People Foundation; US 

Geological Survey; Yale 

University 

Eastern deciduous forest/ 

Suburban Agriculture fringe, 

urban parks, residential and 

commercial patches, riparian 

and stream habitats 

Patch dynamics of built, social, 

biological, and hydrological 

components of the metropolitan 

area; feedback's between social, 

economic, and ecological 

components of an urban ecosystem; 

effect of infrastructure and 

development on fluxes of nutrients, 

energy, and water in upland, stream, 

and coastal regions of metropolitan 

Baltimore 

Bonanza 

Creek 

Experimental 

Forest (BNZ) 

64.8, −148.0 

Path 69, Row 15; 

Lat/Long: 64°45'N/ 

148°00'W 

University of Alaska; 

Institute of Northern 

Forestry, USDA Forest 

Service, Pacific 

Northwest Research 

Station 

Taiga. Areas of boreal forest 

including permafrost-free 

uplands and permafrostdominated north slopes and 

lowlands; floodplain seres 

Successional processes associated 

with wildfire and floodplains; 

facilitative and competitive 

interactions among plant species 

throughout succession; plantmediated changes in resource and 

energy availability for decomposers; 

herbivorous control of plant species 

composition; hydrologic regime and 

stream ecology 

Cedar Creek 

Natural 

History Area 

(CDR) 45.4, 

−93.2 

Path 27, Row 28; 

Lat/Long: 45°24'N/ 

93°12'W 

University of Minnesota Eastern deciduous forest and 

tallgrass prairie. Old fields; oak 

savanna and forest, conifer 

bog; lakes; pine forest; wetland 

marsh and carr 

Successional dynamics; primary 

productivity and disturbance 

patterns; nutrient budgets and 

cycles; climatic variation and the 

wetland/upland boundary; plantherbivore dynamics 

 A-10
Central 

Arizona - 

Phoenix 

(CAP) 33.5, 

−11.2 

Path 36, Row 37 and 

Path 36, Row 36° 

These have been 

used by CAP although 

Path 37, Row 37 is 

centered more closely 

in the City of Phoenix 

Arizona State University 

(Main and West) 

Sonoran Desert scrub. Urban 

parks, residential, interior 

remnant desert patches, 

commercial and industrial 

patches, urban fringe, 

regulated river and floodplain 

(dry), effluent-dominated river 

Interactions of ecological and socioeconomic systems in an urban 

environment; influence of land use 

change on ecological patterns and 

processes; movement of nutrients 

through highly manipulated, urban 

flowpaths; interactions of introduced 

and native species in urban 

environment; millennium- and 

century-scale geomorphic change in 

landforms and interaction with 

engineered landscapes 

Coweeta 

Hydrologic 

Laboratory 

(CWT) 35.0, 

−83.5 

Path 18, Row 36; 

Lat/Long: 35°00'N/ 

83°30'W 

University of Georgia; 

USDA Forest Service, 

Southeastern Forest 

Experiment Station 

Eastern deciduous forest. 

Hardwood forests and white 

pine plantations 

Long-term dynamics of forest 

ecosystems including forest 

disturbance and stress along an 

environmental gradient; stream 

ecosystems along an environmental 

gradient; and the riparian zone as a 

regulator of terrestrial-aquatic 

linkages 

Harvard 

Forest (HFR) 

42.5, −72.2 

Path 13, Row 30; 

Lat/Long: 42°32'N/ 

72°10'W 

Harvard University; 

Universities of New 

Hampshire and 

Massachusetts; The 

Ecosystem Center, 

Marine Biological 

Laboratory 

Eastern deciduous forest. 

Hardwood-white-pine-hemlock 

forest; spruce swamp forest; 

conifer plantations 

Long-term climate change, 

disturbance history and vegetation 

dynamics; comparison of 

community, population, and plant 

architectural responses to human 

and natural disturbance; forestatmosphere trace gas fluxes; 

organic matter accumulation, 

decomposition and mineralization; 

element cycling, fine root dynamics 

and forest microbiology 

Hubbard 

Brook 

Experimental 

Forest 

(HBR) 43.9, 

−71.8 

Path 13, Row 29; 

Lat/Long: 43°56'N/ 

71°45'W 

Yale, Cornell, and 

Syracuse Universities; 

Institute of Ecosystem 

Studies; USDA Forest 

Service, Northeastern 

Forest Experiment 

Station 

Eastern deciduous forest. 

Northern hardwood forests in 

various developmental stages, 

spruce-fir forests; streams and 

lakes 

Vegetation structure and production; 

dynamics of detritus in terrestrial 

and aquatic ecosystems; 

atmosphere-terrestrial-aquatic 

ecosystem linkages; heterotroph 

population dynamics; effects of 

human activities on ecosystems 

Jornada 

Experimental 

Range (JRN) 

32.5, −106.8 

Path 33, Row 37; 

Lat/Long: 32°30'N/ 

106°45'W 

New Mexico State 

University; USDA ARS 

Jornada Experimental 

Range; Duke University; 

NOAA, RTP, NC; 

University of New 

Mexico; Dartmouth 

College, NH; Oregon 

Graduate Center; Texas 

Technological University; 

SUNY Buffalo; University 

of Keele, UK; Kings 

College, London, UK; 

U.S. EPA-EMAP, Las 

Vegas, NV 

Hot desert. Playa, piedmont, 

and swale; bajada, basin, 

mountain and swale shrubland; 

mesquite dunes 

Desertification; factors affecting 

primary production; animal-induced 

soil disturbances; direct and indirect 

consumer effects; vertebrate and 

invertebrate population dynamics; 

grazing effects on ecosystem 

structure and function; biodiversity 

and ecosystem function; small 

mammal effects on soil and 

vegetation heterogeneity; soil 

microbial processes; surface 

hydrology; trace gas emissions from 

soils; eolian processes 

 A-11
W.K. Kellogg 

Biological 

Station 

(KBS) 42.4, 

−5.4 

Path 21, Row 31; 

Lat/Long: 85°24'W/ 

42°24'N 

Michigan State 

University, Michigan 

Agricultural Experiment 

Station 

Row-crop agriculture. 

Conventional and organicbased corn-soybean-wheat 

cultivation; perennial biomass 

cultivation; native successional 

communities 

Ecological interactions underlying 

the productivity and environmental 

impact of production-level cropping 

systems; patterns, causes, and 

consequences of microbial, plant, 

and insect diversity in agricultural 

landscapes; gene transfer, 

community dynamics, 

biogeochemical fluxes 

Konza 

Prairie 

Research 

Natural 

Area(KNZ) 

39.1, −94.6 

Path 28, Row 33; 

Lat/Long: 39°05'N/ 

96°35'W 

Kansas State University Tallgrass prairie. Tallgrass 

prairie; gallery forest; prairie 

stream 

Effects of fire, grazing and climatic 

variability on ecological patterns and 

processes in tallgrass prairie 

ecosystems, use of remotely sensed 

data and geographic information 

systems to evaluate grassland 

structure and dynamics 

Luquillo 

Experimental 

Forest (LUQ) 

18.3, −65.8 

Path 4, Row 47 and 

48; Lat/Long: 

18°18'N/ 65°47'W 

Center for Energy and 

Environment Research, 

University of Puerto 

Rico; Institute of Tropical 

Forestry, USDA Forest 

Service, Southern 

Experiment Station 

Tropical rainforest. Tabonuco 

forest; palo Colorado forest; 

palm brake; dwarf forest and 

montane streams 

Patterns of and ecosystem 

response to different patterns of 

disturbance; land-stream 

interactions; effect of management 

on ecosystem properties; integration 

of ecosystem models and 

geographic information systems 

McMurdo 

Dry Valleys - 

Antarctica 

(MCM) 

−78.0, 

+165.0 

Path 56, Row 116 Desert Research 

Institute, Reno, Nevada; 

U.S. Geological Survey, 

Boulder, Colorado 

Polar desert oases Microbial ecosystem dynamics in 

arid soils, ephemeral streams, and 

closed basin lakes; resource and 

environmental controls on terrestrial, 

stream and lake ecosystems; 

material transport between aquatic 

and terrestrial ecosystems; 

ecosystem response to greater 

hydrologic flux driven by warming 

climate 

Niwot Ridge/ 

Green Lakes 

Valley 

(NWT) 40.1, 

−105.6 

Path 34, Row 32; 

Lat/Long: 40°03'N/ 

105°37'W 

Institute of Arctic and 

Alpine Research, 

University of Colorado 

Alpine tundra; Fellfield; 

meadow; herbaceous and 

shrub tundras; cliffs and talus; 

glacial lakes; streams and 

wetlands 

Patterns and controls of nutrient 

cycling; trace gas dynamics, plant 

primary productivity and species 

composition; geomorphology, and 

paleoecology 

North 

Temperate 

Lakes (NTL) 

46.0, −89.7 

and 43.1, 

89.4 

Path 25, Row 28 and 

Path 24, Row 30 

Lat/Long: 46°00'N/ 

89°40'W and 

89°24/ 43°06 

Center for Limnology, 

University of WisconsinMadison, Wisconsin 

Northern temperate lakes in 

glacial landscapes in urban, 

agricultural and forested 

watersheds. Oligotrophic, 

dystrophic and eutrophic lakes; 

temporary forest ponds; warm 

and cold streams; sphagnumleatherleaf bog; conifer swamp; 

mixed deciduous and 

coniferous forests 

Physical, chemical and biological 

limnology; hydrology and 

geochemistry; climate forcing; 

producer and consumer ecology; 

ecology of invasions; ecosystem 

variability; lakescape and landscape 

ecology 

 A-12
Palmer 

Station 

(PAL) 

Antarctica 

−64.7, −64.0 

Path 219 , Row 105; 

Lat/Long: 64°40'S/ 

64°W 

University of California, 

Santa Barbara; Old 

Dominion University 

Polar marine. Coastal and 

open ocean pelagic 

communities; seabird nesting 

areas 

Oceanic-ice circulation and models; 

sea-ice dynamics; 

biological/physical interactions; 

effect of sea ice on primary 

production, consumer populations 

and apex predators; bio-optical 

models of primary production; 

spatial distribution and recruitment 

in consumer populations; seabird 

population dynamics and 

reproductive ecology 

Plum Island 

Sound (PIE) 

42.67, 

−70.99 

Path 12, Row 30; Lat/ 

Long: 42°40'/ 70°59' 

Site has the following 

X and Y bounds in 

decimal coordinates: 

X min = −71.22 X max 

= −70.75. Y min = 

42.50 Y max = 42.83. 

The total area is 

approximately 37 km 

x 37 km or 1369 km2

The Ecosystems Center, 

Marine Biological 

Laboratory; Universities 

of South Carolina and 

New Hampshire; 

Massachusetts 

Audubon; Wells, Maine, 

NERRS 

Coastal estuary Linkages between land and coastal 

waters involving organic carbon and 

organic N inputs to estuarine 

ecosystems from watersheds with 

various land covers and uses 

Sevilleta 

National 

Wildlife 

Refuge 

(SEV) 34.3, 

−106.8 

Path 33, Row 36; 

To acquire entire site 

area, Path 32, Row 

36, Path 32, Row 37 

and Path 33, Row 37 

are also needed. Lat/ 

Long: 34°19'/ 

106°62'W 

University of New 

Mexico; U.S. Fish and 

Wildlife Service 

Multiple: intersection 

of subalpine mixed-conifer 

forest/meadow, riparian 

cottonwood forest, dry 

mountainland, grassland, cold 

desert, hot desert. Conifer 

savanna; creosote bush; 

desert grassland; mesquite 

and sand dunes; Great Basin 

shrub and shortgrass steppes; 

tallgrass swales; riparian 

communities 

Landscape and organism population 

dynamics in a biome tension zone; 

semiarid watershed ecology; climate 

change; biospheric/atmospheric 

interactions; paleobotany/ 

archaeology; microbial role in gas 

flux; and control of landscape 

heterogeneity; scale effects on 

spatial and temporal variability 

Shortgrass 

Steppe 

(SGS) 40.8, 

−104.8 

Path 33, Row 32; 

Lat/Long: 40°49'N/ 

104°46'W 

Colorado State 

University; USDA Forest 

Service; USDA 

Agricultural Research 

Service 

Floodplain; shrubland; 

saltmeadow 

Soil water; above- and belowground 

net primary production; plant 

population and community dynamics; effects of livestock grazing; 

soil organic matter accumulation 

and losses, soil nutrient dynamics; 

and ecosystem recovery from 

cultivation 

Virginia 

Coast 

Reserve 

(VCR) 37.5, 

−74.8 

Path 14, Row 34; 

Lat/Long: 37°30'N 

75°40'W 

University of Virginia Coastal barrier islands. Sandy 

intertidal; open beach; 

shrubthicket; mature pine 

forest; salt marsh; estuary 

Holocene barrier island geology; salt 

marsh ecology, geology, and 

hydrology; ecology/ evolution of 

insular vertebrates; primary/ 

secondary succession; life-form 

modeling of succession 

A.2.4.1. Hubbard Brook Experimental Forest 

The Hubbard Brook Ecosystem Study (HBES) at Hubbard Brook Experimental Forest (HBEF) is 

the longest-running precipitation and stream chemistry (1963 to present) monitoring program in the U.S. 

 A-13
(see http://www.hubbardbrook.org). HBEF was established in 1955 as a major center for hydrologic 

research in New England. The site is located within the boundaries of the White Mountain National Forest 

in central New Hampshire. The 3138–ha, bowl-shaped valley has hilly terrain, ranging from 222 to 1015 

m elevation. The HBES originated in 1960 with the intention of applying the small watershed approach to 

the study of element fluxes and cycles. The goal of the study is to develop a better understanding of 

ecological patterns and processes that characterize the northern forest in eastern North America, and its 

response to both natural and human disturbances. In 1987, HBEF joined the NSF’s LTER network 

(http://www.lternet.edu). Hubbard Brook is renowned for its long-term record of measurements, 

landscape-scale experiments of whole watersheds, and the involvement of scientists from diverse 

disciplines and institutions. 

The HBEF is entirely forested, mainly with deciduous northern hardwoods: sugar maple (Acer 

saccharum), beech (Fagus grandifolia), and yellow birch (Betula allegheniensis), and some white ash 

(Fraxinus americana) on the lower and middle slopes. Other less abundant species include mountain 

maple (Acer spicatum), striped maple (Acer pensylvanicum), and trembling aspen (Populus tremuloides). 

Red spruce (Picea rubens), balsam fir (Abies balsamea), and white birch (Betula papyrifera var. 

cordifolia) are abundant at higher elevations and on rock outcrops. Hemlock (Tsuga canadensis) is found 

along the main Hubbard Brook. Pin cherry (Prunus pensylvanica), a shade intolerant species, dominates 

all sites for the first decade following a major forest disturbance. Logging operations ending around 

1915–1917 removed large portions of the conifers and better quality, accessible hardwoods. The present 

second-growth forest is even-aged and composed of about 80 to 90% hardwoods and 10 to 20% conifers. 

The HBEF is an oblong basin about 8 km long by 5 km wide. Hubbard Brook is the single major 

stream draining the basin. Numerous smaller tributary streams of varying size drain into Hubbard Brook 

including Watershed 6 (WS-6), which is the biogeochemical reference watershed. 

One of the strengths of the HBES is the long-term monitoring program. Table A-2 lists the major 

parameters included in the HBES long-term monitoring study. The monitoring data illustrate that shortterm observations can be misleading and that decades of monitoring may be required to detect real 

changes in complex ecosystems. The long-term record at the HBEF provides: insight into ecosystem 

function; empirical data for testing models and generating hypotheses; a record of extreme or unusual 

events; and information that is relevant to regional national and global environmental issues. 

Table A-2. Current long-term monitoring data sets developed through the Hubbard Brook Ecosystem 

Study 

PHYSICAL/HYDROLOGIC MONITORING 

Instantaneous streamflow (9 stations) 

Daily precipitation (24 stations) 

Class A weather station data 

Weekly snow depth on snow courses 

Daily soil temperature and moisture 

AIR CHEMISTRY 

(SO2, HNO3, particulates, ozone) 

MIRROR LAKE 

Instantaneous streamflow (3 inlets,outlet) 

Daily precipitation (2 stations) 

Weekly chemistry (3 inlets, outlet) 

Bi-monthly limnology (temp, chemistry, plankton) 

SOLUTION CHEMISTRY 

Weekly bulk precipitation (6-10 stations) 

Monthly soil solution WS5, WS6 

Weekly stream at weirs of WS19 

Monthly stream within WS5, WS6 

ORGANISMS 

Bird populations 

Phytophagous insect populations WS2, WS4, WS5, WS6 

Vegetation biomass, chemistry 

SOILS 

Forest floor mass, chemistry (WS6, WS5; 5-yr intervals) 

Chemical and physical properties from soil pits (WS5) 

Chemical and physical properties from soil bags 

 A-14
Some of the monitoring is done on experimentally manipulated watersheds. There are nine gauged 

watersheds at the HBEF, four of which have been treated experimentally. A tenth ungauged watershed 

was also treated. Table A-3 includes summary data on the various watersheds. Datasets for long-term 

monitoring can be found at http://www.hubbardbrook.org/data/dataset_search.php. The datasets most 

often used to examine ecosystem response to ambient deposition of N and S are from WS-6 and Mirror 

Lake, since they have not been experimentally manipulated. Watershed 6 is the biogeochemical reference 

catchment at HBEF where monitoring began in June 1963. Measured stream chemistry parameters 

include major anions and cations, pH, silica, dissolved organic and inorganic carbon, specific 

conductance, dissolved O2, ANC, and PO4. Stream chemistry data can be accessed at 

http://www.hubbardbrook.org/data/dataset.php?id=8. The normal sampling interval for WS-6 is weekly, 

with more frequent samples taken at times of increased discharge. 

Table A-3. Study watersheds at HBEF. 

WS Area (ha) Slope (°) Aspect Elevation (m) Gauge Type Initial Yr. 

1 11.8 18.6 S22°E 488–747 V-notch weir 1956 

2 15.6 18.5 S31°E 503−716 V-notch weir 1957 

3 42.4 12.1 S23°W 527–732 V-notch weir 1957 

4 36.1 15.6 S40°E 442–747 V-notch weir 1960 

5 21.9 15.4 S24°E 488–762 V-notch weir, San Dimas flume 1962 

6 13.2 15.8 S32°E 549–792 V-notch weir, San Dimas flume 1963 

7 77.4 12.4 N16°W 619–899 V-notch weir, San Dimas flume 1965 

8 59.4 14.0 N12°W 610–905 V-notch weir, San Dimas flume 1968 

9 68.4 NE 685–910 V-notch weir 1995 

10 12.1 SE 470–595 None 1970 

Monitoring of streamflow and water chemistry has shown that the study watersheds have similar 

characteristics. Within each watershed there are a variety of soils, vegetation, microtopographical 

features, and micro-climate. Nevertheless, the composition of these variables seems to be similar from 

watershed-to-watershed. Thus, the effects of experimental manipulations of watersheds can be adequately 

evaluated by comparison with neighboring unmanipulated watersheds. 

The most conspicuous streamflow characteristic is the seasonal shift from large volume of flow in 

spring to very low flow in late summer and early autumn. These yearly highs and lows reflect seasonal 

spring snowmelt that often occurs over a few days or weeks and the slow progressive decrease in flow 

from the transpirational draft in summer, respectively. The numerous streams in the HBEF range from 

small ephemeral channels that often dry up during summer to a large perennial 5th-order stream (Hubbard 

Brook). 

Mirror Lake is a 15–ha oligotrophic clearwater lake adjacent to HBEF. The lake normally mixes in 

spring and fall, and is ice-covered from about December 1st to April 15th each year. Part of the drainage 

 A-15
to the lake originates in the Experimental Forest. The lake water is dilute, slightly acidic, and quite clear, 

with low productivity and low concentrations of nutrients in the water. Numerous studies have been 

conducted on Mirror Lake since the mid-1960s, including extensive physical, chemical, biological, and 

paleoecological research (Likens, 1985). Data are available since 1967 for lakewater concentrations at 

discrete depths for base cations, pH, and dissolved O2. Ammonium, major anions, phosphate, and 

dissolved silica have been measured routinely since 1970, although some data are available before these 

dates for each solute. Other standard monitoring data include temperature and specific conductance at 

each depth. Before 1990, not all records had complete solute arrays. Since 1990, DIC and ANC have also 

been measured on a routine basis, although some prior data do exist for those parameters. The usual 

sampling interval for Mirror Lake is four to six times each year, especially at times of maximum and 

minimum thermal stratification. Data for Mirror Lake and inlet and outlet streams can be found at: 

http://www.hubbardbrook.org/data/dataset_search.php. 

The soils, vegetation, and climate at the HBEF are characteristic of the northern hardwood forest 

complex, which spans much of the north-central and northeastern U.S. and southeastern Canada. 

Streamflow and stream chemistry reflect the landscape characteristics of the drainage area. Consequently, 

results from the relatively small watersheds at the HBEF are to a first approximation representative of a 

much larger regional area. During the scientific debate that occurred before passage of the CAAA of 

1990, the trends in sulfate (SO4

2−

) concentrations in streamwater and precipitation at HBEF were very 

influential in convincing scientists and policy makers that decreasing S emissions would yield large 

decreases in the concentration of SO4

2−

 in precipitation and streamwater in the northeastern U.S. (Lovett 

et al., 2007). Monitoring data collected since 1963 (Figure A-3) played a major role in development of the 

watershed-ecosystem concept and methods for analyzing and understanding watershed biogeochemical 

cycles (Bormann and Likens, 1967; Likens et al., 1978; Likens and Bormann, 1995; Lovett et al., 2007). 

An extensive effort has been made to bring together some of the results of research that had been 

done at Hubbard Brook over the last several decades. Over the duration of the HBES there have been six 

books and more than 1,000 papers published. In addition, more than 500 abstracts were published and 

more than 100 graduate theses completed. A complete list of titles is available at 

http://www.hubbardbrook.org/pubs/pub_search.php. To date, four synthesis volumes have been completed 

(Bormann and Likens, 1979; Likens et al., 1977; Likens, 1985, 1992). 

Source: Lovett et al. (2007); updated from Likens et al. (2002). 

Figure A-3. Long-term record of SO4

2− concentration in streamwater and precipitation at Watershed 6 of 

HBEF. 

 A-16
A.2.4.2. Coweeta 

The Coweeta LTER research program (http://coweeta.ecology.uga.edu/) in North Carolina is based 

in the eastern deciduous forest of the Blue Ridge Physiographic Province of the southern Appalachian 

Mountains. The program entails long-term cooperation between the University of Georgia and the U.S. 

Department of Agriculture (USDA) Forest Service Coweeta Hydrologic Laboratory. The research 

program centers on the effects of disturbance and environmental gradients on biogeochemical cycling, 

and the underlying watershed ecosystem processes that regulate and respond to those cycles. Coweeta 

represents one of the longest continuous environmental studies of any North American landscape. 

The research at Coweeta focuses largely on how water, soil, and forest resources respond to 

management practices, natural disturbances, and the atmospheric environment. It also aims to identify 

practices that mitigate impacts on these watershed resources. Current topics of emphasis include analyses 

of long-term changes in hydrology, nutrient cycling, and productivity in response to management 

practices and natural disturbances; assessment of prescribed burning effects on the forest environment; 

interdisciplinary implementation of ecosystem management on the national forests; effects of climatic 

change on productivity; impacts of atmospheric deposition on forest processes and ecosystems; 

cumulative effects of land use practices on water quality; physiological studies of forest carbon balance 

and competition; and biodiversity. 

Investigators at the Coweeta Hydrologic Laboratory have recorded N dynamics of streams and 

precipitation in mature mixed hardwood-covered watersheds since 1972. Research has been conducted on 

responses to management practices such as clearcutting, selective cutting, conversion of native hardwood 

to coniferous forest, and old-field succession. Reference watersheds were characterized as in a transition 

phase between stage 0 and stage 1 of watershed N saturation. Evidence for stage 3 of N saturation, where 

the watershed is a net source of N rather than a N sink, was found for the most disturbed watershed at 

Coweeta. 

The Coweeta Basin comprises 2185 hectares within the Blue Ridge geologic province in North 

Carolina. The laboratory has been dedicated to forest hydrology research since its establishment in 1933. 

Elevations range from 679 to 1592 m. More than 50 km of streams drain the area. 

Coweeta is the first major mountain range contacted by air masses moving over the industrialized 

Piedmont region to the south. Analyses of precipitation chemistry have shown the influence of both local 

and regional activities on nutrient inputs to forest ecosystems. 

Since Coweeta was established, 32 weirs have been installed on streams. Seventeen of these weirs 

are currently operational. Stream gauging was initiated on most watersheds between 1934 and 1938, and 

stream chemistry measurements date back to 1968. 

Research has been conducted on eight mixed hardwood control areas and 13 catchments where 

forest management prescriptions have been applied. Past treatments have included varying intensities of 

cutting, ranging from light selection through clear-cutting; conversion of hardwoods to grass and 

subsequent succession to hardwoods; multiple-use management; mountain farming; and the application of 

herbicides and fertilizers. 

Research and monitoring data from Coweeta has been extensively analyzed and reported in the 

scientific literature. Example recent publications include Swank and Vose (1997), Grossman et al. (1998), 

Schofield et al. (2001), and Scott and Helfman (2001). 

Long-term changes in soils have been identified in reference and managed watersheds over two 

decades (Knoepp and Swank, 1994). For example, changes in exchangeable soil cation content varied 

with aspect: concentrations decreased on a north-facing slope but were stable on a south-facing slope. The 

demonstrated impacts of forest management practices have varied considerably. Soils in a white pine 

plantation showed stable C levels, but cations declined. 

Commercial sawlog harvest resulted in large increases in soil C and cation concentrations, which 

remained elevated for 17 years. Whole-tree harvest resulted in decreased soil C for the next 14 years. 

Clearly soil response to harvest varies with type of harvest and site. Long-term studies like these have 

 A-17
proven useful in guiding ecosystem management projects in the southern Appalachians (Meyer and 

Swank, 1996). 

A.2.4.3. Walker Branch 

Walker Branch Watershed is located on the U.S. Department of Energy’s Oak Ridge Reservation in 

Tennessee. The 97.5 ha Walker Branch watershed has been the site of long-term, intensive environmental 

studies since the late-1960s (see http://walkerbranch.ornl.gov/). 

The forest soils are acidic, very cherty, infertile, and permeable. They are formed over dolomitic 

bedrock, but retain little evidence of their carbonate parent material. The forest vegetation is primarily 

oak-hickory with scattered pine on the ridges and mesophytic hardwoods in the valleys. 

Initially, the research and monitoring of Walker Branch centered primarily on the geologic and 

hydrologic processes that control the amounts and chemistry of water moving through the watershed. Past 

projects have included: 

 watershed hydrology and forest nutrient dynamics, 

 forest micrometeorology, 

 atmospheric deposition, 

 International Biological Program Eastern Deciduous Forest Biome Project, 

 trace element cycling and stream nutrient spiraling, and 

 effects of acidic deposition on canopy processes and soil chemistry. 

These projects have all contributed to a more complete understanding of how forest watersheds 

function and have provided insights into the solution of energy-related problems associated with air 

pollution, contaminant transport, and forest nutrient dynamics. Available long-term data at this site 

include: 

 Daily climate data 

 Monthly climate data 

 Precipitation 

 Atmospheric deposition 

 Stream discharge and annual runoff 

 Stream chemistry 

 Vegetation 

A.2.5. Water, Energy, and Biogeochemical Budgets Program 

The Water, Energy, and Biogeochemical Budgets (WEBB) Program was started in 1991 at five 

small watersheds in the U.S. to examine water, energy, and biogeochemical fluxes and to determine the 

effects of atmospheric deposition, climatic variables, and human influences on watershed processes. The 

five sites are at Loch Vale, Colorado; Luquillo Experimental Forest, Puerto Rico; Panola Mountain, 

Georgia; Sleepers River, Vermont; and Trout Lake, Wisconsin. These sites are supported, in part, by other 

programs in the USGS, other Federal and State Agencies, and Universities. Two of these sites, Loch Vale 

and Sleepers River, have been used extensively to evaluate the effects of atmospheric sulfur and N 

deposition, and are described here. Each of those sites is also part of the LTER network. 

 A-18
A.2.5.1. Sleepers River 

The Sleepers River Research Watershed in northeastern Vermont was established by the 

Agricultural Research Service (ARS) of the USDA in 1959 and is now operated jointly by the USGS and 

the U.S. Army Cold Regions Research and Engineering Laboratory (CRREL), with collaboration from 

several other Federal agencies and universities (see http://nh.water.usgs.gov/projects/sleepers/index.htm). 

The USGS uses hydrologic measurements and chemical and isotopic tracing techniques to determine how 

water moves from the hillslope to the stream, and what processes cause chemical changes, including the 

neutralization of acid rain. Research results provide insights on how pollutants move through ecosystems, 

and how ecosystems may respond to climatic change. 

The watershed is covered by 1 to 4 m of glacial till, a compacted fine silty material that formed 

underneath glacial ice as it moved overland. The till was formed primarily from local bedrock, which is a 

calcareous granulite/quartz-mica phyllite. About 60 to 80 cm of soil has developed in the till. Weathering 

of calcite in the till and bedrock causes highly buffered streamflow, compared to most streams in New 

England, and a nutrient-rich biological environment. Sleepers River is, therefore, an end member in 

regional biogeochemical cycling studies (Hornbeck et al., 1997). 

The Sleepers River area has reverted from a predominately cleared, agricultural landscape to a 

mostly forested one. A Northern Hardwood forest, dominated by sugar maple, white ash, yellow birch, 

and beech, with lesser amounts of red spruce and balsam fir, now covers two-thirds of the area; the 

remaining open land is primarily pasture and hayfields. Dairy farming and logging are the primary human 

enterprises in the watershed. The average annual temperature is 6 °C and the average annual precipitation 

is 1.1 m, 20% to 30% of which falls as snow. 

Sleepers River has one of the longest historical hydrologic and climatologic data bases for a coldregion area in the U.S., featuring measurements of precipitation and streamflow since 1959, snow depth 

and corresponding water content since 1960, soil frost depth since 1984 (Shanley and Chalmers, 1999), 

and ground-water levels since 1991. These and other measurements constitute a valuable resource for 

hydrologic modeling and for the evaluation of climatic changes. Sampling site locations are shown in 

Figure A-4. 

 A-19
Figure A-4. Location of sampling stations in Sleepers River watershed, Vermont. 

Recent research findings include the following: 

 Precipitation is acidic, but streamflow is well-buffered from calcite weathering in till and 

bedrock. 

 Infiltrating snowmelt causes ground water to rise into the permeable soil zone, where it moves 

rapidly downslope. 

 Naturally occurring isotopic and chemical tracers indicate that “old” water dominates streamflow, 

and that water acquires solutes from weathering and biogeochemical processes along both deep 

and shallow flowpaths. 

 Nitrate (NO3

−

) in streamflow is supplied primarily by mineralization and nitrification in the soil, 

rather than directly by the N content of precipitation. 

The fate of NO3

−

 in the forest ecosystem is being investigated by analysis of both the N and O 

isotopes of the NO3

−

 ion. The isotopic composition of NO3

−

 in streamflow matches that of NO3

−

 produced 

by mineralization and nitrification in the soil, indicating that streamflow NO3

−

 is derived from the soil and 

not from the rain or snowmelt that causes the high flow (Kendall et al., 1995). This finding suggests that 

most incoming atmospheric N is incorporated at least temporarily in the soil where it is utilized by the 

biota. 

 A-20
A.2.5.2. Loch Vale 

The Loch Vale Watershed is a 661–ha alpine/subalpine basin located in the south-central Rocky 

Mountains, about 100 km northwest of Denver, Colorado. The basin is in a roadless area in Rocky 

Mountain National Park and is accessed by a 5–km hike or ski from the trailhead near Bear Lake. The 

western boundary of the basin is the Continental Divide; streams drain to the northeast. Basin elevations 

range from 4192 m (13,153 ft) at Taylor Peak to 3110 m (10,200 ft) at the outlet. There are two main 

subbasins in Loch Vale: Andrews Creek drains the northern subbasin, and Icy Brook drains the southern 

subbasin. These two creeks join above The Loch, which is the lowest of three lakes in the basin. Stream 

gauges are operated on Andrews Creek, Icy Brook, and at The Loch outlet. Water chemistry monitoring 

occurs at The Loch and on both inlet streams (see http://nh.water.usgs.gov/projects/sleepers/index.htm). 

Large glaciers that covered much of Rocky Mountain National Park during the late Pleistocene 

sculpted the basin into characteristic glacial landforms, including steep U-shaped valleys, cirques, and 

arêtes. When the glaciers retreated about 12,500 years ago, they deposited till of varying thickness, which 

is confined mostly to the forested, lower part of the basin. Smaller, more recent glacial advances left 

younger till, talus, and rock deposits in the upper parts of the basin. The younger glacial and periglacial 

deposits are largely unvegetated. 

Available water chemistry data include major ions, nutrients, DIC, DOC, and, for selected samples, 

a range of isotopes including 2

H/H, 18O/16O, 15N/14N and 18O/16O in nitrate ion, 35S, 34S/32S, 87Sr/86Sr, 13C, 

12C). Monitoring of precipitation and hydrology include the following elements: 

 Precipitation: quantity — 3 sites continuous; chemistry, 1 site, biweekly. 

 Stream discharge, 2 sites (Andrews Creek-Loch Vale, Icy Brook-Loch Vale). 

 Stream chemistry, (Andrews Creek, Icy Brook-Loch Vale). 

 Spring discharge, conductance, and temperature, 3 sites continuous. 

 Spring water chemistry, 3 sites biweekly, 20–30 sites once during low-flow season. 

 Soil lysimeters, 5 sites, biweekly to monthly during summer and fall. 

 Snowpack amount and chemistry (depth, snow-water equivalent) basin-wide survey at maximum 

accumulation, index sites biweekly to monthly. 

 Selected microenvironment runoff, e.g., rock outcrop, talus fields, weekly to monthly. 

 Meteorology: 3 sites (wind speed, wind direction, air temperature, incoming and outgoing 

radiation, relative humidity), continuous. 

 Gas flux (CO2 and CH4) in wetland, forest, and talus soils, weekly to monthly; CO2

concentrations in surface waters at 10–15 sites several times annually. 

 Snowmelt lysimeter discharge and chemistry, monitored for three years, currently inactive. 

Atmospheric deposition of N to Loch Vale is high compared to most other sites in the Rockies, 

although considerably lower than most impacted sites in eastern North America and Europe. The 

alpine/subalpine ecosystem at Loch Vale exhibits symptoms of advanced watershed N saturation, 

indicating sensitivity to N deposition. Talus landscapes contribute substantially to N export in streamflow, 

and soil microbial processes are important in cycling N, even in areas such as talus that have little soil 

development. Research at this site indicates that N export is a function of both deposition and internal Ncycling processes that are affected by variability in climate. 

 A-21
A.2.6. Other Monitoring Programs 

A.2.6.1. Bear Brook 

The Bear Brook Watershed (BBW) is located in eastern Maine (44°52'15" latitude, 68°06'25" 

longitude), approximately 60 km from the Atlantic coastline. The BBW is a paired watershed study 

funded by the U.S. EPA since 1987 as part of The Watershed Manipulation Project (WMP) within the 

National Acid Precipitation Assessment Program (NAPAP) (see http://hydromodel.com/bbwm.htm; 

http://www.umaine.edu/DrSoils/bbwm/bbwm.html). As a long-term research watershed, the BBW 

includes bench-scale, micro-site, plot, and whole watershed investigations. The major purposes of the 

BBW project are to: 

 Identify and quantify the major processes that control surface water acidity, with a major 

emphasis on the role of excess SO4

2−

 and nitrate provided via atmospheric deposition and 

experimental application, and the rate of cation supply from chemical weathering and cation 

desorption. 

 Assess the quantitative and qualitative responses at the watershed level to different (both 

increased and decreased) levels of acidic deposition. 

 Evaluate the ability of existing models of water acidification to predict short- and long-term 

chemical variations in surface water chemistry and to predict watershed soil responses to 

increased and decreased loading of strong acids. 

The watershed includes two first order streams: East Bear Brook (EBB) and West Bear Brook 

(WBB). On each stream, a catchment outlet was selected and gauged so that both streams have about the 

same catchment area (EBB = 10.7 ha and WBB = 10.2 ha). Since streams are close and face the same 

slope direction, the watersheds are geographically similar and are appropriate for a paired watershed 

studies. Both watersheds have a maximum discharge of about 0.01 mm/ha/s or 0.15 m3

/s. Annual water 

yield relative to incoming precipitation for WBB ranges from 68 to 77% and EBB ranges from 62–68%. 

Stream channels in each watershed are well defined. Each stream bed is approximately 1 m wide at 

the weir and water flows over exposed bedrock in places. Elsewhere, the streambeds are comprised of 

boulders and gravel. Both streams have undergone intermittent dry periods during summer over the 

course of the study. One V-notch weir was constructed on each of the streams during winter 1987–1988. 

Mean discharge in each stream is about 0.13 cubic feet per second. 

Sampling frequency at the weirs was every three weeks during the winter of 1986–1987 and at 

least weekly thereafter. On the basis of sampling conducted before beginning the manipulation 

experiment (1987–1989), the streams had the following characteristics: ANC, −5 to 90 μeq/L; airequilibrated pH, 4.7 to 7.2; specific conductance of approximately 26 μS/cm; and DOC of 1 to 4 mg C/l. 

Soils in the Bear Brook watersheds are primarily Spodosols. The average depth of the overburden 

in the watersheds is 0.5 m, with a range of 0 to 5.2 m. Soil pH (0.01M CaCl2) values ranged from 2.9 in 

the O horizon, to 3.9 in the B horizon, to 4.4 in the C horizon. The bedrock is primarily metamorphosed 

and folded politic graded beds and quartzites, with granitic dikes. The surficial material is till. 

The forest is comprised primarily of deciduous species with areas of conifers. Tree species include 

American beech (Fagus grandifolia), birch (Betula sp.), maple (Acer sp.), red spruce (Picea rubens), 

balsam fir (Abies balsamea), white pine (Pinus strobus), and hemlock (Tsuga canadensis). Coniferous 

stands, which occupy approximately 17% of the total watershed area, occur more commonly in the upper, 

steeper portions of the watersheds. 

Although the Bear Brook project was intended as an experimental manipulation of West Bear 

Brook, there is also great value in the long-term monitoring data collected at East Bear Brook, the nonmanipulated reference watershed. This two-decade long monitoring record provides information on the 

 A-22
response of an acid-sensitive low-order stream in Maine to changes that have occurred in atmospheric 

deposition since 1986. 

Results of the Bear Brook project have been widely published (cf. Kahl et al., 1993b; Norton et al., 

1999a, 1999b, 1994). 

A.2.6.2. Shenandoah Watershed Study 

The Shenandoah Watershed Study (SWAS) program is a monitoring and research network focused 

on low-order, high-gradient streams associated with public lands in western Virginia (see 

http://swas.evsc.virginia.edu/). The objectives of the program are to increase understanding of factors that 

govern biogeochemical conditions and stressor-response relationships in forested mountain watersheds of 

the central Appalachian region. Success in addressing these scientific and problem-oriented objectives has 

been achieved through development of a data collection network that accounts for spatial gradients, as 

well as temporal variation, in the chemical composition of the region’s relatively undisturbed headwater 

streams. 

The program is notable for the length of the continuous data record that has been obtained, 

including the longest-running record (28 years) of stream water composition and discharge in the National 

Park System. The SWAS component of the program, which now includes 14 streams in Shenandoah 

National Park, was initiated in 1979. The Virginia Trout Stream Sensitivity Study (VTSSS) component, 

which now includes 51 streams in National Forests and other conservation lands, was initiated in 1987. 

The distribution of SWAS-VTSSS study sites in relation to public lands is shown in Figure A-5 

The SWAS-VTSSS program has been maintained as a cooperative effort involving the Department 

of Environmental Sciences at the University of Virginia, the National Park Service, the U.S. EPA, the 

USDA Forest Service, the U.S. Geological Survey, the Virginia Department of Game and Inland 

Fisheries, and Trout Unlimited. The monitoring sites account for ecological variation among the region’s 

forested mountain watersheds with a data-collection strategy that represents: spatial variation through the 

distribution of hydrochemical monitoring within a lithologic classification system; and temporal variation 

through long-term data collection at fixed locations sampled at different frequencies. 

 A-23
Figure A-5. SWAS-VTSS Program Study Sites. The length of the stream water chemical composition 

record for SWAS-VTSSS study watersheds is 20 to 28 years. SWAS sites are located in 

Shenandoah National Park (shaded blue). VTSSS sites are mainly located in Virginia’s 

National Forests (shaded green). 

The lithologic classification system includes 6 classes based on the physical and chemical 

properties of bedrock formations in the region. ANC and concentrations of related acid-base constituents 

in stream waters, as well as other biotic and abiotic properties of watersheds, differ among the lithologic 

classes. 

The SWAS-VTSSS data collection framework is most-well developed in the Blue Ridge Mountains 

Province within Shenandoah National Park, where stream water composition data are collected seasonally 

at 14 sites, weekly at 6 sites, and every four hours during episodic high-flow conditions at 3 sites with 

continuous discharge gauging. Stream water composition data are collected on a seasonal basis at an 

additional 51 sites located outside of the Park, in both the Blue Ridge Mountains and Ridge and Valley 

Provinces. 

Stream water samples collected through the SWAS and VTSSS programs are analyzed for ANC, 

pH, and the major anions (SO4

2−

, nitrate, and chloride) and cations (calcium, magnesium, potassium, and 

sodium) by methods appropriate for low-ionic strength natural waters. Both the SWAS and VTSSS 

sample streams were selected based on geographic distribution, representation of the major bedrock types 

underlying the mountain ridges in the region, and minimization of recent watershed disturbance. All but a 

few of the sample streams currently support reproducing populations of native brook trout. All of the 

sample streams supported brook trout populations historically. 

Sustained data collection in a network constructed of intensively studied sites nested within a 

geographically extensive set of less intensively studied sites has allowed detection and interpretation of 

change that has occurred in a context of multiple time scales and stressors. Responses to multi-year 

changes in acidic deposition have been reflected in long-term trends in quarterly concentrations of SO4

2−

, 

ANC, and other acid-base constituents of streams in the network. Expectations for southeastern 

watersheds with soils that retain sulfur, for example, have been confirmed by the lack of regional 

improvement in stream water quality following reductions in acidic deposition mandated by the CAA. 

The acid-base chemistry of streams in the network also varies seasonally and on shorter time scales. 

Weekly and higher-frequency automated stream water sampling during periods of high runoff have 

supported the study of episodically more-acidic conditions, including the study of fish sensitivity with instream bioassays and development of models to predict severity and recurrence intervals. 

 A-24
By accounting for significant spatial gradients and temporal patterns in the region, the SWASVTSSS hydrochemical data collection program provides a basis for both observing and interpreting 

watershed-scale change, as well as an informed foundation for process-oriented research. Monitoring data 

and research findings obtained through the SWAS-VTSSS program have contributed to increased 

scientific understanding, as well as to policy formulation and implementation. 

The mathematical model, Model of Acidification of Groundwater in Catchments (MAGIC), was 

first calibrated using data obtained for White Oak Run, a SWAS-VTSSS study stream in Shenandoah 

National Park. MAGIC is the most widely used acid-base chemistry model in the U.S. and Europe and the 

principal model used by the National Acid Precipitation Assessment Program in the 1980s to estimate 

future damage to lakes and streams in the eastern U.S. The MAGIC model has since been applied in a 

number of regional assessments that relied extensively on stream water and soils data obtained through 

the SWAS-VTSSS program. Among these are: 

 The Southern Appalachian Mountain Initiative, a multi-state effort to evaluate alternative 

approaches to solving regional air-pollution problems. MAGIC projections indicated that even 

ambitious emission control strategies would not result in near-term recovery of the region’s most 

acidified surface waters – a consequence of base-cation depletion in soils exposed to decades of 

acidic deposition. 

 The Shenandoah Assessment, an assessment of acidification effects on aquatic systems in 

Shenandoah National Park. MAGIC reconstructions indicated that Park streams associated with 

base-poor bedrock lost about 70 μeq/L between 1900 and 1990. MAGIC projections indicated 

that some streams may recover given prospective reductions in acidic deposition, but others will 

not. 

Data and findings provided through the SWAS-VTSSS program have also proven relevant to the 

evaluation and implementation of national air pollution control policies. The SWAS-VTSSS program 

provides data for the U.S. EPA long-term monitoring of surface water response to legislated reductions in 

sulfur emissions. Whereas SO4

2−

 concentrations in surface water declined during the 1990–2000 period 

for four northeastern regions with sensitive surface waters, the SWAS-VTSSS study region, in contrast, 

experienced increasing stream-water SO4

2−

 concentrations and continuing acidification. 

Recent publications that were based on analyses of SWAS-VTSSS data include Cosby et al. (1991), 

Stoddard et al. (1993), Sullivan et al. (2003), and Webb et al. (2004). 

A.2.6.3. Fernow 

The Fernow Experimental Forest, established in 1934, is located just south of the city of Parsons in 

the most mountainous region of West Virginia. It is surrounded by the Monongahela National Forest, 

which comprises about 900,000 acres of rugged, hilly terrain. Most research at Fernow is focused on 

improvement of forest management (see http://www.fs.fed.us/ne/parsons/fefhome.htm). 

Scientists at Fernow are developing information and techniques for sustainably managing 

hardwood forests in the central Appalachians. The mixed hardwood forest covers about 78% of West 

Virginia and supplies important timber products, provides recreational opportunities, and supports a 

diverse assemblage of wildlife and plant species. 

The Fernow Experimental Forest was heavily logged between 1905 and 1911. The forest now 

contains about 1900 ha of second- and third-growth Appalachian hardwood stands, which are 

representative of average to better than average sites found on approximately 4 million ha of the forest 

type in West Virginia and surrounding states. At the lowest elevations, the original forests consisted 

mainly of hardwoods, with eastern hemlock (Tsuga canadensis [L.] Carr.) along stream bottoms and on 

north slopes. Forests at the higher elevations were dominated by red spruce (Picea rubens Sarg.) and 

hemlock. Small patches of pure spruce occurred on the tops of the mountains. 

 A-25
Elevations in the Fernow range from 533 to 1112 m, with slopes of 10% to 60%. A rock layer 

composed of fractured hard sandstone and shale underlies most of the Fernow. A majority of the soils are 

of the Calvin and Dekalb series, which originated from these rocky materials (loamy-skeletal mixed 

mesic Typic Dystrochrepts). On the southeastern part of the forest, Greenbrier limestone outcrops to 

produce a midslope zone of limestone soil of the Belmont series (fine-loamy mixed mesic Typic 

Hapludalfs). Almost all Fernow soils, including the sandstone, shale, and limestone soils, are welldrained, medium textured loams and silt loams. Average soil depth is about 1 m, and average soil pH is 

about 4.5. 

A rainy, cool climate is typical on the Experimental Forest. Precipitation, which averages about 145 

cm per year, is evenly distributed throughout the year. Mean annual temperature is about 9 °C, and the 

length of the growing season is approximately 145 days. 

The forest types and conditions today reflect the site qualities and past history of the area. Oaks 

(Quercus spp.) are most common and are found on all sites along with American beech (Fagus 

grandifolia Ehrh.) and sweet birth (Betula lenta [L].). Excellent sites in coves and on north slopes support 

primarily northern red oak (Quercus rubra L.), sugar maple (Acer saccharum Marsh.), yellow-poplar 

(Liriodendron tulipifera L.), black cherry (Prunus serotina Ehrh.), white ash (Fraxinus americana L.), 

basswood (Tilia americana L.), cucumbertree (Magnolia acuminata L.), and beech. Fair sites on south 

and east slopes usually support oak stands composed of red oak, white oak (Quercus alba L.), chestnut 

oak (Quercus prinus L.), and scarlet oak (Quercus coccinea Muenchh.). Other fair site species include red 

maple (Acer rubrum L.), sweet birch, black gum (Nyssa sylvatica Marsh.), sassafras (Sassafras albidum

Nutt.), and sourwood (Oxydendrum arboreum [L.] DC.). Good sites commonly support a mixture of 

excellent and fair site species. Black locust (Robinia pseudoacacia L.), sweet birch, and Fraser magnolia 

(Magnolia fraseri Walt.) are consistent but generally minor components of the forest on all sites. 

American chestnut was a major forest component until it was eliminated by the chestnut blight. 

The Fernow Experimental Forest encompasses practically the entire Elk Lick Run drainage, which 

is about 5.8 km long and 3.5 km across at the widest point. Elk Lick Run has seven major tributaries 

including Big Spring, which drains a headwater limestone formation. Headwater areas on two of these 

tributaries have been gauged to show how forest management influences streamflow. 

Research on the Fernow Experimental Forest by the Timber and Watershed Project scientists is 

done in cooperation with the Monongahela National Forest, West Virginia University, Marshall 

University, Pennsylvania State University, Virginia Tech, and the West Virginia Division of Natural 

Resources. 

Scientific studies on the Fernow have followed two lines of research, with considerable overlap. 

Silvicultural research, focused mostly on mixed hardwood stands, addresses questions relating to 

regenerating, growing, tending, and harvesting trees and stands. Watershed research has addressed some 

of the more basic questions about water use by forests and forest hydrology, as well as critical issues 

affecting roads, best management practices, and forest management effects on water and soil resources. 

The Fernow also has been in the forefront of research on acidic deposition and N saturation. A wholewatershed acidification study has been conducted since 1989. Recently, research on threatened and 

endangered species has assumed a more prominent role, due to the presence of Indiana bat and running 

buffalo clover on the Fernow. 

A.2.6.4. National Ecological Observatory Network 

The National Ecological Observatory Network (NEON) is a continental-scale research platform 

that is primarily focused on discovering and understanding the impacts of climate change, land-use 

change, and invasive species on ecology. It will also generate data that will be useful for assessing effects 

of NOX and SOX deposition on ecosystems. NEON has not yet been implemented; it is described here 

because it represents an ambitious monitoring program that is expected to be very useful in the near 

future. NEON will gather long-term data on ecological responses of the biosphere to changes in land use 

 A-26
and climate, and on feedbacks with the geosphere, hydrosphere, and atmosphere. NEON is proposed as a 

national observatory, consisting of distributed sensor networks and experiments, linked by advanced 

cyber infrastructure to record and archive ecological data for at least 30 years. Using standardized 

protocols and an open data policy, NEON is intended to gather essential data for developing scientific 

understanding and theory required to manage the nation’s ecological challenges. The program description 

is found at www.neoninc.org/. 

A.3. Modeling 

A.3.1. Principal Ecosystem Models Used in the U.S. 

It is particularly difficult to study endpoints at the larger levels of biological organization (e.g., at 

the population, community, biogeochemical, and ecosystem-level) with monitoring studies. Geographic 

areas are larger, and timeframes are longer, rendering it difficult to obtain data in sufficient quantity to 

detect impacts unless they are exceptionally severe. Therefore, the most common approach to study 

endpoints at these scales is to develop and apply a model. Models may be calibrated using data from 

monitoring, survey, or laboratory or field experiments and are useful tools in predicting larger-scale, 

longer-term impacts. However, verifying the predictions and assessing the overall validity of the model 

can be challenging. 

Some of the most frequently used ecosystem models designed to quantify effects of atmospheric N 

and S deposition are discussed below. It is important to note that the ecosystem models are parameterized 

for specific areas and may not be readily applicable to other locations without significant reparameterization. 

There are four principal models that are currently being used in the U.S. to assess the effects of S 

and N deposition on terrestrial and freshwater aquatic ecosystems: MAGIC, NuCM, PnET/BGC, and 

DayCent-Chem. Two models, SPARROW and WATERSN, are commonly used to evaluate N loading to 

large river systems and to estuaries. These six models are briefly reviewed in the following sections. Each 

review begins with a summary of the provenance and conceptual basis of the model and contains 

references to some of the published applications. This is followed by a more detailed description of the 

processes included in the model, the inputs required, and the output variables simulated by the model. 

The ranges of process complexity, temporal resolution and spatial discretization represented in 

these models are considerable. These ranges make comparative summaries of inputs, outputs, and 

processes across the models problematic. The models are all currently in use because they are, in a sense, 

complementary to each other, with each providing an approach or satisfying requirements unique to their 

own structure and intended applications. As a result, there is no good way to develop satisfying 

comparative equivalences among the components of the various structures. It is also beyond the scope of 

this document to present the level of detail necessary to run any of the models. The descriptions below 

must of necessity be brief. References to appropriate texts designed to provide more detail are given for 

each model. 

Following the discussion of the four models most frequently used in the U.S., there are brief 

descriptions of the most important models of S and N deposition effects that are being used in Europe and 

elsewhere. 

A.3.1.1. MAGIC 

The MAGIC model (Cosby et al., 1985a; 1985b; 1985c) is a mathematical model of soil and 

surface water acidification in response to atmospheric deposition based on process-level information 

 A-27
about acidification. MAGIC has been applied extensively in North America and Europe to both individual 

sites and regional networks of sites, and has also been used in Asia, Africa and South America. The utility 

of MAGIC for simulating a variety of water and soil acidification responses at the laboratory, plot, 

hillslope, and catchment scales has been tested using long-term monitoring and experimental 

manipulation data. 

Figure A-6. Conceptual structure of the MAGIC model showing major pools and fluxes included in 

simulation of effects of S and N deposition. 

MAGIC has been widely used in policy and assessment activities in the U.S. and in several 

countries in Europe (e.g., Beier et al., 1995; Clair, 2004; Cosby, 1985b; Cosby et al., 1990, 1995, 1996; 

Ferrier et al., 2001; Hornberger et al., 1989; Jenkins et al., 1990; Moldan et al., 1998; Sullivan et al., 

1998, 2006b; Whitehead et al., 1988, 1997; Wright et al., 1994, 1998). 

MAGIC Model Structure 

MAGIC is a lumped-parameter model of intermediate complexity, developed to predict the longterm effects of acidic deposition on surface water chemistry (see Figure A-6). The model simulates soil 

solution chemistry and surface water chemistry to predict the monthly and annual average concentrations 

of the major ions in these waters. MAGIC consists of: a section in which the concentrations of major ions 

are assumed to be governed by simultaneous reactions involving SO4

2−

 adsorption, cation exchange, 

dissolution-precipitation- speciation of aluminum, and dissolution-speciation of inorganic carbon; and a 

mass balance section in which the flux of major ions to and from the soil is assumed to be controlled by 

atmospheric inputs, chemical weathering, net uptake and loss in biomass and losses to runoff. At the heart 

of MAGIC is the size of the pool of exchangeable base cations in the soil. As the fluxes to and from this 

pool change over time owing to changes in atmospheric deposition, the chemical equilibria between soil 

and soil solution shift to give changes in surface water chemistry. The degree and rate of change of 

surface water acidity thus depend both on flux factors and the inherent characteristics of the affected soils. 

Cation exchange is modeled using equilibrium (Gaines-Thomas) equations with selectivity 

coefficients for each base cation and aluminum. SO4

2−

 adsorption is represented by a Langmuir isotherm. 

Aluminum dissolution and precipitation are assumed to be controlled by equilibrium with a solid phase of 

 A-28
aluminum trihydroxide. Aluminum speciation is calculated by considering hydrolysis reactions as well as 

complexation with SO4

2−

, fluoride and dissolved organic compounds. Effects of carbon dioxide on pH and 

on the speciation of inorganic carbon are computed from equilibrium equations. Organic acids are 

represented in the model as tri-protic analogues. Weathering rates are assumed to be constant. Two 

alternate mechanisms are offered for simulation of nitrate and ammonium in soils and water: either first 

order equations representing net uptake and retention; or a set of equations and compartments describing 

process-based N dynamics controlled by C and N pools and fluxes in the compartments. 

Atmospheric deposition fluxes for the base cations and strong acid anions are required as inputs to 

the model. These inputs are generally assumed to be uniform over the catchment. Atmospheric fluxes are 

calculated from concentrations of the ions in precipitation and the rainfall volume into the catchment. The 

atmospheric fluxes of the ions must be corrected for dry deposition of gas, particulates and aerosols and 

for inputs in cloud/fog water. The volume discharge for the catchment must also be provided to the model. 

In general, the model is implemented using average hydrologic conditions and meteorological conditions 

in annual or seasonal simulations, i.e., mean annual or mean monthly deposition, precipitation and lake 

discharge are used to drive the model. Values for soil and surface water temperature, partial pressure of 

carbon dioxide and organic acid concentrations must also be provided at the appropriate temporal 

resolution. 

The MAGIC model can be implemented as a one- or two-soil representation of a catchment with or 

without wetlands. Atmospheric deposition enters the soil compartment(s) and the equilibrium equations 

are used to calculate soil water chemistry. The water is then routed to the stream compartment, and the 

appropriate equilibrium equations are reapplied to calculate runoff chemistry. Input-output mass balance 

equations are provided for base cations and strong acid anions, and charge balance is required for all ions 

in each compartment (for complete details of the model see Cosby et al., 1985a, 1985b, 1985c; 2001). 

For most applications, model outputs for 15 stream water variables are used. These variables 

consist of the concentrations of 10 ions (H; Ca; Mg; Na; K; NH4; SO4

2−

; NO3; Cl; and total inorganic Al), 

the stream discharge (Q), stream pH, sum of base cation (SBC) concentrations (SBC = Ca + Mg + Na + K 

+ NH4), sum of mineral acid anion (SAA) concentrations (SAA = Cl + SO4

2−

 + NO3) and the charge 

balance acid neutralizing capacity (ANC = SBC – SAA). These variables are expressed in units of m/yr 

(or m/mo) for Q, μmol/L for inorganic Al, and μeq/L for all other variables. In addition, model output for 

7 soil and soilwater variables are frequently used, the total base saturation and individual cation 

saturations for Ca, Mg, Na, and K, the soilwater pH and the Ca/Al ratio in soil water. 

The aggregated nature of the model requires that it be calibrated to observed data from a system 

before it can be used to examine potential system response. Calibrations are based on volume weighted 

mean annual or seasonal fluxes for a given period of observation. The length of the period of observation 

used for calibration is not arbitrary. Model output will be more reliable if the annual flux estimates used in 

calibration are based on a number of years rather than just one year. There is a lot of year-to-year 

variability in atmospheric deposition and catchment runoff. Averaging over a number of years reduces the 

likelihood that an “outlier” year (very dry, etc.) is used to specify the primary data on which model 

forecasts are based. On the other hand, averaging over too long a period may remove important trends in 

the data that need to be simulated by the model. 

The calibration procedure requires that stream water quality, soil chemical and physical 

characteristics, and atmospheric deposition data be available for each catchment. The water quality data 

needed for calibration are the concentrations of the individual base cations (Ca, Mg, Na, and K) and acid 

anions (Cl, SO4

2−

, and NO3) and the pH. The soil data used in the model include soil depth and bulk 

density, soil pH, soil cation-exchange capacity, and exchangeable bases in the soil (Ca, Mg, Na, and K). 

The atmospheric deposition inputs to the model must be estimates of total deposition, not just wet 

deposition. In some instances, direct measurements of either atmospheric deposition or soil properties 

may not be available for a given site with stream water data. In these cases, the required data can often be 

estimated by: (a) assigning soil properties based on some landscape classification of the catchment; and 

 A-29
(b) assigning deposition using model extrapolations from some national or regional atmospheric 

deposition monitoring network. 

Soil data for model calibration are usually derived as aerially averaged values of soil parameters 

within a catchment. If soils data for a given location are vertically stratified, the soils data for the 

individual soil horizons at that sampling site can be aggregated based on horizon, depth, and bulk density 

to obtain single vertically aggregated values for the site, or the stratified data can be used directly in the 

model. 

Calibration of the model (and estimation of historical changes at the modeled sites) requires a 

temporal sequence of historical anthropogenic deposition. Current understanding of ecosystem responses 

to acidic deposition suggests that future ecosystem responses can be strongly conditioned by historical 

acid loadings. Thus, as part of the model calibration process, the model should be constrained by some 

measure of historical deposition to the site. However, such long-term, continuous historical deposition 

data may not exist. The usual approach is to use historical emissions data as a surrogate for deposition. 

The emissions for each year in the historical period can be normalized to emissions in a reference year (a 

year for which observed deposition data are available). Using this scaled sequence of emissions, historical 

deposition can be estimated by multiplying the total deposition estimated for each site in reference year 

by the emissions scale factor for any year in the past to obtain deposition for that year. 

A.3.1.2. NuCM 

The current NuCM model is based on the original Integrated Lake Watershed Acidification Study 

(ILWAS) model of the 1980s (cf. Chen et al., 1984; Gherini et al., 1985; Goldstein et al., 1984). NuCM 

was developed as an extension to the ILWAS model by investigators in the Integrated Forest Study see 

and the model code was written by Tetra-Tech, Inc. (Liu et al., 1991). NuCM was developed to explore 

potential effects of atmospheric deposition, fertilization and harvesting in forest ecosystems. Because 

NuCM was designed primarily for simulating the effects of atmospheric deposition on nutrient cycling 

processes, its construction emphasizes soil and soil solution chemistry (Liu et al., 1991). As a stand-level 

model, NuCM incorporates all major nutrient cycling processes (uptake, translocation, leaching, 

weathering, organic matter decay, and accumulation). Vegetation is divided into leaf, bole and root 

compartments for under- and overstory vegetation. NuCM simulates the cycling of N, P, K, Ca, Mg, Na, 

and S based on expected optimal growth rates (input by the user and reduced in the event of nutrient 

limitation), user-defined litterfall, weathering, N and S mineralization rates, soil minerals composition, 

initial litter, soil organic matter pools, and C/N ratios. 

The model has been calibrated for different vegetation types, including a loblolly pine (Pinus taeda

L.) stand at Duke University (Johnson et al., 1995), a mixed deciduous stand at Walker Branch (Johnson 

et al., 1993) and a red spruce (Picea rubens Sarg.) stand in the Great Smoky Mountains (Johnson et al., 

1996). The NuCM model was used as part of the Southern Appalachian Mountain Assessment (Sullivan 

et al., 2002a). 

NuCM Model Structure 

In NuCM, the ecosystem is represented as a series of vegetation and soil components. The 

overstory consists of one generic conifer and one generic deciduous species of specified biomass and 

nutrient concentration (foliage, branch, bole, roots). For mixed species stands, average values for biomass 

and nutrient concentration by component must be used. NuCM also includes an understory, which can be 

divided into canopy, bole, and roots. Maximum potential vegetative growth in the model is defined by the 

user and is constrained in the model by the availability of nutrients and moisture. The forest floor is 

simulated from litterfall inputs and litter decay. Litterfall mass inputs are defined by the user, and litter 

decay is represented as a four stage process where: coarse litter decays to fine litter; fine litter decays to 

 A-30
humus and cations; humus decays to organic acids, NH4

+

, SO4

2−

, H+, and CO2; and organic acids decay to 

NH4

+

, SO4

2−

, H+, and CO2. Each stage is represented as a first-order equation. 

The soil includes multiple layers (up to 10), and each layer can have different physical and 

chemical characteristics. The user defines bulk density, cation exchange capacity, exchangeable cations, 

adsorbed phosphate and SO4

2−

, and four soil minerals and their composition. These inputs define the 

initial soil exchangeable/adsorbed pools and total pools. Initial total soil N pools are simulated from 

litterfall and decay, as described above, and user-defined C/N ratios. Vegetation, litter, and soil pools 

change over a simulation in response to growth, litterfall and decomposition, and nutrient fluxes via 

deposition, leaching and weathering. 

The processes that govern interactions among these pools include translocation, uptake, foliar 

exudation and leaching, organic matter decay, nitrification, anion adsorption, cation exchange and mineral 

weathering. Translocation, defined as the removal of nutrients from foliage before litterfall, is userspecified. Maximum uptake is calculated from biomass and nutrient concentrations; actual uptake is equal 

to this maximum value when sufficient nutrients are available and reduced when nutrients become 

limiting. Reduced uptake first allows reduced nutrient concentrations in plant tissues, then causes a 

reduction in growth. Foliar exudation and leaching rates are simulated as proportional to foliar 

concentrations using user-defined coefficients. 

Mineral weathering reactions are described in the model using rate expressions with dependencies 

on the mass of mineral present and solution-phase hydrogen-ion concentration taken to a fractional power. 

Cation exchange is represented by the Gapon equation. The model simulates a tri-protic organic acid with 

a fixed charge density. Nitrification is represented in the form of a Michaelis–Menton rate expression. 

Phosphate adsorption is represented by a linear isotherm, and SO4

2−

 adsorption is represented by a 

Langmuir adsorption isotherm. 

Climate inputs to the NuCM model are through input meteorological files (typically 1 to 5 years 

long), which are repeated to generate long-term simulations. The meteorological files contain daily values 

for precipitation quantity, maximum and minimum air temperature, cloud cover, dewpoint, atmospheric 

pressure, and wind speed. Monthly soil temperature data are also required. 

Precipitation is routed through the canopy and soil layers and evapotranspiration, deep seepage, 

and lateral flow are simulated. The movement of water through the system is simulated using the 

continuity equation, Darcy’s equation for permeable media flow, and Manning’s equation for free surface 

flow. Percolation occurs between layers as a function of layer permeability’s and differences in moisture 

content. Nutrient pools associated with soil solution, the ion exchange complex, minerals, and soil organic 

matter are all tracked explicitly by NuCM. 

Wet deposition is calculated from precipitation amounts and user-input air quality files which 

define precipitation concentrations on a monthly basis. Dry deposition is calculated from air 

concentrations in the air quality files combined with user-defined deposition velocities and simulated leaf 

areas. Leaching is calculated from soilwater percolation and simulated soil solution concentrations using 

the soil chemical and biological algorithms defined above for each soil horizon. 

The only processes in the NuCM model that are explicitly temperature-dependent are evaporation, 

occurrence of precipitation as rainfall versus snowfall, snowpack melting, litter decay, and nitrification. 

Temperature affects processes such as cation exchange, mineral weathering, and uptake only indirectly. 

Precipitation effects are manifested strictly through the hydrologic simulations; none of the nutrient 

processes are dependent explicitly upon moisture. 

A.3.1.3. PnET-BGC

PnET-BGC is an integrated dynamic biogeochemical model that simulates chemical transformations of vegetation, soil and drainage water. The PnET-BGC model was formulated by linking two 

submodels (vegetation and biogeochemical) to allow for the simultaneous simulation of major element 

cycles in forest and interconnected aquatic ecosystems. The vegetation submodel is based on PnET-CN 

 A-31
(Aber, 1992; Aber and Driscoll, 1997; Aber et al., 1997), a simple generalized model of monthly carbon, 

water, and N balances that provides estimates of net primary productivity, N uptake, and water balances. 

The biogeochemical submodel BGC (Gbondo-Tugbawa et al., 2001), expands PnET to include vegetation 

and organic matter interactions of other elements (Ca2+, Mg2+, K+

, Na+, Si, S, P, Al3+, Cl−, and F−), abiotic 

soil processes, solution speciation, and surface water process. 

PnET-BGC was initially developed for and applied to the northern hardwood forest ecosystem. The 

model has been tested using vegetation, soil and water chemistry data from the Hubbard Brook 

Experiment Forest (HBEF) (Gbondo-Tugbawa et al., 2001). The model has subsequently been applied to 

intensively studied watersheds in the Adirondack and Catskill regions of New York and applied regionally 

to the Adirondacks (Chen and Driscoll, 2005b) and northern New England (Chen and Driscoll, 2005a, 

2005c). PnET-BGC has also been used to evaluate the effects of current and future atmospheric deposition 

scenarios (Gbondo-Tugbawa, 2002a; Sullivan, 2006b). 

PnET-BGC Model Structure 

PnET/BGC simulates major biogeochemical processes, such as forest canopy element 

transformations, hydrology, soil organic matter dynamics, N cycling, geochemical weathering, and 

chemical equilibrium reactions in solid and solution phases, and allows for simulations of land 

disturbance (see Figure A-7) (Gbondo-Tugbawa et al., 2001). The model uses mass transfer relationships 

to describe weathering, canopy interactions and surface water processes. Chemical equilibrium 

relationships describe anion adsorption, cation exchange and soil solution and surface water speciation. 

Soil solution equilibrium reactions are described using the tableau approach (Morel and Hering, 1993). A 

more detailed description of the model can be found in Gbondo-Tugbawa et al. (2001). 

 A-32
Source: Gbondo-Tugbawa et al. (2001). 

Figure A-7. The PnET-BGC model illustrating the compartments and flow paths of carbon and nutrients 

(C/Nut) within the model. 

The model operates on a monthly time step and is applied at the stand to small-watershed scale. 

The process of photosynthesis, growth and productivity, litter production and decay, mineralization of 

organic matter, immobilization, and nitrification in PnET have been described in Aber and Federer (1992) 

and Aber et al. (1997). The BGC submodel uses the Gaines–Thomas formulation (White and Zelazny, 

1986) to describe cation exchange reactions within the soil. The exchangeable cations considered in the 

model include Ca2+, Mg2+, Na+

, H+, Al3+, K+, and NH4

+

. A pH-dependent adsorption isotherm is used to 

describe the SO4

2−

 adsorption process. The speciation of monomeric aluminum is calculated in the model, 

including both organic and inorganic forms. Organic acids are described using a triprotic analogue 

(Driscoll et al., 1994b), and the total amount of organic acids is estimated as a certain fraction (based on 

the charge density) of DOC. The model simulates ANC in surface waters as an analogue to ANC 

measured by Gran plot analysis, by considering the contributions of DIC, organic anions and Al 

complexes (Driscoll et al., 1994b). 

The PnET/BGC model requires inputs of climate, wet and dry deposition chemistry, and 

weathering data. Climate inputs consist of minimum and maximum air temperature, solar radiation, and 

precipitation. The model uses a constant dry-to-wet deposition ratio by default, but a variable ratio can 

also be applied (Chen and Driscoll, 2005b). The model inputs utilize canopy enhancement factors to 

depict the increased dry deposition observed in coniferous and mixed forest stands compared to hardwood 

forests. Deposition and weathering fluxes for all major elements are required as model inputs. Weathering 

rates are assumed to remain constant over time. 

Calibration of PnET-BGC is based on empirical relationships and observations. The model uses 

historical reconstructions of climate, atmospheric deposition, and land disturbance to construct hindcasts 

of the response of forests to past acidic deposition. The model can also be used to predict the response of 

 A-33
acid-sensitive forest ecosystems to future changes in acidic deposition, for example in response to 

controls on atmospheric emissions. A detailed description of the model, including a detailed uncertainty 

analysis of parameter values, is available in Gbondo-Tugbawa et al. (2001). 

A.3.1.4. DayCent-Chem 

DayCent-Chem links two widely accepted and tested models, one of daily biogeochemistry for 

forest, grassland, cropland, and savanna systems, DayCent (Parton et al., 1998), and the other of soil and 

water geochemical equilibrium, PHREEQC (Parkhurst and Appelo, 1999). The linked 

DayCent/PHREEQC model was created to capture the biogeochemical responses to atmospheric 

deposition and to explicitly consider those biogeochemical influences on soil and surface water chemistry. 

The linked model expands on DayCent’s ability to simulate N, P, S, and C ecosystem dynamics by 

incorporating the reactions of many other chemical species in surface water. 

Hartman et al. (2007) et al. used DayCent-Chem to investigate how wet and dry deposition affect 

biological assimilation, soil organic matter composition, ANC and pH of surface waters, and also Al 

mobilization, soil base cation depletion, and base cation flux. Model results were tested against a longterm data set available from Andrews Creek in Loch Vale Watershed, Rocky Mountain National Park, 

Colorado. 

DayCent-Chem Model Structure 

DayCent is the daily time-step version of CENTURY, a non-spatial, lumped parameter model that 

simulates C, N, P, S, and water dynamics in the soil-plant system at a monthly timestep over time scales 

of centuries and millennia (Parton et al., 1994). CENTURY can represent a grassland, crop, forest, or 

savanna system with parameters that describe the site-specific plant community and soil properties. 

DayCent, the daily timestep version of CENTURY, adds layered soil temperature, a trace gas submodel, a 

more detailed soil hydrology submodel, and explicitly represents inorganic N as either NO3

−

 or NH4

+

 (Del 

Grosso et al., 2001; Kelly et al., 2000; Parton et al., 1998). DayCent 5 is an object-oriented model written 

in the C++ programming language that implements a layered soil structure and algorithms to manage soil 

layers. The model is initialized with an organic soil depth and up to 10 soil layers, where each layer has a 

specified thickness, texture, bulk density, field capacity, wilting point, and saturated hydraulic 

conductivity. 

PHREEQC is a model based on equilibrium chemistry of aqueous solutions interacting with 

minerals, gases, exchangers, and sorption surfaces. The model is written in the C programming language 

and has an extensible chemical database. Version 2.7 of PHREEQC is used in the linked DayCent-Chem 

model to compute aqueous speciation, ion-exchange equilibria, fixed-pressure gas-phase equilibria, 

dissolution and precipitation of mineral phases to achieve equilibrium, and irreversible aqueous mineral 

phase reactions. The aqueous model uses ion-association and Debye Huckel expressions. Ion-exchange 

reactions are modeled with the Gaines-Thomas convention and equilibrium constants are derived from 

Appelo and Postma (1993). 

The DayCent-Chem model inputs are climate drivers consisting of daily precipitation, and minimum and maximum air temperatures. The model also requires daily atmospheric wet deposition concentrations for precipitation species Ca2+, Cl−

, K+, Mg2+, Na+, NH4

+

, NO3

−

, SO4

2−

, and H+ and daily dry deposition amounts or dry/wet ratios for all precipitation species. Initial conditions for model simulations include: initial snowpack water content and chemical composition; initial soil solution concentrations; and 

initial exchangeable cations in each soil layer. Potential annual denudation rates for each mineral phase 

that could be dissolved in the soil, groundwater, or stream solutions must also be provided. 

DayCent-Chem implements a geochemical submodel of layered pools and properties that provides 

information exchange, such as of water fluxes and solute concentrations, between the coupled models, 

and calculates daily geochemical outputs. The geochemical submodel defines soil layers and a 

 A-34
groundwater pool that correspond to those in Day-Cent 5’s original soil class. Surface water 

concentrations are computed in a two-step process where solutes are first transported, and then 

PHREEQC undertakes solution reactions. At each timestep, the model updates exchangeable base cation 

pools and soil solutions in each soil layer, along with groundwater and stream solutions. 

DayCent 5 output includes daily evapotranspiration; soilwater content; outflow; inorganic and 

organic C, N, P, and S stream fluxes; C, N, P, and S contents in soil and plant pools; net primary 

production (NPP); nutrient uptake; trace gas flux; and heterotrophic respiration. In addition to standard 

DayCent 5 outputs, at each daily timestep the model writes the solution chemistry for soil layers, 

groundwater, and stream. 

A.3.1.5. SPARROW 

SPAtially Referenced Regressions on Watersheds (SPARROW) is a hybrid statistical/deterministic 

model used to estimate pollutant sources and contaminant transport in surface waters. SPARROW can be 

used to estimate pollutant loading to downstream receiving waters for a number of water quality 

constituents. The model as constructed for evaluating N export to estuaries will be presented here. 

SPARROW was first described by Smith et al. (1997) as a water quality model designed to reduce 

problems with interpreting watershed data as a result of sparse sampling, network bias, and basin heterogeneity. SPARROW combines regression techniques and process information regarding contaminant 

transport and retention in watershed and riverine systems. Literature values for watershed retention rates 

are used; in-stream retention of N is estimated by a first-order decay function (Smith et al., 1997). 

Others have developed similar regression models relating in-stream water quality measurements to 

watershed nutrient sources and basin attributes (Howarth et al., 1996; Jaworski et al., 1997; Mueller et al., 

1997). These simple correlative models assume that contaminate sources and sinks are homogenously 

distributed and do not make a distinction between watershed and in-stream loss processes. SPARROW is 

distinct from these methods by incorporating spatial representation of basin attributes in the model. Model 

correlations between basin attributes and water quality measurements are strengthened by incorporating 

these spatial references (Alexander et al., 2001; Smith et al., 1997). Spatially referenced basin attributes 

include land use, point and non-point N sources, temperature, soil permeability, and stream density, 

among others. Figure A-8 shows the mathematical form of the SPARROW model (Preston and Brakebill, 

1999). 

 A-35
Source: Preston and Brakebill (1999). 

Figure A-8. Mathematical form of the SPARROW model. 

Smith et al. (1997) provided an example of SPARROW model development for application to the 

conterminous U.S. Their exploratory model included five N sources and eight land surface characteristics 

as potential factors that deliver N from land to water. In-stream decay coefficients for three stream size 

classes were also tested for significance (Table A-4). 

The final model resulted in the inclusion of each of the five N sources and three (temperature, soil 

permeability, and stream density) of the eight land to water delivery factors. Parameter selection was 

primarily based on statistical significance. Further discussion regarding the exclusion of precipitation and 

irrigated land, both of which were determined to be significant, can be found in Smith et al. (1997). 

Parameter estimates are evaluated for robustness through the use of bootstrap analysis. 

The bootstrap procedure involves randomly selecting, with replacement, M monitored loads and 

associated predictor variables from among the observations in the data set (M is the number of monitored 

reaches in the reach network). Where a sampled observation has an upstream monitored load as one of its 

predictors, the monitored value is used, regardless of whether the upstream station appears in the 

bootstrap sample. Coefficient values are estimated from the bootstrap sample. The bootstrap process is 

repeated 200 times, resulting in 200 estimates of each coefficient. From these estimates, the mean 

coefficient value (called the bootstrap estimate), minimum confidence interval, and probability that the 

estimated coefficient has the wrong sign are determined (Smith et al., 1997). 

Spatial referencing in the model occurs in two ways: land surface polygons are mapped in 

conjunction with nonpoint contaminant sources and the land-water delivery variables (temperature, soil 

permeability, stream density, etc.); and the stream reach network is mapped along with point sources, 

channel transport characteristics, and measured transport rates. The positive impacts of this spatial 

referencing can be quantified by eliminating the channel decay coefficients from the model and creating a 

new model with only the contaminant sources and land-water delivery variables in the original model 

(Smith et al., 1997). Removing this spatial reference provided by the reach network results in a model 

with significantly higher mean squared error and lower predictive capacity (Table A-5). 

 A-36
Table A-4. Parameter estimates, probability levels, and regression results of parametric and bootstrap 

regressions of total nitrogen at 414 national stream quality accounting network stations on 

basin attributes, for the Chesapeake Bay total nitrogen SPARROW model. 

Exploratory Model Final Model 

Model 

Parameters 

Coefficient 

Unitsa Parametric 

Coefficient P Parametric

Coefficient

Parametric 

P 

Bootstrap

Coefficient

Lower 

90% CIb

Upper 

90% CIb

Bootstrap 

p

NITROGEN SOURCE Β 

Point sources Dimensionless 0.4112 0.0004 0.3464 0.0049 0.4401 0.0864 0.8173 <0.005 

Fertilizer 

application 

Dimensionless 2.798 0.0154 1.278 0.0022 1.433 0.6149 2.373 <0.005 

Livestock waste 

production 

Dimensionless 1.340 0.1553 0.9723 0.0629 1.058 0.0859 1.919 0.005 

Atmospheric 

deposition 

Dimensionless 3.334 0.2513 6.465 0.0033 6.555 3.270 9.323 <0.005 

Nonagricultural 

land 

kg N/ha/yr 38.49 0.0154 14.67 0.0005 16.65 7.130 29.89 <0.005 

LAND TO WATER DELIVERY Α

Temperature oF−1 0.0228 0.0001 0.0196 0.0001 0.0198 0.0117 0.0259 <0.005 

Slopec % 0.2034 0.2187

Soil permeability h/cm 0.0295 0.0022 0.0442 0.0001 0.0447 0.0334 0.0572 <0.005 

Stream densityc km–1 0.0205 0.0124 0.0215 0.0095 0.0243 -0.0003 0.0450 0.025 

Wetlandd Dimensionless 0.7177 0.2962

Irrigated lande Dimensionless 1.101 0.0001

Precipitationf cm 38.52 0.0057

Irrigated water 

useg 

cm 0.0772 0.3117 

 A-37
Exploratory Model Final Model 

Model 

Parameters 

Coefficient 

Unitsa Parametric 

Coefficient P Parametric Parametric 

P 

Bootstrap Lower 

90% CIb

Upper 

90% CIb

Bootstrap 

Coefficient Coefficient p

IN-STREAM DECAYH Δ

δ 1 (Q <28.3 

m3/s) 

D–1 0.2917 0.0001 0.3758 0.0001 0.3842 0.2981 0.4768 <0.005 

δ 2 (28.3 m3/s <Q 

<283 m3 s) 

D–1 0.1099 0.0001 0.1233 0.0001 0.1227 0.0621 0.1710 <0.005 

δ 3 (Q >283 m3/s) D–1 0.0352 0.1794 0.0406 0.1321 0.0408 0.0176 0.0685 0.015 

R2 0.8822 0.8743 

Mean square error 0.4310 0.4543 

Number of 

observations 

414 414 

aDependent variable (nitrogen transport) in kilograms per year. bMinimum bootstrap confidence intervals (CI). c

Variable enters the model in reciprocal form. dRatio of wetland area to 

total land area. eRatio of irrigated land area to total cropland area. f

Product of reciprocal precipitation and one minus the ratio of irrigated land area to total cropland area. gRatio of 

irrigated land area to irrigated water use. hDecay coefficients fit separately for stream reaches with mean streamflow (Q) corresponding to indicated intervals. The streamflow interval 

breakpoints of 28.3 and 282 m3/s correspond to 1000 and 10,000 ft3/s, respectively . Source: Smith et al. (1997). 

SPARROW has also been applied to spatially identify N sources at the scale of the Chesapeake Bay 

watershed (Preston and Brakebill, 1999). A similar set of N sources, land-to-water delivery parameters, 

and in-stream loss rates to those used in Smith et al. (1997) were considered for this model (Preston and 

Brakebill, 1999). Only estimates for parameters that were used in the final model are given in Table A-6. 

The final model included five N sources, one land-to-water deliver parameter (soil permeability), 

and four in-stream loss rates (including reservoir retention). Comparisons between predicted and observed 

N loading provided an r2

 value of 0.961 (Preston and Brakebill, 1999). Because the data that are used in 

SPARROW are spatially referenced, model results can be mapped. 

Table A-5. Effect of spatial referencing on measures of regression model performance for predicting 

total N flux using the sparrow model. 

Model Components Mean Square Error R2

Includes full spatial referencing (SPARROW)a 0.4544 0.8743 

Excludes in-stream decay and reservoir retention 0.9659b 0.7307 

 A-38
Table A-6. Parameter estimates, probability levels, and regression results for the Chesapeake Bay total N 

SPARROW model 

Explanatory Variables Parameter Estimates Probability Level 

Nitrogen sources β

Point Sources 1.496 <0.005 

Urban area (acres) 7.008 0.010 

Fertilizer application (lb/yr) 0.2790 <0.005 

Livestock waste production (lb/yr) 0.3361 <0.005 

Atmospheric deposition (lb/yr) 1.024 <0.005 

Land-to-water delivery α

Temperature (ΕF) – – 

Precipitation (in) – – 

Avg slope (%) – – 

Soil permeability (in/h) 0.0754 0.095 

Stream density (l/mi) – – 

Wetland (%) – – 

Instream loss (days)a δ

T1 (Q #200 ft3/s) 0.7595 <0.005 

T2 (200 ft3/s < Q #1,000 ft3/s) 0.3021 <0.005 

T3 (Q > 1,000 ft3/s) 0.0669 <0.005 

Tu (reservoir retention) 0.4145 <0.005 

R-squared 0.961 

Mean square error 0.1669 

Number of observations 79 

aT, travel time 

Q, stream discharge 

lb/yr, pounds per yr 

°F, degrees Fahrenheit 

in/h, inches per hour 

ft3/s, cubic feet per second 

–, value not statistically significant 

A.3.1.6. WATERSN 

The Watershed Assessment Tool for Evaluating Reduction Strategies for Nitrogen (WATERSN) 

model is a steady-state numerical N budgeting model that estimates the amount of N exported to rivers 

and estuaries from forest, agricultural and urban land uses. The model is intended to provide an 

understanding of the relative contribution of N export from these land uses to estuaries, and to evaluate N 

 A-39
export reduction strategies that are specific to each land use type (Driscoll et al., 2007a). Figure A-9 

shows a conceptual diagram of the N budgeting system used in WATERSN. 

A detailed description of the original model calculations is provided in Castro et al. (2001). 

Subsequent model applications (Castro et al., 2003; Castro and Driscoll, 2002; Driscoll et al., 2003a; 

Whitall et al., 2004) have developed modifications to the approach originally described in Castro et al. 

(2001). 

WATERSN uses calculations described in Jordan and Weller (Jordan and Weller, 1996) to estimate 

N inputs to the watershed/estuary system. Estimated anthropogenic sources of N inputs to the modeled 

watershed/estuary system include: crop and lawn fertilizer application; biotic N fixation by leguminous 

crops and pastures; atmospheric deposition of wet and dry inorganic N (NH4

+

, NO3

−

) net N import of food 

for human consumption; and net N import of feed for livestock (Castro and Driscoll, 2002). 

Agricultural Areas 

N available for water-borne export to estuaries from agricultural lands is determined as the 

difference between N inputs and outputs (Castro et al., 2001). Modeled N inputs to agricultural lands 

consist of wet and dry atmospheric NH4

+

 and NO3

−

 deposition, N fertilization, biotic N fixation, and 

livestock waste (Castro and Driscoll, 2002). Wet and dry deposition are derived from NADP and 

CASTNet data. Average annual wet deposition rates of NH4

+

 and NO3

−

 are taken from NADP sites in or 

near the study watersheds. Dry deposition of NH4

+

 and NO3

−

 is calculated as an average of all CASTNet 

sites nearest to the study watersheds. WATERSN assumes that dry deposition of both NH4

+

 and NO3

−

 to 

the estuary surface is 25% less than dry deposition to the watershed (Castro and Driscoll, 2002). Meyers 

et al. (2001) described the uncertainty of estimates of wet and dry deposition and considered it to be no 

less than a factor of 2. Estimates of N fertilization are taken from agricultural census data. WATERSN 

assumes that all fertilizer sold in a county is applied in that county. This is considered to be the most 

certain N input to the model (±25%) (Castro and Driscoll, 2002). WATERSN estimates both nonsymbiotic and symbiotic N fixation for crops, pastures, hay fields and upland forests. Non-symbiotic rates 

were taken from literature values for crops, orchards, upland forests, and non-wooded pastures 

(Hendrickson, 1990; Stevenson, 1982; Woodmansee, 1978). Symbiotic rates of N fixation are based on 

type of legume, crop N harvest, N in unharvested portions of crops, soil N availability, and fertilization 

rate. These estimates are less certain than for N fertilization, but are noted as being a relatively minor N 

source in most of the study watersheds. Livestock waste was calculated as the difference between 

livestock consumption of N in feed and production of N in meat, milk, and eggs for human consumption 

(Jordan and Weller, 1996). 

 A-40
Source: Castro et al. (2003). 

Figure A-9. Schematic diagram of the WATERSN approach to estimate the contribution made by different 

N sources to the total N inputs an estuary. 

N outputs from agricultural land include crop harvest, pasture grazing, volatilization of NH3 and 

denitrification. Data regarding crop harvest are obtained from agricultural census. N removed through 

crop harvest is estimated by multiplying the crop harvest by the percent N in each crop. Estimates for 

grazing are based on sheep, cattle, and horse populations (USDA online database in Castro and Driscoll, 

2002, their dietary N requirements, and proportion of dietary N obtained from grazing (Jordan and Weller, 

1996). NH3 volatilization is assumed to be 10% of the N input from fertilizer and atmospheric deposition, 

and 20% of livestock manure inputs (Schlesinger and Hartley, 1992). Denitrification rates were originally 

estimated as 10 to 30% of the N inputs from fertilizer and atmospheric deposition and 20% of livestock N 

waste. Subsequent applications of WATERSN (Castro et al., 2003) modified denitrification rates from 

agricultural lands to vary with the mean watershed temperature and are based on a denitrification activity 

Q10 value of 2 (Maag et al., 1997; Stanford et al., 1975). A Q10 value of 2 suggests that the denitrification 

rate used by the model will change by a factor of 2 for every 10 degree change in temperature based on a 

direct relationship between temperature and denitrification. 

Urban Areas 

N inputs to urban areas include atmospheric and non-atmospheric sources. The total atmospheric N 

deposition input to urban areas is taken as the total (wet + dry) inorganic (NO3

−

 and NH4

+

) N deposition 

rate to the watershed multiplied by the total urban area in the watershed. Non-atmospheric sources include 

point sources (primarily waste water treatment plants) and non-point sources (septic systems and 

pervious/impervious surface runoff) of N in urban areas (Castro et al., 2003). 

N outputs from urban areas include waste water treatment plant effluent, septic system leachate, 

and total N runoff from pervious and impervious lands. Measured total N data are used to calculate N 

export for wastewater treatment plants that have available data. A strong regression relationship between 

measured total N discharged from wastewater treatment plants and human populations that use 

 A-41
wastewater treatment facilities is used to estimate total N discharges from wastewater treatment plants 

that do not have total N monitoring data available. Septic system output is determined by multiplying 

watershed specific human per capita N excretion rates by the human population of the watershed. 

WASTERSN assumes that 75% of this N is exported to the estuary (Castro and Driscoll, 2002). The soil 

water assessment tool (SWAT) is used to estimate non-point source non-atmospheric total N runoff from 

pervious and impervious urban lands. SWAT is a distributed parameter, continuous time model applicable 

at the watershed scale. Required inputs to SWAT include climatic variables, soil properties, elevation, 

vegetation information, and land use. SWAT is designed to predict land use and land management impacts 

on water, sediment, and agricultural yields in large watersheds (Castro and Driscoll, 2002). The model 

assumes that 75% of atmospheric N inputs to urban areas is exported to the estuary (Fisher and 

Oppenheimer, 1991). Alternatively, this N export term can be modified. 

Upland Forests 

N inputs to forests are assumed to be in the form of atmospheric deposition and non-symbiotic N 

fixation. Outputs from forests are estimated with a non-linear regression relationship between wet 

deposition of inorganic N and stream water export of dissolved inorganic nitrogen (DIN) developed using 

results from a multitude of forest watershed studies. Exported dissolved organic nitrogen (DON) was 

assumed to be equal to 50% of the inorganic N export (Castro and Driscoll, 2002). 

Watershed and In-Stream N Retention 

Model validation efforts using measured N fluxes from the USGS National Stream Quality 

Accounting Network (NASQAN) have shown that WATERSN tends to overestimate N export from 

watersheds to estuaries (Castro and Driscoll, 2002). These differences are not unexpected since 

WATERSN does not account for watershed and in-stream N sinks. Attempts have been made to improve 

flux estimates by accounting for watershed and in-stream N retention (Castro and Driscoll, 2002; Castro 

et al., 2001; 2003). A summary of the N retention rates applied to WATERSN in these studies is given in 

Table A-7. 

Castro and Driscoll (2002) assumed that 30% of the total N that entered rivers above the fall line 

was lost during transport to the fall line and that inputs that enter the river below the fall line were not 

attenuated because of the relatively short travel times to the estuary (See Table A-7). This 30% in-stream 

N retention value represents the median retention value obtained in previous studies of northeastern U.S. 

rivers (Castro et al., 2001) and falls within the range of retention values estimated by Howarth et al. 

(1996) and Alexander et al. (2000). Castro and Driscoll (2002) also incorporated watershed N retention 

fractions specific to individual land uses. They assumed that 60% of the excess N from agricultural land 

and septic systems was lost (retained within the watershed) due to watershed processes. Support for this 

value of N retention was given by several reports of riparian N removal rates from agricultural land, 

ranging from about 50 to 90% (Jacobs and Gilliam, 1985; Jordan et al., 1993; Lowrance et al., 1983; 

Peterjohn and Correll, 1984). After incorporating these assumptions, predicted fluxes closely matched 

(r2

 = 0.909) measured fluxes. 

Table A-7. Summary of N retention rates used in recent WATERSN studies. 

Study Retention Type % N Retention 

Castro et al. (2001) In Stream* 30% 

Agriculture 50% 

Castro and Driscoll (2002) In Stream* 30% 

 A-42
Agriculture 60% 

Castro et al. (2003) Agriculture 40% 

Septic System 40% 

In Stream Adjusted until predicted N flux matched observed fluxes 

*In-stream N retention was only applied to lengths of river located above the “fall line.” Below fall line N inputs to streams were assumed to not be attenuated due to the relatively short 

travel time to the estuary. The “fall line” is defined as the boundary between the Piedmont and Coastal Plain physiographic provinces in the eastern U.S. 

Source: Castro and Driscoll (2002) 

Driscoll et al. (2003a) applied WATERSN to investigate anthropogenic N loading to estuaries in the 

northeastern U.S. The objectives of the study were to apply WATERSN to quantify the inputs of Nr to the 

region (Figure A-10), discuss the ecological effects of regional elevated anthropogenic Nr inputs, and 

evaluate management options aimed at mitigating the effects of these elevated anthropogenic N inputs. 

Modeled N reduction scenarios included reductions atmospheric N emissions, increased N removal 

efficiencies of wastewater treatment plants, offshore pumping of wastewater, reductions in agricultural N 

runoff to surface waters, and an integrated management scenario consisting of a combination of N 

reductions from multiple sources. Other studies have applied WATERSN to address similar issues related 

to N loading to estuaries in other regions of the U.S. (Castro et al., 2003; Whitall et al., 2004; Whitall and 

Bricker, 2006). 

Source: Driscoll et al. (2003a). 

Figure A-10. WATERSN model estimates of anthropogenic N inputs to the estuaries of the northeastern 

U.S., in kilograms per hectare per year. 

 A-43
A.3.2. Additional Effects Models Used Widely in Europe 

The models of the effects of S and N deposition described below have been used primarily in 

Europe. These descriptions are derived in part from the UNECE Convention of Long-Range 

Transboundary Air Pollution Modelling and Mapping manual (Posch et al., 2003). 

A.3.2.1.

A.3.2.2.

The Very Simple Dynamic Model 

The Very Simple Dynamic (VSD) soil acidification model is frequently used in Europe to simulate 

acidification effects in soils when observed data are sparse. It only includes weathering, cation exchange, 

N immobilization processes, and a mass balance for cations, sulfur and N. It resembles the model 

presented by Reuss (1980) which, however, did not consider N processes. In the VSD model, the various 

ecosystem processes have been limited to a few key processes. Processes that are not taken into account 

include canopy interactions; nutrient cycling processes; N fixation and NH4 adsorption; SO4

2−

transformations (adsorption, uptake, immobilization, and reduction); formation and protonation of organic 

anions; and complexation of Al. 

The VSD model consists of a set of mass balance equations, describing the soil input-output 

relationships, and a set of equations describing the rate-limited and equilibrium soil processes. The soil 

solution chemistry in VSD depends solely on the net element input from the atmosphere (deposition 

minus net uptake minus net immobilization) and the geochemical interaction in the soil (CO2 equilibria, 

weathering of carbonates and silicates, and cation exchange). Soil interactions are described by simple 

rate-limited (zero-order) reactions (e.g., uptake and silicate weathering) or by equilibrium reactions (e.g., 

cation exchange). It models the exchange of Al, H, and Ca + Mg + K with Gaines-Thomas or Gapon 

equations. 

Solute transport in VSD is described by assuming complete mixing of the element input within one 

homogeneous soil compartment with a constant density and a fixed depth. Since VSD is a single layer soil 

model neglecting vertical heterogeneity, it predicts the concentration of the soil water leaving this layer 

(mostly the rootzone). The annual water flux percolating from this layer is taken as being equal to the 

annual precipitation excess. The time step of the model is one year, and therefore seasonal variations are 

not considered. A detailed description of the VSD model can be found in Posch and Reinds (2003). 

SMART 

The Simulation Model for Acidification’s Regional Trends (SMART) model is similar to the VSD 

model, but somewhat extended. It is described in De Vries et al. (1989) and Posch et al. (1993). As with 

the VSD model, the SMART model consists of a set of mass balance equations, describing soil inputoutput relationships, and a set of equations describing the rate-limited and equilibrium soil processes. It 

includes most of the assumptions and simplifications given for the VSD model; and justifications for 

them can be found in De Vries et al. (1989). 

SMART models the exchange of Al, H, and divalent base cations using Gaines Thomas equations. 

Additionally, SO4

2−

 adsorption is modeled using a Langmuir equation (as in MAGIC) and organic acids 

can be described as mono-, di-, or tri-protic. Furthermore, it does include a balance for carbonate and Al, 

thus allowing application to a range of site conditions, from calcareous soils to completely acidified soils 

that do not have an Al buffer left. Recently, a description of the complexation of aluminum with organic 

acids has been included. The SMART model has been developed with regional applications in mind, and 

an early example of an application to Europe can be found in De Vries et al. (1994). 

 A-44
A.3.2.3. SAFE 

The Soil Acidification in Forest Ecosystems (SAFE) model has been developed at the University of 

Lund (Warfvinge et al., 1993) and a recent description of the model can be found in Alveteg and Sverdrup 

(2002). The main differences between the SMART and MAGIC models are: (a) weathering of base 

cations is not a model input, but it is modeled with the PROFILE (sub-)model, using soil mineralogy as 

input (Warfvinge and Sverdrup, 1992) SAFE is oriented to soil profiles in which water is assumed to 

move vertically through several soil layers (usually 4); and (c) Cation exchange between Al, H, and 

(divalent) base cations is modeled with Gapon exchange reactions, and the exchange between soil matrix 

and the soil solution is diffusion-limited. 

The standard version of SAFE does not include SO4

2−

 adsorption although a version, in which 

SO4

2−

 adsorption is dependent on SO4

2−

 concentration and pH has recently been developed (Martinson 

et al., 2003). The SAFE model has been applied to many sites and more recently also regional 

applications have been carried out for Sweden (Alveteg and Sverdrup, 2002) and Switzerland (Kurz et al., 

1998). 

A.3.3. Other Models 

There are scores of models that can be useful in the context of developing a better understanding of 

the ecological effects of atmospheric S and N deposition. The preceding sections have summarized a 

relatively small number of models that are most commonly used for this purpose in the U.S. and Europe, 

in particular those that contribute to substantive conclusions presented in the ISA. There are many other 

models that are not covered in the discussion presented in this Annex. Several are highlighted in Table A8. 

Table A-8. Some examples of models that could contribute to development of a better understanding of 

the ecological efforts of atmospheric S and N deposition, but that are not explicitly addressed 

in this annex. 

Model Name Type1 Support2 Reference3 Notes 

QUAL2K A S 1 QUAL2K is one dimensional river and stream water quality 

model. QUAL2K assumes: that he channel is well-mixed 

vertically and laterally; steady state hydraulics; diurnal waterquality kinetics. QUAL2K addresses point and non-point loads, 

BOD/DO, non-living particulate organic matter (detritus); 

denitrification; sediment-water interactions; bottom algae; pH 

(both alkalinity and total inorganic carbon). 

WASP7 Water Quality Analysis 

Simulation Program 

A S 2 WASP is a dynamic compartment-modeling program for aquatic 

systems, including both the water column and the underlying 

benthos. WASP allows the user to investigate 1, 2, and 3 

dimensional systems, and a variety of pollutant types. The time 

varying processes of advection, dispersion, point and diffuse 

mass loading and boundary exchange are represented in the 

model. WASP also can be linked with hydrodynamic and 

sediment transport models that can provide flows, depths 

velocities, temperature, salinity and sediment fluxes. 

 A-45
Model Name Type1 Support2 Reference3 Notes 

CE-QUALRIV1; CEQUAL-R1; 

CE-QUAL-W2; 

CE-QUAL-ICM 

Water quality models 

(river, reservoir, and 

estuary/ coastal) 

supported by USACE 

A N 3 CE-QUAL-R1 is a one-dimensional, vertical reservoir model and 

CE-QUAL-W2 is a two-dimensional (vertical and longitudinal), 

laterally averaged, hydrodynamic and water quality model. 

These two models are widely used by the Corps of Engineers, 

other federal and state agencies, the private sector, and 

agencies in other countries. CE-QUAL-RIV1 was developed for 

highly unsteady flow conditions, such as storm water flows and 

streams below peaking hydropower dams. CE-QUAL-ICM run in 

a 2D mode. This approach has been used for large, shallow 

waterways, harbors, and embayments. 

RCA Row Column AESOP A N 4 RCA evaluates the fate and transport of conventional and toxic 

pollutants in surface waterbodies in one, two, or three 

dimensions. RCA has been linked to various hydrodynamic 

models. Subroutines have been developed to model coliforms, 

pathogens, BOD/DO, simple and advanced eutrophication, 

wetland systems, and toxic contaminants. A sediment nutrient 

flux subroutine permits the coupling of the water column and 

sediment bed. 

WARMS Waterfowl Acidification 

Response Modeling 

System 

A N McNicol et al. 

(1995), 

McNicol 

(2002) 

WARMS includes an acidification model linked to fish and 

waterfowl models. WARMS uses pH, area, dissolved organic 

carbon, total P, and presence of fish to estimate preacidification, 

present and eventual steady-state values for pH, fish presence 

and waterfowl breeding parameters under proposed SO2 

emission scenarios. 

GT-MEL Georgia Tech 

hydrologic model and 

the Multiple Element 

Limitation model 

I N 5 GT-MEL is a spatially distributed, process-based ecohydrology 

model that links a land surface hydrology model with a terrestrial 

biogeochemistry model. GT-MEL differs from other available 

ecohydrology models in its simplicity, flexibility, and theoretical 

foundation. The coupled GT-MEL simulates the cycling and 

transport of water and nutrients (C, N and P) within hillslopes 

and watersheds. The model runs on a daily time step and can be 

applied to user-defined landscape units that may vary in shape 

and size (m2 to km2). Thus, GT-MEL can provide detailed spatial 

and temporal information on nutrient acquisition and turnover in 

plants and soils, and terrestrial flow pathways and discharge of 

water and nutrients to surface waters. The same set of model 

equations applies to any terrestrial ecosystem – agricultural 

crops, forests, grasslands, wetlands, tundra, etc. GT-MEL 

simulates the effects of multiple interacting stressors, including 

changes in land use, land cover, climate, and atmospheric CO2

and N deposition. 

ILWAS Integrated LakeWatershed Acidification 

Study 

I N Gherini et al. 

(1985) 

ILWAS was developed to predict changes in surface water 

acidity given changes in the acidity of precipitation and dry 

deposition. The model routes precipitation through the forest 

canopy, soil horizons, streams and lakes using mass balance 

concepts and equations which relate flow to hydraulic gradients. 

The physical-chemical processes which change the acid-base 

characteristics of the water are simulated by rate (kinetic) and 

equilibrium expressions and include mass transfers between 

gas, liquid and solid phases. 

 A-46
Model Name Type1 Support2 Reference3 Notes 

THMB/IBIS I N 6, 7 THMB is a mechanistic simulation model of large river systems 

that has been used recently in combination with a dynamic 

terrestrial ecosystem model IBIS to quantify nitrate flux in the 

Mississippi River Basin. The coupled models simulate timevarying flow and storage of water and N in rivers, wetlands, and 

reservoirs, based on major source inputs, subsurface drainage 

and N leaching, topography, and precipitation and evaporation. 

Evaluations of the model in the Mississippi basin indicated that 

the model accurately simulated inter-annual variability in the 

water and N budget from 1960 to 1994. 

BIOME-BGC Biome-BGC is a multibiome generalization of 

FOREST-BGC 

T N 8 Biome-BGC is a computer program that estimates fluxes and 

storage of energy, water, carbon, and N for the vegetation and 

soil components of terrestrial ecosystems. The primary model 

purpose is to study global and regional interactions between 

climate, disturbance, and biogeochemical cycles. 

DNDC Denitrificationdecomposition model T N 9 DNDC was initially developed to quantifying nitrous oxide (N2O) 

emissions from agricultural soils in the U.S. The capability of the 

model to simulate soil biogeochemistry also allows DNDC to 

model emissions from other ecosystems through linkages with 

vegetation models; the model can be applied from field site to 

regional scales. The core of DNDC is a soil biogeochemistry 

model. 

EPIC Agricultural dynamic 

simulation model 

T N 11 EPIC is a widely used dynamic simulation model that describes 

the influence of agricultural management on crop productivity 

and erosion. The model has been used in studies of climate 

change, agricultural management and policy, and water-quality. 

EPIC simulates N cycling processes in soils-including 

mineralization, nitrification, immobilization, NH3 volatilization and 

denitrification, runoff and subsurface leaching based on physical 

principles and parameter values derived from extensive model 

testing and specific field validation. 

GLEAMS Groundwater Loading 

Effects of Agricultural 

Management Systems 

T N 12 GLEAMS was developed from both EPIC and CREAMS and 

employs a more explicit description of soil water content. In 

GLEAMS, the concentration of nitrate-N removed via 

denitrification is a function of the factors describing the soil water 

content, the soil temperature, and the organic C content. Under 

this formulation, denitrification only occurs if the soil water 

content is greater than a parameter related to the soil water 

content at field capacity and saturation. The fraction of soil 

nitrate- N lost to denitrification increases quickly as soil water 

content increases beyond the field capacity. The EPIC and 

GLEAMS method of simulating denitrification neglects 

denitrification that may occur in anaerobic micro-zones when the 

soil is not at field capacity or saturation. 

Hole-in-the-pipe Hole-in-the-pipe T N Davidson 

et al. (2000) 

The Hole-in-Pipe model relates the emissions of nitrous oxides 

to common soil processes. It regulates soil emissions of NO and 

N2O at two levels: 1st, the rate of N cycling through ecosystems, 

which is symbolized by the amount of N flowing through the 

pipes, affects total emissions of NO and N2O; 2nd, soil water 

content and perhaps other factors affect the ratio of N2O:NO 

emissions, symbolized by the relative sizes of the holes through 

which nitric oxide and nitrous oxide "leak.” Soil water content is 

so important because it controls the transport of O2 into soil and 

the transport of NO, N2O, and N2

 A-47
Model Name Type1 Support2 Reference3 Notes 

MERLIN Model of Ecosystem 

Retention and Loss of 

Inorganic Nitrogen 

T N Cosby et al. 

(1997), 

Kjonaas and 

Wright (1998) 

MERLIN is a catchment-scale mass-balance model of linked 

carbon and N cycling in ecosystems for simulating leaching 

losses of inorganic N. It considers linked biotic and abiotic 

processes affecting the cycling and storage of N. The model is 

aggregated in space and time and contains compartments 

intended to be observable and/or interpretable at the plot or 

catchment scale. The structure of the model includes the 

inorganic soil, a plant compartment, and two soil organic 

compartments. Fluxes in and out of the ecosystem and between 

compartments are regulated by atmospheric deposition, 

hydrological discharge, plant uptake, litter production, wood 

production, microbial immobilization, mineralization, nitrification, 

and denitrification. N fluxes are controlled by carbon productivity, 

the C:N ratios of organic compartments and inorganic N in soil 

solution. Inputs include time series, constants, rates, source 

terms, and soil characteristics (Cosby et al., 1997). 

NLM Waquoit Bay Nitrogen 

Loading Model 

T N RTI 

International 

(2001) 

The Waquoit Bay Nitrogen Loading model estimates inputs from 

different N sources to defined land use categories and then 

estimates losses of N in various compartments of the watershed 

ecosystem, including the groundwater. This empirical N loading 

model produces long-term average output. It is not currently 

endorsed by a federal agency but has been published in peerreviewed journals. Most applications of the model have focused 

on the Cape Cod area of Massachusetts. The empirical data for 

this model are specific to that area and the model simulates N 

transport exclusively in the subsurface (i.e., overland transport is 

not considered). 

Simple Mass 

Balance 

Method/Steady 

State Mass 

Balance 

“Mass balance 

approach” 

T N Bhattacharya 

et al. (2004), 

Likens et al. 

(1996), 

Rodriguez 

and Macias 

(2006) 

Examples of two mass balance approaches are: 

Simple Mass Balance: This model is based on a balance of 

inputs and outputs of N according to the equation: 

Ndep + Nfix = Ni + Nu + Nad + Nde + Nfire + Neros + Nvol + Nle where 

the subscripts denote: dep (deposition); fix (fixation); i 

(immobilization); u (uptake); ad (adsorption); de (denitrification); 

fire (N loss during combustion); eros (erosion); vol (volatilization); 

le (leaching). 

Steady State Mass Balance: This method is the most commonly 

used method for analysis of critical loads of acid deposition. Its 

basic principle is based on identifying the long-term average 

sources of acidity and alkalinity in order to determine the 

maximum acid input that will balance the system at a 

biogeochemical safe-limit. Several assumptions have been made 

in the steady state calculations. First, it is assumed that ion 

exchange is at steady state and there is no net change in base 

saturation or no net transfer of ANC from soil solution to the ion 

exchange matrix. It is assumed that for N there is no net 

denitrification, adsorption or desorption and the N cycle is at 

steady state. Sulfate is also assumed to be at steady state: no 

sulfide oxidation, sulfate uptake, sulfate permanent fixation or 

sulfate reduction are significant. Simple hydrology is assumed 

where there is straight infiltration through the soil profile. 

 A-48
Model Name Type1 Support2 Reference3 Notes 

HSPF/LSPC Hydrological 

Simulation Program - 

FORTRAN 

T* S 12, 13 HSPF simulates watershed hydrology and water quality for 

conventional and toxic pollutants. HSPF incorporates watershedscale ARM and NPS models into a basin-scale analysis 

framework that includes fate and transport in stream channels. It 

is a comprehensive model of watershed hydrology and water 

quality that allows the integrated simulation of land and soil 

contaminant runoff processes with In-stream hydraulic and 

sediment-chemical interactions. LSPC is a simplified version of 

HSPF. Although LSPC was designed to provide a less data 

intensive alternative to HSPF for modeling very large scale 

watersheds, it can also be used to model smaller, more detailed 

watersheds. The primary disadvantage of this simplified version 

of HSPF is that the developers eliminated the atmospheric 

deposition routines found in HSPF. For each model run, it 

automatically generates comprehensive text-file output by 

subwatershed for all land-layers, reaches, and simulated 

modules, which can be expressed on hourly or daily intervals. 

Output from LSPC has been linked to other model applications 

such as EFDC, WASP, and CE-QUAL-W2. 

PLOAD Pollutant Loading 

Model 

T* S 14 PLOAD is part of U.S. EPA’s BASINS (Better Assessment 

Science Integrating Point and Nonpoint Sources) program and 

estimates nonpoint pollution sources on an annual basis. PLOAD 

can be combined with geographic information system (GIS)–

based data coverages to rapidly estimate N loading to the bay 

using pass-through rates based on land uses from U.S. EPA 

guidance documents, literature, or other studies. This model is 

not precipitation driven; it does it include N speciation. However, 

the model does capture differences in N transport for different 

land uses in the watersheds. 

SWAT Soil and Water 

Assessment Tool 

T* S van Griensven 

and Bauwens 

(2001), 15 

SWAT is a public domain river basin scale model actively 

developed and primarily supported by the USDA (and included 

within U.S. EPA BASINS framework), that quantifies the impact 

of land management practices in large, complex watersheds. 

SWAT is a physically based model that applies to all land uses 

and to include stormwater runoff in its calculations. The model 

simulates NH3, nitrate, and organic N throughout the 

waterbodies and vegetation in the modeled system. At this time 

SWAT only accepts the nitrate concentration in the rain as the N 

atmospheric component. 

WARMF Watershed Analysis 

Risk Management 

Framework 

T* S 16 WARMF includes a GIS-based watershed model that calculates 

daily runoff, shallow groundwater flow, hydrology and water 

quality of a river basin. A river basin is divided into a network of 

land catchments (including canopy and soil layers), stream 

segments, and lake layers for hydrologic and water quality 

simulations. Inputs include meteorology, air quality, point source, 

reservoir release, and flow diversion data. WARMF also includes 

two watershed approach modules for Consensus building and 

TMDL calculation. 

 A-49
Model Name Type1 Support2 Reference3 Notes 

DRAINMOD T* N 17 DRAINMOD quantifies N losses and transport from agricultural 

lands with shallow water tables where artificial drainage systems 

are used. Watershed-scale versions of DRAINMOD have been 

developed and evaluated based on data collected on a NC 

coastal plain. DRAINMOD is based on water balances in the soil 

and at the soil surface. It uses functional methods to quantify 

infiltration, subsurface drainage, surface drainage, 

evapotranspiration, seepage, freezing, thawing, snowmelt, and 

seepage. The model predicts the water table depth and soil 

water contents above the water table, drainage rates and the 

other hydrologic components on an hourly and daily basis for 

long periods of hydrologic record. Hydrologic predictions of the 

model have been tested and found to be reliable for a wide 

range of soil, crop, and climatological conditions. 

INCA T* N Wade et al. 

(2005) 

INCA is a water and N mass balance simulation model; it 

estimates the integrated effects of point and diffuse N sources on 

stream nitrate and ammonium concentrations and loads and also 

estimates the N loads related to processes in the plant/soil 

system. It has been most commonly applied to watersheds within 

the UK, but more recently has been modified for use in other 

European watersheds. INCA quantifies plant uptake of nitrate 

and ammonium, nitrification, denitrification, and mineralization 

and immobilization within each land-use type and subcatchment. 

Biogeochemical reactions are limited to the soil zone from which 

water and N are leached to deeper groundwater. 

LWWM Linked 

Watershed/Waterbody 

Model 

T* N 18 The original release of the LWWM coupled the RUNOFF Block of 

the U.S. EPA SWMM model (Version 4.21) with the U.S. EPA’s 

Water Quality Analysis Program (WASP5). All components were 

accessed via a user-friendly operating shell. The LWWM 

included a GIS interface based on Arc/INFO to automate the 

reduction of spatial data within a watershed (i.e., land use and 

soils) for input into the RUNOFF Block of SWMM. The LWWM 

included pre-processors for inputting data into the RUNOFF 

Block of SWMM, WASP5 (Eutro and Toxi), and two 

hydrodynamic models associated with WASP5 (RIVMOD and 

DYNHYD5), as well as a graphical post-processor for the review 

of output from all model components. The post-processor was 

also the means by which nonpoint source loading files from 

RUNOFF were mapped to WASP segments. 

ReNuMa Regional Nutrient 

Management Model 

T* N 19 ReNuMa is based on the Generalized Watershed Loading 

Function (GWLF) model that has been used widely for purposes 

such as TMDL development. ReNuMa improves on GWLF by 

incorporating Net Anthropogenic N Inputs (NANI) accounting 

system. The model now considers atmospheric deposition, 

fertilizer application, septic system effluents, N fixation, and 

denitrification. 

RHESS T* N Boyer et al. 

(2006b) 

RHESS has been used to explore N dynamics at the watershed 

scale. RHESSys simulates the coupled effects of C, N, and 

hydrological processes by coupling biogeochemical dynamics 

from the BIOME_BGC and the NGAS model used in DAYCENT. 

Streamflow is based on the implementation of variable sourcearea concepts based on topography, quantifying routing of water 

through the landscape from patch to patch using either a lumped 

topographic approach adapted from TOPMODEL or a distributed 

approach adapted from the DHSVM model 

 A-50
 A-51

Model Name Type1 Support2 Reference3 Notes 

1Type: A = aquatic; I = integrated aquatic/terrestrial; T = terrestrial; T* = watershed 

2 Support: S = currently supported by U.S. EPA; N = currently not supported by U.S. EPA 3Websites: 

 1: River and Stream Water Quality Model (QUAL2K); http://www.epa.gov/athens/wwqtsc/html/qual2k.html 

 2: U.S. EPA's WASP Website; http://epawasp.com/ 

 3: U.S. Army Core of Engineers Environmental Laboratory - Water Quality Models; http://el.erdc.usace.army.mil/products.cfm?Topic=model&Type=watqual 

 4: Hydroqual: Row Column AESOP (RCA) Modeling Code Description and Technical Capabilities; http://www.hydroqual.com/pdf/RCA_Desc_doc.pdf 

 5: Woods Hole Marine Lab, Ecosystems Center MEL home page http://ecosystems.mbl.edu/Research/Models/mel/welcome.htmll 

 6: IBIS (Integrated Biosphere Simulator); http://water.usgs.gov/software/hspf.html 

 7: THMB (Terrestrial Hydrology Model with Biogeochemistry) - formerly HYDRA; http://www.sage.wisc.edu/download/HYDRA/hydra.html 

 8: Biome-BGC: Terrestrial Ecosystem Process Model, Version 4.1.1; http://www.daac.ornl.gov/MODELS/guides/biome-bgc_guide.html 

 9: Denitrification Modeling Workshop: Model Summary; http://marine.rutgers.edu/BGC/RCNsite/WS1/WS1models/DNDC-2.pdf 

 10: EPIC Fact Sheet; http://www.brc.tamus.edu/epic/epfact2004.htm 

 11: GLEAMS Y2K Update Website; http://www.tifton.uga.edu/sewrl/Gleams/gleams_y2k_update.htm 

 12: U.S. EPA’s HSPF Website; http://www.epa.gov/ceampubl/swater/hspf/ 

 13: USGS’s Water Resources Applications Software: HSPF Website; http://water.usgs.gov/software/hspf.htm 

 14: U.S. EPA's Better Assessment Science Integrating Point & Nonpoint Sources (BASINS) Website; http://www.epa.gov/waterscience/basins/ 

 15: Soil & Water Assessment Tool; http://www.brc.tamus.edu/swat/ 

 16: US EPA's Watershed Analysis Risk Management Framework (WARMF) Website; http://www.epa.gov/athens/wwqtsc/html/warmf.html 

 17: DRAINMOD Download Website; http://www.bae.ncsu.edu/soil_water/drainmod/ 

 18: Linked Watershed Waterbody Model at the Southwest Florida Water Management District; http://www.swfwmd.state.fl.us/software/lwwm.htm 

 19: Regional Nutrient Management (ReNuMa) at Cornell University College of Agriculture and Life Sciences; 

 http://www.eeb.cornell.edu/biogeo/nanc/usda/renuma.htm 
Annex B. Acidification Effects 

B.1. Effects on Biogeochemical Processes along 

Acidification Pathways 

B.1.1. Atmospheric Deposition and Canopy Interaction 

Inputs of N and S in wet, dry, and occult deposition first interact with the vegetative canopy. This 

interaction can occur a few centimeters above the ground in some alpine or grassland ecosystems to over 

100 m above the ground in some forest canopies. In the canopy, deposited pollutants (especially N) can be 

taken up by the plants or by organisms that live within the canopy or on the leaf surface. Most of the 

deposited S moves as throughfall to the soil where it can be temporarily, or permanently, adsorbed on the 

soil. Sulfur that is not adsorbed on the soil moves readily into drainage water. 

Earlier reviews (i.e., Hosker and Lindberg, 1982; Taylor et al., 1988) summarized information on 

the deposition of N to vegetation surfaces and interactions between pollutant deposition and canopy and 

leaf surfaces. Deposited N that is not taken up within the canopy then falls to the ground as throughfall, 

where plants, bacteria, and fungi compete for it. This competition for deposited N has long been known to 

play an important role in determining the extent to which N deposition will stimulate plant growth and the 

degree to which added N is retained within the ecosystem (U.S. EPA, 1993a). The available surface area 

of vegetation, onto which N gasses readily diffuse, has a significant effect on the dry deposition of N 

(Heil and Bruggink, 1987). Coniferous forests tend to increase deposition rates (both dry and wet) relative 

to deciduous forests, and landscape features such as elevation, aspect, and forest edge can play an 

important role in creating high levels of variability in deposition rates in complex terrain (Weathers et al., 

2000). 

B.1.2. Interactions with Soil 

Air pollution is not the sole cause of soil acidity. High rates of soil acidification occur in lowdeposition regions of the western U.S. because of internal soil processes, including tree N uptake and 

nitrification associated with extensive N fixation, for example on sites occupied by red alder trees (Alnus 

rubra) (Johnson et al., 1991a). Acidic deposition is not a necessary condition for having acidic soils, as 

evidenced by the common occurrence of acidic soils in unpolluted forests of the northwestern U.S. and 

Alaska (Johnson et al., 1991a). 

B.1.2.1. Sulfur Retention and Release 

Soils in the U.S. that most effectively adsorb SO4

2−

 occur south of the maximum extent of 

glaciation that occurred during the most recent ice age. (Rochelle and Church, 1987). Sulfate adsorption is 

strongly pH dependent, and a decrease in soil pH resulting from acidic deposition can enhance the ability 

of soil to adsorb SO4

2−

 (Fuller et al., 1987). 

Considerable effort in the 1980s went into the computation of S budgets for watersheds and forest 

plots, to evaluate S retention and release. These budgets were subject to complications from fluxes that 

could not be measured directly, such as dry deposition and weathering, but generally indicated net S 

 B-1
retention at sites south of the line of glaciation—a result attributed to net adsorption of SO4

2−

 (Cappellato 

et al., 1998; Rochelle et al., 1987). Through the 1990s little or no decrease in SO4

2−

 concentrations 

occurred in streams below the glaciation line, despite regional decreases in atmospheric deposition of S 

(Webb et al., 2004). This lack of response has been generally attributed to the net release of adsorbed 

SO4

2−

, resulting from a shift in equilibrium between the adsorbed and solution phases under conditions of 

decreased atmospheric inputs of SO4

2−

. This interpretation is supported by a decrease in concentrations of 

adsorbed SO4

2−

 from 1982 to 1990 in a Piedmont soil in South Carolina that received decreasing levels of 

S deposition during this period (Markewitz et al., 1998). This same soil also experienced an increase in 

adsorbed SO4

2−

 from 1962 to 1972 (Markewitz et al., 1998). The only published S budget more recent 

than 1992 for an unglaciated site in the U.S. (Castro and Morgan, 2000) also suggests a net release of 

SO4

2−

. This upland Maryland watershed released 1.6 times more SO4

2−

 than measured in throughfall in 

1996–97. Additional information was obtained in the German study of Martinson et al. (2005) in which a 

“clean-roof” was used to exclude acidic deposition since 1989. Data collection enabled calibration of a 

model that predicted elevated concentrations of desorbed SO4

2−

 in soil water for at least several decades. 

Although decreased levels of deposition are most likely resulting in net SO4

2−

 desorption, limited research 

is available on sulfate desorption over time periods relevant to the time scale of decreased levels of S 

deposition (Johnson and Mitchell, 1998). 

Numerous S budgets were also compiled in the 1980’s for glaciated sites, and results generally 

indicated that inputs approximately equaled outputs on an annual basis (Rochelle et al., 1987). Little or no 

S retention at glaciated sites was attributed to relatively low SO4

2−

 adsorption capacity in soils. Balanced 

S budgets implied that decreases in atmospheric deposition of S would lead directly to decreases in SO4

2−

leaching, and the strong correlation between decreases in atmospheric deposition and decreases in SO4

2−

concentrations in surface waters is widely recognized as an indication of this direct linkage (Stoddard 

et al., 2003). However, considerable evidence also indicates that S inputs in glaciated ecosystems do not 

behave conservatively, but instead are cycled through microbial and plant biomass (Alewell and Gehre, 

1999; David et al., 1987; Likens et al., 2002). As a result, large quantities of S are stored in organic forms 

within the soil. David et al. (1987) found that annual S deposition (wet plus dry) at a site in the central 

Adirondack region of New York was about 1% of the organic S pool in the soil. Houle et al. (2001) 

estimated that annual S deposition at 11 sites in North America ranged from 1% to 13% of the organic S 

pool in soil. 

Courchesne et al. (2005) measured a downward trend in water-soluble SO4

2−

 from 1993 to 2002 in 

glaciated soils in Quebec, and attributed this response to net desorption of SO4

2−

 rather than release of 

organically associated S. However, during this period, deposition of SO4

2−

 was essentially unchanged. 

They attributed this discrepancy to a delay in the release of adsorbed SO4

2−

 in response to a decrease in S 

deposition over the previous decade. These authors did not provide a mechanism to explain how 

desorption can continue under conditions of constant SO4

2−

 inputs, however. On the basis of the abundant 

evidence of biological S cycling, it seems more likely that the delay observed by Courchesne et al. (2005) 

is the result of biological controls over the release of S. 

Much of the organic S stored in soil is in carbon–bonded forms that are relatively unreactive, but 

can be mineralized to SO4

2−

 in oxic conditions, typically found in moderately well-drained to well-drained 

soils (Johnson and Mitchell, 1998). Furthermore, strong correlations have been shown between levels of 

atmospheric deposition of S and concentrations of S in soil (Driscoll et al., 2001b; Novák et al., 2001). 

Long-term increases in concentrations of total S in soils that are at least partially attributable to increases 

in organic S have also been documented (Knights et al., 2000; Lapenis et al., 2004). The study of Houle 

et al. (2001) did not find a relation between these factors, however. A Swedish “clean-roof” study also 

provides some insights into the role of organic S in possibly delaying recovery (Mörth et al., 2005). After 

9 years of pre-industrial levels of S deposition, the amount of S in runoff still exceeded inputs by 30%. 

Most of the S in runoff was attributed to mineralization of organic S in the O horizon. 

 B-2
B.1.2.2. Base Cation Depletion 

Base cations are common in rocks and soils, but largely in forms that are unavailable to plants. 

There is a pool of bioavailable base cations (termed exchangeable base cations) that are adsorbed to 

negatively charged surfaces of soil particles. They can enter solution by exchanging with other dissolved 

cations including acidic cations such as H+

 or Al3+. Base cations in this pool are gradually leached from 

the soil in drainage water, but are constantly resupplied through weathering. Weathering slowly breaks 

down rocks and minerals, releasing base cations to the pool of adsorbed base cations in the soil. The 

balance between base cation supply and base cation loss determines whether the pool of available base 

cations is increasing or decreasing in size. Net forest growth can also potentially lower exchangeable base 

cation concentrations through uptake of nutrient cations (Ca, Mg, and K), but these cations remain in the 

terrestrial ecosystem and can become available in the future through mineralization or canopy leaching. It 

has long been known that leaching of base cations by acidic deposition might deplete the soil of 

exchangeable bases faster than they are resupplied (Cowling and Dochinger, 1980). However, base cation 

depletion of soils had not been demonstrated at the time of the 1982 PM-SOX AQCD (U.S. EPA, 1982b). 

Data that clearly showed soil base cation depletion in the U.S. did not become available until the 

1990s, although decreases in exchangeable Ca2+ concentrations between the periods 1947 to 1950 and 

1987 to 1988 had been identified in European soils through repeated sampling (Billett et al., 1990; 

Falkengren-Grerup and Eriksson, 1990). In the only repeated sampling in the U.S. in which the original 

soil sample pre-dated acidic deposition, Johnson et al. (1994b) documented a decrease in exchangeable 

Ca2+ concentrations in both the O (combined Oa and Oe horizons) and B horizons from 1930 to 1984. 

Richter et al. (1994) also observed Ca2+ depletion in the B horizon from 1960 to 1990, in repeated 

sampling of Piedmont soil in South Carolina. The studies of Johnson et al. (1994b) and Richter et al. 

(1994) acknowledged the potential role of acidic deposition in causing the loss of Ca2+, but focused on net 

forest growth as the primary cause. 

Through reanalysis of archived soils, Lawrence et al. (1995) measured decreases in concentrations 

of exchangeable Ca2+ and acid-extractable Ca2+ in Oa horizons of spruce stands from 1969–70 to 1987–92 

and presented relationships in soil chemistry that were not consistent with changes expected from 

vegetation uptake effects, but that could be explained by acidic deposition. Drohan and Sharpe (1997) 

also observed a decrease in Ca2+ concentrations in Oa and A horizons at 11 sites across Pennsylvania that 

were sampled in 1957 or 1959 and again in 1993, although effects of vegetation and acidic deposition 

were not distinguished. 

A thorough soil re-sampling study in the U.S. was conducted by Bailey et al. (2003) in 

northwestern Pennsylvania. Between 1967 and 1997, pronounced decreases, attributed largely to acidic 

deposition, were measured in exchangeable Ca2+ and Mg2+ concentrations in Oa/A horizons and 

throughout the B horizon. Courchesne et al. (2005) found higher concentrations of exchangeable Ca2+ in 

the O horizon (combined Oe and Oa horizons) in 2002 than in 1994 at one of three sampling areas within 

a 5.1 ha watershed, but no significant differences at the other two locations. No significant differences 

were found for exchangeable Mg2+ at the three locations in the O horizon. In the upper 10 cm of the 

B horizon, no significant differences were found in exchangeable Ca2+, but at two of three locations, 

exchangeable Mg2+ concentrations were lower in 2002 than in 1994. The 8–yr interval between sampling 

in this study is the shortest time in which changes in exchangeable base cations have been reported for 

North American soils. 

In a regionally designed assessment of changes in soil-exchange chemistry, Sullivan et al. (2006b) 

found that base saturation and exchangeable Ca2+ concentrations in the Adirondack region of New York 

had decreased in the upper 10 cm of the B horizon between the mid 1980s and 2003, in watersheds of 

lakes with ANC less than 200 μeq/L. Soil chemistry in 36 lake watersheds in the mid 1980s was 

compared to soil chemistry in 32 lake watersheds in 2003. Although this study did not involve repeated 

sampling of the same sites, the comparison could be made on a regional basis because the sampling 

locations were selected randomly in both the mid 1980s and in 2003, and a large and similar number of 

sites were included in both samplings. 

 B-3
In a widely cited article, Likens et al. (1996) used a watershed mass balance approach to estimate 

changes in ecosystem Ca2+ pools at HBEF and found a sustained decrease in exchangeable Ca2+

concentrations from 1963 to 1993. The maximum depletion rate occurred in 1972, at the estimated peak 

in acidic deposition levels. The dependence on Ca:Na ratios to estimate Ca2+ weathering fluxes in this 

analysis adds uncertainty to the magnitude of changes reported for the exchangeable Ca2+ pool (Bailey 

et al., 2003). Two additional mass balance studies used Sr isotopes to evaluate changes in soil Ca2+ pools 

and fluxes. The study of Bailey et al. (1996) estimated substantial depletion rates in a watershed in the 

White Mountains of New Hampshire. Miller et al. (1993) estimated that inputs from weathering and 

atmospheric deposition approximately equaled leaching losses at a site in the Adirondack Mountains in 

New York. The different findings in these two studies are related to differences in the mineralogical 

composition of the respective soils. However, the Miller et al. (1993) study also estimated that 50 to 60% 

of the Ca2+ in vegetation and the forest floor was derived from the atmosphere, despite the fact that the 

weathering flux was estimated to be three times the rate of atmospheric inputs. This result suggests that 

Ca2+ supply from weathering in the lower profile is not reaching the upper soil where most root activity 

occurs, and that Ca2+ depletion has occurred in the upper soil. 

The study of Yanai et al. (1999) investigated changes in Ca2+ and Mg2+ concentrations and content 

in northeastern hardwood stands over time intervals ranging from 10 to 21 years. The general conclusion 

of this study was that little or no change in O horizon (Oi, Oe, and Oa horizons) exchange chemistry 

occurred. However, a decrease in exchangeable Ca2+ concentrations in the Oa horizon was observed in 

this study at the HBEF from 1978 to 1997, although no change was observed in this soil in exchangeable 

Mg2+ concentrations, or Ca2+ or Mg2+ content in the Oa horizon. Results of this study were complicated 

by high spatial variability and differences in field sampling techniques between the original collection and 

the resampling (Yanai et al., 1999). Yanai et al. (2005) also found little difference over 15 years in 

exchangeable and extractable Ca2+ and Mg2+ concentrations in Oa horizons at 6 sites, and in O horizons 

(combined Oe and Oa horizons) at 13 sites, in hardwood stands in New Hampshire. In this study, it was 

also estimated that a difference greater than 50% would be needed to be statistically detected due to a 

large degree of spatial variability. Although most repeated sampling studies did identify decreases in 

exchangeable base cations in the Oa or O horizon, the results of Yanai et al. (2005) indicated that this 

change may not occur at all sites and may be difficult to detect in some soils due to inconsistencies in 

identifying horizon separations during sampling. 

Through direct and inferred evidence of Ca2+ depletion, and additional research on soil processes, a 

detailed understanding of the mechanisms of Ca2+ depletion has developed over the past two decades. 

Ulrich (1983) explained Ca2+ depletion as a three-stage process in which buffering of acidity in the 

mineral soil is first accomplished by weathering of carbonates and other mineral forms that weather 

relatively rapidly. Once these mineral forms are depleted, buffering is accomplished largely by cation 

exchange, in which H+

 is substituted for base cations and concentrations of exchangeable base cations 

decrease. Once the buffering capacity provided by cation exchange is depleted, acid neutralization is 

accomplished by weathering of crystalline minerals that contain large amounts of silicon (Si) and Al and 

relatively small amounts of base cations. At this stage, Al is mobilized within the soil and exchangeable 

Al concentrations increase. The shift in acid buffering from base cation exchange to alumino-silicate 

weathering and exchangeable Al was documented in Russian soils sampled three times over 75 years 

(Lawrence et al., 1995). 

The effect of decreasing concentrations of exchangeable base cations on cation leaching in mineral 

soil was shown in simulation modeling by Reuss (1983). Below a base saturation of 20%, leaching of 

Ca2+ decreases substantially and becomes less sensitive to variations in acid inputs as base saturation 

decreases further. This relationship was later shown experimentally by Lawrence et al. (1999). Samples 

from the upper B-horizon in nearly all of the Adirondack lake watersheds sampled by Sullivan et al. 

(2006b) had base saturation values less than 20%, as did soils at 11 sites in New York, Vermont, New 

Hampshire, and Maine in a regional study of mature spruce-fir forests (David and Lawrence, 1996). 

Exchangeable Ca2+ concentrations (expressed as a percentage of cation exchange capacity [CEC]) in the 

regional spruce-fir study were weakly correlated with an estimate of the relative weathering potential of 

 B-4
parent material in the upper 10 cm of the B horizon (r2

 = 0.44). However, these factors were strongly 

correlated in the Oa horizon (r2

 = 0.92) (Lawrence and David, 1997). Because mineral weathering in the 

B horizon is the primary source of soil Ca2+, a strong relationship between weathering potential and 

exchangeable Ca2+ concentrations would be expected in this horizon. The weak correlation suggests that 

concentrations of Ca2+ had decreased into the Al-buffering range sometime in the past. The parent 

material signature in the Oa horizon was likely maintained through vegetative recycling—uptake of Ca2+

from the O and B horizons, followed by transport back into the O horizon in litterfall. 

In summary, evidence from repeated sampling and studies of soil processes indicate that decreases 

in exchangeable base cation concentrations in both Oa and B horizons are common and widespread in the 

eastern U.S. Factors such as logging and net forest growth are likely to have contributed to this decrease 

in varying degrees, but acidic deposition has played a major role (Huntington, 2000; Lawrence et al., 

1987). The magnitudes and rates at which Ca2+ depletion has occurred are less clear. 

These base cation depletion issues relate directly to the chemical recovery potential of acidified 

soils and surface waters. Replenishment of exchangeable base cation concentrations on soils will require 

that inputs from weathering and atmospheric deposition exceed losses from leaching and vegetative 

uptake. Inputs of Ca2+ from atmospheric deposition decreased sharply in the east through the 1980s 

(Hedin et al., 1994), and have remained relatively stable since that time (http://nadp.sws.uiuc.edu/). 

Atmospheric deposition of SO4

2−

 currently remains several factors higher than that of Ca2+ even at sites 

where SO4

2−

 levels are relatively low (http://nadp.sws.uiuc.edu/), so chemical recovery at current acidic 

deposition levels will require inputs of base cations from weathering that are considerably greater than 

inputs from the atmosphere. 

Because of the importance of weathering to the base-cation status of soils, a great deal of effort has 

been made to estimate in situ weathering flux with a variety of methods (Bailey et al., 2003; Likens et al., 

1996; Miller et al., 1993). The complexity and variability of factors that affect weathering flux rates, such 

as soil mineralogy, particle surfaces, soil organic matter, moisture flux, and a host of other factors that are 

difficult to quantify, add large uncertainties to weathering flux estimates. Weathering rates estimated in 

geochemical models are generally assumed to be constant overtime, but lower weathering rates were 

observed in a soil sampled in 1987 than in the same soil sampled and archived in 1949–50 (Zulla and 

Billett, 1994). Further complexity in weathering flux rates results from the possible role of mycorrhizae in 

penetrating silicate minerals to extract base cations while remaining isolated from the soil solution (Blum 

et al., 2002; van Breemen et al., 2000). Lastly, as yet unidentified sources of base cations may exist in 

forest soils. Bailey et al. (2003) found that elevated rates of Ca2+ loss from forest harvesting continued for 

30 years after disturbance, but the source of the additional Ca2+ being lost could not be identified. Until 

estimates of in situ weathering fluxes are better constrained and more data become available from 

repeated soil sampling, predictions of recovery of exchangeable base cation concentrations will be highly 

uncertain. 

B.1.2.3. Aluminum Mobilization 

Through the natural process of podzolization, dissolved organic acids derived from partially 

decomposed organic matter in the O horizon move into the mineral soil where they weather soil particles 

and release Al into solution. As soil solution moves deeper into the profile, acidity is neutralized and Al is 

deposited as a secondary mineral or more likely as an organic Al (Alo) complex (De Coninck, 1980). The 

limited mobility of organic anions results in retention of most Al within the mineral soil (often in the 

Bh horizon). Complexation with dissolved organic matter can increase the mobility of Al within the soil 

and lead to transport of organic Al into surface waters from shallow soils that are high in organic matter 

(Lawrence et al., 1987). 

Increased concentrations of exchangeable Al in the mineral soil have been identified through 

repeated sampling in the U.S. and Europe over periods ranging from 17 years to 41 years in studies by 

Billet et al. (1990), Bailey et al. (2005), Falkengren-Grerup and Eriksson (1990), and Lawrence et al. 

 B-5
(1995). In areas of Europe with excessively high acidic deposition levels, evidence of Al depletion in the 

mineral soil has also been found (Lapenis et al., 2004; Mulder et al., 1989), but Al depletion has not been 

documented in the U.S. 

Increases in exchangeable Al concentrations in the O horizon have been documented over periods 

from 17 to 30 years (Bailey et al., 2005; Drohan and Sharpe, 1997; Lawrence et al., 1995), although the 

study of Yanai et al. (2005) did not find consistent changes in Oa horizons over 15 years. 

Numerous papers have evaluated solubility controls on Al in both the mineral soil and the 

O horizon. These papers have commonly related Al solubility to gibbsite (Al(OH)3) or a gibbsite-like 

mineral to determine if inorganic Al concentrations could be predicted from gibbsite solubility constants 

and pH (Cronan and Goldstein, 1989; David and Driscoll, 1984; Johnson et al., 1981; Lawrence and 

David, 1997). These efforts have shown that inorganic Al concentrations are often undersaturated with 

respect to gibbsite and do not support Al-trihydroxide as the primary control in natural systems. Gibbsite 

solubility should therefore be considered a useful point of reference in evaluating Al-solubility rather a 

mineral form that is an important control of Al solubility in natural systems. 

Through the 1990s, evidence accumulated to indicate that secondary Al in the mineral soil is in a 

form associated with organic matter, and in some soils, imogolite (Berggren and Mulder, 1995; Dahlgren 

and Walker, 1993; Mulder and Stein, 1994; Simonsson, 1998; Skyllberg, 1999). Organic matter also plays 

a major role in controlling Al solubility in O horizons. This interaction has been described by Cronan 

et al. (1986) in O horizons through the bound Al ratio, which reflects the equivalents of adsorbed Al per 

mol of carboxyl groups (Cronan et al., 1986). Tipping et al. (1995) described Al solubility on organic and 

mineral soil horizons through equilibrium humic ion binding. Each of these approaches has had success in 

describing dissolved Al concentrations in organic soils as a function of pH through formulations that rely 

on concentrations of solid-phase organic bound Al. Further work has shown these relationships to be 

specific to the particular horizon, and the pool sizes of Al and humic substances (Lofts et al., 2001). 

However, inputs of acidity may alter concentrations of solid-phase organic bound Al (Lawrence and 

David, 1997). Changes in atmospheric deposition levels may therefore shift these relationships over time 

as soils further acidify or recover. 

B.1.2.4. Soil Acidification 

In the B horizon of soils north of the maximum extent of glaciation, CEC is largely derived from 

organic matter, whereas in older southern soils the surface charge of highly weathered clay minerals is the 

primary source of CEC. The CEC derived from organic matter is pH-dependent. Decreases in pH result in 

a decreases in CEC. In both cases, the CEC of the B horizon is much lower than in organic-rich 

surface horizons (Oa or A horizons). Less acidity from organic matter and a limited capacity for buffering 

due to low CEC makes the B horizon more susceptible to a lowering of pH from acidic deposition, and 

decreases in pH lower the CEC, further reducing the acid-buffering capacity from cation exchange. Two 

studies in the U.S. have provided measurements to assess changes in soil pH in the B horizon from acidic 

deposition. Bailey et al. (2005) found lower pH values in the upper B horizon in northwestern 

Pennsylvania soils in 1967 than in 1997, at 50 cm depth (p <0.001) and at 100 cm depth (p <0.001), 

which were largely attributable to acidic deposition. Markewitz et al. (1998) also found pronounced 

decreases in soil pH down to 60 cm in highly weathered Piedmont soils from 1962 to 1990. The latter 

study was conducted in a former cotton field in which loblolly pines were planted in 1956–57. Forest 

regrowth undoubtedly played a large role in the soil pH changes that were measured, but atmospheric 

deposition was estimated to account for 38% of the H+

 inputs during the 28 years that elapsed between 

measurements. 

Other studies in Europe have found similar decreases in soil pH of the B horizon that could be 

attributed, at least in part, to acidic deposition. These include the study of Lawrence et al. (1995) in 

northwestern Russia, which documented decreases in soil pH in the B horizon down to 90 cm, from 1926 

to 1964, and further decreases from 1964 to 2001. Acidic deposition was identified as the probable 

 B-6
primary cause of decreasing pH in this study. The study of Lawrence et al. (1995) also observed a 

decrease in CEC in this soil, as did a previous study of Russian soils (Lapenis et al., 2004). The decrease 

in pH was likely to have contributed to the decreased CEC of these soils, but a more important factor may 

have been a decrease in organic carbon concentrations that was also measured. To our knowledge, data to 

assess possible changes in CEC in soils in the U.S. has not become available, but change in CEC has 

implications for recovery potential of soils from acidic deposition effects (Sullivan et al., 2006a). 

Increased CEC driven by increases in pH could foster soil recovery by increasing the opportunity for 

adsorption of base cations, as soil solution becomes less acidic. Decreases in soil organic matter driven by 

climate and/or vegetation changes, such as those seen in Russian soils, would result in a decrease in acidbuffering capacity through cation-exchange. There are currently no data in the U.S. that indicate increases 

in soil pH associated with recent declines in acidic deposition levels. These data limitations make future 

projections of recovery of soil pH highly uncertain. 

B.1.2.5. Nitrogen Saturation 

Severe symptoms of N saturation, have been observed in high-elevation, nonaggrading spruce-fir 

ecosystems in the Appalachian Mountains, as well as in the eastern hardwood watersheds at Fernow 

Experimental Forest near Parsons, WV and throughout the northeastern U.S. Mixed conifer forests and 

chaparral watersheds with high smog exposure in the Los Angeles Air Basin also are N-saturated and 

exhibit the highest stream water NO3

−

 concentrations documented within wildlands in North America 

(Bytnerowicz and Fenn, 1996; Fenn et al., 1998). 

Some examples of N-saturated forests in North America, including estimated inputs and outputs, 

are shown in Table B-1 (Fenn et al., 1998). The Harvard Forest hardwood stand in western Massachusetts 

absorbed >900 kg N/ha without significant NO3

−

 leaching during an 8–yr N amendment study (Fenn 

et al., 1998). In contrast, NO3

−

 leaching losses were high at the Harvard Forest pine sites. In the 8–yr 

experimental study, NO3

−

 leaching was observed in the pine stand after the first year (1989) in the high-N 

application plots, and further increases were observed in 1995 and 1996. The hardwood stand did not 

show significant increases in NO3

−

 leaching until 1996. The differences in response of the pine and 

hardwood stands indicate that the mosaic of community types across the landscape must be considered 

when evaluating landscape-scale responses to N deposition (Magill et al., 2000). 

Utilization of N in the terrestrial ecosystem is accomplished through complex interactions between 

plants and microbes that are not fully understood (Schimel and Bennett, 2004). Long-term N retention is 

largely accomplished by incorporation of N into soil organic matter through biological assimilation (Aber 

et al., 1998), and to a lesser extent by abiotic processes that are not well understood (Dail et al., 2001). 

The forms in which N is assimilated by plants and microbes are determined by availability, as described 

in Schimel and Bennett (2004). In the most N-limited ecosystems, competition between plants and 

microbes is high and N is assimilated primarily in depolymerized organic forms, resulting in low 

mineralization rates and minimal buildup of inorganic N in the soil. Increased availability of N increases 

the mineralization rate, which enhances competition between plants and microbes for available NH4

+

produced by mineralization. Further increase in the availability of N (for example by high levels of 

atmospheric N deposition) lessens competition for NH4

+

 between plants and microbes and leads to 

increased production of NO3

−

 by autotrophic nitrifying bacteria. Some of this NO3

−

 can be taken up by 

plants and microbes, but because much of the N demand is satisfied by NH4

+

 under these conditions, 

NO3

−

 tends to be mobile within the soil, enabling it to leach to drainage water. Based on the definitions of 

Aber et al. (1989, 1998) and Stoddard (1994), the first stage of N saturation is reached when competition 

between plants and microbes for NH4

+

 has decreased to the point that net nitrification occurs. 

Substantial leaching of NO3

−

 from forest soils to streamwater can acidify downstream waters 

(Webb et al., 1995), eutrophy estuaries and marine waters (Fisher and Oppenheimer, 1991), and deplete 

soils of nutrient base cations, especially Ca2+ and Mg2+ (Likens et al., 1998). Considerable evidence is 

available to link N deposition to acidification of soils. Much of this evidence comes from the northeastern 

 B-7
U.S., where increased accumulation of N in soil is suggested by a strong positive correlation between 

atmospheric deposition levels and total N concentration in the Oa horizon, at sites in New York, Vermont, 

New Hampshire, and Maine (Driscoll et al., 2001b). Further evidence that atmospheric deposition has 

increased availability of N in soil is shown by a strong negative correlation between atmospheric 

deposition levels and the C:N ratio of the Oa horizon in this region (Aber et al., 2003). If the C:N ratio 

falls below about 25, nitrification is stimulated, resulting in elevated NO3

−

 in surface waters (Aber et al., 

2003). Similar results were found in Europe, where a C:N ratio of 24 was identified as the critical level 

below which nitrification occurred (Emmett et al., 1998). 

Analyses have been conducted in the northeastern U.S. and Europe to examine the relationships 

between N deposition and NO3

−

 leaching to surface waters. The relationship between measured wet 

deposition of N and streamwater output of NO3

−

 was evaluated by Driscoll et al. (1989) for sites in North 

America (mostly eastern areas), and augmented by Stoddard (1994). The resulting data showed a pattern 

of N leaching at wet inputs greater than approximately 5.6 kg N/ha/yr. Stoddard (1994) presented a 

geographical analysis of patterns of watershed loss of N throughout the northeastern U.S. He identified 

approximately 100 surface water sites in the region with sufficiently intensive data to determine their N 

status. Sites were coded according to their presumed stage of N retention, and sites ranged from Stage 0 

(background condition) through Stage 2 (chronic effects). The geographic pattern in watershed N 

retention depicted by Stoddard (1994) followed the geographic pattern of N deposition. Sites in the 

Adirondack and Catskill Mountains in New York, where N deposition was about 11 to 13 kg N/ha/yr, 

were typically identified as Stage 1 (episodic effects) or Stage 2. Sites in Maine, where N deposition was 

about half as high, were nearly all Stage 0. Sites in New Hampshire and Vermont, which received 

intermediate levels of N deposition, were identified as primarily Stage 0, with some Stage 1 sites. Based 

on this analysis, a reasonable threshold of N deposition for transforming a northeastern site from the 

“natural” Stage 0 condition to Stage 1 would correspond to the deposition levels found throughout New 

Hampshire and Vermont, approximately 8 kg N/ha/yr. This is in agreement with the interpretation by 

Driscoll et al. (1989), which would probably correspond to total N inputs near 8 to10 kg N/ha/yr. This is 

probably the approximate level at which episodic aquatic effects of N deposition would become apparent 

in many watersheds of the eastern U.S. 

Analysis of data from surveys of N outputs from 65 forested plots and catchments throughout 

Europe were conducted by Dise and Wright (1995) and Tietema and Beier (1995). Below the throughfall 

inputs of about 10 kg N/ha/yr, there was very little N leaching at any of the study sites. At throughfall 

inputs greater than 25 kg N/ha/yr, the study catchments consistently leached high concentrations of 

inorganic N. At intermediate deposition values (10 to 25 kg N/ha/yr), Dise and Wright (1995) observed a 

broad range of watershed responses. Nitrogen output was most highly correlated with N input (r2

 = 0.69), 

but also significantly correlated with S input, soil pH, percent slope, bedrock type, and latitude. A 

combination of N input (positive correlation) and soil pH (negative correlation) explained 87% of the 

variation in N output at the study sites (Dise and Wright, 1995). 

The threshold level of atmospheric deposition that causes release of NO3

−

 to surface waters was 

identified by Aber et al. (2003) as approximately 7 kg N/ha/yr for the northeastern U.S. In watersheds 

receiving N deposition above this level, concentrations of NO3

−

 in surface waters were positively 

correlated with atmospheric deposition, whereas most watersheds with deposition less than 7 kg/ha/yr had 

little or no NO3

−

 (undetectable at most sites) in their surface waters (Aber et al., 2003). The threshold 

value of 7 kg/ha/yr was based on atmospheric deposition levels for the base of forested watersheds. When 

scaled to include higher deposition levels expected at upper elevations this value was estimated to equal 

about 10 kg/ha/yr, similar to the European estimate of Dise et al. (1998). 

The common deposition threshold for release of NO3

−

 to surface waters in forested watersheds 

found in the northeastern U.S. and Europe represents an important advance in relating N inputs to 

ecosystem effects, but a considerable amount of variability in ecosystem response has also been 

demonstrated. Lovett et al. (2000) found that 39 watersheds in the Catskill region of New York State 

retained from 49% to 90% of atmospheric N inputs. Castro and Morgan (2000) showed that NO3

−

 export 

 B-8
from watersheds in eastern North America can range from nearly 0 to over 400 eq/ha/yr in watersheds 

that receive similar levels of inorganic N in wet deposition in the range of 400 to 500 eq/ha/yr. 

Experimental additions of N to plots and watersheds have also demonstrated variations in terrestrial 

retention of N. Additions of N (approximately twice ambient deposition) to hardwood watersheds in 

Maine (25 kg N/ha/yr) and West Virginia (35.5 kg N/ha/yr), which were releasing NO3

−

 to surface waters 

before the additions, resulted in substantial increases in NO3

−

 concentrations in soil water and stream 

water within the first treatment year (Kahl et al., 1993; Peterjohn et al., 1996) Additions of 25 kg N/ha/yr 

to spruce plots in Vermont (ambient bulk deposition 5.4 kg N/ha/yr), in which net nitrification did not 

occur before treatment, triggered net nitrification in the second year of treatment, whereas nitrification 

was not triggered until the third year in plots receiving 19.8 kg N/ha/yr (McNulty et al., 1996). Similar 

results to these were seen in two studies from Colorado. Additions of 25 kg N/ha/yr to old-growth spruce 

plots in Loch Vale watershed (ambient bulk deposition ~4–5 kg N/ha/yr) doubled N mineralization rates 

and stimulated nitrification, while the addition of the same amount to plots receiving ambient bulk 

deposition of ~2.0 kg N/ha/yr in Fraser Experimental Forest elicited no microbial response but 

significantly increased foliar and organic soil horizon N (Rueth et al., 2003). A comparison study of oldgrowth spruce plots across a depositional gradient in Colorado found mineralization rates to be higher 

where N deposition ranged from 3 to 5 kg N/ha/yr than where N deposition ranged from 1 to 2 kg 

N/ha/yr, with measurable nitrification rates at sites with the highest deposition amounts (Rueth and Baron, 

2002). In marked contrast to these results, concentrations of NO3

−

 plus NH4

+

 were not detected until the 

seventh year in hardwood plots in Harvard Forest, which received additions of 150 kg N/ha/yr (Magill 

and Aber, 2004). Concentrations of (NO3

−

 + NH4

+

) in hardwood plots receiving 50 kg N/ha/yr were not 

yet detectable in the 15th year of treatments. The same treatments were applied to red pine (Pinus 

resinosa) plots, which exhibited elevated concentrations of (NO3

−

 + NH4

+

) in soil water after 1 year of 

150 kg N/ha/yr doses, and after 5 years of 50 kg N/ha/yr doses. 

In general, deciduous forest stands in the eastern U.S. have not progressed toward N-saturation as 

rapidly or as far as spruce-fir stands. Deciduous forests may have a greater capacity for N retention than 

coniferous forests. In addition, deciduous forests tend to be located at lower elevation and receive lower 

atmospheric inputs of N. Many deciduous forests have higher rates of N uptake and greater N requirement 

than spruce-fir forests. Decreased growth and increased mortality have more commonly been observed in 

high-elevation coniferous stands than in lower elevation hardwood forests, and these differences have 

been partially attributed to excess inputs of N (Aber et al., 1998). Indeed, many of the lower elevation 

deciduous stands are N-deficient and are therefore likely to benefit (i.e., grow faster), at least up to a 

point, with increased inputs of N. 

There are examples of N saturation in lower-elevation eastern forests, especially in West Virginia. 

For example, progressive increases in streamwater NO3

−

 and Ca2+ concentrations were measured at the 

Fernow Experimental Forest in the 1970s and 1980s (Adams et al., 1997, 2000; Edwards and Helvey, 

1991; Peterjohn et al., 1996). This watershed has received higher N deposition (average throughfall input 

of 22 kg/ha/yr of N in the 1980s) than is typical for low-elevation areas of the eastern U.S., however 

(Eagar et al., 1996), and this may help to explain the observed N saturation. 

Varying responses to N additions reflect differences in N status of the treatment sites. These 

variations have most often been attributed to disturbance history, dating back a century or more (Goodale 

and Aber, 2001). Sites which have undergone disturbances that cause loss of soil N, such as logging, fire, 

and agriculture, tend to be most effective at retaining atmospheric and experimental inputs of N. Nitrogen 

retention capability often decreases with stand age, which suggests that older forests are more susceptible 

than younger forests to becoming N-saturated (Hedin et al., 1995). Aber et al. (1998) surmised that land 

use history may be more important than cumulative atmospheric deposition of N in determining the N 

status of a forest ecosystem. 

Although considerable progress has been made in understanding the factors that control N 

retention, efforts to quantify net N retention through known processes have not been fully successful. 

Assimilation of N by mycorrhizae followed by exudation as dissolved organic matter was proposed by 

 B-9
Aber et al. (1998) as a possible explanation for unaccounted conversion of inorganic N into soil organic 

matter. However, Frey et al. (2004) found that elevated N inputs reduced active mycorrhizal biomass, 

fungal diversity and fungal:bacterial biomass ratios. These results suggested a decreased role for 

mycorrhizae in fixation of N under elevated N inputs. 

Abiotic transformation of inorganic N into soil organic matter has also been proposed as a possible 

mechanism to explain high rates of N retention in soil, and some evidence has been presented to support 

this possibility. Dail et al. (2001) observed retention of 15NO3

−

 and 15NO2 in sterile soil, but the method of 

sterilization may have increased dissolved organic carbon (DOC) concentrations and artificially increased 

the opportunity for formation of soluble organic N compounds. Davidson et al. (2003) developed the 

ferrous wheel hypothesis to explain incorporation of inorganic N into organic matter. The hypothesized 

mechanism involves conversion of NO3

−

 to NO2

−

 through oxidation of Fe2

+

. Testing of this hypothesis in 

situ was not found in the literature, but the small amount of Fe2

+

 that typically occurs in the forest floor, 

where presumably much of the conversion to organic N occurs, may limit the importance of this pathway. 

Fitzhugh et al. (2003) showed that NO2

−

 produced in the first step of nitrification may be directly 

converted to soluble organic N rather than becoming fully oxidized to NO3

−

. However, concentrations of 

introduced 15NO2 in this experiment were several orders of magnitude higher than that normally seen in 

forest soils. Therefore, the evidence at this time for abiotic retention of N is not fully convincing, and the 

importance of this process requires further research. 

In addition to our limited understanding of N retention mechanisms, there is no direct information 

on ecosystem recovery from N saturation in the U.S. This may be at least partly because atmospheric 

deposition of N has been relatively stable in the eastern U.S. over the past two to three decades. An 

important source of information on N recovery responses has been provided by the European NITREX 

study, which reduced ambient N deposition for 5 years with roofs constructed over experimental plots in 

Germany and The Netherlands. At the German site, deposition was reduced from approximately 38 kg 

N/ha/yr to levels that varied from 10 to 20 kg N/ha/yr. At the Dutch site, deposition was reduced from 

45 kg N/ha/yr to levels that varied from 1 to 10 kg N/ha/yr. At both of these sites, deposition levels before 

the experiment were approximately three to four times greater than the highest deposition levels 

commonly found in the eastern U.S., whereas after the reduction, levels at the Dutch site fell within the 

range of deposition in the eastern U.S. over the past two decades, and values at the German site were 

somewhat higher than this range (Emmett et al., 1998; Ollinger et al., 1993). The decrease in ambient N 

inputs resulted in a marked decrease in N outputs at each site within 2 to 3 years. The responses at the two 

sites were somewhat different, however. At the Dutch site, outputs of N exceeded inputs both before and 

after experimental reduction of inputs. At the German site, inputs exceeded outputs before and after 

reduction of inputs, but outputs were more similar to inputs after the reduction. At both sites, outputs after 

the reduction in deposition remained two to three times higher than outputs commonly measured in the 

eastern U.S. 

Thus, atmospheric deposition of N has increased N availability in soils, which has led to increased 

nitrification and associated acidification of soil and soil water. The N retention capacity of soils is 

strongly dependant on land-use history, however, so the relationships between N deposition and 

ecosystem N status and percent of terrestrial retention are variable. In general, however, atmospheric 

deposition of 10 kg N/ha/yr or higher is required for appreciable amounts of NO3

−

 to leach to surface 

waters in the eastern U.S. and northern Europe. Future projections of chemical recovery from N-driven 

acidification are uncertain because retention mechanisms are not fully understood, and there are only 

limited data on recovery responses. European experiments that reduced inputs of N found decreased 

outputs of N within 2 to 3 years, which indicates a relatively rapid response to decreased deposition 

levels. However, these studies are difficult to directly apply to the U.S. because deposition levels were 

much higher at the European sites before the experiment, and the 5–yearr duration of the experiments 

only demonstrated recovery to levels of N saturation that are higher than the more heavily affected sites in 

the eastern U.S. 

High concentrations of lake or streamwater NO3

−

, indicative of ecosystem saturation, have been 

found at a variety of locations throughout the U.S., including the San Bernardino and San Gabriel 

 B-10
Mountains within the Los Angeles Air Basin (Fenn et al., 1996), the Front Range of Colorado (Baron 

et al., 1994; Williams et al., 1996a, 1996b), the Allegheny Mountains of West Virginia (Gilliam et al., 

1996), the Catskill Mountains of New York (Murdoch and Stoddard, 1992; Stoddard, 1994), the 

Adirondack Mountains of New York (Wigington et al., 1996), and the Great Smoky Mountains in 

Tennessee (Cook et al., 1994). All of these regions, except Colorado, received relatively high (more than 

about 10 kg N/ha/yr) atmospheric deposition of oxidized N throughout the 1980s and 1990s. In contrast, 

the Front Range of Colorado receives less than about 4 or 5 kg N/ha/yr of total (wet plus dry) deposition 

(Sullivan et al., 2005), less than half of the total N deposition received at these other locations. The cause 

of N-saturation at high-elevation western watersheds that receive low to moderate levels of atmospheric 

deposition has been a subject of debate. High concentrations of NO3

−

 in surface waters in the western 

U.S. are not widespread. Nitrate concentrations during the fall sampling season were low in most western 

lakes sampled in the Western Lakes Survey (WLS). Only 24 sampled lakes were found to have NO3

−

concentrations greater than 10 μeq/L. Of those, 19 lakes were situated at high elevation, most above 3,000 

m. Cold temperatures in such lakes undoubtedly play an important role in maintaining high NO3

−

concentrations by limiting biological uptake processes. The high NO3

−

 concentrations are likely to affect 

acid-base chemistry only where ANC is low. Eight lakes showed high NO3

−

 (>10 μeq/L) and low ANC 

(<50 μeq/L), all of which occurred at elevations higher than 3,100 m. Four were located in Colorado, two 

in Wyoming, and one each in California and Utah. In all cases, pH was above 6.5 and ANC was greater 

than or equal to 15 μeq/L. Such lakes are sensitive to episodic pulses of NO3

−

 acidity; such pulses have 

been reported from Colorado Front Range lakes (Williams and Tonnessen, 2000). Episodic acidification 

of western lakes could be important biologically. 

In the Uinta Mountains of Utah and the Bighorn Mountains of central Wyoming, 19% of the lakes 

included within the WLS had NO3

−

 >10 μeq/L. This suggests that N deposition in these areas may have 

exceeded the capability of these systems to assimilate N. It is unknown if these concentrations of NO3

−

represent effects from anthropogenic sources or if this constituted a natural condition associated with 

inhibited NO3

−

 assimilation in cold alpine environments. 

Williams et al. (1996a; 1996b) contended that N-saturation is occurring throughout high-elevation 

catchments of the Colorado Front Range. Many lakes in the Colorado Front Range have chronic NO3

−

concentrations greater than 10 μeq/L and concentrations during snowmelt are frequently much higher, due 

at least in part to leaching from tundra, exposed bedrock, and talus areas. Although biological N demand 

may be high in subalpine forests, uptake is limited in alpine areas by large N inputs from snowmelt, steep 

watershed gradients, rapid water flushing, extensive areas having little or no soil development, and 

limitations on the growth of phytoplankton in some alpine lakes by factors other than N (e.g., phosphorus, 

temperature) (Baron et al., 1994). 

B.1.2.6. Nitrate Leaching 

Nitrate leaching losses from soils to drainage waters are governed by a complex suite of ecosystem 

processes in addition to N inputs from atmospheric deposition. In particular, mineralization and 

nitrification processes play important roles in regulating the quantity of, and temporal variability in, the 

concentration of NO3

−

 in soil solution, and consequently leaching losses from the rooting zone (Johnson 

et al., 1991b, 1991c; Joslin et al., 1987; Reuss and Johnson, 1985)(Johnson et al., 1991b; 1991c). Thus, 

NO3

−

 leaching is mostly under biological control and typically shows pronounced seasonal variability 

(Van Miegroet and Johnson, 1993). Peak concentrations of NO3

−

 in soil solution appear to be largely 

responsible for the potentially toxic peaks in Al concentration that sometimes occur in soil solution, 

although SO4

2−

 may also play a role by serving to elevate chronic Al concentrations (Eagar et al., 1996). 

High leaching of NO3

−

 in soil water and streamwater draining high-elevation spruce-fir forests has 

been documented in numerous studies in the Southern Appalachian Mountain region (Joslin et al., 1992; 

Joslin and Wolfe, 1992b, 1994; Nodvin et al., 1995; Van Miegroet et al., 1992). This high NO3

−

 leaching 

has been attributed to a combination of high N deposition, low N uptake by forest vegetation, and 

 B-11
inherently high N release from soils. Forest age is another major factor-affecting uptake, with mature 

forests requiring minimal N for new growth and, hence, often exhibiting higher NO3

−

 leaching that 

younger, faster growing stands (Goodale and Aber, 2001). Old-growth red spruce stands in the Southern 

Appalachians have been demonstrated to have significantly slower growth rates than stands younger than 

120 years (Smith and Nicholas, 1999). The latter feature is associated with low C:N ratios in mineral soil, 

high N mineralization potential and high nitrification (Eagar et al., 1996; Joslin et al., 1992). 

In most terrestrial ecosystems in the U.S., N is strongly retained and there is limited mobility of 

NO3

−

. Exceptions to this pattern tend to occur in spatially limited regions that receive high levels of total 

N deposition (higher than about 10 to 20 kg N/ha/yr) and in alpine and subalpine environments that have 

little soil or vegetative development over substantial portions of the watersheds. 

B.1.3. Interactions with Transitional Ecosystems 

B.1.3.1.

B.1.3.2.

Sulfur Storage and Release in Transitional Ecosystems 

Although S is generally mobile in upland soils in most parts of the U.S., wetlands act as both 

sources and sinks of atmospherically deposited S. Wetlands retain and release S in response to variations 

in hydrology, which in turn affect oxidation and the reduction process in wetland soils. Ito et al. (2005) 

evaluated the influence of land cover types on SO4

2−

 fluxes in Adirondack lake watersheds. They found 

that SO4

2−

 concentration in drainage water decreased in association with increased wetland area within the 

lake watershed (adj. r2

 = 0.58, p ≥ 0.001). They attributed this observed pattern to dissimilatory SO4

2−

reduction in anaerobic wetland soils. 

Sulfur storage in wetland soils to some degree prevents or delays the acidification of downstream 

surface waters with mineral acidity. However, the water table in wetland areas typically drops during 

drought conditions, and this allows development of aerobic conditions in surface wetland soils. Under 

aerobic conditions, stored S is re-oxidized to SO4

2−

, which can then be rapidly mobilized under high-flow 

conditions that occur in response to rainfall or snowmelt. This can cause substantial episodic pulses of 

acidity in surface waters that receive drainage water from wetlands. Thus, wetlands buffer downstream 

receiving waters against chronic acidity to some degree, but can be an important source of periodic 

episodes of more extreme acidity. 

Organic Acidity in Transitional Ecosystems and Downstream Surface Waters 

Organic acids in fresh water originate from the degradation of biomass in upland areas, wetlands, 

near-stream riparian zones, the water column, and stream and lake sediments (Hemond, 1994). The 

watersheds of surface waters that have high concentrations of organic matter (DOC >about 400 μM) often 

contain extensive wetlands and/or extensive organic-rich riparian areas (Hemond, 1990; Sullivan, 2000a). 

Organic acids contributed by wetlands to downstream drainage waters can influence surface water 

acid-base chemistry, particularly in dilute waters having moderate to high (greater than about 400 μM) 

DOC concentrations. Organic acids in surface waters include a mixture of functional groups having both 

strong and weak acid character. Some lakes and streams are naturally acidic as a consequence of organic 

acids contributed to solution by wetlands. The presence of organic acids also provides buffering to 

minimize pH change in response to changes in the amount of SO4

2−

 and NO3

−

 derived from acidic 

deposition. 

There are many lakes and streams that are chronically acidic or low in ANC mainly due to the 

presence of organic acids. In many cases, the principal source of these organic acids is the wetlands 

within the watershed. The NAPAP (Sullivan et al., 1992) concluded that about one-fourth of all acidic 

lakes and streams surveyed in the National Surface Water Survey (NSWS) (Kaufmann et al., 1988; 

 B-12
Linthurst et al., 1986a) were acidic largely as a consequence of organic acids. A survey of 1400 lakes in 

the Adirondack Mountains by the Adirondack Lake Survey Corporation (ALSC) (Kretser et al., 1989), 

which included many small lakes and ponds (1 to 4 ha) having relatively high DOC, revealed that about 

38% of the lakes had pH <5 due to the presence of organic acids, and that organic acids generally 

depressed the pH of Adirondack lakes by 0.5 to 2.5 pH units in the ANC range of 0 to 50 μeq/L (Baker 

et al., 1990a). 

Specification of the acid-base character of water high in DOC is somewhat uncertain. Attempts 

have been made to describe the acid-base behavior of organic acids using a single H+

 dissociation 

constant (pKa), despite the fact that organic acids in natural waters are made up of a complex mixture of 

acidic functional groups. A portion (perhaps one-third) of the acidity in organic acids is quite strong, with 

some ionization occurring at pH values well below 4.0 (Driscoll et al., 1994b; Hemond, 1994). A number 

of modeling approaches have been used to estimate the acidity of organic acids in fresh waters, often as 

simple organic acid analogs having different pKa values (Oliver et al., 1983; Perdue et al., 1984; Driscoll 

et al., 1994b). 

The importance of naturally occurring organic acids as agents of surface water acidification was 

reinforced by a modeling study (Sullivan and Eilers, 1996) that showed that inclusion of organic acids in 

the Model of Acidification of Groundwater in Catchments (MAGIC) had a substantial effect on model 

predictions of surface water pH, even in waters where DOC concentrations were only moderate. MAGIC 

hindcasts of pre-industrial lakewater pH of Adirondack lakes showed poor agreement with diatom 

inferences of pre-industrial pH when organic acids were not considered in the MAGIC model (Sullivan 

and Eilers, 1996). Revised MAGIC hindcasts of pre-industrial lakewater pH that included an organic acid 

representation (Driscoll et al., 1994b) showed considerably closer agreement with diatom inferences 

(Figure B-1). The mean difference between MAGIC and diatom estimates of pre-industrial pH was 

reduced from 0.6 pH units to 0.2 pH units when organic acids were included in the model, and the 

agreement for individual lakes improved by up to a full pH unit (Sullivan et al., 1996a). 

Rosenqvist (1978) and Krug et al. (1985) hypothesized that a significant component of the mobile 

acid anions contributed from atmospheric deposition (e.g., SO4

2−

, NO3

−

) replace organic anions that were 

previously present in solution. Under this anion substitution hypothesis, the net result of acidic deposition 

is not so much an increase in cations (including potentially toxic H+

 and Aln+) as much as an exchange of 

SO4

2−

 and NO3

−

 anions for organic anions, with little or no change in ANC and pH. This hypothesis has 

received some support from paleolimnological studies, which suggested historic decreases in DOC 

concentrations during the period of lakewater acidification in the 1900s (Davis et al., 1985; Dixit et al., 

2001; Kingston and Birks, 1990). Other studies have found a decrease in organic acidity which was at 

least partly attributable to the extent of organic acid protonation. David et al. (1999) measured a decrease 

in organic anion concentrations in stream water in response to the experimental whole-watershed 

acidification experiment at the Bear Brook Watershed in Maine. Wright et al. (1993) concluded that ANC 

increases in a small watershed in Norway, where rates of acidic deposition were experimentally reduced, 

were limited by the increasing role of organic acids that accompanied decreasing acid deposition load. 

Complexation of organic acids by metals (Almer et al., 1974; Cronan and Aiken, 1985; Dickson, 

1978; Lind and Hem, 1975) and pH-dependent changes in dissociation of organic acids (Oliver et al., 

1983; Wright et al., 1988a) are probably important components of the organic acidity response. Loss of 

DOC in response to acidic deposition can also cause a shift in Al species composition towards lesser 

complexation with organic ligands. Such a shift from ALo to Ali increases toxicity of the Al to aquatic 

biota (Baker and Schofield, 1982). Changes in pH can alter the charge density of organic solutes and thus 

influence organic contributions to acidity (Wright et al., 1988a, 1988b). David et al. (1999) found that the 

charge density of organic acids decreased by about 1 μeq/L/mg C at West Bear Brook in Maine, in 

response to 6 years of experimental acidification, probably due to greater protonation of organic acid 

anions at the lower pH. Similar results were reported by Lydersen et al. (1996) at Lake Skjervatjern in 

Norway. Values of the organic acid charge density in the ALSC lakes in the Adirondack Mountains 

 B-13
increased with increasing pH between pH values of 5.0 to 7.0 due to the presence of weakly acidic 

functional groups (Driscoll et al., 1994b). 

Source: Sullivan et al. (1996). 

Figure B-1. MAGIC model hindcast estimates of pre-industrial pH versus diatom-inferred pH for 33 

statistically selected Adirondack lakes: a) without including organic acid representation in 

the MAGIC simulations, and b) including a triprotic organic acid analog model in the MAGIC 

simulations. 

Hedin et al. (1990) artificially acidified a small, moderately high-DOC (725 μM C) stream with 

H2SO4 at HBEF in New Hampshire. Streamwater pH (4.4) was near the range of reported average pKa

values for organic acids, suggesting that the capacity of organic acids to buffer mineral acidity should be 

high. The acid loading rate was adjusted to achieve an increased streamwater SO4

2−

 concentrations of 

150 μeq/L at the downstream sampling point 108 m below the point of acid addition. Adjustments were 

made for dilution by soil water or inflow from small tributaries. Although streamwater DOC did not 

change significantly, the concentration of organic anions (as calculated from the charge balance) 

decreased by 17 μeq/L. Thus, the overall capacity of organic anions to neutralize mineral acid inputs 

offset about 11% of the added acid (Hedin et al., 1990). This experiment only considered interactions 

between mineral acid and organic matter within the stream. Any additional buffering that may have been 

provided within the terrestrial catchment was not represented in the experimental design. Also, any 

 B-14
possible catchment-mediated influences of the experimental acidification on organic acid properties or 

terrestrial DOC mobilization were excluded from the experiment because the acid was not applied to the 

catchment soils. 

Source: Stoddard et al. (2003). 

Figure B-2. Cumlative frequency diagram (distribution) of slopes for SO4

2− , NO3−, Gran ANC, hydrogen 

ion, [Ca2+ + Mg2+], and DOC concentrations in LTM surface water monitoring sites, by region, 

for the period 1990–2000. The Ridge/Blue Ridge Province did not have sufficient DOC data to 

allow trend analysis. 

 B-15
Results of a resurvey of 485 Norwegian lakes sampled in both 1986 and 1995 provided evidence in 

support of an increase in organic acid anion concentrations in association with decreased lakewater SO4

2−

concentration (Skjelkvåle et al., 1998). The organic acid anion concentration increased by an amount 

equal to between 9% and 15% of the decrease in SO4

2−

concentration in the four regions of the country 

most heavily affected by the decrease in S deposition during the intervening 10 year period. Lakewater 

SO4

2−

 concentrations decreased by 9 μeq/L (western and northern Norway) to 20 to 21 μeq/L (eastern and 

southern Norway). Only in mid-Norway, where average SO4

2−

 concentration decreased by only 6 μeq/L, 

did the organic acid anion concentration remain unchanged between 1986 and 1995 (Skjelkvåle et al., 

1998). 

Recent monitoring data have shown that DOC and organic acid anion concentrations in many lakes 

and streams in the U.S. have increased in association with decreased S deposition. It is likely that a high 

percentage of this DOC originates from wetland soils within the monitored watersheds. This result 

appears to be partly responsible for the limited lakewater ANC and pH recovery that has occurred at many 

locations. The response of surface waters to changes in acidic deposition has included a general increase 

in surface water DOC (Figure B-2). All regions of the eastern U.S. analyzed by Stoddard et al. (2003) that 

had sufficient DOC data for analysis exhibited increases in DOC concentrations during the 1990s. All 

regional trends were significant with the exception of the Northern Appalachian Plateau, the region with 

the lowest median DOC concentration. The median increase in DOC of 0.05 mg/L/yr reported by 

Stoddard et al. (2003) corresponds to an overall increase of about 10% across study regions, similar to 

trends reported elsewhere in the northern hemisphere (Evans and Monteith, 2002; Skjelkvåle et al., 2001). 

This suggests a common cause. Both climate warming and decreasing acidic deposition are possible 

causal agents. 

B.2. Factors That Determine Ecosystem Sensitivity 

B.2.1. Transitional Ecosystems 

B.2.1.1. Wetlands and Peatlands 

Wetlands and peatlands often contain highly acidic soils. Their acidity is mainly attributable to the 

presence of large quantities of naturally occurring organic materials. Fulvic and humic acids, formed 

during the breakdown of organic matter, contribute substantial organic acidity to soil and surface waters 

in wetland and peatland environments. In the case of ombotrophic bogs and poor fens, there is also a 

scarcity of base cations, which would serve to buffer both organic and mineral acidity. 

Because wetland and peatland vegetative communities are adapted to high levels of natural organic 

acidity, it is unlikely that S or N deposition would cause any acidification-related effects at levels of 

acidic deposition commonly found in the U.S. Nevertheless, wetlands are closely tied to a number of 

important biogeochemical processes that regulate watershed response to acidic deposition. The major 

interactions are described below. 

High concentrations of DOC in brownwater lakes and streams are often due to the influence of 

wetlands on hydrography within the watershed. This presence of high concentrations (higher than about 

500 μM) of DOC can substantially reduce the pH and ANC of surface waters, buffer those waters against 

pH changes in response to added mineral acidity, and form stable complexes with dissolved Al, thereby 

reducing its toxicity to aquatic life. Therefore, the response of surface waters to acidic deposition is 

strongly influenced by the extent of upstream and shoreline wetland development. 

 B-16
Wetlands also serve as a (sometimes-temporary) sink for atmospheric S and N. Chemical reduction 

reactions and biological uptake contribute to S and N storage in wetland soils. Oxidation during drought 

periods, when water levels recede, followed by flushing from wetland to downstream surface water 

during subsequent storm flow, can cause substantial pulses of mineral acidity in downstream receiving 

waters. On a chronic basis, the concentration of SO4

2−

 (and associated acidity) in surface water can be 

substantially lower as a consequence of dissimulatory S reduction in upslope wetlands. On an episodic 

basis, wetlands can contribute to wide fluctuations in downstream surface water acid-base chemistry. 

Such fluctuations can include pulses of acidity that may be toxic to aquatic biota. 

Wetlands provide anaerobic substrate for S-reducing bacteria. These bacteria are also partly 

responsible for the increased rate of mercury (Hg) methylation that is known to occur in wetlands. As a 

consequence, fish in lakes drained by wetlands often have much higher concentrations of tissue methyl 

mercury, as compared with fish in lakes that lack watershed wetlands (Driscoll et al., 2007b). 

B.2.1.2. Ponds 

The factors that determine the sensitivity of ponds to acidification from acidic deposition are 

generally similar to those that determine the sensitivity of lakes (discussed in the following section). In 

general, however, ponds and small lakes tend to exhibit low ANC and pH at a greater frequency than do 

larger lakes (Sullivan et al., 1990b). This pattern is mainly a consequence of the higher concentrations of 

DOC frequently found in ponds as compared with larger lakes. In addition, because larger bodies of water 

tend to have larger watersheds, there is a greater likelihood that they will integrate conditions across a 

broader landscape, increasing the possibility of receiving at least a moderate level of base cation supply 

(Sullivan et al., 1990b). Thus, where lakes are acid-sensitive, it is likely that ponds are also acid-sensitive. 

However, synoptic databases of pond acid-base chemistry are generally not available. 

B.2.2. Streams and Lakes 

Acidic deposition that falls as precipitation directly on the lake surface may eventually be 

neutralized by in-lake reduction processes which are controlled in part by hydraulic residence time. 

Natural hydrologic events also alter acidification and neutralization processes during snowmelt and 

change flowpaths during extended droughts (Webster et al., 1990). 

Leaching of base cations by acidic deposition can deplete the soil of exchangeable bases. The 

importance of this response has recently been widely recognized because most watersheds are not 

exhibiting much ANC and pH recovery of drainage water in response to recent large decreases in S 

deposition. This limited recovery can be at least partially attributed to decreased base cation 

concentrations in surface water. This understanding of the base cation response has developed slowly. 

During the 1980s, the generally accepted paradigm of watershed response to acidic deposition was 

analogous to a large-scale titration of ANC (Henriksen, 1984). Atmospheric inputs of acidic anions were 

believed to result in movement of those anions through soils into drainage waters with near proportional 

loss of surface water ANC. This view was modified by Henriksen (1984), who suggested that a modest 

component of the added SO4

2−

 (up to a maximum of about 40%) could be charge-balanced by increased 

mobilization of base cations from soils, and the remaining 60% to 100% of the added SO4

2−

 resulted in 

loss of ANC in surface waters. During the latter part of the 1980s, it became increasingly clear that a 

larger component (>40%) of the added SO4

2−

 was in fact neutralized by base cation release in most cases 

and the ANC (and therefore also pH) of surface waters typically did not change as much as was earlier 

believed. This understanding developed in large part from paleoecological studies (Charles et al., 1990b; 

Sullivan et al., 1990b), which indicated that past changes in lakewater pH and ANC had been small 

relative to estimated increases in lakewater SO4

2−

 concentrations since pre-industrial times (Sullivan, 

2000a). The belief that changes in acidic deposition were accompanied mainly by changes in ANC and 

 B-17
pH has been replaced by the realization that changes in SO4

2−

 were accompanied mainly by changes in 

base cations. Thus, surface waters have not been acidified as much by historical deposition as was earlier 

believed. Furthermore, surface water ANC and pH should not be expected to show substantial chemical 

recovery upon reduced emissions and deposition of S and N. The magnitude of the base cation response 

has clearly limited the extent of surface water acidification caused by acidic deposition. However, this 

same response has contributed to base cation deficiencies in some soils, with associated adverse terrestrial 

effects. 

B.2.3. Other Types of Ecosystems 

There has been little work on the rates of atmospheric deposition to urban ecosystems despite 

extensive data on concentrations and chemical reactions of air pollutants in cities (U.S. EPA, 2004). 

Nevertheless, urban ecosystems are often subjected to large rates of deposition of anthropogenic 

pollutants (Lovett et al., 2000a). Decades of research on urban air quality indicate that cities are often 

important sources of emissions of NOX, SOX, and dust. Urban N deposition may affect nutrient cycles and 

soil acid-base chemistry in vegetated areas in and around cities, but such possible effects have not been 

studied sufficiently to draw conclusions about sensitivities or effects. 

To determine the patterns of atmospheric deposition and throughfall in the vicinity of a large city, 

Lovett et al. (2000a) measured bulk deposition, oak forest throughfall, and particulate dust at sites along a 

transect within and to the north of New York City. They found that throughfall N was twice as high in the 

urban areas compared with suburban and rural areas. Most of the urban dry deposition of NO3

−

 was from 

gaseous NOX. Because there is limited biological uptake of throughfall N in an urban setting, it is 

believed that a relatively high (but unknown) percentage of N deposited to the urban landscape leaches to 

surface waters. Aquatic effects associated with N leaching from urban environments would be expected to 

be most pronounced near coastal cities. This is because atmospheric deposition to near-coastal urban 

environments can provide a substantial N load to estuaries and near shore oceanic environments, which 

tend to be N-limited. See further discussion in ISA Section 3.3.2.4. 

B.3. Distribution and Extent of Ecosystem Effects 

B.3.1. Terrestrial Ecosystems 

Coniferous forests, with soils that are naturally more acidic, generally have lower pH and base 

saturation than soils in deciduous forests (Fernandez et al., 2003). In a paired watershed study at Bear 

Brook Watershed in Maine, one watershed with mixed coniferous and deciduous species received 

(NH4)2SO4 corresponding to about 25 kg N/ha/yr and 29 kg S/ha/yr. After a decade of experimental 

acidification, the treated watershed had 66 kg/ha/yr less exchangeable Ca2+ and 27 kg/ha/yr less 

exchangeable Mg2+ than the untreated watershed (Fernandez et al., 2003). Soils under conifers (red 

spruce, balsam fir [Abies balsamea], hemlock [Tsuga canadensis]) appeared to be more sensitive to 

acidification than those under hardwoods (American beech [Fagus grandifolia], yellow birch [Betula 

alleghaniensis], sugar, and red maples [Acer rubrum]). The hardwoods demonstrated no significant 

effects from (NH4)2SO4 addition. Differences in response to acid treatment among vegetation covers were 

most pronounced in upper soil (O horizon and upper 5 cm of the B horizon). The study did not distinguish 

between effects from NH4

+

 versus SO4

2−

 additions. 

 B-18
Kozlowski (1985) suggested that plants and soils act as sinks for SO2 deposition at low exposures 

of 1 to 4 μg/m3

, with no discernible effects on ecosystem structure at those levels. Shugart and 

McLaughlin (1985) cautioned that forest responses to SO2 and other stressors are strongly controlled by 

the successional dynamics of impacted forests. Thus, efforts to better understand and quantify forest 

dynamics and development will be paramount to predicting chronic pollution effects. 

Results of N fertilization studies have been used to infer the response of forests to atmospheric N 

deposition. Such studies were reviewed by Johnson (1991) and U.S. EPA (1993a), illustrating that forests 

can respond differently to periodic large pulsed fertilizer inputs, as compared with steady, low-level 

inputs from atmospheric deposition. For example, multiple or continuous inputs of N may stimulate 

populations of nitrifying bacteria (U.S. EPA, 1993a). This might be expected to modify the competitive 

interactions between trees and microbes and affect both the forest growth response and the extent of NO3

−

leaching and associated acidification. 

In the southern Appalachian Mountains, acidification sensitivity has been evaluated for two 

common tree species: red spruce (sensitive) and loblolly pine (Pinus taeda; insensitive). Dendrochronological analyses of tree cores collected for permanent plots in the Great Smoky Mountains National 

Park (37 trees cores from low elevation [~1500 m]; 35 tree cores from high elevation sites [~2000 m]), 

demonstrated a positive correlation between temporal and spatial trends in red spruce growth and acidic 

deposition, with a greater response in trees on ridges than in draws. Ridges are naturally more acidified, 

receive higher levels of acidic deposition, and have shallower soils with lower base saturation (Webster 

et al., 2004). 

Loblolly pine seems to have low susceptibility to adverse effects from acidic deposition. A 

simulated acid addition experiment showed no significant effect of acidification on foliar nutrition in 

loblolly pine seedlings, at application levels of 21 to 26 kg/ha SO4-S and 8 to 10 kg/ha NO3

−

N (Baker 

et al., 1994). Loblolly pines grown on old agricultural fields showed signs of N deficiency over 25 years 

of growth despite atmospheric deposition of 5 to 10 kg N/ha/yr (Richter et al., 2000). 

In the northeastern U.S., two species of coniferous tree (red spruce and red pine) have been shown 

to be sensitive to acidification. Aber et al. (2003) reported a decrease in C:N ratio from about 35 to about 

25 along an increasing N deposition gradient of 3 to12 kg N/ha/yr across the Northeast. At the Harvard 

Forest LTER site, at chronic experimental N addition levels of 50 and 150 kg N/ha/yr, Magill et al. (2004) 

found 31% and 54% decreases, respectively, in red pine growth after 15 years of chronic N application. 

No additive effect of S was seen after 11 years of a combined N and S treatment, with an application of 

74 kg S/ha/yr and 50 kg N/ha/yr. There were no significant differences in baseline measurements between 

the low N and combined N and S treatments. 

Recent evidence indicates that mortality in red spruce in the southern Appalachian Mountains is not 

abnormal when compared to historical rates, and that Fraser fir stands killed by the balsam woolly adelgid 

(Adelges piceae) are largely being replaced by vigorous re-growth of young stands of that species (Van 

Miegroet et al., 2007). To what extent spruce or fir mortality in the southern Appalachian Mountains will 

be replaced with a species mix similar to that existing before the mortality remains to be seen. 

At the Fernow Experimental Forest in West Virginia, (NH4)2SO4 inputs of 54 kg N/ha/yr and 61 kg 

S/ha/yr (application plus ambient atmospheric deposition), each about three times the ambient deposition 

level, were applied to one watershed for 4 years. Few differences in soil and forest floor chemistry were 

found in response to the N addition, although pH was significantly lower in the treatment watershed, 

corresponding to increased base-flow concentrations of NO3

−

 and Ca2+ (Gilliam et al., 1996). 

Deciduous forests show variable responses to acidification depending on the tree species present. 

Along an increasing N deposition gradient in the northeastern U.S., from 4.2 to 11.1 kg N/ha/yr, Lovett 

and Rueth (1999) found a twofold increase in mineralization in soils of sugar maple stands, but no 

significant relationship between increased deposition and mineralization in American beech stands. This 

difference might be attributable to the lower litter quality in beech stands. Thus, sugar maple appears to be 

more susceptible to effects of increasing deposition and concomitant soil acidification from either direct 

leaching of NO3

−

 or enhanced nitrification. For northeastern hardwoods, Aber et al. (2003) found a 

 B-19
decrease in C:N ratio from 24 to 17 over a deposition gradient of 3 to 12 kg N/ha/yr. This decrease was 

similar but less steep than the decrease seen in conifers. 

Across an 800 km pollution gradient (3 to 11 kg SO4 -S/ha/yr; 2 to 4 kg NO3 -N/ha/yr) in northern 

hardwood forests, with maples dominant, Pregitzer et al. (1992) found a 200 to 300 μg/g increase in foliar 

S, and litter fall S content ranged from 872 to 1356 μg/g. While foliar N did not change across the 

gradient, litterfall N was correlated with changing deposition. Pregitzer and Burton assert that their data 

did not suggest a causal link between acid deposition and forest decline. Decline would be impossible to 

document in the short 5–year time frame of their study. They did, however, assert that their results 

supported the plausibility of altered tree nutrition across large geographic regions due to atmospheric 

deposition. 

B.3.2. Transitional Ecosystems 

Wetlands are common in many areas that contain acid-sensitive surface waters. For example, 

wetlands constitute about 14% of the land surface in the Oswegatchie/Black River watershed in the 

southwestern Adirondack Mountains (Ito et al., 2005), one of the regions of the U.S. most affected by 

surface water acidification from acidic deposition. There are no studies, however, that have documented 

the extent or magnitude of acidification effects of S and N deposition on wetland ecosystems in the U.S. 

The topic of acidification effects on wetlands is not well represented in the literature, and therefore 

the distribution of ecosystem effects for these systems is not presented. Because levels of natural organic 

acidity tend to be high in wetland soils and water, it is not likely that such ecosystems are affected by the 

levels of acidic deposition commonly encountered in the U.S. It is more likely that atmospheric 

deposition affects wetlands via nutrient N enrichment pathways. (See Discussion in ISA Sections 3.3.2.2 

and 3.3.5.2) Gorham et al. (1987) hypothesized that acidic deposition to mineral-poor fens might cause 

depletion of exchangeable base cations and decreased pH of soil water. This mechanism was suggested as 

a possible cause of transition from mineral-poor fen to Sphagnum bog. Such an effect has not been 

observed in response to acidic deposition at levels found in the U.S. 

Synoptic surveys of ponded waters generally are restricted to lakes larger than 1 ha, 4 ha, or 10 ha. 

Reasons for this limitation are varied and can include the perception that larger lakes are more important, 

the failure of regional topographic map coverages to include the smaller lakes and ponds, and the fact that 

smaller lakes tend to be much more numerous than larger lakes within the major lake districts of the U.S. 

In general, if the larger lakes in a given region are sensitive to acidification, the smaller ponds would also 

be expected to be sensitive. In most cases, data to demonstrate this are not available. 

Ponds that have been observed to be sensitive to acidic deposition have been found in the 

Adirondack Mountains in New York (Kretser et al., 1989) and the Mount Zirkel Wilderness Area located 

in the Colorado Rockies (Campbell et al., 2004). Acid-sensitive ponds are likely to be found elsewhere as 

well. 

B.3.3. Aquatic Ecosystems 

In most regions of the U.S., most lakes and streams are not highly sensitive to existing levels of 

ambient air pollution. In addition, air pollution levels are generally decreasing in many parts of the 

country, especially in the eastern U.S., in response to federal and state air pollution control regulations. 

Therefore, the highly sensitive, and acidified, systems tend to be restricted to a relatively small percentage 

of the overall aquatic resource base. There are exceptions to this generalization, such as for example in 

Monongahela National Forest, WV, where a high percentage of the streams are acid-sensitive and highly 

acid-affected (Sullivan et al., 2002b). Similarly, a high percentage of Adirondack lakes (and presumably 

 B-20
also streams) are acid-sensitive and have been acidified by atmospheric deposition of S and N (Driscoll 

et al., 1991). 

Studies to assess relationships between atmospheric deposition loading (N and S) and the estimated 

or expected extent, magnitude, and timing of aquatic acidification effects (NAPAP, 1998; U.S. EPA, 

1995a; Van Sickle and Church, 1995) often employ a “weight of evidence” evaluation of the relationships 

between deposition and effects, as followed by NAPAP in the Integrated Assessment (IA) (NAPAP, 

1991). 

Several kinds of evidence were used in the IA to assess the extent and magnitude of acidification in 

sensitive regions of the U.S. These included: 

 results of watershed simulation models that projected past or future chemical changes in response 

to changes in S deposition 

 empirical biological dose/response models 

 improved relationships between surface water chemistry and ambient acidic deposition 

 trend analyses of long-term monitoring chemical data in regions that have experienced large 

recent changes in acidic deposition levels 

 paleolimnological reconstructions of past water chemistry using fossil remains of algae deposited 

in lake sediments 

 results from whole-watershed or whole-lake acidification or deacidification field experiments 

Evidence from each type of study contributes to understanding of the quantitative importance of 

acidification and neutralization processes and their effects on the chemistry and biology of affected 

ecosystems. 

B.3.3.1. Status of Surface Waters – Regional Overview 

In the NSS, DOC concentrations were much higher in lowland coastal streams, compared with 

inland streams. National Stream Survey data also supported the hypothesis that atmospheric sources and 

watershed retention of S control regional patterns in streamwater SO4

2−

 concentrations. Most NSS 

watersheds retained the vast majority of the total N loading from wet deposition. The 1986 data 

suggested, however, that some atmospherically deposited N may have been reaching streams in the 

northern Appalachians (Kaufmann et al., 1991). 

In the 1990 NAPAP State of Science/Technology (SOS/T) summary, Baker et al. (1991a) identified 

six high interest subpopulations that accounted for most of the U.S. surface waters that were acidic with 

acidic deposition having been identified as the likely source (Figure B-3; Table B-2): 

 Southwestern Adirondacks 

 New England Uplands 

 Low Si Eastern Upper Midwest 

 Forested Mid-Atlantic Highlands 

 Mid-Atlantic Coastal Plain 

 Northern Florida Highlands 

Stream data for the NSS was unavailable for three of these high-interest areas: the Adirondacks, 

New England, and Upper Midwest. The national WSA data indicated that acidic streams in the Upper 

Midwest are likely to be rare but there are acidic streams in the Adirondacks/New England region. 

Specific areas of interest within the other three high-interest regions are described below. 

 B-21
In addition to the large water chemistry databases developed by the U.S. EPA, there are also some 

important supplemental databases in some regions. For example, based on results of lake surveys 

conducted during the 1980s, about 70% of the known acidic lakes in Maine were either seepage type or 

high elevation (Kahl et al., 1991). The Maine seepage lake dataset includes 120 of the estimated 150 lakes 

in Maine that meet the following criteria: located in sand and gravel mapped by the USGS or Maine 

Geological Surveys; depth at least 1 m; and area at least 0.4 ha (1 ac). Sampling was conducted in 1986–

87 and 1998–2000, and included at least one fall index sample for each lake. There were 87 lakes with 

Gran ANC less than 100 μeq/L. 

The Maine high elevation lake dataset includes 90 lakes above 600 m elevation. Sampling was 

conducted during the periods 1986–88 and 1997–99. The study included the vast majority of Maine lakes 

that are at least 1 m deep and at least 0.4 ha (1 ac) in area. There were 64 lakes with Gran ANC less than 

100 μeq/L. 

Figure B-3. Location and percentage of acidic surface waters in U.S. high-interest subpopulations with 

respect to acidic deposition effects. Estimates are for the upstream reach ends in the NSS 

data. Population estimates in the Mid-Atlantic Highlands apply only to the forested 

watersheds, while estimates in the eastern Upper Midwest apply only to low-silica (≥ 1 mg/L) 

lakes. Figure taken from Figure 9–106 in the 1990 NAPAP SOS/T report. 

B.3.3.2. Recent Changes in Surface Water Chemistry 

Surface water acid-base chemistry monitoring throughout the eastern U.S. occurs primarily in two 

EPA programs: the Temporally Integrated Monitoring of Ecosystems (TIME) project (Stoddard, 1990) 

and the Long-Term Monitoring (LTM) project (Ford et al., 1993; Stoddard et al., 1998a). Both projects 

are operated in cooperation with numerous state agencies, academic institutions and other federal 

agencies. Each is described below. 

The regions represented by the LTM and TIME monitoring programs (Table B-3) are estimated to 

contain 95% of the lakes and 84% of the streams in the U.S. that have been anthropogenically acidified by 

acidic deposition. The Adirondacks had a large proportion of acidic surface waters (14%) in the NSWS; 

from 1984 to 1987, the ALSC sampled 1,469 Adirondack lakes greater than 0.5 ha in size and estimated 

 B-22
that many more (26%) were acidic (Driscoll et al., 1991). The higher percentage of acidic lakes in the 

ALSC sample was due to inclusion of smaller lakes and ponds (1 to 4 ha in area), many of which were 

acidic as a consequence of naturally occurring organic acids (Sullivan et al., 1990a). The proportions of 

lakes estimated by NSWS to be acidic were smaller in New England and the Upper Midwest (5% and 3%, 

respectively), but because of the large numbers of lakes in these regions, there were several hundred 

acidic waters in each of these two regions. 

The Valley and Ridge Province and Northern Appalachian Plateau had 5% and 6% acidic sites, 

respectively. The only potentially acid-sensitive region in the eastern U.S. not assessed in the Stoddard 

et al. (2003) report was Florida, where the high proportion of naturally acidic lakes, and a lack of longterm monitoring data, make assessment of the effects of acidic deposition problematic (Stoddard et al., 

2003). 

The TIME project is structured as a probability sampling. Each site is chosen statistically to be 

representative of a target population. In the Northeast (New England and Adirondacks), this target 

population consists of lakes with Gran ANC <100 μeq/L, which are those likely to be most responsive to 

changes in acidic deposition. In the Mid-Atlantic, the target population is upland streams with ANC 

<100 μeq/L. Each lake or stream is sampled annually, and results are extrapolated to the target 

populations (Larsen and Urquhart, 1993; Larsen et al., 1994; Stoddard et al., 1996; Urquhart et al., 1998). 

The TIME project began sampling northeastern lakes in 1991. Data from 43 Adirondack lakes can be 

extrapolated to the target population of about 1,000 lakes having ANC <100 μeq/L, out of a total 

population of 1,830 lakes with surface area >1 ha. Data from 30 lakes representing about 1,500 lakes 

having ANC <100 μeq/L, out of a total population of 6,800 lakes, are included in the TIME program in 

New England. 

As a compliment to lake and stream sampling in the statistical populations of lakes in TIME, the 

LTM project samples a subset of sensitive lakes and streams with long-term data, many dating back to the 

early 1980s. Each LTM site is sampled 3 to 15 times per year, and the resulting data are used to 

characterize the response of the most sensitive aquatic systems in each region to changing levels of acidic 

deposition. In most regions, a small number of higher ANC (e.g., Gran ANC >100 μeq/L) sites are also 

sampled. Because of the long-term records at most LTM sites, their trends can also be placed in a better 

historical context than those of TIME sites, where data are only available from the 1990s. Monitoring 

results from the LTM project have been widely published (DeWalle and Swistock, 1994; Driscoll et al., 

1995; Driscoll and Van Dreason, 1993; Kahl et al., 1991, 1993; Murdoch and Stoddard, 1993; Stoddard 

et al., 1998; Stoddard and Kellogg, 1993; Webster et al., 1993). Overall results were summarized by 

Stoddard and Kellog (1993). 

Monitoring data from the LTM and TIME projects were used to evaluate recent changes in lake and 

stream chemistry from 1990 to 2000 in many of the sensitive areas of the eastern U.S. including New 

England, the Adirondack Mountains, the Northern Appalachian Plateau, the Ridge and Blue Ridge 

provinces of Virginia, and the Upper Midwest (Stoddard et al., 2003). There are also substantial numbers 

of acid-sensitive streams in the Blue Ridge Province in North Carolina and portions of South Carolina 

and Tennessee that have been affected by acidic deposition but that were not included in this analysis. In 

general, the results of the TIME/LTM data analysis suggest that about one-quarter to one-third of the 

lakes and streams that were chronically acidic in the 1980s were no longer chronically acidic in the year 

2000. However many still had low ANC and were potentially susceptible to episodic acidification 

(Stoddard et al., 2003). 

Stoddard et al. (2003) found little evidence of regional change in the acidity status of lakes in New 

England or streams in the Ridge/Blue Ridge regions. Furthermore, none of the study regions showed an 

increase in the number of chronically acidic waters, even though there was a decline in base cation 

concentrations and a likely increase in natural organic acidity (Stoddard et al., 2003). An important caveat 

in this analysis is that changes in Gran ANC used in the analysis were based on the median change of all 

sites in a region (Table B-4). However, as shown in (Table B-5), the rates of ANC increase were generally 

more rapid in chronically acidic lakes with ANC less than 0 μeq/L and streams with ANC between 0 and 

 B-23
25 μeq/L. If acidic sites are recovering more rapidly than the population of sites as a whole, then the 

estimates of change in the number of acidic lakes and streams presented would be conservative. 

While general estimates for large regions are useful in providing a broad picture of the extent and 

status of surface water acidity, specific results from studies within those regions can help isolate trends 

and determine the specific mechanisms that contribute to change. The following sections report on the 

current status, past acidification, and potential future conditions for lakes and streams in acid sensitive 

areas of the Northeast, Southeast, Upper Midwest, and Western U.S. 

B.3.4. Regional Assessments 

B.3.4.1. Northeastern Surface Waters 

Current Status 

The Adirondacks and New England are two of the most acid sensitive and intensively studied 

regions in the Northeast. The glaciated soils and location downwind from emissions sources have made 

these areas the subject of intense scientific study over the past four decades. Most of this research has 

focused on lake ecosystems, though important stream studies have been undertaken at specific research 

sites and more regional stream survey work is being conducted. As discussed below, the surface water 

chemistry in these areas integrates atmospheric deposition, local geology, and upland watershed 

characteristics. 

Available surface water datasets for Adirondack lakes include TIME, EMAP, and ALSC, each of 

which is useful for documenting chemical status and recent chemical changes. Population estimates from 

the TIME dataset suggest that 13.0% of Adirondack lakes (238 lakes) were chronically acidic in the early 

1990s during baseflow conditions in the summer. By applying an approximate rate of change in Gran 

ANC of +0.8 μeq/L/yr to these estimates (based on trend slopes for TIME and LTM data, (Table B-6), 

Stoddard et al. (2003) projected that approximately 8.1% of the population (149 Adirondack lakes) 

remained chronically acidic in 2000. This finding suggests that roughly 38% of lakes in the Adirondacks 

that were chronically acidic in the early 1990s were not chronically acidic a decade later. Certain caveats 

need to be included with the results of this analysis, however. Summertime baseflow sampling reflects the 

least acidic conditions experienced throughout the year. In addition, LTM trends, which are based on yearround sampling, may not be representative of trends in the summer-only sampling of the TIME program, 

and the rate of change determined through the TIME program was not controlled for possible differences 

in flow conditions between the two sample periods. Lastly, the ANC value of 0 μeq/L/yr used to define 

acidic waters has been shown to be below the level needed to protect aquatic ecosystems in the 

Adirondack region (Baldigo et al., 2007; Lawrence et al., 2007). 

A study by Driscoll et al. (2001b) used EMAP data from 1991 to 1994 to evaluate the extent of 

acidic lakes in the Adirondacks for that period. The EMAP survey is a probability based survey 

representative of lakes with surface area greater than 1 ha (1,812 lakes). The survey was conducted during 

low-flow summer conditions, and the results therefore likely reflect the highest ANC values for the year. 

Results from the survey indicate that 10% of the population of Adirondack lakes were chronically acidic 

(ANC values of less than 0) and 31% were sensitive to episodic acidification (ANC values between 0 and 

50) during the study period (Driscoll et al., 2001b). 

The ALSC conducted a comprehensive survey of Adirondack lakes greater than 0.2 ha in surface 

area between 1984 and 1987 (Kretser et al., 1989). Of the 1,489 lakes surveyed, 24% had summer pH 

values below 5.0, 27% were chronically acidic (ANC <0), and an additional 21% were probably 

susceptible to episodic acidification (ANC between 0 and 50) (Driscoll et al., 2007a). 

For the New England region, the TIME population data indicates that 5.6% of the population (386 

lakes) in New England exhibited Gran ANC <0 μeq/L during the period of 1991 to 1994. This result is 

 B-24
similar to the EMAP findings which indicate that 5% of lakes in New England and in the eastern Catskill 

region of New York had ANC values less than 0 μeq/L. The EMAP analysis also estimated that an 

additional 10% of the population had low ANC values, between 0 and 50 μeq/L, and were probably 

sensitive to episodic acidification (Driscoll et al., 2001b). 

Both TIME and LTM data from the New England region indicate that only a small increase in Gran 

ANC had occurred during the reported monitoring period (+0.3 μeq/L/yr). As a result, it is estimated that 

the proportion of chronically acidic lakes decreased only 0.1% from 5.6% to 5.5% over the previous 10 

years (Table B-4) (Stoddard et al., 2003). 

State surveys within New England provide additional information on the variation in lakewater 

chemistry across the region. In Maine, approximately 100 clearwater lakes in that state have been 

classified as acidic, based on surveys conducted by the U.S. EPA and the University of Maine (Kahl et al., 

1999). An estimated 13% of the high-elevation lakes in Maine are acidic, compared to less than 1% of 

Maine lakes (>4 ha) represented in EPA’s Eastern Lakes Survey (ELS) (Kahl et al., 1991; Linthurst et al., 

1986a). Most acidic lakes in Maine are either seepage lakes located in sand and gravel deposits, or highelevation lakes located above 600 m elevation. Roughly 60% of the acidic lakes are seepage lakes. The 

acid-sensitive seepage lakes are located in mapped sand and gravel deposits, are at least 1 m deep, and are 

at least 0.4 ha (1 acre) in surface area. About 45 of the 150 lakes of this type in Maine are acidic (Kahl 

et al., 1999). 

Whereas lakes in the Adirondacks and New England have been intensively studied, there are no 

published data which describe the status of streamwater acid-base chemistry at a regional scale, except for 

the Catskill Mountains. 

In the absence of regional streamwater studies, insights can be gained from site-specific long-term 

studies in the region. The HBEF has one of the longest continuous records of precipitation and 

streamwater chemistry in the U.S. Compared to model hindcast approximations, current conditions at 

HBEF indicate that soil percent base saturation has decreased in response to acidic deposition and 

because of accumulation of nutrient cations by forest vegetation. Further, acidic deposition has 

contributed to a nearly fourfold increase in stream SO4

2−

 concentration; a decrease in ANC from positive 

to negative values; a decrease in stream pH to 5.0; and an increase in stream Al, largely occurring as the 

inorganic form which has been shown by Lawrence at al. (2007) to be an unequivocal indication of the 

effects of acidic deposition. Driscoll et al. (2001b) estimated that roughly 6% of lakes and streams in the 

Northeast are considered more sensitive to acidic deposition than the stream monitored at HBEF (Driscoll 

et al., 2001b). 

Past Acidification 

There are limited surface water data that directly document historic conditions and the response to 

atmospheric deposition since the time of the Industrial Revolution (Charles, 1991). To address this gap, 

scientists use sediment cores from lakes and detailed computer models to try to reconstruct past 

conditions as well as understand the mechanisms that contribute to changing conditions. 

Paleolimnological Studies 

Paleolimnological studies use the remains of diatoms and other algae embedded in lake sediments 

to reconstruct historical water chemistry. In the Adirondack Mountains and northern New England, both 

diatom and chrysophyte algal remains in sediment cores have been used to evaluate patterns of past 

acidification in a large number of lakes. 

Major findings of the Paleoecological Investigation of Recent Lakewater Acidification (PIRLA)-I 

and PIRLA-II research programs in the Adirondack Mountains suggested that: Adirondack lakes had not 

acidified as much since pre-industrial times as had been widely believed before 1990; many Adirondack 

lakes with ambient pH greater than 6.0 had not acidified historically, whereas many of the lakes having 

pH less than about 6.0 had acidified; many of the lakes having high pH and ANC had increased in pH and 

ANC since the previous century; and the average F-factor for acid-sensitive Adirondack lakes was near 

 B-25
0.8 (Charles et al., 1990b; Sullivan et al., 1990). The results of these studies had major effects on 

scientific understanding of the extent to which lakes had acidified in response to acidic deposition. The 

view of surface water acidification as a large-scale titration of ANC (Henriksen, 1980, 1984) was replaced 

by the realization that base cation concentrations typically changed more than ANC in response to acidic 

deposition. This realization also modified expectations for chemical recovery of surface waters as acidic 

deposition levels have decreased (Sullivan, 2000). 

Diatom and chrysophyte reconstructions of pH and ANC for a statistically selected group of 

Adirondack lakes suggested that about 25% to 35% of the Adirondack lakes that are larger than 4 ha had 

acidified since preindustrial time (Cumming et al., 1992). Low-ANC lakes of the southwestern 

Adirondacks acidified the most, probably due to low initial buffering capacity and high rainfall and 

deposition of S and N in that area. Cumming et al. (1992) estimated that 80% of the Adirondack lakes that 

had ambient pH ≥ 5.2 had experienced large declines in pH and ANC since the previous century, and that 

30% to 45% of the lakes with pH between 5.2 and 6.0 had also acidified. 

Cumming et al. (1994) reported the results of chrysophyte inferences of pH in recently deposited 

lake sediments to assess acidification timing for 20 low-ANC Adirondack lakes. Lakes that were inferred 

to have acidified since about 1900 tended to be small, high-elevation lakes with lower inferred preindustrial pH than the group of study lakes as a whole. These were probably the most acid-sensitive and 

were the first to acidify with increasing acidic deposition. Husar and Sullivan (1991) estimated that S 

deposition was about 4 kg S/ha/yr at that time. These lakes are located in the high peaks area and in the 

southwestern portion of Adirondack Park. A second category of acidification response included highelevation lakes that were historically low in pH (<5.5) but that acidified further beginning about 1900. 

The third identified type of response included lakes with pre-industrial pH in the range of about 5.7 to 6.3 

that started to acidify around 1900 but showed their greatest pH change around 1930 to 1950. The final 

category included lakes that were not inferred to have acidified. They had pre-industrial pH around 6.0 

and are located at lower elevation. 

Davis et al. (1994) conducted paleolimnological studies of 12 lakes in northern New England that 

were low in pH and ANC. Past logging, forest fire, and vegetation composition in the watersheds were 

estimated from oral and written historical information, aerial photographs, and tree ring analyses. Lake 

sediment cores were collected and analyzed for pollen, diatoms, and chemistry to reconstruct past lake 

conditions for several hundred years. All 12 lakes were historically low in pH and ANC, with diatominferred pre-industrial ANC of –12 to 31 μeq/L. The inferred pH and ANC values of the lakes were 

relatively stable throughout the one to three centuries of sediment record before watershed disturbance by 

Euro-Americans. From the early 19th into the 20th century, however, all of the lakes showed increased 

diatom-inferred pH changes of about 0.05 to 0.6 pH units and increased diatom-inferred ANC of about 5 

to 40 μeq/L. Most of these inferred increases in pH and ANC coincided with watershed logging. For all 

study lakes, recovery to pre-logging acid-base lake chemistry conditions was followed by continued 

decline in pH by 0.05 to 0.44 pH units and ANC by up to 26 μeq/L, probably because of acidic 

deposition. The 12–lake mean inferred decreases in pH and ANC in response to acidic deposition were 

0.24 pH units and 14 μeq/L, respectively (Davis et al., 1994). 

Modeling Studies 

The most extensive regional modeling study that provides estimates of past acidification of 

Adirondack lakes is that of Sullivan et al. (2006b). They modeled past changes in the acid-base chemistry 

of 70 Adirondack lake watersheds, including 44 that were statistically selected to be representative of the 

approximately 1,320 lake watersheds in the Adirondacks that have lakes larger than 1 ha and deeper than 

1 m and that have ANC ≥ 200 μeq/L. Model hindcasts were constructed using both the MAGIC and 

PnET-BGC models. 

Based on MAGIC model outputs extrapolated to the regional Adirondack lake population, 

maximum past acidification occurred by about 1980 or 1990, with median ANC of the population under 

investigation of about 61 μeq/L (reduced from a median of 92 μeq/L estimated for the pre-industrial 

 B-26
period). By 1990, 10% of the population target lakes had decreased in ANC to below −16 μeq/L and 25% 

had ANC <28 μeq/L. Percentile values in 2000 illustrated limited chemical recovery (3 to 5 μeq/L) 

compared with simulated values in 1980 and 1990. 

The MAGIC model simulations suggest that none of the target lakes were chronically acidic (had 

ANC <0 μeq/L) under pre-industrial conditions, but that by 1980 there were about 204 acidic Adirondack 

lakes. That number decreased by an estimated 14% between 1980 and 2000. Similarly, the MAGIC model 

simulations suggested that there were no Adirondack lakes having ANC <20 μeq/L in 1850, but by 1990 

there were 263 such lakes. Many lakes (N = 191) were estimated to have had pre-industrial ANC below 

50 μeq/L, and this estimate increased threefold by 1990, followed by a decrease to 399 lakes in 2000. 

PnET-BGC model simulations generated output generally similar to results provided by MAGIC 

model simulations. PnET-BGC simulations suggested that lakewater SO4

2−

, NO3

−

, and base cation 

concentrations under pre-industrial conditions were much lower than current values. In 1850, simulated 

SO4

2−

 concentrations in all study lakes were less than 25 μeq/L, and the median value was about 

15 μeq/L. By 1980, the median simulated SO4

2−

 concentration had increased more than sixfold to about 

100 μeq/L. Simulated lake NO3

−

 concentrations also increased markedly during that time, with the 

median value increasing from about 4 μeq/L in 1850 to 12 μeq/L in 1980. Simulated increases in the sum 

of divalent base cation concentrations were less than for SO4

2−

 concentrations, with the median value 

increasing from 93 μeq/L in 1850 to 140 μeq/L in 1980. This large change in SO4

2−

 + NO3

−

 relative to the 

change in the sum of base cation concentrations was the major mechanism driving the decreases in ANC 

and pH associated with historical increases in acidic deposition. 

Simulated lakewater ANC and pH and soil base saturation decreased from pre-industrial conditions 

to recent times. Results from PnET-BGC suggested that the median Adirondack lake, from among the 

estimated 1,320 lakes in the population larger than 1 ha that had measured recent ANC <200 μeq/L, had 

pre-industrial ANC near 80 μeq/L; an estimated 10% of the lake population had pre-industrial ANC 

<41 μeq/L; and one-fourth had pre-industrial ANC <64 μeq/L. Percentiles for the year 2000 suggest 

decreases in SO4

2−

, NO3

−

, and sum of base cations, and small increases in ANC since 1990 for lowerANC lakes. 

Results from PnET-BGC suggest that none of the lakes in the Adirondack population had preindustrial ANC below 20 μeq/L. By 1990, there were 289 lakes having ANC <20 μeq/L and 217 

chronically acidic (ANC ≥ 0 μeq/L) lakes according to PnET-BGC simulations. There were 202 lakes in 

the population simulated to have had pre-industrial ANC below 50 μeq/L, and this number increased 2.8 

times by 1980 under the PnET-BGC simulations. 

PnET-BGC has also been used to characterize past conditions at streams within the HBEF. 

Gbondo-Tugbawa et al. (2002) used relationships between current emissions and deposition, and 

estimates of past emissions to reconstruct historical deposition conditions. Their analysis also considered 

land disturbances such as logging in 1918–1920 and hurricane damage in 1938. Using this approach, they 

estimated that past soil base saturation in the mineral soil (circa 1850) was approximately 20%, stream 

SO4

2−

 concentration was approximately 10 μeq/L, stream ANC was about 40 μeq/L, stream pH was 

approximately 6.3, and stream Ali concentration was below 1 μmol/L (Driscoll et al., 2001b). 

Recent Trends 

Sulfur deposition has contributed to chronic soil and surface water acidification in the eastern U.S. 

to a much greater extent than has N deposition. Nitrate concentrations in acid-sensitive drainage waters in 

the eastern U.S. are generally much lower than SO4

2−

 concentrations. 

The concentration of SO4

2−

 in precipitation has declined for about the past three decades throughout 

the northeastern U.S., in response to decreased atmospheric emissions and deposition. EPA’s LTM 

Program has been collecting monitoring data since the early 1980s for many lakes and streams in acidsensitive areas of the U.S., including the Northeast. These data allow evaluation of trends and variability 

in key components of lake and streamwater chemistry before, during, and subsequent to Title IV 

implementation. Throughout the northeastern U.S., the concentration of SO4

2−

 in surface waters has 

 B-27
decreased substantially (Figure B-4) in response to decreased emissions and atmospheric deposition of S. 

Decreased concentrations of SO4

2−

 in lakes and streams of a third, or more, have been commonly 

observed. 

Lakewater SO4

2−

 concentrations have decreased steadily in Adirondack lakes, at least since 1978 

(Driscoll et al., 1995; Stoddard et al., 2003). Initially, there was not a systematic increase in lakewater pH 

or ANC in response to the decreased SO4

2−

 concentrations. Rather, the decline in lakewater SO4

2−

 during 

the 1980s was charge-balanced by a nearly equivalent decrease in concentrations of base cations in many 

of the low-ANC lakes (Driscoll et al., 1995). Similar findings were reported by Stoddard and Kellog 

(1993) for lakes in Vermont. F-factors for the nine ALTM lakes that showed significant declines in both 

the sum of base cations (SBC) and (SO4

2−

 + NO3

−

) concentration ranged from 0.55 to greater than 1.0, 

with a mean of 0.93 (Driscoll et al., 1995). These high F-factor values for chemical recovery from 

acidification were similar to results of historical acidification obtained by Sullivan et al. (1990), based on 

diatom reconstructions of historical change for 33 Adirondack lakes. 

Trend analysis results for the period 1982 to 1994 were reported by Stoddard et al. (1998) for 36 

lakes in the northeastern U.S. having ANC ≥ 100 μeq/L. Trend statistics among sites were combined 

through a meta-analytical technique to determine whether the combined results from multiple sites had 

more significance than the individual Seasonal Kendall Test statistics. All lakes showed significant 

declining trends in SO4

2−

 concentration (Δ SO4

2−

 = −1.7 μeq/L/yr; p ≥ 0.001). Lakewater ANC responses 

were regionally variable. Lakes in New England showed evidence of ANC recovery (Δ ANC = 

0.8 μeq/L/yr; p ≥ 0.001), whereas Adirondack lakes exhibited either no trend or further acidification, 

largely because of declines in base cation concentrations. 

The observed changes in the concentration of NO3

−

 in some surface waters have likely been due to 

a variety of factors, including climate. During the 1980s, NO3

− concentration increased in many surface 

waters in the Adirondack and Catskill Mountains in New York (Driscoll and Van Dreason, 1993; Murdoch 

and Stoddard, 1993). There was concern that northeastern forests were becoming N-saturated, leading to 

increased NO3

−

 leaching from forest soils throughout the region. Such a response could negate the 

benefits of decreased SO4

2−

 concentrations in lake and stream waters. However, this trend was reversed in 

about 1990, and the reversal could not be attributed to a change in N deposition. Nitrate leaching through 

soils to drainage waters is the result of a complex set of biological and hydrological processes. Key 

components include N uptake by plants and microbes, transformations between the various forms of 

inorganic and organic N, and local precipitation patterns. Most of the major processes are influenced by 

climatic factors, including temperature, moisture, and snowpack development. Therefore, NO3

−

concentrations in surface waters respond to many factors in addition to N deposition and can be difficult 

to predict. It is likely that monitoring programs of several decades or longer will be needed to separate 

trends in NO3

−

 leaching from climatic variability in forested watersheds (Driscoll et al, 1995). 

Monitoring data collected during the 1990s in the LTM and TIME projects illustrated that most 

regions included in the monitoring efforts showed large declines in SO4

2−

 concentrations in surface waters 

over the 10 years of monitoring, with rates of change ranging from 1.5 to 3 μeq/L/yr (Figure B-5). These 

declines in lake and stream SO4

2−

 concentration were considered consistent with observed declines in S 

wet deposition. Surface water NO3

−

 concentrations also decreased, but only in the two regions that had 

the highest ambient surface water NO3

−

 concentrations (Adirondacks and Northern Appalachian Plateau), 

but were relatively unchanged in regions with lower concentrations. DOC increased in each region over 

time. This finding suggests an increase in the contribution of natural organic acidity, which would 

partially offset the expected chemical recovery from decreased acidic deposition. 

 B-28
Figure B-4. Measured concentration of SO4

2− in selected representative lakes and streams in six regions 

of the U.S. during the past approximately 15 years. Data were taken from EPA’s Long-Term 

Monitoring (LTM) program. 

ANC increased in the Adirondacks at a rate of about +1 μeq/L/yr, despite a decline in surface water 

base cation concentrations (Ca2+ + Mg2+. The decline in base cations offset some of the decline in SO4

2−

, 

 B-29
and thus limited the increase in ANC or pH that occurred in response to lower SO4

2−

 concentrations. 

Surface water ANC and pH increased significantly in the 1990s; Ali concentrations declined slightly. 

Regional surface water ANC did not change significantly in New England (Stoddard et al., 2003). 

Moderate increases in surface water ANC during the 1990s reduced the estimated number of acidic 

lakes and stream segments in the northeastern U.S. Stoddard et al. (2003) estimated that there were 150 

Adirondack lakes in the year 2000 that had ANC less than 0 (8.1% of the lake population), compared to 

13% (240 lakes) in the early 1990s. 

Lakewater SO4

2−

 concentrations in the most acid-sensitive Maine lakes declined by about 12% to 

22% during the period 1982 to 1998 (Kahl, 1999). Only in the seepage lakes, however, was there 

evidence of a small decline in lakewater acidity during that period (Table B-7). 

Source: Stoddard et al. (2003). 

Figure B-5. Summary of regional trends in surface water chemistry from 1990 through 2000 in regions 

covered by the Stoddard et al. (2003) report. 

However, evidence for reductions in lakewater ANC in seepage lakes from the mid-1980s to 1998 

were based on a comparison of only two sampling points, which may have been influenced by climatic 

variation. Therefore, the conclusion of decreasing acidity of seepage lakes is considered preliminary. The 

seepage lakes are generally hydrologically isolated from their surrounding soil environment. They 

therefore did not show a clear decreasing trend in base cation concentrations, as has been found in 

drainage lakes throughout the Northeast. The high-elevation lakes, in contrast, showed small declines in 

lakewater acidity during the 1980s, but that trend slowed or reversed in the 1990s (Kahl, 1999). Both the 

seepage and high elevation lakes showed increased DOC concentrations of 10% to 20%, generally by 

about 0.5 to 1.0 mg/L. The increase in dissolved organic matter would be expected to limit the extent of 

ANC and pH recovery that would otherwise accompany the observed decreases in SO4

2−

 concentration. 

Whereas NO3

−

 concentrations decreased during the 1990s in many lake chemistry datasets (Stoddard 

et al., 2003), the high-elevation lakes in Maine continued to show high concentrations. 

 B-30
The reference stream of the Bear Brook Watershed Study (East Bear Brook) has the longest 

continuous, high-frequency data record of stream chemistry in Maine. Sulfate and NO3

−

 concentration 

have both declined substantially since 1987. Base cations declined by an almost equivalent amount, and 

the increase in ANC has been limited (Kahl, 1999). 

Long-term stream water data from the HBEF reveal a number of changes that are consistent with 

trends in lakes and streams across Europe and eastern North America (Evans and Monteith, 2002; 

Stoddard et al., 2003, 1999). Stream water draining the HBEF reference watershed (Watershed 6) had a 

32% decline in annual volume-weighted concentrations of SO4

2−

 (−1.1 μeq/L) between 1963 and 2000 

(Driscoll et al., 2007a). This decrease in stream SO4

2−

 concentration corresponds to both decreases in 

atmospheric emissions of SO2 and to bulk precipitation concentrations of SO4

2−

 (Likens et al., 2001). In 

addition, there has been a long-term decrease in stream concentrations of NO3

−

 that is not correlated with 

a commensurate change in emissions of NOX or in bulk deposition of NO3

−

. The long-term declines in 

stream concentrations of strong acids (SO4

2

 + NO3

−

; −1.9 μeq/L/yr) have resulted in small but significant 

increases in pH, from 4.8 to 5.0 (Driscoll et al., 2007a). Streams at HBEF remain acidic compared to 

background conditions, when stream pH was estimated to be approximately 6.0 (Driscoll et al., 2007). 

The increase in stream pH has been limited because of marked concurrent decreases in the sum of base 

cations (−1.6 μeq/L/yr; Driscoll et al., 2001b). 

Future Projections 

MAGIC model simulations were conducted for the NAPAP IA to forecast the response of lakes and 

streams in the eastern U.S. to S deposition. Results were reported by NAPAP (1991), Sullivan et al. 

(1992), and Turner et al. (1992). The projected median change in lakewater or streamwater ANC during 

50–year simulations was similar among regions, except in the Southern Blue Ridge and Mid-Atlantic 

Highlands, where acidification was delayed due to S adsorption on watershed soils. MAGIC projected 

relatively small future loss of ANC in most northeastern watersheds under continued constant deposition. 

These modeled changes were due to a simulated slight depletion of the supply of base cations from soils 

(Turner et al., 1992). 

On average, each kg/ha/yr change in S deposition was projected by MAGIC to cause a 3 to 4 μeq/L 

median change in surface water ANC. Such projected changes in ANC, while considerably smaller than 

was generally thought to occur in the 1980s, nevertheless suggested widespread sensitivity of surface 

water ANC to changes in S deposition throughout the northeastern U.S. (Sullivan, 2000). 

Since 1990, adjustments have been made to the MAGIC model and its application method in 

response to model testing using paleolimnological data (Sullivan et al., 1992, 1996a) and the results of 

acidification and deacidification experiments (Cosby et al., 1995, 1996; Norton et al., 1982) and empirical 

studies (Sullivan and Cosby, 1998). The net effect has been that the model projects somewhat less 

sensitivity of Adirondack lakes to change in S deposition as compared with the version of MAGIC 

applied in 1990 (Sullivan and Cosby, 1998). 

Model projections of future acid-base chemistry under three scenarios of future atmospheric 

emissions controls were presented by Sullivan et al. (2006a) and Zhai et al. (2008) for lakes in the 

Adirondack Mountains to evaluate the extent to which lakes might be expected to continue to increase in 

ANC in the future. Estimated levels of S deposition at one representative watershed are shown in Figure 

B-6 for the hindcast period and in the future under the three emissions control scenarios. Model 

simulations for 44 statistically selected Adirondack lakes using the MAGIC and PnET-BGC models were 

extrapolated to the regional lake population. Cumulative distribution frequencies of ANC response 

projected by MAGIC are shown in Figure B-7 for the past (1850), peak acidification period (1990), and 

future (2100). Results for the future are given for each of the scenarios. 

Results suggested that the ongoing trend of increasing lakewater ANC for the most acid-sensitive 

lakes would not continue under future emissions and deposition levels anticipated as of 2003 (Base Case 

Scenario). The numbers of Adirondack lakes having ANC below 20 and below 50 μeq/L were projected to 

increase between 2000 and 2100 under that scenario, and the number of chronically acidic Adirondack 

 B-31
lakes (i.e., ANC less than 0) was projected to stabilize at the level reached in 2000. This projected reversal 

of chemical recovery of acid-sensitive lakes was due to a continuing decline in the simulated pool of 

exchangeable base cations in watershed soils. 

Simulations suggested that re-acidification might be prevented with further reductions in emissions 

and deposition. 

Chen and Driscoll (2005b) applied the PnET-BGC model to 44 EMAP lake watersheds in the 

Adirondacks. PnET-BGC was used to predict the acid-base chemistry of soils and surface waters, and to 

assess the fisheries status during pre-industrial conditions (~1850) and under three future acidic 

deposition scenarios. 

Source: Sullivan et al. (2006b). 

Figure B-6. Estimated time series of S deposition at one example watershed in the SW Adirondack 

Mountains used by Sullivan et al. (2006b) as input to the MAGIC model for projecting past and 

future changes in lakewater chemistry attributable to acidic deposition. Future deposition 

estimates were based on three emissions control scenarios (Base Case, Moderate Additional 

Controls, Aggressive Additional Controls). 

 B-32
Source: Sullivan et al. (2006b). 

Figure B-7. Simulated cumulative frequency distributions of lakewater ANC at three points in time for the 

population of Adirondack lakes. 

Model hindcasts using PnET-BGC indicated that acidic deposition has greatly altered surface 

waters and soils in the Adirondacks over the past 150 years, and that some ecosystems are continuing to 

acidify despite decreases in S deposition. The model was applied to three future emissions scenarios: base 

case, moderate emissions reductions, and aggressive emissions reductions. A case study for Indian Lake 

in the Adirondacks illustrated that larger reductions in deposition caused greater decreases in SO4

2−

 and 

base cation concentrations in stream water and greater recovery in pH and ANC. Within the full 

population of lake-watersheds, some showed decreasing ANC and pH values from 1990 to 2050 even 

under the moderate and aggressive reduction scenarios. By 2050 to 2100, however, nearly all lakes 

experienced increasing ANC and pH. The rate of soil base saturation regeneration increased very slowly 

over the modeled time period, compared to changes in surface water chemistry. For 95% of the lakewatersheds studied, simulated soil base saturation remained below 20% in 2100 under all emissions 

scenarios. 

There are few streams in the northeastern U.S. for which future acid-base chemistry status has been 

modeled. One notable exception is the modeling conducted for streams at the HBEF and in the Catskill 

Mountains. Calculations performed by Driscoll et al. (2003a) using the PnET-BGC model suggested that 

“aggressive reductions in N emissions alone will not result in marked improvements in the acid-base 

status of forest streams.” For example, in response to an aggressive utility emissions control scenario 

(hypothesized 75% reduction in utility N emissions beyond the CAAA), the ANC values of Watershed 6 

at HBEF in New Hampshire and Biscuit Brook in the Catskill Mountains in New York were only 

projected to increase by 1 and 2 μeq/L, respectively, by the year 2050 (Driscoll et al., 2003a). Projected 

changes in water chemistry in response to differing levels of N deposition were small in comparison with 

model projections of variations resulting from climatic factors (Aber, 1997; Driscoll et al., 2003a). 

 B-33
B.3.4.2. Southeastern Surface Waters 

The two regions in the Southeast that were identified by Charles (1991) as containing low-ANC 

surface waters are the Appalachian Mountains and Northern Florida. The Appalachian Mountain region 

contains many streams that have low ANC, and it receives one of the highest rates of acidic deposition in 

the U.S. (Herlihy et al., 1993). Streamwater acid-base chemistry has been extensively studied in this 

region (Church et al., 1992; Herlihy et al., 1993; Sullivan et al., 2002, 2003; Van Sickle and Church, 

1995). 

Northern Florida contains the highest percentage of acidic lakes of any lake population in the U.S. 

(Linthurst et al., 1986a, b). Most lakes in Florida are located in marine sands overlying carbonate bedrock 

and the Floridan aquifer, a series of limestone and dolomite formations that underlies most of Florida. 

Most of the acidic and low-ANC lakes are located in the Panhandle and north central lake districts. 

The current status, past acidification and recent trends in surface waters chemistry for both the 

Appalachian Mountains and northern Florida are discussed below. 

Appalachian Mountains 

Current Status 

One of the most important processes affecting watershed acid-neutralization throughout much of 

the Southeast is S-adsorption on soil. If S adsorption on soil is high, relatively high levels of S deposition 

have little or no effect on stream acid-base chemistry, at least in the short-term. However, this 

S-adsorption capacity can become depleted over time under continued S deposition, and this causes a 

delayed acidification response. 

Sulfur-adsorption varies by physiographic province. It is highest in the soils of the Southern Blue 

Ridge, where typically about half of the incoming S is retained. Adsorption is lower in the Valley and 

Ridge watersheds and especially in the Appalachian Plateau (Herlihy et al., 1993). In general, S 

adsorption is higher in the southern portions of the Appalachian Mountain region. 

The Mid-Atlantic Highlands consists of the portions of the Blue Ridge Mountains, Ridge and 

Valley, and Appalachian Plateau ecoregions between the Virginia-North Carolina border and the Catskill 

Mountains in southeastern New York. Acid mine drainage (AMD) is a major source of acidity to streams 

in the Mid-Atlantic Highlands but in many cases is easy to identify due to the high concentrations of 

SO4

2−

 in the streams that are influenced by AMD (Herlihy et al., 1991). Acidic and low-ANC streams 

affected by AMD were removed before analyses of acid-base chemistry population statistics. 

Streams in the Appalachian Mountain portion of the mid-Atlantic region receive some of the largest 

acidic deposition loadings of any region of the U.S. A compilation of survey data from the midAppalachians yields a consistent picture of the acid-base status of streams. Acidic streams, and streams 

with very low ANC, are almost all located in small (watershed area <20 km2

), upland, forested 

catchments in areas of base-poor bedrock. Acidic surface waters in this region are nearly always found in 

forested watersheds because the thin soils and steep slopes that make these watersheds unsuitable for 

agriculture and other development also contribute to their sensitivity to acidic deposition (Baker et al., 

1991b). 

In the subpopulation of upland forested streams, which comprises about half of the total stream 

population in the mid-Appalachian area, data from various local surveys showed that 5% to 20% of the 

streams were acidic, and about 25 to 50% had ANC <50 μeq/L (Herlihy et al., 1993). NSS estimates for 

the whole region showed that there were 2330 km of acidic streams and 7500 km of streams with ANC 

<50 μeq/L. In these forested reaches, 12% of the upstream reach ends were acidic and 17% had pH ≥ 5.5 

(Table B-4). Sulfate from atmospheric deposition was the dominant source of acid anions in acidic midAppalachian streams. In these acidic streams, the low pH (median 4.9) and high levels of Ali (median 

 B-34
129 μg/L) leached through soils by acidic deposition were considered to be sufficiently high to cause 

damage to aquatic biota. Acidic streams in this subpopulation typically had low DOC (mean 1.5 mg/L). 

Localized studies have clearly shown that streamwater ANC is closely related to bedrock 

mineralogy (Herlihy et al., 1993). Sullivan et al. (2007a) delimited a high-interest area for streamwater 

acidification sensitivity within the Southern Appalachian Mountain region (Virginia/West Virginia to 

Georgia) based on geological classification and elevation. It covered only 28% of the region, and yet 

included almost all known acidic and low ANC (<20 μeq/L) streams, based on evaluation of about 1,000 

streams for which water chemistry data were available. They found that the vast majority of low ANC 

sample streams were underlain by the siliceous geologic sensitivity class, which is represented by such 

lithologies as sandstone and quartzite. Low ANC streamwater throughout the region was also found to be 

associated with a number of watershed features in addition to lithology and elevation, including 

ecoregion, physiographic province, soil type, forest type and watershed area. 

Sulfate mass balance analyses indicated that, because of watershed SO4

2−

 retention, soils and 

surface waters of the region have not yet realized the full effects of elevated S deposition. On average, 

based on NSS data, sites in the Blue Ridge Mountains retain 35% of incoming SO4

2−

 from atmospheric 

deposition. The amount of SO4

2−

 retention was strongly related to ecoregion in the order, Piedmont >Blue 

Ridge and Ridge & Valley >Appalachian Plateau. 

Herlihy et al. (1993) believed that the observed differences were due to the effects of cumulative 

loadings from atmospheric S deposition and not due to inherent ecoregional differences in the soils. They 

also concluded that S retention will likely continue to decrease in the future, resulting in further losses of 

stream ANC. 

Both mineral acids and organic acids play important roles in the acid-base status of streams in the 

Mid-Atlantic Coastal Plain (Baker et al., 1991b). Acidic streams in the New Jersey Pine Barrens (Table B2) are largely inorganically dominated, but most likely they were naturally organically acidic in the past. 

It is uncertain what effect the addition of inorganic acids from acidic deposition has had on these low 

ionic strength colored systems. Over half the streams in the Pine Barrens included in the NSS were acidic, 

and virtually all (96%) had ANC less than 50 μeq/L. Human disturbances in the Pine Barrens often result 

in the alkalinization of streams (increases in pH and ANC) that alter the natural Pine Barrens aquatic 

biota. Outside of the Pine Barrens in the NSS, the remainder of the acidic streams in the Coastal Plain 

were all high DOC organically dominated systems. Low DOC (<4 mg/L), acidic streams have been 

observed, however, in other Mid-Atlantic Coastal Plain surveys. 

The Virginia Trout Stream Sensitivity Study (VTSSS) surveyed streamwater chemistry for 344 

(~80%) of the native brook trout (Salvelinus fontinalis) streams in western Virginia. About half of the 

streams included in the VTSSS had ANC <50 μeq/L. In contrast, the NSS (Kaufmann et al., 1988) data 

for western Virginia suggested that only about 15% of the streams in the NSS target population had ANC 

<50 μeq/L. These differences may reflect the smaller watershed size, more mountainous topography, and 

generally more inert bedrock of the VTSSS watersheds, as compared with the overall NSS stream 

population. 

In the Appalachian Plateau of West Virginia there are two wilderness areas located in close 

proximity in an area of base-poor bedrock — Dolly Sods and Otter Creek Wilderness Areas. Most streams 

draining these wilderness areas are acidic or low in ANC. 

In the Great Smoky Mountains National Park in North Carolina and Tennessee, Cook et al. (1994) 

reported high NO3

−

 concentrations (~100 μeq/L) in upland streams which were correlated with elevation 

and forest stand age. The old growth sites at higher elevation showed the highest NO3

−

 concentrations. 

This pattern could have been due to the higher rates of N deposition and flashier hydrology at high 

elevation, and also decreased N uptake by trees in older forest stands. High N deposition at these sites has 

likely contributed to both chronic and episodic acidification of streamwater (Flum and Nodvin, 1995; 

Nodvin et al., 1995). 

 B-35
Recent Trends 

Population estimates from TIME surveys of streams in the Northern Appalachian Plateau region 

suggested that 5014 km of streams (11.8% of the stream length) were acidic in 1993−94, but that only 

3600 km of streams (8.5% of the stream population) remained acidic in this region in 2000. The 

approximate rate of estimated change in Gran ANC in the region (Table B-5) was +0.79 μeq/L/yr. On this 

basis, Stoddard et al. (2003) estimated that roughly 3600 km of stream (8.5%) remained acidic 10 years 

later. This represents about a 28% decrease in acidic stream length over the preceding decade. 

Future Projections 

Model projections of future changes in acid-base chemistry of streams in the southeastern U.S. 

were presented by Sullivan et al. (2002, 2003, 2005). In the eight-state Southern Appalachian Mountains 

region, Sullivan et al. (2005) modeled future effects of atmospheric S and N deposition on aquatic 

resources. Modeling was conducted with the MAGIC model for 40 to 50 sites within each of three 

physiographic provinces, stratified by stream water ANC class. Simulations were based on assumed 

constant future atmospheric deposition at 1995 levels and on three regional strategies of emissions 

controls provided by the Southern Appalachian Mountains Initiative (SAMI), based on the Urban to 

Regional Multiscale One-Atmosphere model (Odman et al., 2002). 

The NSS statistical frame (Kaufmann et al., 1991) was used to estimate the number and percentage 

of stream reaches in the region that were projected to change their chemistry in response to the emissions 

control strategies. There was a small decline in the estimated length of projected acidic (ANC ≥ 0) 

streams in 2040 from the least to the most restrictive emissions control strategy, but there was little 

difference in projected stream length in the other ANC classes as a consequence of adopting one or 

another strategy. However, projections of continued future acidification were substantially larger under a 

scenario in which S and N deposition were held constant into the future at 1995 levels. Turner et al. 

(1992) also reported MAGIC model simulation results that suggested substantial acidification 

(~20 μeq/L) of aquatic systems would occur in the southeastern U.S. if deposition remained constant at 

1985 levels. Those model analyses were conducted as part of the National Acid Precipitation Assessment 

Program (NAPAP, 1991). 

The SAMI emissions control strategies used in the modeling represented air regulatory 

requirements being implemented at the time of SAMI’s formation, expected reductions under recent 

federal regulatory actions, and additional emissions controls applied to all emissions sectors in the eight 

SAMI states. The spatial variability of these emissions controls resulted in varying estimated future 

changes in S and N deposition at different locations within the SAMI region. The SAMI strategies were 

designated A2, B1, and B3. A2 is the reference strategy that represented SAMI’s best estimates for air 

emission controls under regulations for which implementation strategies were relatively certain at the 

time of the study (about the year 2000). Emissions reductions under the A2 strategy included the acid rain 

controls under Title IV of the 1990 Amendments to the CAAA, the 1-h O3 standard, NOX reductions 

required under EPA’s call for revised State Implementation Plans (SIPs), and several highway vehicle and 

fuel reductions. The A2 strategy was applied for all eastern states and focused on the utility and highway 

vehicle sectors. The B1 and B3 strategies assumed progressively larger emissions reductions, targeted 

only to the eight SAMI states but covering all emissions sectors. 

Streams exhibited a broad range of response to the cumulative S deposition loadings received to 

date and the large simulated decreases in S deposition in the future under the emissions control strategies 

(Figure B-8). Some streams showed modeled stream water SO4

2−

 concentrations increasing in the future, 

even while S deposition was reduced by more than two-thirds. These were mostly sites that had relatively 

low SO4

2−

 concentrations in 1995 (≥ about 50 μeq/L) because of S adsorption on soils. They generally 

showed simulated future acidification, which was most pronounced for the A2 strategy. Other streams 

were simulated to show relatively large decreases in future stream water SO4

2−

 concentrations and 

concurrent increases in ANC in response to the strategies, with progressively larger changes from the A2 

to the B3 strategy. These tended to be streams that had relatively high concentrations of SO4

2−

 B-36
(>50 μeq/L) in 1995, suggesting that they were closer to steady state with respect to S inputs and outputs. 

Some streams were projected to exhibit future decreases in both SO4

2−

 and NO3

−

 concentrations but 

nevertheless to continue to acidify. This response was attributed by Sullivan et al. (2004) to large 

simulated decreases in base cation concentrations at these sites due to soil base cation depletion. 

Source: Sullivan et al. (2004) 

Figure B-8. Map showing simulated changes in streamwater ANC from 1995 to 2040 in response to the 

SAMI A2 emmissions control strategy, representing existing emissions control regulations. 

Most simulated changes in stream water ANC from 1995 to 2040 were rather modest (Table B-8), 

given the very large estimates of decreased S deposition. Few modeled streams showed projected change 

in ANC of more than about 20 μeq/L. Some of the largest changes were simulated for some of the streams 

that were most acidic in 1995. For such streams, however, even relatively large increases in ANC would 

still result in negative ANC stream water, and therefore little biological benefit would be expected from 

the simulated improvement in chemistry. The model results suggested, however, that benefits would 

continue to accrue well beyond 2040 for all strategies, even if deposition was held constant at 2040 levels 

into the future. 

 B-37
Florida Surface Waters 

Current Status 

According to the ELS survey conducted in 1984, 75% of the Florida Panhandle lakes were acidic at 

that time, as were 26% of the lakes in the northern peninsula. Most of the acidic lakes were clearwater 

(DOC <400 μmol) seepage lakes in which the dominant acid anions were Cl−

 and SO4

2−

. Most of the 

acidic and low-ANC lakes are located in the Panhandle and north central lake districts. In these areas, the 

Floridan aquifer is separated from overlying sand deposits by a confining layer called the Hawthorne 

formation. The major lake districts are located in karst terrain, where lakes formed through dissolution of 

the underlying limestone followed by movement of surficial deposits into solution cavities (Arrington and 

Lindquist, 1987). 

Flow of water from most of the lakes is downward, recharging the Floridan aquifer. Lake stage 

varies in response to long-term trends in precipitation, and perhaps in response to groundwater 

withdrawals. ANC generation in most lakes that have been studied appears to be due primarily to in-lake 

SO4

2−

 and NO3

−

 reduction (Baker et al, 1988; Pollman and Canfield, 1991). Retention of SO4

2−

 by 

watershed soils may also be important. Lakes can be highly alkaline where groundwater interacts with the 

deeper aquifer. Lakes with hydrologic contributions from shallow aquifers in highly weathered sands can 

be quite acidic and may be sensitive to acidic deposition. 

DOC concentrations are high in many Florida lakes, but organic anions are less important than 

SO4

2−

 in most low-ANC and acidic lakes (Pollman and Canfield, 1991). Aluminum concentrations tend to 

be very low in Florida lakes despite the high lakewater acidity because most of the Aln+ is removed from 

soil solution by precipitation and ion exchange reactions within 75 cm depths (Graetz et al., 1985), and 

relatively little Aln+ is transported in groundwater to lake waters. 

Baker et al. (1988) reported that retention of inorganic N is nearly 100% in most Florida lakes. 

ANC generation from SO4

2−

 retention may approach 100 μeq/L in some Florida lakes (Pollman and 

Canfield, 1991). These in-lake processes are important for generating ANC. Base cation deposition and 

NH4

+

 assimilation can also influence the acid-base status of lakes in Florida. 

The Northern Florida Highlands high interest area identified by Baker et al. (1991a) consists of the 

northern portion (north of 29°N latitude) of the Central Lake District and the Florida Panhandle (Figure 

B-3). Acidic streams were located in the Florida Panhandle and were mildly acidic (mean pH 5.0) and 

extremely dilute, with very low sea salt-corrected SBC (mean 21 μeq/L) and sea salt corrected SO4

2−

concentrations (mean 16 μeq/L). One-fourth of these acidic Panhandle streams were organic-dominated 

but the remaining sites all had DOC <2 mg/L and were inorganically dominated. Inorganic monomeric Al 

concentrations in these acidic streams were very low (mean 11 μg/L). In these low DOC, low ANC 

Panhandle streams, it was suggested that the degree of SO4

2−

 and NO3

−

 retention was an important control 

on streamwater ANC (Baker et al., 1991a). 

Past Acidification 

Considerable research has been conducted on past acidification in Florida lakes. Historical analyses 

of lake chemistry (Battoe and Lowe, 1992), inferred historical deposition (Hendry and Brezonik, 1984; 

Husar and Sullivan, 1991), and paleolimnological reconstructions of lake pH (Sweets, 1992; Sweets et al., 

1990) suggest evidence that some Florida lakes have acidified in response to acidic deposition. However, 

the role of acid deposition in lakewater acidification is not entirely clear (Pollman and Canfield, 1991), 

and the interpretation is complicated by regional and local changes in land use and hydrology (Sullivan, 

2000). 

An alternative explanation for the apparent acidification of some lakes is the regional decline in the 

potentiometric surface of the groundwater (Sweets et al., 1990). Large groundwater withdrawals of the 

Floridan aquifer for residential and agricultural purposes may have reduced groundwater inflow of base 

cations into seepage lakes, and therefore caused less buffering of acidity. Other land use changes may 

 B-38
have increased lake pH by providing inputs of fertilizer, which would increase lake productivity. 

Paleolimnological evidence of this effect was provided by Brenner and Binford (1988) and Deevey et al. 

(1986). 

It is likely that lake chemistry in Florida has been heavily effected by land use changes. More than 

half of the Florida lakes included in the ELS showed evidence of disturbance based on deviations from 

expected geochemistry (Pollman and Canfield, 1991). Such effects complicate efforts to determine the 

role of acidic deposition in controlling lakewater acid-base chemistry. 

Diatom-inferred pH reconstructions of lakewater chemistry of six seepage lakes in Florida were 

calculated as part of the PIRLA-I project and reported by Sweets et al. (1990). An additional 10 seepage 

lakes were cored as part of PIRLA-II, and results of those analyses were reported by Sweets (1992). 

Paleolimnological study lakes are located in the Panhandle, the Trail Ridge Lake District, and Ocala 

National Forest, generally in terraces of highly weathered loose sand that were deposited on top of the 

clay-confining layer. 

Of the six lakes analyzed in PIRLA-I, two (Barco and Suggs) were inferred to have acidified since 

1950 (Sweets et al., 1990). The timing of the onset of inferred acidification correlated with increases in 

SO2 emissions and S deposition, which increased consistently between about 1945 and 1985 (Husar and 

Sullivan, 1991). 

Of the 16 Florida seepage lakes studied in the PIRLA-II projects, 5 were located in or near the Trail 

Ridge Lake District, and all showed diatom-inferred acidification of at least 0.2 pH units (Sweets, 1992). 

Lakes located in the Panhandle region and Ocala National Forest generally did not show evidence of 

recent acidification. The exception was Lake Five-O, which was inferred to have decreased by 2 pH units. 

However, the diatom data suggested that this pH decline was associated with a sudden change in 

chemistry, probably caused by a catastrophic disturbance such as sinkhole development, rather than by 

acidification from atmospheric deposition (Pollman and Sweets, 1990; Sweets, 1992). 

B.3.4.3. Upper Midwest 

The Upper Midwest contains numerous lakes created by glaciation. The region has little 

topographic relief and with its deep glacial overburden, it also has little or no exposed bedrock. Acidsensitive surface waters in the Upper Midwest are mainly seepage lakes (Eilers, 1983). Most drainage 

lakes and some of the seepage lakes in the Upper Midwest region receive substantial inflow from 

groundwater, which is generally high in base cation concentrations from dissolution of carbonate and 

silicate minerals. Relatively high concentrations of base cations in these lakes make them insensitive to 

acidification from acidic deposition. The seepage lakes that have low base cation concentrations, and that 

are therefore more acid-sensitive, generally receive most of their water input from precipitation directly 

on the lake surface (Baker et al., 1991b). 

Current Status 

Based on the ELS survey, the Upper Midwest has a large population of low ANC lakes, but 

relatively few chronically acidic (ANC ≥ 0) lakes (Linthurst et al., 1986a; 1986b). Acidic lakes in the 

Upper Midwest are primarily small, shallow, seepage lakes that have low concentrations of base cations 

and Al and moderate SO4

2−

 concentrations. Organic anions, estimated by both the Oliver et al. (1983) 

method and the anion deficit, tend to be less than half the measured SO4

2−

 concentrations in the acidic 

lakes (Eilers et al., 1988), but much higher in many of the drainage lakes that are less sensitive to 

acidification from acidic deposition. 

Groundwater flow-through lakes in the Upper Midwest can be identified on the basis of having Si 

concentration greater than about 1 mg/L (Baker et al., 1991b). They generally have high pH and ANC, 

due to groundwater inputs of base cations (e.g., (Baker et al., 1991b). Based on results from the ELS 

survey, only 6% of these lakes had ANC ≥ 50 μeq/L and none were acidic. Groundwater recharge lakes 

 B-39
(those having Si concentration less than 1 mg/L) constituted 71% of the seepage lakes in the Upper 

Midwest, and were more frequently low pH and ANC. Five percent were acidic and 9% had pH ≥ 5.5. 

Nearly 90% of Upper Midwestern lakes that had ANC ≥ 50 μeq/L were in this category (Baker et al., 

1991b). Such lakes tend to be susceptible to acidification from acidic deposition. 

Sullivan (2000) summarized patterns in lakewater chemistry across the Upper Midwest from the 

ELS survey. Lakewater pH, ANC, base cations, and DOC all decreased from west to east across the 

region. Lakewater SO4

2−

 concentrations did not show a comparable change, despite a substantial increase 

in wet SO4

2−

 deposition from Wisconsin to Michigan. Cook and Jager (1991) attributed the absence of a 

more pronounced gradient in lakewater SO4

2−

 concentration across the region to watershed sources of S in 

Minnesota and high anion retention in seepage lakes, which predominate in the eastern portion of the 

region. The retention of SO4

2−

 by dissimilatory reduction is generally high for seepage lakes because of 

their long hydraulic retention times (τw). For example, an Upper Midwestern seepage lake with mean 

depth of 3 m and hydraulic retention time of 7.5 years would be expected to lose about 50 μeq/L of SO4

2−

from the water column by this process (Cook and Jager, 1991). 

Lakewater concentrations of inorganic N reported by the ELS were low throughout the Upper 

Midwest. In addition, snowmelt would not be expected to provide any significant NO3

−

 influx to lakes in 

the Upper Midwest because most snowmelt infiltrates the soil before reaching the drainage lakes, and 

because snowmelt input of N into seepage lakes would be limited mainly to the snow on the lake surface 

and immediate near-shore environment. Aluminum concentrations are far lower in the Upper Midwest 

than in lakes of similar pH in the Northeast. 

Wetlands are common throughout the Upper Midwest. They contribute to high production of 

organic matter which is reflected in high DOC concentrations in many lakes. Despite the abundant 

wetlands, SO4

2−

 is the dominant anion in the low-ANC (≥ 50 μeq/L) groundwater recharge seepage lakes. 

Base cation production is the dominant ion-enrichment process in most Upper Midwestern lakes. 

Even in low-ANC groundwater-recharge seepage lakes, base cation production accounts for 72% to 86% 

of total ANC production (Cook and Jager, 1991). 

Past Acidification 

Space-for-time substitution analysis was used to infer the general levels of past change in lake 

water acid-base chemistry in the Upper Midwest. Such an analysis assumes that study lakes were 

generally similar in acid-base chemistry before the onset of acidic deposition and that the only substantial 

driver of recent change in acid-base chemistry has been the level of acidic deposition. Across an 

increasing S depositional gradient from eastern Minnesota eastward to eastern Michigan, ANC expressed 

as (HCO3

−

 - H+) decreased and the ratio SO4

2−

 to SBC increased in the groundwater recharge seepage 

lakes. In Michigan and Wisconsin, many lakes had SO4

2−

 >SBC, indicating that the acidity was due to 

high SO4

2−

 relative to SBC concentration. There were also many lakes that had high concentrations of 

DOC, and organic acidity probably accounted for many of these lakes having ANC <0. The spatial pattern 

in (HCO3

−

 - H+) could not be attributed to DOC, which generally showed a decreasing trend with 

increasing acidic deposition. 

The concentration of lakewater (Ca2+ + Mg2+) also decreased with increasing acidic deposition, 

probably due to lower levels of base cation deposition and greater amounts of precipitation in the eastern 

portion of the region. Atmospheric deposition is an important source of base cations for groundwater 

recharge seepage lakes because of minimal groundwater inputs. In the eastern portion of the region, such 

lakes are more sensitive to pH and ANC depression in response to either elevated SO4

2−

 or DOC. The 

spatial patterns for low ANC groundwater recharge lakes in the Upper Midwest are consistent with the 

following hypotheses (Sullivan et al, 1990a; Sullivan, 1990, 2000a): 

1. Sensitivity to mineral and organic acidity increased from west to east because of decreasing 

lakewater base cation concentrations, and this may have been due, in part, to changes in base 

cation deposition and precipitation volume along this gradient. 

 B-40
2. High concentrations of DOC were responsible for the acidic conditions in some of the lakes, and 

DOC may have decreased in response to acidic deposition. 

3. Many of the lakes in eastern Michigan, and some in Wisconsin, were acidic because of high SO4

2−

relative to base cation concentration, and had probably been acidified by acidic deposition. 

Diatom-inferred pH reconstructions were completed for 15 lakes in the Upper Midwest region, and 

summarized by Kingston et al. (1990) and Cook and Jager (1991). Four lakes, all of which had measured 

pH <5.7, showed a diatom-inferred pH decline of 0.2 to 0.5 pH units during the preceding 50 to 100 

years. Diatom-inferred pH increased in one lake by 0.2 pH units. No change was inferred for the other 10 

lakes, including 4 lakes with pH >6.0. No major, recent, regional acidification was indicated by the 

diatom-inferred pH reconstructions. Inferred changes in most lakes were small, and were no greater 

during the industrial period than during the pre-industrial period (Sullivan, 2000a). 

Although diatom data suggested that some Upper Midwestern lakes may have acidified since preindustrial times, there is little paleolimnological evidence indicating substantial widespread acidification 

in this region (Cook and Jager, 1991; Kingston and Birks, 1990). Land use changes and other human 

disturbances of Upper Midwestern lakes and their watersheds have probably exerted more influence on 

the acid-base chemistry of lakes than has acidic deposition (Kingston and Birks, 1990; Sullivan et al, 

1990a; Sullivan, 1990, 2000a). The portion of the region most likely to have experienced acidification 

from acidic deposition is the Upper Peninsula of Michigan, where acidic seepage lakes are particularly 

numerous (Baker et al., 1991b); acidic deposition is highest for the region, and the [SO4

2−

]/[SBC] ratio is 

commonly >1.0. The percentage of acidic lakes in the eastern portion of the Upper Peninsula of Michigan 

(east of longitude 87°) was estimated to be 18% to 19% in 1984 (Eilers et al., 1988; Schnoor et al., 1986). 

Recent Trends 

Regional trend values for long-term monitoring lakes during the period 1990 to 2000 suggested 

that SO4

2−

 declined in lakewater by 3.63 μeq/L/yr, whereas lakewater NO3

−

 concentrations were relatively 

constant. The large decrease in SO4

2−

 concentration was mainly balanced by a large decrease in base 

cation concentrations (−1.42 μeq/L/yr) and an increase in ANC (+1.07 μeq/L/yr). All of these trends were 

significant at p <0.01 (Stoddard et al., 2003). In the Upper Midwest, an estimated 80 of 251 lakes that 

were acidic in the mid-1980s were no longer acidic in 2000. This change may be due to reduced levels of 

S deposition (Stoddard et al., 2003). 

B.3.4.4. West 

Portions of the mountainous West contain large areas of exposed bedrock, with little soil or 

vegetative cover to neutralize acidic inputs. This is particularly true of alpine regions of the Sierra 

Nevada, northern Washington Cascades, the Idaho batholith, and portions of the Rocky Mountains in 

Wyoming and Colorado. However, the percentage of exposed bedrock in a watershed does not always 

indicate acid-sensitivity. If the bedrock contains even small deposits of calcareous minerals or if physical 

weathering such as that caused by glaciers causes a high production of base cations within the watershed 

(Drever and Hurcomb, 1986), surface waters may be alkaline, and are not sensitive to acidification from 

acidic deposition. 

The areas that are sensitive to adverse effects from acidic deposition in the western U.S. form two 

nearly continuous ranges, the Sierra Nevada, which extends through most of the length of California, and 

the Cascade Mountains, which extend from northern California to northern Washington (see Figure B-9.). 

The sensitivity of the Rocky Mountains varies widely because the ranges are discontinuous with highly 

variable geological composition. For that reason, assessments of the sensitivity of Rocky Mountain 

aquatic resources to acidification should be specific to individual ranges (Turk and Spahr, 1991). 

 B-41
Source: Landers et al. (1987). 

Figure B-9. Major geomorphic units and locations of lakes sampled in the Western Lake Survey. Those 

areas known to contain sensitive lake resources are shaded with cross-hatching. 

The NAPAP SOS/T Reports and the IA (NAPAP, 1991) provided only a cursory treatment of 

aquatic effects issues in the West, largely because it was well known that atmospheric deposition of S and 

N were generally low compared to highly affected areas in the East (Sullivan, 2000a) and because results 

from the WLS (Landers et al., 1987) indicated that there were virtually no acidic (ANC ≥ 0) lakes in the 

West. NAPAP (1991) indicated, however, that high-elevation areas of the West contained many of the 

watersheds most sensitive to the potential effects of acidic deposition. 

Because of the proximity of western urban and industrial pollution sources to individual mountain 

ranges, it is important to consider emissions in the immediate vicinity of sensitive resources as well as 

regional emissions. Atmospheric deposition in the far western ranges (i.e., Sierra Nevada, Cascade 

Mountains) is largely influenced by local emissions, particularly emission sources to the west (upwind) of 

sensitive resources. In the Rocky Mountains, deposition chemistry is often influenced by a more complex 

collection of emission sources. For example, in the Mt. Zirkel Wilderness of northwestern Colorado, 

SO4

2−

and NO3

−

 in the snow appeared to originate largely from sources in the Yampa Valley, about 75 km 

to the west (Turk, 1992). Rocky Mountain National Park is affected by emissions from the Front Range to 

the southeast. 

 B-42
Source: Sullivan et al. (2004). 

Figure B-10. Estimated percent changes in the total deposition of sulfur, reduced nitrogen, and nitratenitrogen at MAGIC modeling sites from 1995 to 2040 under each of the emissions control 

strategies 

The acid base chemistry of lake and stream waters in Rocky Mountain National Park appears to be 

primarily a function of the interactions among several key parameters and associated processes: 

atmospheric deposition, bedrock geology, the depth and composition of surficial deposits and associated 

hydrologic flowpaths, and the occurrence of soils, tundra, and forest vegetation (Sullivan, 2000a). 

Potential biological effects of acidic deposition on lakes in the Rocky Mountains are primarily attributable 

to acidification from high NO3

−

 concentration. In general, such effects tend to be episodic, rather than 

chronic. Highest NO3

−

 concentrations in both precipitation and surface waters are found above timberline 

in Colorado, where biological activity, and therefore NO3

−

 uptake, by terrestrial and aquatic biota is 

lowest. 

Current Status 

The available information on acid-base chemistry of surface waters in the West is based mostly on 

synoptic data from the WLS (Landers et al., 1987) and some more localized studies. Acid anion 

concentrations in most western lakes are low during fall, but can be higher during snowmelt (Williams 

and Melack, 1991). Available data from intensive study sites in the West (e.g., Loch Vale, CO, Emerald 

Lake Basin, CA, and the Glacier Lakes Watershed, WY) suggest that episodic depression of stream pH 

may be more pronounced than for lakes. However, there are no available systematic regional stream 

chemistry data with which to assess regional sensitivity of streams to acidic deposition. 

In most western lakes concentrations of SO4

2−

 are low, although watershed sources of S are 

substantial in some cases (Table B-9). Turk and Spahr (1991) presented a conceptual model for expected 

SO4

2−

 distributions in lakewaters in the West that can be used as an aid in identifying the proportion of 

watersheds with significant watershed sources of S. Considering that atmospheric sources can account for 

generally <30 μeq/L of SO4

2−

 in the West, it appears that many lakes, particularly in Colorado, receive 

variable amounts of watershed S (Sullivan, 2000a) (Figure B-10). 

 B-43
Nitrate concentrations in most WLS lakes were near 0 during fall (Landers et al., 1987), although 

fall NO3

−

 concentrations were high in some cases (Table B-10). For example, nearly one fourth of the 

lakes in northwest Wyoming had NO3

−

 >5 μeq/L and almost 10% had NO3

−

 >10 μeq/L (Table B-10). In 

both the Sierra Nevada and Colorado Rockies subregions, about 10% of the lakes had fall NO3

−

concentrations above 5 μeq/L (Table B-10). 

It is important to note that even low to moderate concentrations of NO3

−

 in western lakes might be 

significant in view of: the low base cation concentrations in many lakes; potential for continuing N 

deposition to eventually exhaust natural assimilative capabilities; and the fact that these distributions are 

based on fall data. Time-intensive discharge and chemical data for two alpine streams in Loch Vale 

watershed identified strong seasonal control on streamwater NO3

−

 concentrations (Campbell et al., 1995). 

In spite of the paucity of soil cover, the chemical composition of streams is regulated much as in typical 

forested watersheds. Soils and other shallow groundwater matrices such as boulder fields are more 

important in controlling surface water chemistry than their abundance would indicate. Spring streamwater 

NO3

−

 concentrations ranged to 40 μeq/L, compared with summer minimum values near 10 μeq/L. Elution 

of acidic waters from snowpack along with dilution of base cations originating in shallow groundwater 

caused episodes of decreased ANC in alpine streams (Campbell et al., 1995). A subalpine stream in the 

same watershed similarly displayed decreased ANC and elevated monomeric Al concentrations during 

snowmelt over 2 years of intensive sampling, related to elevated concentrations of SO4

2−

, NO3

−

, and DOC 

(Denning et al., 1991). Limited data collected during snowmelt suggest that spring concentrations could 

be several times higher than samples collected during the fall (e.g., Reuss et al., 1995). 

The surface water chemistry data for the West indicate that the Sierra Nevada and Cascade 

Mountains constitute the mountain ranges with the greatest number of sensitive resources (Table B-9 and 

Table B-10). Lakes in the Sierra Nevada are especially sensitive to effects from acidic deposition because 

of the predominance of granitic bedrock, thin acidic soils, large amounts of precipitation, coniferous 

vegetation, and dilute nature of the lakes (Melack et al., 1985; Melack and Stoddard, 1991). Surface 

waters in this region are among the poorly buffered surface waters in the U.S. (Landers et al., 1987; 

Melack and Stoddard, 1991). The hydrologic cycle is dominated by the annual accumulation and melting 

of a dilute, mildly acidic (pH about 5.5) snowpack. 

During the 1980s, an Integrated Watershed Study (IWS) was conducted at seven lakes in the Sierra 

Nevada, including Emerald Lake and surrounding watersheds (~3,000 m elevation) to determine the 

effects of acidification on surface waters (Tonnessen, 1991). Three lakes (Lost, Pear, and Emerald) had 

volume-weighted mean ANC in the range of 15 to 30 μeq/L. Moderate ANC waters (Topaz, Spuller, and 

Marble Fork) exhibited mean ANC in the range of 30 to 50 μeq/L. Crystal and Ruby Lakes had mean 

annual ANC >50 μeq/L. 

Many Cascade and Rocky Mountain lakes are also highly sensitive to potential acidic deposition 

effects (Nelson, 1991; Turk and Spahr, 1991). It does not appear that chronic acidification has occurred to 

any significant degree, although episodic acidification has been reported for lakes in the Colorado Front 

Range (Williams and Tonnessen, 2000). The data that would be needed for determining the extent and 

magnitude of episodic acidification have not been collected to a sufficient degree in acid-sensitive areas 

of the West to support regional assessment of episodic acidification (Sullivan, 2000a). 

Along the eastern edge of the Continental Divide in Colorado and southeastern Wyoming, 

Musselman et al. (1996) conducted a synoptic survey of surface water chemistry in the mountainous areas 

that are exposed to relatively high (by western standards) deposition of N. A total of 267 high-elevation 

lakes situated in watersheds having a high percentage of exposed bedrock or glaciated landscape were 

selected for sampling. None of the lakes were chronically acidic (ANC <0), although several had ANC 

<10 μeq/L, and more than 10% of the lakes had ANC <50 μeq/L. The WLS data for lakes in Colorado 

and Wyoming demonstrate that surface waters in this area had fall concentrations of NO3

−

 in the range of 

10 to 30 μeq/L, and likely had substantially higher NO3

−

 concentrations during spring. 

The weight of evidence suggests many high-elevation lakes in the West receive N deposition 

sufficiently high to cause chronic NO3

−

 leaching, and likely some degree of associated chronic and 

 B-44
episodic acidification. However, existing data are insufficient to make a conclusive determination 

(Sullivan, 2000a). 

Past Acidification 

The limited paleolimnological data available for lakes in the western U.S. suggest that widespread 

chronic acidification probably has not occurred. Some lakes may have experienced recent pH declines, 

but the magnitude of such changes has likely been small (Sullivan, 2000a). 

In the Sierra Nevada, paleolimnological reconstructions of lakewater pH and ANC were calculated 

by Holmes et al. (1989) at 24 depth intervals at Emerald Lake, for the period 1825 to the present. 

Significant trends were not found for either pH or ANC, and the authors concluded that Emerald Lake had 

not been acidified by acidic deposition. Whiting et al. (1989) completed paleolimnological analyses of 

three additional lakes in the Sierra Nevada. Eastern Book Lake (pH = 7.06) showed evidence of both 

long-term alkalization (~0.3 pH units over the past 200 years) and pH fluctuations since 1970. Lake 45 

(pH = 5.16) may have acidified slightly (~0.2 pH units) over the last 60 years. Lake Harriet (pH = 6.52) 

showed no significant change. 

In Rocky Mountain National Park, Colorado, Baron et al. (1994) investigated metal stratigraphy, 

diatom stratigraphy, and inferred pH profiles of four subalpine lakes. They found no evidence of historical 

influence on pH attributable to atmospheric deposition. Other paleolimnological studies of Rocky 

Mountain lakes report similar results: metals (primarily lead) exhibit temporal dynamics related to the 

increase and decline of precious metal mining in the region, but these are asynchronous with other metal 

or biological indicators of acidification (Wolfe et al., 2003). Both the study by Wolfe et al. (2003) and a 

study by Saros et al. (2003) showed no evidence of acidification of lake waters over time, but increasing 

evidence of eutrophication from atmospheric N deposition (see Annex C). 

DayCent-Chem, a model that simulates the daily dynamics of plant production, soil organic matter, 

cation exchange, mineral weathering, elution, stream discharge, and stream solute concentrations, was 

able to recreate daily stream chemistry dynamics over 13 years for an alpine watershed in the Colorado 

Front Range (Hartman et al., 2007). Using the model to hindcast stream chemical dynamics back to 1900 

revealed changes in simulated pH coincident with maximum SO2 emissions in the late 1960s and early 

1970s. Model simulations suggested annual mean pH values decreased to 5.6 to 5.8 during the years of 

maximum regional SO2 emissions, and have since recovered to circumneutral values. Simulated ANC 

responded to both SO2 and NOX emissions, decreasing to annual values of 20 to 25 μeq/L during years of 

highest SO2 or NOX emissions compared with current mean annual ANC values near 50 μeq/L Hartman. 

Recent Trends 

Limited monitoring data are available on recent trends in surface water chemistry in the western 

regions and are mostly limited to the recent past and a number of reconnaissance studies (Melack and 

Stoddard, 1991; Nelson, 1991; Turk and Spahr, 1991). Existing information on recent trends in surface 

water chemistry since the 1980s suggests that conditions vary widely across the West. Parts of Colorado, 

Wyoming and the western Cascades showed decreased ANC, while Emerald Lake experienced reduced 

NO3

−

 concentrations. 

Turk et al. (1993) reported the results of 5 years of monitoring for ten lakes in the Mt. Zirkel and 

Weminuche Wilderness areas in Colorado. Based on lake concentrations of SO4

2−

 and Cl− and on wet 

deposition concentrations of SO4

2−

, NO3

−

, and H+, Turk and Spahr (1991) concluded that low-ANC lakes 

had lost no more than 5 μeq/L ANC in the Bitterroot Range of the Northern Rocky Mountains, 12 μeq/L 

ANC in the Wind River Range of Wyoming, and 10 μeq/L ANC in the Front Range of Colorado. It is 

likely that the actual ANC losses had been much less than these estimates (Sullivan, 2000a). 

 B-45
Future Projections 

The DayCent-Chem model was used to project a timeline to acidification for an alpine watershed 

of Rocky Mountain National Park (Hartman et al., 2007). At current levels of N deposition of 4 to 6 kg 

N/ha/yr, acidification does not occur over 48 years of simulation, but increasing deposition amounts lead 

to first episodic acidification over time at deposition of 7.0 to 7.5 kg N/ha/yr. MAGIC model simulation 

results suggested that a sustained N deposition load of 12.2 kg N/ha/yr would be required over a period of 

50 years to cause chronic acidification of the Andrews Creek watershed in Rocky Mountain National Park 

(Sullivan et al., 2005). 

B.3.4.5. Temporal Variability in Water Chemistry 

Water chemistry changes on both intra-annual and inter-annual time scales in response to changes 

in environmental conditions. Because of this variability, many years of data are required to establish the 

existence of trends in surface water chemistry. Assignment of causality to changes that are found to occur 

is even more difficult. 

Temporal variability in surface water and soil solution chemistry, and patterns in nutrient uptake by 

terrestrial and aquatic biota, influence acidification processes and pathways. Thus, conditions are 

constantly changing in response to episodic, seasonal, and inter-annual cycles and processes. In particular, 

climatic fluctuations that govern the amount and timing of precipitation inputs, snowmelt, vegetative 

growth, depth to groundwater tables, and evapoconcentration of solutes influence soil and surface water 

chemistry and the interactions between pollution stress and sensitive aquatic and terrestrial biological 

receptors. 

Decreases in pH with increases in flow are nearly ubiquitous in drainage waters throughout the 

U.S. (Wigington et al., 1991). Chemical changes during episodes are controlled in part by acidic 

deposition and in part by natural processes, including dilution of base cation concentrations, nitrification, 

flushing of organic acids from terrestrial to aquatic systems, and the neutral salt effect. Episodic 

acidification pulses may last for hours to weeks, and sometimes result in depletion of ANC in acidsensitive streams and lakes to negative values and concomitant increases in Ali in solution to toxic levels. 

During episodes, which are driven by rainstorms and/or snowmelt events, both discharge 

(streamflow volume per unit time) and water chemistry change, sometimes dramatically. This is important 

because streams may in some cases exhibit chronic chemistry that is still suitable for aquatic biota, but 

nevertheless experience occasional episodic acidification with lethal consequences (Wigington et al., 

1993). 

The most important factor governing watershed sensitivity to episodic acidification is the pathways 

followed by snowmelt water and stormflow water through the watershed. These pathways determine the 

extent of acid neutralization provided by the soils and bedrock in that watershed. High-elevation 

watersheds with steep topography, extensive areas of exposed bedrock, deep snowpack accumulation, and 

shallow, base-poor soils tend to be most sensitive to episodic acidification. 

Rainfall and snowmelt typically pass through the soil profile before reach a stream channel. The 

typical soil profile in acid-sensitive watersheds has lowest pH in upper organic soil horizons, increasing 

down the profile to higher pH at depth. Drainage water chemistry during baseflow conditions is generally 

reflective of conditions in the lower soil horizons and the subsoil. During high flows during snowmelt or 

rainfall events, however, flow-routing favors water flowpaths through upper horizons. During such 

events, drainage water chemistry, therefore, typically reflects the lower pH, higher organic content, and 

lower ANC of these upper soil horizons (Sullivan, 2000a). As such, storm flow and snowmelt are often 

associated with episodes of extreme surface water acidity due to an increase in the proportion of flow 

derived from water that has moved laterally through the surface soil without infiltration to deeper 

soil horizons (Wigington et al., 1991). 

 B-46
The routing of water as it flows through a watershed determines the degree of contact with 

acidifying or neutralizing materials and therefore influences (along with soils and bedrock characteristics) 

the amount of episodic acidification that occurs. In any given watershed, surface water ANC may vary in 

time depending upon the proportion of the flow that has contact with deep versus shallow soil horizons; 

the more subsurface contact, the higher the surface water ANC (Turner et al., 1991). This can be 

attributed in part to higher base saturation and (in some watersheds) greater SO4

2−

 adsorption capacity in 

subsurface soils. It may also relate to the accumulation in the upper soil horizons of acidic material 

derived from atmospheric deposition and decay processes (Lynch and Corbett, 1989; Turner et al., 1991). 

Episodic acidification is often the limiting condition for aquatic organisms in streams that can be suitable 

for aquatic life under baseflow conditions. 

Episodes are generally accompanied by changes in at least two or more of the following chemical 

parameters: ANC, pH, base cations, SO4

2−

, NO3

−

, Aln+, organic acid anions, and DOC (Sullivan, 2000a). 

The U.S. EPA Episodic Response Project (ERP) confirmed the chemical and biological effects of episodic 

pH depressions in lakes and streams in parts of the U.S. (Wigington et al., 1993). The ERP illustrated that 

episodic processes are mostly natural, that SO4

2−

 and especially NO3

−

 attributable to atmospheric 

deposition play important roles in the episodic acidification of some surface waters, and that the chemical 

response that has the greatest effect on biota is increased Ali concentration. Similar findings had been 

reported elsewhere, especially in Europe, but the ERP helped to clarify the extent, causes, and magnitude 

of episodic acidification in portions of the U.S. (Sullivan, 2000a). 

Water chemistry trends documented by long-term monitoring programs and reported here represent 

recovery from chronic acidification. Most surface waters exhibit seasonally lower ANC and pH values 

than would be captured by trend analysis that considers only chronic chemistry data. In many cases, sites 

that are relatively low in ANC, but not chronically acidic, undergo short-term episodic acidification to 

negative ANC values during spring snowmelt, or during intense rain events. Lawrence (2002) found that 

16% of total stream reaches in the West Branch Neversink River, in the Catskill Mountains of New York, 

were chronically acidic, whereas 66% of total stream reaches has a high likelihood of becoming acidic 

during high flows. 

Most research on episodic processes has been conducted on stream systems, which tend to be more 

susceptible to such effects than lakes. Spatial variability can be considerable in lakes, and this complicates 

efforts to quantify the magnitude of episodic effects (Gubala et al., 1991). Moreover, synoptic lake 

surveys are typically conducted during the autumn “index period,” during which time lakewater chemistry 

exhibits low temporal variability. Although autumn is an ideal time for surveying lakewater chemistry in 

terms of minimizing variability, lakewater samples collected during autumn provide little relevant data on 

episodic processes, and in particular on the dynamics or importance of N as an agent of acidification. 

Nitrate concentrations in lakewater are elevated during the autumn season only in lakes having 

watersheds that exhibit fairly advanced symptoms of N saturation (Stoddard, 1994). 

Mixing zones have received little attention despite the fact that they can be acutely toxic to aquatic 

biota. Whether an area of acidic water that comes in contact with non-acidic water is a safe haven or a 

toxic zone depends on many parameters, one of the most important of which is the amount and form of Al 

species produced at the boundaries. For example, Al hydroxide (Al(OH3)) can precipitate out of solution 

if pH is suddenly increased within a mixing zone. This form of Al is acutely toxic to fish. 

The mechanisms that produce acidic episodes can include dilution of base cations and flushing of 

NO3

−

, SO4

2−

 and/or organic acids from forest soils to drainage water (Kahl et al., 1992; Lawrence, 2002; 

Wigington et al., 1996; Wigington, 1999). Acidic deposition can contribute to episodic acidification of 

surface water both by supplying N which can produce pulses of NO3

−

 during high flow periods, 

contributing hydrologically mobile SO4

2−

 through dry deposition, and by lowering baseline pH and ANC, 

so that episodes are sufficient to produce biologically harmful conditions (Stoddard et al., 2003). 

Episodic acidification due to atmospheric deposition is most commonly associated with N 

deposition, and effects tend to be most pronounced during snowmelt. However, snowmelt can flush into 

surface waters N that was deposited from the atmosphere to the snowpack and also N that was 

mineralized within the soil under the snowpack during winter. A substantial component of the NO3

−

 flux 

 B-47
may have been derived from mineralization of organic N (Ley et al., 2004). Much of the N released from 

the snowpack during the melting period is retained in underlying soils and only a component of that is 

flushed to surface waters. Where soils are sparse, as in alpine regions of the western U.S., most snowpack 

N is flushed to surface waters, and even though there is evidence through use of isotopic tracers that much 

of the N was cycled microbially, snowpack N has been reported to caused temporary acidification of 

alpine streams (Campbell et al., 2002; Williams and Tonnessen, 2000). 

Episodic pH and ANC depressions during snowmelt are largely driven by base cation dilution and 

NO3

−

 enrichment in most areas (Campbell et al., 1995; Stoddard, 1995; Wigington et al., 1991, 1993), 

although Denning et al. (1991) found a significant decline of both pH and ANC associated with DOC 

flushing from forest soils. Pulses of increased SO4

2−

 during hydrological episodes are usually attributable 

to S storage and release in soils (for example, in the southeastern U.S.) or wetlands. More commonly, lake 

and streamwater concentrations of SO4

2−

 decrease or remain stable during snowmelt. This is probably 

because most stream flow during episodes is derived from water previously stored in watershed soils that 

is then forced into streams and lakes by the piston effect. 

In the Northeast, the most severe acidification of surface waters generally occurs during spring 

snowmelt (Charles, 1991). Stoddard et al. (2003) found that on average, spring ANC values in New 

England, the Adirondacks, and the Northern Appalachian Plateau were about 30 μeq/L lower than 

summer values during the period 1990 to 2000 (Figure B-11). This implies that lakes and streams in these 

regions would need to recover to chronic Gran ANC values above about 30 μeq/L before they could be 

expected to not experience acidic episodes (Stoddard et al., 2003). However, the estimate of 30 μeq/L is 

certain to be low because the comparison was made with non-episodic sampling in spring. 

In the West, episodic acidification is an especially important issue for surface waters throughout 

high-elevation areas. A number of factors pre-dispose western systems to potential episodic effects 

(Peterson et al., 1998; Sullivan, 2000a), including: 

 the abundance of dilute to ultradilute lakes which exhibit very low concentrations of base cations, 

and therefore ANC, throughout the year; 

 large snowpack accumulations at the high-elevation sites, thus causing substantial episodic 

acidification via the natural process of base cation dilution; and 

 short hydraulic retention times for many of the high-elevation drainage lakes, thus enabling 

snowmelt to rapidly flush lake basins with highly dilute meltwater. 

Based on measurements of microbial biomass, CO2 flux through the snowpack, and soil N pools, 

Williams et al. (1996a) concluded N cycling under the snowpack in Colorado during the winter and spring 

was sufficient to supply the NO3

−

 measured in stream waters. Brooks et al. (1996) investigated soil N 

dynamics throughout the snow-covered season on Niwot Ridge, CO. Sites with consistent snow cover had 

a 3 to 8 cm layer of thawed soil under the snowpack for several months before snowmelt began. Nitrogen 

mineralization in this thawed layer contributed to Nr pools that were significantly larger than the pool of 

N stored in the snowpack. As snowmelt began, soil inorganic N pools decreased sharply, concurrent with 

a large increase in microbial biomass N. As snowmelt continued, both microbial N and soil inorganic N 

decreased, presumably due to increased demand by growing vegetation (Brooks et al., 1996). 

 B-48
Source: Stoddard et al. (2003). 

Figure B-11. Relationship between mean summer and spring ANC values at LTM sites in New England, the 

Adirondacks, and the Northern Appalachian Plateau. 

In the Sierra Nevada, the hydrology of alpine and subalpine ecosystems is dominated by snowfall 

and snowmelt, with over 90% of the annual precipitation falling as snow. The relatively small loads of 

acidic deposition can supply relatively high concentrations of SO4

2−

 and NO3

−

 to lakes and streams during 

the early phase of snowmelt (Stoddard, 1995) through the process of preferential elution (Johannessen 

and Henriksen, 1978). 

Lakewater pH and ANC in the Sierra Nevada generally decrease with increasing runoff, reaching 

minima near peak snowmelt discharge. Most other solutes exhibit temporal patterns that indicate dilution 

or a pulse of increased concentration followed by either dilution or biological uptake. Williams and 

Melack (1991) and Williams et al. (1995) documented ionic pulses (2 to 10 days in duration) in meltwater 

concentrations in the Emerald Lake watershed twofold to twelvefold greater than the snowpack average. 

Sulfate and NO3

−

 concentrations in meltwater decreased to below the initial bulk concentrations after 

about 30% of the snowpack had melted. The initial meltwater draining from the snowpack had 

concentrations of NO3

−

 and NH4

+

 as high as 28 μeq/L, compared to bulk snowpack concentrations 

<5 μeq/L (Williams et al., 1995). Streamwater NO3

−

 concentrations peaked during the early snowmelt 

period, with maximum streamwater concentrations of 18 μeq/L. During summer, streamwater NO3

−

concentrations were always near or below detection limit. 

Stoddard (1995) reported results for two lakes in the Sierra Episodes Study, one of which (Treasure 

Lake) typified the response of most high elevation lakes in the study and one whose response was most 

extreme (High Lake). At Treasure Lake, ANC began to decline at the onset of snowmelt and reached a 

minimum at peak runoff, corresponding with minimum base cation, NO3

−

, and SO4

2−

 concentrations. The 

lakewater did not become acidic. High Lake watershed contained a deeper snowpack, and began melting 

later in the season. ANC fell to 0 and below twice during the first 10 days of snowmelt. The ANC 

minimum corresponded with maximum concentrations of base cations, NO3

−

 and Al. Nitrate 

concentrations increased to values greater than 40 μeq/L, exceeding concurrent increases in base cations 

and causing the lake to become acidic for brief periods. Stoddard (1995) concluded that High Lake 

appeared to be representative of the most extreme conditions of episodic acid-sensitivity in the Sierra 

Nevada. 

 B-49
Source: Sullivan et al. (2003). 

Figure B-12. Minimum streamwater ANC sampled at each site during each year versus median spring ANC 

for all samples collected at that site during that spring season. Data are provided for all 

intensively studied streams within Shenandoah National Park during the period 1993−1999. A 

1:1 line is provided for reference. The vertical distance from each sample point upwards to the 

1:1 line indicates the ANC difference between the median spring value and the lowest sample 

value for each site and year. 

Data regarding episodic variability in streamwater ANC for six intensively studied sites within 

Shenandoah National Park for the period 1993 to 1999 are presented in Figure B-12 (Sullivan et al, 2003). 

The minimum measured ANC each year at each site (which generally is recorded during a large rain or 

snowmelt episode) is plotted against the median spring ANC for that year at that site. Sites that exhibited 

median spring ANC below about 20 μeq/L (Paine Run, White Oak Run, Deep Run) generally had 

minimum measured ANC about 10 μeq/L lower than median spring ANC. 

In contrast, at the high-ANC Piney River site (median spring ANC >150 μeq/L), the minimum 

measured ANC was generally more than about 40 μeq/L lower than the respective median spring ANC. At 

sites having intermediate ANC values, with median spring ANC in the range of about 30 to 90 μeq/L, the 

minimum ANC measured each year was generally about 20 to 30 μeq/L lower than the respective median 

spring ANC. Thus, there is a rather clear pattern of larger episodic ANC depressions in streams having 

higher median ANC and smaller episodic ANC depressions in streams having lower median ANC. The 

two sites that had median spring ANC between about 0 and 10 μeq/L consistently showed minimum 

measured values below 0. Streams having low chronic ANC can be expected to experience relatively 

small episodic ANC depressions. However, those depressions can result in minimum ANC values that are 

associated with toxicity to aquatic biota. 

A recent study by Deviney et al. (2006) Deviney used hourly ANC predictions over short time 

periods to compute recurrence intervals of annual water-year minimum ANC values for periods of 6, 24, 

72, and 168 h. They extrapolated the results to the rest of the Shenandoah National Park catchments using 

catchment geology and topography. On the basis of the models, they conclude that large number of 

Shenandoah National Park streams have 6- to 168-h periods of low ANC values, which may stress 

resident fish populations (Deviney et al., 2006). Specifically, on the basis of a 4–year recurrence interval, 

approximately 23% of the land area (44% of the catchments) can be expected to have conditions that are 

indeterminate (ANC 20 to 50), episodically acidic (ANC 0 to 20) or chronically acidic (ANC less than 0) 

for 72 continuous hours. Many catchments are predicted to have successive years of low-ANC values 

potentially sufficient to extirpate some species (Deviney et al., 2006). The authors of the study reported 

 B-50
that smaller catchments are more vulnerable to episodic acidification than large catchments underlain by 

the same bedrock. Catchments with similar topography and size are more vulnerable if underlain by less 

basaltic and carbonate bedrock. 

There are several different mechanisms of episodic acidification in operation in the streams in 

Shenandoah National Park, depending at least in part on the bedrock geology of the stream. The most 

acidic conditions in Shenandoah National Park streams occur during high-flow periods, in conjunction 

with storm or snowmelt runoff. The general relationship between flow level and ANC is evident in Figure 

B-13, which plots ANC measurements against flow for three intensively studied streams representing the 

major bedrock types in the park. The response of all three streams is similar in that most of the lower 

ANC values occur in the upper range of flow levels. 

Consistent with observations by Eshleman (1988), the minimum ANC values that occur in response 

to high flow are related to baseflow ANC values. Paine Run (siliciclastic bedrock) had a mean weekly 

ANC value of about 6 μeq/L and often had high-flow ANC values that were less than 0 μeq/L. Staunton 

River (granitic bedrock) had a mean weekly ANC value of about 82 μeq/L and had only a few high-flow 

ANC values less than 50 μeq/L. Piney River (basaltic bedrock) had a mean weekly ANC value of 

217 μeq/L and no values as low as 50 μeq/L. 

Eshleman and Hyer (2000) estimated the contribution of each major ion to observed episodic ANC 

depressions in Paine Run, Staunton River, and Piney River during a 3−year period. During the study, 33 

discrete storm events were sampled and water chemistry values were compared between antecedent 

baseflow and the point of minimum measured ANC (near peak discharge). The relative contribution of 

each ion to the ANC depressions was estimated using the method of Molot et al. (1989), which 

normalized the change in ion concentration by the overall change in ANC during the episode. At the lowANC (~0) Paine Run site on siliciclastic bedrock, increases in NO3

−

 and SO4

2−

, and to a lesser extent 

organic acid anions, were the primary causes of episodic acidification. Base cations tended to compensate 

for most of the increases in acid anion concentration. ANC declined by 3 to 21 μeq/L (median 7 μeq/L) 

during the episodes studied. 

 B-51
Source: Sullivan et al. (2003). 

Figure B-13 Relationship between ANC and runoff for streamwater samples collected at intensively 

studied sites in Shenandoah National Park. The data represent samples collected during the 

1992–1997 period. 

At the intermediate-ANC (~60 to 120 μeq/L) Staunton River site on granitic bedrock, increases in 

SO4

2−

 and organic acid anions, and to a lesser extent NO3

−

, were the primary causes of episodic 

acidification. Base cation increases compensated these changes to a large degree, and ANC declined by 2 

to 68 μeq/L during the episodes (median decrease in ANC was 21 μeq/L). 

 B-52
At the high-ANC (~150 to 200 μeq/L) Piney River site on basaltic (69%) and granitic (31%) 

bedrock, base cation concentrations declined during episodes (in contrast with the other two sites where 

base cation concentrations increased). Sulfate and NO3

−

 usually increased. The change in ANC during the 

episodes studied ranged from 9 to 163 μeq/L (median 57 μeq/L; Eshleman and Hyer, 2000). 

Previous studies have shown that mobilization of dissolved Al during episodic acidification is a 

primary cause of fish mortality in streams that have low ANC under baseflow conditions (Wigington 

et al., 1993). Streams with higher ANC during baseflow are less likely to become sufficiently acidic 

during episodes to bring much Al into solution. 

Figure B-14 provides an example of changes in ANC, pH, and total monomeric Al that occurred in 

Paine Run, Staunton River, and Piney River during a high-flow episode in January 1995. Under baseflow 

conditions, ANC at the Paine Run site was above 0 μeq/L, pH was above 5.5, and Al concentration was 

less than about 1 μM. Discharge levels increased dramatically during the episode, resulting in depression 

of ANC to less than 0 μeq/L, pH values less than 5.5, and an increase in Al concentration to near 3 μM, 

above the threshold for adverse effects on some species of aquatic biota. 

Source: Sullivan et al. (2003). 

Figure B-14. Decrease in ANC and pH and increase in dissolved aluminum in response to a sharp increase 

in streamflow in three watersheds within Shenandoah National Park during a hydrological 

episode in 1995. The watersheds were selected to be representative of the three geologic 

sensitivity classes within the park. Data are shown for the month of January 1995. 

The same episode also resulted in substantial declines in ANC in the granitic (Staunton River) and 

basaltic (Piney River) watersheds. However, ANC values at these two sites were relatively high before the 

episode (about 75 and 175 μeq/L, respectively) and did not decline to below about 50 μeq/L during the 

episode at either site, and pH values remained above 6.0 and 6.5, respectively. 

In general, pre-episode ANC is a good predictor of minimum episodic ANC and also a reasonable 

predictor of episodic ΔANC. Higher values of pre-episode ANC lead to larger ΔANC values, but 

minimum ANC values of such streams are generally not especially low. Lowest minimum ANC values are 

reached in streams that have low pre-episode ANC, but the ΔANC values for such streams are generally 

small. 

Webb et al. (1994) developed an approach to calibration of an episodic acidification model for 

VTSSS long-term monitoring streams in western Virginia that was based on the regression method 

 B-53
described by Eshleman (1988). Median, spring quarter ANC concentrations for the period 1988 to 1993 

were used to represent chronic ANC, from which episodic ANC was predicted. Regression results were 

very similar for the four lowest ANC watershed classes, and they were therefore combined to yield a 

single regression model to predict the minimum measured ANC from the chronic ANC. Extreme ANC 

values were about 20% lower than chronic values, based on the regression equation: 

ANCmin = 0.79 ANCchronic − 5.88 (r2 = 0.97; se of slope = 0.02, p = 0.001) 

Because the model was based on estimation of the minimum ANC measured in the quarterly 

sampling program, it is probable that the true minimum ANC values were actually somewhat lower than 

20% below the measured chronic ANC. Nevertheless, regression approaches for estimation of the 

minimum episodic ANC of surface waters, such as was employed by Webb et al. (1994) for western 

Virginia, provide a basis for predicting future episodic acidification. It must be recognized, however, that 

future episodic behavior might vary from current behavior if chronic conditions change dramatically. 

The relative importance of the major processes that contribute to episodic acidification varies 

among the streams within Shenandoah National Park, in part as a function of bedrock geology and 

baseflow streamwater ANC. Sulfur-driven acidification was an important contributor to episodic loss of 

ANC at all three study sites, probably because S adsorption by soils occurs to a lesser extent during highflow periods. This is due, at least in part, to diminished contact between drainage water and potentially 

adsorbing soils surfaces. Dilution of base cation concentrations was most important at the high-ANC site. 

The documented importance of NO3

−

 to episodic acidification was a relatively recent development, 

attributed to the effects of gypsy moth (Lymantria dispar) infestation in many watersheds within 

Shenandoah National Park (Webb et al., 1995). Consumption of foliage by the moth larvae converted 

foliar N, which is normally tied up in long-term N cycling processes, into more labile N forms on the 

forest floor. 

Thus, episodic acidification of streams in Shenandoah National Park can be attributed to a number 

of causes, including dilution of base cations and increased concentrations of sulfuric, nitric, and organic 

acids (Eshleman et al., 1995; Hyer et al., 1995). For streams having low pre-episodic ANC, episodic 

decreases in pH and ANC and increases in toxic Al concentrations can have adverse effects on fish 

populations. Not all of the causes of episodic acidification are related to acidic deposition. Base-cation 

dilution and increase in organic acid anions during high-flow conditions are natural processes. The 

contribution of nitric acid, indicated by increased NO3

−

 concentrations, has evidently been (at least for 

streams in the park) related to forest defoliation by the gypsy moth (Eshleman et al., 1998; Webb et al., 

1995). However, significant contributions of sulfuric acid, indicated by increased SO4

2−

 concentrations 

during episodes in some streams, is an effect of atmospheric deposition and the dynamics of S adsorption 

on soils (Eshleman and Hyer, 2000). 

B.4. Effects on Biota 

Soil and surface water acidification involve changes in a number of chemical parameters, each of 

which has the potential to influence the health and vigor of biological communities and the species that 

comprise them. In most cases where biological effects of acidification have been documented, the most 

important chemical parameters involved in those effects have been pH, Ali, and Ca2+. Less commonly, one 

or more base cations other than Ca2+ (e.g., Mg2+, K+

) or C are also involved. This is true for both aquatic 

and terrestrial effects. 

A number of authors have examined the complex interactions between pH, Al, and Ca2+ that must 

be considered when attempting to determine the effects of acidification on both aquatic and terrestrial 

biota (e.g., Mount et al., 1988; Wood et al., 1990). Calcium concentration significantly affects the 

distribution of species and their ability to survive in acidified environments. Aluminum, leached by acid 

 B-54
precipitation from soils in the watershed, complicates the response considerably because some forms of 

Al are highly toxic to both aquatic and terrestrial species. Aluminum and hydrogen ions interact both 

synergistically and antagonistically depending on conditions (Havas, 1985; Rosseland and Staurnes, 

1994). In the presence of naturally occurring organic acids, Al toxicity can be reduced or eliminated. A 

number of authors have examined the complex interactions between pH, Al, and Ca2+ that must be 

considered when attempting to determine the effects of acidification on both aquatic and terrestrial biota 

(e.g., Ingersoll et al., 1990a; Mount et al., 1988; Wood et al., 1990). 

B.4.1. Types of Effects of Acidification on Biota 

Ecological effects occur at four levels of biological organization: the individual; the population, 

comprised of many individuals; the biological community, composed of many species, (Billings, 1978); 

and the ecosystem. Several metrics have been developed to describe the effects of acidification at each of 

these levels of organization. For the individual, effects are assessed in terms of sublethal effects on 

condition. At the population level, effects are measured by changes in the population of a certain species. 

At the community level, species richness and community structure can be used to evaluate effects, and at 

the ecosystem level, changes in nutrient cycling and ecosystem processes are assessed. Most of these 

indices have been applied primarily to aquatic ecosystems. Each is discussed below. 

Baker et al. (1990b) conducted a rigorous review of the effects of acidification on aquatic biota for 

the 1990 NAPAP State of Science/Technology reports. They evaluated hundreds of laboratory, in situ 

bioassay, field surveys, whole-system field experiments, and smaller mesocosm studies on the effects of 

acidification on aquatic biota. Their 381–page report is the most exhaustive source summarizing the 

aquatic biological effects of acidification from acidic deposition. The summaries provided here in 

Sections B.4 and B.6 rely heavily on this source. 

In Shenandoah National Park, a statistically robust relationship between acid-base status of streams 

and fish species richness was documented. The 3 year Fish in Sensitive Habitats (FISH) study of stream 

acidification in Shenandoah National Park demonstrated negative effects on fish from both chronic and 

episodic acidification (Bulger et al., 1999). Biological differences in low- versus high-ANC streams 

included species richness, population density, condition factor, age, size, and field bioassay survival. Of 

particular note was that both episodic and chronic mortality occurred in young brook trout exposed in a 

low-ANC stream, but not in a high-ANC stream (MacAvoy and Bulger, 1995), and that blacknose dace 

(Rhinichthys atratulus) in low-ANC streams were in poor condition relative to blacknose dace in higherANC streams (Dennis et al., 1995; Dennis and Bulger, 1995). 

B.4.1.1. Individual Condition Factor 

Relatively little is known about changes in the condition of fish or other aquatic biota resulting 

from acidification. It is expected that sublethal effects will occur in acid-sensitive species well before the 

species is eliminated from a particular lake, stream, or terrestrial habitat. For that reason, loss of an acidsensitive species is not necessarily an ideal indicator of acid stress. Clearly, stress begins to occur before 

species elimination. Sublethal effects are more difficult to quantify, but are nevertheless important. 

Condition factor is one measure of sublethal effect that has been used to quantify effects of 

acidification on fish. Condition factor is an index to describe the relationship between fish weight and 

length. Expressed as fish weight/length3

, multiplied by a scaling constant, this index reflects potential 

depletion of stored energy reserves (Dennis et al., 1995; Everhart and Youngs, 1981; Goede and Barton, 

1990). Condition factor is interpreted as depletion of energy resources such as stored liver glycogen and 

body fat (Goede and Barton, 1990). Fish with higher condition factor are more robust than fish having 

low condition factor. 

 B-55
Source: Bulger et al. (1999). 

Figure B-15. Length-adjusted condition factor (K), a measure of body size in blacknose dace (Rhinichthys 

atratulus) compared with mean stream pH among 11 populations (n = 442) in Shenandoah 

National Park. Values of pH are means based on quarterly measurements, 1991–94; K was 

measured in 1994. The regression analysis showed a highly significant relationship (p <0.001) 

between mean stream pH and body size, such that fish from acidified streams were less 

robust than fish from circumneutral streams. 

Field studies have shown lower condition factor in fish found in more acidic streams (Dennis et al., 

1995). Condition factor has been developed and applied mainly for blacknose dace. This species is widely 

distributed in Appalachian Mountain streams and is moderately tolerant of low pH and ANC, relative to 

other fish species in the region. However, the concept is probably applicable to other species as well. 

Condition factor may be a useful metric for many species in aquatic ecosystems that are only marginally 

affected by acidification. 

Bulger et al. (1999) observed a positive relationship between condition factor and pH in streams in 

Shenandoah National Park. Dennis and Bulger (1995) found a reduction in the condition factor for 

blacknose dace in waters near pH 6.0. The four populations shown in Figure B-15 with the lowest 

condition factor have mean habitat pH values within or below the range of critical pH values at which 

Baker and Christensen (1991) estimated that negative population effects for blacknose dace are likely for 

the species. The mean length-adjusted condition factor of fish from the study stream with the lowest ANC 

was about 20% lower than that of the fish in best condition. Comparisons with the work of Schofield and 

Driscoll (1987) and Kretser et al. (1989) suggest that pH in the low-pH Shenandoah National Park 

streams is near or below the limit of occurrence for blacknose dace populations in the Adirondack region 

of New York (Sullivan et al., 2003). 

Chronic sublethal stress caused by pH below about 6.0 may have serious effects on a variety of 

wild fish populations. There is an energy cost in maintaining physiological homeostasis; the calories used 

to respond to stress are a part of the fish’s total energy budget and are unavailable for other functions, 

such as growth and reproduction (Schreck, 1981, 1982; Wedemeyer et al., 1990) 

 B-56
Observed differences in condition factor may occur because maintenance of internal chemistry in 

the more acidic streams would require energy that otherwise would be available for growth and weight 

gain (Dennis and Bulger, 1999; Sullivan et al., 2003). The energy costs to fish for active ionoosmoregulation can be substantial (Bulger, 1986; Farmer and Beamish, 1969). Because of the steep 

gradient in Na+

 and Cl− concentrations between fish blood and freshwater, there is constant diffusional 

loss of these ions, that must be replaced by energy-requiring active transport. Low pH increases the rate 

of passive loss of blood electrolytes (especially Na+

 and Cl−), and Al elevates losses of Na+ and Cl− above 

the levels that occur due to acid stress alone (Wood, 1989). 

It is also possible that the loss of sensitive individuals or early life stages within species may reduce 

competition for food among the survivors, resulting in better growth rates, survival, or condition. 

Similarly, competitive release (increase in growth or abundance subsequent to removal of a competitor) 

may result from the loss of a sensitive species, with positive effects on the density, growth, or survival of 

competitor population(s) of other species (Baker et al., 1990b). However, in some cases where 

acidification continued, transient positive effects on size of surviving fish were shortly followed by 

extirpation (Bulger et al., 1993). 

Acid stress is at least partly responsible for the lower condition of blacknose dace populations in 

Shenandoah National Park, though reduced access to food or lower food quality (Baker et al., 1990b), 

either resulting from the nature of soft water streams or exacerbated by acidification, cannot be ruled out. 

Primary productivity is low in headwater streams and lower still in soft water headwaters, which are more 

likely to be acidified. Production of invertebrates is likely to be low in such streams as well (Wallace 

et al., 1992). Thus, lower food availability cannot be discounted as a potential contributor to lowered 

condition in Shenandoah National Park blacknose dace populations in low-pH streams. Nevertheless, 

reduced growth rates have been attributed to acid stress in a number of other fish species, including 

Atlantic salmon (Salmo salar), Chinook salmon (Oncorhynchus tshawytscha), lake trout (Salvelinus 

namaycush), rainbow trout (Oncorhynchus mykiss), brook trout, brown trout (Salmo trutta), and Arctic 

char (Salvelinus alpines). 

B.4.1.2.

B.4.1.3.

Species Composition 

Species composition refers to the mix of species that are represented in a particular ecosystem. 

Acidification alters species composition in aquatic ecosystems. There are a number of species common to 

many oligotrophic waters that are sensitive to acidic deposition and that cannot survive, compete, or 

reproduce in acidic waters. In response to small to moderate changes in acidity, acid-sensitive species are 

often replaced by other more acid-tolerant species, resulting in changes in community composition, but 

little or no change in total community abundance or biomass. The effects of acidification are continuous, 

with more species being affected at higher degrees of acidification. Therefore, the degree of alteration of 

surface water biological community composition increases as surface waters become more acidic. There 

is a consistent pattern of lower community diversity with increased acidification. 

Taxonomic Richness 

Taxonomic richness is a metric that is commonly used to quantify the effects of an environmental 

stress such as acidification or eutrophication. The richness metric can be applied at various taxonomic 

levels. For example, the number of fish species can be used as an index of acidification (Bulger et al., 

1999). Similarly, acidification effects on aquatic insects can be evaluated on the basis of the number of 

families or genera of mayflies (order Ephemeroptera) (Sullivan et al., 2003). 

Acidification results in the loss of acid-sensitive species, with more species lost with higher 

degrees of acidification. A direct outcome of population loss caused by acidification is a decline in species 

 B-57
richness (the total number of species in a stream or lake). This is a highly predictable outcome of regional 

acidification, although the pattern and rate of species loss varies from region to region. 

Decreases in ANC and pH and increases in Ali concentration contribute to declines in species 

richness and abundance of zooplankton, macroinvertebrates, and fish (Keller and Gunn, 1995; Schindler 

et al., 1985). Species richness is positively correlated with pH and ANC (Kretser et al., 1989; Rago and 

Wiener, 1986) because of the elimination of acid-sensitive species (Schindler et al., 1985). Knowledge of 

the spatial distribution of pH and other water quality variables is necessary to explain the presence or 

absence of species within heterogeneous environments. Organisms that are mobile and can sense the pH 

of their environment can move to areas (called refugia) that have more favorable water chemistry. 

Although some species are favored by increased acidity, species diversity generally decreases as surface 

water acidity increases. 

Decreases in species richness have been observed for all major trophic groups of aquatic organisms 

(Baker et al., 1990b). Baker et al. (1990b) discussed 10 selected studies that documented this 

phenomenon, with sample sizes ranging from 12 to nearly 3,000 lakes and streams analyzed per study. 

Lake and stream size can be an important complicating factor in interpreting species richness data. 

Larger lakes and streams in larger watersheds would generally be expected to contain more species than 

smaller lakes or streams in smaller watersheds, irrespective of acid-base chemistry. Nevertheless, when 

adjusted for lake size, lakes with pH less than approximately 6.0 contain significantly fewer species than 

lakes with pH above 6.0 (Figure B-17) (Frenette et al., 1986; Matuszek and Beggs, 1988; Rago and 

Wiener, 1986; Schofield and Driscoll, 1987). 

Studies in the Adirondack Mountains demonstrated the effect of acidification on species richness; 

of the 53 fish species recorded in Adirondack lakes by the ALSC, about half (26 species) were absent 

from lakes with pH below 6.0. Those 26 species included important recreational species, such as Atlantic 

salmon, tiger trout (Salmo trutta X Salvelinus fontinalis), redbreast sunfish (Lepomis auritus), bluegill 

(Lepomis macrochirus), tiger musky (ESOX masquinongy X lucius), walleye (Sander vitreus), alewife 

(Alosa pseudoharengus), and kokanee (Oncorhynchus nerka) (Kretser et al., 1989), plus ecologically 

important minnows that serve as forage for sport fish. Fully 346 of 1,469 lakes surveyed by the ALSC 

supported no fish at all at the time of the survey. These lakes were significantly lower in pH, dissolved 

Ca2+, and ANC, and had higher concentrations of Ali than lakes hosting one or more species of fish 

(Gallagher and Baker, 1990). Among lakes with fish, there was an unambiguous relationship between the 

number of fish species and lake pH, ranging from about one species per lake for lakes having pH less than 

4.5 to about six species per lake for lakes having pH >6.5 (Kretser et al., 1989; Driscoll et al., 2001). 

Figure B-17 shows the mean number of fish species for pH classes from 4.0 to 8.0 in lakes in the 

Adirondacks. It is important to note, however, that there are many possible causes of fish absence in 

addition to acidification. These include lack of suitable habitat (especially for spawning), winter kill, 

blocked access, etc. 

 B-58
Source: Matuszek and Beggs (1988). 

Figure B-16. Mean residual number of species per lake for lakes in Ontario, by pH interval. The residual 

number of species for a lake is the deviation of the observed number from the number 

predicted by lake area. 

Sullivan et al. (2006b) developed a relationship between fish species richness and ANC class for 

Adirondack lakes. Fish species richness observations, as a function of ANC (μeq/L) class, were fit to a 

logistic relationship by a non-linear regression analysis. Under chronically acidic conditions (summer 

index or annual average ANC <0 μeq/L), Adirondack lakes are generally fishless. There was a marked 

increase in mean species richness with increases in ANC up to values of approximately 100 μeq/L. The 

asymptote for the fish species equation was 5.7 species. This analysis suggests that there could be loss of 

fish species with decreases in ANC below approximately 100 μeq/L. It does not account, however, for the 

possibility that lakes having higher ANC are often larger, and therefore support more fish species because 

of increased habitat diversity and complexity. 

As an element of the FISH project (Bulger et al., 1999), numbers of fish species were compared 

among 13 Shenandoah National Park streams spanning a range of pH and ANC conditions. There was a 

highly significant (p <0.0001) relationship between stream acid-base status (during the 7–year period of 

record) and fish species richness among the 13 streams. The streams with the lowest ANC hosted the 

fewest species (see Figure B-18). 

 B-59
Figure B-17. Number of fish species per lake or stream versus acidity statues, expressed either as pH or 

ANC. (A) Adirondack lakes (Sullivan et al., 2006b); (B) streams in Shenandoah National Park 

(Bulger et al., 1999). The data for the Adirondacks are presented as mean and range of species 

richness within 10 μeq/L ANC categories, based on data collected by the Adirondack Lakes 

Survey Corporation. 

Median stream ANC values and watershed areas are shown in Table B-11 for the 14 streams used 

by Bulger et al. (1999) to develop the relationship between ANC and fish species richness shown in 

Figure B-18. Despite the overall similarities, these study streams vary in watershed area by a factor of 10. 

The streams that have larger watershed areas generally have more fish species than the streams having 

smaller watershed areas. All of the “rivers” have watersheds larger than 10 km2

 and ANC higher than 

75 μeq/L. In contrast, the majority (but not all) of the “runs” have watershed area smaller than 10 km2

 and 

ANC less than 20 μeq/L. All of the streams that have watershed areas smaller than 10 km2

 have three or 

fewer known species of fish present. All of the streams having larger watersheds (>10 km2

) have three or 

more known fish species; seven of nine have five or more species; and the average number of fish species 

is six. There is no clear distinction between river and run, but it is clear that as small streams in 

Shenandoah National Park combine and flow into larger streams and eventually to rivers, two things 

happen: acid-sensitivity generally declines, and habitat generally becomes suitable for additional fish 

species (Sullivan et al., 2003). 

 B-60
Source: Redrawn from Bulger et al. (1999). 

Figure B-18. Number of fish species among 13 streams in Shenandoah National Park. Values of ANC are 

means based on quarterly measurements, 1987–94. The regression analysis shoed a highly 

significant relationship (p <0.0001) between mean stream ANC and number of fish species. 

Streams having ANC consistently <75 μeq/L had three or fewer species. 

South of Shenandoah National Park the effects of surface water acidification on fish species 

richness have been studied in some detail in the St. Marys River in Virginia. Fish species richness was 

closely associated with surface water acid-base chemistry. Bugas et al. (1999) conducted electrofishing in 

the St. Marys River in 1976, and every 2 years from 1986 through 1998. Systemic stream acidification 

occurred during the study period. Sampling occurred at six sites between the downstream end of the St. 

Marys Wilderness and the headwaters over a distance of about 8 km. The number of fish species in the St. 

Marys River within the wilderness declined from 12 in 1976 to 4 in 1998. Three of the four species 

present in 1998 (brook trout, blacknose dace, fantail darter [Etheostoma flabellare]) are tolerant of low 

pH and are typically the only fish species present in streams having similar levels of acidity in 

Shenandoah National Park, which is also located in Virginia (Bulger et al., 1999). Bugas et al. (1999) 

reported that successful brook trout reproduction in the St. Marys River occurred only 1 year out of 4 

during the period 1995 through 1998. Eight of the fish species recorded in one or more early years have 

not been observed in more recent years. Several, including blacknose dace, rainbow trout, and torrent 

sucker (Thoburnia rhothoeca), showed a pattern of being progressively restricted over time to lower river 

reaches, which generally have higher ANC. The number of fish species decreased with decreasing 

minimum ANC, from nine species at ANC of about 160 μeq/L to one to three species at ANC near 0. The 

best fit regression line suggested, on average, a loss of one species for every 21 μeq/L decline in annual 

minimum recorded ANC value. 

Dynamic water chemistry model projections have been combined with biological dose-response 

relationships to estimate declines in fish species richness with acidification. A relationship derived from 

the data in Figure B-18 was used by Sullivan et al. (2003) with stream ANC values predicted by the 

MAGIC model to provide estimates of the expected number of fish species in each of the modeled 

streams for the past, present, and future chemical conditions simulated for each stream. The coupled 

geochemical and biological model predictions were evaluated by comparing the predicted species 

richness in each of the 13 streams with the observed number of species that occur in each stream. The 

agreement between predicted and observed species numbers was good, with a root mean squared error 

(RMSE) in predicted number of species across the 13 streams of 1.2 species. The average error was 0.3 

 B-61
species, indicating that the coupled models were unbiased in their predictions. Model reconstructions of 

past species richness in the streams suggested that historical loss of species had been greatest in the 

streams located on the most sensitive geological class (siliciclastic). The average number of species lost 

from streams on the three bedrock types examined were estimated as: 1.6 species on siliciclastic bedrock; 

0.4 species on granitic bedrock; and 0.4 species on basaltic bedrock. In the case of the siliciclastic 

streams, the projected past changes were much larger than the average error and RMSE of the coupled 

models, suggesting that the projections were reasonably robust. 

It appears that fish species richness is controlled by multiple factors, of which both acidification 

and watershed area can be important. Watershed area might be important in this context because smaller 

watersheds may contain smaller streams having less diversity of habitat, more pronounced effects on fish 

from high-flow periods, or lower food availability. Such issues interact with other stresses, including 

acidification, to determine overall habitat suitability. 

For Shenandoah National Park, Bulger et al. (1999) concluded that the most important cause of the 

observed decline in species richness with decreasing ANC was acid stress associated with acidification. 

However, an additional causal factor may have been the decrease in the number of available aquatic 

niches when moving from downstream locations (which are seldom low in pH and ANC) to upstream 

locations (which are often low in pH and ANC in this region; (Sullivan et al., 2003). The relative 

importance of this latter factor, compared with the importance of acid stress, in determining this 

relationship is unknown. 

In the Adirondack region, Driscoll et al. (2001b) concluded that high-elevation lakes are more 

likely to be fishless than larger lakes at low elevation (Gallagher and Baker, 1990) because they have poor 

access for fish immigration, poor fish spawning substrate, or low pH, or they may be susceptible to 

periodic winter kills. Nevertheless, small, high-elevation Adirondack lakes with fish also had significantly 

higher pH compared with fishless lakes; acidity is likely to play an important role in the absences of fish 

from such lakes (Driscoll et al., 2001b). 

B.4.1.4. Community Structure 

Ecosystem response to pollutant deposition is a direct function of the ecosystem’s ability to 

ameliorate resulting changes in individual species (Strickland et al., 1993). To determine ecosystem 

response and the possible effects on community structure, species responses must be scaled in both time 

and space and be propagated from the individual to the more complex levels of community interaction 

within an ecosystem. 

Individuals within a population vary in their ability to withstand a stress. The response of each 

individual is based on its genetic constitution (genotype), its stage of growth at time of exposure to the 

stress, and the microhabitat in which it lives (Levin, 1998). The range within which individuals in the 

population can exist and function determines the ability of the population to survive when exposed to a 

chronic stress. Those individuals that are able to cope with the stress survive and reproduce. The same 

kinds of pressures act on populations of different species. Competition among species results in 

community change over time and eventually produces ecosystems composed of populations of species 

that have the capability to tolerate the stress (Guderian et al., 1985; Rapport and Whitford, 1999; 

U.S. EPA, 2004). 

Work conducted on the biological effects of acidification has largely been focused on the response 

of fish, especially salmonids (trout and salmon). This focus tends to be driven by the value people place 

on fish and fishing, rather than any ecological consideration. Other vertebrate, invertebrate, plant, and 

algal communities are also sensitive to acidification. In general, higher order trophic groups are more 

susceptible to acidification. Thus, in terms of changes in community structure in response to aquatic 

acidification, the general progression of sensitivity is as follows: fish >invertebrates (benthic and 

zooplankton) >algae >microbes (Baker et al., 1990b). Population-level fish response to acidification is 

primarily through recruitment failure, a result of increased mortality of early life stages or indirect effects 

 B-62
through the food chain (loss of prey species). Ali, pH, and Ca2+ have been identified as the variables most 

likely to have the greatest influence on fish community structure. 

B.4.1.5.

B.4.2.1.

Indices of Ecological Effects 

The most widely used index of acidification effect is the Acid Stress Index (ASI) developed by 

Baker et al. (1990b). This index uses fish bioassay survival data fitted to a maximum likelihood logistic 

regression model as a function of exposure to pH, Al, and Ca2+ to predict the probability of fish survival 

expressed as a percent mortality. This approach can aid in determination of effects on species composition 

by predicting the probability of occurrence of species of varying acid sensitivity. Separate ASI models 

were developed for tolerant, intermediate, and sensitive fish species. Approximate ASI reference levels 

were established for various species based on logistic regression of fish presence as a function of the 

sensitive, intermediate, and tolerant ASI values for brown bullhead (Ameiurus nebulosus), brook trout, 

lake trout, and common shiner (Luxilus cornutus). They are presented in Table B-12. 

The ASI was deemed a useful index of stress by Baker et al. (1990b), even though the relationships 

between ASIs and fish population status could not be quantified precisely because of confounding factors. 

Such factors included the abundance and types of food species, competitors and predators present, 

variations in habitat quality, and density-dependent effects on fecundity. 

B.4.2. Timing of Effects 

Life Stage Differences in Sensitivity 

Episodic and chronic changes in the chemistry of surface waters can have different effects on 

aquatic organisms and populations depending on species and the life history stages present. More is 

known about the sensitivity to acidification of the life stages of fish than is known for other aquatic 

organisms. In general, early life stages are more sensitive to acidic conditions than the young-of-the-year, 

yearlings, and adults (Baker and Schofield, 1985; Baker et al., 1990b; Johnson et al., 1987). Also, small 

fish, especially swim-up fry, are probably less mobile and less able to avoid exposure to adverse chemical 

conditions than the relatively larger adults (Baker et al., 1996). 

There are a number of issues of acidification timing that are important to determination of the 

extent and magnitude of effects. One important issue concerns the timing of acidity exposure relative to 

life stage. For example, adult fish are generally more tolerant of acidity than early life stages such as eggs, 

fry and juveniles. There could be substantial differences in effect based on small differences in age or 

timing of exposure to acidity. No definite pattern was observed by Baker et al. (1990b) across all studies 

or species. This may reflect either differences in the test conditions or actual differences among species. 

The presence of early life stages of brook trout, which are most sensitive to adverse effects from 

acidification (Bulger et al., 2000), varies with season. For example, the most acid-sensitive stages of 

brook trout development are present in Virginia streams throughout the cold season in general, and the 

winter in particular (Figure B-19). 

 B-63
Source: Sullivan et al. (2003). 

Figure B-19. Life stages of brook trout. 

The processes of oogenesis and fertilization in fish and aquatic invertebrates are especially 

sensitive to low pH (Havas et al., 1995; Muniz, 1991). In fish, this sensitivity is most likely due to 

adverse effects on the female spawner. For instance, Beamish et al. (1976) reported that reduced serum 

and plasma Ca2+ in female fish in acidified Canadian lakes lead to a higher probability for failure in 

producing viable eggs. A depletion of Ca2+ from bone and increased numbers of females with unshed eggs 

have also been linked to sensitivity at this life stage (cf. Muniz, 1991; Rosseland, 1986). 

After fertilization, the embryo seems to be susceptible to acidic waters throughout the whole period 

of development. The periods shortly after fertilization and before hatching seem to be most critical 

(Rosseland, 1986). The susceptibility of the embryo can be the result of direct exposure to elevated H+

concentrations and also to the toxic effects of Ali at intermediate pH-values. Low pH in the surrounding 

water also results in pH-depression inside the egg, leading to either a prolongation of the hatching or to a 

reduced hatching success (Rosseland, 1986). Eggs lying in gravel on stream and lake beds are to some 

extent protected from exposure to rapid changes in pH (Gunn and Keller, 1984b; Lacroix, 1985). 

Nevertheless they can experience high mortality during periods of acid runoff, such as snowmelt (Gunn 

and Keller, 1984a). 

In fish, emergent alevins show susceptibility to the adverse effects of Ali and H+

 that increases with 

age (Baker and Schofield, 1982; Wood and McDonald, 1982). Rosseland (1986) indicated that this 

increasing sensitivity results from changes that take place in the respiratory system. Shortly after hatch, 

alevins still respire through their skin but gradually gills become the primary organ of gas and ion 

exchange. Gills are the locus for interference of H+

 and Ali with iono-regulatory exchange. Woodward 

et al. (1989) exposed cutthroat trout (Oncorhynchus clarki) from the Snake River in Wyoming to pH 

depressions from pH 4.5 to 6.5 in the laboratory. Fertilized egg, eyed embryo, alevin, and swim-up larval 

stages were exposed to low pH for a period of seven days. Each life stage was monitored for mortality, 

growth, and development for 40 days after hatching. Reductions in pH from 6.5 to 6.0 in low-Ca2+ water 

(70 μeq/L) did not affect survival, but reduced growth of swim-up larvae. The eggs, alevin, and swim-up 

larval stages showed significantly higher mortality at pH 4.5 than at pH 6.5. Mortality was also higher at 

pH 5.0 than at pH 6.5, but only statistically higher for eggs. 

Woodward (1991) exposed greenback cutthroat trout (Oncorhynchus clarki stomias) in the 

laboratory to 7–day pH depressions. Low-Ca2+ (65 μeq/L) water at pH 6.5 was experimentally reduced to 

pH values of 6.0, 5.5, 5.0, and 4.5. Four life stages were exposed: freshly fertilized egg, eyed embryo, 

alevin, and swim-up larva. Alevin survival was reduced at pH 5.0, whereas survival of eggs, embryos, and 

swim-up larvae was reduced at pH 4.5. Swim-up larvae showed feeding inhibitions at pH 4.5. The authors 

concluded that the threshold for effects of acidity on greenback cutthroat trout in the absence of Al was 

pH 5.0 (Woodward, 1991). 

 B-64
Yellowstone cutthroat trout (O. c. bouveri) were exposed to 7–day pH depressions by Farag et al. 

(1993). Of the four life stages studied, eggs were most sensitive to low pH. Eggs exposed for seven days 

to pH 5.0 test water showed a statistically significant reduction in survival compared with eggs exposed 

for seven days to pH 6.5 water. Survival of alevin and swim-up larvae were significantly reduced from 

near 100% at pH 6.5 to near 0% at pH 4.5. Intermediate pH values (6.0, 5.5) in all cases showed reduced 

survival compared with the control (6.5) but not by statistically significant amounts. Eyed embryos were 

not sensitive to any of the exposures. 

According to Bulger et al. (1999), adult brook trout in Shenandoah National Park streams are more 

tolerant of acidity than are adult blacknose dace. For both species, the early life stages are more sensitive 

than the adults, and brook trout young are actually more sensitive than blacknose dace adults (Bulger 

et al., 1999). Blacknose dace spawn during summer and the eggs and very young fry are therefore 

somewhat protected from the most acidic episodes, which typically occur during cold-season, high-flow 

conditions. 

B.4.2.2. Biological Effects of Episodes 

Episodic decreases in pH and ANC can produce chemical conditions in lakes, and especially in 

streams, that are as harmful to biota as chronic acidification (Baker et al., 1996). Adverse effects on biota 

occur particularly when changes involve pH, Ali, or Ca2+ (Baker et al., 1990b). Aquatic biota vary greatly 

in their sensitivity to episodic decreases in pH and increases in Ali in waters having low Ca2+

concentration. However, Baker et al. (1990b) concluded that episodes are most likely to affect biota if the 

episode occurs in waters with pre-episode pH above 5.5 and minimum pH during the episode of less than 

5.0. 

Results from the ERP demonstrated that episodic acidification can have long-term adverse effects 

on fish populations. Streams with suitable chemistry during low flow, but low pH and high Ali levels 

during high flow, had substantially lower numbers and biomass of brook trout than in non-acidic streams 

(Wigington et al., 1996). Streams having acidic episodes showed significant mortality of fish. 

Some brook trout avoided exposure to stressful chemical conditions during episodes by moving 

downstream or into areas with higher pH and lower Ali. This movement of brook trout only partially 

mitigated the adverse effects of episodic acidification, however, and was not sufficient to sustain fish 

biomass or species composition at levels that would be expected in the absence of acidic episodes. Just as 

spatially heterogeneous environments or refugia enable some species to survive in otherwise unfavorable 

conditions, temporal heterogeneity often has the opposite effect. These findings suggest that stream 

assessments based solely on chemical measurements during low-flow conditions will not accurately 

predict the status of fish populations and communities in small mountain streams unless some adjustment 

is made for episodic processes (Baker et al., 1996a, 1990b; Sullivan, 2000a; Wigington et al., 1996). 

In Shenandoah National Park, MacAvoy and Bulger (1995) used multiple bioassays over 3 years in 

one of the low-ANC streams as part of the FISH project to determine the effect of stream baseflow and 

acid episode stream chemistry on the survival of brook trout eggs and fry. Simultaneous bioassays took 

place in mid- and higher-ANC reference streams. Acid episodes (with associated low pH and elevated Ali

concentrations, and high streamwater discharge) induced rapid mortality in the low-ANC stream, while 

the test fish in the higher-ANC stream survived (Bulger et al., 1999). 

In the West, it has also been shown that native trout are sensitive to short-term increases in acidity. 

For example, Woodward et al. (1989) exposed native western cutthroat trout to pH depressions (pH 4.5 to 

6.5) in the laboratory. Reductions in pH from 6.5 to 6.0 in low-Ca2+ water (70 μeq/L) did not affect 

survival, but did reduce growth of swim-up larvae. Eggs, alevins, and swim-up larvae showed 

significantly higher mortality at pH 4.5 as compared to pH 6.5. Mortality was also somewhat higher at pH 

5.0, but only statistically higher for eggs. Some species of aquatic biota in western aquatic ecosystems 

have been shown to be somewhat more sensitive to pH and ANC change than are cutthroat trout (Baker 

et al., 1990b). 

 B-65
Multiple logistic regression models were used by Van Sickle et al. (1996) to relate fish bioassay 

mortality rates to summary statistics of time-varying stream chemistry over the 20–day bioassay periods. 

Higher mortality of all three test fish species (brook trout, dace, sculpin [Cottus spp.]) during the in situ 

bioassays was clearly associated with increased Ali. In addition, individual bioassays conducted during 

chronically or episodically acidified conditions had higher median mortality than did those during non 

acidic conditions, but no mortality differences were detected between chronically acidic and episodically 

acidic conditions. Time-weighted median Ali was the best single predictor of 20–day mortality for both 

brook trout and sculpin, whereas the number of days with Ali >200 μg/L provided the best prediction of 

blacknose dace mortality. 

In the Northeast, Baker et al. (1996) studied the effects of episodic acidification on fish in 13 small 

streams in the Adirondack and Catskill Mountains of New York and the Northern Appalachian Plateau in 

Pennsylvania. They conducted in situ bioassays with brook trout and blacknose dace, mottled sculpin 

(Cottus bairdi) or slimy sculpin (Cottus cognatus) depending on the region, to measure direct toxicity. 

Movements of brook trout individuals in relation to stream chemistry were tracked using radiotelemetry. 

Electrofishing surveys assessed fish community status and the abundance and biomass of brook trout in 

each stream. Streams with suitable conditions during low flow, but moderate-to-severe episodic 

acidification during high flow, had higher fish mortality in bioassays, higher net downstream movement 

of brook trout during events, and lower brook abundance and biomass compared to nonacidic streams. 

These streams lacked the more acid-sensitive fish species (blacknose dace and sculpin). Movement of 

trout into refugia (areas with higher pH and lower Al) during episodes partially mitigated the adverse 

effects of episodes. 

Chemical measurements by ERP during high flow correlated with fish community status. In 

general, reduced trout abundance occurred in ERP streams with median high flow pH <5.0 and Ali >100 

to 200 μg/L. Acid -sensitive fish species were absent from streams with median high flow pH <5.2 and 

Ali >100 μg/L. More recently, Baldigo et al. (2007) found that mortality of brook trout young of the year 

occurred at concentrations as low as 54 μg/L .Ali was the single best predictor of fish mortality in ERP 

bioassays (Van Sickle et al., 1996) and has been identified as an important toxic factor in other bioassays 

and field studies (Ingersoll et al., 1990b; Mount et al., 1988; Rosseland et al., 1990). The relationships 

between pH and Ali or ANC and Ali vary among streams (Wigington et al., 1996), and therefore 

predictions of potential effects on fish based solely on pH or ANC may be misleading. High Ali

concentrations during episodes are probably the dominant cause of adverse effects on fish during episodic 

acidity events. 

Biological Effects of Chronic Acidification 

Changes in surface water acid-base chemistry, including pH, ANC, Ali, and Ca2+, can affect instream and in-lake biota. Adverse biological effects may be seen at pH less than about 6.0 to 6.5 and Ali

greater than about 30 to 50 μg/L (1 to 2 μM). It tends to increase with decreasing pH, and reaches 

potentially toxic concentrations (>~2 μM) in surface drainage waters having pH less than about 5.5. 

Effects vary substantially by organism, life stage, and the concentration of DOC. Inorganic Al in solution 

is also toxic to plants. 

Calcium can ameliorate the toxic effects of acidity and Al on biota. Most organisms can tolerate 

lower pH and higher Ali at higher Ca2+ concentrations, but in natural environments, elevated 

concentrations of Ali are only found in Ca2+-depleted systems. This effect is most important at low Ca2+

levels. Overall biological effects noted with decreasing pH are described in Table B-13 (Baker et al., 

1990b). The organisms most likely to respond to such changes in water chemistry include fish, aquatic 

insects, zooplankton, and diatoms. In some cases, amphibians are also important sensitive biological 

receptors. Most available data are for fish response. 

In most stream or lake survey areas, direct quantification of biological responses to surface water 

acidification is not possible, given the scarcity or absence of biological long-term monitoring and dose-

 B-66
response data. Few biological long-term monitoring studies have been conducted. Much of the available 

in situ dose-response data have been generated from studies of streams in Virginia and Pennsylvania and 

lakes in New York. Data with which to evaluate acidification relationships have been scarce in most other 

regions. 

Lakes 

Fish status assessments for the eastern and upper midwestern U.S. were conducted by Baker et al. 

(1990b), by region, using a variety of assessment methods. For the northeast region, two water chemistry 

models were linked to fish response models: the Integrated Lake-Watershed Acidification Study (ILWAS) 

model and MAGIC. For the Adirondack subregion, three process models were used: ILWAS, MAGIC, 

and Regional MAGIC. For other areas in the eastern U.S. and for the Upper Midwest, analysis of fish 

status was limited to application of the sensitive, intermediate, and tolerant toxicity models. 

Assessment results reported by Baker et al. (1990b) for the Adirondack region are presented in 

Table B-14 showing results based on the ASI. Table B-15 shows the estimated percentage of Adirondack 

lakes with acid-base chemistry unsuitable for fish population survival according to various assessment 

models based on responses for brook trout, lake trout, and common shiner. Assessment results for the 

Northeast region are presented in Tables B-16 and B-17. 

In acid-sensitive lakes in the western U.S., the focus is often mainly on native cutthroat trout. It is 

important to note, however, that many high-elevation western lakes and streams were historically fishless. 

The top predators in such aquatic ecosystems were often amphibians or crustaceans. Thus, even though 

cutthroat trout might be considered native to the region, they are not necessarily native to a particular lake 

or stream. 

Streams 

In streams, the major organisms of concern with respect to water acidification are fish, amphibians, 

benthic macroinvertebrates, and periphyton (attached algae). All of these groups have shown adverse 

effects in response to acidification (see Annex B-6). Most available data are for fish and aquatic insects, 

mainly in the southeastern U.S. Streams affected by acidic deposition tend to occur at high elevation. 

They are often high-gradient and flow through base-poor geology. 

Baker et al. (1990b) presented assessment results for the mid-Appalachian region as the 

distribution (percent) of NSS lower node, upper node, and total streams classified in various ASI values 

(Baker et al., 1990b). Most of the streams were classified in the lowest ASI category (Table B-18). 

Assessment results for the interior Southeast region were similar (Table B-19). 

Some fish response research has also been conducted for streams in the Catskill Mountains. Baker 

and Christensen (1991) estimated that the fish species found in the Neversink River Basin in the Catskill 

Mountains are typically lost when pH decreases to the range of 4.7 to 5.2 (brook trout), 5.5 to 5.9 (slimy 

sculpin), 4.7 to 5.7 (brown trout), 5.6 to 6.2 (blacknose dace), and 4.9 to 5.3 (Atlantic salmon). 

The Shenandoah National Park FISH Project evaluated the effects of streamwater acidification on 

fish populations and communities in streams in Shenandoah National Park. Fish species richness, 

population density, condition factor, age distribution, size, and bioassay survival were all lower in streams 

having low-ANC compared to intermediate-ANC and high-ANC streams (Bulger et al., 1995; Dennis 

et al., 1995; Dennis and Bulger, 1995; MacAvoy and Bulger, 1995). 

Bulger et al. (2000) developed model-based projections using the MAGIC model to evaluate the 

potential effect of reductions in S deposition of 40% and 70% from 1991 levels using data from VTSSS 

and SWAS. Projections were based on four brook trout stream categories: Suitable , ANC >50 μeq/L; 

Indeterminate, ANC 20 to 50 μeq/L; Marginal, ANC 0 to 20 μeq/L; and Unsuitable, ANC <0 μeq/L. 

Three scenarios of future acidic deposition were modeled: constant deposition at 1991 levels, 40% 

reduction from 1991 deposition levels, and 70% reduction from 1991 deposition levels. Based on 

observed 1991 ANC values, approximately 30% of all trout streams in Virginia were marginal or 

unsuitable for brook trout because they were either episodically (24%) or chronically (6%) acidic. In 

 B-67
addition, another 20% of the streams were classified as indeterminate, and brook trout in these streams 

may or may not have been affected. Based on the model simulations, 82% of these streams would not 

have been acidic before the onset of acidic deposition and would likely have been suitable for brook trout. 

The model projections suggested that neither the 40% nor the 70% reductions in acidic deposition 

would be expected to increase the number of streams that were suitable for brook trout above the ambient 

50%. In fact, the results suggested that a 70% reduction in deposition would be needed in the long-term 

just to maintain the number of streams that were considered suitable for brook trout. Because of the length 

of time required to restore buffering capacity in watershed soils, most of the marginal or unsuitable 

streams were expected to remain marginal or unsuitable for the foreseeable future. 

To develop projections of probable past and future responses of aquatic biota to changing S 

deposition in Shenandoah National Park, the MAGIC model was coupled by Sullivan et al. (2003) with 

several empirical models that linked biological response to past and future model projections of water 

quality. Unlike MAGIC, which is a geochemical, process-based model, the biological effects estimates 

were based on observed empirical relationships rooted in correlation and expressed as linear relationships. 

Correlation does not necessarily imply cause, but an observed pattern of co-variation between variables 

does provide a context for analysis of a possible relationship. In this case, the projections did not require 

extrapolation and are, therefore, statistically robust. To the extent that the observed empirical relationships 

used in the coupled models do in fact reflect the effects of acid stress on aquatic biota, the projections 

were also biologically robust. 

The geochemical and biological response models also differ in that MAGIC is a dynamic model 

and explicitly predicts the time course of changing water quality, whereas the empirical relationships used 

for estimating biological response were static. These relationships reflected a point in time (when the 

observations were made) and provided no information concerning the dynamics of biological response. 

That is, the empirical models predicted a new biological status for a new water chemistry, but gave no 

indication of the time required to achieve the biological status once the water quality change had 

occurred. 

There are thus two considerations that must be kept in mind when interpreting the biological 

responses predicted using a combination of process-based and empirical modeling approaches: the 

causality of the relationship between water quality and response, and the dynamics of biological response. 

With respect to the issue of causality, acidification is a disturbance and disturbance usually lowers species 

richness. In turn, loss of species usually lowers ecosystem stability. Biodiversity loss is a predictable and 

proven consequence of acidification, and there are abundant examples of this in North America and 

Europe (Bulger et al., 2000). With respect to the timing of biological response, it can be variable and 

difficult to predict. 

B.4.2.3. Timing of Recovery from Acidification 

Lakes and streams show spatial and temporal variability in response to a host of biotic and abiotic 

factors. Against this background of variability, it is difficult to detect changes in biological communities 

in response to changes in an individual environmental stressor without long-term biological data 

(Schindler, 1990; Lancaster et al., 1996). Long-term data sets are rare, and there are few well-documented 

instances of temporal changes in biological communities in response to changes in water chemistry. 

Regardless, it is known that surface water acidification affects virtually all trophic levels (e.g., Flower and 

Battarbee, 1983; Lancaster et al., 1996; Økland and Økland, 1986; Ormerod and Tyler, 1991; Rundle and 

Hildrew, 1990; St. Louis et al., 1990; Siminon et al., 1993; Sullivan, 2000a). 

Biological recovery can occur only if chemical recovery is sufficient to allow survival and 

reproduction of acid-sensitive plants and animals. The time required for biological recovery is uncertain. 

For terrestrial ecosystems, it may be decades after soil chemistry is restored because of the long life of 

many plant species and the complex interactions of soil, roots, microbes, and soil biota. For aquatic 

systems, research suggests that stream macroinvertebrate populations may recover relatively rapidly 

 B-68
(within approximately 3 years), whereas lake populations of zooplankton recover more slowly (Gunn and 

Mills, 1998). 

The timing of fish recovery is highly uncertain, and probably will depend heavily on dispersal 

opportunities. Stocking could accelerate fish population recovery (Driscoll et al., 2001b). Fish populations 

have recovered in acidified lakes when the pH and ANC have been raised through liming or reduction of 

acidic deposition (Hultberg and Andersson, 1982; Beggs and Gunn, 1986; Dillon et al., 1986; Keller and 

Pitblado, 1986; Raddum et al., 1986; Gunn et al., 1988; Kelso and Jeffries, 1988). 

Studies in Canada have improved understanding of the feasibility and complexity of biological 

recovery in response to chemical recovery from acidification. Biological recovery of previously acidified 

lakes is expected to be a slower process than chemical recovery. Sometimes there are other environmental 

stresses in addition to acidity, such as metal contamination (Gundersen and Rasmussen, 1995; Havas 

et al., 1995; Jackson and Harvey, 1995; McNicol et al., 1995; Yan et al., 1996b). Barriers can be imposed 

by water drainage patterns between lakes that hinder re-colonization by some fish species (Jackson and 

Harvey, 1995). Predation by non-acid-sensitive fish species can affect the recovery of zooplankton and 

macroinvertebrate communities (McNicol et al., 1995). Finally, tributary-spawned fish can be preyed 

upon when they move downstream into lakes inhabited by predatory fish and birds (Schofield and 

Keleher, 1996). 

Changes in surface water chemistry as a direct response to changes in S and N deposition are 

difficult to predict. Both chemical and biological effects of changing deposition can lag as the ecosystem 

comes into equilibrium with the modified deposition inputs. Soils or wetlands may continue to release S 

at a high rate for many years subsequent to a decrease in S deposition. As a result, surface water SO4

2−

concentrations may decrease in the future as a consequence of deposition changes that have already 

occurred. If soil base cations have become depleted, base cation concentrations in some surface waters 

could decrease in the future irrespective of any further changes in SO4

2−

 concentrations. This would be 

expected to contribute to additional acidification. 

Studies in the U.S., Canada, and Europe have illustrated the feasibility and complexity of biological 

recovery in response to decreased surface water acidity. There is currently no theoretical basis on which to 

predict the paths of biological recovery. At some scale, each stream or river is unique. The null hypothesis 

is that recovery will proceed in the same fashion as acidification, only backwards. Thus, for example, the 

last species lost (the most acid-tolerant) would be the first to return. However, time lags are expected to 

differ widely among species and among water bodies. Biological recovery of previously acidified lakes or 

streams can lag behind chemical recovery because of such factors as (a) limits on dispersal and 

recolonization; (b) barriers imposed by water drainage patterns (Jackson and Harvey, 1995); (c) the 

influence of predation (McNicol et al., 1995); and (d) other environmental stresses (Gunn et al., 1995; 

Havas et al., 1995; Jackson and Harvey, 1995; McNicol et al., 1995; Yan et al., 1996a,1996b). 

Limitations on dispersal and recolonization can hamper biological recovery from chronic and 

episodic acidification. If fish move into refugia areas during low pH and then return, behavioral 

avoidance would reduce the overall effect of acidification on fish populations. However, if fish move out 

of the stream system in response to sublethal episodes, as suggested by Baker et al. (1996), and do not 

return or return in smaller numbers, then the population level effects of episodic acidification would be 

greater than predicted based on mortality tests alone. 

Stream macroinvertebrate communities are often dominated by immature life stages of flying 

insects, such as mayflies, dragonflies, and stoneflies. Such species have rather rapid colonization times, 

such that a functional stream macroinvertebrate community may return in only a few years in response to 

improved chemistry. However, fish community recovery is expected to be quite variable, depending on 

sources of colonists. In streams, fish could be introduced as soon as the water quality becomes suitable or 

the macroinvertebrate community becomes established. In streams that had simple fish communities in 

the past, a fish community might become rapidly established. It might take decades for complex 

communities without species introductions. 

The Sudbury region of Ontario, Canada has been important for studying the chemical and 

biological effects of S deposition. Mining and smelting of copper-nickel ore began in the 1880s. By the 

 B-69
1950s and 1960s, SO2 emissions from the mining and smelting operations peaked at over 5,000 tons/day 

and extensive acidification of nearby surface waters was documented (Beamish and Harvey, 1972). 

Emissions of SO2 then decreased during the 1970s to less than one-third of the peak values. This region 

has been the focus of extensive chemical and biological effects work since the 1980s (Keller, 1992). 

Sulfur emission reductions resulted in improved water quality in many lakes (Keller and Pitblado, 1986; 

Keller et al., 1986), and some fisheries recovery was also documented (Gunn and Keller, 1990; Keller and 

Yan, 1991). Griffiths and Keller (1992) found changes in the occurrence and abundance of benthic 

invertebrates that were consistent with a direct effect of reduced lakewater acidity. A more recent 

assessment of recovery of ecosystems in Canada provided further evidence of biological recovery, but 

also showed that the spatial extent of recovery was limited to lakes that had been severely acidified by the 

Sudbury smelter (Jeffries et al., 2003). 

Whitepine Lake, located 90 km north of Sudbury, had low pH (5.4) and ANC (1 μeq/L) in 1980 

and its fish populations displayed symptoms of acid stress. Acid-tolerant yellow perch (Perca flavescens) 

were abundant, but the more acid-sensitive species lake trout and white sucker (Catostomus commersoni) 

were rare and not reproducing. Fish populations were studied by Gunn and Keller (1990) from 1978 

through 1987, and zooplankton were sampled at least monthly during the open-water periods of 1980 

through 1988. During the period between 1980 and 1988, pH increased to 5.9 and ANC increased to 

11 μeq/L. Young lake trout first reappeared in 1982 and became increasingly abundant throughout the 

study. The number of benthic invertebrate taxa increased from 39 in 1982/83 to 72 in 1988, and the 

relative abundance of many of the invertebrates found in 1982 changed along with the changes in water 

chemistry (Gunn and Keller, 1990). Research at Sudbury clearly documented that chemical recovery of 

lakes was possible upon reduced emissions and deposition of S, and also that biological recovery, 

involving multiple trophic levels, would soon follow. 

Baker et al. (1990b) used field-based models to test the potential for biological recovery. The 

models were calibrated from the observed among-lake or among-stream associations between fish status 

and the chemical and physical characteristics measured in the surface water. The models were generally 

calibrated using chemistry data collected in conjunction with surveys of fish status. It was assumed that 

the systems surveyed were at steady state and that the observed status of the fish community was 

determined by the observed chemical and physical conditions in the lake or stream. For each species 

considered, the current presence or absence of the species was analyzed as a function of the water quality 

variables associated with acidification (e.g., pH, Al, Ca2+, ANC, and DOC) using maximum likelihood 

logistic regression (Reckhow et al., 1987). Models developed from data from the ELS and the ALSC were 

calibrated against data from Ontario lakes. 

The results from the various models were compared to their prediction of the change in the number 

of Adirondack lakes with unsuitable acid-base chemistry, given a 50% decrease or a 30% increase in S 

deposition relative to the existing conditions. All the models provided similar results (Figure B-20) with 

the exception of those that relied on the pCa/pH term to predict fish status. Those models seemed to 

overestimate the effect of Ca2+, and thus underestimate predicted fish response to changes in acidic 

deposition. 

 B-70
Source: Baker et al. (1990b). 

Figure B-20. Example model application. Projected changes in the percentage of Adirondack lakes 

(Direct/Delayed Response project target population) with acid-base chemistry unsuitable for 

the survival of fish populations in the year 2034, versus current simulated conditions, based 

on projected changes in water chemistry from the Model of Acidification of Groundwater in 

Catchments (MAGIC) and using alternative models of fish response, given a 50% decrease in 

deposition or a 30% increase in deposition. (a) brook trout, (b) lake trout, (c) common shiner. 

An important consideration for measuring the success of S and N emissions controls is the 

development of appropriate expectations for the magnitude of potential chemical recovery. Most lakes 

inferred to have been measurably acidified by atmospheric deposition were already marginally acidic, 

typically with pH less than about 6, before anthropogenic atmospheric pollution began before 1900. 

Therefore, full recovery of currently acidic lakes would not be expected to yield neutral pH. Nevertheless, 

increases in ANC may allow recovery of fish populations even if pH remains relatively low (Stoddard 

et al., 2003). 

B.4.3. Effects by Ecosystem Type 

B.4.3.1. Terrestrial Ecosystems 

Due to a strong dependency on atmospheric deposition and exposure to gaseous compounds as the 

major sources of nutrients, lichens are affected by changes in these conditions. Vulnerability of lichens to 

increased N input is generally greater than that of vascular plants (Fremstad et al., 2005). Even in the 

Pacific Northwest, which receives uniformly low levels of N deposition, changes from acid-sensitive and 

N-sensitive to pollution-tolerant and nitrophilic lichen taxa are occurring in some areas (Fenn, 2003). In 

eastern North America and central Europe, areas experiencing relatively high levels of acidic deposition 

have experienced noticeable reductions in cyanolichen abundance on both coniferous and deciduous trees 

(Richardson and Cameron, 2004). Effects on lichen species biodiversity are also likely (McCune, 1988; 

van Haluwyn and van Herk, 2002). 

 B-71
Fenn et al. (2007) speculated that large, pollution-sensitive macrolichens, including epiphytic 

cyanolichens, will be replaced by N-tolerant species in areas where development expands in western 

Oregon and Washington into N-limited Coast Range forests. Currently, in the Pacific Northwest, 

nitrophilic lichen species are common in and around Seattle, Portland, Spokane, the Tri-cities, Salem, 

Oregon’s agricultural lands in the northeast and southwest, and the Willamette Valley (Fenn et al., 2007). 

The USDA Forest Service website contains information about lichen species pollution tolerance, 

diversity, and preferred habitat in relation to exposure to N (http://www.nacse.org/lichenair). 

In London, epiphyte diversity, including a majority of the lichen taxa, declined in areas where NO2

surpassed 40 μg/m3

 and NOX surpassed 70 μg/m3. Lichens remaining in areas affected by these levels of 

exposure contained almost exclusively families Candelariaceae, Physciaceae and Teloschistaceae

(Davies et al., 2007). 

Progressive decline in ectomycorrhizal fungal (EMF) community structure and species richness 

was observed at five Alaskan coniferous forest sites (white spruce [Picea glauca] dominant) along an 

N deposition gradient (1 to 20 kg N/ha/yr) downwind from a large industrial complex on the Kenai 

Peninsula. The effects were attributed to both acidification and fertilization processes (Lilleskov et al., 

2002). EMF communities are important in tree nutrition and C balance, and EMF trees tend to be 

dominant in N-limited forest ecosystems. A shift in EMF community structure could result in changes in 

tree species. 

Westman et al. (1985) summarized the literature of negative effects of SO2 on native plants, 

including decreased pollen germination and tube elongation in both angiosperms and gymnosperms. It is 

often difficult to separate the effects of SO2 exposure on plants from the effects of S deposition. This is 

because areas that experience high SO2 exposure generally also receive high S deposition. Kozlowski 

(1985) summarized relative susceptibility of different trees, lichens, and bryophytes to SO2. 

Available information is not sufficient to draw conclusions regarding the increased likelihood of 

future effects on the condition of hardwood forests in the Southern Appalachian Mountain region 

(Sullivan et al., 2002). Certainly, such effects are less likely for hardwood forests than for spruce-fir 

forests. Red oak seedlings grown in a greenhouse in deciduous forest soils exhibited no response to 

acidified soil (pH 4.0 from 9:1 H2SO4:HNO3) or to high or low SO4

2−

 inputs (12.8 to 24.8 mg/L). The lack 

of response suggested that red oak seedlings are not sensitive indicators of acidification effects from S 

deposition (McClenahen, 1987). 

Grasslands and Alpine Tundra 

Alpine communities are considered very sensitive to changes in N deposition, but documented 

effects in the scientific literature have been attributed to nutrient enrichment, rather than acidification 

(Seastedt et al., 2004; Bowman et al., 2006). Lower-elevation grasslands, especially those in semi-arid 

environments, would be expected to be even less sensitive to acidification because of low water leaching 

potential and the common presence of base-rich Mollisol and Aridisol soils. However, some effects of 

acidification may be manifested in mesic grasslands. 

In a review of SO2

 effects on grasses in the United Kingdom, Bell (1985) suggested that damage 

can occur at levels as low as 150 μg/m3

. However, he asserted that any ubiquitous critical load value must 

be modified to include variations due to environmental conditions and combined effects with other 

pollutants. He also suggested that many grass species exhibit a tolerance to SO2

, resulting from more 

intraspecific competition in agricultural grasslands. Westman et al. (1985) also provided evidence of the 

evolution of a tolerant grass species, Bromus rubens, in southern California coastal sage scrub, influenced 

by an average of 3.7 μmol/m3

 of SO2 over 25 years. 

Studies of SO2

 effects on timothy grass (Phleum pratense) showed diminished leaf production and 

increased leaf senescence in seedlings exposed to 0.120 ppm SO2

 for 35 days (Mansfield and Jones, 

1985). In another experiment, Mansfield and Jones (1985) reported that exposure to 0.120 ppm SO2

 in 

seedlings over 40 days resulted in a 62% reduction in the dry weight of roots and 51% reduction in the 

 B-72
dry weight of shoots, as well as a significant decline in leaf-area ratio (LAR) and specific leaf area (SLA) 

by the end of the experiment. They suggested that decreased growth and shifts in LAR and SLA could 

lead to decreased hardiness and increased susceptibility to water stress. 

In a 5–year exposure of native mixed prairie grassland in Montana, Lauenroth and Milchunas 

(1985) exposed grasses to a control (~20 μg/m3

) and three elevated levels of SO2 (~60, 106, 184 μg/m3). 

Year-to-year S accumulation did not appear to occur over the 5–year course of the treatment, though 

progressive increases in root and rhizome S concentrations were observed seasonally. No significant 

negative effects on either above-ground net primary productivity or below-ground biomass dynamics in 

grasses were observed, except a decrease in biomass for Bromus japonicus. However, lichen cover 

declined after 1–year of exposure at the low treatment level. Though no biomass or cover effects were 

observed at the community level, there were minor population changes. These results are consistent with 

the nature of semi-arid grasslands that typically adjust well to perturbations (Lauenroth and Milchunas, 

1985). 

Arid Lands 

At the time of the previous AQCD, it was believed that arid and semi-arid ecosystems were not as 

susceptible to soil acidification and high NO3

−

 leaching as are forested ecosystems. This is because of a 

scarcity of water for NO3

−

 leaching, except on an episodic basis, and because arid soils tend to be more 

alkaline than soils in more humid environments. No new research has altered that conclusion. Arid lands 

in the U.S. generally receive low levels of S deposition. However, N deposition can be quite high, 

especially in southern California in the vicinity of the Los Angeles Basin. Little work has been done on 

the effects of acidification on arid land ecosystems. As reviewed by Fenn et al. (2003), acidification 

effects have not been demonstrated at the Central Arizona-Phoenix LTER site, despite the almost 30 kg 

N/ha/yr of deposition received. Nevertheless, N deposition has the potential to increase plant growth and 

denitrification and alter community composition in arid environments (Egerton-Warburton and Allen, 

2000; Allen et al., 2005). Such changes could alter key ecosystem processes and, as such, merit 

consideration. There has been little research to examine these issues and, therefore, the state of knowledge 

is similar to what it was in 1993. 

B.4.3.2. Aquatic Ecosystem 

Chronic Effects 

Sulfate 

The study of Clow and Mast (1999) is unique in that SO4

2−

 trends were evaluated with both raw 

data and data adjusted for existing trends in flow. From 1967 to 1983, Clow and Mast (1999) showed a 

decreasing trend in SO4

2−

 concentrations in a Catskill river, no trend in three rivers in Maine, 

Pennsylvania, and Virginia, and an increasing trend in a river in Ohio. The Maine river did show a 

decreasing trend before flow adjustment of the data. From 1984 to 1996, however, Clow and Mast (1999) 

found decreasing trends in SO4

2−

 concentrations in all five rivers, both with and without flow adjustment. 

The rivers in Pennsylvania, Ohio, and Virginia were south of the maximum southern extent of glaciation, 

and therefore were more likely to be subject to the effects of SO4

2−

 adsorption in soils of their watersheds. 

In such streams, decreasing S-adsorption on soils would be expected to counteract the effects of 

decreasing S deposition in terms of effects on stream SO4

2−

 concentration. 

Surface waters in other unglaciated regions exhibited decreasing trends in SO4

2−

 by the 1980s. 

Concentrations of SO4

2−

 in 130 northeastern lakes in 1984 were compared to those in the same lakes in 

2001 (Warby et al., 2005). Median concentrations in each subregion were lower in 2001 than 1984, and in 

the region as a whole, the overall median decrease was 1.53 μeq/L/yr. A decrease in SO4

2−

 concentrations 

 B-73
that averaged 2.16 μeq/L/yr was also observed in 47 of 48 Adirondack lakes from 1992 to 2004, and a 

similar decrease of 2.09 μeq/L/yr was observed in a subset of these lakes from 1982 to 2004 (Driscoll 

et al., 2007a). 

The pattern of increasing concentrations of SO4

2−

 in surface waters before the year of peak S 

emissions in 1973, followed by widespread decreasing trends in SO4

2−

 concentrations after the peak (with 

the only exception being the Blue Ridge Mountain region in Virginia), provides convincing evidence of 

the link between S emissions and SO4

2−

 concentrations in surface waters. A similar link has been shown in 

Europe (Stoddard et al., 1999). On this basis, continued decreases in S emissions would be expected to 

result in further decreases in SO4

2−

 concentrations in surface waters, although the rate of response is 

uncertain due to an incomplete knowledge of S retention mechanisms in terrestrial systems. Also, in a 

detailed analysis of flow effects on SO4

2−

 trends, Murdoch and Shanley (Murdoch and Shanley, 2006) 

found SO4

2−

that higher concentrations of SO4

2−

 occurred at corresponding high, medium and low flows in 

2000 to 2002 than in 1997 to 1999 in two of the rivers studied by Clow and Mast (1999), and at high and 

medium flows in a third river. Continued monitoring of surface waters will be needed to verify a future 

link between emissions and SO4

2−

 concentrations in surface waters. 

Nitrate 

Driscoll et al. (1985) found that NO3

−

 concentrations in 20 lakes in the early 1980s in the 

Adirondack region of New York averaged 12% of SO4

2−

 concentrations, whereas Lovett et al. (2000) 

found that baseflow NO3

−

 concentrations in 1994–97 were an average of 37% of SO4

2−

 concentrations in 

39 streams in the Catskill region of New York. Average concentrations of NO3

−

 in most southeastern 

streams also tend to be considerably less than SO4

2−

 concentrations (Webb et al., 2004). 

High-frequency sampling in the study of Murdoch and Stoddard (1993) demonstrated the 

importance of NO3

−

 during high-flow conditions in Catskill streams in which concentrations periodically 

equaled or exceeded SO4

2−

 concentrations. This study also reported increasing trends in NO3

−

concentrations during the period of 1970 to 1990 in all 16 Catskill streams for which data were available. 

A similar increase in NO3

−

 concentrations was reported for Adirondack lakes in the 1980s (Stoddard et al., 

1999). These increasing trends in NO3

−

 concentrations were attributed to N saturation in response to 

atmospheric deposition (Aber et al., 1998). 

The relationship between N deposition and surface water NO3

−

 concentrations up through the 

1980s suggested that continued N deposition would further the accumulation of N in terrestrial 

ecosystems and drive continued increases in surface water NO3

−

 concentrations. However, more recent 

information on NO3

−

 concentrations have been less consistent with the concept of N saturation. Goodale 

et al. (2003) resampled New Hampshire streams in 1996–97 that had been previously sampled in 

1973−74 and found substantially lower NO3

−

 concentrations in the more recent sampling, despite two 

decades of relatively stable levels of deposition to otherwise undisturbed forests. The lower NO3

−

concentrations could not be accounted for by differences in flow or forest succession, but interannual 

climate variation was proposed as a possible cause. The long-term record of dissolved inorganic N (which 

is largely NO3

−

) concentrations at the HBEF showed a similar pattern; high concentrations in the late 

1960s and 1970s, followed by decreases to minimum values in the mid-1990s (Aber et al., 2002). These 

authors attributed this pattern to a combination of environmental factors, but did not identify a single most 

important control variable. A reversal from increasing trends in NO3

−

 concentrations in the 1980s to 

decreasing trends in the 1990s was also observed in Adirondack lakes (Driscoll et al., 2003a). A small 

decrease in atmospheric deposition of N also occurred in this region through the 1990s, but was not 

considered sufficient by these authors to explain the decreasing trend in lakewater NO3

−

 concentrations. 

Rather, they proposed that increased concentrations of atmospheric CO2 may have resulted in a 

fertilization effect that increased N assimilation (Driscoll et al., 2007a). 

In general, trends in surface water NO3

−

 concentrations during the 1990s were much smaller than 

trends in SO4

2−

, with the only ecologically significant changes occurring in the two regions with the 

highest ambient NO3

−

 concentrations. Lakes in the Adirondacks and streams in the Northern Appalachian 

 B-74
Plateau both exhibited small but significant downward trends in NO3

−

 in the 1990s (Table B-6). Both of 

these regions are central to the debate over whether N saturation is a legitimate threat to the health of 

forests and surface waters (Aber et al., 1998; Stoddard, 1994). While declining NO3

−

 concentrations in 

these regions is a positive development for these ecosystems, it is not known if these trends will continue, 

especially because they do not appear to reflect changes in N emissions or deposition. The presence of 

strong upward trends in NO3

−

 in these same regions in the 1980s (Murdoch and Stoddard, 1992; Stoddard, 

1994) suggests that trends measured on the scale of a single decade may reflect variability in long-term 

patterns of changing NO3

−

 leakage from forested watersheds. Such patterns are controlled by factors that 

may take many years of additional research to determine. While great uncertainty exists and the time 

scales of N saturation may be longer than previously considered (e.g., centuries rather than decades), the 

long-term retention of N deposition in forested regions is unlikely to continue indefinitely (Aber et al., 

2003). 

In New England and the Upper Midwest, where ambient NO3

−

 concentrations are much lower than 

in the Adirondacks and Northern Appalachian Plateau, NO3

−

 concentrations in surface waters were 

unchanged during the 1990s. The Ridge/Blue Ridge province registered a small, but significant, decrease 

in NO3

−

 during the 1990s, but interpretation of trends for NO3

−

 in this region is complicated by an 

outbreak of gypsy moths that also occurred during this period. Forest defoliation by gypsy moths was the 

most likely cause of a pulse in NO3

−

 export from many streams in this region in the mid-1990s (Eshleman 

et al., 1998). 

Some evidence of climate effects on long-term trends in NO3

−

 concentrations in surface waters was 

provided by studies of Mitchell et al. (1996) and Murdoch et al. (1998). A synchronous pattern in NO3

−

concentrations was observed from 1983 to 1993 in four small watersheds in New York, New Hampshire, 

and Maine, which included anomalously high concentrations during the snowmelt period of 1990. The 

region-wide spike in NO3

−

 concentrations followed an unusually cold December that may have disrupted 

soil N cycling processes (Mitchell et al., 1996). Murdoch et al. (1998) also found that mean annual air 

temperatures were strongly related to average annual NO3

−

 concentrations in most years in a Catskill 

watershed with elevated NO3

−

 concentrations in stream water. Those relationships were explained by 

microbial control of N release in watersheds that were considered to be N-saturated. 

Efforts to explain the decreasing trends in NO3

−

 concentrations under conditions of reasonably 

stable atmospheric N deposition have focused on terrestrial N cycling and N-saturation theory. However, 

processes within lakes may have also played a role in the trends in Adirondack lakes. In a study of 30 of 

the 48 lakes studied by Driscoll et al. (2003a; 2007a), Momen et al. (2006) found that concentrations of 

NO3

−

 were inversely correlated with concentrations of Chl a in 11 lakes, and that Chl a was increasing in 

concentration in 9 lakes. The increase in pH observed in most of these lakes may have stimulated 

productivity so that N assimilation by plankton increased (Momen et al., 2006). 

Thus, there is little or no apparent relationship between recent trends in N deposition and trends of 

NO3

−

 concentrations in surface waters, in sharp contrast to S deposition and SO4

2−

 concentrations. Rather 

than disprove the concept of N-saturation; however, these studies more likely reflect the complexities of 

N utilization within terrestrial and aquatic ecosystems. These complexities create considerable uncertainty 

with regard to how future trends in NO3

−

 concentrations in surface waters will respond to changing levels 

of deposition. 

 B-75
Figure B-21. Time series data for SO4

2−, NO3−, base cations [Ca2+ + Mg2+], Gran ANC, pH, and DOC in 

example Long Term Monitoring Lakes and streams that have relatively low ANC. The example 

surface waters include Salmon Pond, Maine (New England region); Darts Lake, NY 

(Adirondack region); East Branch Neversink River (Appalachian Plateau region); and 

Vandercook Lake, Wisconsin (Upper Midwest region). Significant trends are indicated by trend 

lines. Shaded box indicates time period of analyses reported by Stoddard et al. (2003). 

Base Cations 

The earliest trends of base cation concentrations in acid-sensitive surface waters of the U.S. were 

presented by Stoddard (1991) for 12 streams in the Catskill region. In 5 of 12 streams, concentrations of 

(Ca2+ + Mg2+) increased from 1915–22 to 1945, but decreased from 1945–46 to 1990. In the remaining 

seven streams, concentrations increased during both periods, but at a lower rate in the more recent period 

in five of the seven streams. In streams that showed an increase in concentrations during both periods, the 

average rate of increase from 1915 to 1922 was 2.8 μeq/L, whereas the average rate of increase from 

1945 to 1990 was 1.2 μeq/L. Data on SO4

2−

 trends were not available for the early period, but the trends 

in (Ca2+ + Mg2+) concentrations were consistent with the expected pattern of high rates of cation leaching 

during the early stages of acidification from S deposition. 

Clow and Mast (1999) observed trends in (Ca2+ + Mg2+) concentrations that were generally 

consistent with SO4

2−

 trends in five eastern rivers from 1968 to 1983. Decreasing trends in concentrations 

of (Ca2+ + Mg2+) and SO4

2−

 concentrations were observed in a Maine river, and increasing trends in (Ca2+

 B-76
+ Mg2+) and SO4

2−

 concentrations were observed in an Ohio river. None of the three other rivers showed a 

decrease in concentrations of (Ca2+ + Mg2+), and only one showed a decreasing trend in SO4

2−

concentrations. For the period 1984 to 1996, the trend in SO4

2−

 concentrations was negative in the Ohio 

River and the concentrations of (Ca2+ + Mg2+) showed no trend. Also, a negative trend in (Ca2+ + Mg2+) 

concentrations in a Virginia river was coupled with a negative trend in SO4

2−

 concentrations. Relations for 

the other three rivers were similar to the earlier period of 1984 to 1996. 

The study of Likens et al. (1996) evaluated trends in base cations in relation to trends in (SO4

2−

 + 

NO3

−

) in the long-term record for the HBEF. This record showed an approximately linear, increasing 

relationship between concentrations of base cations and (SO4

2−

 + NO3

−

) from 1964 to 1969, then a 

reversal in 1970 to a decreasing trend up to 1994. The slope of the phase with increasing anion 

concentrations was steeper than the slope for the phase with decreasing anion concentrations. This 

indicates lower base cation leaching per equivalent of mobile anion, and therefore suggests depletion of 

base cations stored in soil. The study of Lawrence et al. (1999b) showed decreased concentrations of base 

cations at a rate that exceeded decreases in (SO4

2−

 + NO3

−

) in Catskill streams from 1984 to 1997. In 

streams within western Virginia and in Shenandoah National Park, concentrations of base cations did not 

exhibit significant trends from 1988 to 2001, perhaps due to the influence of S adsorption on streamwater 

SO4

2−

 concentrations. 

Regional declines in base cation concentrations were measured in the LTM Program from 1990 to 

2000 in New England lakes, Adirondack lakes, Appalachian streams, and upper Midwest lakes (Stoddard 

et al., 2003). These results were consistent with decreased Ca2+ concentrations measured by Warby et al. 

(2005) in 130 acid-sensitive lakes in the Northeast between 1984 and 2001. The rate of decrease 

identified by Warby et al. (2005) for base cations (1.73 μeq/L) was somewhat less than the rate of 

decrease in SO4

2−

 concentrations (1.53 μeq/L). Driscoll et al. (2007), also documented decreasing trends 

in base cation concentrations in 16 Adirondack Lakes from 1982 to 2004, and similar rates of decrease in 

48 lakes (including the 16) from 1992 to 2004. 

In summary, decreases in base cation concentrations over the past two to three decades are 

ubiquitous and closely tied to trends in SO4

2−

 concentrations in acid-sensitive regions of the U.S. Reports 

of increases in concentrations of base cations in acid-sensitive regions were not found in the literature. In 

most regions, rates of decrease for base cations have been similar to those for SO4

2−

 and NO3

−

, with the 

exception of streams in Shenandoah National Park. Decreasing trends of base cation concentrations do 

not necessarily indicate further acidification or recovery of surface waters, but do indicate lower leaching 

rates in soils, a prerequisite for recovery of soil base saturation. However, decreased concentrations of 

base cations, particularly Ca2+, would also be expected to lower productivity in oligotrophic surface 

waters. 

Acid Cations 

Measurements of pH (sometimes expressed as H+) have been routinely collected in surface waters 

in the U.S. where effects of acidic deposition have been monitored, but a long-standing reliance on 

titrated ANC rather than pH as the primary chemical measurement has limited the amount of pH data 

published. The longest continuous record of pH in surface waters dates back to 1963 at the HBEF 

(Driscoll et al., 2001b). This record shows an overall increasing trend from 1963 to 1994, although most 

of the increase occurred after 1980. In Adirondack lakes, 12 of 16 monitored from 1982 to 2004 showed 

an increase in pH, but the rates of change among lakes were highly variable, and one lake showed a 

decrease in pH (Driscoll et al., 2007a). When expressed as H+

 concentration, the average increase for the 

12 lakes was 0.18 μeq/L/yr. In this same region, pH also increased in 31 of 48 lakes (including the 16 

lakes monitored from 1982) from 1992 to 2004. Two lakes showed increases in pH over the 12 years. 

Comparison of pH measurements of 130 lakes in 1984 and in 2001, in the northeastern U.S., 

showed an overall average increase in pH of 0.002 units (Warby et al., 2005). However, in this 

assessment, lakes in the Adirondack region did not show a significant increase, nor did lakes in central 

New England, or Maine. The Catskill/Poconos region of New York and Pennsylvania showed an average 

 B-77
increase of 0.008 pH units per year, and southern New England showed an average increase of 0.002 pH 

units per year. Through continuous monitoring from 1990 to 2000, Stoddard et al. (2003) found a decrease 

in H+

 (0.19 μeq/L/yr) similar to that observed in the same Adirondack lakes by Driscoll et al. (200a7) 

from 1992 to 2004 (0.18 μeq/L), and an increase in Appalachian streams (0.08 μeq/L/yr) and Midwest 

lakes (0.01 μeq/L/yr). No trends were found in New England lakes or Blue Ridge streams in Virginia in 

this study. Stream monitoring in the Adirondack region from 1991 to 2001 showed an increase in H+

 in 

one stream, no trend in a second stream, and also an increase in a third stream near the outlet of a lake 

(Lawrence et al., 2004). In summary, decreasing trends in pH in surface waters are common through the 

1990s up to 2004, but many exceptions occur, and overall, the rates of change have been small. 

The discovery that Ali was toxic to aquatic life resulted in a considerable amount of data on Al 

concentrations in surface waters in the 1980s, but most of this sampling was done either once or for a 

limited period of time (Johnson et al., 1981; Driscoll and Newton, 1985; Driscoll et al., 1987; Lawrence 

et al., 1987; Cronan et al., 1990). Monitoring of Ali concentrations was begun in 16 Adirondack lakes in 

1982 and expanded to 48 lakes in 1990. From 1982 to 2004, 5 of the original 16 Adirondack monitoring 

lakes showed decreasing trends in Ali concentrations at rates that ranged from 0.02 μM/yr to 0.18 μM/yr 

(Driscoll et al., 2007a). From 1992 to 2004, 24 of the 48 lakes showed decreasing trends in Ali

concentrations (Driscoll et al., 2007a). The analysis of Stoddard et al. (2003) also observed an average 

decrease in Ali concentrations from 1990 to 2000 in the same group of Adirondack lakes reported on by 

Driscoll et al. (2007a), but observed no trend for this period in New England lakes, Appalachian streams, 

or Midwest lakes. 

Monthly stream chemistry monitoring at the HBEF showed decreases in Ali concentrations at four 

locations along the reference stream for the experimental forest from 1982 to 2000, but no trends at two 

other locations along this stream (Palmer et al., 2004). These data also showed a surprising decrease in pH 

at two of the locations where Ali decreased, and no pH trend at the other two locations where Ali

decreased (Palmer et al., 2004). Comparison of total Al concentrations in 130 lakes in the northeastern 

U.S. in 1984 with those measured in 2001 showed lower average concentrations in 2001 in the 

Adirondack region, the Catskill/Pocono region, central New England, southern New England, and Maine 

(Warby et al., 2005). Because these measurements are of total Al, they are not directly comparable to Ali. 

Most recently, Lawrence et al. (in review) found that 49 of 195 streams (25%) during August base flow in 

the western Adirondack region had Ali concentrations above 2.0 μM, the level above which toxic effects 

on biota have been shown (Driscoll et al., 2001b; Baldigo et al., 2007). 

Acid Neutralizing Capacity 

In response to reduced levels of acidic deposition required by the CAA and other emissions control 

legislation, Stoddard et al. (2003) found trends during the 1990s toward increasing Gran ANC (Figure B21) in all of the glaciated regions of the eastern U.S. (i.e., New England, Adirondacks, Northern 

Appalachian Plateau) and Upper Midwest, and decreasing Gran ANC in the Ridge/Blue Ridge province. 

Changes were relatively modest compared with observed reductions in SO4

2−

 concentrations. Only the 

regional increases in the Adirondacks, Northern Appalachian Plateau, and Upper Midwest were 

statistically significant (Figure B-21). Median increases of about +1 μeq/L/yr in the Northern Appalachian 

Plateau, Adirondacks and Upper Midwest represent significant movement towards ecological recovery 

from acidification (Stoddard et al., 2003). 

It has been hypothesized that decreases in acidic deposition will yield the most chemical recovery 

in lakes and streams that have experienced the most severe acidification. Using data from all of the sites 

in regions where decreases in surface water SO4

2−

 and NO3

−

 have occurred, Stoddard et al. (2003) found 

that acidic lakes and streams exhibited a highly significant median increase in Gran ANC of 

+1.3 μeq/L/yr during the 1990s. Low-ANC sites (0 to 25 μeq/L) showed a smaller significant median 

ANC increase of +0.8 μeq/L/yr. Moderate ANC sites, those with mean ANC values greater than 25 μeq/L, 

showed no significant change in Gran ANC (Figure B-21). 

 B-78
All of the glaciated regions in the eastern U.S. showed declines in base cation (Ca2+ + Mg2+) 

concentrations during the 1990s, with the average changes in the range of −1.5 to −3.4 μeq/L/yr. All of 

the regional trends were highly significant (Figure B-21). Across the eastern U.S., surface water SO4

2−

 has 

decreased at a rate of about −2.5 μeq/L/yr (the mean of regional median slopes), and NO3

−

 at a rate of 

−0.5 μeq/L/yr, in surface waters on glaciated terrain during the 1990s. These rates of change set an upper 

limit to our expectation of ANC recovery of +3 μeq/L/yr (i.e., the sum of SO4

2−

 and NO3

−

 trend 

magnitudes). The Gran ANC increase reported by Stoddard et al. (2003) was actually about one-third of 

this maximum, +1 μeq/L/yr. The difference between the observed Gran ANC trend and the maximum 

trend estimated from rates of acid anion change can largely be explained by the average regional median 

decline in (Ca2+ + Mg2+ concentrations, which was about −2.0 μeq/L/yr (Stoddard et al., 2003). 

Episodic Effects 

Episodic acidification can result naturally from the mobilization of organic acids and from dilution 

of base cation concentrations, but decreases in pH and ANC associated with increases in SO4

2−

 and NO3

−

are largely attributable to acidic deposition (Wigington et al., 1996). Episodic acidification is most 

common in the early spring and late fall as a result of snowmelt and rainstorms, and is least common in 

summer, when high flows tend to be infrequent. Seasonal variations in stream flow also result in seasonal 

patterns of surface water chemistry at base flow. Lakes and streams at base flow tend to be more acidic in 

early spring than at other times of the year and low flows in late summer tend to be least acidic (Lawrence 

et al., 2007). 

The transient nature of high flows makes episodic acidification difficult to measure. Therefore, 

assessments have generally estimated the number of lakes and streams prone to episodic acidification by 

combining episode information from a few sites with base flow values of ANC determined in large 

surveys (Eshleman et al., 1995; Bulger et al., 2000; Driscoll et al., 2001b). Inclusion of episodically 

acidified water bodies in regional assessments substantially increases estimates of the extent of surface 

water acidification. For example, baseflow samples collected from 1991 to 1994 through the U.S. EPA 

TIME Program indicated that 10% of the 1,812 lakes (>1 ha surface area) in the Adirondack region of 

New York could be considered chronically acidic on the basis of ANC values less than 0 μeq/L, but that 

an additional 31% of these lakes had baseflow ANC values less than 50 μeq/L and were, therefore, 

estimated to be susceptible to episodic acidification (Driscoll et al., 2001b). Lawrence (2002) also 

estimated the extent of episodically acidified stream reaches in a Catskill, NY watershed (area = 85 km2

) 

through the use of an index site at the base of the watershed that became episodically acidified at high 

flows. Upstream sites with a lower base flow ANC than the index site at the same date and time were 

found to have a high likelihood of becoming episodically acidified. Base flow sampling of 122 upstream 

sites indicated that approximately 16% of the total upstream reaches were chronically acidified (ANC 

<10 μeq/L), but that 66% of the stream reaches became episodically acidified. 

Stoddard et al. (2003) compared seasonal data from New England lakes, Adirondack lakes and 

Northern Appalachian streams, collected monthly to quarterly, to evaluate the difference between the 

chemistry of surface waters in the summer and in the spring. Results indicated that spring values of ANC 

were an average of 30 μeq/L lower than summer ANC. This study referred to samples collected in spring 

as “episodic samples,” although sampling was done independent of flow. Therefore, the 30 μeq/L 

difference should be considered a seasonal effect rather than an episodic effect. 

The most thorough characterization of episodic variations in stream chemistry was conducted 

through the ERP, in which 13 low-order streams (watershed areas less than 24 km2

) in the Adirondack and 

Catskill regions of New York, and the Appalachian Plateau in Pennsylvania were monitored from 1988 to 

1990 (Wigington et al., 1996). Acid episodes with chemical concentrations within the 90th percentile 

involved decreases in ANC of up to 200 μeq/L, decreases in pH of up to one unit, and increases in 

concentrations of Ali of up to 15 μM (Wigington et al., 1996). Results also showed that acid episodes 

reduced the size of fish populations and eliminated acid-sensitive species if median high-flow pH was less 

than 5.2 and Ali concentrations exceeded 3.7 μM, despite the relatively short duration of episodes (Baker 

 B-79
et al., 1996). Baker et al. (1996) concluded that effect on biota from episodic acidification were likely to 

be similar to those from chronic acidification. Elimination of an annual age class can result from an 

episode that occurred in the presence of a sensitive life stage. Largely on the basis of this study, the EPA 

concluded that reversal of effects from episodic acidification could be used as a key ecological endpoint 

for an acid deposition standard for protection of the environment (U.S. EPA, 1995a). 

Despite the significance of the findings of ERP, little assessment or monitoring of episodes was 

done in the 1990s. One exception was the work of Hyer et al. (1995) in three watersheds of differing 

geology in Shenandoah National Park. Results suggested that episodic acidification was occurring 

throughout the park on all bedrock types, although acidification was not sufficient to cause elevated Al 

concentrations. Lawrence (2002) also documented severe episodic acidification in August 1998 in a 

tributary of an ERP stream, where Ali concentrations increased from 1.6 to 7.3 μM in 6.5 h. 

In the first large-scale study designed to sample streams during high-flow conditions, Lawrence 

et al. (2007) found that 124 out of 188 (66%) western Adirondack streams were prone to acidification to 

the level at which Ali becomes mobilized. Only streams accessible with less than a 60–min hike were 

sampled in this study. The March, 2004 survey was chosen to represent episodic conditions, and a survey 

conducted August 16–18, 2004 was chosen to represent base flow conditions. Based on this comparison, 

35% of the streams were chronically acidified, 30% of the streams were episodically acidified, and 34% 

were not acidified. Survey results were also used to estimate that 718 km of stream reaches were prone to 

acidification, although 3085 km of stream reaches within the study region could not be assessed because 

of inaccessibility. 

There have been no studies in the U.S. that determine if either the severity or frequency of episodic 

acidification has lessened. In a study of two streams in Nova Scotia (Laudon et al, 2002), trends in ANC 

in four phases of storm hydrographs from 1983 to 1998, were not detected other than in the peak-flow 

phase of one stream (an increase of 0.87 μeq/L). In Sweden, the anthropogenic contribution to episodic 

decreases in ANC were estimated to range from 40 to 80% in five streams from 1990 to 1999 (Laudon et 

al, 2002). 

B.5. Effects on Watersheds and Landscapes 

B.5.1. Interactions among Terrestrial, Transitional, and Aquatic 

Ecosystems 

Acidification has pronounced effects on nutrient cycling in terrestrial, transitional, and aquatic 

ecosystems. Of particular importance in this regard is the role of N deposition in influencing N cycling. 

This topic is discussed in detail in Section 5. Also important are the influences of acidification on the 

availability of Ca2+ and other nutrient base cations (Mg2+, K+

). 

In general, decomposition, nutrient cycling, productivity, and other system-level processes in 

surface waters are not as sensitive as species composition and richness to relatively small amounts of 

acidification. Such effects only seem to occur at high levels of acidification (e.g., pH <5). This is because 

acid-sensitive species are often replaced by more acid-tolerant species that perform the same function 

until acidification becomes severe. For example, whereas changes in microbial composition and 

abundance have been observed with acidification, they appear to have minimal effect on overall microbial 

respiration and nutrient cycling. At extreme levels of acidity, however, these system-level functions may 

also decrease. Thus, system-level functions are not generally good indicators of light to moderate levels 

of acidification. 

 B-80
Integrating the effects of atmospheric deposition across spatial scales is difficult. The response of a 

single plant, or small group of plants, cannot be easily scaled up to examine effects on plant communities, 

ecosystems, watersheds, or geographic regions. Integration typically requires a combination of 

approaches, including ecosystem modeling, experimental manipulation studies, surveys across pollution 

deposition gradients, and long-term monitoring studies. Similarly, aquatic effects at the population level 

can be readily quantified, but extrapolation to the community or aquatic ecosystem level is problematic. 

Linking research results across scales will be an important component of future research. Measurements 

that correlate with ecosystem processes, such as foliar N concentration, leaf area index, or spectral 

reflectance, can in some cases be remotely sensed. They offer great promise for future assessment of 

terrestrial effects across spatial scales. 

Effects of atmospheric deposition of acidifying substances on soil, vegetation, and surface water 

are manifested in specific processes, affecting energy, water and nutrient flow, intra- and inter-species 

competitive interactions, and ecosystem primary production. Therefore, effects on sensitive species (only 

some of which have been documented) have the potential to cascade throughout the ecosystem and 

become manifested at a variety of scales. Such ecosystem- to landscape-scale effects from atmospheric 

deposition of acidifying substances are known to occur, but the results of these interacting processes have 

not been conclusively demonstrated. 

It is also evident that acidification from natural and human-caused disturbances, including climatic 

stressors (temperature, moisture availability, wind), insect infestation, disease, fire, and timber harvest, 

can affect the severity of effect of atmospheric deposition of SOX and NOY. Although it is clear that such 

interactions can occur, there are no studies that have clearly documented that acidic deposition at levels 

that commonly occur across broad landscapes in the U.S. has conclusively altered ecosystem structure or 

function. Similarly, although it is widely believed that acidic atmospheric deposition can make plants 

more susceptible to the adverse effects of other natural and human-caused stressors, such effects have not 

been conclusively demonstrated in more than a few cases. The data demonstrating and quantifying the 

extent to which SOX and NOY deposition are altering natural terrestrial ecosystems via acidification 

pathways are sparse. In particular, effects of soil and soil water acidification on soil ecosystem processes 

and nutrient cycling are poorly known. Even less is known about effects on soil microorganisms and food 

webs, or how such effects interact with the above-ground vegetation community. 

B.5.2. Interactions with Land Use and Disturbance 

The prevailing scientific consensus during the 1980s held that most lakes in eastern North America 

that had pH less than about 5.5 to 6.0 had been acidified by acidic deposition. Reports that acidic lakes 

and streams were rare or absent in similar areas not receiving acidic deposition were used as evidence of 

acidification by acidic deposition in many regions (e.g., Baker et al., 1991b; Murdoch and Stoddard, 

1992; Neary and Dillon, 1988; Sullivan et al., 1988). An alternative hypothesis had been advanced by 

Rosenquist (1978), Krug (1989, 1991), and Krug and Frink (1983) that land use changes could explain 

recent lake acidification in southern Norway and the northeastern U.S. According to this hypothesis, 

natural soil processes and changes in vegetation can generate more acidity than is received from 

atmospheric deposition. For example, an increase in acidic humus formation in response to decreased 

upland agriculture was suggested as being responsible for regional acidification in southern Norway, 

rather than acidic deposition (Rosenqvist, 1978). Subsequent acidic deposition effects research in some 

cases seemed to be designed to refute this hypothesis rather than to explore the relationships between land 

use and acid-base chemistry (cf. Havas et al., 1984; Birks et al., 1990). Research intended to discriminate 

between acidic deposition and land use as the major cause of acidification generally concluded that acidic 

deposition was the principal cause of regional acidification in certain areas of North America and Europe. 

Perhaps more appropriate research questions might have focused on quantifying the relative importance 

of land use activities or landscape change in exacerbating or ameliorating acidic deposition effects. The 

 B-81
importance of acidic deposition as an agent of acidification does not preclude the importance of land use 

and landscape changes which, in some cases, may actually be more important than acidic deposition 

(Sullivan et al., 1996b). 

There has not been a regional evaluation of land use changes in areas of the U.S. susceptible to 

surface water acidification from acidic deposition. It has therefore not been possible to quantify the extent 

or magnitude of land use effects on acidification. It is clear, however, that such changes can have 

important effects on acid-base status (Sullivan, 2000a), especially as influenced by N deposition (Goodale 

and Aber, 2001). 

Changes in human land use activity, and associated changes in vegetative structure, influence 

ecosystem response to external stressors such as acidic deposition, exposure to O3, natural disturbance 

factors such as wind and fire, and climatic changes. Some activities contribute to the acidification of soil 

and surface waters; other activities decrease acidity (Sullivan et al., 1996b) (Table B-21). 

Forest management practices, especially those that have occurred over many generations of trees, 

can have important effects on soil erosion, nutrient supplies, and organic material. Such effects can 

influence the availability of base cations for acid neutralization and/or aspects of N cycling. 

Forests are efficient at scavenging S and N from the atmosphere. Differences in forest canopy, 

particularly between deciduous and coniferous trees, can cause large differences in dry deposition, and 

therefore total deposition of S and N. In regions that receive high levels of acidic deposition, the presence 

of forest vegetation, especially coniferous trees, enhances total deposition of acid-forming precursors 

(Rustad et al., 1994). In addition to the enhanced deposition caused by the presence of large trees, there 

are also differences in nutrient uptake. In particular, younger trees take up larger quantities of N and other 

nutrients than do trees in older forests. Therefore, changes in the occurrence and age or species 

composition of the forest can influence the rates of atmospheric deposition to the site as well as the fate of 

atmospherically deposited substances. 

Landscape processes and watershed disturbance can influence soil and water acidification in many 

ways. Land use practices and vegetation patterns have been changing in various parts of the U.S. for 

decades to centuries. These changes in human activity can influence the response of forested ecosystems 

to external stressors, including atmospheric deposition of S or N, natural disturbance factors such as wind 

and fire, and climatic changes. Some processes contribute to the acidification of soil and surface waters or 

reduce the base saturation of the soils thereby increasing their sensitivity to acidic deposition. Other 

processes cause decreased acidity (Sullivan et al., 1996b; Sullivan, 2000a). 

Watershed disturbance from logging, blowdown, and fire disrupts the normal flow of water and can 

cause increased contact between runoff water and soil surfaces, leading to increased base cation 

concentration and ANC in drainage water. Recovery from disturbance can cause a decrease in drainage 

water ANC as the system returns to pre-disturbance conditions. In particular, soil loss through erosion can 

reduce the base cation pool size, thereby limiting the capacity of soils to neutralize atmospheric acidity. In 

addition, forest harvesting has an important effect on forest N-demand, thereby reducing the likelihood of 

future N-saturation in response to high N deposition. Forest management practices, especially those that 

have occurred over many generations, have had important effects on soil chemistry, nutrient supplies, and 

organic material. 

Watershed disturbances, including road building, agriculture, mining, urbanization, logging, 

blowdown, and fire can alter various aspects of ecosystems biogeochemistry. Such disturbances can 

influence the water budget, base cation mobilization, routing of drainage water, nutrient input, and S and 

N cycling in ways that affect the acid-base chemistry and nutrient dynamics of soils and drainage waters 

(Sullivan et al., 1996b). The effects of such disturbances can greatly modify the response of a given 

watershed to atmospheric inputs of S and N. 

 B-82
B.5.2.1. Timber Harvest 

Removal of the forest affects drainage water quality in several ways. Deposition of S and N are 

reduced; leaching of NO3

−

 increases and, in some cases, causes a pulse of surface water acidification. 

Base cations tied up in wood are lost when wood is transported off-site. Regrowth of the forest may 

further affect drainage water quality through vegetative uptake of N and base cations. Trees accumulate 

base cations to a greater degree than anions. To balance the charge discrepancy, roots release an 

equivalent amount of protons and acidify the soil. Base cation accumulation by trees is age-dependent. 

Young forests grow faster and are therefore more acidifying than older forests (Nilsson et al., 1982; 

Nilsson, 1993). They also retain greater amounts of N. 

Most forests in the northeastern U.S. are recovering from extensive human disturbance that 

occurred over a period of about 200 years. Landscapes were mainly forested during pre-colonial times, 

logged or cleared for agriculture in the mid to late 19th century, and are now largely early to mid-seral 

stage regenerating forests (Niering, 1998). 

In some areas that experience relatively high levels of acidic deposition, there is growing concern 

about sustainable timber productivity (Adams, 1999). Harvest-induced leaching losses have been 

estimated to range from 6 to 60 kg/ha/yr of N, 28 to 48 kg/ha/yr of Ca2+, and 7 to 16 kg/ha/yr of Mg2+

(Federer et al., 1989). Timber harvesting also increases leaching losses from the site because of the 

reduction in transpirational water loss. The increased water flux after tree removal increases the 

opportunity to leach base cations from the soil. 

If base cations sequestered in tree wood are removed from the site by tree harvesting, the result is a 

decrease in the available base cation pool on the site. Physical disturbances to forest soils during logging 

operations and increased soil temperature that results from exposure of the forest floor to sunlight may 

also cause a short-term increase in the rates of N mineralization and nitrification (Joslin et al., 1992). The 

resulting increase in NO3

−

 production and leaching further depletes base cations from the soil pool. 

Johnson et al. (1991a) measured short-term (3 years) effects of logging at HBEF in New 

Hampshire on soil acid-base chemistry. Base saturation of the mineral soil Bh horizon decreased from 14 

to 11% and pH decreased by 0.24 pH units. 

Likens et al. (2002) reported results of a 34–year study of the biogeochemistry of forest ecosystems 

at HBEF. Part of the study evaluated the effects of tree removal on S cycling and related biogeochemical 

processes. Vegetation removal resulted in increased decomposition of organic matter and nitrification. 

These changes, in turn, lowered soil water pH, enhanced SO4

2−

 adsorption on mineral soil, and therefore 

decreased the flux of SO4

2−

 in stream water. With subsequent vegetation regrowth, the adsorbed SO4

2−

was released from the soil to drainage water, and streamwater SO4

2−

 concentrations increased. 

Baldigo et al. (2005) compared the effects of clear-cut and timber-stand improvement (TSI) 

harvests on water chemistry and mortality of caged brook trout in three Catskill Mountain streams. 

Harvests removed 73% of tree basal area from a clearcut subbasin, 5% basal area from a TSI subbasin, 

and 14% basal area at a site below the confluence of both streams (the combined effect of the two harvest 

methods). Water quality and trout mortality were affected only in the clearcut stream. Acidity and 

concentrations of NO3

−

 and Ali increased sharply during high flows after the first growing season (1997). 

Acid-Ali episodes were severe during this period and decreased steadily in magnitude and duration 

thereafter. All trout at the clearcut site died within 7 days during spring 1998, and 85% died during spring 

1999. Only background mortality was observed in other years at this site and at the other three sites 

during all tests. The effects of tree harvests on fish communities are of concern because they might 

interact with effects of acidic deposition and produce more substantial effects on biota than either stress 

factor on its own. 

 B-83
B.5.2.2. Insect Infestation 

Effects of insect-caused defoliation on the N cycle can be pronounced. The foliar N consumed by 

insects is deposited on the forest floor as insect feces (frass), greenfall, and insect biomass. Some of this 

deposited N is subsequently taken up by tree roots and soil microbes, with little effect on the nutritional 

condition of the trees or the site. Where a sizable component of this N is leached in drainage water, the 

nutritional consequences can be more significant. There are also various feedback mechanisms. For 

example, low N supply can slow the population growth of defoliating insects (Mason, 1992) and enhance 

the tree’s chemical defenses against insects (Hunter and Schultz, 1995). The amount of N leaching loss is 

generally small, relative to atmospheric deposition inputs and relative to the amount of N transferred to 

the forest floor with the defoliation (Lovett and Ruesink, 1995; Lovett et al., 2002). Nevertheless, it can 

be high enough to contribute to base cation depletion of soils and effects on downstream receiving waters. 

The extent of NO3

−

 leaching may be partly related to the extent of defoliation and tree mortality that 

occurs and also the amount of precipitation that occurs immediately after the defoliation (Lovett et al., 

2002). 

Forest insect infestation can have profound effects on the acid-base and nutrient chemistry of soils 

and drainage waters. Effects of a gypsy moth infestation in Shenandoah National Park provide a good 

example. Between the mid-1980s and the early 1990s, the southward expanding range of the European 

gypsy moth traversed Shenandoah National Park and affected all of the University of Virginia’s SWAS 

study watersheds (Webb, 1999). Some areas of the park were heavily defoliated 2 to 3 years in a row. The 

White Oak Run watershed, for example, was more than 90% defoliated in both 1991 and 1992. This 

insect infestation of forest ecosystems in Shenandoah National Park resulted in substantial effects on 

streamwater chemistry. The most notable effects of the defoliation on park streams were dramatic 

increases in the concentration and export of N and base cations in streamwater. Following defoliation, 

NO3

−

 export increased to previously unobserved levels and remained high for over 6 years before 

returning to predefoliation levels. The very low levels of pre-disturbance NO3

−

 export in park streams 

were consistent with expectations for N-limited, regenerating forests (Aber et al., 1989; Stoddard, 1994). 

Release of NO3

−

 to surface waters following defoliation was likewise consistent with previous 

observations of increased N export due to forest disturbance (Likens et al., 1970; Swank, 1988). The exact 

mechanisms have not been determined, but it is evident that the repeated consumption and processing of 

foliage by the gypsy moth larva disrupted the ordinarily tight cycling of N in Shenandoah National Park 

forests. 

Although N is thought to play an important role in the chronic acidification of surface waters in 

some areas (Sullivan et al., 1997), the elevated concentrations of NO3

−

 in Shenandoah National Park 

streams following defoliation did not appear to contribute to baseflow acidification in White Oak Run. 

This was due to a concurrent increase in concentrations of base cations in streamwater (Webb et al., 

1995). Both NO3

−

 and base cation concentrations increased during high-runoff conditions, although the 

increase in base cations did not fully compensate for the episodic increase in NO3

−

. Episodic acidification 

following defoliation thus became more frequent and more extreme in terms of observed minimum ANC 

(Webb et al., 1995). 

The full effect of the gypsy moth on aquatic resources in Shenandoah National Park is not well 

understood. One consequence may be a reduction in the supply of available soil base cations and 

associated effects on streamwater ANC. Repeated periods of defoliation would probably increase the 

effect of episodic acidification on sensitive aquatic fauna and may determine the conditions under which 

some species are lost. Ultimately such effects may depend upon both the severity of future gypsy moth or 

other insect outbreaks and possibly on the amount of atmospheric N deposition. Gypsy moth populations 

typically display a pattern of periodic outbreaks and collapse (Cambell, 1981). It remains to be seen what 

the long-term pattern will be (Sullivan et al., 2003). 

Webb et al. (1994) compared pre- and post-defoliation streamwater chemistry for 23 VTSSS 

watersheds. Nitrate concentrations, measured quarterly, increased in most of the streams in response to 

defoliation, typically by 10 to 20 μeq/L or more. The increased streamwater NO3

−

 concentration was 

 B-84
probably derived from the N content of the foliage that had been consumed by the gypsy moth larvae and 

converted to feces on the forest floor. Sulfate concentrations and ANC also decreased in streamwater. 

Although the mechanism for decreased SO4

2−

 was not totally clear, Webb et al. (1994) hypothesized that 

increased nitrification in response to the increased soil N pool may have caused soil acidification, which 

would be expected to have increased soil S adsorption (Johnson and Cole, 1980). Decreased S deposition 

during the comparison period may also have contributed to the SO4

2−

 response. 

Eshleman et al. (1998) reported NO3

−

 outputs from five small (<15 km2) forested watersheds in 

Virginia and Maryland from 1988 to 1995. The study watersheds varied in geology, vegetation, and acid 

sensitivity, with baseflow ANC typically in the range of 0 to 10 μeq/L in Paine Run to the range of 150 to 

350 μeq/L in Piney River. Oak species (Quercus spp.), which are a preferred food source of gypsy moth 

larvae, occupied about 60% to 100% of the study watersheds. Nitrate concentrations increased in at least 

three of the watersheds in association with intense defoliation by the gypsy moth larva during the late 

1980s to early 1990s, to peak annual average NO3

−

 concentrations of about 30 to 55 μeq/L. Most of the 

increased NO3

−

 leaching occurred during storm flow conditions. 

A number of other studies have been conducted that examined the effects of gypsy moth, or other 

forest insect pests, on watershed biogeochemistry. Defoliation of poplars (Populus sp.) by gypsy moth 

larvae in southwestern Michigan did not result in appreciable NO3

−

 leaching (Russell et al., 2004). 

Other pest species can have similar effects. For example, spruce-fir forests throughout the southern 

Appalachian Mountains have been subjected to significant disturbance, especially from the balsam wooly 

adelgid, a European pest which has infested Fraser fir since about the 1960s. Severe fir mortality has 

occurred in many areas. This disturbance factor has the potential to interact with acidic deposition and 

other ecosystem stresses, and contribute to multiple-stress tree mortality and to changes in 

biogeochemical cycling. 

Defoliation by the elm spanworm (Ennomos subsignarius Hübner) larvae in old-growth hemlockhardwood forests on the Allegheny High Plateau of northwestern Pennsylvania increased streamwater 

NO3

−

 concentrations from pre-defoliation levels of about 29 μeq/L to peak values the summer after 

defoliation of about 100 μeq/L (Lewis and Likens, 2007). 

B.5.3. Wind or Ice Storm Damage 

Forest blowdown might affect surface water acid-base chemistry by changing the pathway 

followed by drainage water through watershed soils (Dobson et al., 1990). Pipes formed in the soil by 

decaying tree roots can alter hydrologic flow so that less water enters the soil matrix, where neutralization 

processes buffer the acidity of rainwater and snowmelt. Pipes tend to occur most commonly in nearsurface soil horizons where most tree rooting occurs. Contact between drainage water and mineral soil is 

reduced when runoff is routed through them. If enhanced pipeflow, resulting from sudden extensive tree 

mortality, affects a large portion of a watershed, runoff water may have less opportunity for acid 

neutralization than would be the case in the absence of such pipeflow. 

Severe canopy damage occurred in 1998 in response to an ice storm at HBEF and surrounding 

areas in the White Mountains. Houlton et al. (2003) reported effects of this disturbance on N cycling and 

leaching losses. Subsequent to the ice storm, drainage water NO3

−

 concentrations increased sevenfold to 

tenfold. Peak streamwater NO3

−

 concentrations during spring months reached or exceeded 50 μeq/L at 

many sites. There were no significant differences, however, in N mineralization, nitrification, or 

denitrification rates between damaged and undamaged areas. Houlton et al. (2003) interpreted these 

results as an indication that increased NO3

−

 leaching was probably due to decreased root uptake rather 

than accelerated N cycling by soil microbes. The amount of NO3

−

 leaching loss was estimated to be more 

than half of the entire year’s worth of atmospheric N deposition. 

 B-85
B.5.3.1.

B.5.3.2.

Fire 

Fire can increase concentrations of NO3

−

 and SO4

2−

 in soils and drainage water (cf. Chorover et al., 

1994; Riggan et al., 1994). Fenn and Poth (1998) hypothesized that successful fire suppression efforts 

may have contributed to the development of N-saturation in fire-adapted ecosystems in southern 

California by allowing N to accumulate in soil and in the forest floor, and by maintaining dense mature 

stands with reduced N demand. 

The effects of fire on NO3

−

 leaching in chaparral stands in the San Gabriel Mountains, CA that 

received high atmospheric N deposition were investigated by Riggan et al. (1994). Study watersheds were 

burned with fires of different intensity and, after rainfall, NO3

−

 and NH4

+

 were measured in watershed 

streams. Nitrogen release was up to 40 times greater in burned watersheds than in unburned watersheds, 

and the amount and concentration of N release were found to be related to fire intensity. 

Chorover et al. (1994) evaluated the effects of fire on soil and stream water chemistry in Sequoia 

National Park, CA. Burning increased concentrations of NO3

−

 and SO4

2−

 in soil water and stream water. 

Sulfate concentrations increased 100 fold. Nitrate concentrations also increased and remained higher in 

soils and stream water for about 3 years. These results suggest that successful fire suppression may have 

contributed to the development of N saturation in fire-adapted ecosystems in southern California by 

allowing N to accumulate in the soil and forest floor, and by maintaining dense mature stands with 

reduced N demand (Fenn and Poth, 1998). 

Multiple Stress Response 

Acidification-related effects of S and N deposition do not occur in isolation; they interact with 

disturbances of various types, both natural and human-caused. They also influence a range of 

biogeochemical processes that may be difficult to predict. Overall, the interactions between disturbance 

and ecosystem acidification as a consequence of acidic deposition are not well understood. 

It is believed that high rates of N deposition cause increased susceptibility of forests to other 

stressors, including reducing the resistance of some tree species to frost, insect damage, or drought. The 

effects of acidic deposition can interact with a variety of stressors, both natural and human-caused. The 

end result might include adverse effects that would not occur solely in response to acidic deposition, or in 

response to any one of the other stressors. 

Watershed disturbance might also effect Hg cycling and its relationship to S deposition. For 

example, Garcia and Carignan (2000), in a study of 20 watersheds in Quebec, Canada, found that the 

average Hg concentration in 560–mm northern pike (ESOX lucius) was significantly higher in lakes 

whose watersheds had recently (1995) been logged (3.4 μg/g), as compared with reference lake 

watersheds (1.9 μg/g), that had remained undisturbed for at least 40 years. Fish tissue Hg concentration 

also increased with increasing DOC and lakewater SO4

2−

 concentration, and with decreasing pH. 

B.6. Ecological indicators of acidification 

B.6.1. Biological Indicators 

Surface water acidification from acidic deposition causes effects on organisms at all trophic levels. 

Early studies focused on the loss of fish populations, especially salmonids. Later studies also reported that 

many species of phytoplankton, zooplankton, insect larvae, crayfish, snails, and freshwater mussels are 

sensitive and are often reduced or absent from acidified lakes and streams (Havas, 1986; Baker et al., 

 B-86
1990b). Similarly, many species of microrhizal fungi and lichens have been reported to be particularly 

sensitive to acidic deposition in terrestrial ecosystems. 

Effects of acidification on aquatic biota have been demonstrated in laboratory and field bioassays 

(e.g., Baker et al., 1996), whole-ecosystem acidification experiments (e.g., Schindler et al., 1985), and 

field surveys (e.g., Baker and Schofield, 1982; Gallagher and Baker, 1990). Many of the species that 

commonly occur in acid-sensitive surface waters susceptible to acidic deposition cannot reproduce or 

survive if the water is acidic. Some sensitive species of fish, invertebrates, and algae cannot survive at 

moderate levels of acidity. For example, some zooplankton predators, sensitive mayfly species, and 

sensitive fish species are affected at pH values below the range of 5.6 to 6.0 (Baker and Christensen, 

1991). Such pH values generally equate to ANC below about 25 to 50 μeq/L. 

There are few published examples of long-term monitoring data for biological assemblages in acidsensitive surface waters, and none in the U.S. Therefore, conclusions about the effect of acidic deposition 

on the distribution of sensitive species are based on other kinds of data (Stoddard et al., 2003). For 

example, the number of fish species increases with increasing pH and ANC when evaluated for multiple 

water bodies across the landscape. This result has been shown for streams in Virginia, lakes in the 

Adirondacks, and both high-elevation and seepage lakes in Maine (Figure B-17). 

Given the available data, it is clear that acidification from acidic deposition limits the distribution 

of acid-sensitive fish, benthic invertebrate, phytoplankton, and zooplankton species, but a lack of 

adequate data makes it difficult to quantify the magnitude of change in biota from historical condition or 

in response to recent (past two to three decades) decreases in acidic deposition in individual lakes or 

streams. Studies in Canada and Europe have illustrated the feasibility and complexity of biological 

recovery in response to decreased acidity. 

Threshold pH levels for adverse biological effects have been summarized for a variety of aquatic 

organisms (Haines and Baker, 1986; Baker et al., 1990b). The effects of low pH are specific to the 

organism, and perhaps region, under consideration and depend also upon the concentrations of other 

chemical constituents in the water, notably Ali and Ca2+. In general, populations of salmonid fish are not 

found at pH levels less than 5.0, and smallmouth bass (Micropterus dolomieu) populations are usually not 

found at pH values less than 5.2 to 5.5 (Haines and Baker, 1986). A number of synoptic surveys indicate 

loss of species diversity and absence of many other fish species in the pH range of 5.0 to 5.5 (Haines and 

Baker, 1986). Levels of pH less than 6.0 to 6.5 have been associated with adverse effects on populations 

of dace, minnows, and shiners (family Cyprinidae), and bioassays suggest that given sufficient Al 

concentrations, pH less than 6.5 can lead to increased egg and larval mortality in blueback herring (Alosa 

aestivalis) and striped bass (Morone saxatilis) (Hall, 1987; Klauda et al., 1987). 

Mycorrhizal fungi have been suggested as possible biological indicators of atmospheric deposition 

effects by Løkke et al. (1996) because they are intimately associated with tree roots, depend on plant 

assimilates, and play essential roles in plant nutrient uptake. Thus, mycorrhizal fungi can influence the 

ability of their host plants to tolerate different anthropogenically generated stresses. Mycorrhizae and 

associated fine roots have short lifespans and their turnover appears to be controlled by environmental 

factors. Changes in mycorrhizal species composition, or the loss of dominant mycorrhizal species in areas 

where diversity is already low, may cause increased susceptibility of plants to stress (Løkke et al., 1996). 

Mycorrhizal fungi are dependent for their nutrition on the supply of assimilates from the host plant. 

Stresses that shift the allocation of C reserves to the production of new leaves at the expense of supporting 

tissues will be reflected rapidly in decreased fine root and mycorrhizal biomass (Winner and Atkinson, 

1986). Decreased C allocation to roots could also affect soil carbon and rhizosphere organisms. For 

example, earthworms are believed to decrease in abundance, and in species number, in acidified soils 

(Løkke et al., 1996). Soil dwelling animals, including earthworms, are important for decomposition, soil 

aeration, and nutrient redistribution in the soil. They contribute to decomposition and nutrient availability, 

mainly by increasing the accessibility of dead plant material to microorganisms. 

 B-87
B.6.1.1.

B.6.1.2.

Phytoplankton 

Phytoplankton are the small microscopic plants or plant-like organisms that live suspended in the 

water column of lakes and large rivers. Acidification results in decreased species richness and diversity of 

phytoplankton communities. There is also a shift in the composition of dominant taxa, but species 

composition shifts cannot be accurately predicted (though it is clear that community restructuring occurs 

with acidification). This effect is most prevalent in the pH 5 to 6 range (Baker et al., 1990b). Acidification 

has also been found to cause decreases in food web complexity (indicated by the number of trophic links 

or species) in the Adirondack Mountains (Havens and Carlson, 1998). Both Al toxicity and P limitation 

may also be responsible for shifts in phytoplankton community composition. Neither grazing pressure nor 

changes in water clarity associated with acidification seem to have a major effect on phytoplankton 

community structure. There is no consistent pattern of acidification effects on phytoplankton biomass. 

Various lakes have shown increases, decreases, or no change in phytoplankton biomass with acidification 

(Baker et al., 1990b). Leavitt et al. (1999) suggested that the complex interactions between pH, DOC, and 

light explain the high variability in the algal biomass-acidification relationship. In most lakes, 

acidification has a negligible effect on primary productivity. 

Diatoms, which comprise an important component of the phytoplankton, are excellent indicators of 

environmental change in aquatic ecosystems, including acidity, nutrient status, salinity, and climatic 

features (Stoermer and Smol, 1999; Sullivan and Charles, 1994). There are thousands of different species, 

many of which have rather narrow ecological tolerance ranges. Diatoms have been widely used as 

indicators of past lake acidification. Inference based on diatom fossil remains preserved in lake sediments 

is an excellent approach for quantifying historical chemical change (Charles and Norton, 1986). 

Paleolimnological reconstructions of past lakewater chemistry are based on transfer functions 

derived from relationships between current lakewater chemistry and diatom (or, in some cases, 

chrysophyte) algal remains in surface sediments. Predictive relationships are developed using regional 

lake datasets, and are then applied to diatom assemblage data collected from horizontal slices of lake 

sediment cores to infer past lakewater conditions (Charles et al., 1990a; Husar and Sullivan, 1991). 

Periphyton are the small microscopic plants (or plant-like organisms) that live on submerged 

substrates in aquatic systems (e.g., stream or lake bottoms). As seen in phytoplankton communities, 

acidification results in decreased species richness, community alteration, and emergence of new dominant 

species in periphyton communities. Many diatom and blue-green bacterial periphyton species cannot 

tolerate acidic conditions. On the other hand, green algae, particularly the filamentous Zygnemataceae, 

increase in relative abundance at lower pH (Baker et al., 1990b). Unlike for phytoplankton, there is 

evidence that the biomass of attached periphyton increases at lower pH. 

Studies of phytoplankton recovery from acidification indicate that there is an increase in 

phytoplankton species richness and diversity as pH increases. In the Experimental Lakes area of Ontario, 

previously acidified lakes have been experimentally de-acidified. In Lake 223, there was little increase in 

phytoplankton diversity as pH changed from 5.0 to 5.8 but a strong recovery of diversity at pH above 6 

(Findlay and Kasian, 1996). In Lake 302S, profound change began at pH 5.5; phytoplankton assemblages 

at pH below 5.5 resembled acidified lakes. Cyanobacteria were among the first to recover at pH 5.5 to 5.8 

(Findlay et al., 1999). In the Killarney Park area of Ontario, Findlay (2003) reported that lakes that were 

previously low in pH (5.0 to 5.5) and are now above pH 6 have shifted towards phytoplankton 

assemblages typical of circumneutral environments. 

Zooplankton 

Field survey and experimental lake studies both indicate that acidification reduces zooplankton 

species richness. Effects of acidification on community biomass and abundance, however, were not 

definitive. Some studies indicated a lower biomass under low pH conditions, whereas other studies 

showed no consistent pattern in the biomass-pH relationship. Limited data indicated that acidification 

 B-88
does not alter zooplankton community grazing rates. Zooplankton species that have been shown to be 

sensitive to low pH include Diaptomus sicilis, Epischura lacustris, Tropocyclops parsinus mexicanus, 

Daphnia galeata mendotae, Daphnia rosea, Diaphanosoma birgei, Leptodora kindtii, Asplanchna 

priodonta, and Conochilus unicornis. In North America, species reported to have increased dominance in 

acidic lakes (acid-tolerants) include Keratella taurocephala, Bosmina longirostris, and Diaptomus 

minutus. Possible mechanisms for zooplankton sensitivity to low pH include ion regulation failure, 

reduced O2 uptake, inability to reproduce, and Al toxicity. Indirect effects of acidification on zooplankton 

communities are also possible due to pH-induced shifts in higher trophic level zooplankton predators. 

This mechanism is probably of less importance than the direct effects of low pH. It is also probable that 

under acidic conditions, zooplankton communities are less able to ameliorate nutrient additions or control 

algal densities (Baker et al., 1990b). 

Reported pH thresholds for zooplankton community alteration range from 5 to 6. Holt and Yan 

(2003) reported a threshold of community change at pH 6 for lakes in southern Ontario. Locke and 

Sprules (1994) reported that acidification below pH 5 in the 1970s overcame the resistance stability of the 

zooplankton community in Ontario Precambrian Shield lakes. The subset of study lakes that showed pH 

recovery from acidification 20 years later in 1990 also showed recovery in the stability of the zooplankton 

community. Holt and Yan (2003) also noted recovery in zooplankton community composition (based on 

similarity to neutral lakes) in the subset of Killarney Park (Ontario) lakes in which the pH increased to 

over 6 during the 1971 to 2000 study period. They did not, however, note any time trend of increasing 

species richness between recovering lakes and non-recovering lakes. 

Recovery in experimentally acidified Lake 223 back to pH 6.1 was studied by Malley and Chang 

(Malley and Chang, 1995). They reported that the zooplankton community was still in a state of flux. 

Species diversity that had been reduced during the acidification phase had partially returned to 

preacidification levels. Rotifers had recovered less than crustaceans. One decade after cessation of the 

experimental acidification of Little Rock Lake in Wisconsin, recovery of the zooplankton community was 

complete (Frost et al., 2006). Recovery did not follow the same trajectory as the initial acidification, 

however, indicating a substantial hysteresis in zooplankton community recovery. About 40% of the 

zooplankton species in the lake exhibited a lag of 1 to 6 years to recover to levels noted in the neutral 

reference basin. 

In situ enclosure studies were conducted for 35 days at Emerald Lake in the Sierra Nevada by 

Barmuta et al. (1990). The lake sediments were included within the experimental enclosures. This allowed 

the investigators to document the response of zoobenthos as well as zooplankton. Treatments included a 

control (pH 6.3) and acid addition to reach pH levels of 5.8, 5.4, 5.3, 5.0, and 4.7. Results indicated that 

zooplankton were sensitive to acidification but zoobenthos were unaffected by the experimental 

treatment. Daphnia rosea and Diaptomus signicauda decreased in abundance below the range of pH 5.5 

to 5.8 and were eliminated below about pH 5.0. Bosnia longirostris and Keratella taurocephala generally 

became more abundant with decreasing pH. Barmuta et al. (1990) concluded that even slight acidification 

of high-elevation lakes in the Sierra Nevada might alter the structure of the zooplankton community. 

Sullivan et al. (2006a) found that zooplankton taxonomic richness varied with ANC in Adirondack 

lakes (Table B-22). Taxonomic richness expressed as number of species of crustaceans, rotifers, and total 

zooplankton, increased with increasing ANC. In general, lakewater ANC explained nearly half of the 

variation in total zooplankton and crustacean taxonomic richness, but less for rotifer richness. These 

results (Table B-22) provided the basis for estimating changes in zooplankton richness in response to past 

or future changes in lakewater ANC. Several zooplankton species found in lakes in the Sierra Nevada are 

also known to be sensitive to acidity status (Gerritsen et al., 1998). 

B.6.1.3. Benthic Invertebrates 

Within stream systems, macroinvertebrate communities are among the most sensitive life forms to 

disturbances, including those associated with atmospheric deposition (Cairns and Pratt, 1993). In 

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addition, they are relatively easy to sample in the field (Plafkin et al., 1989; Resh et al., 1995; Karr and 

Chu, 1999). 

Acidification results in the loss of acid-sensitive benthic invertebrates and decreases in pH of one 

unit or more typically result in species loss. Invertebrate taxa that are most sensitive to acidification are 

mayflies, amphipods, snails, and clams. At low levels of acidification (pH 5.5 to 6.0), acid-sensitive 

species are replaced by more acid-tolerant species, yielding little or no change in total community species 

richness, diversity, density, or biomass. If pH decreases are larger, more species will be lost without 

replacement, resulting in decreased richness and diversity. Many sites also note decreases in invertebrate 

biomass and productivity (more so in streams than lakes). High levels of acidification (pH <5) were found 

to virtually eliminate all mayflies, crustaceans and mollusks from French streams (Guerold et al., 2000). 

Examples of sensitive benthic invertebrate species include Baetis rhodani, Gammarus lacustris, Hyalella 

azteca, Asellus aquaticus, Orconectes rusticus, and O. propinquous. Stoneflies are generally more acidtolerant than mayflies and caddisflies. 

Possible mechanisms for acidification effects on invertebrates include direct toxicity of H+

 and Al, 

disruption of ion regulation, and reproductive failure. Indirect effects due to acidification-induced changes 

to invertebrate predator populations are also possible (Baker et al., 1990b). Acidic episodes in streams can 

cause increased downstream drift of acid-sensitive species, particularly Baetis (Kratz et al., 1994; Smock 

and Gazzera, 1996). 

It has been well documented that low streamwater pH can be associated with reductions in 

invertebrate species richness or diversity (Townsend et al., 1983; Raddum and Fjellheim, 1984; Burton 

et al., 1985; Kimmel et al., 1985; Hall and Ide, 1987; Peterson and Van Eechhaute, 1992; Rosemond et al., 

1992; Sullivan et al., 2003), and sometimes density (Hall et al., 1980; Townsend et al., 1983; Burton et al., 

1985; Kimmel et al., 1985). Effects on invertebrate density are not universal; a number of studies have 

found no density effects (Harriman and Morrison, 1982; Simpson et al., 1985; Ormerod and Tyler, 1987; 

Winterbourn and Collier, 1987). However, a decrease in species richness with decreasing pH has been 

found in almost all such studies (Rosemond et al., 1992), and this finding has been especially pronounced 

in streams for order Ephemeroptera (mayflies). 

The Ephemeroptera-Plecoptera-Tricoptera (EPT) Index is a common measure of stream 

macroinvertebrate community integrity. The EPT metric is the total number of families present in those 

three insect orders (mayflies, stoneflies, and caddisflies, respectively). The total number of families is 

generally lower at acidified sites because species within those families tend to exhibit varying acid 

sensitivity (USDA, 1996). Mayflies tend to be most sensitive of the three, and stoneflies tend to be least 

sensitive (Peterson and Van Eechhaute, 1992). 

There has been some recovery in benthic invertebrate communities in surface waters exhibiting 

chemical recovery from acidification. In Scotland, Soulsby et al. (1995) reported an increase in acidsensitive mayflies in some streams that showed recent ANC increases. However, no increases in 

invertebrates were observed in the most acidic streams despite observed increases in ANC. They 

suggested that further acidic deposition reductions and sufficient time for reversal of soil acidification 

may be required before biotic recovery can occur. Tipping et al. (2002) noted increases of invertebrate 

richness and diversity in the English Lake District in their study streams that had pH increases of 0.3 to 

0.5 units since about 1970. 

Responses of aquatic macroinvertebrates to acidification were evaluated by Kratz et al. (1994) in 

12 streamside channels in Sequoia National Park, CA. Replicated treatments included a control (pH 6.5 to 

6.7) and experimental exposure at pH levels of 5.1 to 5.2 and 4.4 to 4.6. Invertebrate drift was monitored 

continuously and benthic densities were determined before and after acidification. Single 8-h acid pulses 

increased the drift of sensitive taxa, and benthic densities were reduced. Baetis showed reduced density 

post-treatment to less than 25% of control densities in both pH reduction treatments (5.2, 4.6) and two 

different experimental exposures. Densities of Paraleptophlebia appeared to be reduced by the 

acidification, but most treatment effects were not statistically significant. Kratz et al. (1994) suggested 

that the effects of acid inputs on benthic species densities depended on microhabitat preferences. Baetis

nymphs are epibenthic and active. They are often found on the upper surfaces of rocks where they are 

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directly exposed to acidified water. This may have been responsible for their greater response to 

acidification. 

B.6.1.4. Fish 

By 1990 it was well established that pH in the range of 4.0–6.5 could cause significant adverse 

biological effects on fish. Low pH was one of the most important factors resulting in adverse effects. The 

toxicity of pH was, in most cases, the result of impaired body salt regulation. Decreased pH in the water 

inhibited the active uptake of Na+

 and Cl− and stimulated the passive loss of these ions (Baker et al., 

1990b). 

The response to acidification was not uniform, however. Some species and life stages experienced 

significant mortality in bioassays at relatively high pH (e.g., pH 6.0–6.5 for eggs and fry of striped bass 

and fathead minnow) (Buckler et al., 1987; McCormick et al., 1989), whereas others were able to persist 

at quite low pH without adverse effect (Mudminnow; [Umbra spp.] at pH 4.0 and Umbra pygmaea at pH 

3.5) (Dederen, 1987). Many minnows and dace (Cyprinidae) are sensitive to acidity (threshold effects at 

pH <5.5 to 6.0), but some common game species such as brook trout, largemouth bass, and small mouth 

bass are relatively insensitive (threshold effects at pH <5.0 to 5.5). A summary of studies that 

demonstrated the difference among species is shown in Table B-23. Table B-24 summarizes the results 

from a variety of studies that determined the threshold values of pH for various taxa and kinds of effects. 

The effect of acidification on aquatic organisms, especially fish, is due in large part to the toxic 

effect of Ali that is released from watershed soils. A number of studies reviewed by Baker et al. (1990b) 

reported threshold values of Ali for various species and effects. Those results are presented in Table B-25. 

The effects of low pH and high Ali can be ameliorated to an extent in the presence of increased Ca2+

concentration. A summary of the effect of increasing Ca2+ concentration is presented in Table B-26. 

Fish populations in acidified streams and lakes of Europe and North America have declined, and 

some have become extinct as a result of atmospheric deposition of acids and the resulting changes in 

water quality (Baker et al., 1990b). A variety of factors, including Ali, DOC, and Ca2+, along with the 

timing and magnitude of episodic fluctuations in toxic acid and Ali concentrations, are related to the 

degree to which surface water acidification influences fish survival in natural systems (Baker et al., 

1990b; Baldigo and Murdoch, 1997; Gagen et al., 1993; Siminon et al., 1993; Van Sickle et al., 1996). 

Aluminum fractionation and Ali concentration are directly dependent upon pH levels (Driscoll et al., 

1985). 

Fish communities of acid-sensitive streams and lakes may contain a variety of species, but are 

often dominated by trout. Across the eastern U.S., brook trout is often selected as an indicator of 

acidification effects on aquatic biota because it is native to many eastern streams and lakes and because 

residents place great recreational and aesthetic value on this species. It must be emphasized, however, that 

brook trout is a relatively acid-tolerant species. Many other fish species, including rainbow and brown 

trout, as well as a variety of other fish species, are more acid-sensitive than brook trout. In many 

Appalachian Mountain streams that have been acidified by acidic deposition, brook trout is the last 

species to disappear; it is generally lost at pH near 5.0 (MacAvoy and Bulger, 1995), which usually 

corresponds in these streams with ANC near 0 (Sullivan et al., 2003). 

Although there are known differences in acid sensitivity among fish species, experimentally 

determined acid sensitivities are available for only a minority of freshwater fish species. Baker and 

Christensen (1991) reported critical pH values for 25 species of fish. They defined critical pH as the 

threshold for significant adverse effects on fish populations. The reported range of pH values represents 

the authors’ estimate of the uncertainty of this threshold. The range of response within species depends on 

differences in sensitivity among life stages, and on different exposure concentrations of Ca2+ and Al. To 

cite a few examples, blacknose dace is regarded as very sensitive to acid stress, because population loss 

due to acidification has been documented in this species at pH values as high as 6.1; in field bioassays, 

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embryo mortality has been attributed to acid stress at pH values as high as 5.9. Embryo mortality has 

occurred in common shiner at pH values as high as 6.0. Although the critical pH range for rainbow trout 

is designated as 4.9–5.6, adult and juvenile mortality have occurred at pH values as high as 5.9. Brown 

trout population loss has occurred over the pH range of 4.8–6.0, and brook trout fry mortality has 

occurred over the range of 4.8–5.9 (Baker and Christensen, 1991). Relative sensitivities can be suggested 

by regional surveys as well, although interpretation of such data is complicated by factors that correlate 

with elevation. Such factors, including habitat complexity and refugia from high-flow conditions, often 

vary with elevation in parallel with acid sensitivity. It is noteworthy, however that about half of the 53 fish 

species found in Adirondack Mountain waters in New York never occur at pH values below 6.0 (Kretser 

et al., 1989; Driscoll et al., 2001b); for those species whose acid tolerances are unknown, it is probable 

that acid sensitivity is responsible for at least some of these absences. It is the difference in acid tolerance 

among species that produces a gradual decline in species richness as acidification progresses, with the 

most sensitive species lost first. 

Effects on biota can be assessed as effects on a particular sensitive species or species perceived to 

be important, or as effects on the richness or diversity of fish or other potentially sensitive life form. For 

example, Bulger et al. (2000) developed ANC thresholds for brook trout in Virginia, which are presented 

in (Table B-27). These values were based on annual average stream water chemistry, and therefore 

represent chronic exposure conditions. The likelihood of additional episodic stress is incorporated into the 

response categories in the manner in which they are interpreted. For example, the episodically acidic 

response category, which has chronic ANC in the range of 0 to 20 μeq/L, represents streams that are 

expected to acidify to ANC near or below 0 during rainfall or snowmelt episodes. In such streams, 

sublethal and/or lethal effects on brook trout are possible (Bulger et al., 2000; Sullivan et al., 2003). 

Fish species richness, population density, condition factor, age distribution, size, and bioassay 

survival have all been shown to be reduced in low-ANC streams as compared to intermediate-ANC and 

high-ANC streams (Bulger et al., 1995; Dennis et al., 1995; Dennis and Bulger, 1995; MacAvoy and 

Bulger, 1995). Fish species richness is a good indicator of acidification response. Lakes or streams having 

pH below about 5.0 or ANC below about 0 generally do not support fish. Depending on the region, waters 

having pH above about 6.5 and ANC above about 50 μeq/L support large, but variable, numbers of 

species. There is often a positive relationship between pH and number of fish species, at least for pH 

values between about 5.0 and 6.5, or ANC values between about 0 and 50 to 100 μeq/L (Bulger et al., 

1999; Sullivan et al., 2006a). Such observed relationships are complicated, however, by the tendency for 

smaller lakes and streams, having smaller watersheds, to also support fewer fish species, irrespective of 

acid-base chemistry. This pattern may be due to a decrease in the number of available niches as stream or 

lake size decreases. Nevertheless, fish species richness is one of the most useful indicators of biological 

effects of surface water acidification. 

Acidification and the associated elevated concentrations of Ali in surface waters have adversely 

affected fish populations and communities in parts of the Adirondack Mountains of northern New York 

(Baker and Schofield, 1982; Johnson et al., 1987; Kretser et al., 1989; Schofield and Driscoll, 1987; 

Siminon et al., 1993) and in acid-sensitive streams of the Catskill Mountains of southeastern New York 

(Stoddard and Murdoch, 1991) and the Appalachian Mountains from Pennsylvania to Tennessee and 

South Carolina (USDA, 1996; Bulger et al., 1999, 2000). 

Adverse effects of low pH and high Ali concentration on fish include increased mortality, decreased 

growth, decreased reproductive potential, and ionoregulatory impairment. A partial list of studies 

demonstrating such effects is provided in Table B-28 from Baker et al. (1990b). It has been shown, 

however, that there is marked variability among species and among life stages within species in the 

specific levels of pH and Ali that produce measurable responses. 

Surface-water acidification can affect fish populations by a number of mechanisms ranging from 

increased morality and emigration to decreased food supplies (Baker et al., 1990b). The primary reason 

for population decline and extinction, however, is usually the failure of a species to successfully recruit 

young-of-the-year fish (Mills et al., 1987; Brezonik et al., 1993). The response of aquatic communities to 

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acidification, therefore, should appear first as changes in age distribution and decreased health of 

individual fish (growth and condition), then as decreased biomass and density in populations of acidintolerant fish species, and finally as elimination of sensitive species (Baker et al., 1990b). 

The primary mechanism for the toxic effects of low pH and elevated Al on fish involves disruption 

of normal ion regulation at the gill surface resulting in increased rates of ion loss and inhibition of ion 

uptake (McWilliams and Potts, 1978; Leivestad, 1982; Wood and McDonald, 1987; Bergman et al., 

1988). Additional effects might include disruption of Ca2+ metabolism (Peterson and Martin-Robichaud, 

1986; Gunn and Noakes, 1987; Reader et al., 1988), and decreased hatching success (Runn et al., 1977; 

Peterson et al., 1980; Haya and Waiwood, 1981; Waiwood and Haya, 1983). 

Prominent physiological disturbance for fish exposed to acid waters are iono- and osmoregulatory 

failure, acid-base regulatory failure, and respiratory and circulatory failure. Most of these effects can be 

directly attributed to effects on gill function or structure. The acute toxicity of low pH in acidic waters 

results in the loss of Ca2+ from important binding sites in the gill epithelium, which reduces the ability of 

the gill to control membrane permeability (McDonald, 1983; Havas, 1986; Exley and Phillips, 1988). 

The energy costs to fish for active iono-osmoregulation can be substantial (Farmer and Beamish, 

1969; Bulger, 1986). The concentrations of serum electrolytes (such as Na+

 and Cl−) are many times 

higher (often 100–fold higher) in fish blood than in the fresh waters in which they live. The active uptake 

of these ions occurs at the gills. Because of the steep gradient in Na+

 and Cl− concentrations between the 

blood and fresh water, there is constant diffusional loss of these ions, which must be replaced by energyrequiring active transport. Low pH increases the rate of passive loss of blood electrolytes (especially Na+

and Cl−

); and Al elevates losses of Na+ and Cl− above the levels due to acid stress alone (Wood, 1989). 

For example, dace in an acidified stream maintain whole-body Na+

 at levels similar to dace in a highANC stream (Dennis, 1995), despite probable higher gill losses of electrolytes due to acid/Al stress. 

Therefore, the homeostatic mechanisms at the gill responsible for maintaining blood electrolyte levels 

must work harder and use more energy to maintain these levels for dace in the acidified stream. 

Whole lake experiments and artificial stream channel experiments have shown that acidification 

can lead to loss of fish species. A summary of the work on Lake 223 in the Experimental Lakes Area in 

Canada is provided in Table B-29. Work at Little Rock Lake in Wisconsin suggested that rock bass 

suffered recruitment failure at pH 5.6 or below. Artificial channel studies showed poor survival and 

reproductive success for fathead minnow at pH 5.9 to 6.0. 

ANC criteria have been used for evaluation of potential acidification effects on fish communities. 

The utility of these criteria lies in the association between ANC and the surface water constituents that 

directly contribute to or ameliorate acidity-related stress, in particular pH, Ca2+, and Al. Bulger et al. 

(2000) developed ANC thresholds for brook trout response to acidification in forested headwater 

catchments in western Virginia (See Table B-27). Note that because brook trout are comparatively acid 

tolerant, adverse effects on many other fish species should be expected at relatively higher ANC values. 

Streams with chronic ANC greater than about 50 μeq/L are generally considered suitable for brook 

trout in southeastern U.S. streams because they have a large enough buffering capacity that persistent 

acidification poses no threat to this species, and there is little likelihood of storm-induced acidic episodes 

lethal to brook trout. In such streams, reproducing brook trout populations are expected if the habitat is 

otherwise suitable (Bulger et al., 2000), although some streams may periodically experience episodic 

chemistry that affects species more sensitive than brook trout. Streams having annual average ANC from 

20 to 50 μeq/L may or may not experience episodic acidification during storms that can be lethal to 

juvenile brook trout, as well as other fish. Streams that are designated as episodically acidic (chronic ANC 

from 0 to 20 μeq/L) are considered marginal for brook trout because acidic episodes are likely (Hyer 

et al., 1995), although the frequency and magnitude of episodes vary. Streams that are chronically acidic 

(chronic ANC less than 0 μeq/L) are not expected to support healthy brook trout populations (Bulger 

et al., 2000). 

Field surveys provided a regional context for fish response to acidification. Although there were 

some variations, the results of field surveys generally confirmed the results of bioassays, field 

 B-93
experiments, and other intensive field studies. The results of many field surveys were summarized in 

Baker et al. (1990b) and are compiled in (Table B-30). 

It is important to note, however, that the absence of fish from a given lake or stream in an area that 

experiences surface water acidification does not necessarily imply that acidification is responsible for the 

absence of fish. For example, results of fisheries research in the Adirondacks has indicated that many 

Adirondack lakes always had marginal spawning habitat for brook trout (Schofield, 1993), and some of 

the currently fishless acidic lakes probably never supported fish. 

Many of the data for the assessment of fish status in the Adirondack region of New York come 

from the reports by Kretser et al. (1989) and Baker et al. (1990a). The status of fish and of the presence of 

individual species were related to a variety of lake characteristics. Of the lakes without fish, 42% had high 

organic acid content that may have caused the observed low pH, 13% were bog lakes of high acidity and 

naturally poor fish habitat, 9% had pH >5.5 suggesting other factors were likely responsible for the lack 

of fish, and 3% were small high-elevation lakes that were unlikely to have fish regardless of acid-base 

chemistry. However, 34% of the lakes surveyed (112 lakes) that had no fish at the time of survey had low 

pH that was most likely the result of acid deposition and no other obvious explanation for the lack of fish. 

Multivariate regression of the presence/absence of brook trout in Adirondack waters produced a 

ranking of factors that appeared to influence the presence of brook trout when biological factors were 

excluded from the analysis (stocking, presence of associated species, presence of competitors). Among 

contributing factors, including SiO2, ANC, DOC, substrate, and distance to the nearest road, pH ranked 

first as a predictor of brook trout presence (Christensen et al., 1990). The results of this analysis supported 

the hypothesis that 1990 levels of pH and related variables restricted the distribution of some fish in 

Adirondack waters. 

Fish toxicity models have been developed as mathematical regression functions fit to observations 

of fish mortality when exposed to constant levels of pH, Ali, and Ca2+ in laboratory toxicity tests. These 

models had the advantage that they dealt directly with the interaction effects of pH, Al, and Ca2+, but they 

did not account for the effects of variations in other aspects of surface water quality, and they could not be 

directly interpreted in terms of population-level response. 

The many bioassays conducted of pH effects were screened by Baker et al. (1990b) to provide data 

most suitable for model development. Bioassays selected for inclusion were those that measured the 

mortality of early life stages, those that incorporated different combinations of pH, Al, and Ca2+, and those 

that used fish of varying sensitivity (Bergman et al., 1988). 

Acidity and Al toxicity are not the only stress factors that influence the distribution of fish in acidsensitive streams. Other habitat characteristics, including water temperature and stream channel 

morphology, can be important (Sullivan et al., 2003). In addition, it is probable that some trout 

populations have been affected by competition with other introduced species (Larson and Moore, 1985). 

B.6.1.5. Amphibians 

Some species of amphibians are considered to be highly sensitive to changes in environmental 

conditions and some species have probably been adversely affected by acidic deposition in some areas. 

Furthermore, several species of amphibian have exhibited marked declines in abundance throughout the 

western U.S. in recent decades and there has been much speculation concerning the cause(s) of these 

declines in abundance. 

Populations of many species of amphibians have declined or become eradicated throughout the 

world in recent decades (Barinaga, 1990; Wake, 1991). The causes have not been evident and some of the 

declines have occurred in remote pristine areas. For example, in the Sierra Nevada, at least two of five 

species of aquatic-breeding amphibians, Rana muscosa (mountain yellow-legged frog) and Bufo canorus

(Yosemite toad) have been declining (Phillips, 1990). A number of hypotheses have been proposed for 

amphibian decline, including acidic deposition. In the western U.S., however, acidic deposition has been 

discounted as the primary cause of the decline of R. muscosa and B. canorus in the Sierra Nevada and of 

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R. pipiens and B. boreas in the Rocky Mountains (Corn et al., 1989; Bradford et al., 1992). Grant et al. 

(2005) reported little relationship between streamwater ANC and the adjacent salamander community in 

Shenandoah National Park. 

In some cases, population fragmentation as a consequence of fish predation may be a more likely 

cause (Bradford et al., 1993). It is generally recognized that R. muscosa was eliminated by introduced fish 

early in the 20th century in many lakes and streams in Sequoia and Kings Canyon National Parks. The 

amphibians have been eliminated from nearly all waters inhabited by fish, presumably by predation on 

tadpoles. Before 1870, virtually all of the high-elevation (>2500 m) lakes in the Sierra Nevada were 

barren of fish, but have since been stocked with fish. Fish introductions may have contributed to recent 

amphibian declines because amphibian populations are now more isolated from each other than formerly. 

The role of atmospheric deposition as an additional stressor is not clear. 

The acidification sensitivity of temporary ponds, where many amphibians live or reproduce, have 

not been well studied. These ponds tend to fill directly from rain or snowmelt and thus can be more acidic 

than surrounding lakes and streams. There is a correlation between pond acidity and amphibian 

abundance. 

There are both acid-sensitive and acid-tolerant amphibians. Examples of acid-sensitive amphibians 

include the spotted salamander (Ambystoma maculatum) and Jefferson salamander (Ambystoma 

jeffersonianum). Embryos of acid-sensitive species are killed by water with pH less than about 4.5. Acidtolerant embryos may survive at a pH of 3.7. Toxicity is not solely a matter of pH, but is also influenced 

by Ca2+, Ali, and DOC concentrations. It is also dependent on the life stages present and water 

temperature (Baker et al., 1990b). Large-scale amphibian extinctions in any geographic region due to 

acidic deposition have not been detected. 

Although acidic deposition may play a role in some areas, there is no evidence to suggest that it is a 

primary factor. Other issues, including fish introductions, are probably more important as stressors on 

amphibian populations across broad regional to national scales. 

B.6.1.6. Fish-Eating Birds 

Relative to other trophic groups, there are few studies assessing acidification effects on fish-eating 

birds. Limited data suggest that fish-eating birds are adversely affected by acidification. Acidification 

effects on birds may be indirect, related to changes in the quantity and quality of food. Other potential 

causal pathways include delayed egg laying, lighter/thinner egg shells, and reduced chick growth in acidic 

waters (Tyler and Ormerod, 1992). There is also concern about increased metal and Hg concentrations in 

fish-eating birds associated with bioaccumulation from contaminated fish in known areas of acidification 

(Baker et al., 1990b). 

Fish-eating birds can serve as biological indicators of lakes affected by acidic deposition (McNicol, 

2002). Lack of prey resources, decreased food quality, and elevated lake water methylmercury (MeHg) 

concentrations that could be associated with acidification may negatively effect foraging, breeding, and/or 

reproduction for the common loon (Gavia immer), common merganser (Mergus merganser), belted 

kingfisher (Ceryle alcyon), osprey (Pandion haliatus), American black duck (Anas rubripes), ring-necked 

duck (Aythya collaris), eastern kingbird (Tyrannus tyrannus), and tree swallow (Tachycineta bicolor) 

(Table B-31) (Longcore and Gill, 1993). Breeding distribution for the common goldeneye (Bucephala 

clangula), an insectivorous bird, may be positively effected by acidic deposition (Longcore and Gill, 

1993). Reduced prey diversity and quantity have been observed to create feeding problems for nesting 

pairs of loons on low-pH lakes in the Adirondacks (Parker, 1988). 

Since the mid 1980s, a statistically significant increase in fish-eating birds has been observed in the 

Sudbury region of Ontario, Canada, which has corresponded with a decreasing abundance of common 

goldeneye (McNicol, 2002). This interaction has been attributed to an increase in prey for pisciverous 

birds and a decrease in available prey for insectivorous birds as a result of stricter S emissions controls in 

the U.S. and Canada (McNicol, 2002). Logistic regression modeling with measured pH and species 

 B-95
occurrence data for acid-sensitive lakes in the Algoma region of Ontario showed that the occurrence of 

fish, common loons, and common mergansers is positively related to lake water pH (McNicol, 2002). 

Predictions of common loon and merganser recovery for this area were made using the Waterfowl 

Acidification Response Modeling System (WARMS) under varying S emissions control scenarios 

targeted for 2010 (McNicol, 2002). The modeled emissions scenarios include: 

 S1: sulfate emissions equal to those in the early 1980s (base case) 

 S2: sulfate emissions equal to that in 1994 (full Canadian emissions reductions based on the 1991 

Canada/U.S. Air Quality Agreement) 

 S3: expected sulfate emissions in 2010 (full implementation of U.S. emissions reductions based 

on the 1991 agreement) 

 S4: a hypothetical 50% reduction in expected 2010 sulfate emissions 

 S5: a hypothetical 75% reduction in expected 2010 sulfate emissions 

The number of lakes projected to be suitable for supporting breeding pairs and broods increased 

with lake pH and stricter emissions controls (Table B-32) (McNicol, 2002). 

Marginal improvements to fish-eating bird habitat were predicted to occur by 2010 (S3), with more 

significant improvements expected under hypothetical S emissions reductions of 50% and 75% (S4 and 

S5) for lakes with pH below 6.5 (McNicol, 2002). Fundamental to the predicted improvement of these 

fish-eating bird populations is the expected increase in food availability with lake pH recovery. 

Elevated MeHg accumulation in fish-eating birds in Wisconsin and the northeastern U.S. has been 

linked to lake acidification (Meyer et al., 1995; (Hrabik and Watras, 2002)Evers et al., 2007). This form 

of Hg is toxic, bioavailable, and accumulates in top predators to levels of concern for both human health 

and the environment (Table B-33) (Evers et al., 2007). 

Acidic deposition might contribute to Hg toxicity in fish-eating birds because SO4

2−

 addition to 

wetland environments could stimulate the production of MeHg, thereby increasing lake water 

concentrations of MeHg (Jeremiason et al., 2006). Kramar et al. (2005) determined that the extent of 

wetland located in close proximity (less than150 m) to loon territory was positively correlated with Hg 

concentrations in loon blood. Wetland MeHg production is discussed in greater detail in Section 6.3. 

Accumulation of MeHg in fish-eating birds can result in damage to nervous, excretory, and 

reproductive systems (Wolfe et al., 1998). Table B-34 lists several studies indicating effects related to 

mercury bioaccumulation in avian eggs and tissues. Reproduction is considered one of the most sensitive 

endpoints to chronic low-level MeHg exposure for fish-eating birds (Wolfe et al., 1998). Reduced clutch 

size, increased number of eggs laid outside the nest, eggshell thinning, and increased embryo mortality 

have all been documented (Wolfe et al., 1998). 

Table B-1. N-saturated forests in North America, including estimated N inputs and outputs. 

Location Forest Type Elevation (m) N Input 

(kg/ha/yr) 

N Output 

(kg/ha/yr) Reference 

Adirondack Mts., 

northeastern New York 

Northern hardwoods or 

hardwood/conifer mix 

396–661 9.3a Stage 1 N 

lossb 

Driscoll and Van 

Dreason (1993) 

Catskill Mts., 

southeastern New York 

Mainly hardwood; 

some eastern hemlock 

335–675 10.2a Stage 1 and 2 

N lossb 

Stoddard (1994) 

Turkey Lakes Watershed, 

Ontario, Canada 

Sugar maple and 

yellow birch 

350–400 7.0–7.7 

(as throughfall) 

17.9–23.6 Foster et al. (1989); 

Johnson and 

Lindberg (1992) 

 B-96
Whitetop Mt., 

southwestern, Virginia 

Red spruce 1,650 32c 47c Joslin and Wolfe 

(1992); Joslin et al. 

(1992) 

Fernow, West Virginia Mixed hardwood 735–870 15–20 6.1 Gilliam et al. ; 

Peterjohn et al. 

(1996) 

Great Smoky Mts. 

National Park, Tennessee 

American beech 1,600 3.1d 2.9 Johnson and 

Lindberg (1992) 

Great Smoky Mts. 

National Park, Becking 

Site, North Carolina 

Red spruce 1,800 10.3d 19.2 Johnson et al. 

(1991c) 

Great Smoky Mts. 

National Park, Tower Site, 

North Carolina 

Red spruce 1,740 26.6 20.3 Johnson et al. 

(1991c) 

Front Range, Colorado Alpine tundra, 

subalpine conifer 

3,000–4,000 7.5–8.0 7.5 Williams et al. 

(1996a) 

San Dimas, San Gabriel 

Mts., southern California 

Chaparral and 

grasslands 

580–1,080 23.3e 0.04–19.4 Riggan et al. (1985) 

Camp Paivika, San 

Bernardino Mts., southern 

California 

Mixed conifer 1,600 30 7–26 Fenn et al. (1996) 

Klamath Mts., northern 

California 

Western coniferous NA Mainly 

geologicg 

NAg Dahlgren (1994) 

Thompson Forest, 

Cascade Mts., 

Washington 

Red alder 220 4.7 plus >100 

as N2 fixation 

38.9 Johnson and 

Lindberg (1992) 

a Estimated total N deposition from wet deposition data is from Driscoll et al. (1991) for the Adirondacks, and from Stoddard and Murdoch (1991) for the Catskills. Total deposition was 

estimated based on the wet deposition: total N deposition ratio (0.56) at Huntington Forest in the Adirondacks (Johnson, 1992). N deposition can be higher in some areas, especially at 

high-elevation sites such as Whiteface Mountain (15.9 kg N/ha/yr); (Johnson, 1992). b Stage 1 and 2 of N loss according to the watershed conceptual model of Stoddard (1994). N 

discharge (kg N/ha/yr) data are not available, only stream water NO3

− concentration trend data were collected. c

 Values appear high compared to other sites, especially N leaching 

losses. Joslin and Wolfe (1992) concede that “there is considerable uncertainty associated with the estimates of atmospheric deposition and leaching fluxes.” However, elevated NO3

−

concentrations in soil solution and lack of a growth response to N fertilization ratio (Joslin and Wolfe, 1994) support the hypothesis that the forest at Whitetop Mountain is N -saturated. d

Estimated total N deposition from throughfall data. Total deposition was estimated based on the throughfall/total N deposition ratio from the nearby Smokes Tower site (Johnson, 1992). e Annual throughfall deposition to the chaparral ecosystem. f

 N output is from unpublished stream water data (Fenn and Poth, 1999). The low value represents a year of average 

precipitation, and the high value is for 1995, when precipitation was nearly double the long-term average. N output includes N export in stream water and to groundwater. g Annual input 

and output data are not known, although N deposition in this forest is probably typical for much of the rural western U.S. (2–3 kg N/ha/yr; Young et al., 1988). Excess N is from 

weathering of ammonium in mica schist bedrock. The ammonium was rapidly nitrified, leading to high NO3

− concentrations in soil solution (Dahlgren, 1994). 

Table B-2 Summary of measured ANC, pH, and Al concentrations compared with reference values in the 

six high-interest areas. 

Percent of Population with 

Area n* N* 

ANC ≥ 0 pH ≥ 5.5 Ali >100 μg/L 

ADIRONDACKS 

Southwest lakes 52 450 38 51 36 

Other lakes 84 707 0 3 0 

 B-97
NEW ENGLAND 

Seaboard Lowlands lakes 94 848 8 11 0 

Highland lakes 354 3,574 2 5 2 

MID-ATLANTIC HIGHLANDS 

Forested lakes 91 433 10 9 1 

Other lakes 52 791 0 0 0 

Forested streams 78 11,631 12 17 8 

Other streams 69 10,172 0 2 0 

ATLANTIC COASTAL PLAIN 

Northeast lakes 22 187 11 15 7 

Pine Barrens streams 12 675 56 92 56 

Other streams 31 7,452 10 24 15 

FLORIDA 

Northern Highland lakes 32 522 63 53 10 

Northern Highland streams 18 669 28 55 0 

EASTERN UPPER MIDWEST 

Low silica lakes 155 1,254 16 19 1 

High silica lakes 125 1,673 3 4 2 

* n = sample size, N = estimated number of lakes or upstream reach ends in population. 

Source: Baker et al. (1990b). 

Table B-3 Sources of data and sample sizes for datasets analyzed by Stoddard et al. (2003), along with 

estimates of the condition of surface waters in each region in the 1980s. 

Source of Data and Region No. of Sites1 Size of Population2 Percent Acidic in 1980s3

STATISTICAL SURVEYS 

New England Lakes4 30 4,327 lakes 5% 

Adirondack Lakes4 43 1,290 lakes 14% 

Appalachian Plateau Streams 31 72,000 stream miles 6% 

SENSITIVE SURFACE WATERS 

New England Lakes 24 N.A. 5% 

Adirondack Lakes 48 N.A. 14% 

Northern Appalachian Streams 9 N.A. 6% 

Upper Midwest Lakes 38 N.A. 3% 

 B-98
Ridge/Blue Ridge Streams 69 N.A. 5% 

1 Number of monitoring sites with monitoring data available (1990–2000). 2 Total number of lakes, or stream length, for which statistical survey results can be inferred. Site selection for 

LTM (sensitive surface waters) is not statistically based, and results cannot be expanded to population level. 3 Estimates of extent of acidification, based on National Surface Water 

Survey results (Linthurst et al., 1986; Kaufmann et al., 1988). 4 Estimates are for lakes with surface areas >4 ha; estimates based on populations including smaller lakes are likely to be 

higher, due to the increased incidence of acidification in small lakes. 

Statistical survey data are from the EMAP and TIME projects. Sensitive surface water data are from the LTM project, as well as other contributed studies. Source: Stoddard et al. (2003) 

Table B-4 Estimates of change in number and proportion of acidic surface waters in acid-sensitive 

regions of the North and East, based on applying current rates of change in Gran ANC to past 

estimates of population characteristics from probability surveys. 

Results of Regional Survey Results of Monitoring during 1990s 

Region Population 

Size 

Number 

Acidic1

% 

Acidic2

Time 

Period of 

Estimate 

Rate of 

ANC 

change3

Estimated 

Number 

Acidic in 

2000 

% 

Acidic 

in 2000 

% Change in 

Number of 

Acidic 

Systems 

New England 6,834 lakes 386 lakes 5.6% 1991–94 +0.3 374 lakes 5.5% −2% 

Adirondacks. 1830 lakes 238 lakes 13.0% 1991–94 +0.8 149 lakes 8.1% −38% 

No. 

Appalachians 

42,426 km 5014 km 11.8% 1993−94 +0.7 3600 km 8.5% −28% 

Ridge/Blue 

Ridge 

32,687 km 1634 km 5.0% 1987 −0.0 1634 km 5.0% 0% 

Upper 

Midwest 

8,574 lakes 251 lakes 2.9% 1984 +1.0 80 lakes 0.9% −68% 

Source: Stoddard et al. (2003) 

Table B-5 Regional trend results for long-term monitoring sites for the period 1990 through 2000. 

Region SO4

2−

(μeq/L/yr) 

NO3

−

(μeq/L/yr) 

Base Cations 

[Ca2+ + Mg2+] 

(μeq/L/yr) 

Gran ANC 

(μeq/L/yr) 

Hydrogen

(μeq/L/yr)

DOC 

(mg/L/yr) 

Aluminum 

(μg/L/yr) 

New England Lakes −1.77** +0.01ns −1.48** +0.11ns −0.01ns +0.03* +0.09ns 

Adirondack Lakes −2.26** −0.47** −2.29** +1.03** −0.19** +0.06** −1.12** 

Appalachian Streams −2.27* −1.37** −3.40** +0.79* −0.08* +0.03ns +0.56ns 

Upper Midwest Lakes −3.36** +0.02ns −1.42** +1.07** −0.01* +0.06** −0.06ns 

Ridge/Blue Ridge Streams +0.29** −0.07** −0.01ns −0.07ns +0.01ns NA NA 

ns regional trend not significant (p >0.05). * p <0.05. ** p <0.01. NA insufficient data. Note: Values are median slopes for the group of sites in each region.Source: Stoddard et al. (2003) 

 B-99
Table B-6 Slopes of trends in Gran ANC in acidic, low, and moderate ANC lakes and streams, 1990–2000. 

ANC Class Number of Sites Change in Gran ANC (μeq/L/yr) 

Acidic (ANC <0 μeq/L) 26 +1.29** 

Low ANC (0 <ANC <25 μeq/L) 51 +0.84** 

Moderate ANC (25 <ANC <200 μeq/L) 43 +0.32 ns 

ns trend not significant (p >0.05). ** p <0.01. Note: Analysis includes all sites in New England, Adirondacks, Appalachian Plateau, and Upper Midwest; Ridge and Blue Ridge sites excluded.

Source: Stoddard et al. (2003) 

Table B-7 Changes in key chemical characteristics during periods of record in Maine aquatic systems. 

Change in (all in μeq/L) 

Years 

Sulfate Nitrate Base Cations Calculated ANCa ANC DOCb

Acadia NP lakes (22) 17 −10 0 −17 −7 0 −2 

LTM lakes @ Tunk Mtn (6) − spring 17 −9 0 −10 −1 1 1 

LTM lakes @ Tunk Mtn (6) − fall 17 −7 0 −9 −2 −2 1 

LTM lakes since 1990 − fall only 8 −9 0 −10 −1 −1 0 

High elevation lakes (90) 12 −16 1 −23 −8 −2 4 

Seepage lakes (120) 12 −9 1 −1 7 7 4 

East Bear Brook at BBWM 11 −22 −16 −44 −6 −4 1 

RLTM lakes (16) 7 −6 1 −17 −12 −4 2 

a Calculated ANC = [change in base cations] minus [change in (sulfate + nitrate)]. b DOC (μeq/L) = DOC in mg/l * 4 (e.g., Kahl et al., 1999). Source: Kahl et al. (1999) 

Table B-8 Projected changes (μeq/L) in median values of streamwater chemistry at the regional 

modeling sites from 1995 to 2040 in each of the three emissions control strategies, stratified 

into two segments of the SAMI region (northeast and southwest) and by physiographic 

province. 

Physiographic Province Number of Sites Δ Sulfate Δ Nitrate Δ SBC ΔANC 

A2 STRATEGY1

VIRGINIA AND WEST VIRGINIA 

Blue Ridge 16 1.8 0.03 −2.2 −4.0 

Valley and Ridge 41 −0.45 0.02 −6.8 −6.6 

Appalachian Plateau 34 −31.2 −3.5 −33.8 −4.4 

 B-100
NORTH CAROLINA, TENNESSEE, SOUTH CAROLINA, GEORGIA, AND ALABAMA 

Blue Ridge 33 8.8 0.15 1.0 −8.0 

Appalachian Plateau 6 15.3 0.15 −1.2 −15.9 

VIRGINIA AND WEST VIRGINIA 

Blue Ridge 16 −2.7 −0.04 −3.0 −1.0 

Valley and Ridge 41 −5.6 −0.37 −8.2 −4.7 

Appalachian Plateau 34 −36.3 −4.9 −38.4 −1.4 

NORTH CAROLINA, TENNESSEE, SOUTH CAROLINA, GEORGIA, AND ALABAMA 

Blue Ridge 33 5.6 −0.48 −0.60 −5.4 

Appalachian Plateau 6 11.6 −0.23 −1.9 −13.3 

B3 STRATEGY1

VIRGINIA AND WEST VIRGINIA 

Blue Ridge 16 −7.4 −0.09 −5.1 2.9 

Valley and Ridge 41 −13.8 −0.36 −10.3 −0.83 

Appalachian Plateau 34 −40.4 −5.8 −39.3 2.6 

NORTH CAROLINA, TENNESSEE, SOUTH CAROLINA, GEORGIA, AND ALABAMA 

Blue Ridge 33 3.2 −1.0 −2.3 −3.2 

Appalachian Plateau 6 7.2 −0.53 −3.1 −10.4 

1 Emissions control strategies were based on existing regulations (A2), moderate additional controls (B1), and more aggressive additional controls (B3) 

Source: Sullivan et al. (2004) 

Table B-9. Population estimates of water chemistry percentiles for selected lake populations in the 

western U.S.a

pH ANC 

(μeq/L) 

SBC 

(μeq/L) 

SO4

2−

(μeq/L) 

NO3

−

(μeq/L) 

DOC 

(mg/L) Population n N 

P1 P5 P1 P5 P1 P5 P95 P99 P95 P99 P50 P99 

Sierra Nevada 114 2,119 5.84 6.31 15 16 21 26 90 386 8 10 0.8 2.7 

Cascades 146 1,473 5.95 6.25 11 18 20 31 60 97 3 6 1.3 2.6 

Idaho Batholith 88 937 6.34 6.42 21 33 30 45 30 43 3 4 1.2 2.4 

NW Wyoming 38 648 6.56 6.56 38 38 64 66 41 2,909 13 32 1.0 4.8 

Colorado 

Rockies 

121 1,173 6.02 6.65 25 42 58 80 915 2,212 10 13 1.3 5.7 

aData from Landers et al. (1987). bExcluding Fern Lake (4D3−017) which is naturally acidic. Note: The 1st and 5th percentiles (P1, P5) are presented for pH, ANC (μeq/L), and SBC 

(μeq/L) and the 95th and 99th (P95, P99) percentiles are shown for SO4

2- (μeq/L) and NO3− (μeq/L). The median (P50) and 90th percentiles are shown for DOC (mg/L). 

 B-101
Table B-10. Population estimates of the percentage of lakes in selected subregions of the West with ANC 

and NO3

− within defined ranges. 

ANC (μeq/L) NO3⎯ (μeq/L) 

<0 <25 <50 >5 >10 

Sierra Nevada 0 8.7 39.3 10.6 1.5 

Cascades 0 10.2 22.4 1.5 0.0 

Idaho Batholith 0 2.0 23.6 4.6 3.9 

NY Wyominga 0 2.3 12.8 22.8 8.9 

Colorado Rockies 0 0.9 5.5 9.8 1.8 

a Excluding Fern Lake (4D3−017) which is a naturally acidic lake. Source: Landers et al. (1987) 

Table B-11. Median streamwater ANC and watershed area of streams in Shenandoah National Park that 

have water chemistry and fish species richness data. 

Site ID Watershed Area (km2) Median ANC (μeq/L) # Fish Species 

SMALLER WATERSHEDS (<10 KM2) 

North Fork Dry Run 2.3 48.7 2 

Deep Run 3.6 0.3 N.D.a

White Oak Run 4.9 16.2 3 

Two Mile Run 5.4 10.0 2 

Meadow Run 8.8 −3.1 1 

LARGER WATERSHEDS (>10 KM2) 

Brokenback Run 10.1 74.4 3 

Staunton River 10.6 76.8 5 

Piney River 12.4 191.9 7 

Paine Run 12.7 3.7 3 

Hazel River 13.2 86.8 6 

White Oak Canyon 14.0 119.3 7 

N. Fork Thornton River 18.9 249.1 9 

Jeremy’s Run 22.0 158.5 6 

Rose River 23.6 133.6 8 

a No data were available regarding the number of fish species in Deep Run. Source: Sullivan (2003) 

 B-102
Table B-12. Reference levels for the Acidic Stress Index based on logistic regression of fish presence as a 

function of the sensitive intermediate and tolerant ASI values for brown bullhead, brook trout, 

lake trout, and common shiner. 

Reference Acid Stress Index 

Lakes Streams 

Fish Response 

Tolerant ASI >30 Intermediate ASI >30 Loss of all fish species 

Tolerant ASI >10 Sensitive ASI >30 Loss of brook trout 

Intermediate ASI >80 Loss of other sport fish, such as smallmouth bass and lake trout 

Sensitive ASI >80 Sensitive ASI >10 Loss of acid-sensitive species, such as minnows. 

Source: Baker et al. (1990b). 

Table B-13. General summary of biological changes anticipated with surface water acidification, 

expressed as a decrease in surface water pH. 

pH 

Decrease General Biological Effects 

6.5 to 6.0 Small decrease in species richness of plankton and benthic invertebrate communities resulting from the loss of a few highly 

acid-sensitive species, but no measurable change in total community abundance or production. 

Some adverse effects (decreased reproductive success) may occur for highly acid-sensitive fish species (e.g., fathead 

minnow, striped bass). 

6.0 to 5.5 Loss of sensitive species of minnows and dace, such as fathead minnow and blacknose dace; in some waters, decreased 

reproductive success of lake trout and walleye, which are important sport fish species in some areas. 

Visual accumulation of filamentous green algae in the near-shore zone of many lakes and in some streams. 

Distinct decrease in species richness and change in species composition of plankton and benthic invertebrate communities, 

although little if any change in total community abundance or production. 

Loss of some common invertebrate species from zooplankton and benthic communities, including many species of snails, 

clams, mayflies, and amphipods, and some crayfish. 

5.5 to 5.0 Loss of several important sport fish species, including lake trout, walleye, rainbow trout, and smallmouth bass, as well as 

additional nongame species such as creek chub. 

Further increase in the extent and abundance of filamentous green algae in lake near-shore areas and streams. 

Continued shift in species composition and decline in species richness of plankton, periphyton, and benthic invertebrate 

communities; decreases in total abundance and biomass of benthic invertebrates and zooplankton may occur in some waters. 

Loss of several additional invertebrate species common in surface waters, including all snails, most species of clams, and 

many species of mayflies, stoneflies, and other benthic invertebrates. 

Inhibition of nitrification. 

5.0 to 4.5 Loss of most fish species, including most important sport fish species such as brook trout and Atlantic salmon. A few fish 

species are able to survive and reproduce in water below pH 4.5 (e.g., central mudminnow, yellow perch, and in some waters, 

largemouth bass). 

 B-103
Measurable decline in the whole-system rates of decomposition of some forms of organic matter, potentially resulting in 

decreased rates of nutrient cycling. 

 Substantial decrease in number of species of plankton and benthic invertebrates and further decline in species richness of 

plankton and periphyton communities; measurable decrease in total community biomass of plankton and benthic invertebrates 

of most waters. 

Loss of additional species of plankton and benthic invertebrate species, including all clams and many insects and crustaceans.

Reproductive failure of some acid-sensitive species of amphibians, such as spotted salamanders, Jefferson salamanders, and 

the leopard frog. 

Source: Baker et al. (1990b). 

Table B-14. Estimated percentage of Adirondack lakes with and Acidic Stress Index exceeding the 

reference levels for effects on fish populations, based on diatom-inferred historical (preindustrial) chemistry and present-day measured and inferred acid-base chemistry. 

DDRP Target Population 

ASI Reference Level Diatom Inferred 

Historical Current Net Change Measured ELS/NSWS Target Population Measured 

Tolerant ASI >30 0.0 3.6 +3.6 1.8 2.2 

Tolerant ASI >10 0.0 9.1 +9.1 10.9 6.5 

Intermediate ASI >80 7.3 21.8 +14.5 21.8 15.2 

Sensitive ASI >80 28.5 41.2 +12.7 32.7 20.0 

Source: Baker et al. (1990b). 

Table B-15. Estimated percentage of Adirondack lakes with acid-base chemistry unsuitable for fish 

population survival, based on diatom-inferred historical (pre-industrial) chemistry and 

present-day measured and inferred acid-base chemistry. 

DDRP Target Populationb

Diatom-Inferredc

ELS/NWS 

Target Populationb Fish Species ALSC 

Modela

Historical Current Net Change Measured Measured Measured 

BROOK TROUT 

Bayesian 2.7 13.0 +10.3 14.2 10.1 21.8 

LAF framework - - - - 15.8 24.6 

pH 2.3 11.3 +9.0 12.8 9.3 22.2 

pCa/pH 16.0 13.3 −2.6 14.5 10.3 23.0 

pCa/pH, Al/DOC 16.6 15.6 −1.0 19.2 13.9 23.5 

LAKE TROUT 

 B-104
DDRP Target Populationb

Diatom-Inferredc 

ELS/NWS 

Target Populationb Fish Species ALSC 

Modela

Historical Current Net Change Measured Measured Measured 

pH 6.4 18.1 +11.2 21.4 14.4 30.9 

pCa/pH 31.7 25.1 −6.6 29.0 18.9 - 

Inorg. Al 23.9 38.6 +14.7 26.2 17.1 - 

COMMON SHINER 

pH 19.2 29.6 +10.5 33.5 21.3 42.3 

pCa/pH 45.8 37.7 −8.1 40.2 29.1 - 

a All models, except the brook trout Bayesian model (Section 3.5) and LAF framework (Section 3.4), are field-based acidification response models as defined in Section 3.3.3. b ELS/NSWS target population in Subregion 1A, defined in Section 3.1 (N = 1,290 lakes); a subset of these lakes was considered for the DDRP and sediment diatom analyses, for 

example, excluding lakes with ANC >400 μeq/L and with site depths <1.5 m (N = 675 lakes). See Sullivan (1990) for further details. c

 Estimates of acid-base chemistry inferred from 

sediment diatom analysis; methods and water chemistry described in Sullivan (1990). - Analysis not conducted. Source: Baker et al. (1990b). 

Table B-16. Estimated percentage of the lakes in the Northeast and Upper Midwest, ELS/NSWS target 

population with an Acidic Stress Index exceeding the reference levels for fish populations 

defined in Table C-12. 

ASI Reference Level Subregion 1A Northeast Region Upper Midwest Region 

Tolerant ASI >30 2.2 1.0 0.5 

Tolerant ASI >10 6.5 2.4 1.0 

Intermediate ASI >80 15.2 5.7 2.0 

Sensitive ASI >80 20.0 8.6 3.1 

Source: Baker et al. (1990b). 

Table B-17. Estimated percentage of lakes in the Northeast, ELS/NSWS target populations with acid-base 

chemistry unsuitable for fish population survival. 

Fish Species/Model Subregion 1A Entire Northeast 

BROOK TROUT 

Bayesian 10.1 3.7 

LAF Framework 15.8 8.9 

pH 9.3 3.5 

pCa/pH 10.3 4.4 

pCa/pH, Al/DOC 13.9 7.0 

LAKE TROUT 

 B-105
Fish Species/Model Subregion 1A Entire Northeast 

pH 14.4 5.8 

pCa/pH 18.9 8.9 

Inorganic Al 17.1 6.3 

COMMON SHINER 

pH 21.3 9.5 

pCa/pH 29.1 19.7 

Source: Baker et al. (1990b) 

Table B-18. Distribution of acidic stress index values among the NSS-1 Target populations for the midAppalachian region. 

Number (%) 

Lower Node Upper Node 

Total Length (%) 

SENSITIVE ASI 

≥ 10 84.6 66.7 76.1 

10–30 10.1 18.9 14.0 

30–50 1.4 1.9 1.9 

50–80 1.8 2.6 1.6 

>80 2.0 9.8 6.4 

INTERMEDIATE ASI 

≥ 10 97.8 89.3 88.9 

10–30 0.2 2.5 0.7 

30–50 0.6 1.3 0.4 

50–80 0.1 1.4 0.4 

>80 1.3 5.4 1.9 

TOLERANT ASI 

≥ 10 99.4 97.1 98.1 

10–30 0.6 1.4 0.9 

30–50 0.0 0.6 0.3 

50–80 0.0 0.9 0.7 

>80 0.0 0.0 0.0 

Source: Baker et al. (1990b). 

 B-106
Table B-19. Distribution of acidic stress index values among the NSS-1 target populations for the interior 

Southeast region. 

Number (%) 

Lower Node Upper Node 

Total Length (%) 

SENSITIVE ASI 

≥ 10 79.4 70.1 75.9 

10–30 18.8 21.1 18.8 

30–50 0.0 1.7 2.0 

50–80 1.7 5.2 2.5 

>80 0.0 1.7 0.7 

INTERMEDIATE ASI 

≥ 10 100.0 98.3 99.3 

10–30 0.0 0.0 0.0 

30–50 0.0 0.0 0.0 

50–80 0.0 0.0 0.0 

>80 0.0 1.7 0.7 

TOLERANT ASI 

≥ 10 100.0 100.0 100.0 

10–30 0.0 0.0 0.0 

30–50 0.0 0.0 0.0 

50–80 0.0 0.0 0.0 

>80 0.0 0.0 0.0 

Source: Baker et al. (1990b). 

 B-107
Table B-20. Comparison of solution and tissue chemistries at threshold treatment levels where significant 

impacts on tree growth or nutrient content were first observed. In many cases, adverse 

impacts were observed at the lowest Al treatment level. Hence, the actual threshold Ca/Al ratio 

may be higher than reported. Results are from a variety of studies reported in the literature. 

Study 

Solution 

Al 

(μmol/L) 

Solution 

Ca/Al 

(M) 

Foliar 

Ca 

(mg/km) 

Foliar 

Al 

(mg/km)

Foliar 

Ca/Al 

(M) 

Root Ca 

(mg/km)

Root Al 

(mg/kg)

Root 

Ca/Al(M)

Type of 

Study or 

Experimenta

Response 

Variableb

Al 

Analysis 

Used in 

Ratioc

NORWAY SPRUCE 

Godbold 

et al. (1988) 

100 1.3 H N Ali 

Matzner 

et al. (1989) 

100+ 0.3 to 1.8 F N Alt 

Stienen and 

Bauch 

(1988) 

1500 0.66 1470 32 31 770 1890 0.28 H N,B Alt 

Schroder 

et al. (1988) 

2000 1 H N Ali 

RED SPRUCE 

Thornton 

et al. (1987) 

250 1 1100 65 11.4 650 6000 0.07 H B Ali 

Hutchinson 

et al. (1986) 

185 2.2 S B Alt 

Joslin and 

Wolfe (1988) 

200 nd ~3000 ~2000 S B Ali 

Schier 

(1985) 

1850 1.35 12.9 0.43 H B,N Ali 

Ohno et al. 

(1988) 

250 0.8 14 S N Ala in soil 

paste 

Joslin and 

Wolfe (1992) 

0.45 F B Alt 

WHITE SPRUCE 

Nosko et al. 

(1988) 

50 0.2 H B Alt 

RED OAK 

Joslin and 

Wolfe (1989) 

300 4.05 11.9 0.06 S B Alt in 

SrCl2 

DeWald 

et al. (1990) 

115 4.48 3630 75 32.4 3630 6415 0.38 S B Alt 

 B-108
Al 

Analysis 

Used in 

Ratioc

Foliar 

Ca/Al 

(M) 

Root Ca 

(mg/km)

Type of 

Study or 

Experimenta

Response 

Variableb Study 

Solution 

Al 

(μmol/L) 

Solution 

Ca/Al 

(M) 

Foliar 

Ca 

(mg/km) 

Foliar 

Al 

(mg/km)

Root Al 

(mg/kg)

Root 

Ca/Al(M)

McCormick 

and Steiner 

(1978) 

7405 0.54 H B Alt 

HONEYLOCUST 

Thornton 

et al. 

(1986b ;198

6c) 

50 1.1 8.4 0.21 to 

0.32 

H B Ali 

Sucoff et al. 

(1990) 

100 1.4 0.35 S B Ali 

Wolfe and 

Joslin (1989) 

100 4.3 0.71 S B Ali 

SUGAR MAPLE 

Thornton 

et al. 

(1986a) 

100–600 0.42 to 

2.5 

~2000 ~190 9.9 ~1500 ~2700 0.4 H B,N Ali 

LOBLOLLY PINE 

Cronan et al. 

(1989) 

Thornton 

(unpubl.) 

500–3000 0.5 900 260 2.3 3700 7770 0.32 H N Ali 

AMERICAN BEECH 

Cronan et al. 

(1989) 

500–3000 0.5 2670 69 26.1 1140 7930 0.1 H N Ali 

EUROPEAN BEECH 

Asp and 

Berggren 

(1990) 

300 0.35 3.8 0.2 H N Ali 

Cronan et al. 

(1989) 

500 0.5 H N Ali 

PEACH 

Edwards 

and Horton 

(1977) 

222 10.8 0.008 H N Ali 

SCOTCH PINE 

Ilvesniemi 

(1992) 

185 nd 400 300 0.9 2300 S N,B Alt 

McCormick 

and Steiner 

(1978) 

2960 1.35 H B Alt 

 B-109
Al 

Analysis 

Used in 

Ratioc

Foliar 

Ca/Al 

(M) 

Root Ca 

(mg/km)

Type of 

Study or 

Experimenta

Response 

Variableb Study 

Solution 

Al 

(μmol/L) 

Solution 

Ca/Al 

(M) 

Foliar 

Ca 

(mg/km) 

Foliar 

Al 

(mg/km)

Root Al 

(mg/kg)

Root 

Ca/Al(M)

VIRGINIA PINE 

McCormick 

and Steiner 

(1978) 

2960 1.35 H B Alt 

PITCH PINE 

McCormick 

and Steiner 

(1978) 

2960 1.35 H B Alt 

Cumming 

and 

Weinstein 

(1990) 

50 20 S N,B Alt 

BIRCHES: GRAY, PAPER AND YELLOW 

McCormick 

and Steiner 

(1978) 

4444 0.9 H B Alt 

EUROPEAN BIRCH 

Goransson 

and 

Eldhuset 

(1987) 

1000 0.02 0.17 H N Ali 

RADIATA PINE 

Truman et al. 

(1986)

17 10.5 2120 800 1.8 1320 1850 0.48 H N Alt 

DOUGLAS-FIR 

Keltjens and 

Van Loenen 

(1989)

370 0.54 2300 300 5.2 H B Alt 

LARCH 

Keltjens and 

Van Loenen 

(1989)

555 0.36 1800 250 4.9 H B Alt 

a Types of study include hydroponic (H), soil or sand culture (S), or existing forest (F). b Response variables include biomass (B), or nutrient content (N). 

c Aluminum measurements include Ali (Ali), monomeric Al (Ala), and total Al (Alt). Since most of the lab studies were conducted under conditions of low pH and minimal DOC, 

measurements of total Al, Ali, and labile Al are very comparable. Source: Cronan and Grigal (1995) 

 B-110
Table B-21. Overview of selected major processes by which landscape change can alter drainage 

water acid-base chemistry 

Landscape Change Effect on Acid-Base Chemistry 

Logging, blowdown Dilution 

Lower deposition, less acidity 

Pulse of NO3

− acidity initially 

Less base cation neutralization, more acidity 

Less water contact with mineral soils, less neutralization of acidic deposition inputs 

Road building and construction More base cation neutralization, less acidity initially 

Depletion of base cation reserves in soils, more acidity long-term 

Drainage of wetlands Re-oxidation of stored S, pulses of acidity with increased discharge 

Drought Reduced groundwater inputs to seepage lakes with consequent increased acidity 

Increased relative baseflow to drainage waters with consequent decreased acidity 

Lake shore development Decreased acidity 

Insect damage Pulse of NO3

− acidity initially 

Source: Sullivan (2000) 

Table B-22. Observed relationships between zooplankton species richness (R) and lakewater ANC. 

Taxonomic Group Equation r2 p 

Total Zooplankton R = 15.65 + 0.089ANC 0.46 0.001 

Crustaceans R = 6.35 + 0.028ANC 0.47 0.001 

Rotifers R = 9.04 + 0.053ANC 0.30 0.001 

Source: Sullivan et al. (2006a). Reprinted with permission. 

 B-111
Table B-23. Threshold response of increased mortality of fish to low pH listed from least sensitive to most 

sensitive. 

Study Species Increased Mortality 

Threshold, pH Study Conditions 

Johnson et al. (1987) Blacknose dace, creek chub 5.9 - 6.0 

Brook trout 4.8 - 5.1 

In situ bioassay with early life stages in 

Adirondack surface waters 

Common shiner 5.4 - 6.0 

Lake whitefish, white sucker, 

walleye 

5.1 - 5.2 

Holtze and Hutchinson 

(1989) 

Smallmouth bass 4.8 

Laboratory exposure of early life stages to pH 

and Al. 

Atlantic salmon 5.0 

Brown trout 4.5 - 5.0 

Johansson et al. (1977) 

Brook Trout 4.5 

Laboratory tests with eggs exposed to low pH, 

no Al. 

Black crappie 5.5 

Rock bass 5.0 

Swenson et al. (1989) 

Yellow perch, largemouth 

bass 

4.5 

Laboratory tests with early life stages exposed 

to pH and Al. 

Fathead minnow 5.9 

Slimy sculpin 5.6 - 5.9 

Lake Trout 5.6 

Pearl dace 5.1 

Mills et al. (1987) 

White sucker 5.0 - 5.1 

Whole-lake treatment (fish population 

recruitment failure) 

Source: Baker et al. (1990b). 

 B-112
Table B-24. Threshold values of pH for various taxa and effects. 

Reference Type of Study Taxa pH Observed Effects 

Buckler et al. (1987) Lab bioassay Striped bass 6.5 >50% larval mortality 

McCormick et al. (1989) Lab bioassay Fathead minnow 6.0 Significant decrease in embryo survival 

Mills et al. (1987) Whole-lake 

experiment 

Fathead minnow 5.9 Population recruitment failure 

Klauda et al. (1987) Lab bioassay Blueback herring 5.7 >50% mortality of larvae 

Holtze and Hutchinson (1989) Lab bioassay Common shiner 5.4 >50% embryo mortality 

Baker and Schofield (1980) Lab bioassay White sucker 5.2 Substantial reduction in embryo survival 

Kane and Rabeni (1987) Lab bioassay Smallmouth bass 5.1 >50% mortality of larvae after 30–day exposure 

Leino et al. (1987) Whole-Lake 

experiment 

Adult fathead minnow 5.2–

5.8 

Increased numbers of chloride (ionoregulatory) 

cells on the gills 

Lacroix (1985a) Field survey Atlantic salmon parr 

(age 1+) 

4.9–

5.3 

Significantly lower blood Cl levels; high K levels 

McDonald and Milligan (1988) Lab bioassay Adult brook trout 5.2 Reduced Na transport activity 

McWilliams and Potts (1978) Lab bioassay Adult brown trout 5.0 Net Na loss; major shift in the gill tansepithelial 

potential 

Tietge et al. (1988) Lab bioassay Adult brook trout 4.9 Increased volume density of lamellar chloride 

cells on gills 

Booth et al. (1988) Lab bioassay Adult brook trout 4.8 Net loss of Na and Cl 

Audet and Wood (1988) Lab bioassay Adult rainbow trout 4.8 Decreased plasma Na and Cl levels 

Peterson and MartinRobichaud (1986) Lab experiment Atlantic salmon larvae 4.5 Reduced accumulation of Na, K, and Ca 

Powell and McKeown (1986) Lab bioassay Coho salmon parr and 

smolts 

4.4 Net decrease in plasma Na 

Source: Baker et al. (1990b). 

 B-113
Table B-25. Threshold values of Al for various species and effects (form of Al not specified for most 

studies). 

Reference Type of Study Taxa pH Al 

(μg/L) 

Observed Effect (at similar pH without 

added Al) 

Sadler and Lynam 

(1988) 

LB Brown trout 5.2 30 Significant reduction in fish growth 

Turnpenny et al. 

(1987) 

Field survey Broen trout —- 40 Fish absent or rare in streams in Wales and 

England 

Holtze and 

Hutchinson (1989) 

LB Walleye 4.9 50 >50% mortality of embryos to 4–d post-hatch 

Skogheim and 

Roseland (1986) 

Field mesocosm 

experiment 

Atlantic salmon 5.1 75 >50% mortality of smolts 

Klauda and Palmer 

(1987) 

LB Blueback herring 5.5–

5.6 

100 >50% larval mortality 

Rosseland and 

Skogheim (1984) 

LB Atlantic salmon 4.9–

5.0 

130 Significant increase in mortality of presmolts 

Baker and Schofield 

(1982) 

LB White sucker 5.2 200 >50% larval mortality 

Fjellheim et al. (1985) LB Eel 5.1 230 Significant increase in elver mortality 

Brown (1983) LB Brown trout 4.5–

5.4 

250 >50% fry mortality 

Schofield and Trojnar 

(1980) 

Field study Brook trout 4.9 286 No survival of trout stocked into lakes with higher 

total Al (even after accounting for pH effects). 

Ormerod et al. (1987) Whole-stream 

experiment 

Atlantic salmon and 

brown trout 

5.0 350 >50% mortality of young-of-the-year. 

Source: Baker et al. (1990b) 

 B-114
Table B-26. The effects of increasing Ca2+ to ameliorate low pH and high Al. 

Reference Type of 

Study Taxa pH Al Range (μg/L) 

Ca2+ Range 

(μeq/L) Observed Response to Increasing Ca2+

Brown (1982, 

1983) 

LB Brown trout 4.5–

5.1 

—- 12–400 Increased embryo survival and hatch 

Wright and 

Snekvik (1978) 

Field 

survey 

Brown trout 4.5–

7.5 

—- 20–200 Trouot status significantly correlated with log 

Ca2+ and pH. 

Brown (1983) LB Brown trout 4.5–

5.4 

0–500 12–100 Increased fry survival in low pH or high Al 

waters 

McDonald (1983) LB Rainbow trout 4.3 —- 69–223 Decreased adult mortality and net loss of Na 

and Cl 

Edwards et al. 

(1987) 

LB Brown trout 4.2 —- 100–5600 Lower loss of plasma Na and Cl ions 

Freda and 

McDonald (1988) 

LB Common shiner, 

rainbow trout 

4.0 —- 70–1000 Significant decrease in Na loss 

Mount et al. 

(1988) 

LB Brook trout 5.0–

6.3 

0–500 25–400 Increased survival and growth of adults; 

increased progeny survival with adult exposure 

to low and high Al 

Sadler and 

Lynam (1988) 

LB Brown trout 5.2 0–80 8–800 Increased yearling survival and growth in 

waters with elevated Al. 

Source: Baker et al. (1990b). 

Table B-27. Brook trout acidification response categories developed by Bulger et al. (2000) for streams in 

Virginia. 

Response Category Chronic ANC Range (μeq/L) Expected Response 

Suitable >50 Reproducing brook trout expected if other habitat features are also suitable 

Indeterminate 20 to 50 Brook trout response expected to be variable 

Episodically acidic 0 to 20 Sub-lethal and/or lethal effects on brook trout are possible 

Chronically acidic <0 Lethal effects on brook trout probable 

 B-115
Table B-28. Partial listing of bioassays demonstrating decreased fish survival in waters with low pH and 

(or) elevated aluminum. 

Reference Species Life Stage Lab/Field 

Johansson and Kihlström (1975) Northern pike Fry Lab 

Johansson and Milbrink (1976) Roach 

European perch 

Egg Lab, field 

Johansson et al. (1977) Brown trout; Brook trout Egg and fry Lab 

Trojnar (1977) White sucker Egg and fry Lab 

Peterson et al. (1980) Atlantic salmon Egg Lab 

Schofield and Trojnar (1980) Brook trout Fry Lab 

Baker and Schofield (1982) Brook trout; White sucker Egg and fry Lab 

Brown (1983) Brown trout Fry Lab 

Hulsman et al. (1983) Walleye 

Rainbow trout 

Egg 

Fry 

Field 

Sharpe et al. (1983) Brook trout; Brown trout 

Rainbow trout; Mottled sculpin 

Fry and adult Field 

Jagoe et al. (1984) Arctic char Egg and fry Lab 

Lacroix (1985b) Atlantic salmon Egg and fry Field 

Ingersoll (1986) Brook trout Egg and fry Lab 

Buckler et al. (1987) Striped bass Fry Lab 

Johnson et al. (1987) Brook trout; Lake trout 

Creek chub; Blacknose dace 

Egg, fry, and young-of-year Field 

Klauda and Palmer (1987) Blueback herring Egg and fry Lab 

Lacroix and Townsend (1987) Atlantic salmon Juvenile Lab 

Wales and Liimatainen (1987) Walleye Egg Field 

Palmer et al. (1988) Bluegill; Fathead minnow Juvenile Lab 

Gunn (1989) Lake trout Egg and fry Lab, field 

Holtze and Hutchinson (1989) Common shiner; Lake whitefish 

White sucker; Walleye 

Smallmouth bass; largemouth bass 

Egg and fry Lab 

Hutchinson et al. (1989) Lake trout 

Brook trout 

Egg and fry Lab 

 B-116
Table B-29. Mills et al., 1987. Shows effect of various pH on fish forage fish and lake trout. 

Biota 1977 

pH 6.13 

1978 

pH 5.93 

1979 

pH 5.64 

1980 

pH 5.59 

1981 

pH 5.02 

1982 

pH 5.09 

1983 

pH 5.13 

Forage 

fish 

Fathead 

minnow 

experience 

recruitment 

failure 

Fathead 

minnow near 

extinction; 

slimy sculpin 

decline in 

abundance 

Increase in 

abundance of 

pearl dace, 

Suckers very 

abundant. 

White sucker 

recruitment 

failure; no effect 

on adult growth 

and survival 

Recruitment 

failure for all 

species 

Recruitment 

failure for all 

species 

Lake 

trout 

Increase 

in 

condition 

i.e., “fatter” 

Increased 

abundance of 

young-of-theyear 

Lake trout 

recruitment failure; 

condition similar to 

preacidification 

Recruitment 

failure; no effect 

on adult growth 

and survival 

Lake trout 

condition poor; 

recruitment 

failure; reduced 

adult survival 

Lake trout 

condition very 

poor; recruitment 

failure; reduced 

adult survival 

Source: (Baker et al., 1990b). 

Table B-30. Range of minimum pH of fish species occurrence in 11 lake surveys. 

Family and Species High Minimum pH Low Minimum pH 

CYPRINIDAE 

Bluntnose minnow 6.6 5.6 

Fathead minnow 6.3 5.1 

Blacknose dace 6.8 5.6 

Pearl dace 5.9 4.7 

Northern redbelly dace 5.9 4.7 

Common shiner 6.2 4.9 

Golden shiner 5.5 4.5 

Creek chub 5.9 4.6 

SALMONIDAE 

Brook trout 5.6 4.6 

Lake trout 5.2 4.9 

Brown trout 5.0 4.6 

Atlantic salmon 6.3 5.3 

CENTRARCHIDAE 

Smallmouth bass 7.0 4.9 

Largemouth bass 5.0 4.6 

 B-117
 B-118

Family and Species High Minimum pH Low Minimum pH 

Pumpkinseed 6.6 4.6 

Bluegill 4.5 4.5 

Rock bass 6.2 4.6 

Black crappie 5.6 5.6 

PERCIDAE 

Yellow perch 5.8 4.4 

Walleye 6.9 5.2 

Johnny darter 6.2 4.9 

Iowa darter 6.2 4.6 

ESOCIDAE 

Northern pike 5.9 4.0 

CATASTOMIDAE 

White sucker 5.5 4.6 

ICTALURIDAE 

Brown bullhead 5.6 4.5 

UMBRIDAE 

Central mudminnow 4.5 4.2 

GASTEROSTEIDAE 

Brook stickleback 5.4 4.6 

Source: Baker et al. (1990b). 
 B-119 

Table B-31. Studiesa that either did (yes) or did not (no) yield evidence that acidic deposition affected 

certain species of birds 

Diet/ Foraging Breeding Distribution Reproductive Measures 

Species 

Yes No Yes No Yes No Referencea

Common loon x x x x x 1–3, 19,20 

Common merganser x x 19 

Belted kingfisher x 4 

Osprey x x x 5,6 

Black duck x X xb 7–9 

Common goldeneye xb 8 

Ring-necked duck x x 10,11 

Eurasian dipper x x x 12–14 

Eastern kingbird x x 15 

Tree swallow x x x 16–18 

a1= Alvo et al. (1988); 2 = Parker (1988); 3 = Wayland and McNicol (1990); 4 = Goriup (1989); 5 = Eriksson (1983); 6 = Eriksson (1986); 7 = Hunter et al. (1986); 8 = DesGranges and 

Darveau (1985); 9 = Rattner et al. (1987); 10,11 = McAuley and Longcore (1988a,b) 12,13 = Ormerod et al. (1985, 1986); 14 = Ormerod and Tyler (1987); 15 = Glooschenko et al. 

(1986); 16,17 = Blancher and McNicol (1988, 1991); 18 = St. Louis et al. (1990); 19 = Blancher and McNicol (1991); 20 = Blair (1990).. bThe effect was beneficial 

Source: Longcore and Gill (1993) 

Table B-32. Predicted habitat suitability for lakes in the Algona Model Dataset 

Number of Lakes with Suitable Habitat Under Each Emission Scenario 

Group Current pH <6 Current pH 6–6.5 Current pH >6.5 

Total 

Model 

Lakes 

Current 

Suitable Lakes 

S1 S2 S3 S4 S5 S1 S2 S3 S4 S5 S1 S2 S3 S4 S5 

Fish 526 338 29 34 40 77 100 97 100 107 124 133 196 196 197 197 197 

Common loon 

pairs 

433 100 12 14 14 16 17 22 22 23 24 24 66 66 66 67 67 

Common loon 

broods 

433 36 2 2 2 2 2 6 6 7 7 7 28 28 28 28 28 

Common 

merganser pairs 

433 52 6 12 27 68 86 14 17 20 33 44 31 31 32 34 35 

Common 

merganser broods 

433 31 6 10 18 69 89 14 15 16 21 29 12 11 10 10 10 

Results are expressed as the number of lakes with suitable habitat for fish, common loons (pairs and broods) and common mergansers (pairs and broods) under each emission 

scenario (S1, S2, S3, S4, S5) according to current pH classes (<6, 6–6.5, >6.5). Habitat suitability is calculated by probability of presence at time t from WARMS output (# of suitable 

lakes at time t/total number of lakes), for fish [n = 526], and for loons and mergansers [n = 433]. 
 B-120 

Table B-33. Summary statistics of biological data layers for mercury (Hg) concentrations in fish and 

wildlife (μg/g) in the northeastern U.S. and southeastern Canada. 

Hg Concentrations 

Category/Species Sample 

Size 

Data layer 

Designation 

Mean ± 

Standard 

Deviation Range 

Hg Level 

of Concern 

(Tissue Type) 

Percentage of 

Samples with 

Concentration > 

Level of Concern 

HUMAN HEALTH 

 Yellow percha 4089 Primary 0.39 ± 0.49 <0.05–5.24 0.30 (fillet) 50 

 Largemouth bassb 934 Secondary 0.54 ± 0.35 <0.05–2.66 0.30 (fillet) 75 

ECOLOGICAL HEALTH 

 Brook trout 319 Secondary 0.31 ± 0.28 <0.05–2.07 0.16 (whole fish) 75 

 Yellow perchc (841)d Secondary 0.23 ± 0.35 <0.05–3.18 0.16 (whole fish) 48 

 Common loone 1546 Primary 1.74 ± 1.20 0.11–14.20 3.0 (blood) 11 

 Bald eagle 217 Secondary 0.52 ± 0.20 0.08–1.27 1.0 (blood) 6 

 Mink 126 Secondary 19.50 ± 12.1 2.80–68.50 30.0 (fur) 11 

 River otter 80 Secondary 20.20 ± 9.30 1.14–37.80 30.0 (fur) 15 

Note: All data are in wet weight except for fur, which is on a fresh-weight basis aFillet Hg in yellow perch is based on individuals with a standardized length of 20 cm. 

bFillet Hg in largemouth bass is based on individuals with a standardized length of 36 cm. 

c

Whole-fish Hg in yellow perch is based on individuals with a standardized length of 13 cm. Whole-fish Hg for yellow perch was converted to fillet Hg. dThe sample population of 841 yellow perch examined for whole-fish Hg is included with the 4089 fillets (i.e., the total number of all biotic data layers does not double-count yellow 

perch). 

eEgg Hg for the common loon was converted to the adult blood equivalent 

Table B-34. Mercury concentrations in avian eggs and tissues and related effects. 

Tissue Concen. 

(ppm) 

Wet (w) 

or Dry 

(d) Endpoint Species Reference 

Liver 1.06 w No effect Common tern Gochfeld (1980) 

Liver 22.2 w Abnormal feather loss in juveniles Common tern Gochfeld (1980) 

Liver 5 w Conservative threshold for major toxic 

effects 

Water birds Zillioux et al. (1993) 

Liver 7.2 w Increased disease and emaciation Common tern Spalding and Forrester 

(1991) 

Liver 9.08 w Nesting success Common tern Finley and Stendall (1978) 

Liver 20.7 w Hatching success Common tern Finley and Stendall (1978) 
 B-121 

Tissue Concen. 

(ppm) 

Wet (w) 

or Dry 

(d) Endpoint Species Reference 

Liver 30 W Neurologic effects Osprey Heinz (1974) 

Liver 35 w Death Common loon Wiemeyer et al. (1987) 

Liver 54.5 w LD33a European starling Finley et al. (1979) 

Liver 97.7 w Death Gannet 

Liver 103.6 w LD33 European starling Finley et al. (1979) 

Liver 126.5 w LD33 Red-winged 

blackbird 

Finley et al. (1979) 

Liver 306 total/ 

20.4 MeHg 

d No adverse effects observed Black-footed 

albatross 

Gochfeld (1980) 

Brain 4–6 w Failure to hatch Black duck Hoffman and Moore (1979) 

Brain 20 w 25% mortality Zebra finch Scheuhammer (1988) 

Egg 1–5/0.2–1.0 d Reduced productivity in one half of the 

population 

Merlin Newton and Hass (1988) 

Egg 0.5–1.5 w Decreased hatchability Pheasant Heinz (1979) 

Egg 0.86 w Aberrant nesting behavior Common loon Heinz (1979) 

Egg 1.0 w Successful reproduction Common tern Finley and Stendall (1978) 

Egg 1.0–3.6 w Residue threshold for significant toxic 

effects 

Variety of water 

birds 

Zillioux et al. (1993) 

Egg 2–16 w No decreased hatchability Herring gull Finley and Stendall (1978) 

Egg 3.65 w 27% hatching, 10–12% fledging Common tern Finley and Stendall (1978) 

Kidney 37.4 total/ 

6.2 MeHg 

d No adverse effect observed Black-footed 

albatross 

Kim et al. (1996) 

Kidney 40.4 w LD33 Grackle Finley et al. (1979) 

Kidney 74.3 w LD33 Red-winged 

blackbird 

Finley et al. (1979) 

Kidney 86.4 w LD33 European starling Finley et al. (1979) 

aLD33 = lethal dose, 33%. Source: Wolfe et al. (1998) 
Annex C. Nutrient Enrichment Effects 

from Nitrogen 

C.1. Effects on Biogeochemical Pathways and Cycles 

C.1.1. Nitrogen Cycling in Terrestrial Ecosystems 

C.1.1.1.

C.1.1.2.

C.1.1.3.

Nitrogen Deposition Effects on DON Leaching 

Some N fertilization experiments suggest that increasing N deposition drives an increase in 

production of dissolved organic nitrogen (DON) in soil (McDowell et al., 2004; Seely and Lajtha, 1997), 

but there is little evidence that elevated N deposition increases the export and loss of DON from terrestrial 

ecosystems. Essentially all of the increase in N export across gradients of N deposition occurs as an 

increase in NO3

−

 rather than DON export. The latter is typically less than 2 kg N/ha/yr from most 

northeastern-forested watersheds (Campbell et al., 2000b; Aber et al., 2003; Goodale et al., 2000; Lovett 

et al., 2000). 

Interactions Between Snow Melt and Nitrate Leaching 

Changes in other environmental parameters can also be important. Measurement of nutrient 

concentrations in Emerald Lake (Sierra Nevadas) over a period of 19 years suggested that NO3

−

concentration declined between 1983 and 1995. This was likely caused by changes in the snow regime 

induced by a drought during the period 1987 to 1992 (Sickman et al., 2003). Years that had shallow and 

early melting snowpacks generally had lower snowmelt NO3

−

 concentration. In addition, declines in NO3

−

concentration during the growing season even in the wet years of 1993 through 2000 were likely the 

result of increased P loading to Emerald Lake and the consequent release of phytoplankton from P 

limitation (Sickman et al., 2003). 

Denitrification: NO and N2O Flux 

Davidson et al. (2000) described N gas loss from terrestrial ecosystems using a conceptual model 

called “hole-in-the-pipe.” In this model, production of NO, N2O, and N2 gas are functions of the general 

rate of N cycling processes through soil (i.e., the N flux “flowing through the pipe”), combined with 

information on soil water content, a key determinant of the ratio of NO:N2O (relative “hole size” for NO 

and N2O gas “leakage”). The model formulation has been supported by a range of field measurements in 

temperate and especially tropical ecosystems (Davidson et al., 2000), and suggests that processes that 

increase the rate of N cycling through soils should also increase the rate of N gas loss from these systems. 

Production of NO and N2O tend to be lower in temperate than in tropical ecosystems, largely because of 

colder temperatures and slower rates of N cycling in temperate systems, and the frequency of P rather 

than N limitation in tropical systems. However, increased availability of N through fertilization can 

increase the rate of NO and N2O gas loss from temperate forests. 

 C-1
Early studies of N gas emission in response to N fertilization experiments at the Harvard Forest, 

MA, found small increases in N2O production in response to the highest N treatment (150 kg N/ha/yr) to a 

red pine (Pinus resinosa) stand, but N2O losses accounted for <0.4% of N additions (Magill et al., 2000). 

However, later studies found that NO emission rates can be more than an order of magnitude greater than 

N2O emissions, with NO emissions amounting to 3-4% to 8% (4 to 5 kg N/ha/yr) of the N additions to the 

fertilized pine stands (Venterea et al., 2003, 2004). Emissions of NO and N2O increased with fertilization 

rate (0, 50, and 150 kg N/ha/yr) in both the red pine and a nearby red oak (Quercus rubra)/red maple 

(Acer rubrum) stand (Venterea et al., 2003, 2004). A study of the response of Scots pine (Pinus sylvestris) 

stands across a gradient of N deposition in Germany found a threefold to fourfold increase in the rate of 

NO and N2O production as N deposition increased from 15 to 22 kg N/ha/yr. In these forests, both gases 

were produced in roughly equal amounts, although as N deposition increased, the rate of NO production 

increased more steeply than did the rate of N2O production (Butterbach-Bahl et al., 2002). 

At Höglwald, a German site receiving 20 to 30 kg N/ha/yr in throughfall, Butterbach-Bahl et al. 

(2002) reported higher emissions of NO than N2O in both a spruce and a beech stand, with N oxide 

emissions totaling 4.5 to 6.8 kg N/ha/yr. Intensive laboratory studies suggested additional emissions of N2

gas amounting to 7.2 and 12.4 kg N/ha/yr in the spruce and beech stands, respectively. This is the only 

known forest site for which a complete (NO, NO2, N2O, and N2) N gas budget has been estimated, and in 

total, these measurements suggest that soil emissions may balance 46% to 78% of the N received in 

throughfall at this site. This result suggests somewhat higher rates of N gas loss than might be inferred 

from a series of 15N tracer studies in conifer stands across Europe, which spanned a range of rates of N 

input from atmospheric and experimental sources of 3 to 91 kg N/ha/yr. Across all sites, total recovery of 

added 15N in soil, vegetation, and lysimeter leachate after 9 to 21 months amounted to 65% to 105% of 

added 15N (Tietema et al., 1998), providing a broad constraint on N gas emissions of no more than 35% of 

added 15N. The lowest rates of 15N recovery (65% to 67%) occurred at Speuld and Ysselsteyn, two sites in 

The Netherlands with the highest rates of chronic throughfall N input (35 to 53 kg N/ha/yr). Although 

much more work is needed on complete N gas budgets, several lines of evidence suggest that trace gas 

emissions of N may constitute an increasing pathway of N loss with increasing rates of N deposition. 

C.1.1.4. Climate and N2O Interactions 

Rainfall events are an important feature controlling N2O produced via denitrification. Rainfall 

increases soil moisture. This inhibits O2 diffusion creating anoxic conditions, which increases rates of 

denitrification. A study of a spruce forest sites under ambient and elevated N deposition (20 and 30 kg 

N/ha/yr, respectively) indicated through most of the study period N2O emission was equivalent between 

the sites (Mohn et al., 2000). However, after rainfall events the maximum rate of N2O emission was much 

higher for the +N plots, especially when rainfall caused low soil redox potential (an indicator of anoxic 

conditions). Another study of mixed spruce, pine and birch forest (100 years old) under well- and poorly 

drained soil moisture conditions indicated poorly drained soils produced 1/3 more N2O (118 g N2O−

 N/ 

ha/yr) than well drained soils. In this study, N deposition was increased in the poorly drained soil from 

ambient (12 kg N/ha/yr) to elevated (42 kg N/ha/yr), N2O emissions increased by a factor of more than 2 

(254 kg N/ha/yr) (Klemedtsson et al., 1997). 

In addition to soil moisture, temperature also influences denitrification. The PnET-N-DNDC model 

is designed to simulate and predict soil C and N biogeochemistry in temperate forest ecosystems and to 

simulate the emissions of N2O and NO from forest soils. The model couples the PnET model 

(Photosynthesis-Evapotranspiartion-Model), the Denitrification-Decomposition (DNDC) model, and an N 

module that are further described in Li et al. (1992; 1996; 2000a), Li (2000), and Stange et al. (2000). The 

PnET-N-DNDC model is designed to simulate and predict soil C and N biogeochemistry in temperate 

forest ecosystems and to simulate the emissions of N2O and NO from forest soils. Denitrification is 

described in the model as a series of sequential reductions driven by microorganisms using N oxides as 

electron acceptors under anaerobic conditions. As intermediates of the processes, NO and N2O are tightly 

 C-2
controlled by the kinetics of each step in the sequential reactions. The capacity of this model to simulate 

N trace gas emissions from forest soils was tested by comparing model results with results form field 

measurements at 19 different field sites across Europe and 1 site in the U.S. (Kesik et al., 2005). Possible 

feedbacks of temperature and precipitationchange on forest soil NO and N2O emissions in Europe were 

investigated using PnET-N-DNDC (Kesik et al., 2006). The model results indicated decreasing 

precipitation and increasing temperature in areas with light texture soils (below 15%) resulted in 

decreased soil moisture values; in turn, N2O production by denitrification decreases. Under these same 

environmental conditions, NO production by nitrification increases. Most laboratory studies show 

increasing temperature increases N2O production, however if water filled pore space (WFPS) increases to 

70-80%, then N2 rather than N2O is the main product of denitrification and N2O emissions go down. This 

illustrates how N2O emissions increase with increasing soil moisture until soil moisture become more 

conducive to N2 emission. 

C.1.2. Nitrogen Cycling in Transitional Ecosystems 

C.1.2.1.

C.1.2.2.

Denitrification: Measurement Techniques 

There are a variety of methods for measuring denitrification rates in wetland, freshwater and 

marine sediments, including measurements of NO3

−

 loss, N2 production, N2O accumulation in response to 

acetylene inhibition of N2O reduction, isotopic methods, and N2:argon (Ar) measurement by membrane 

inlet mass spectrometry (MIMS) (Smith, 2006). Most direct measurements of denitrification have 

measured only rates of production of N2O, or used the “acetylene block” technique of inhibiting 

transformation of N2O to N2 and monitoring the accumulation of N2O as a surrogate of the sum of N2O 

and N2. The acetylene block method is highly problematic, however, as it also inhibits rates of 

nitrification, and so denitrification rates are strongly underestimated where nitrification and denitrification 

processes are coupled closely in space or time (Groffman et al., 2006). 

Techniques can be based on laboratory incubation of sediment cores or in situ studies. Each 

method has advantages and disadvantages and many studies have been conducted to compare results 

among the various methods (e.g., Bernot et al., 2003; Seitzinger, 1988, 1993, 2002b; Smith et al., 2006a; 

Groffman et al., 2006). Kana et al. (1998) described the MIMS method to measure small changes in 

dissolved N2 caused by denitrification in sediments. This technique allows measurement of N2 flux in 

unperturbed sediment cores with high temporal resolution (Kana et al., 1998). This is especially useful 

during summer conditions when NO3

−

 concentration in the water is typically low, but the high 

temperature can support high rates of denitrification. Under such conditions, it is likely that a coupled 

sequence of nitrification and denitrification accounts for substantial N loss from estuarine sediments 

(Kemp et al., 1990). Constraints regarding field and analytical methods have seriously limited 

understanding of the magnitude and controls on denitrification (Groffman et al., 2006). 

Nitrogen Deposition Effects on Methane 

Increased N loading to transitional ecosystems can affect both methane (CH4)-producing and CH4-

oxidizing microbial activity. The difference between the CH4 production and oxidation determines the 

magnitude of CH4 emission from soils. There is evidence to support that ammonium compounds reduce 

CH4 oxidation (Gulledge et al., 1997; King and Schnell, 1994; Steudler et al., 1989), but ammonium 

compounds have also been observed to increase methanotropic bacterial activity (Bodelier et al., 2000). In 

general CH4 emissions from saturated soils have been observed to increase with N addition (Granberg 

et al., 2001; Saarnio et al., 2003; Zhang et al., 2007). A hypothesis for explaining this effect is that 

increases in vegetative cover caused by N addition increase C availability through root exudates, which in 

 C-3
turn stimulates methanogenic bacteria and CH4 emissions (Granberg et al., 2001; Saarnio et al., 2003; 

Zhang et al., 2007). 

Saarnio et al. (2003) observed moderate increases in CH4 emissions from boreal wetland soils with 

N fertilization rates of 30 kg N/ha/yr as ammonium nitrate (NH4NO3). Comparable N application rates 

and effects on CH4 emissions were also observed by Granberg et al. (2001) in a similar ecosystem type. 

Zhang et al. (2007) observed elevated CH4 emissions from freshwater wetland soils with experimental N 

additions of 240 kg N/ha/yr. They postulated that additional N increased abundance of Deyeucia 

angustifolia which increased CH4 emissions by supplying methanogenic bacteria with additional substrate 

in the form of root exudates. Other studies have shown that N addition had little or no effect on CH4

emissions across a variety of ecosystem types (Ambus and Robertson, 2006; Saarnio et al., 2000; Silvola 

et al., 2003). Note that the N enrichment rates employed in all of the above reported studies related to N 

effects on soil CH4 emissions were greater (30 to 240 kg N/ha/yr) than atmospheric N inputs in most areas 

of the U.S. that are heavily effected by elevated atmospheric N deposition. See ISA Section 3.4 for a 

discussion of methane flux from terrestrial, transition and aquatic ecosystems. 

C.1.3. Nitrogen Cycling in Estuarine Ecosystems 

C.1.3.1.

C.1.3.2.

Denitrification and Anammox in Estuarine Ecosystems 

Denitrification is a major factor governing the loss of N from estuarine ecosystems. Denitrification 

by microbes found in estuarine and marine sediments releases much of the added N inputs back into the 

atmosphere (Arrigo, 2005; Vitousek et al., 1997). Collection of quantitative data on this process has been 

hampered, however, by the complexity of environmental controls on the denitrification process and 

difficulties in measuring denitrification rates (Kana et al., 1998). Major environmental controls include 

temperature and the availability of NO3

−

, O2, and organic materials (Rysgaard et al., 1994; Seitzinger, 

1988). 

Marine microbial ecology is highly complex and poorly understood. Relatively new knowledge 

about anammox bacteria has completely altered scientific understanding of N cycling in the oceans. 

Although it was previously believed that denitrification was responsible for virtually all of the transfer of 

Nr in the ocean to the atmosphere as N2 gas, it now appears that anaerobic ammonium oxidation 

(anammox) may account for up to 50% of the N2 production in the oceans (Dalsgaard et al., 2005; Devol, 

2003; Kuypers et al., 2005; Ward, 2003). This reaction uses NO2

−

 as the primary electron acceptor and is 

catalyzed by planctomycete bacteria of the genera Brocadia, Kuenenia, and Scalindua. That NH4

+

 could 

be oxidized under anoxic conditions was theorized several decades ago based on calculations of the ratios 

among N, P, and C in marine ecosystems. Nevertheless, the process was not experimentally documented 

until the 1990s (van de Graaf et al., 1995). More recently, anammox has been detected in a variety of 

freshwater, estuarine, and marine waters and sediments (Dalsgaard et al., 2005; Devol, 2003; Engstrom 

et al., 2005; Jetten et al., 2003, 2005; Kuypers et al., 2005; Op Den Camp et al., 2006; Pilcher, 2005; 

Rysgaard et al., 2004; Ward, 2003) 

Nitrogen Budgets 

The greatest uncertainty in the development of detailed N budgets for coastal ecosystems is 

quantifying how much of the N deposited on the watershed is transferred through the terrestrial watershed 

to the estuary. The difficulty stems from multiple agricultural and mobile and stationary fuel combustion 

emission sources in an estuary watershed; quantifying dry deposition to the estuary surface and to the 

watershed; measuring gaseous losses of NH3 and NOX compounds to the atmosphere; and complex N 

flow pathways through the watershed (NRC, 2000). Published estimates of the contribution of 

 C-4
atmospheric deposition to estuary N load exhibit wide variability. Such estimates for a specific estuary 

may differ. Some examples are summarized in Table C-1. Despite the variability, it appears that 

atmospheric sources of N loading to estuaries in the U.S. can be quantitatively important. The major 

sources of N to estuaries and near-coastal marine waters in the U.S., in addition to atmospheric 

deposition, include wastewater effluent derived mainly from food imports and consumption, fertilizer 

application, livestock feed imports, and N-fixing crops (Boyer et al., 2002; Driscoll et al., 2003). 

Table C-1. Estimated percent of total N load to Delaware Bay and Hudson River/Raritan Bay contributed 

by atmospheric deposition. 

Percent of N Load Contributed by Atmospheric Deposition 

Reference 

Delaware Bay Hudson River/Raritan Bay 

Paerl (1985) 44 — 

Hinga et al. (1991) — 33 

Scudlark and Church (1993) 15 — 

Paerl (1995) — 68 

Jaworski et al. (1997) 44 68 

Alexander et al. (2001) 22 26 

Castro et al. (2001) — 10 

Stacey et al. (2001) 

Land-based 16 10 

Sparrow model 25 27 

Castro and Driscoll (2002) 20 17 

Castro et al. (2003) 23 18 

Boyer et al. (2002) estimated that atmospheric deposition averaged 31% of total N inputs over the 

combined area of the 16 northeastern river basins. Contributions from atmospheric deposition ranged 

from 60% of N inputs for the basins in northern Maine to 15 to 20% for the Schuylkill and Potomac River 

Basins, the latter of which had large agricultural N inputs. Across all basins, estimated riverine export of 

N amounted to 25% of total N inputs, and ranged from 11% to 40%. This result is consistent with a 

similar analysis by Howarth et al. (1996), who found that basins draining to the North Atlantic exported 

approximately 25% of anthropogenic N inputs on average. 

Turner et al. (2001) found a strong correlation between population density (persons/ km2

) and the 

total N loading from watershed to estuary (r2

 = 0.78) for coastal watersheds in the U.S. This finding is 

likely due to the prevalence of automobiles in heavily populated areas, along with their associated N 

emissions and deposition, plus the myriad non-atmospheric sources of N from human activities, 

particularly sewage releases. They also determined that direct atmospheric deposition becomes 

increasingly more important as a contributor to the total N loading to an estuary as the water surface area 

increases relative to total watershed area (terrestrial plus water surfaces). Turner et al. (2001) found that, 

on average, direct atmospheric deposition of N accounted for more than an estimated 25% of the estuarine 

 C-5
N load when the estuary surface occupied 20% or more of the overall watershed area. Few of the estuaries 

in the eastern U.S. comprise such a large percentage of their watershed (Castro et al., 2001). 

The estimates of the effect of direct atmospheric deposition to estuary surfaces are hampered by 

uncertainties in dry deposition rates. Many published studies have assumed that dry N deposition is equal 

to measured wet deposition (Fisher and Oppenheimer, 1991; Hinga et al., 1991; Scudlark and Church, 

1993), and this is probably biased high (Baker, 1991a). However, other studies have assumed dry N 

deposition rates for estuarine and near coastal areas are equal to 40% of wet (Jaworski et al., 1997) or 

67% of wet (Meyers et al., 2000b). Of particular importance, the rate of dry deposition to open water 

surfaces is much lower than the rate of dry deposition to vegetated terrestrial surfaces. Paerl et al. (2001a) 

estimated that dry deposition to open estuarine surfaces is three to five times lower than to vegetated 

surfaces. This difference is seldom considered in N-budgeting studies, and can have a substantial effect 

on estimates of direct atmospheric loading to estuary surfaces, which is especially important for estuaries 

having low watershed area to estuary surface area ratio. 

A number of empirical approaches have been developed to quantify N fluxes to the coastal zone 

which rely on estimates of N sources within the watershed and characteristics of the landscape. Alexander 

et al. (2002) compared several of these empirical methods, the most accurate and least biased of which 

was that of Howarth et al. (1996). A modified version of the Howarth et al. (1996) methodology was 

published by Boyer et al. (2006). More mechanistic approaches include those of Bouwman et al. (2005), 

Van Drecht et al. (2003), and Green et al. (2004). 

C.1.4. Timing of Chemical Change 

C.1.4.1.

C.1.4.2.

Interannual Change: Nitrate Leaching 

Interannual changes in N cycling can be reflected in changes in streamwater chemistry. NO3

−

leaching from terrestrial ecosystems throughout the 1980s was observed in many of the original lakes in 

EPA’s Adirondack Long Term Monitoring (ALTM) program (Driscoll and Van Dreason, 1993), which 

was followed by a decline during the 1990s. As a consequence of this subsequent decline, Driscoll (2003) 

reported an overall significant (p <0.1) decrease in NO3

−

 concentration for the period 1982 to 2000 for 8 

of the 16 original ALTM monitoring sites. Only the one mounded seepage lake in the study (Little Echo 

Pond) had a small, but statistically significant, increase in NO3

−

 concentration (0.01 μeq/L/yr, p <0.06). It 

is not clear why many Adirondack watershed soils leached NO3

−

 to a lesser extent during the 1990s than 

they did during the 1980s (Driscoll et al., 2003). Decreasing stream NO3

−

 concentrations during the 1990s 

was also observed in the Catskill Mountains (Stoddard et al., 2003) and in New Hampshire (Goodale 

et al., 2003). There was not a substantial change in N emissions or deposition in the Northeast region over 

that period. Climatic factors, insect defoliation, increases in atmospheric CO2, and interactions with 

increasing availability of DOC have been proposed as possible contributing factors to regional decreases 

in NO3

−

 leaching (Aber et al., 2002; Driscoll et al., 2003; Goodale et al., 2003; 2005; Mitchell et al., 

1996), but the driver of this decadal scale pattern remains uncertain. 

Episodic Change 

Nutrient enrichment effects of N deposition are controlled to a large degree by biological and 

hydrological processes that operate on episodic (hours to days), seasonal, and interannual time scales. 

Nitrogen uptake and transformation reactions and processes vary greatly with season and with climatic 

factors. In particular, N export from terrestrial and transitional ecosystems to aquatic ecosystems is 

governed by seasonal fluctuations in temperature and biological uptake, and episodic fluctuations in water 

movement associated with rainstorms and snowmelt. The role of N in driving biotic change in stream 

 C-6
ecosystems due to episodic pulses of NO3

−

 associated with spring snowmelt are discussed in detail in ISA 

Section 3.2. 

C.1.4.3. Reversibility of Impacts 

Although there are relatively few studies of the reversibility of the biogeochemical effects of 

elevated N deposition, the few to date suggest the possibility of recovery. Experimental studies in Europe 

suggest that some ecosystem processes and characteristics are likely to recover rapidly following a 

reduction in N deposition. In a study in northern Sweden, high levels of fertilization (90 kg N/ha/yr) over 

20 years induced substantial soil acidification, including loss of over half of the base cations in the 

mineral soil, a decrease in pH, and an increase in soluble Al (Högberg et al., 2006). However, 10 years 

after this treatment was stopped, the pH of the mineral soil had increased, and extractable NO3

−

 was no 

higher than in the control plot. Stem volume growth did not substantially increase relative to the 

acidification period. “Clean roof” experiments that prevent N deposition inputs at sites receiving >40 kg 

N/ha/yr ambient atmospheric N deposition in The Netherlands increased wood and root production soon 

after the roof was installed (Boxman et al., 1998), and NO3

− exports below the rooting zone were reduced 

dramatically within 2 years (Bredemeier et al., 1998). 

A study of alpine lake sediment cores in Rocky Mountain National Park, CO (Wolfe et al., 2003) 

suggested the possible reversibility of N enrichment effects on lake biota. Although increased dominance 

of mesotrophic diatom species was correlated with increased N inputs during the 20th century, it did not 

appear that any of the oligotrophic species had been totally lost from study lakes. Thus, reduced future N 

loading may allow renewed dominance by oligotrophic diatom species. 

It is not necessarily true, however, that nutrient enrichment effects of N deposition will, in all 

cases, be easily reversible. For example, it has been suggested that vegetation conversion in the coastal 

sage scrub community in California has altered hydrologic function to an extent that may be difficult to 

reverse. The depth of rainwater percolation into soil has been reduced as a result of invasion of non-native 

annual grasses. This hydrologic change inhibits the growth of deep-rooting native shrubs (Wood et al., 

2006). 

 C-7
C.1.5. Tables Supporting Cross Ecosystem Evaluation of N2O, CH4 and 

CO2 Flux 

Table C-2 summarizes key information from the experiments included in the meta-analysis 

presented in ISA Section 3.3.4. 

Table C-2 The study site, experimental condition, ecosystem type, N form, amount of N addition and 

citations is presented for all studies used in NEE, EC, CH4 uptake, CH4 emission and N2O 

emission meta analyses. 

Site Experimental 

Condition Ecosystem N Form 

N 

Addition 

(kg 

ha/yr) 

Reference 

NET ECOSYSTEM CARBON EXCHANGE (NEE) 

Swiss FACE (Lolium) field grassland NH4NO3 320 Aeschlimann et al. 2005 

Swiss FACE (Trifolium) field grassland NH4NO3 320 Aeschlimann et al. 2005 

Ottawa, Canada field wetland NH4NO3 32 Basiliko et al. 2006 

Ottawa, Canada field wetland NH4NO3 64 Basiliko et al. 2006 

Ottawa, Canada field wetland NH4NO3 16 Bubrie et al. 2007 

Ottawa, Canada field wetland NH4NO3 32 Bubrie et al. 2007 

Ottawa, Canada field wetland NH4NO3 64 Bubrie et al. 2007 

Laqueuille, France field grassland 175 Soussana et al. 2007 

Oensingen, Switzerland field grassland slurry 200 Soussana et al. 2007 

Toolik Lake, AK(Inlet) field tundra NH4NO3 100 Shaver et al. 1998 

Toolik Lake, AK (Outlet) field tundra NH4NO3 100 Shaver et al. 1998 

Orange county,CA field,w/o water addition grassland NO3

– 100 Harpole et al., 2007 

Orange county,CA field, with water addition grassland NO3

– 100 Harpole et al., 2007 

Switzerland field grassland 40 Diemer 1997 

Finland field wetland NH4NO3 30 Saarnio et al. 2003 

Abisko, Sweden field tundra NH4NO3 100 Christensen et al. 1997 

ecosystem carbon content (ec) 

WA field coniferous . . Canary et al. 2000 

Placerville CA field coniferous NH4

+ 100 Johnson et al. 2006 

Placerville CA field coniferous NH4

+ 200 Johnson et al. 2006 

 C-8
Clayey, AL field coniferous DAP 45 Leggett et al. 2006 

Sandy, MS field coniferous DAP 45 Leggett et al. 2006 

Harvard forest, MA field deciduous NH4NO3 150 Magill et al. 2004 

Harvard forest, MA field coniferous NH4NO3 150 Magill et al. 2004 

Harvard forest, MA field, w/o S addition deciduous NH4NO3 50 Magill et al. 2004 

Harvard forest, MA field, with S additon deciduous NH4NO3 50 Magill et al. 2004 

Harvard forest, MA field, w/o S addition coniferous NH4NO3 50 Magill et al. 2004 

Harvard forest, MA field, with S addition coniferous NH4NO3 50 Magill et al. 2004 

BBWM, ME field deciduous NH4

+ 25.2 Parker et al. 2001; Elvir 

et al. 2006 

MI (site A) field deciduous NO3

– 30 Pregitzer et al. 2008; 

Burton et al. 2000 

MI (site B) field deciduous NO3

– 30 Pregitzer et al. 2008; 

Burton et al. 2000 

MI (site C) field deciduous NO3

– 30 Pregitzer et al. 2008; 

Burton et al. 2000 

MI (site D) field deciduous NO3

– 30 Pregitzer et al. 2008; 

Burton et al. 2000 

FL field coniferous . . Shan et al. 2001 

CH4 EMISSION 

Netherland (eutrophic site) Incubation wetland NH4

+ Aerts and Caluwe 1999 

Netherland (mesotrophic site) incubation wetland NH4

+ Aerts and Caluwe 1999 

Polish (mesotrophic site) incubation wetland NH4

+ Aerts and Caluwe 1999 

Netherland incubation wetland NH4

+ Aerts and Caluwe 1999 

Swiss FACE field, ambient CO2 grassland NH4NO3 84 Baggs and Blum 2004 

Swiss FACE field, elevated CO2 grassland NH4NO3 84 Baggs and Blum 2004 

Minnesota Incubation wetland NH4

+ 20 Keller et al., 2005 

Minnesota incubation wetland NH4

+ 60 Keller et al., 2005 

Niwot Ridge, CO field grassland urea 250 Neef et al. 1994 

Finland incubation wetland NH4NO3 30 Nykanen et al. 2002 

Finland incubation wetland NH4NO3 100 Nykanen et al. 2002 

Salmisuo, Finland field, ambient CO2 wetland NH4NO3 30 Sarrnio and Silvola 1999 

Salmisuo, Finland field, elevated CO2 wetland NH4NO3 30 Sarrnio and Silvola 1999 

Sanjiang mire, China field wetland NH4NO3 240 Zhang et al. 2007 

 C-9
ch4 uptake 

Quebec, Canada incubation coniferous NH4

+ Adamsen and King 1993 

Quebec, Canada incubation coniferous NO3

– Adamsen and King 1993 

Michigan field coniferous NH4NO3 10 Ambus and Robertson 

2006 

Michigan field coniferous NH4NO3 30 Ambus and Robertson 

2006 

Michigan field deciduous NH4NO3 10 Ambus and Robertson 

2006 

Michigan field deciduous NH4NO3 30 Ambus and Robertson 

2006 

Michigan field grassland NH4NO3 10 Ambus and Robertson 

2006 

Solling, Germany field, with clean rain roof coniferous NH4NO3 18.5 Borken et al. 2002 

Solling, Germany field, with clean rain roof coniferous NH4NO3 24.2 Borken et al. 2002 

Solling, Germany field, w/o clean rain roof coniferous NH4NO3 18.5 Borken et al. 2002 

Solling, Germany field, w/o clean rain roof coniferous NH4NO3 24.2 Borken et al. 2002 

Bousson, PA field deciduous NH4NO3 100 Bowen et al. 2000 

Perridge Forest, UK incubation deciduous NO3

– Low N Bradford et al. 2001 

Perridge Forest, UK incubation deciduous NO3

– High N Bradford et al. 2001 

MT Ascutney, VT field coniferous NH4

+ 31.4 Castro et al. 1992 

Bousson, PA field deciduous NH4NO3 100 Chan et al. 2005 

Abisko, Sweden field tundra NH4NO3 100 Christensen et al. 1997 

Finland field wetland NH4

+ 100 Crill et al. 1994 

Finland field wetland NO3

– 100 Crill et al. 1994 

Finland field wetland urea 100 Crill et al. 1994 

Villingen, Germany field coniferous NH4

+ 150 Gulledge and Schimel 

2000 

LTER Alaska field deciduous NH4NO3 66.7 Gulledge and Schimel 

2000 

LTER Alaska field coniferous NH4NO3 12.3 Gulledge and Schimel 

2000 

LTER Alaska field deciduous NH4NO3 171.4 Gulledge and Schimel 

2000 

LTER Alaska field coniferous NH4NO3 142.9 Gulledge and Schimel 

2000 

 C-10
Swiss FACE field, ambient CO2 grassland NH4NO3 560 Ineson et al. 1998 

Swiss FACE field, elevated CO2 grassland NH4NO3 560 Ineson et al. 1998 

Scotland incubation heathland NH4

+ 40 Macdonald et al. 1997 

Scotland incubation heathland NH4NO3 40 Macdonald et al. 1997 

Scotland incubation heathland NO3

– 40 Macdonald et al. 1997 

Finland field coniferous NH4NO3 200 Maljane et al. 2006 

Swale, CO field grassland urea 450 Mosier et al., 1991 

Midslope, CO field grassland urea 450 Mosier et al., 1991 

Passture, CO field grassland urea 450 Mosier et al., 1991 

Niwot Ridge, CO field grassland urea 250 Neef et al. 1994 

Villingen, Germany field coniferous NH4

+ 150 Papen et. al 2001 

Finland field wetland NH4NO3 30 Saarnio et al. 2003 

Gjovelandsnesset, Norway incubation coniferous NH4NO3 30 Sitaula et al. 1995 

Gjovelandsnesset, Norway incubation coniferous NH4NO3 90 Sitaula et al. 1995 

Harvard forest, MA field coniferous NH4NO3 37 Steudler et al. 1989 

Harvard forest, MA field deciduous NH4NO3 37 Steudler et al. 1989 

Harvard forest, MA field coniferous NH4NO3 120 Steudler et al. 1989 

Harvard forest, MA field deciduous NH4NO3 120 Steudler et al. 1989 

Belgium (Bulrush site) incubation wetland NH4

+ VanderNat et al. 1997 

Belgium (Reed site) incubation wetland NH4

+ VanderNat et al. 1997 

Costa Rica (Loam site) field tropical 

forest 

NH4

+ 65 Weitz et al. 1999 

Costa Rica (Clay site) field tropical 

forest 

NH4

+ 65 Weitz et al. 1999 

N2O EMISSION 

Michigan biological Station Incubation, ambient CO2 deciduous NH4NO3 Low N Ambus and Robertson 

1999 

Michigan biological Station Incubation, ambient CO2 deciduous NH4NO3 High N Ambus and Robertson 

1999 

Michigan biological Station Incubation, elevated CO2 deciduous NH4NO3 Low N Ambus and Robertson 

1999 

Michigan biological Station Incubation, elevated CO2 deciduous NH4NO3 High N Ambus and Robertson 

1999 

Michigan field coniferous NH4NO3 10 Ambus and Robertson 

2006 

 C-11
Michigan field deciduous NH4NO3 10 Ambus and Robertson 

2006 

Michigan field grassland NH4NO3 10 Ambus and Robertson 

2006 

Michigan field coniferous NH4NO3 30 Ambus and Robertson 

2006 

Michigan field deciduous NH4NO3 30 Ambus and Robertson 

2006 

Hyytiälä incubation coniferous NH4

+ Ambus et al. 2006 

Speulderbos incubation coniferous NH4

+ Ambus et al. 2006 

San Rossore incubation coniferous NH4

+ Ambus et al. 2006 

Glencorse incubation coniferous NH4

+ Ambus et al. 2006 

Nyirjes incubation coniferous NH4

+ Ambus et al. 2006 

Achenkirch incubation coniferous NH4

+ Ambus et al. 2006 

Höglwald incubation coniferous NH4

+ Ambus et al. 2006 

Sorø incubation deciduous NH4

+ Ambus et al. 2006 

Bosco negri incubation deciduous NH4

+ Ambus et al. 2006 

Schottenwald incubation deciduous NH4

+ Ambus et al. 2006 

Hyytiälä incubation coniferous NO3

– Ambus et al. 2006 

Speulderbos incubation coniferous NO3

– Ambus et al. 2006 

San Rossore incubation coniferous NO3

– Ambus et al. 2006 

Glencorse incubation coniferous NO3

– Ambus et al. 2006 

Nyirjes incubation coniferous NO3

– Ambus et al. 2006 

Achenkirch incubation coniferous NO3

– Ambus et al. 2006 

Höglwald incubation coniferous NO3

– Ambus et al. 2006 

Sorø incubation deciduous NO3

– Ambus et al. 2006 

Bosco negri incubation deciduous NO3

– Ambus et al. 2006 

Schottenwald incubation deciduous NO3

– Ambus et al. 2006 

Swiss FACE field, ambient CO2 grassland NH4NO3 84 Baggs and Blum 2004 

Swiss FACE field, elevated CO2 grassland NH4NO3 84 Baggs and Blum 2004 

Swiss FACE (Lolium) field, ambient CO2 grassland NH4NO3 420 Baggs et al., 2003 

Swiss FACE (Lolium/ Trifolium) field, ambient CO2 grassland NH4NO3 420 Baggs et al., 2003 

Swiss FACE (Trifolium) field, ambient CO2 grassland NH4NO3 420 Baggs et al., 2003 

Swiss FACE (Lolium) field, elevated CO2 grassland NH4NO3 420 Baggs et al., 2003 

 C-12
Swiss FACE (Lolium/ Trifolium) field, elevated CO2 grassland NH4NO3 420 Baggs et al., 2003 

Swiss FACE (Trifolium) field, elevated CO2 grassland NH4NO3 420 Baggs et al., 2003 

Solling, Germany field coniferous NH4NO3 18.5 Borken et al. 2002 

Solling, Germany field coniferous NH4NO3 24.2 Borken et al. 2002 

Solling, Germany field coniferous NH4NO3 18.5 Borken et al. 2002 

Solling, Germany field coniferous NH4NO3 24.2 Borken et al. 2002 

Harvard forest field deciduous NH4NO3 37 bowden et al. 1991 

Harvard forest field coniferous NH4NO3 37 bowden et al. 1991 

Harvard forest field deciduous NH4NO3 120 bowden et al. 1991 

Harvard forest field coniferous NH4NO3 120 bowden et al. 1991 

Solling, Germany field deciduous NH4

+ 140 Brumme and Beese 

1992 

Gullane incubation deciduous NH4

+ Castaldi and Smith 1998 

Gullane incubation deciduous NO3

– Castaldi and Smith 1998 

Ascutney , VI field coniferous NH4

+ 31.4 Castro et al. 1993 

Allt, UK (site M3) field heathland NH4NO3 20 Curitis et al., 2006 

Allt, UK (site M4) field heathland NH4NO3 20 Curitis et al., 2006 

Afon Gwy, UK (site G1) field grassland NH4NO3 20 Curitis et al., 2006 

Afon Gwy, UK (site G2) field grassland NH4NO3 20 Curitis et al., 2006 

Afon Gwy, UK (site G3) field grassland NH4NO3 20 Curitis et al., 2006 

River Etherow, UK (site E1) field heathland NH4NO3 20 Curitis et al., 2006 

River Etherow, UK (site E2) field heathland NH4NO3 20 Curitis et al., 2006 

St. James Parish, LO field wetland NO3

– 100 Delaune et al. 1998 

St. James Parish, LO field wetland NH4

+ 100 Delaune et al. 1998 

Saratoga field grassland NH4NO3 168 Delgado et al. 1996 

Puerto Rico field tropical 

forest 

urea 300 Erickson et al. 2001 

Jasper Ridge , CA incubation, ambient CO2 grassland urea 200 Hungate et al. 1997 

Jasper Ridge , CA incubation, elevated CO2 grassland urea 200 Hungate et al. 1997 

Swiss FACE field, ambient CO2 grassland NH4NO3 560 Ineson et al. 1998 

Swiss FACE field, elevated CO2 grassland NH4NO3 560 Ineson et al. 1998 

Manaus, Brazil (site 1) field tropical 

forest 

NO3

– Keller et al. 1988 

 C-13
Manaus, Brazil (site 2) field tropical 

forest 

NO3

– Keller et al. 1988 

Manaus, Brazil (site 3) field tropical 

forest 

NO3

– Keller et al. 1988 

Manaus, Brazil (site 4) field tropical 

forest 

NO3

– Keller et al. 1988 

Manaus, Brazil (site 1) field tropical 

forest 

NH4

+ Keller et al. 1988 

Manaus, Brazil (site 2) field tropical 

forest 

NH4

+ Keller et al. 1988 

Manaus, Brazil (site 3) field tropical 

forest 

NH4

+ Keller et al. 1988 

Manaus, Brazil (site 4) field tropical 

forest 

NH4

+ Keller et al. 1988 

gardsjon watershed field, well drained coniferous NH4NO3 35 Klemedtsson et. al 1997 

gardsjon watershed field, wet coniferous NH4NO3 35 Klemedtsson et. al 1997 

Avoyelles, LO field wetland NH4

+ 100 Lindau et al. 1994 

Avoyelles, LO field wetland NH4

+ 300 Lindau et al. 1994 

Avoyelles, LO field wetland NO3

– 100 Lindau et al. 1994 

Avoyelles, LO field wetland NO3

– 300 Lindau et al. 1994 

Harvard forest field coniferous NH4NO3 113 Magill et al. 1997 

Finland field coniferous 200 Maljane et al. 2006 

Swale, CO field grassland urea 450 Mosier et al., 1991 

Midslope, CO field grassland urea 450 Mosier et al., 1991 

Passture, CO field grassland urea 450 Mosier et al., 1991 

Puerto Rico field, low tide wetland NH4

+ 15.4 Munoz -Hincapie et al. 

2002 

Puerto Rico field, low tide wetland NH4

+ 130.2 Munoz-Hincapie et al. 

2002 

Puerto Rico field, low tide wetland NH4

+ 266 Munoz -Hincapie et al. 

2002 

Puerto Rico field, low tide wetland NO3

– 15.4 Munoz -Hincapie et al. 

2002 

Puerto Rico field, low tide wetland NO3

– 130.2 Munoz -Hincapie et al. 

2002 

Puerto Rico field, low tide wetland NO3

– 266 Munoz -Hincapie et al. 

2002 

Puerto Rico field, high tide wetland NH4

+ 15.4 Munoz -Hincapie et al. 

2002 

 C-14
Puerto Rico field, high tide wetland NH4

+ 266 Munoz -Hincapie et al. 

2002 

Puerto Rico field, high tide wetland NO3

– 15.4 Munoz -Hincapie et al. 

2002 

Niwot Ridge, CO (wet meadow) field grassland urea 250 Neef et al. 1994 

Niwot Ridge, CO (dry meadow) field grassland urea 250 Neef et al. 1994 

Villingen field coniferous NH4

+ 150 Papen et. al (2001) 

Duke FACE incubation, ambient CO2 coniferous NO3

– Phillips et al. 2001 

Duke FACE incubation, elevated CO2 coniferous NO3

– Phillips et al. 2001 

 Finland field wetland NO3

– 100 Regina et al. 1998 

 Finland field wetland NH4

+ 100 Regina et al. 1998 

 Finland field wetland urea 100 Regina et al. 1998 

Mojave incubation desert NO3

– Schaeffer et al. 2003 

Gjovelandsnesset, Sweden field, pH=3 coniferous NH4NO3 30 Sitaula et al. 1995 

Gjovelandsnesset, Sweden field, pH=3 coniferous NH4NO3 90 Sitaula et al. 1995 

Gjovelandsnesset, Sweden field, pH=4 coniferous NH4NO3 30 Sitaula et al. 1995 

Gjovelandsnesset, Sweden field, pH=4 coniferous NH4NO3 90 Sitaula et al. 1995 

Gjovelandsnesset, Sweden field, pH=5 coniferous NH4NO3 30 Sitaula et al. 1995 

Gjovelandsnesset, Sweden field, pH=5 coniferous NH4NO3 90 Sitaula et al. 1995 

Scotland field coniferous NH4NO3 48 Skiba et al. 1998 

Deepsyke forest field coniferous NH4NO3 48 Skiba et al. 1999 

Deepsyke forest field coniferous NH4NO3 96 Skiba et al. 1999 

Costa Rica (Loam site) field tropical 

forest 

65 Weitz et al. 1999 

Costa Rica (Clay site) field tropical 

forest 

65 Weitz et al. 1999 

Sanhuabg Mire, China field wetland NH4NO3 240 Zhang et al. 2007 

C.2. Terrestrial Ecosystems 

The following sections are organized by ecosystem type and combine information that is 

supplemental to Section 3.3 of the ISA. 

 C-15
C.2.1. General C Cyling 

C cycling is a complex process that includes C capture from the atmosphere by autotrophic biota, 

the primary producers of the ecosystem, and respiration (autotrophic + heterotrophic). In general, 

atmospheric nutrient (e.g., N) deposition on an ecosystem that is deficient in that nutrient will often cause 

an increase in growth, at least initially, especially of the primary producers. If that same nutrient is 

deposited on an ecosystem that has an adequate supply of that nutrient, there may be no appreciable 

nutrient enrichment effect, at least up to a point. Nutrient input that is greatly in excess of biological 

demand will often cause toxicity, reduced growth, or problems other than those associated with nutrient 

enrichment (i.e., N-saturation, acidification, base cation depletion) (Figure C-1). 

Source: U.S. EPA (1993a) 

Figure C-1. Schematic representation of the response of vegetation to nutrient addition. 

C.2.2. Forest Growth Interactions with Herbivores 

Light availability, nutrient balance, and C:N:P stoichiometry are closely relate, and affect the 

composition of autotrophic species that will occupy a particular habitat. The resulting stoichiometric 

balance of C:N:P in the autotrophic community can have additional feedbacks on nutrient cycling by 

herbivores, detritivores, and decomposers (Sterner and Elser, 2002). Such effects also extend to 

herbivores, and likely other members of the food web. Forkner and Hunter (2000) altered plant growth of 

oak (Quercus prinus and Q. rubra) saplings through fertilizer (N, P, K) addition and then censused the 

densities of insect herbivore guilds and predaceous anthropods on experimental and control trees. In 

general, leaf chewers, phloem feeders, and leaf miners were more common on fertilized, as compared 

with non-fertilized, trees. Predaceous arthropods were also more abundant on fertilized trees and their 

densities were correlated with herbivore densities. 

C.2.3. Southern California Coniferous Forest 

Wet N deposition is generally low throughout the region, in the range of 1 to 3 kg N/ha/yr. 

However, dry deposition is highly variable, but ranges up to about 30 kg N/ha/yr or more (Bytnerowicz 

and Fenn, 1996; Fenn, 1997; Takemoto et al., 2001). Available data (e.g., Minnich et al., 1995) suggest 

progression toward less needle retention, higher shoot:root biomass ratios, increasing depth of litter, and 

high NO3

−

 in soil solution in response to high N deposition. These changes may eventually lead to 

 C-16
replacement of pine species with nitrophilous and O3-tolerant species such as fir and cedar (Takemoto 

et al., 2001). 

Streamwater NO3

−

 concentrations in montane watersheds that are downwind of the greater Los 

Angeles area are the highest in North America. Some streams in the San Gabriel and the San Bernardino 

Mountains have been documented to have levels of NO3

−

 in stream water with peaks as high as 370 μeq/L 

(Fenn and Poth 1999), reflecting very high N deposition and N-saturation of the terrestrial ecosystem. In 

contrast, N leaching is low in most watersheds in the Sierra Nevada, and NO3

−

 concentrations in streams 

are usually below 1 μeq/L. Nevertheless, some of the higher elevation watersheds in the Sierra Nevada 

export appreciable NO3

− from the terrestrial environment, particularly during the early phases of 

snowmelt. Fenn et al. (2002) reported springtime peaks of NO3

−

concentration in lakewater up to 38 μeq/L 

at high elevation and for watersheds dominated by talus. At lower elevation areas, however, most of the 

inorganic N deposition loading is retained within the watersheds and concentrations of NO3

−

 in stream 

and lake waters are low (Fenn et al., 2003a). Surface water NO3

−

 concentrations in these areas provide an 

index reflecting the general levels of N deposition. For example, where surface water NO3

−

concentrations are high, N deposition to the terrestrial watershed is also high. 

C.2.4. Boreal Forests 

The boreal forest represents the largest terrestrial biome on Earth, and as such can have a large 

influence on global cycling of N and other nutrients. Plant growth in the boreal forest is limited mainly by 

N availability, in part because of slow mineralization of organic materials in the harsh climate (Vitousek 

and Howarth, 1991). Conceptual models of N cycling in the boreal forest have typically assumed that 

mineralization of organic N is required for plant uptake of N (Näsholm et al., 1998). However, it has been 

demonstrated in laboratory studies (Chapin et al., 1993) and field studies (Näsholm et al., 1998) that some 

boreal plants are capable of directly taking up amino acids from the soil, and therefore bypassing the need 

for prior mineralization. The interactions among soil abiotic processes, mycorrhizal associations, 

microbes, and plants are complex and poorly understood. Nevertheless, these interactions are important to 

global N cycling and to boreal plant species composition because organic N concentrations are typically 

high in the soil of boreal forests. It appears that atmospheric N deposition and climate warming have the 

potential to alter boreal forest plant communities by shifting nutritional processes from organic to 

inorganic N uptake (Näsholm et al., 1998). 

C.2.5. Alpine 

The western U.S. contains extensive land areas that receive low levels of atmospheric N deposition, 

interspersed with hot spots of relatively higher N deposition downwind of large metropolitan centers and 

agricultural areas (Fenn, 2003). Alpine plant communities occur in some of the areas that receive 

moderately elevated atmospheric N deposition such as those located in the Sierra Nevada in southern 

California, the Front Range in Colorado, and the Cascade Mountains in Washington (Figure C-2). 

 C-17
a. 

 

b. 

c. 

Source: Vegetative distribution data were taken from the national map LANDFIRE (September 2006) (http://gisdata.usgs.net/website/landfire/). 

Figure C-2. Distribution of alpine vegetation in three western regions that are in close proximity to urban 

and agricultural sources of atmospheric N emissions : a) the Denver-Fort Collins region of 

Colorado, b) the Seattle-Tacoma region of Washington, and c) the Fresno-Los Angeles area of 

California (the blue line on the map is the California/Nevada border). Alpine vegetation in 

these areas is sensitive to nutrient enrichment effects from atomospheric N deposition. 

 C-18
Alpine plant species are typically adapted to low nutrient availability and their soil-forming 

processes are poorly developed, therefore they are often sensitive to effects from N enrichment (Bowman 

et al., 2006) including changes in species composition (Bowman et al., 1995; Seastedt and Vaccaro, 

2001). Other reasons alpine tundra are sensitive to N enrichment include factors such as low rates of 

primary production, short growing season, low temperature, and a wide variation in moisture availability 

(Bowman and Fisk, 2001). 

Nitrogen cycling in alpine environments is strongly tied to variations in moisture regime (Bowman 

et al., 1993; Bowman, 1994; Fisk et al., 1998). Blowing snow is transported across alpine landscapes by 

wind and tends to accumulate in certain depression areas. These areas receive much higher levels of 

moisture and winter season N deposition than other more wind-swept portions of the alpine environment 

(Bowman, 1992). Fenn et al. (2003) suggested that as much as 10 kg N/ha/yr may leach through the snow 

during the initial phases of snowmelt in some of the alpine areas in Colorado that accumulate substantial 

snowpack. It is these moist meadow areas that may be most affected by N deposition and are also the 

areas most likely to show changes in plant species composition and impacts on N cycling (Bowman and 

Steltzer, 1998). 

Nitrogen deposition to the alpine tundra of Niwot Ridge in the Colorado Front Range altered N 

cycling and provided the potential for replacement of some native plant species by more competitive, 

faster-growing native species (Baron, 2000; Bowman and Steltzer, 1998; Bowman, 2000). Many plants 

that grow in alpine tundra, as is true of plants growing in other low resource environments (e.g., infertile 

soil, desert), tend to have some similar characteristics, including slow growth rate, low photosynthetic 

rate, low capacity for nutrient uptake, and low soil microbial activity (Bowman and Steltzer, 1998; 

Bowman, 2000). Such plants generally continue to grow slowly when provided with an optimal supply 

and balance of resources (Pearcy et al., 1987; Chapin, 1991). In addition, plants adapted to cold, moist 

environments grow more leaves than roots as the relative availability of N increases. These patterns of 

vegetative development and their response to added N affect plant capacity to respond to variation in 

available resources and to environmental stresses such as frost, high winds, and drought. Vegetation in the 

southern Rocky Mountains responds to increased N supply by increasing plant productivity for some 

species, but this increase in productivity is also accompanied by changes in species composition and 

abundance (Bowman et al., 1993). Many of the dominant plant species do not respond to additional N 

supply with increased production. Rather, many subdominant species, primarily grasses and some forbs, 

increase in abundance when the N supply is increased (Fenn et al., 2003c). 

In alpine ecosystems, changes in plant species composition due to N deposition can result in 

increased leaching of NO3

−

 from the soils because the plant species favored by higher N supply are often 

associated with greater rates of N mineralization and nitrification than the pre-existing species (Bowman 

et al., 1993, 2006; Steltzer and Bowman, 1998; Suding et al., 2006). 

Total organic N pools in the soils of dry alpine meadows are large compared to pools of NH4

+

 and 

NO3

−

 (Fisk and Schmidt, 1996). However, positive response to inorganic N fertilization has been 

demonstrated, and thus some plant species appear to be restricted in their ability to take up organic N 

from the soil and are growth-limited by the availability of inorganic N (Bowman et al., 1993, 1995; 

Theodose and Bowman, 1997). Miller and Bowman (2002) analyzed patterns of foliar 15N, NO3

−

reductase activity, and mycorrhizal infection compared with N uptake quantified by stable isotope tracer 

additions in the greenhouse. 13C enrichment subsequent to 13C, 15N-glycine addition indicated that all of 

the 11 genera studied were able to take-up labeled glycine to some extent. Glycine uptake ranged from 

about 35% to more than 100 % of NH4

+

 uptake. Only Festuca (fescue grass) showed glycine uptake 

exceeding both NH4

+

 and NO3

− uptake (Miller and Bowman, 2002). 

 C-19
C.2.6. Arctic Tundra 

Soluble N in tundra soil solution is dominated by organic N, including free amino acids, rather than 

NH4

+

 or NO3

− (Kielland, 1995). Tundra plants appear to exhibit a range of interspecific differences that 

allow coexistence under conditions that reflect a single limiting element. Species differ in rooting depth, 

phenology, and uptake preferences for organic and inorganic forms of N (Shaver and Billings, 1975; 

Chapin et al., 1993; Kielland, 1994; McKane et al., 2002). McKane et al. (2002) demonstrated, based on 

15N field experiments, that arctic tundra plant species were differentiated in timing, depth, and chemical 

form of N utilization. Furthermore, the species that exhibited greatest productivity were those that 

efficiently used the most abundant N forms. 

Ericoid mycorrhizae provide host plants with the capacity to take up N in the form of amino acids 

(Stribley and Read, 1980; Bajwa and Read, 1985). This is important in arctic plant communities that 

occur on acidic organic soils because amino acids are typically readily available in such soils, and N 

availability generally limits primary productivity. 

Future climate warming could have important effects on N cycling in arctic tundra ecosystems. In 

the past, organic materials have accumulated in tundra soils, largely because decomposition has been 

slower than plant growth. Climate warming may increase the decomposition of soil organic matter, 

thereby increasing the availability of stored N (Weintraub and Schimel, 2005). The distributions of woody 

plant species are also increasing in response to warming, with likely feedbacks on C and N cycling. For 

example, the dominant shrub species in the arctic tundra in Alaska, Betula nana, is expanding its 

distribution in tussock vegetation communities (Weintraub and Schimel, 2005). 

Poor soil aeration is caused by permafrost, resulting in poor water drainage and the development of 

anaerobic conditions. Vegetation composition and primary productivity vary in response to differences in 

soil moisture and aeration (Everett and Brown, 1982; Gebauer et al., 1995). Reduced soil O2 can limit 

nutrient availability. For example, under anaerobic conditions, N mineralization and nitrification rates 

decrease while denitrification increases (Ponnamperuma, 1972; Gebauer et al., 1995). 

C.2.7. Arid Land 

From 1989 to 2004 in the Chihuahuan desert, Baez et al. (2007) observed a 43% increase in 

ambient N deposition, from 1.71 to 2.45 kg N/ha/yr, resulting in an additional 5.88 kg N/ha/yr deposition 

over that time period. They suggest that these deposition trends may result in significant plant community 

changes, as indicated by fertilization studies of blue gramma (Bouteloua gracilis) and black gramma (B. 

eriopoda). In a field addition with additions of 20 kg N/ha/yr in one season, blue gramma was favored 

over black gramma, the current dominant species (Baez et al., 2007). 

C.2.8. Lichens 

There are several potential uses of lichens for air pollution and deposition monitoring. These 

include measurement of tissue lichen concentrations of specific pollutants (i.e., lichens as passive 

monitors), determination of changes in species composition or the presence/absence of sensitive species, 

and identification of areas having relatively high levels of air pollution, where monitoring instrumentation 

could be installed to more quantitatively measure pollution levels. Assessment of long-term change in the 

epiphytic lichen community can be especially valuable to provide an early indication of either improving 

or deteriorating air quality and atmospheric deposition. Such monitoring was incorporated in 1994 into 

the USFS Forest Inventory and Analysis (FIA) Program (See Annex A). 

 C-20
Lichen communities in the Pacific Northwest show signs of air pollution damage under current air 

pollution levels. Symptons include decreases in the occurrences of sensitive taxa and replacement by 

pollution-tolerant and nitrophilous taxa (Fenn et al., 2003a; Geiser and Neitlich, 2007). Indicators of clean 

sites and polluted sites (Table C-3) were used by Geiser and Neitlich (2007) to create six lichen zones of 

air quality within the region, from worst (all sensitive species absent) to best (all sensitive species 

present). Air pollution was associated with effects on community composition of lichens, rather than 

species richness. The most widely observed effects included paucity of sensitive, endemic species, and 

enhancement of nitrophilous and non-native species (Geiser and Neitlich, 2007). The strongest 

relationship was with wet NH4

+

 deposition, consistent with findings in California (Jovan and McCune, 

2005) and Europe (van Dobben et al., 2001). The zone of worst air quality was associated with absence of 

sensitive lichens, enhancement of nitrophyllous lichens, mean wet NH4

+

 deposition >0.06 mg N/L, lichen 

tissue N and S concentrations >0.6% and 0.07 %, and SO2 levels harmful to sensitive lichens. 

Jovan and McCune (2005) constructed a model based on non-metric multidimensional scaling 

ordination to analyze lichen species distribution from 98 FIA plots in the greater Central Valley of 

California. The model used epiphytic macrolichen community data to reflect air quality and climate in 

forested areas. Some species respond negatively to NOX and SOX deposition (Gauslaa, 1995; McCune, 

1988; van Haluwyn and van Herk, 2002). Other species respond positively to NHY deposition (de Bakker, 

1989; van Dobben and de Bakker, 1996; van Herk, 1999, 2001; Jovan and McCune, 2005). 

Similarly, Jovan and McCune (2006) developed a model of NH3 exposure to epiphytic 

macrolichens in the Sierra Nevada region. They found that lichens provide a relatively inexpensive tool 

for estimating fine-scale distributions of NH3 exposure to terrestrial ecosystems. Because NH3 has a high 

deposition velocity (Asman and van Jaarsveld, 1992), dry deposition of reduced N exhibits high spatial 

variability. Monitoring of species composition of epiphytic lichen communities can therefore help 

quantify spatially variable eutrophication risk to forest health in the Sierra Nevada region (Jovan and 

McCune, 2006). 

Table C-3. Principal Air Quality Indicator Lichen Species in Oregon and Washington 

Group Sub-Group Indicator Species 

Clean Air Regional distribution Bryoria capillaris, Lobaria oregana, 

Sphaerophorus globosus, Usnea filipendula, 

Usnea scabrata 

Sub-regional distribution Ahtiana pallidula, Alectoria sarmentosa, Bryoria 

fuscescens, Hypogymnia enteromorpha, 

Nephroma bellum, Nodobryoria oregana 

Polluted Air Regional nitrophytes Candelaria concolor, Physcia adscendens, 

Xanthoria polycarpa 

* Includes only species with highest indicator value, used by Geiser and Neitlich (2007) to define air quality 

zones. 

Source: Geiser and Neitlich (2007). 

 C-21
C.3. Transitional Ecosystems 

The sensitivity of wetlands is particularly important given that they contain a disproportionately 

high number of rare plant species (Figure C-3) (Moore et al., 1989). EPA reported that, of the 130 plant 

species from the conterminous U.S. that were listed as threatened or endangered in 1987, 14% occurred 

principally in wetlands (U.S. EPA, 1993b). Bedford and Godwin (2003) indicated that a 

disproportionately high number of rare plant species occur in fens relative to their percent land cover 

(Table C-4) (Bedford and Godwin, 2003). For example, fens comprise only 0.01% of northeastern Iowa 

but contain 12% of the region’s rare plant species and 17% of the listed endangered, threatened, and 

species of concern (Table C-4). 

Source: Moore et al. (1989). 

Figure C-3. Number of nationally rare species versus standing crop in each of 401 quadrants from 

wetlands in Ontario, Quebec, and Nova Scotia. 

Table C-4. Contribution of fens to support of plant species diversity in selected states. 

Number of 

Vascular 

Species 

Found in Fens 

(# Native) 

Percent of 

State Vascular 

Flora Found in 

Fens (% 

Native) 

NonVascular 

Species 

Found in 

Fens 

Number of 

Uncommon & 

Rare Species 

Found in Fens 

Percent of State 

Uncommon & 

Rare Species 

Found in Fens 

Percent of 

State Area 

in Fens 

Colorado ~500 ~14 20 3.3 0.08–0.15 

Idaho 327 20 35 12.0 

Iowa 320 18 134 12.0 0.01 

(307) (17.2) 

Montana 174 60 40 0.0015 

 C-22
New 

Hampshire 

340 17.2 91 52 13.7 0.078 

Calcareous 

fens 

0.0058 

Open fens 0.0726 

New Jersey 96 13.5 0.00733 

Calcareous 

fens 

245 60 0.0073 

Acidic 

seeps 

169 13 36 0.00003 

New York 440 13.8 77 55 7.0 0.07 

(397) (19.0) 

North 

Carolina 

77 11.0 0.0023 

Source: Bedford and Godwin (2003). 

C.4. Aquatic Ecosystems 

Aquatic systems can be subdivided into major types based on hydrology. At the broadest level, 

freshwater aquatic ecosystems can be classified as riverine, lacustrine, and palustrine systems. Riverine 

systems can be identified at varying scales, including valley segment, river reach, and channel unit. 

Lacustrine systems include deepwater habitats associated with lakes and reservoirs. Palustrine systems 

include small, shallow, or intermittent water bodies, including ponds. Each type of aquatic ecosystem is 

potentially sensitive to nutrient enrichment effects from N deposition. Nevertheless, available data 

documenting such effects are limited. 

The dose-response data for aquatic organisms such as those cited here are generally expressed in 

concentration units, as mg/L or μmol/L of N, for example. Such exposure concentration data cannot be 

directly related to ecosystem exposure, which is generally expressed in such units as kg/ha. This is 

because a given N deposition exposure can result in widely varying concentrations of N compounds 

(especially NO3

−

) in water. For convenience, a concentration of 1 mg/L of N (as, for example, in the case 

of NO3

−

-N or NH4

+

-N) is equal to 71.4 μmol/L or 71.4 μeq/L of NO3

−

 or NH4

+

. 

C.4.1. History of Evaluating Nitrogen Enrichment in Freshwater 

Aquatic Ecosystems 

The role of N deposition in freshwater eutrophication and acidification processes has been 

considered secondary to P and S, and only within the past 20 years have there been studies questioning 

the established science and showing N-limitation in some fresh waters, N excess in some terrestrial 

systems, and N-caused acidification in poorly buffered fresh waters. A number of things have conspired to 

prevent extensive evaluation of the effects of atmospheric N deposition on aquatic organisms via nutrient-

 C-23
enrichment pathways. These include assumptions, or prevailing paradigms, that have channeled scientific 

thought in one direction and away from others. 

First were the assumptions for many years that atmospheric deposition was caused primarily by 

sulfur (S) emissions and that effects on aquatic ecosystems were primarily caused by acidification 

processes. Only after S emissions began to decrease substantially in response to the CAAA did the role of 

NOX, and still later, NH3, emissions become recognized as potential agents of environmental change. And 

even then, that role was assumed to be restricted mainly to acidification from NO3

−

, a strong acid anion, 

not eutrophication (Reuss and Johnson, 1985). Second, because N is the nutrient most limiting to primary 

production in most ecosystems, it was assumed until fairly recently that N was tightly cycled in terrestrial 

systems, and that excess NO3

− leaching rarely occurred in natural environments (Vitousek and Howarth, 

1991). Finally, the attention of aquatic biologists has been strongly focused on the role of P in 

eutrophication of freshwaters for the past 40 years, largely due to the demonstrated role of P in causing 

large increases in algal productivity worldwide (Schindler et al., 1971; Schindler, 1974). P is an essential, 

and often limiting, nutrient to aquatic organisms. A large number of highly influential studies in the 1960s 

and 1970s exposed the role of wastewater, in particular phosphate detergents, in causing excessive algal 

production and anoxia in Lake Mendota (Wisconsin), Lake Washington (Washington), Lake Erie, and 

many other locations (Hasler, 1947; Vollenweider, 1968; Edmondson, 1969, 1991). Because of the 

emphasis on P as a major cause of fresh water eutrophication, Downing and McCauley (1992) wrote as 

recently as 1992: “opinions differ on the role of N as a limiting nutrient in lakes.” 

C.4.2. Interactions between Nitrogen and P loading 

Results from surveys, paleolimnological reconstructions of past conditions, experimental results, 

and meta-analyses of hundreds of studies all consistently show N-limitation to be common in fresh 

waters, especially in remote areas, and there is a nearly universal eutrophication response to N-enrichment 

in lakes and streams that are N-limited. Surveys of lake N concentrations and trophic status along 

gradients of N deposition show increased inorganic N and increased productivity to be strongly related to 

atmospheric N deposition. Where N-enrichment has occurred, P limitation, N+P colimitation, and a few 

instances of Si depletion have been reported. Paleolimnological records show increases in productivity 

and changes in algal assemblages in the recent past (since 1950) that are correlated with increased societal 

use of synthetic N fertilizers and human. The paleolimnological evidence is strongest in regions with the 

highest N deposition, and is weaker where N deposition is lower (Wolfe et al., 2001; 2003; 2006; Saros 

et al., 2003). In additions to changes in productivity, algal community reorganization has been observed 

in the paleolimnological record, experiments, and observations of N-enriched lakes, especially those 

where enrichment has come from N deposition. A summary of additional studies addressing N-limitation 

is given in Table C-5. 

It is generally believed that the Laurentian Great Lakes are P-Limited (Schelske, 1991; Downing 

and McCauley, 1992; Rose and Axler, 1998). Water quality in the open waters of these lakes has been 

improving in recent years in response to controls on point sources of P (Nicholls et al., 2001). Work by 

Levine et al. (1997), however, suggested a more complicated pattern of response to nutrient addition for 

Lake Champlain. They added nutrients to in situ enclosures and measured indicators of P status, including 

alkaline phosphatase activity and orthophosphate turnover time. Although P appeared to be the principal 

limiting nutrient during summer, N addition also resulted in algal growth stimulation. During spring, 

phytoplankton growth was not limited by P, N, or silica (Si), but perhaps by light or temperature (Levine 

et al., 1997). 

Data from 28 Sierra Nevada lakes sampled in 1985 and again in 1999 suggested that NO3

−

concentrations decreased during that period and total P concentrations increased in more than 70% of the 

lakes sampled. Sickman et al. (2003a) concluded that lakes throughout the Sierra Nevada appear to be 

experiencing measurable eutrophication in response to atmospheric deposition of nutrients, but N 

 C-24
deposition is only part of the process. Based on the evidence of increased P loading throughout the Sierra 

Nevada, Sickman et al. (2003a) concluded that site-specific P sources were unlikely to be the cause of 

observed trends. They proposed that atmospheric deposition and accelerated internal cycling of P in 

response to changes in climatic factors were the most likely sources of increased P loading to the Sierra 

Nevada Lakes, but it is not known why atmospheric deposition of P to these lakes has increased over 

time. Possibilities include use of organo-phosphate pesticides and aeolian transport of soils and dust that 

are high in P from the San Joaquin Valley to the Sierra Nevada Mountains (Bergametti et al., 1992; 

Lesack and Melack, 1996; Sickman et al., 2003a). 

Data from a survey of 44 lakes east and west of the Continental Divide in Colorado indicated that 

lakes on the western side of the Continental Divide averaged 6.6 μeq/L of NO3

−

, whereas lakes on the 

eastern side of the Continental Divide averaged 10.5 μeq/L of NO3

−

 concentration. In the Colorado Front 

Range, NO3

−

 concentrations in lakes above 15 μeq/L have commonly been measured, suggesting some 

degree of N-saturation (Baron, 1992). A meta-analysis of 42 regions in Europe and North America 

suggested that a majority of lakes in the northern hemisphere were limited by N in their natural state 

(Bergström and Jansson, 2006). While many of these lakes now receive sufficient N from deposition that 

they are no longer N-limited, some lakes in remote regions still maintain their original oligotrophic or 

ultra-oligotrophic status. 

Table C-5. Summary of additional evidence for N limitation on productivity of freshwater ecosystems. 

Region Endpoint Observation Ecosystem 

Type Reference 

Sweden N 

limitation 

a consistent pattern of nutrient limitation showing N limitation for 

deposition below approximately 2.5 kg N/ha/yr, co-limitation of N and P 

for deposition between ~2.5 and 5.0 kg N/ha/yr, and P limitation in areas 

with N deposition greater than 5.0 kg N/ha/yr. 

lakes Bergström 

et al. (2005) 

Rocky 

Mountains of 

Colorado and 

Wyoming 

N 

limitation 

Review: the author concluded that the effects of atmospheric N deposition 

were uncertain and that a widespread shift from N to P limitation had not 

been clearly demonstrated. 

Burns (2004) 

Texas N 

limitation 

some instances of seasonsal N-limitation, and other instances of yearround N-limitation rivers Stanley et al., 

(1990) 

Rocky 

Mountains of 

Colorado and 

Wyoming 

N 

limitation 

Review: Author stated that recent studies did suggest a change in diatom 

species dominance in the 1950s, but widespread species changes across 

lakes in the region and the role of N deposition in these changes needed 

confirmation. Thus, the available data were not clear at that time, but 

suggested that some changes had likely occurred in some aquatic 

ecosystems. 

lake Burns (2004) 

C.4.3. Aquatic Species Affected 

The following Section contains studies in which the amount of N added was less than 10 mg NO3

−

N/L, or 714 μM, and most studies tested the effects of 5 mg NO3

−

-N/L or less. Many of these studies are 

summarized in Table C.6. Overall, several major effects were reported on biota treated with N 

enrichment: the effects on algae included growth stimulation, increased cell densities, decline or 

 C-25
stimulation of individual taxa, and decline in diversity; the amount of N required to stimulate growth in 

phytoplankton is extremely low: 3 μM or less; and animal responses included no response, decreased 

reproductive capability, declines in growth rate and biomass, mortality, and in one case, increased fitness 

because NO3

−

 was detrimental to a fungal parasite. 

C.4.3.1. Phytoplankton and Plants 

Two species of diatom, Asterionella formosa and Fragilaria crotonensis, now dominate the flora of 

at least several alpine and montane Rocky Mountain lakes (Baron et al., 2000; Interlandi and Kilham, 

1998; Saros et al., 2003, 2005; Wolfe et al., 2001, 2003). These species are opportunistic algae that have 

been observed to respond rapidly to disturbance and slight nutrient enrichment in many parts of the world. 

They were among the first diatoms to increase in abundance following watershed settlement and 

agricultural development in European lake watersheds in the 12th and 13th centuries (Anderson et al., 

1995; Lotter, 1998), and North American settlements in the 18th and 19th centuries (Christie and Smol, 

1993; Hall et al., 1999). In these studies, as well as in a Swedish lake influenced by acidic deposition, 

these two diatom species expanded following initial disturbance, and were later replaced by other species 

more tolerant of either acidification or eutrophication (Renberg et al., 1993; Hall et al., 1999). Moreover, 

the growth of A. formosa has been stimulated with N amendments during in situ incubations, using 

bioassays and mesocosms (6.4 to 1616 μM N/L; McKnight et al., 1990) (76 μM N/L; Lafrancois et al., 

2004) (18 μM N/L; Saros et al., 2005). 

It may seem obvious that additions of N stimulate cell growth, but not all species of diatoms or 

other algae are equally responsive to N supply. A. formosa and F. crotonensis have extremely low 

resource requirements for P, enabling them to outcompete other algae for resources and such differences 

in resource requirements allow some species to gain a competitive edge over others upon nutrient 

addition, and as a consequence, shifts in assemblages have been observed (Lafrancois et al., 2004; Saros 

et al., 2005; Wolfe et al., 2001; 2003). This is in keeping with findings of Interlandi and Kilham (2001), 

who demonstrated that maximum species diversity was maintained when N levels were extremely low 

(<3 μM N) in lakes in the Yellowstone National Park (Wyoming, Montana) region. The implication is 

that species diversity declines with increasing availability of N, and this finding complements the results 

of terrestrial studies that also showed a negative relationship between species diversity and N availability 

(Gilliam 2006; Suding et al., 2005; Stevens 2004). 

P limitation and co-limitation of both N and P are reported for fresh waters in the literature, 

particularly during summer (Downing and McCauley, 1992; Elser 1990; Morris and Lewis 1988; 

Sickman et al., 2003b). Because diatoms in northern temperate freshwaters respond rapidly and favorably 

to N enrichment and also have relatively high Si requirements, Si can be depleted, at least seasonally, 

from waters that are relatively high in N and P. 

Silica depletion due to nutrient enrichment has been reported for the Great Lakes (Conley et al., 

1993). Increased growth of silicate-utilizing diatoms as a result of NO3

−

 and phosphate (PO4

3−

)-induced 

eutrophication, and subsequent removal of fixed biogenic Si via sedimentation has brought about changes 

in the ratios of nutrient elements Si, N, and P. In turn, such changes can cause shifts from diatoms to nonsiliceous phytoplankton in large rivers and coastal marine regions (Ittekot, 2003). Reduction in dissolved 

Si in lakewater corresponded to phytoplankton blooms under ice and large numbers of diatoms during 

spring in Loch Vale Watershed, Rocky Mountain National Park (Campbell et al., 1995). This is a 

potential seasonal issue in water bodies underlain by aluminosilicate rocks because mineral weathering 

can replenish the Si supply. 

 C-26
C.4.3.2.

C.4.3.3.

Seasonal Nitrogen Input and Cyanobacteria 

Some ecosystems are seasonally enhanced with N from atmospheric deposition, either from 

snowmelt flushing of accumulated N in winter snow, or from flushing during dormancy of terrestrial 

vegetation (Stoddard, 1994). While many eutrophic and hypereutrophic freshwater ecosystems have 

seasonal or perennial cynaobacteria that fix atmospheric N, obviating the need for an external source of N 

(Wetzel, 2001), only one oligotrophic lake with obligate N-fixing bacteria has been reported (Reuter 

et al., 1985). N-fixation is energy expensive and sometimes limited by trace metal availability, so obligate 

N-fixing cyanobacteria (formerly called blue-green algae) are rarely found in ultra-oligotrophic waters 

(McKnight et al., 1990; Vitousek and Howarth, 1991). Because of this, oligotrophic and ultraoligotrophic 

waters are extremely sensitive to even low inputs of N from atmospheric deposition. Anabaena circinalis, 

an obligate N-fixing cyanobacterium, was suppressed with additions of 500 μM/L N (Higley et al., 2001), 

and DIN levels >~200 μM/L N completely inhibited N fixation in Castle Lake, CA (Reuter et al., 1985). 

Nitrate Toxicity: Invertebrates 

Toxic responses to N exposure by aquatic invertebrates have been identified in a number of studies. 

Toxic response thresholds are typically much higher than the levels of N in surface waters that could be 

attributable to N deposition in the U.S. Safe Concentrations (SC), or threshold values of N, were 

determined by Camargo and Ward (1995) for several aquatic insects at different life stages. Early instars 

are generally more sensitive to N in solution than later or adult stages. The SC for late instars of 

Hydropsyche occidentalis, a caddis fly, was found to be 171 μM/L, and concentrations greater than this 

value induced mortality. The SC was 100 μM/L for early instars of the same species (Camargo and Ward, 

1995). Another caddis fly, Cheumatopsyche pettiti, tolerated higher concentrations, with safe 

concentrations of 171 and 250 μM N/L, respectively for early and late instars (Camargo and Ward, 1995). 

Two species of amphipod did not survive after 120-h exposure to NO3

−

 concentrations of 200 μM N/L for 

one species, and 314 μM N/L for the other (Camargo et al., 2005). No observable effect concentrations 

above which Ceriodaphinia dubia exhibited reduced reproductive capability ranged broadly in laboratory 

experiments, but some effects were seen at concentrations greater than 507 μM N/L (Scott and 

Crunkilton, 2000). A decline in Daphnia spp. was observed in mesocosm nutrient enrichment 

experiments where 75 μM N/L was added, but this was attributed to lower food quality of the algal 

assemblage that replaced the original species as a result of fertilization (Lafrancois et al., 2004). Thus, 

toxic responses seem to occur at N concentrations that are much higher than the concentrations required 

to elicit a response in competitive interactions. 

A whole-ecosystem experiment at the Bear Brook watershed, ME simulated the effects of N and S 

deposition by means of experimental (NH4)2SO4 addition over a period of 10 years. Researchers found 

that elevated N inputs had minimal effect on stream detritus processing (Chadwick and Huryn, 2003). 

They also found that N additions had no significant effect on stream macroinvertebrate secondary 

production or varying production by functional feeding groups. They concluded that climate-related 

variables such as flow duration and litter inputs controlled secondary production when N was not limiting 

(Chadwick and Huryn, 2005). 

Changes to aquatic food webs have not been as thoroughly explored as changes to algal 

assemblages, but a few studies have shown declines in zooplankton biomass (Paul et al., 1995; Lafrancois 

et al., 2004) in response to N-related shifts in phytoplankton biomass toward less palatable taxa with 

higher C:P ratios (Elser et al., 2001). N enrichment of arctic streams not only increased periphyton 

biomass and productivity, but also stimulated the entire ecosystem, increasing decomposition rates, fungal 

biomass, and invertebrates (Benstead et al., 2005). 

 C-27
C.4.4. Nitrate Toxicity: Amphibians and Fish 

A summary of studies on the effects of nitrate on amphibians and fish is given by Table C-6. It 

appears that very high NO3

−

 concentrations in surface water are required to elicit a toxic response in 

amphibian populations. Concentrations that caused no observed effects and no observed adverse effects 

ranged from 357 to 714 μM N/L for frogs, salamanders, and the American toad (Bufo americanus) 

(Hecnar, 1995; Laposata and Dunson, 1998; Johansson et al., 2001; Romansic et al., 2006). In one 

experiment, the red-legged frog (Rana aurora) exhibited a decreased susceptibility to Saprolegnia mold 

when exposed to elevated NO3

−

 concentrations (Romansic et al., 2006). 

According to one review, adverse direct effects of N deposition on fish due to nutrient enrichment 

are probably minimal (Burns, 2004). N concentrations alone are not high enough to influence fish 

metabolism, and the extent of eutrophication is insufficient (due to induced P limitation in oligotrophic 

waters) to cause O2 depletion. 

Other research suggests that the eggs and fry of rainbow trout (Oncorhyncus mykiss; including 

steelhead), cutthroat trout (O. clarki), and chinook salmon (O. tshawytscha) are susceptible to elevated 

concentrations of NO3

−

, with rainbow trout mortality occurring after 30 day incubations in concentrations 

>79 μM N/L (Kincheloe et al., 1979). There were no observed effects reported below this concentration. 

Chinook salmon and cutthroat trout eggs and fry responded to slightly higher concentrations; no observed 

effects occurred below 164 μM N/L, but mortality occurred at higher concentrations (Kincheloe et al., 

1979). Lake whitefish (Coregonus clupeaformis) and lake trout (Salvelinus namaycush) embryos 

displayed developmental delays at concentrations greater than 446 and 114 μM N/L, respectively 

(McGurk et al., 2006). All of these toxic threshold concentrations are much higher than the concentrations 

of NO3

−

 in surface water that would routinely be expected to occur solely in response to atmospheric N 

deposition in the U.S. Nevertheless, such high concentrations of streamwater NO3

−

 have been measured in 

the Great Smoky Mountains, NC (Cook et al., 1994) and in mixed conifer forests in southern California 

(Fenn and Poth, 1999). 

Table C-6. Summary of effects of N enrichment on aquatic biota in freshwater ecosystems. 

Species Common 

Name 

Life 

Stage 

N Concentration 

(mg NO3

–N/L) Observed Effects Reference 

ALGAE: PHYTOPLANKTON 

Asterionella formosa diatoms 0.252 mg/L stimulated growth Saros et al. 

(2005) 

Asterionella formosa diatoms 6.4 μmol/L stimulated growth McKnight et al. 

(1990) 

Multiple species diatoms 1.06 mg/L (low ambient N-deposition):increase in 

chlorophyll-a content and growth rate; 

no cell density effect 

Lafrancois 

et al. (2004) 

Asterionella formosa diatoms 5.7 × 10−4 (>0.041 

uM) (high light) 

increased growth rate (measured at 

half the maximum growth rate) 

Michel et al. 

(2006) 

Fragilaria crotonensis diatoms 0.252 mg/L stimulated growth Saros et al. 

(2005) 

Multiple species diatoms 1.06 mg/L (low ambient N deposition):increase in 

chlorophyll-a content and growth rate; 

no cell density effect 

Lafrancois 

et al. (2004) 

 C-28
Fragilaria crotonensis diatoms 3.5 × 10−4 (>0.028 

uM) (high light) 

increased growth rate (measured at 

half the maximum growth rate) 

Michel et al. 

(2006) 

Fragilaria crotonensis diatoms 8.4 × 10−6 (>0.006 

uM) (med light) 

increased growth rate (measured at 

half the maximum growth rate) 

Michel et al. 

(2006) 

Staurosirella pinnata diatoms 8.4 × 10−6 (>0.006 

uM) (med light) 

increased growth rate (measured at 

half the maximum growth rate) 

Michel et al. 

(2006) 

Tetracyclus glans benthic diatoms 1.7 × 10−4 (>0.012 

uM) (low light) 

increased growth rate (measured at 

half the maximum growth rate) 

Michel et al. 

(2006) 

Multiple species phytoplankton 

assemblages 

3.0 uM N saturation value for maximum 

diversity in WY low N lakes 

Interlandi et al. 

(1999) 

not identified phytoplankton 

assemblages 

0.5 mg/L NO3

–- stimulated growth seasonally, 

while tributary periphyton communities 

were P limited 

Stanley et al. 

(1990) 

not identified phytoplankton 

assemblages 

0.3 uM NH4 additions more effective at 

stimulating growth than NO3

–

Levine and 

Whalen (2001) 

not identified phytoplankton 

assemblages 

 100 μg/L stimulated NO3

– uptake Axler and 

Reuter (1996) 

Multiple species crysophytes 1.21 mg/L (elevated ambient N deposition): no 

response to NO3

– additions; increased 

chlorophyll-a and cell density when 

NO3

– combined with acid and P 

Lafrancois 

et al. (2004) 

Multiple species epilimnetic algae 0.012 mg/L increased growth rate (measured at 

half the maximum growth rate) 

Priscu et al. 

(1985) 

Multiple species hypolimnetic 

algae 

0.050 mg/L increased growth rate (measured at 

half the maximum growth rate) 

Priscu et al. 

(1985) 

PERIPHYTON 

attached benthic 

algae 

0.5 M NaNO3 in 2% 

agar 

biomass increased in response to N 

and N & P additions during period of 

seasonal N-limitation (July-August) 

Smith and Lee 

(2006) 

attached benthic 

algae 

2.5 M NO3

– stimulated stream algal growth 

during seasonal N-limitation 

Bushong and 

Bachmann 

(1989) 

not identified attached benthic 

algae 

0.5 M NaNO3 in 3% 

agar 

NO3

– alone stimulated stream algal 

growth during seasonal N-limitation, 

while N & P co-limited growth in other 

times 

Wold and 

Hershey 

(1999) 

not identified attached benthic 

algae 

0.5 M NaNO3 in 3% 

agar 

NO3

– alone stimulated stream algal 

growth during seasonal N-limitation, 

while N & P co-limited growth in other 

times 

Allen and 

Hershey 

(1996) 

not identified attached benthic 

algae 

 0.036 mg/L stimulated NO3

– uptake Axler and 

Reuter (1996) 

not identified attached benthic 

algae 

0.5 M NaNO3 in 2% 

agar 

no growth response Higley et al. 

(2001) 

 C-29
not identified epilithic ~700 μg NO3

–-N suppressed N2-fixation Reuter et al. 

(1985) 

not identified attached benthic 

algae 

0.16 mg/L increased summer growth rate 

(measured at half the maximum growth 

rate) 

Reuter et al. 

(1985) 

not identified attached benthic 

algae 

0.32 mg/L increased winter growth rate 

(measured at half the maximum growth 

rate) 

Reuter et al. 

(1986) 

not identified sublittoral 

epilithic algae 

0.259 mg/L increased growth rate (measured at 

half the maximum growth rate) 

Reuter and 

Axler (1992) 

not identified eulittoral epilithic 

algae 

0.126 mg/L increased growth rate (measured at 

half the maximum growth rate) 

Reuter and 

Axler (1992) 

not identified epipelic algae 0.713 mg/L increased growth rate (measured at 

half the maximum growth rate) 

Reuter and 

Axler (1992) 

CYANOBACTERIA 

Anabaena circinalis N fixing 

cyanobacteria 

0.5 M NaNO3 in 2% 

agar 

decreased abundance Higley et al. 

(2001) 

Microcycstis sp. non-N-fixing 

cyanobacteria 

0.28 mg/L increased growth rate; increased 

mycrocystin and anatoxin-a 

concentrations 

Gobler et al. 

(2007) 

INVERTEBRATES 

Hydropsyche 

occidentalis 

caddis fly early 

instaar 

1.4 (SC) mortality Camargo and 

Ward (1995) 

late 

instaar 

2.2 (SC) mortality Camargo and 

Ward (1995) 

Cheumatopsyche 

pettiti 

caddis fly early 

instaar 

2.4 (SC) mortality Camargo and 

Ward (1995) 

late 

instaar 

3.5 (SC) mortality Camargo and 

Ward (1995) 

Echinogammarus 

echinosetosus 

amphipod adult 2.8 (120 h LC0.01) mortality Camargo et al. 

(2005) 

Eulimnogammarus 

toletanus 

amphipod adult 4.4 (120 h LC0.01) mortality Camargo et al. 

(2005) 

Ceriodaphnia dubia water 

flea/cladoceran 

adult 7.1-56.5 (7d 

NOEC) 

decreased reproductive ability; fewer 

neonates produced per female 

Scott and 

Crunkilton 

(2000) 

Daphnia pulex Water flea adult 1.06 (low ambient N-deposition): decreased 

biomass in response to NO3

–

Lafrancois 

et al. (2004) 

Daphnia schoedleri Water flea adult 1.06 (low ambient N-deposition): decreased 

biomass in response to NO3

–

Lafrancois 

et al. (2004) 

VERTEBRATES: AMPHIBIANS 

Rana temporaria common frog larvae 5 (70-d NOEC) delayed development, lower growth 

rate and body mass at metamorphosis 

Johansson 

et al. (2001) 

 C-30
Rana sylvatica wood frog fertilized 

eggs 

9 (NOAEL) no effect of NO3

– on survivorship Laposata and 

Dunson (1998) 

Ambystoma 

jeffersonianum 

Jefferson’s 

salamander 

fertilized 

eggs 

9 (NOAEL) no effect of NO3

– on survivorship Laposata and 

Dunson (1998) 

Ambystoma 

maculatum 

spotted 

salamander 

fertilized 

eggs 

9 (NOAEL) no effect of NO3

– on survivorship Laposata and 

Dunson (1998) 

Ambystoma gracile northwestern 

salamander 

larvae 5-20 no effect of NO3

– on survivorship Romansic 

et al. (2006) 

Rana aurora red-legged frog larvae 5-20 no effect of NO3

– on survivorship; NO3–

decreased susceptibility to Saprolegnia 

mold 

Romansic 

et al. (2006) 

Hyla regilla Pacific tree frog larvae 5-20 no effect of NO3

– on survivorship Romansic 

et al. (2006) 

Pseudacris triseriata striped chorus 

frog 

tadpole 10 (100-d LOEC) mortality Hecnar (1995) 

Rana pipiens northern leopard 

frog 

tadpole 10 (100-d LOEC) mortality Hecnar (1995) 

Bufo americanus American toad fertilized 

eggs 

9.0 (NOAEL) no effect of NO3

– on survivorship Laposata and 

Dunson (1998) 

FISH 

Oncorynchus mykiss 

(anadromous)

steelhead eggs 1.1 (30-d NOEC) mortality occurred above this value Kincheloe 

et al. (1979) 

Oncorynchus mykiss 

(nonanadromous)

rainbow trout eggs 1.1 (30-d NOEC) mortality occurred above this value Kincheloe 

et al. (1979) 

Oncorynchus mykiss 

(nonanadromous)

rainbow trout fry 1.1 (30-d NOEC) mortality occurred above this value Kincheloe 

et al. (1979) 

Oncorynchus 

tshawytscha 

chinook salmon fry 2.3 (30-d NOEC) mortality occurred above this value Kincheloe 

et al. (1979) 

Salmo clarki cutthroat trout 

(Lahontan) 

eggs 2.3 (30-d NOEC) mortality occurred above this value Kincheloe 

et al. (1979) 

Salmo clarki cutthroat trout 

(Lahontan) 

fry 4.5 (30-d NOEC) mortality occurred above this value Kincheloe 

et al. (1979) 

Coregonus 

clupeaformis 

lake whitefish embryo 6.25 (~120-d 

NOEC) 

hatching and developmental delays McGurk et al. 

(2006) 

MISCELLANEOUS 

Saprolegnia spp. pathogenic water 

mold 

5-20 mg NO3

–/L decreased ability to infect and kill the 

larvae of the red-legged frog, Rana 

aurora 

Romansic 

et al. (2006) 

NOEC = No-observed-effect concentration; NOAEL = No-observed-adverse-effect level; SC = Safe concentration 

 C-31
C.5. Estuary and Coastal Ecosystems 

There are a variety of factors that govern the sensitivity of estuaries and near-coastal marine waters 

to eutrophication from atmospheric N deposition. Of critical importance is the total N input from all 

sources, including both atmospheric and non-atmospheric sources. Other key elements include the 

dilution capacity of the watershed, which reflects the volume of water available to dilute added N, and 

flushing rate, which reflects the time required for inflowing water to replace estuary volume, (Bricker 

et al., 1999; NRC, 2000). Other potentially important factors can include the following (NRC, 2000): 

 Physiography (geomorphology, dominant biological communities, biogeographic province); 

 Type of primary production base (i.e., seagrasses, phytoplankton, coral, attached intertidal algae, 

etc.); 

 Stratification and extent to which phytoplankton occupy the nutrient-rich photic zone; and 

 Allochthonous inputs of organic matter. 

A number of factors control the N loading rates to estuaries and the potential effects of N 

deposition on nutrient loading and algal blooms. Estuaries communicate with fresh water on the upstream 

side and with the ocean on the downstream side. The flushing of fresh river water through the system and 

the movement and mixing of salt water from the ocean are complicated and are always changing in 

response to weather and tidal cycles. The surface area, volume, and depth of the estuary are also critical 

factors governing the sensitivity of an estuary to N inputs. Decreases in grazer, filter-feeder, and higher 

trophic level populations of fish and shellfish exacerbate problems associated with nutrient overenrichment (Jackson et al., 2001). 

At the upstream end of an estuary, the water is primarily fresh much of the time. Discharge of N 

from the land surface, only a part of which is of atmospheric origin (mainly as deposition to the land that 

subsequently leached to the river water), dominates new N inputs. Further downstream within the estuary, 

where fresh water is more thoroughly mixed with saltwater, much of the terrestrial N load is assimilated 

by phytoplankton and benthic flora or removed by microbes in the process of denitrification (Paerl, 2002). 

The importance of atmospheric N as a contributor to the total N load beyond this zone probably increases, 

but there are no data to evaluate that. 

The principal watershed features that control the amount of increased N flux to estuaries in the U.S. 

include human population, agricultural production, and the size of the estuary relative to its drainage 

basin (Caddy, 1993; Fisher et al., 2006; Peierls et al., 1991). Dense human populations generate large 

volumes of nutrient-rich wastewater. Tertiary sewage treatment can reduce effluent N concentrations to 

less than 35 μM, but these technologies have not been promoted as aggressively in the U.S. as elsewhere 

(Conley et al., 2002; U.S. EPA, 2003). Agricultural production is heavily dependent on fertilizer 

application to generate high yields from small areas. Fertilizer application has dramatically increased 

NO3

−

 concentrations in ground water in many agricultural areas (Bohlke and Denver, 1995), which can 

leach to surface waters. Large terrestrial drainage basins that drain into small estuaries tend to have high 

nutrient flux if the land is heavily populated or used for agriculture. 

In addition to estuaries, coastal marine ecosystems are highly susceptible to nutrient enrichment, 

especially from N. Land clearing, agricultural land use, sewage treatment discharge, and atmospheric 

deposition can all result in high loadings of N to the coastal zone. Excessive N inputs contribute to a 

range of impacts, including enhanced algal blooms, decreased distribution of seagrasses, and decreased 

dissolved oxygen (DO) concentration (Borum, 1996; Bricker et al., 1999; Nixon, 1995; Valiela et al., 

1992). Because of human population growth and the great popularity of coastal areas, there is substantial 

potential for increased N loading to coastal ecosystems from both atmospheric and non-atmospheric 

sources. 

 C-32
C.5.1. Interacting Factors with Productivity 

Results of empirical observations and short-term (3 weeks) marine mesocosm experiments suggest 

that there can be wide variation in the response of autotroph biomass to nutrient addition (Cloern, 2001; 

Olsen et al., 2006). Such variation may be attributable to the time scale of the observations, rate of water 

exchange, grazing pressure, and other environmental factors (Olsen et al., 2006). 

C.5.2. Hydrology Interactions with Phytoplankton Biomass 

River discharge has a huge influence on the hydrology and nutrient cycling of estuaries. For 

example, discharge from the large watershed of the Susquehanna River is important to the seasonal and 

interannual variability in the hydrology of Chesapeake Bay (Fisher et al., 1988; Malone et al., 1988). 

When discharge from the Susquehanna River is low, summer phytoplankton biomass in Chesapeake Bay 

tends to be low compared to spring conditions, and the phytoplankton community is dominated by small 

and flagellated forms (Marshall and Lacouture, 1986). Under higher river flows, summer phytoplankton 

biomass in the bay is higher, and has an increased prevalence of diatoms (Paerl et al., 2006). 

Hydrologic variation interacts with nutrient supply to control phytoplankton seasonal patterns in 

Chesapeake Bay. High biomass during the spring diatom bloom leads to consequent sedimentation of 

organic material out of the photic zone during the transition to summer (Malone et al., 1996; Harding 

et al., 2002). Microbial decomposition of this material then fuels the pattern of summer anoxia in bottom 

waters (Paerl et al., 2006). N loading to Chesapeake Bay and its tributaries during spring high runoff 

periods contributes to periods of P limitation and co-limitation (Boynton et al., 1995). The ecosystem then 

returns to N limitation during low flow summer months (Paerl, 2002). 

C.6. Watersheds, Landscapes and Disturbance 

C.6.1. Interactions among Terrestrial, Transitional, and Aquatic 

Ecosystems 

Streams, and to a lesser extent lakes, can serve as indicators of regional environmental change 

(Seastedt et al., 2004), partly because they integrate conditions within their watersheds including 

atmospheric, edaphic, geologic, and hydrologic conditions. Streams reflect the terrestrial environment 

most closely during high flow when much of the stream water enters the channel from the upper soil 

where most of the biological activity occurs. The terrestrial signal can be less clear in lakes because they 

have the capacity to store water and modify water chemistry through internal processes to a greater degree 

than streams (Lawrence et al., 2008). 

Young and Sanzone (2002) provided a checklist of ecological attributes that should be considered 

when evaluating the effects of an environmental stressor on the integrity of ecological systems 

(Table C-7). The Essential Ecological Attributes (EEAs) listed in the table represent groups of related 

ecological characteristics (Harwell et al., 1999), including landscape condition, biotic condition, chemical 

and physical characteristics, ecological processes, hydrology and geomorphology, and natural disturbance 

regimes. The first three ecological attributes listed in Table C-7 can be classified primarily as “patterns,” 

whereas the last three are “processes” (Bormann and Likens, 1979). They can be affected by a variety of 

environmental stressors (Figure C-4). 

 C-33
Of concern in this annex are relationships between NOX atmospheric deposition, derived from 

anthropogenic sources, and one or more of the EEAs. The ranges of likely changes in ecosystem patterns 

and processes associated with changes in deposition are discussed in the subsections that follow. 

The following discussion assesses and characterizes the overall condition or integrity of 

ecosystems within the U.S. that are affected by the deposition of atmospheric N and its role as a nutrient. 

The six EEAs – landscape condition, biotic condition, chemical/physical characteristics, ecological 

processes, hydrology/geomorphology, and natural disturbance regimes (Table C-7) – provide a 

hierarchical framework for assessing ecosystem status. Characteristics related to structure, composition, 

or functioning of ecological systems may be determined by the use of endpoints or ecological indicators 

of condition that are measureable and significant either ecologically or to society (Harwell et al., 1999). 

Table C-7. Essential ecological attributes and reporting categories. 

Landscape Condition Ecological Processes 

 Extent of Ecological System/Habitat Types Energy Flow 

 Landscape Composition Primary Production 

 Landscape Pattern and Structure Net Ecosystem Production 

Biotic Condition Growth Efficiency 

 Ecosystems and Communities Material Flow 

 Community Extent Organic Carbon Cycling 

 Community Composition N and P Cycling 

 Trophic Structure Other Nutrient Cycling 

 Community Dynamics Hydrology and Geomorphology 

 Physical Structure Surface and Groundwater flows 

 Species and Populations Pattern of Surface flows 

 Population Size Hydrodynamics 

 Genetic Diversity Pattern of Groundwater flows 

 Population Structure Salinity Patterns 

 Population Dynamics Water Storage 

 Habitat Suitability Dynamic Structural Characteristics 

 Organism Condition Channel/Shoreline Morphology, Complexity 

 Physiological Status Extent/Distribution of Connected Floodplain 

 Symptoms of Disease or Trauma Aquatic Physical Habitat Complexity 

 Signs of Disease Sediment and Material Transport 

Chemical and Physical Characteristics Sediment Supply/Movement 

(Water, Air, Soil, and Sediment) Particle Size Distribution Patterns 

 Nutrient Concentrations Other Material Flux 

 C-34
 N Natural Disturbance Regimes 

 P Frequency 

 Other Nutrients Intensity 

Trace Inorganic and Organic Chemicals Extent 

 Metals Duration 

 Other Trace Elements 

 Organic Compounds 

Other Chemical Parameters 

 pH 

 Dissolved O2

 Salinity 

 Organic Matter 

 Other 

Physical Parameters 

Source: Young and Sanzone (2002). 

The relationships among the EEAs are complex because all are interrelated. Changes in one EEA 

may affect, directly or indirectly, every other EEA. Ecological processes create and maintain 

environmental patterns, and these patterns affect how the processes are expressed (Young and Sanzone, 

2002). Changes in patterns or processes can result in changes in the status and functioning of an 

ecosystem. 

Changes in the biodiversity, composition, and structure of ecosystems relate directly to functional 

integrity. Changes in biodiversity are of particular significance in altering ecosystem function. The energy 

obtained by plants (producers) from sunlight during photosynthesis and the chemical nutrients taken up 

by those plants from the soil and the atmosphere are transferred to other species (consumers) within the 

ecosystem through food webs. The movement of chemical nutrients through an ecosystem is cyclic, as the 

nutrients are used or stored and eventually returned to the soil by microorganisms and fungi 

(decomposers). Energy is transferred among organisms through the food webs and eventually is 

dissipated into the environment as heat. The flows of energy and cycling of nutrients provide the 

interconnectedness among the elements of the ecosystem and transform the community from a random 

collection of numerous species into an integrated whole. 

Human existence and welfare on this planet depend on life-support services provided by the 

interaction of the EEAs. Both ecosystem structure and function play essential roles in providing goods 

and services (Table C-8; Daily, 1997). Ecosystem processes provide diverse benefits including absorption 

and breakdown of pollutants, cycling of nutrients, binding of soil, degradation of organic waste, 

maintenance of a balance of gases in the air, regulation of radiation balance and climate, and fixation of 

solar energy (Daily, 1997; Westman, 1977; World Resources Institute, 2000). These ecological benefits, in 

turn, provide economic benefits and values to society (Costanza et al., 1997; Pimentel et al., 1997). Goods 

such as food crops, timber, livestock, fish, and drinking water have market value. The values of 

ecosystem services such as flood-control, wildlife habitat, cycling of nutrients, and removal of air 

pollutants are more difficult to measure (Goulder and Kennedy, 1997). See discussion in Annex F. 

Biodiversity is an important consideration at all levels of biological organization, including species, 

individuals, populations, and ecosystems. Human-induced changes in biotic diversity and alterations in 

the structure and functioning of ecosystems are the two most dramatic ecological trends of the past 

 C-35
century (U.S. EPA, 2004; Vitousek et al., 1997b). The deposition of nutrient N from the atmosphere has 

the potential to alter ecosystem structure and function by altering nutrient cycling and changing 

biodiversity. It is important to understand how ecosystems respond to stress to determine the extent to 

which anthropogenic stresses, including N deposition, affect ecosystem services and products (Table C-8). 

Particular concern has developed within the past decade regarding the consequences of decreasing 

biological diversity (Ayensu et al., 1999; Chapin et al., 1998; Hooper and Vitousek, 1997; Tilman, 2000; 

Wall, 1999). Human activities that decrease biodiversity also alter the complexity and stability of 

ecosystems, and change ecological processes. In response, ecosystem structure, composition and function 

can be affected (Figure C-5) (Chapin et al., 1998; Daily and Ehrlich, 1999; Levlin, 1998; Peterson et al., 

1998b; Pimm, 1984; Tilman and Downing, 1994; Tilman, 1996; Wall, 1999). 

Source: Young and Sanzone (2002). 

Figure C-4. Sample stressors and the essential ecological attributes they affect. 

Table C-8. Primary goods and services provided by ecosystems. 

Ecosystem Goods Services 

Agroecosystems Food crops 

Fiber crops 

Crop genetic resources 

Maintain limited watershed functions (infiltration, flow control, partial soil protection) 

Provide habitat for birds, pollinators, and soil organisms important to agriculture 

Sequester atmospheric carbon 

Provide employment 

 C-36
Coastal 

Ecosystems 

Fish and shellfish 

Fishmeat (animal feed) 

Seaweeds (for food and industrial 

use) 

Salt 

Genetic resources 

Moderate storm impacts (mangroves, barrier islands) 

Provide wildlife (marine and terrestrial (habitat and breeding 

areas/hatcheries/nurseries 

Maintain biodiversity 

Dilute and treat wastes 

Provide harbors and transportations routes 

Provide human and wildlife habitat 

Provide employment 

Contribute aesthetic beauty and provide recreations 

Forest 

Ecosystems 

Timber 

Fuelwood 

Drinking and irrigation water 

Fodder 

Nontimber products (vines, bamboos, 

leaves, etc.) 

Food (honey, mushrooms, fruit, and 

other edible plants; game) 

Genetic resources 

Remove air pollutants, emit O2

Cycle nutrients 

Maintain array of watershed functions (infiltration, purification, flow control, soil 

stabilization) 

Maintain biodiversity 

Sequester atmospheric carbon 

Moderate weather extremes and impacts 

Generate soil 

Provide employment 

Provide human and wildlife habitat 

Contribute aesthetic beauty and provide recreation 

Freshwater Drinking and irrigation water 

Fish 

Hydroelectricity 

Genetic resources 

Buffer water flow (control timing and volume) 

Dilute and carry away wastes 

Cycle nutrients 

Maintain biodiversity 

Provide aquatic habitat 

Provide transportation corridor 

Provide employment 

Contribute aesthetic beauty and provide recreation 

Grassland 

Ecosystems 

Livestock (food, game, hides, and 

fiber) 

Drinking and irrigation water 

Genetic resources 

Maintain array of watershed functions (infiltration, purification, flow control, soil 

stabilization) 

Cycle nutrients 

Remove air pollutants and emit O2

Maintain biodiversity 

Generate soil 

Sequester Atmospheric carbon 

Provide human and wildlife habitat 

Provide employment 

Contribute aesthetic beauty and provide recreation 

Source: World Resources Institute (2000). 

 C-37
Source: Modified from Ayensu et al. (1999). 

Figure C-5. Linkages among various ecosystem goods and services (food, water, biodiversity, forest 

products) and other driving forces (climate change). 

C.6.2. Interactions with Land Use and Disturbance 

Scientific understanding of N cycling in forested watersheds is complicated by ecosystem response 

to climatic variation, human land use, and various kinds of landscape disturbance, including insect 

infestation, wind storm, fire, and disease (Aber and Driscoll., 1997; Goodale et al., 2000; Mitchell et al., 

2006). N dynamics in watersheds of mixed land use (i.e., agriculture, urban, forest) are even more 

complicated. It is clear that disturbances have major impacts on nutrient enrichment from N deposition, 

and that these effects can be long-lasting. Nevertheless, the scientific community is only in the early 

stages of learning how to quantify these interactions. 

Changes in land use can affect nutrient heterogeneity in the mineral soil of forest stands. For 

example, Fraterrigo et al. (2005) found that patterns of variance in soil C, N, and Ca2+ concentration 

increased with the extent of intensive past land use in western North Carolina. Land use might alter the 

local patchiness of soil nutrients by decoupling interactions among microclimate, topography, vegetation, 

and soil biota. In particular, mechanical soil mixing and maintenance of agricultural monocultures can 

homogenize soils in cultivated systems (Robertson et al., 1993; Paz-Gonzalez and Taboada, 2000). Such 

effects may be important if the land use is changed to forest. Similarly, changes in species composition 

can alter the spatial distribution of nutrients in litter inputs (Dijkstra and Smits, 2002; Fraterrigo et al., 

2005). 

In the northeastern U.S., concentrations of N in streams of upland forested watersheds tend to be 

considerably lower than in streams draining watersheds with other land uses. In a comparison of small 

watersheds in eastern New York, concentrations of N were highest and most variable in a stream draining 

a watershed where the predominant land use was row crop production. Total dissolved N concentration in 

streams in sewered suburban and urban watersheds were somewhat lower and less variable than in 

streams draining the agricultural watershed. Streams in urban and suburban watersheds may also 

experience high episodic N loading caused by combined sewer overflows (Driscoll et al., 2003c). 

 C-38
C.6.3. Timber Harvest and Fire 

Timber harvest contributes to nutrient removal from the ecosystem via biomass export and 

acceleration of leaching losses (Bormann et al., 1968; Mann et al., 1988). In particular, logging 

contributes to loss of N and Ca2+ from the soil (Tritton et al., 1987; Latty, 2004). The extent of nutrient 

loss is determined, at least in part, by the intensity of the logging and whether or not it is accompanied by 

fire (Latty, 2004). The species composition of the regrowth vegetation also has important effects on 

nutrient cycling. Fire is sometimes followed by establishment of N-fixing vegetation that provides 

substantial sources of Nr (Johnson, 1995; Johnson et al., 2004). Tree species also vary dramatically in 

their N cycling properties, especially in their influence on litter mass and quality (Finzi et al., 1998; 

Ferrari, 1999; Ollinger, 2002). Thus, over time, the extent of effect of logging and fire on nutrient cycling 

can increase, depending largely on shifts in tree species composition and the degree to which C and N 

pools are altered in the mineral soil and the forest floor. 

Dissolved N exports have been clearly shown to increase substantially after major watershed 

disturbance, often reaching peak concentrations in streamwater within 1 to 3 years of disturbance, and 

then returning to background concentrations after about 5 to 10 years (Likens et al., 1978; Bormann and 

Likens, 1979; Eshleman et al., 2000). Such transient NO3

−

 leakage has been shown to occur subsequent to 

both logging (Dahlgren and Driscoll, 1994; Martin et al., 1984; Yeakley et al., 2003) and insect 

infestation (Eshleman et al., 1998, 2004). 

The extent to which timber harvesting influences leaching of NO3

−

 and base cations from soils to 

drainage waters depends on changes in primary productivity, nutrient uptake by plants and 

microorganisms within the terrestrial ecosystem, and hydrological pathways for transferring nutrients to 

drainage water (Hazlett et al., 2007). Because of the variety of responses and interactions of 

environmental and forest litter and soil conditions, it is difficult to generalize about the influence of 

harvesting on N cycling (Grenon et al., 2004; Hazlett et al., 2007). 

It is known, however, that land use history constitutes a major influence on N leaching from 

forested watersheds that receive moderate to high levels of atmospheric N deposition (Pardo et al., 1995; 

Aber and Driscoll, 1997; Goodale et al., 2000; Lovett et al., 2000a). The severity of effect and length of 

the recovery period probably vary according to the nature of the past disturbance. Extensive past logging 

appears to have considerable and long-lasting effects on nutrient cycling (Goodale and Aber, 2001; Fisk 

et al., 2002). Latty et al. (2004) compared soil nutrient pools and N cycling among three forest stands in 

the Adirondack Mountains: old growth, selectively logged, and selectively logged and then burned. The 

logging and fire had occurred about 100 years previously. Results suggested that even relatively light 

logging, plus burning, may influence the extent of subsequent N limitation over time scales of decades to 

centuries (Latty, 2004). Models of forest ecosystem response to disturbance incorporate such long-lasting 

effects of land use on C and N storage, cycling, and release (Aber et al., 1997). 

Chen and Driscoll (2004) simulated the response of five forested watersheds in the Adirondack and 

Catskill regions of New York to changes in atmospheric deposition and land disturbance. Simulation 

results suggested that forest harvesting caused increased leaching of base cations and NO3

−

 from the 

watersheds. These changes also affected model projections of future pH and acid neutralizing capacity 

(ANC) of lake water. Model results suggested that lakewater pH and ANC were lower in response to 

forest cutting as compared with undisturbed conditions. 

Nitrification rates at old growth sites in the White Mountains of New Hampshire (63 ± 4.3 kg 

N/ha/yr) were approximately double those at previously burned (34 ± 4.4 kg N/ha/yr) and previously 

logged (29 ± 4.7 kg N/ha/yr) sites (Goodale, 2001). Fire and logging disturbances had occurred about 100 

years previously on these study sites. Nitrification increased as forest floor C:N ratio decreased, resulting 

in higher NO3

−

 concentrations in streamwater. These results suggest that forest disturbance can have longlasting effects on N cycling and the potential for N saturation. 

Thus, disturbance can affect N cycling and the response of forest ecosystems to N deposition. In 

addition, it also appears that vegetative changes stimulated by N deposition may affect the frequency and 

 C-39
severity of disturbance. Excess Nr deposition is thought to be impacting essential ecological attributes 

associated with terrestrial ecosystems and how they respond to disturbance. Effects of Nr deposition 

influence habitat suitability, genetic diversity, community dynamics and composition, nutrient status, 

energy and nutrient cycling, and frequency and intensity of natural fire disturbance regimes. For example, 

several lines of evidence suggest that Nr deposition may be contributing to greater fuel loads and thus 

altering the fire cycle in a variety of ecosystem types (Fenn et al., 2003c). Invasive grasses, which can be 

favored by high N deposition, promote a rapid fire cycle in many locations (D’Antonio and Vitousek, 

1992). The increased productivity of flammable understory grasses increases the spread of fire and has 

been hypothesized as one mechanism for the recent conversion of coastal sage shrub (CSS) to grassland 

in California (Minnich and Dezzani, 1998). 

High grass biomass has also been associated with increased fire frequency in the Mojave Desert 

(Brooks, 1999; Brooks and Esque, 2002; Brooks et al., 2004). This effect is most pronounced at higher 

elevation, probably because the increased precipitation at higher elevation contributes to greater grass 

productivity. Increased N supply at lower elevation in arid lands can only increase productivity to the 

point at which moisture limitation prevents additional growth. Fire was relatively rare in the Mojave 

Desert until the past two decades, but now fire occurs frequently in areas that have experienced invasion 

of exotic grasses (Brooks, 1999). 

C.6.4. Insect Infestation and Disease 

Insect infestation and plant disease, via atmospheric N deposition, can alter the effects of nutrient 

enrichment on forest ecosystems. Such disturbances alter the pool of Nr in the forest floor with short-term 

impacts on NO3

−

 and base cation leaching. Positive influences of N deposition on root and seed biomass 

of an annual plant, common ragweed, were suppressed by herbivory, which increased with higher 

available plant shoot N (Throop, 2005). 

Eshleman et al. (2004) applied a regional lithology-based unit N export response function model to 

simulate NO3

−

 export to streams in the Chesapeake Bay watershed. The model considered the geographic 

distribution of bedrock classes and the timing and extent of defoliation by gypsy moth (Lymantria dispar) 

larvae. Modeling results suggested that the regional annual NO3

−

-N export increased during the year 

following peak insect defoliation by about 1500%, from an initial rate of 0.1 kg N/ha/yr to nearly 1.5 kg 

N/ha/yr. 

Between the mid-1980s and the early 1990s, the southward expanding range of the gypsy moth 

traversed Shenandoah National Park, VA (Webb, 1999). Some areas of the park were heavily defoliated 2 

to 3 years in a row. The White Oak Run watershed, for example, was more than 90% defoliated in both 

1991 and 1992. The gypsy moth population in White Oak Run then collapsed due to pathogen outbreak. 

This insect infestation of the forest ecosystem resulted in substantial effects on streamwater chemistry. 

The most notable effects of the defoliation on park streams were dramatic increases in the concentration 

and export of N and base cations in streamwater. Figure C-6 shows the increase in NO3

−

 export that 

occurred in White Oak Run. Following defoliation, NO3

−

 export increased to previously unobserved 

levels and remained high for over 6 years before returning to predefoliation levels. The very low baseline 

levels of NO3

−

 export in park streams were consistent with expectations for N-limited, regenerating 

forests (e.g., Aber et al., 1989; Stoddard, 1994). Release of NO3

−

 to surface waters following defoliation 

was likewise consistent with previous observations of increased N export due to forest disturbance 

(e.g., Likens et al., 1970; Swank, 1988). The exact mechanisms have not been determined, but it is 

evident that the repeated consumption and processing of foliage by the gypsy moth larva disrupted the 

ordinarily tight cycling of N in the forests within this park. 

 C-40
Source: Sullivan et al. (2003). 

Figure C-6. Effect of watershed defoliation by the gypsy moth caterpillar on NO3

− flux in streamwater. 

White Oak Run was heavily defoliated for three consecutive years. The watershed area 

defoliated was 46.5% in 1990, 92.9% in 1991, and 90.4% in 1992. In 1993, the gypsy moth 

population collapsed and there was no further defoliation. 

The elevated concentrations of NO3

−

 following defoliation did not appear to contribute to baseflow 

acidification in White Oak Run. This was due to a concurrent increase in concentrations of base cations in 

streamwater (Webb et al., 1995). Both NO3

−

 and base cation concentrations also increased during highrunoff conditions, although the increase in base cations did not fully compensate for the episodic increase 

in NO3

−

. As a consequence, episodic acidification became more frequent and more extreme (Webb et al., 

1995). 

Large trees in old growth forests may resorb less N from foliage than do younger trees on 

previously logged or burned sites (Killingbeck, 1996). This process would be expected to contribute to an 

alleviation of N limitation on plant processes in old-growth forests (Latty et al., 2004). This effect might 

also extend to herbivores, which are often N-limited (Mattson, 1980). Latty et al. (2004) attributed the 

high severity of beech bark disease in old growth forests to such a mechanism. Beech bark disease is 

caused by an introduced scale insect (Crytococcus fagisuga), which has high N requirements (Wargo, 

1988; Houston, 1994). The N-rich foliage in the old growth forest may improve insect fitness, 

contributing to a higher rate of infestation in the old growth stands (Latty et al., 2004). 

C.6.5. Urbanization 

Perhaps the most noteworthy impact of urban land use on processes of nutrient enrichment from N 

deposition concerns the transport of Nr to N-limited estuarine and near-coastal waters. In agricultural, and 

especially in forested areas, it is generally expected that most atmospherically deposited N is taken up by 

terrestrial vegetation. Relatively little of the deposited N is available for transport to downstream 

 C-41
receiving waters. This is not the case for urban land use. Urbanization often involves substantial clearing 

of vegetation and compaction of soil (Poff et al., 1997; Burges et al., 1998; Jones et al., 2000; Trombulak 

and Frissell, 2000; Alberti et al., 2007). Due to the relatively large impervious surface area in the urban 

landscape (buildings, roads, parking lots, etc.), a higher percentage of precipitation is routed directly to 

surface waters, with less opportunity for vegetative uptake of deposited N (Arnold and Gibbons, 1996; 

Montgomery and Buffington, 1998). Therefore, atmospheric N deposition contributes proportionately 

more NO3

−

 to surface waters in urban settings than it does with other land uses. The reduction in riparian 

and wetland coverage and functionality also diminishes the ability of the urban watershed to filter 

contaminants from runoff, including atmospherically deposited N (Peterjohn and Correll, 1984). Because 

many large urban areas are both located close to the coastline and expected to receive relatively high NOX

deposition, they can constitute sizeable sources of body contribution to estuarine and marine waters. 

C.6.6. Agriculture 

Agricultural ecosystems are not sensitive to levels of N deposition typically found in the U.S. 

Rather, such ecosystems often act as net sources of NH3 emissions rather than as sinks (Grünhage et al., 

1992; Krupa, 2003). Atmospheric N deposition can contribute a quantitatively important component of 

the Nr requirements of pastures and croplands. In such settings, atmospheric N deposition provides an 

additional chronic source of N fertilizer. This may be viewed as a beneficial outcome. Nevertheless, some 

of the N that is atmospherically deposited on agricultural land may eventually leach to drainage waters 

and contribute to eutrophication, especially in estuarine and near-coastal marine environments. Industrial 

livestock operations also contribute substantial amounts of NHY to the atmosphere, some of which is 

deposited on coastal waters. 

Agriculture also experiences indirect effects of NOX emissions through the formation of groundlevel O3. Such effects are not considered in this review. 

C.6.7. Other Disturbances 

In some ecosystems, chronic additions of atmospherically derived N may have had irreversible 

consequences that involve interactions with invasive, non-native plants. For example, California has 

many plant species that occur in shrub, forb, and grasslands that receive high N deposition. There are up 

to 200 sensitive plant species in southern California coastal sage scrub (CSS) communities alone (Skinner 

and Pavlik, 1994). About 25 plant species are thought to be extinct in California, most of them forbs that 

occurred in sites that have experienced conversion to annual grassland (U.S. EPA, 2005a). As CSS 

vegetation continues to convert to grassland dominated by invasive species, loss of additional rare plant 

species may occur. Invasive plant species are often identified as a major threat to rare native plant species. 

However, the occurrence of invasive species may combine with other stress factors, including N 

deposition, to promote increased productivity of invasive species at the expense of native species. 

As sensitive vegetation is lost, wildlife species that depend on these plants can also be adversely 

affected. There are several threatened or endangered wildlife species listed by the U.S. Fish and Wildlife 

Service, including the desert tortoise (Gopherus agassizii) and checkerspot butterfly that are native to 

plant communities in California thought to be sensitive to atmospheric N input. A native to the San 

Francisco Bay area, the bay checkerspot butterfly has declined in association with invasion of exotic 

grasses that replaced the native forbs on which the butterfly depends. In particular, the larval stage of the 

butterfly is dependent on Plantago erecta, which is increasingly being outcompeted by exotic grasses 

(U.S. EPA, 2005a). 

Decline in the population of the desert tortoise may be due to a number of co-occurring stresses, 

including grazing, habitat destruction, drought, disease, and a declining food base. In the desert shrub 

 C-42
inter-spaces, sites where native forbs once flourished, invasive grasses now dominate, reducing the 

nutritional quality of foods available to the tortoise (Fenn et al., 2003a; Nagy et al., 1998). N deposition 

contributes to the productivity and density of grasses at the expense of native forbs (Brooks, 2003). 

C.6.8. Multiple Stress Response 

Ecosystems are often subjected to multiple stressors, of which nutrient enrichment from 

atmospheric deposition of N is only one. Additional stressors are also important, including O3 exposure, 

climatic variation, natural and human disturbance, the occurrence of invasive non-native plants, native 

and non-native insect pests, and disease. Atmospheric N deposition interacts with these other stressors to 

affect ecosystem patterns and processes in ways that are only beginning to beunderstood. 

For example, terrestrial ecosystems at many locations are subjected to high N deposition and high 

exposure to O3. This is especially true in portions of southern California and the Appalachian Mountains. 

Mixed conifer forests in the San Bernardino and San Gabriel mountains in southern California are 

exposed to high levels of atmospheric O3 and receive atmospheric N deposition in the range of about 5 to 

over 30 kg N/ha/yr (Takemoto et al., 2001). Spatial variability in N deposition is high due to the patchy 

characteristics of these forests and associated canopy effects on dry deposition processes. The forest 

ecosystems have reached N-saturation, as evidenced by high NO3

−

 concentrations in stream water. 

However, evaluation of N effects on vegetation is complicated by the concurrent effects of O3, which has 

damaged sensitive plant species, especially Ponderosa and Jeffrey pine. Bytnerowicz (Bytnerowicz, 2002) 

summarized N/O3 interactions and consequent effects. 

Peak diurnal concentrations of atmospheric O3 and NO2 co-occur at Tanbork Flat in the San 

Bernardino Mountains (Bytnerowicz et al., 1987). They can have counteracting effects, with O3 reducing 

growth and N deposition enhancing growth of pine trees (Grulke and Balduman, 1999). 

Jeffrey and Ponderosa pine are the most sensitive western coniferous tree species to injury from O3

pollution (Miller et al., 1983; Duriscoe and Stolte, 1989). In some areas of the western Sierra Nevada 

Mountains, O3 concentrations have been high enough to cause visible foliar injury to these species and 

reduced needle retention (Bytnerowicz, 2002). Reduced radial growth has also been observed (Peterson 

et al., 1987, 1991). In the San Bernardino Mountains, trees of these species that exhibit severe foliar 

injury from O3 do not show growth reductions (Arbaugh et al., 1999), and this has been attributed to the 

fertilizing effects of high N deposition (Bytnerowicz, 2002; Takemoto et al., 2001). This may be an 

example of counteracting effects from O3 and N air pollution. It is also possible that N deposition in the 

western Sierra Nevada Mountains may increase growth of pines, especially on nutritionally poor granitic 

soils (Takemoto et al., 2001). 

 C-43
Table C-9. Ecological effects of N deposition described for study sites in the Western U.S. 

Ecological or 

Environmental 

Impact 

Location Level of 

Uncertainty 

Possibility of 

Broader Occurrence 

(at other sites) 

Reference 

EFFECTS IN AQUATIC SYSTEMS 

Elevated NO3

− in runoff; 

most severe in southern 

California and in 

chaparral catchments in 

the southwestern Sierra 

Nevada 

Transverse ranges of 

southern California; lowelevation catchments in 

the Sierra Nevada; highelevation catchments in 

the Colorado Front Range 

Well-documented 

response 

It is unclear how 

widespread this 

phenomenon is outside 

the ecosystems listed, 

because there is littler 

information from lowelevation systems in the 

Sierra Nevada and 

elsewhere. 

Fenn and Poth (1999) 

Fenn et al. (2003a) 

Williams et al. (1996a) 

N enrichment and shifts 

in diatom communities 

in alpine lakes 

Colorado Front Range; 

Lake Tahoe 

(California/Nevada border) 

Documented for two 

lakes east of the 

Continental Divide 

and Lake Tahoe 

These effects seem 

likely in other Nenriched lakes but have 

not been investigated. 

Baron et al. (2000) 

Goldman (1988) 

Wolfe et al. (2001) 

Reduced lake water 

clarity and increased 

algal growth 

Lake Tahoe 

(California/Nevada 

border); high-elevation 

lakes throughout central 

and southern Sierra 

Nevada 

Well-documented 

response; N and P 

deposition believed 

to be important 

factors 

Lake Tahoe is an 

unusual case because 

of its renowned lake 

clarity; extent of 

occurrence elsewhere in 

northern Sierra Nevada 

is unknown. 

Jassby et al. (1994) 

Sickman et al. (2003a) 

Increased NO3

−

concentrations in highelevation lakes 

Several regions, mainly 

downwind of urban 

centers 

Fairly well 

established from 

lake surveys, but 

more data needed 

for improved 

definition of 

frequency and 

severity 

Evidence suggests that 

urban plumes and 

agricultural emissions 

affect lake NO3

− levels. 

There is also evidence 

of impacts on lowelevation lakes. 

Sickman et al. (2002) 

EFFECTS IN TERRESTRIAL SYSTEMS 

Enhanced growth of 

invasive species 

Costal sage scrub, 

southern California; San 

Francisco Bay area 

N deposition, 

fertilization studies, 

and plant 

community data 

supportive, but 

moderate 

uncertainty remains 

It is not known if this 

effect occurs elsewhere, 

but it is expected that 

nitrophilous species will 

be selected for if N 

accumulates in soil. 

Allen et al. (2008) 

Weiss (1999) 

Lichen community 

changes 

Parts of the Pacific 

Northwest; many areas in 

California; north and 

central Colorado 

Well-established 

response; a highly 

sensitive air 

pollution indicator 

Because of the 

sensitivity of many 

lichen species, it is likely 

that this effect occurs 

elsewhere. 

Nash and Sigal (1999) 

 C-44
 C-45

Deleterious effects on 

threatened and 

endangered species 

San Francisco Bay area; 

southern California 

Supportive 

evidence, but high 

degree of 

uncertainty about 

the precise role of N 

deposition 

There is a high 

likelihood of effects in 

some habitats where N 

accumulates in soils 

Weiss (1999), Brooks (2003) 

Altered fire cycle Coastal sage scrub in 

southern California 

Hypothesis based 

on observations, 

fertilization studies, 

and N deposition 

and N cycling data; 

high level of 

uncertainty 

Because it has not been 

studied elsewhere, it is 

uncertain whether this 

effect occurs in other 

areas. 

Allen et al. (2008) 

Altered forest C cycling 

and fuel accumulation 

San Bernardino Mountains Documented 

response 

It is uncertain whether 

this effect occurs in 

other areas. 

Grulke and Balduman (1999) 

Physiological 

perturbation of 

overstory species 

San Bernardino Mountains Documented 

response 

This effect has not been 

widely studied but is 

expected for sensitive 

plant species exposed 

to O3 and adapted to N 

limitation but growing in 

N-enriched soils. 

Grulke et al. (1998) 

Grulke and Balduman (1999) 

Takemoto et al. (2001) 

Forest expansion into 

grasslands 

Great Plains of western 

Canada 

Supportive 

evidence found, but 

high degree of 

uncertainty as to the 

role of N deposition 

It is not known whether 

this effect occurs in 

other areas. 

Köchy and Wilson (2001) 

N emissions as a major 

contributor to regional 

haze problem 

National forests and parks 

throughout California, the 

Pacific Northwest, and 

some sites in the Interior 

West 

Well-established 

effect; contribution 

from Nous 

pollutants has been 

quantified 

This is known to occur 

in areas far removed 

from emissions sources 

because of long-range 

transport. 

Fenn et al., (2003a) 

IMPROVE data (4 March 2003; 

http://vista.circa.colostate.edu/ 

improve) 

NOX emissions as 

precursors for 

phytotoxic levels of O3, 

leading to O3 injury to 

sensitive plant species 

Southern California; Sierra 

Nevada 

Well-established 

effect 

Significant O3 injury to 

vegetation has not been 

reported from other 

sites downwind of urban 

centers but cannot be 

ruled out as urban 

regions expand. 

Miller and McBride (1999) 

Carroll et al. (2003) 

Note: Summary includes the degree of uncertainty regarding the role of N deposition in each effect and the likelihood that these effects may occur elsewhere in the West. 
Annex D. Critical Loads 

D.1. Background 

Critical loads and critical levels are used to express how much deposition of an atmospheric 

pollutant (a “load”) or how large a concentration of an airborne pollutant (a “level”) can be tolerated by 

natural or artificial systems without significant harm or change occurring in those systems (see 

Section D.2.1). The critical load and critical level approaches to quantifying the effects of pollutants 

attempt to estimate the atmospheric deposition load or concentration that would be likely to cause 

environmental harm. The expectation is that environmental harm can be avoided by keeping pollution 

levels or loads below these critical values. This approach is commonly used to estimate loads or levels of 

pollution required to protect lakes, streams, or forest soils from environmental harm. The basic principles 

are, however, transferable to any sensitive receptor. Since the present evaluation deals primarily with the 

effects of atmospheric deposition of S and N compounds, this chapter focuses on critical loads more than 

critical levels. 

Most critical load studies in North America have been undertaken in Canada. The critical load 

approach has been used in Canada to design emission reduction programs (Jeffries and Lam, 1993; 

RMCC, 1990). Modeling of critical loads for the 1997 Canadian Acid Rain Assessment (Jeffries, 1997) 

was conducted for six regional clusters of lakes, four in eastern Canada, one in Alberta, but also the 

Adirondack Mountains in New York. More recently, critical loads have been determined and mapped for 

waters (Aherne et al., 2004; Dupont et al., 2005; Henriksen et al., 2002; Hindar et al., 2001; Watmough 

et al., 2005) and forest soils (Arp et al., 1996; Moayeri et al., 2001; Ouimet et al., 2006; Watmough and 

Dillon, 2003), for a number of regions in eastern Canada. There have also been a number of regional 

critical loads studies (cf. Henriksen and Dillion, 2001; Ouimet et al., 2006) focused on acid-sensitive 

lakes on the Canadian pre-Cambrian shield. Much of this work is summarized and presented, along with 

steady state critical load maps for eastern Canada, in the 2004 Canadian Acid Deposition Science 

Assessment (Jeffries et al., 2005). 

At the regional, cross-border level, critical loads in northeastern North America have been 

developed by a joint U.S.-Canadian cooperative. The Conference of New England Governors and Eastern 

Canadian Premiers (NEG/ECP) has undertaken a program with the objective to “estimate sustainable 

acidic deposition rates and exceedances for upland forests representative of the New England States and 

the Eastern Canadian Provinces...” (NEG/ECP Forest Mapping Group, 2001). The Forest Mapping 

Working Group within the NEG/ECP conducts regional assessments of the sensitivity of northeastern 

North American forests to current and projected S and N emissions levels. The group is charged with 

identifying specific forested areas most sensitive to continued S and N deposition and estimating 

deposition rates required to maintain forest health and productivity at large spatial scales (Miller and 

McDonald, 2006). The NEG/ECP has also provided estimates of critical loads for surface waters in 

northeastern North America (Dupont et al., 2005). 

Aside from the NEG/ECP studies, the use of critical loads to assess S and N deposition effects in 

the U.S. has not been as geographically extensive as elsewhere in North America or Europe. Most critical 

loads studies in the U.S. have focused on smaller sub-regional areas or individual sites. Critical loads 

studies for forests in the U.S. have been centered in the northeast and have usually used a catchmentbased approach (Aber et al., 2003; Driscoll et al., 2003d; Pardo and Driscoll, 1993, 1996). Critical load 

studies for surface waters have been more extensive along the eastern seaboard. Critical loads have been 

estimated for lakes in the Northeast (Driscoll et al., 2001b; Pembrook, 2004) and for streams in the MidAtlantic States and central Appalachians (Sverdrup et al., 1992; Sullivan et al., 2004). In the western U.S. 

there have been a few studies of critical loads for acidification of surface waters (Sullivan et al., 2004). 

 D-1
The primary concern in the West, however, has been the critical load of N deposition affecting both 

terrestrial and aquatic resources through eutrophication and/or through N enrichment and its impact on 

community structure (Baron et al., 1994, 2000; Baron, 2006; Bowman et al., 2006; Burns, 2004; Fenn, 

2003a; Nydick et al., 2003, 2004b; Stevens et al., 2004; Williams and Tonnessen, 2000; Wolfe et al., 

2003). 

The critical load approach has been used extensively in Europe for organizing information about 

effects, and for specifying emissions reductions that would be required to protect ecosystems and other 

sensitive receptors from the harmful effects of atmospheric S and N) deposition. During the 1970s, it was 

recognized that transboundary air pollution in Europe had adverse ecological and economic 

consequences. In response, the countries of the UN Economic Commission for Europe (UNECE) 

developed the Convention on Long-range Transboundary Air Pollution (LRTAP), the first international 

legally binding instrument to deal with problems of air pollution on a broad regional basis (see 

http://www.unece.org/env/lrtap). Signed in 1979, it entered into force in 1983. The LRTAP Convention 

requires that its Parties cooperate in research into the effects of S compounds and other major air 

pollutants on the environment, including agriculture, forestry, natural vegetation, aquatic ecosystems, and 

materials. To this end, the Executive Body for the Convention established a Working Group on Effects 

(WGE) that is supported by a number of International Cooperative Programmes (ICPs). The ICP for 

Mapping and Modeling generated maps of critical loads for all of Europe in 1995 (Posch et al., 1995). 

Those maps are modified on a continuing basis (e.g., Posch et al., 2001). By comparing current or 

expected future deposition to the critical loads maps, mapped estimates of exceedances have been 

generated. An exceedance is the amount of S and N deposition that occurs at some specific time (past, 

current, or future), above the critical load of deposition that would be required to protect against adverse 

effects on the environment. The maps of estimated exceedances are used in negotiations to regulate 

pollutant emissions in Europe (for example, the 1999 Gothenberg Protocol via the UNECE Convention 

on LRTAP). 

Outside of North America and Europe, there is an increasing use of critical loads for assessment 

purposes, and to inform policy development. Examples include studies in Siberia (Bashkin et al., 1995), 

Thailand (Milindalekha et al., 2001), and South Africa (Van Tienhoven et al., 1995). In China, several 

studies have been carried out to study the sensitivity of surface waters to acidification and the critical 

loads of acid deposition (Duan et al., 2000; Li et al., 2000; Ye et al., 2002; Hao et al., 2001), and to 

calculate the critical loads of S and N acidity for soils at both the local and regional scales (Xie et al., 

1995; Zhao and Seip, 1991; Duan et al., 2000b, 2001). 

D.1.1. The Critical Load Process 

The process of estimating critical loads is not a purely scientific enterprise. Management or policy 

input to the process is needed to insure that the appropriate science is included and the appropriate 

questions are addressed. The critical load process integrates knowledge of a multitude of physical, 

chemical and biological mechanisms affected by ambient air quality, and presents the current scientific 

understanding in a format that is most useful for assessing current or future management practices and 

policy decisions regarding air quality, or the resources affected. The critical loads process provides 

decision-making insight based on both scientific evidence and policy priorities. 

Science and policy are closely coupled in the critical loads process. In the development of critical 

load estimates, it is important to identify those elements that are essentially scientific in nature as opposed 

to those elements that are driven by management or policy priorities. The scientific elements include tasks 

such as: relating ambient air quality to pollutant deposition, quantifying the relationships between 

pollutant deposition and resource responses, identifying the resources at risk to adverse effects, 

understanding the temporal and spatial responses of resources to pollutant deposition, and more. The 

policy-dependent elements include tasks such as: identifying the environmental resources to be protected, 

 D-2
establishing appropriate criteria for different land use areas (e.g., Class I areas, national parks, wildlife 

refuges), defining significant harm to protected resources, and more. When all elements are integrated, it 

is apparent that the critical load process provides a framework for alternate ways of examining and 

understanding the cascade of effects from ambient air quality to resource effects, described in the 

preceding four annexes. Changing scientific assumptions or understanding may result in different critical 

load estimates for the same resources. Changing policy or management assumptions or priorities may also 

result in different critical load estimates. 

There is, therefore, no single “definitive” critical load for a natural resource. Critical load estimates 

are explicitly linked to policy, but their reliability is conditioned on the soundness of the underlying 

science. As elements of the critical load process change, the critical load estimates will change to reflect 

both the current state-of-knowledge and policy priorities. Changes in scientific understanding may 

include: new dose-response relationships, better resource maps and inventories, larger survey datasets, 

continuing time series monitoring, improved numerical models, etc. Changes in the policy elements may 

include: new definitions of harm, new mandates for resource protection, focus on new pollutants, or 

inclusion of perceived new threats that may exacerbate the pollutant effects (e.g., climate change). 

The critical load process is thus an iterative process — as science changes, the content is updated; 

as policy needs change, the content is re-directed. Being iterative, the process allows incremental 

improvement in understanding resource responses to ambient air quality. Individual elements of the 

process can be replaced as needed to reflect new science or policy. Continuing to update the process may 

reduce uncertainty and risk, as new data or techniques allow refinement of existing pieces. The piecewise 

nature of the process provides adaptability as new policy concerns arise, such as new pollutants or 

mandates. As the critical load process advances, a “library” of critical load estimates will result. 

Examining and comparing these accumulated results, and their underlying scientific and policy bases, 

may produce a “weight of evidence” consensus, even if any single estimate entails substantial uncertainty. 

D.1.2. Organization of this Annex 

This Annex is intended as a review of the current state of critical loads science. It is not the 

intention to address questions of management or policy other than to point out where these activities 

influence the critical loads process. The material in Section D.2 presents necessary definitions and 

describes the conceptual framework for a critical load analysis. This framework identifies those elements 

that are primarily scientific in nature and those elements that require policy input. The framework also 

describes the steps that are taken in deriving a critical load estimate for a given resource. It is not an 

objective of this Annex to provide details of all critical loads studies that have been implemented in the 

U.S. or elsewhere. The conceptual framework, however, provides a generalized summary of the steps 

most critical loads studies have followed. Section D.3 discusses the time frame of responses for 

implementation of a critical load. Time frames of resource response are often ignored, or assumed 

implicitly, in defining a critical load analysis. The time required to implement the policy and technology 

to achieve a critical load can also affect the responses of the resources at risk. The time frames of 

response are important for selecting the data and models used to estimate critical loads. Section D-4 

summarizes the tools (models and modeling approaches) commonly used in calculating critical loads. The 

Annex concludes in Section 5 with a summary of the current agreement on critical loads uses in the U.S. 

that was a product of the Multi-Agency Workshop on Critical Loads held in 2006. The workshop 

produced a series of recommendations for current and future activities related to critical loads analyses in 

the U.S. 

 D-3
D.2. Definitions and Conceptual Approach 

D.2.1. Critical Load Definitions 

Critical loads and critical levels are used to express how much deposition of an atmospheric 

pollutant (a “load”) or how large a concentration of an airborne pollutant (a “level”) can be tolerated by 

natural or artificial systems without significant harm or change occurring in those systems. The generally 

accepted definition of a critical load or a critical level of atmospheric pollutants emerged from a pair of 

international workshops held in the late 1980s. The workshop participants defined a critical load or a 

critical level as “a quantitative estimate of an exposure to one or more pollutants below which significant 

harmful effects on specified sensitive elements of the environment do not occur according to present 

knowledge.” 

This evaluation deals primarily with the effects of atmospheric deposition of S and N compounds. 

This Annex, therefore, will deal exclusively with the concept of critical loads of S and N compounds from 

atmospheric deposition. Critical levels of pollutant concentration will not be addressed. As discussed in 

previous annexes, the deposition of both S and N has acidifying effects on receptors (Annex B), and the 

deposition of oxidized and/or reduced N compounds can produce eutrophication or nutrient-enrichment 

effects in receptors (Annex C). The following material, therefore, will focus on critical loads of S and N 

for acidification effects, and on critical loads of N for nutrient effects. 

In addition to the generic definition of a critical load/level presented above, the participants in the 

second international workshop (the Skokloster Workshop) developed a number of specific definitions 

related to known atmospheric pollutants. Two of those definitions are relevant to this Annex. 

Recognizing that both S and N compounds contribute to the acidity of deposition, the workshop 

participants developed a definition for critical loads of S and N for acidification of an ecosystem: “the 

highest deposition of acidifying compounds that will not cause chemical changes leading to long-term 

harmful effects on ecosystem structure and function according to present knowledge.” Recognizing that N 

in both oxidized (NOY = NO +, NO2 + NO2 +, NO3

−

) and reduced (NHX = NH3 + NH4

+

) forms in 

deposition may influence the eutrophication and nutrient balances of ecosystems, the workshop 

participants defined the critical load of N for nutrient effects in an ecosystem as “the highest deposition of 

N as NHX and/or NOY below which harmful effects in ecosystem structure and function do not occur 

according to present knowledge.” 

All three definitions can be applied to different receptors in a number of different environments 

(e.g., terrestrial ecosystems, transitional ecosystems, aquatic ecosystems, groundwater, agricultural crops, 

etc.). A sensitive element can constitute a part of, or the whole of, an ecosystem. Harmful effects can 

occur to individual organisms, to populations, or to entire communities within an ecosystem. Harmful 

effects can also be defined at the level of the ecosystem itself as changes in ecosystem processes, 

structure, and/or function. 

While the concepts expressed in these definitions of critical loads and levels are easily understood 

and intuitively satisfying, the application of the critical load concept requires careful consideration and 

definition of a number of terms and procedures. It is apparent that there can be many different critical 

load values for a given atmospheric pollutant depending on the receptor or sensitive element(s) being 

considered. There can also be multiple different atmospheric pollutants that can produce the same harmful 

effects in a given receptor. Therefore, the critical load of a given pollutant can potentially be dependent on 

the deposition and/or atmospheric concentration of other pollutant species. Finally, the same atmospheric 

pollutant can produce a variety of different disturbances in a sensitive ecosystem that might occur at 

different pollutant loads. For example, N deposition produces both nutrient and acidification effects and 

the critical load of N for each type of disturbance may be different. 

 D-4
Therefore, to derive a quantitative estimate of the critical load of an atmospheric pollutant, a 

number of factors must be identified and defined Figure D-1. These include disturbance type, receptor, 

sensitive elements, and definition of what constitutes significant harm. In addition, a numerical 

relationship between pollutant deposition and the identified receptor response must be formulated, 

generally based on either an empirical dose-response relationship or a steady state or dynamic numerical 

model simulation. The next section outlines the steps (decisions) that must be taken to implement this 

process. 

D.2.2. Critical Load Analysis Procedures 

The development of a quantitative critical load estimate requires a number of steps. In this 

discussion, Figure D-1 is used to illustrate the procedure. The figure is simplified to facilitate general 

discussion and does not represent the full complexity of the choices that must be made, or the scientific 

understanding underlying those choices. 

Table D-1 An example of the matrix of information that must be considered in the definition and 

calculation of critical loads. Note that multiple alternative biological indicators, critical 

biological responses, chemical indicators, and critical chemical limits 

There are eight general steps that must be taken to define the basic critical load question in any 

analysis. 

1. Identify the ecosystem disturbance of concern (acidification, eutrophication, etc.). Not all 

disturbances will occur in all regions or at all sites, and the degree of disturbance may vary across 

landscape areas within a given region or site. 

 D-5
2. Identify the landscape receptors subjected to the disturbance (forests, surface waters, crops, etc.) 

Receptor sensitivity may vary locally and/or regionally, and the hierarchy of receptors most 

sensitive to a particular type of disturbance may vary as well. 

3. Identify the biological indicators within each receptor that are affected by atmospheric deposition 

(individual organism, species, population, or community characteristics). Indicators will vary 

geographically and perhaps locally within a given receptor type 

4. Establish the critical biological responses that define “significant harm” to the biological 

indicators (presence/absence, loss of condition, reduced productivity, species shifts, etc.). 

Significant harm may be defined differently for biological indicators that are already at risk from 

other stressors, or for indicators that are perceived as “more valued.” 

5. Identify the chemical indicators or variables that produce or are otherwise associated with the 

harmful responses of the biological indicators (stream water pH, Al concentration, soil base 

saturation, etc.). In some cases, the use of relatively easily measured chemical indicators (e.g., 

surface water pH or ANC) may be used as a surrogate for chemical indicators that are more 

difficult to measure (e.g., Al concentration). 

6. Determine the critical chemical limits for the chemical indicators at which the harmful responses 

to the biological indicators occur (e.g., pH <5, base saturation <5%, Al concentrations >100 μg/L, 

etc.). Critical limits may be thresholds for indicator responses such as presence/absence, or may 

take on a continuous range of values for continuous indicator responses such as productivity or 

species richness. Critical limits may vary regionally or locally depending on factors such as 

temperature, existence of refugia, or compensatory factors (e.g., high calcium concentration 

mitigates the toxicity of Al to fish and plant roots). 

7. Identify the atmospheric pollutants that control (affect) the pertinent chemical indicators 

(deposition of SO4

2−

, NO3, NH4, HNO3, etc.). Multiple pollutants can affect the same chemical 

variable. The relative importance of each pollutant in producing a given chemical response can 

vary spatially and temporally. 

8. Determine the critical pollutant loads (often in units of kg/ha/yr deposition of S or NO3

–

N, etc.) at 

which the chemical indicators reach their critical limits. Critical pollutant loads usually include 

both wet and dry forms of pollutant deposition. The critical pollutant load may vary regionally 

within a receptor or locally within a site (as factors such as elevation or soil depth vary) and may 

vary temporally at the same location (as accumulated deposition alters chemical responses). 

The definition of the critical load problem for a region or individual site generally requires working 

down the table from top to bottom (Table D-1) What is the disturbance? What receptors are affected? 

What indicator organisms are, or were previously present and observable? What chemical indicators are 

changing and can be measured? What atmospheric pollutant is driving the changes in the chemical 

indicators? 

The derivation of a quantitative estimate of a critical load generally requires working from the 

bottom of the table back towards the top, as indicated by the arrows in Table D-1. What is the maximum 

load of a pollutant that will cause a shift in the chemical indicator to its critical limit such that a critical 

indicator response occurs, or does not occur? From this point of view, it can be seen that steps 8 and 6 

require the development of dose-response functions for the components of the ecosystem being 

considered (arrows in Table D-1). Step 8 describes the response of the chemical indicator as a function of 

the pollutant load, and Step 6 describes the responses of the biological indicator as a function of the 

chemical variable. As discussed in later sections, these response functions can be derived using empirical 

(e.g., statistical) or process-based (e.g., mechanistic) models that are either time-invariant (static or steady 

state) or time-variable (dynamic). 

 D-6
Each step in the development of the critical load, as summarized in Table D-1 can be classified as 

either a predominantly scientific task or as a task benefiting from, or perhaps requiring, collaboration and 

input from scientists, decision-makers, and other interested parties. For instance, tasks 1, 2, and 3 can be 

viewed as predominantly scientific tasks that can be completed by asking questions of fact. At task 4, 

however, questions of what defines “significant harm” entail subjective elements that cannot be 

determined by scientific techniques alone. In anticipation of the ultimate use of the critical load definition 

to set policy or establish management strategies, it is appropriate that political, socioeconomic, or perhaps 

ethical considerations be brought to bear in defining “significant harm.” To define “harm” is to imply a 

corrective action, the cost of which will have to be borne by someone. Having reached agreement on task 

4, however, tasks 5, 6, 7, and 8 are again predominantly scientific in nature, requiring determination of the 

causal links, represented as response functions or models, leading from the loading of the pollutant to the 

defined “significant harm.” 

This procedure will almost certainly result in calculation of multiple critical load values for a given 

pollutant and analysis location. The multiple solutions derive from the nested sequence of disturbances, 

receptors, and biological indicators that must be considered for a given pollutant. Multiple critical load 

values may also arise from an inability to agree on a single definition of “significant harm” at step 4. 

Finally, there is the inescapable heterogeneity of all natural environments. Consider soils for instance. The 

high spatial variability of soils almost guarantees that for any reasonably sized soil-based “receptor” that 

might be defined in a critical load analysis, there will be a continuum of critical load values for any 

indicator chosen. The range of this continuum of values may be narrow enough to be ignored, but in any 

critical load analysis there is nevertheless an a priori expectation of multiple values, or of a range of 

values. 

The existence of multiple estimates of critical loads for a given pollutant and receptor should 

present no real problem. Examination of the range of critical loads derived may be deemed useful in 

subsequent discussions of the analysis, and in the decision-making steps that may follow critical load 

calculation. For instance, the lowest critical load of all those derived may be adopted as “the” critical 

load, as is often done in Europe. This however, is a policy choice. The scientific task is the derivation of 

the multiple values using best available information. 

D.2.3. Target Load Definition 

As seen in the previous section, it is expected that a potentially large number of critical load values 

may be objectively determined for a given atmospheric pollutant and a given receptor. Like the definition 

of “significant harm,” the choice of which critical load value to use for management or decision-making 

is subjective, and should be driven by socioeconomic, political, and ethical considerations. The target load 

concept was developed to address these issues. Target loads are deposition loads of a given pollutant, 

based on critical load estimates for the pollutant, which incorporate policy and/or management decisions 

about the amount of pollutant deposition, and therefore the amount of resource damage that is deemed 

acceptable. Target loads can be set at, above, or below the various critical loads. If the target load is set 

above some of the estimated critical loads, one accepts the inevitability that some of the ecosystem 

components, generally the most sensitive, will be adversely affected. If the target load is set below all of 

the estimated critical loads, a safety margin has been established to account for uncertainty inherent in the 

process. 

Given the spatial heterogeneity of natural systems, target loads might also be used to provide some 

measure of “cumulative resource protection.” As discussed above, there typically exists a range of critical 

loads for a particular “significant harm” in a particular receptor. Selecting a target load within the range 

will provide protection for the fraction of the receptor with critical loads above the chosen target load, 

whereas that fraction with critical loads below the chosen target load will be expected to suffer some harm 

at that deposition level. In this way, it is possible to use the target load to define protection for some 

 D-7
cumulative proportion of the receptor (i.e., a target load for protection of 95% of the resource from 

“significant harm”). 

While most of the steps involved in estimating critical loads depend on sound, objective scientific 

analysis, the selection of target loads is almost entirely a subjective judgment. The selection of target 

loads must begin with reliable estimates of critical loads to set the constraints, and define the expected 

consequences of the target load choices. Nevertheless, the final decisions of which indicators are the key 

indicators, how much cumulative resource should be protected, how much sooner or later resource 

protection will be implemented, cannot be answered scientifically. Political, socioeconomic, and ethical 

considerations will form the basis of the final target load selections. Frequently, the legal mandates for 

various public lands would have a determinant influence on the selection of target loads. For instance, 

Federal Class I areas may be held to one standard of harm because of mandates to protect “natural 

condition,” whereas Federal mixed-use lands may be held to a different standard of harm, and cropland to 

yet another standard of harm. 

It is also important to note that scientific understanding, modeling approaches, and the data used to 

estimate critical loads are continually improving. Furthermore, the political, economic, and social 

environments surrounding selection of target loads are also constantly shifting. Therefore, the analysis 

and estimation of critical and target loads must be an iterative process. 

D.3. Time Frame of Response 

The critical load definitions and procedures discussed in the previous section do not explicitly 

consider the time frame of ecosystem response. When is “significant harm” expected? How long will it be 

before existing harm is reversed? When should critical loads be implemented? How long should a critical 

load be maintained? The use of critical and target loads in resource management always has some time 

frame of expected response, and some context of management priorities. For instance, it may be that a 

target load well below the critical load would hasten the recovery of a receptor with existing harm. Or, it 

may be that a receptor that has not yet been damaged can sustain a target load above the critical load for 

some finite period before incurring “significant harm.” Such time frames can be very long (many decades 

or centuries). 

The time frame of response between implementation of a critical load and the corresponding 

changes in biological or chemical indicators is a potentially important factor in establishing critical load 

analysis procedures and in selecting the final target load. Analyses can be designed to provide estimates 

of either “steady state critical loads” or “dynamic critical loads” depending on the perceived, or 

mandated, importance of the time frame of response and the types of models (transfer functions) used. 

Steady state critical loads analyses provide estimates of the long-term sustainable deposition of a 

pollutant that will not cause “significant harm” to a receptor. This is the relevant information needed for 

any receptor to provide protection from damage by the pollutant in perpetuity as the receptor comes into 

equilibrium with the pollutant critical load (the implicit purpose of steady state analyses). However, no 

information is given concerning the time to achieve the equilibrium or what may happen to the receptor 

along the path to equilibrium. Estimated steady state critical loads for receptors that are currently 

damaged provide no information concerning when the desired long-term sustainable protection will occur 

and the existing “significant harm” will be mitigated. There exists the possibility that receptors with no 

current damage could suffer “significant harm” while waiting for implementation of the critical load. The 

possible occurrence, timing, and duration of such “interim periods of harm” are not the subject of steady 

state analyses. 

Dynamic critical loads analyses provide estimates of a specifically scheduled deposition load of a 

pollutant that will not result in “significant harm” to a receptor at a specified time. This is the relevant 

information needed for any receptor to provide protection from damage by the pollutant within a specified 

time frame (the explicit purpose of dynamic analyses). However, care should be taken in interpreting the 

 D-8
results of dynamic analyses to ensure that “significant harm” to the receptor does not occur after the 

specific timetable has been completed. Many receptors can tolerate higher loads of a pollutant for a few 

decades (a common length of specified schedules for dynamic analyses) than can be sustained over longer 

periods. The use of dynamic critical load estimates in such cases may provide protection from harm 

during a time frame of immediate interest, but ultimately fail to provide long-term protection, unless these 

issues are considered. 

D.3.1. Steady State Critical Loads 

If the time frame of response is not important, for instance, if the target load is to provide long-term 

sustainable protection and the immediacy of the responses is not relevant, the use of static or steady state 

models (response functions) is justified in the critical load analysis procedure. Using steady state models 

to estimate critical loads and compare the estimated critical load to current or future deposition, only two 

cases can be distinguished: current or future deposition is below the critical load; or current or future 

deposition exceeds the critical load. In the first case, no problem is apparent, and no target load is deemed 

necessary, unless increases in deposition are anticipated. In the second case, there is by definition an 

increased risk of “significant harm” to the receptor and selection of a target load for resource protection is 

indicated. 

The lack of explicit consideration of time in a steady state critical load analysis can lead to 

assumptions that are frequently not warranted. The critical load derived in a steady state analysis is an 

estimate of the long-term, constant deposition that a receptor can tolerate with no significant harm after it 

has equilibrated with the critical load deposition. However, biological and geochemical processes that 

affect a receptor may delay the attainment of equilibrium (steady state condition) for years, decades, or 

even centuries. By definition, steady state critical loads do not provide any information on these time 

scales. As a result, it is often assumed that reducing deposition to, or below the steady state critical load 

value will immediately eliminate or mitigate “significant harm.” That is, it is assumed that the chemical 

indicator affected by the atmospheric pollutant immediately attains a non-critical value upon 

implementation of the critical load, and that there is immediate biological recovery as well. As discussed 

in the next section, these assumptions may not be valid. 

D.3.2. Dynamic Critical Loads 

The time frame of receptor response is important if the establishment of target loads is tied to 

defined schedules of deposition change or receptor responses. The use of time-dependent or dynamic 

model response functions will be necessary if the critical load analysis considers the response time frame. 

In the cascade of events that occur from changed deposition of an atmospheric pollutant to development 

of responses of key biological indicators, there are many processes in natural systems that are time and/or 

resource dependent and therefore can introduce delays in the response pattern. In the decision-making 

process leading to the adoption of target loads, there are likewise considerations of when deposition 

changes can be initiated and completed and when biological indicator responses are desired. With 

dynamic models, either empirical or process-based, a wide range of estimated critical loads can be 

derived for comparison with current or future deposition depending on the temporal constraints imposed 

on the critical load analysis. Temporal constraints that can be imposed on a given critical load analysis are 

determined by: the receptor responses—the characteristic time scales and inherent lags of the receptor 

being analyzed (a function of hydrobiogeochemical processes in the receptor); and the deposition 

schedules—the years designated for beginning and completing the changes in deposition and for 

evaluating the indicator responses (a function of political, socioeconomic, and management constraints). 

 D-9
D.3.3. Receptor Responses 

The general conceptual model of the linkages among pollutant deposition and the responses of 

chemical and biological indicators can be characterized as a series of delays. In the causal chain from 

deposition of pollutant to damage to key biological indicators there are two major links that can give rise 

to delays. First, hydrological and biogeochemical processes in catchments can delay the responses of 

chemical indicators. Second, biological processes and population dynamics can further delay the response 

of biological indicators. The pattern of chemical and biological indicator responses can be represented 

conceptually (Figure D-1) (adapted from Jenkins et al., 2003; Posch et al., 2003). Five stages in the 

conceptual pattern can be distinguished (Figure D-1): 

 Stage 1: Pollutant deposition is below the critical load for either the critical chemical limit or the 

critical biological response, and there is no “significant harm” to the receptor. As long as 

deposition stays below the critical load, this is the ‘ideal’ situation. 

 Stage 2: Pollutant deposition rises above the critical load, but chemical and biological indicators 

still do not violate their respective criteria because there is a delay. No damage is likely to occur 

at this stage, despite the exceedance of the critical load. The time between the first exceedance of 

the CL and first violation of the biological criterion (first occurrence of “significant harm”) is 

called the Damage Delay Time (DDT = t3–t1). 

 Stage 3: Pollutant deposition is above the critical load and both the chemical and biological 

criteria are violated. Measures to reduce emissions are taken to avoid further harm to the receptor 

and pollutant deposition begins to decrease. 

 Stage 4: Pollutant deposition has been reduced to a level below the critical load, but the chemical 

and biological criteria are still violated, and thus “recovery” has not yet occurred. The time 

between the first non-exceedance of the critical load and the subsequent non-violation of both 

criteria can be called the Recovery Delay Time (RDT = t6–t4). 

 Stage 5: This stage is similar to Stage 1. Pollutant deposition has been reduced to a level below 

the critical load and neither the chemical nor biological criteria are violated. Only at this stage can 

the receptor be considered to have recovered to an undamaged level. 

Stages 2 and 4 can be further subdivided into two sub-stages each: chemical damage and recovery 

delay times (DDTc = t2–t1 and RDTc = t5–t4; dark grey in Figure D-1 and (additional) biological damage 

and recovery delay times (DDTb = t3–t2 and RDTb = t6–t5; light grey). Given opportunities for 

“confounding effects” (i.e., mechanisms not related to acidic deposition but affecting biological 

indicators, such as forest pest infestation or climate change) occurring during the “delay periods,” it is 

clear that unambiguous short-term patterns of recovery of biological indicators are unlikely to be 

observed, even in the presence of rather large declines in pollutant deposition. This has important 

implications for recovery expectations. 

D.3.4. Deposition Schedules 

Dynamic critical loads, by definition, must explicitly account for the receptor time scales and lags 

described above. Therefore, the process of estimating dynamic critical load values for a given pollutant 

must be based on a planned or assumed deposition schedule for changing the pollutant deposition and for 

assessing the receptor responses. Three different time periods are specified, as illustrated in Figure D-2. 

[The nomenclature used here for the three years specified in the deposition schedule conforms to that used 

in European “dynamic target loads analyses” (Posch et al., 2003)]. 

 D-10
Figure D-1. Conceptual patterns of pollutant deposition effects on a chemical indicator and a 

corresponding biological indicator during increasing and decreasing deposition. Critical 

limits and responses for the chemical and biological indicators are indicated as horizontal 

lines, along with the critical load of deposition that produces these levels. The delays between 

the exceedance of the critical load (t1), the violation of the critical chemical limit (t2), and the 

crossing of the critical biological response (t3) are indicated in grey shades, highlighting the 

DDT. Similar delays in chemical and biological recovery during deposition reductions (t4, t5, 

and t6) define the RDT of the system. 

 D-11
Figure D-2. Pollutant deposition patterns for defining the temporal parameters of dynamic critical loads 

analyses. The deposition schedule requires that three years be specified: (1) the year in 

which changes in pollutant deposition are begun, called the protocol year; (2) the year in 

which changes in pollutant deposition are completed, called the implementation year; and (3) 

the year in which the chemical or biological response indicator is evaluated, called the target 

year. 

The first time period is the protocol year when deposition changes moving toward the critical load 

are begun. It will be the case that voluntary or mandated changes in deposition will require a number of 

years to get underway once a critical load has been calculated or target load has been selected. These 

delays in moving toward the critical load will affect the dynamic responses of the chemical and biological 

indicators and, therefore, must be included in the dynamic modeling of receptor response. Before the 

protocol year, it must be assumed that pollutant deposition will be continuing along the pattern of recent 

or historical deposition change or along the pattern dictated by future deposition scenarios already 

planned and assumed to take effect. 

The second time period is the implementation year when deposition changes are complete and 

pollutant deposition has reached the desired critical or target load. It is likely that a number of years will 

elapse between the time changes in deposition toward the critical load are initiated, and the time when 

they are completed. During this transition period, pollutant deposition continues at a rate higher, or lower, 

than the critical load. The effects of these years of deposition inputs above or below the critical load value 

will affect the dynamic responses of the chemical and biological indicators, and must also be included in 

the dynamic modeling of receptor response. It is assumed in dynamic critical loads analyses that the 

pollutant deposition to the receptor remains constant at the critical load for all years after the 

implementation year. 

The final time period is the target year when the biological indicators are evaluated. Recognizing 

that there are inherent lags in receptor responses following changes in pollutant deposition, it a number of 

years will frequently be allowed to elapse after the implementation year before the receptor responses are 

assessed. It must also be recognized that receptor responses will continue to change over time. Thus, 

selection of the target year will affect attainment of the critical limit. 

The deposition schedule for a dynamic critical load analysis can be driven by a number of 

considerations, and can be organized from protocol year to target year or vice versa. The selection of the 

protocol and implementation years is often a matter of political will and economic possibility. Large-scale 

pollution abatement programs take time to negotiate. Costs or engineering difficulties may delay the start 

of the abatement program and affect the length of time it takes to complete the program once it is begun. 

Once these constraints have been established, it is then possible to select a reasonable target year for 

 D-12
evaluation of the receptor responses. Alternately, resource management mandates might require that 

“significant harm” to receptor indicators be mitigated or eliminated by a certain time. This establishes the 

target year for the dynamic analysis and the protocol and implementation years must be selected to allow 

time for any lags in the receptor responses to occur. 

Clearly, there is tension between the two approaches when developing a deposition schedule. It is 

possible, for instance, to defer the protocol and implementation years so far into the future that extensive 

“significant harm” occurs to the receptor indicators in the intervening years. If that damage is especially 

severe, the critical load, when the target year is finally reached, may not be achievable. Similarly, if a 

receptor is currently suffering harm, it is possible to choose a target year for receptor response too close to 

the present day to allow time for the receptor to recover, even if the pollutant deposition was reduced to 

zero immediately. 

Both of these hypothetical scenarios raise important points about dynamic critical load estimates. 

Because time is explicitly incorporated, there are certain dynamic critical loads questions that have no 

answer. Commonly called “you can’t get there from here” problems, these deposition schedules choose 

protocol, implementation, or target years that are inconsistent with the time scales of receptor response. 

For example, setting a target year 5 years in the future for achievement of no “significant harm” in a 

receptor that is currently badly damaged, and has a history of high pollutant loading, may be asking the 

impossible. Critical load estimates derived in this case would require having set the pollutant deposition 

to zero some years in the past. In other words, the state of no significant harm cannot be reached within 

the specified five years regardless of how deposition is changed within that 5 year period (“you can’t get 

there from here”). This problem is moot for steady state critical loads. Steady state critical loads analyses 

will always provide some sensible estimate (zero or finite) of long-term sustainable pollutant deposition 

for every receptor because time is not a factor. Dynamic critical loads analyses, on the other hand, may 

frequently provide non-quantitative results, but these results nonetheless convey useful information 

concerning the current status of the receptor and point out the necessity to continue the analysis with 

modified assumptions or expectations to develop realistic and achievable target load values. 

D.3.5. Long-Term Implications 

The explicit inclusion of time in critical loads estimation provides useful information for managers 

or policymakers when deciding when and how much to alter pollutant emissions and deposition, but the 

dynamic approach leads to implicit assumptions that must be recognized. Focus on the near-term aspects 

of receptor responses (the years included in the deposition schedule) can be misleading. The implicit 

assumption is that having attained the desired biological or chemical response in the target year, nothing 

more will happen, or at least that further changes in the receptor, if they do occur, will not produce 

“significant harm.” Available dynamic model critical load estimates suggest that this is not always true, 

and the long-term implications of dynamic critical load estimates should be examined carefully. 

The dynamic critical loads procedure assumes that pollutant deposition remains constant at the 

critical load from the implementation year until the target year, and the assessment of receptor response. 

Model simulations can be continued, assuming deposition at the constant critical load value, for a number 

of years after the specified target year to ensure that lags in the receptor response will not result in 

“significant harm” appearing at some later date, even though it was not present in the target year. Some 

receptors have chemical or biological lags that are many decades long, or longer. Critical loads analyses 

based on deposition schedules that cover only 20 to 30 years can produce the unwanted result of 

estimating a dynamic critical load that avoids “significant harm” to the receptor in the target year, only to 

have the receptor suffer damage some years later. 

It is important to determine which receptor responses and which deposition schedules might lead to 

such an unwanted result. There are some general guidelines concerning this potential problem. For any 

receptor for which there is currently no “significant harm,” the estimated critical load provided by the 

 D-13
dynamic approach will be one that brings the biological or chemical indicators to the threshold of harm 

without crossing it (the definition of the critical load). However, this dynamic critical load has then put 

the biological or chemical indicator on a trajectory away from its currently good status and toward the 

threshold of harm. In most cases, the trajectory toward harm will continue past the target year and 

“significant harm” will occur in these receptors some time after the target year. 

On the other hand, in any receptor for which there currently is “significant harm,” the estimated 

critical load provided by the dynamic approach will be one that brings the biological and chemical 

indicators to the threshold of harm and crosses it, just to return to a state of no harm. This dynamic critical 

load puts the biological or chemical indicator on a trajectory away from harm and towards good status. In 

most cases, it is likely that the upward trajectory will continue past the target year and significant further 

recovery will occur in these receptors after the target year. 

These are, however, merely generalizations. Depending on the geochemical and biological process 

affecting a receptor, there exist possibilities that upward trajectories could become downward trajectories 

sometime after the target year and vice versa. The most straightforward procedure is to run the model(s) 

used in the dynamic critical loads analyses for a sufficiently long time after the target year so that any 

reasonable chance of delayed damage to the receptor or delayed recovery is either discovered or 

discounted. 

D.3.6. Steady State and Dynamic Critical Loads: Complementary 

Information 

There is no “correct” choice to be made between steady state and dynamic critical loads analyses. 

Both provide estimates of pollutant loads that are intended to avoid “significant harm” to a receptor. Both 

are valid scientific expressions of the receptor’s sensitivity to the pollutant. They differ primarily in the 

time scales implicit in their use. Steady state analyses provide critical load estimates for long-term 

sustainable protection, but ignore questions of near-term recovery and avoidance of interim harm. 

Dynamic analyses provide critical load estimates that can be used to examine short-term or long-term 

options for recovery of damaged systems and avoidance of interim harm, but may ignore the ultimate 

long-term sustainability of the estimated deposition, which may evolve over centuries. Clearly, the two 

approaches provide complementary information. 

The complementary nature of the two critical loads approaches can be exploited in the selection of 

a target load estimate for a receptor. Selecting the lower of the two critical load estimates for the receptor 

(steady state or dynamic) should result in facilitation of recovery, or avoidance of harm, in the short-term, 

as well as long-term sustainability once the receptor has reached equilibrium with the selected target load. 

Multiple lines of evidence reflecting multiple critical load values can provide important information that 

collectively provides the foundation for management decision-making. 

The procedures, data requirements, and computational resources needed for each of the two critical 

loads approaches may differ significantly depending on the models (response functions) adopted for the 

analyses. Differences in the approaches may also depend on the disturbance, receptor, or indicator being 

evaluated. The next two sections discuss the disturbances, receptors, and indicators relevant to deposition 

of S and N, and the models used for calculation of critical load estimates by each approach. 

D.4. Calculation of Critical Loads 

The derivation of quantitative estimates of critical loads requires the development of dose-response 

functions (models) for the components of the ecosystem being considered. Models are needed to describe 

two different classes of dose-response function. Geochemical models describe the changes in the 

 D-14
chemical indicators that occur as functions of changes in the pollutant loads. Biological response models 

describe the changes in the biological indicators as functions of changes in the chemical variables. 

Models for either class of dose-response function can be developed using two general approaches. 

Empirical models are based on direct observations of indicator response to pollutant deposition. They are 

usually developed using statistical techniques and generally do not contain a mechanistic pathway linking 

pollutant deposition to indicator response. Process-based models are based on conceptual representations 

of chemical and biological mechanisms, and use mathematical equations to express the inter-relationships 

among system components. Whereas process-based models frequently also use observations of receptor 

responses to pollutant deposition for calibration and validation, they are fundamentally different from 

empirical models in that mechanistic pathways from pollutant deposition to indicator response are 

explicitly included in the model structures. In general, the geochemical models used to link S and N 

deposition to chemical indicator response are mostly process-based, whereas biological responses to 

acidification by S and N are mostly modeled using empirical approaches. Finally, both geochemical 

models and biological response models, whether developed using either empirical or process-based 

approaches, can be further classified as static or dynamic depending on whether or not time is included 

among variables. 

D.4.1. Empirical Models 

Empirical models can be constructed relating either chemical or biological indicators to pollutant 

deposition. The empirical models currently in use for calculating critical loads employ steady state 

approaches. This is not a necessary constraint, however, because even with no knowledge of the 

underlying mechanisms, there exist many statistical techniques for relating the time-series of outputs and 

inputs of ecosystems. The reason empirical critical loads models are usually based on a steady state 

approach is primarily because time-series data of long enough duration to parameterize dynamic 

empirical models are not generally available. In general, empirical models require less complex datasets, 

are more straightforward to implement, and are easier to understand than process-based models. For some 

receptors, the lack of conceptual understanding of the mechanisms of indicator response to pollutant 

deposition renders the use of process-based models problematic, and the use of empirical models is then 

the only viable critical load analysis approach. 

D.4.2. Acidification Effects of Sulfur and Nitrogen 

Empirical models of critical loads for acidity assign critical loads to soils on the basis of soil 

mineralogy and chemistry (UNECE, 2004). For example, at the Critical Loads Workshop at Skokloster 

soil forming materials were divided into five classes on the basis of the dominant weatherable minerals. A 

critical load range, rather than a single value, was assigned to each of these classes according to the 

amount of acidity that could be neutralized by the base cations produced by mineral weathering. The 

classification of soil materials developed at Skokloster used a relatively small range of primary silicate 

minerals and carbonates. A larger range of minerals was classified by Sverdrup and Warfvinge (1988) and 

Sverdrup et al. (1990) for use in the PROFILE model (Warfvinge and Sverdrup, 1992). 

D.4.3. Nutrient Effects of Nitrogen 

Empirical models of critical loads for nutrient N have been developed in Europe within LRTAP to 

set critical loads for atmospheric N deposition (e.g., UNECE, 2004). Empirical critical loads of N for 

natural and semi-natural terrestrial ecosystems and wetland ecosystems were first presented in a 

 D-15
background document for the 1992 workshop on critical loads held under the UNECE LRTAP 

Convention at Lökeberg, Sweden (Bobbink et al., 1992b). A number of European expert workshops have 

taken place to reach agreement among specialists regarding the impacts of N for various ecosystems and 

related critical loads (Achermann and Bobbink, 2003; Bobbink et al., 1992b, 1996.; Hornung et al., 1995; 

Nilsson and Grennfelt, 1988). Empirical relationships have also recently been developed in the U.S., 

particularly for western ecosystems (e.g., Baron et al., 1994, 2000; Burns, 2004; Fenn et al., 2003; Nydick 

et al., 2004a; Williams and Tonnessen, 2000). 

D.4.4. Process-Based Models 

A number of process-based models are currently in use for calculating critical loads using both 

steady state and dynamic approaches. Developing a process-based modeling approach that includes all 

appropriate chemical and biological indicators is a complex task. Some process models incorporate both 

geochemical and biological response mechanisms in one program. An alternate approach is to chain 

individual process-based models, for example taking the output of a geochemical model and passing it as 

input to a biological-response model. In either approach, the level of process complexity varies a great 

deal among the various available models. The choice of a particular process-based modeling approach to 

be used in a critical load analysis (dynamic or steady state, all-in-one or chained, etc.) will depend on the 

scope of the analysis, the quality and quantity of available data, and the availability of resources (time and 

money) for the analysis. The following is a brief overview of some of the process-based biogeochemical 

models that are commonly used to calculate critical loads. 

D.4.5. Steady State Models 

The Simple Mass Balance (SMB) model is the standard model for calculating critical loads for 

terrestrial ecosystems under the LRTAP Convention (Sverdrup et al., 1990; Sverdrup and De Vries, 1994). 

The SMB model is a single-layer model. There also exist multi-layer steady state models for calculating 

critical loads in terrestrial ecosystems. Examples are the MACAL model (De Vries, 1988) and the widely 

used PROFILE model (Warfvinge and Sverdrup, 1992), which has at its core a model for calculating 

weathering rates from total mineral analyses. 

The Steady State Water Chemistry (SSWC) model (Henriksen et al., 1992; Henriksen and Posch, 

2001; Sverdrup et al., 1990) calculates critical loads of acidity for surface waters, based on the principle 

that acid loads should not exceed the balance of non-marine, non-anthropogenic base cation sources and 

sinks in a catchment, minus a buffer to protect selected biota from being damaged. 

The First-order Acidity Balance (FAB) model for calculating critical loads for surface waters takes 

into account sources and sinks within the lake and its terrestrial catchment. The original version of the 

FAB model was developed and applied to Finland, Norway, and Sweden by Henriksen et al. (1992) and 

Posch et al. (1997). A modified version was first reported in Hindar et al. (2000, 2001) and is described in 

more detail by Henriksen and Posch (2001). 

D.4.6. Dynamic Models 

MAGIC is a lumped-parameter model of intermediate complexity, developed to predict the longterm effects of acidic deposition on surface water chemistry (Cosby et al., 1985a, 1985b, 2001). The 

model simulates soil solution chemistry and surface water chemistry to predict the monthly and annual 

average concentrations of the major ions in these waters. MAGIC consists of: a sub-model in which the 

 D-16
concentrations of major ions are assumed to be governed by simultaneous reactions involving SO42– 

adsorption, cation exchange, dissolution-precipitation-speciation of Al, and dissolution-speciation of 

inorganic carbon (C); and a mass balance sub-model in which the flux of major ions to and from the soil 

is assumed to be controlled by atmospheric inputs, chemical weathering, net uptake and loss in biomass, 

and losses to runoff. At the heart of MAGIC is the size of the pool of exchangeable base cations in the 

soil. As the fluxes to and from this pool change over time owing to changes in atmospheric deposition, the 

chemical equilibria between soil and soil solution shift to give changes in surface water chemistry. The 

degree and rate of change of surface water acidity thus depend both on flux factors and the inherent 

characteristics of the affected soils. MAGIC is described in more detail in Annex C. 

PnET-BGC is an integrated dynamic biogeochemical model that simulates chemical 

transformations of vegetation, soil, and drainage water. It was formulated by adding the sub-model BGC 

(biogeochemistry) to PnET-CN, a model of C, water, and N balances (Aber and Federer, 1992; Aber and 

Driscoll, 1997; Aber et al., 1997), to expand the model to include vegetation and organic matter 

interactions of major elements (i.e., Ca2

+

, Mg2+, K+, Na+, Si, S, P, Al3

+

, Cl−, F−), abiotic soil processes, 

solution speciation, and surface water processes (Gbondo-Tugbawa et al., 2001). The model was initially 

developed for, and applied to, the northern hardwood forest ecosystem. It was tested extensively at the 

Hubbard Brook Experimental Forest, New Hampshire, including a detailed sensitivity analysis of 

parameter values. The model has subsequently been applied to intensively-studied watersheds in the 

Adirondack and Catskill regions of New York and applied regionally to the Adirondacks (Chen and 

Driscoll, 2005b) and northern New England (Chen and Driscoll, 2005a, 2005b). See additional 

description in Annex C. 

Simulation Model for Acidification’s Regional Trends (SMART2) is a soil acidification and 

nutrient cycling model and is an extension of the dynamic soil acidification model SMART (Kros et al., 

1995). The original model was a relatively simple simulation of the response of soil and soil water quality 

to atmospheric inputs. Improvements in SMART2 include processes of canopy interactions, litter fall, root 

decay, mineralization, and root uptake of nutrients. SMART2 has been used primarily in European critical 

loads studies. 

The Soil Acidification in Forest Ecosystems model (SAFE) was developed at the University of 

Lund in Sweden (Alveteg and Sverdrup, 2002; Warfvinge et al., 1993). The main differences between the 

SAFE and MAGIC models are: (a) weathering of base cations is not calibrated for SAFE, but it is 

modeled with the PROFILE sub-model, using soil mineralogy as input (Warfvinge and Sverdrup, 1992); 

(b) SAFE is oriented to soil profiles in which water is assumed to move vertically through several soil 

layers, (c) cation exchange between Al, H, and (divalent) base cations is modeled in SAFE with Gapon 

exchange reactions rather than Gaines-Thomas reactions, and the exchange between the soil matrix and 

soil solution is diffusion-limited. The standard version of SAFE does not include S adsorption although a 

version, in which S adsorption is dependent on SO4

2– concentration and pH of soil solution, has recently 

been developed (Martinson et al., 2003). 

ForSAFE is a mechanistic model that simulates N and C cycling and soil chemistry. Climatic 

drivers within the model include temperature, precipitation, radiation, and deposition. ForSAFE combines 

three established models (SAFE, PnET-CN, and DECOMP). SAFE simulates soil chemistry (e.g., 

chemical weathering, cation exchange, leaching, and solution equilibrium reactions). PnET-CN (Aber et 

al., 1997) is used to predict forest growth within ForSAFE, through the simulation of C fixation, litterfall, 

and C and nutrient allocation. DECOMP (Walse et al., 1998) is a dynamic, multi-layered process-oriented 

decomposition model that incorporates the influences of temperature, moisture, pH, and Al. Very Simple 

Dynamic soil acidification model (VSD) only includes a few key processes, such as cation exchange and 

N immobilization, and a mass balance for cations and N (Posch et al., 2003). VSD does not consider 

seasonal variations, as the time step in the model is one year. The VSD model is based on mass balance 

equations that describe soil input-output fluxes and equations describing the rate-limited (e.g., uptake and 

silicate weathering) and equilibrium (e.g., cation exchange) soil processes. Soil solution chemistry is 

based solely on the net element input from the atmosphere (i.e., deposition minus net uptake minus net 

 D-17
immobilization) and geochemical interactions in the soil (i.e., CO2 equilibria, weathering of carbonates 

and silicates, and cation exchange). VSD simulates a single soil layer with a constant density and a fixed 

depth. The concentration of the soil water leaving the compartment is assumed to be equal to the annual 

precipitation excess. 

D.5. Use of Critical Loads in the U.S. – Current Status 

At the Multi-Agency Critical Loads Workshop for Sulfur and Nitrogen Deposition Effects on 

Freshwater and Terrestrial Ecosystems, convened by the U.S. EPA, the U.S. Forest Service (USFS), the 

National Park Service (NPS), and the USGS in May 2006, approximately 75 scientists, conservation 

representatives, and state and federal agency officials gathered to share information, discuss scientific 

advances, and develop a broad federal strategy for advancing critical loads in the U.S. (U.S. EPA, 2006c). 

The conclusions and recommendations of that workshop are presented below. These conclusions and 

recommendations represent the current understanding of critical loads as scientific tool and policy 

instrument in the U.S. 

The conclusions and recommendations below were reached by the Federal Agencies sponsoring the 

workshop. It is worth noting that some state agencies have pursued the use of critical loads independently 

to link science and policy in addressing the management of natural resources. For instance, in the State of 

Colorado, critical loads for N deposition that were developed for Rocky Mountain National Park (Baron, 

2006) are being used to develop goals for N emissions reductions by the State of Colorado, U.S. EPA, and 

NPS. (See “Nitrogen Deposition Reduction Plan” at http://www.cdphe.state.co.us/ap/rmnp.html) 

D.5.1. Current Recommendations on Critical Loads Uses in the U.S. 

The participants in the Multi-Agency Critical Loads Workshop developed a set of findings and 

recommendations to help advance critical loads usage in the U.S. The “areas of agreement” published in 

the workshop report (U.S. EPA, 2006c) included the following: 

 A critical load is defined as: a quantitative estimate of the exposure to one or more pollutants 

below which significant harmful effects on specific sensitive elements of the environment do not 

occur according to present knowledge (Nilsson and Grennfelt, 1988). 

 Despite reductions in S and N emissions in the U.S., deposition rates still exceed preindustrial 

levels and acidification and eutrophication effects remain widespread. 

 Critical loads can be used to better understand impacts of atmospheric deposition, assess the 

effectiveness of emissions programs, and guide natural resource management. 

 The development of critical loads is a process that is subject to continued development and 

improvement as knowledge advances. 

 Adequate information exists to move forward with the development and limited application of 

critical loads in some regions and ecosystems in the U.S. 

 An intensive research and monitoring agenda should be pursued to support the development and 

refinement of critical loads in the U.S. 

 Critical loads should be based on a matrix of biological and chemical indicators for aquatic and 

terrestrial ecosystems that account for acidification, N saturation, and eutrophication effects and 

are relevant to the geographic area or ecosystem of concern. 

 D-18
 Adequate information exists to establish harmful effect thresholds for some indicators based on 

specific protection and recovery objectives defined by policymakers and managers. 

 Dynamic models provide the most accurate site-specific information and account for timedependent processes, but are generally too data intensive to be applied across large geographic 

areas at present. Simple mass balance models can be applied to current conditions in large 

geographic areas, but in some instances do not adequately highlight some sensitive areas because 

they tend to average conditions across the landscape. Hybrid approaches that link observational 

datasets with dynamic and steady state models represent a useful approach for regionalizing sitespecific information. 

D.5.2. Questions and Limitations Regarding Critical Loads Uses in the 

U.S. 

The participants in the Multi-Agency Critical Loads Workshop also developed a set of “Questions 

Needing Further Discussion” (U.S. EPA, 2006c): 

 What are the appropriate applications of critical load estimates to policy and management issues 

given current knowledge? For applications where buy-in to an incremental process does not exist, 

greater investment in critical loads methods may be needed before this application can be 

pursued. 

 How strong is the relationship between specific indicators, thresholds, and biological responses? 

 What are the suitable interpretations and uses of existing databases for the development of 

national simple mass balance critical load models? 

D.5.3. Critical Loads Research and Monitoring Needs 

Finally, the participants in the Multi-Agency Critical Loads Workshop presented a list of “Critical 

Loads Research and Monitoring Needs” (U.S. EPA, 2006c), which are summarized below. 

D.5.3.1. Emissions and Deposition 

 Update N and S emissions inventories on a state-by-state basis back to the 1900s to correspond 

with methods used in current emissions inventories. 

 Develop NH3 emissions inventory. 

 Improve dry deposition estimates for S and N. 

 Improve total S and N deposition estimates. 

 Measure gaseous NH3 concentrations. 

 Add NH3 deposition measurements to current networks. 

 Improve estimates of total deposition in complex terrain. 

 Develop N and S deposition maps for North America. 

 D-19
D.5.3.2.

D.5.3.3.

D.5.3.4.

Soils 

 Improve spatial coverage and representativeness of soil chemistry databases, particularly in 

sensitive terrain. 

 Increase soil monitoring. 

 Improve estimates of mineral weathering rates. 

 Develop soil archiving and well characterized reference samples to promote cross-laboratory 

comparisons. 

 Expand research on the nature and size of soil nutrient pools. 

 Conduct research on threshold values of soil quality for biologic responses. 

 Determine N supply rates in different soil types. 

 Investigate N soil accumulation rates in arid lands and implications for critical loads. 

Surface Waters 

 Incorporate TIME and LTM surface water monitoring programs into a larger network with better 

geographic coverage (e.g., the West and Southeast). 

 Improve spatial coverage and representativeness of surface water chemistry databases, 

particularly in sensitive and complex terrain. 

 Integrate fixed-site monitoring with regional probability monitoring design. 

 Continue to monitor major drivers of acidity. 

 Build critical loads considerations (e.g., validation, improvement, regionalization) into 

monitoring from the start by combining chemistry, hydrology, deposition and biology, and 

integrating site-specific models and measurements into regional contexts. 

 Expand research to understand what is driving dissolved organic carbon (DOC) changes in the 

East. 

 Analyze the impact of groundwater transport on recovery times. 

Biological Effects 

 Develop better understanding of the link between chemical indicators and biological response 

(e.g., quantify the minimum N level at which plankton communities shift). 

 Conduct additional research on the sequential impacts of N and relationship between N 

deposition and ecosystem impacts. 

 Integrate critical load estimates with biodiversity and climate change interactions. 

 Undertake more research on biological change and “harmful effects” to help establish appropriate 

critical loads thresholds (e.g., in arid lands, what level of productivity of exotic invasive species 

will cause the reduction versus the extinction of native species?). 

 Collect sediment cores from lakes that vary in rates of N deposition to track changes in diatom 

assemblages. 

 D-20
 D-21 

D.5.3.5. Critical Loads Models 

 Improve representation of N dynamics in models. 

 Expand models to include NH3. 

 Improve explicit consideration of changing base cations and DOC. 

 Conduct ground-truthing of forest sensitivity and other models. 

 Integrate water flowpaths into nutrient cycling models since lateral and vertically upward 

flowpaths are common. 

 Understand and quantify uncertainties in models. 

 Conduct site level model comparisons of dynamic and simple mass balance models. 

 Integrate observational databases with steady state and dynamic models. 

 Incorporate capacity to understand and evaluate climate change interactions. 

Table D-2. Biological indicators for the effects of elevated N deposition and related empirical critical 

loads for major ecosystem types (according to the Eunis classification) occurring in Europe. 

Ecosystem Type Biological Effect Indicators 

Empirical 

Critical Load 

(kg N/ha/yr) 

GRASSLANDS AND TALL FORB HABITATS (E) 

Sub-Atlantic semi-dry calcareous 

grassland 

Increased mineralization, nitrification and N leaching; increased tall 

grasses; decreased diversity 

15–25 

Non-Mediterranean dry acid and 

neutral closed grassland 

Increase in nitrophilous graminoids, decline of typical species 10–20 

Inland dune grasslands Decrease in lichens, increase in biomass, accelerated succession 10–20 

Low and medium elevation hay 

meadows 

Increased tall grasses, decreased diversity 20–30 

Mountain hay meadows Increase in nitrophilous graminoids, changes in diversity 10–20 

Moist and wet oligotrophic grasslands Increase in tall graminoids, decreased diversity, decrease in bryophytes 10–25 

Alpine and subalpine meadows Increase in nitrophilous graminoids, changes in diversity 10–15 

Moss and lichen dominated mountain 

summits 

Effects on bryophytes and lichens 5–10 

HEATHLAND HABITATS (F) 

Northern wet heaths Decreased heather dominance, transition heather to grass, decline in 

lichens and mosses 

10–20 

Dry heaths Transition heather to grass, decline in lichens 10–20 

Arctic, alpine, and subalpine scrub 

habitats 

Decline in lichens, mosses, and evergreen shrubs 5–15 
 D-22

COASTAL HABItAT (B) 

Shifting coastal dunes Increased biomass, increased N leaching 10–20 

Coastal stable dune grasslands Increase in tall grasses, decreased prostrate plants, increased N leaching 10–20 

Coastal dune heaths Increase in plant production, increased N leaching, accelerated succession 10–20 

Moist to wet dune slacks Increase in biomasss and tall graminoids 10–25 

MIRE, BOG, AND FEN HABITATS (D) 

Raised and blanket bogs Changed species composition, N saturation of Spagnum 5–10 

Poor fens Increased sedges and vascular plant, negative effects on mosses 10–20 

Rich fens Increase in tall graminoids, decreased diversity, decrease of characteristic 

mosses 

15–35 

Mountain rich fens Increase in vascular plants, decrease in bryophytes 15–25 

FOREST HABITATS (G) 

Mycorrhizae Reduced sporocarp production, reduced below ground species 

composition 

10–20 

Ground vegetation Changed species composition, increased nitrophilous species; increased 

susceptibility to parasites (insects, fungi, virus) 

10–15 

Lichens and algae Increase in algae; decrease in lichens 10–15 

Source: Achermann and Bobbink (2003). Reprinted with permission. 
Annex E. Effects of NOY, NHX, and SOX on 

Structures and Materials 

E.1. Introduction 

The purpose of this chapter is to summarize the research published since the most recent AQCDs 

on materials and structures damage caused by: (1) NO, NO2, and their combination (NO + NO2 = NOX); 

the organic and inorganic reaction products of NOX (denoted as NOZ); and the combination of NOX and 

NOZ (NOX + NOZ = NOY), (2) the effects of NH3 and NHX, and of (3) SOX. Materials and structures 

exposed to the environment are subject to damage from exposure to sunlight, moisture, salt, windblown 

dust, and cycles of temperature and humidity, whether or not air pollutants are present. However, NOY, 

NHX, and SOX air pollutants may cause such damage to be greater or occur more rapidly than with natural 

environmental factors alone. Damage to materials and structures may be physical, potentially affecting 

the durability or maintenance needs of a material or structure, or may be purely aesthetic, affecting only 

the outward appearance of the material or structure. In the case of historical buildings, monuments, or 

artifacts, aesthetic damage may be a relevant concern. 

Note that very extensive work related to materials damage from acidic deposition related to S and 

N was conducted in the 1980s as part of the National Acid Precipitation Assessment Program (NAPAP) 

(NAPAP, 1991). The results of that work are well known to the U.S. EPA (1991) and so are not discussed 

here. In compiling information for this chapter on NOY/NHX/SOX effects, the information presented in the 

1993 NOX AQCD (U.S. EPA, 1993a) and the 2004 PM AQCD (U.S. EPA, 2004) was updated by 

literature searches reaching back to approximately 1992. This update was based on peer-reviewed 

literature, with a focus on studies that were conducted in the U.S., that evaluated effects at realistic 

ambient air pollutant levels, and that treated NOY/NHX/SOX as components of a complex mixture of air 

pollutants. These latter two factors result in an emphasis on studies done with exposures to ambient 

atmospheric pollution, rather than exposures at high levels, e.g., in test chambers. The studies cited in this 

chapter were selected from those found in a broad literature search based on criteria that they address 

damage caused by exposure to atmospheric contaminants; focus on S and N containing species; provide a 

clear link between pollutant concentrations and damage; and give complete information on methods and 

data analysis used. 

Broadly speaking, the pace of research on NOY, NHX, and SOX materials effects has slowed 

considerably since the publication of the previous AQCDs. In particular, although the literature searches 

conducted for this update emphasized studies conducted in the U.S., the great majority of the relevant 

publications found originated in Europe or Asia. The relative scarcity of recent U.S. studies on structural 

and materials damage from NOY/NHX/SOX may be a natural fall-off in research in this area, following the 

extensive efforts that were summarized in the previous AQCDs and in the NAPAP report. Certainly the 

greater number and age of aesthetically valuable buildings and archeological sites in Europe and Asia, 

relative to the U.S., may be a driving force for current research in those geographic areas. In this chapter, 

each discussion of the effects of NOY/NHX/SOX on a material type begins with a brief summary of the 

state of knowledge as represented in the previous AQCDs, and then continues with a description of recent 

research on that type of material. 

 E-1
E.2. Environmental Exposures of Materials 

E.2.1. Mechanisms of Materials Damage 

As noted in the introduction to this chapter, materials damage may occur by natural physical 

processes without the involvement of NOY/NHX/SOX air pollutants. When those pollutants are involved, 

the destructive processes may be chemical, physical, or even biological. Chemical processes include 

direct reactions with gaseous pollutants such as NO2, SO2, or nitric acid (HNO3), reaction with 

electrolytes (proton (H+

), ammonium (NH4

+

), nitrate (NO3

−

), SO4

2−

, etc.) in water on material surfaces, 

and reactions with chemicals in deposited particulate matter. An example of a physical process is the 

deterioration of stone that occurs when gypsum (CaSO4·2H2O) forms from reaction of SO2 with the 

calcium carbonate (CaCO3) in the stone. The gypsum thus formed occupies a larger volume than the 

original stone, causing the surface to deteriorate. Biological degradation can occur when deposited 

pollutants are oxidized to acids by fungi or bacteria. 

A key factor affecting damage to certain materials, primarily metals and stone, is the frequency and 

duration of wetting of the surface. Liquid water on materials surfaces can dissolve deposited pollutants, 

producing reactive electrolyte solutions, and can serve as a reaction medium in which S and N oxides are 

converted to more damaging acids. Pollutants deposited on surfaces may contain or form hygroscopic 

salts, which enhance the formation of liquid water and thereby increase materials damage. As Dubowski 

et al. (2004) have shown, the deposition of HNO3 onto surfaces can increase the extent of wetting of 

surfaces, and promote the damaging effects of both HNO3 and other pollutants. 

E.2.2. Deposition Processes 

Air pollutants come into contact with surfaces through both dry and wet depositional processes. 

Dry deposition occurs in the absence of precipitation and is governed by factors such as atmospheric 

turbulence, the chemical and physical properties of the pollutant (e.g., water solubility and reactivity for 

gases; size, density, and shape for particles), and surface properties (e.g., reactivity, roughness, moisture 

level, and pH). The deposition rate of a pollutant is proportional to the atmospheric concentration of that 

pollutant. Dry deposition of gases depends primarily on the water solubility of the gas, the moisture level 

on the surface, and the pH of the electrolyte formed on the surface of a material. Nitric acid and NH3 are 

deposited very efficiently to most surfaces regardless of the surface properties of the material. Particle 

size plays an important role in determining the rate of deposition of particles to a surface. For very small 

particles, Brownian diffusion is the dominant deposition mechanism. For larger particles, inertial 

impaction and gravitational settling are important deposition processes. Particles between 0.05 and 2 μm, 

which include most atmospheric particles containing NO3

−

, SO4

2−

, and NH4

+

, may have long atmospheric 

lifetimes in the absence of moisture. 

Wet deposition occurs when gas or particle species come into contact with moisture (as rain, fog, 

snow, or ice). Atmospheric species can be dissolved into moisture and then deposited as the moisture falls 

to the ground. Solubility and the chemical reactions of the dissolved species determine the degree of wet 

deposition. For acid gases, high dissolution is observed due to the dissociation of the dissolved species in 

water. Wet deposition of pollutants occurs at a faster rate than dry deposition, but is only an important 

mechanism when moisture is present. 

 E-2
E.2.3. Chemical Interactions of Nitrogen and Sulfur Oxide Species 

N and S oxide species are subject to many atmospheric reactions in both the gaseous and 

particulate phase. Emissions of S and N oxides are primarily in the form of gas phase SO2 and NOX. In 

the atmosphere, these species can be oxidized by reaction with other atmospheric species to gas and 

particle phase product species. On the surface of materials, the oxides are generally oxidized to their acid 

forms (nitrous acid (HNO2), HNO3, sulfurous acid (H2SO3), and sulfuric acid (H2SO4)), which then 

dissociate to form nitrite, nitrate, sulfite, and sulfate ions. These acids are the primary species responsible 

for damage to materials by S and N pollutants. NH3, the primary gaseous basic compound in the 

atmosphere, can partly or completely neutralize these acids in particulate matter or in the aqueous phase, 

forming NH4

+

 ions. 

On the surfaces of materials, N and S species can react to form a variety of degradation products. 

On metals and stone, the possible degradation products include nitrite, nitrate, sulfite, and sulfate species 

as well as minerals that incorporate nitrate or sulfate into a more complex composition. These degradation 

products may be more or less reactive to further degradation than the original material. Degradation 

products that are more reactive, or those that are soluble in water, do not have long lifetimes on a material 

surface. They undergo further chemical reactions and are transformed to other species, or they are washed 

off the surface by precipitation. Products which are less reactive and less soluble in water than the original 

material may form a protective layer on the surface of the material which inhibits or prevents further 

damage from atmospheric pollutants. Products that are more reactive or water-soluble than the original 

material are readily removed, exposing the surface to more damage. The protectiveness of the products 

formed depends on the complex mixture of species present and the physical/chemical properties of the 

material. 

Synergistic effects, which influence the rate of degradation of materials, are possible in 

atmospheres containing a complex mixture of pollutants. NOX may enhance the oxidation of sulfite to 

sulfate and lead to faster rates of corrosion. The deposition velocity of SO2 and NOX may be influenced 

by the presence of HNO3 deposited to the surface due to the increased degree of surface wetting. 

E.2.4. Materials Damage Experimental Techniques 

The NOY/NHX/SOX air pollutants are comprised of numerous distinct chemical species, which may 

exist in the gaseous and/or particulate phases in the atmosphere, as well as in dissolved form in 

atmospheric precipitation and in condensed water on surfaces. To test the damaging effects of 

NOY/NHX/SOX species on man-made materials, it is often necessary to simplify the system by testing 

under controlled laboratory conditions, typically with a very limited set of pollutants in a test chamber. 

Such tests generally use pollutant concentrations that are greatly elevated relative to ambient atmospheric 

levels, and may also use exaggerated temperature, humidity, or wetting, to accelerate the development of 

materials damage so that it can be detected. Chamber tests may not accurately mimic the mass transfer of 

pollutants in the atmosphere, and efforts in such tests to isolate the effects of one pollutant from the 

complex mixture present in the atmosphere are unrealistic. As a result, chamber tests may provide 

valuable information on potential effects and mechanisms involving ambient air pollutants, but cannot 

accurately predict the corrosion rates or effects of such pollutants in real situations. 

Exposing materials of interest to the ambient atmosphere for extended time periods can provide a 

realistic look at the effects of air pollutants on materials. However, such ambient exposure tests are 

limited by the occurrence of natural (i.e., non-air pollutant) materials damage, and by the complexity of 

the NOY/NHX/SOX system. While it is relatively easy to determine which materials suffer more or less 

damage during equivalent exposures to ambient air pollution, it is extremely difficult to determine which 

air pollutants are responsible for the observed damage. This is due to the co-occurrence of all air 

 E-3
pollutants simultaneously, complexities in accurately measuring the suite of NOY/NHX/SOX species, and 

interconversions among species (e.g., SO) and SO4

2−

, NOX and HNO3) related to contact with materials or 

with moisture. The amount of time that surfaces are wet is a key factor in the extent of materials damage, 

and this factor may be difficult to determine in ambient exposures, because the presence of air pollutants 

themselves may enhance surface wetness on the microscale beyond that expected based on 

meteorological conditions (Dubowski et al., 2004). Ambient exposure tests lead to retrospective analyses, 

in which meteorological and air pollutant data, surface analyses, and measurements of chemical and 

physical properties are evaluated statistically to estimate the impacts of air pollutants on the exposed 

materials. 

E.3. Effects on Dyes and Textiles 

E.3.1. Fading of Dyes 

The fading of dyes by N oxides has long been recognized, and dye manufacturers have worked to 

produce products less susceptible to this effect, through both improved dye chemicals and the use of 

inhibitors in dye formulations to minimize fading. Fading has been observed with both red and blue dyes, 

both on natural fibers (e.g., cotton, silk, wool) and on synthetics (e.g., nylon, rayon, polyester). The fading 

effect of NO2 is generally reported to be greater than that of NO on various dyes with various fabrics. In 

exposures of dyed fabrics to ambient air, test samples must be shielded from sunlight, to avoid the 

substantial fading of dyes that results from sunlight exposure. Under such conditions, NO2 and ozone (O3) 

are often found to be about equally important in the fading of dyed fabrics. 

E.3.2. Degradation of Textile Fibers 

N oxides can degrade a variety of synthetic fibers, with the greatest effects seen with nylon. With 

NO2, the damage to nylon occurs due to breaking of the polymer chain (i.e., chain-scissioning). Similar 

weakening of nylon has been observed in tests with elevated concentrations of HNO3. A synergistic effect 

was observed between mechanical stress and NOX in the degradation of oriented nylon-6 fibers (Smith 

and DeVries, 1993). 

E.4. Effects on Plastics and Elastomers 

The group of materials called plastics includes a wide variety of polymeric materials such as 

polyethylene, polypropylene, polystyrene, polyurethanes, acrylic polymers, phenolics, and fluorocarbon 

polymers, among others. Plastic materials may include other components such as hardeners or 

plasticizers, and fillers that may impart properties such as physical strength. Elastomers are polymers that 

can stretch to at least their twice their normal dimensions and then return to their original dimensions 

when the stress is removed. Examples of elastomers include various rubber formulations and neoprene. 

Plastics and elastomers can be damaged by NO2, SO2, and O3, as well as by UV radiation in sunlight, and 

some studies have been designed to separate the effects of these factors. 

Chamber studies at relatively high pollutant concentrations with sunlight or UV light have 

generally shown greater damage than from the pollutants alone. NO2 is damaging to a variety of polymers 

 E-4
and elastomers, causing either chain-scissioning or cross-linking (formation of additional bonds between 

polymer chains) depending on the polymer. Polypropylene is reported to be damaged more severely by 

SO2 than by NO2. Elastomers are damaged more severely than plastics. In tests where light and NO2 have 

been present simultaneously, much of the damage observed in chamber tests has been attributed to O3, 

produced by the interactions of the pollutants and UV light, rather than to NO2 alone. In studies in which 

the same pollutant concentrations are present both with and without light, the greater damage observed in 

samples exposed to the light is often attributed to the light itself, when in fact chemical processes initiated 

by light (such as the formation of O3) undoubtedly also play a part. 

Cellulose nitrate can break down through hydrolytic, thermal, and photochemical reactions. 

Addition of plasticizer to cellulose nitrate slows the degradation substantially. NO2 is of particular interest 

with regard to cellulose nitrate because it is not only capable of causing damage, but is also produced as a 

result of damage to the material (Shashoua, 2006). NO2 is formed when N-O bonds connecting cellulose 

rings are broken. The NO2 formed will then further degrade cellulose nitrate, thus the degradation is an 

autocatalytic process. 

E.5. Effects on Metals 

Metals are considered to be the materials most subject to damage from the NOY/NHX/SOX air 

pollutants, and have been the subject of a great deal of research. The nature and concentration of the 

pollutant, its rate of deposition, and especially the duration of wetting of the surface are key factors in the 

corrosion of metals. Numerous studies have indicated corrosion rates of metal surfaces on the order of 

1 to several micrometers per year (μm/yr) under real or simulated atmospheric conditions. 

Table E-1 summarizes the materials tested, exposure conditions, and findings of recent studies 

related to the effects of NOY/NHX/SOX air pollutants on metals. The studies listed in Table E-1 are 

discussed where applicable in the following sections. 

 E-5
Table E-1. Studies on corrosive effects of NOY/NH3/SOX effects on metals. 

Materials Exposure Conditions Findings Reference 

Zinc 

Mild Steel 

Samples were exposed to fly-ash in clean air 

and in air with SO2

 and/or HCl (presentation 

rates of 27 × 10−6 and 4.7 × 10−6 mg/cm2s, 

respectively). A synthetic acid rain solution 

was used to model wet deposition. 

Corrosion was found to depend on the 

surface electrolyte irrespective of the 

presence of particles. Inert particles were 

found to increase corrosion rates in relatively 

unpolluted atmospheres. With higher 

pollution levels, species leached from 

particulate matter contribute to the 

conductance of the surface electrolyte and 

thus increase the corrosion rate. 

Askey et al. 

(1993) 

Copper Copper samples were exposed to 264 ppb 

NO2 in a laboratory setting. Exposures were 

limited to 72 h to study the initial corrosion 

behavior. 

The corrosion rate of copper in the presence 

of NO2 was much greater than in clean air. 

The surface electrolyte was found to contain 

predominantly nitrate with only trace levels of 

nitrite. After 24 h, the electrolyte had become 

sufficiently acidic to dissolve the copper 

oxide layer. Once the copper oxide was 

dissolved, corrosion proceeded at a 

significantly faster rate. 

Dante and 

Kelly (1993) 

Zinc Zinc samples were exposed to SO2

 (0.78 ppm) 

and/or NO2 (1.06 ppm) for 420 h. Some 

samples were treated with NaCl before 

exposure. 

SO2 slowed the corrosion of zinc with 

moderate to high surface concentrations of 

NaCl due to the formation of sodium zinc 

hydroxychloride sulfate. NO2 (which, alone, 

is unreactive toward zinc) accelerated the 

corrosion of zinc in the presence of small 

amounts of NaCl. 

Svensson and 

Johansson 

(1993b) 

Galvanized 

Iron 

Zinc 

Samples were exposed to SO2 or NH3 in the 

laboratory. Air was supplied at 5 cm/s and the 

pollutant gases at 3 cm/s. The concentrations 

of the gases were very high to accelerate the 

tests. 

Corrosion rates in SO2 were found to be 

largely dependent on relative humidity. No 

such humidity dependence was observed for 

corrosion induced by NH3. Corrosion rates in 

both gases decreased sharply with time 

(approaching steady state values after 30 h). 

Dehri et al. 

(1994) 

Aluminum 

Zinc 

Samples were exposed to ambient air for 

4 years at 6 sites. SO2 deposition rates ranged 

from 10 mg/m2day to non-detectablelevels 

across the sites. Time of wetness was also 

measured at each site. 

Corrosion products that developed in rural 

environments were found to be easily 

removed from the surface and thus result in 

poor protectiveness. Corrosion products 

formed in more aggressive environments 

were found to be more protective against 

continuing corrosion. 

Vilche et al. 

(1995) 

Zinc Samples were exposed at temperatures of 4, 

14, 22, and 30 C with 95% relative humidity. 

SO2 was supplied at 500 (± 5) ppb and 107 (± 

2) ppb. 

SO2 induced corrosion was found to be 

inversely dependent on temperature. The 

maximum corrosion rate (at 107 ppb SO2) of 

11 mg/cm2d was observed at 4 C. The 

corrosion rate at 30 C was 6.8 mg/cm2d. 

Svensson and 

Johansson 

(1996) 

 E-6
Materials Exposure Conditions Findings Reference 

Copper 

Zinc 

Aluminum 

Samples were exposed in the laboratory to 

SO2 (1.5 ppb, 0.5 ppm, 10 ppm), NO2

(10 ppm), NO (10 ppm), or O3 (10 ppm). 

NO was found to have no effect on the 

corrosion of copper, zinc, or aluminum. 

Copper in the presence of SO2 (10 ppm) and 

NO2 led to significant material loss initially 

with a slowing of the rate with increasing 

time. O3 was found to have the strongest 

influence on the corrosion of copper. Only 

very slight mass gains were observed for 

copper exposed to 0.5 ppm SO2. A small 

effect on zinc was observed for NO2 with 

SO2 (10 ppm) resulting in the largest weight 

gain. SO2 at 0.5 ppm had a much larger 

effect on zinc than on copper. For aluminum, 

O3 had the largest effect followed by SO2

and NO2. 

Oesch and 

Faller (1997) 

Copper Powdered samples of copper patina 

compounds (tenorite, cuprite, brochantite, 

antlerite, and atacamite) were exposed to SO2

(476 ppb) alone or in combination with NO2

(450 ppb) or O3 (500 ppb). Some samples 

were pretreated with carbon. 

Tenorite reacted rapidly with SO2 to form 

brochantite and other sulfate containing 

products. Cuprite reacted slowly with SO2

alone but addition of O3 formed antlerite and 

brochantite. NO2 did not produce the same 

effect. For samples with carbon on the 

surface, the oxidation reaction was greatly 

enhanced. Brochantite and antlerite were 

found to be stable in atmospheres with SO2

in combination with O3 or NO2. 

Strandberg 

(1998) 

Copper Samples were exposed to atmospheres 

containing approximately 200 ppb of SO2, SO2

and O3, or SO2 and NO2. O3 and NO2 were 

introduced at different times in the exposure 

scenarios. 

Copper sulfite and cuprous oxide formed on 

copper surfaces exposed to SO2. With O3

present, an increased rate of mass gain was 

measured, and copper sulfite was converted 

to copper sulfate. NO2 increased the mass 

gain to a lesser extent than O3 and resulted 

in the formation of copper nitrate in addition 

to copper sulfate. 

Aastrup et al. 

(2000) 

Mild Steel Steel samples were exposed to the 

atmosphere in 47 marine atmospheres with 

varying levels of chloride and SO2. 

Atmospheres were separated for data analysis 

based on chloride and SO2 deposition rates. 

Samples exposed at sites with moderate 

SO2 and chloride deposition rates formed 

compact, rounded corrosion structures. 

Samples at sites with high SO2 and 

moderate chloride exhibited cracking in the 

corrosion products. Samples at sites with 

high chloride and moderate SO2 exhibited 

the highest corrosion rates of the mixed 

atmospheres. The 1 site with high chloride 

and high SO2 exhibited a lower corrosion 

rate than expected. 

Almeida et al. 

(2000) 

 E-7
Materials Exposure Conditions Findings Reference 

Steel Two types of steel were exposed to urbanindustrial and rural atmospheres for 20 years. 

Avg SO2 concentrations were 90 and 

<10 μg/m3 (34 and <4 ppb) for urban and rural 

environments, respectively. 

The corrosion rates of the two grades of 

steel were similar with values of 0.1 and 0.08 

mm/yr for the urban and rural environments, 

respectively. The initial rate of corrosion was 

significantly faster and steady state values 

were approached after 4000 days exposure. 

The similar corrosion rates measured for the 

samples were thought to be due to similar 

time of wetness at both sites. 

Damian and 

Fako (2000) 

Copper 

Zinc 

Samples were exposed in the field at 8 sites. 

SO2, NO2, and O3 concentrations were 

monitored over a 4–yr exposure duration at 

each of the sites. 

The highest corrosion losses for copper were 

observed at the site with the highest 

combination of SO2 and O3. For zinc, the 

highest corrosion losses were observed at 

the site with the highest SO2 concentration. 

Both metals showed a decrease in corrosion 

rate with time. Runoff rates from copper were 

much smaller than from zinc. 90% of the 

corrosion products remained on the copper 

surface after 4 years; only 40% of the zinc 

corrosion products remained after 4 years. 

LeuenbergerMinger et al. 

(2002) 

Nickel Nickel samples were exposed in the field at 3 

sites (urban, industrial, and rural) for 1 year. 

Concentrations of NO (41.1, 9.7, and 

2.9 μg/m3) (33, 8, and 2 ppb), NO2 (50.1, 24.2, 

and 8.7 μg/m3) (26, 13, and 5 ppb), SO2 (22.3, 

29.0, and 12.2 μg/m3) (8, 11, and 5 ppb), and 

O3 (25.8, 47.1, and 60.1 μg/m3) (13, 24, and 

30 ppb) were measured at the urban, 

industrial, and rural sites, respectively. 

Mass loss rates of 320, 570, and 200 

μg/cm2y were determined for urban, 

industrial, and rural environments, 

respectively. Mass loss was found to 

increase with increasing SO2 concentration. 

Soluble corrosion products were formed on 

the surface and then removed by rainfall 

events. Hydrated nickel sulfates were the 

main corrosion products formed on the nickel 

surface. 

Jouen et al. 

(2004) 

Iron Samples were exposed to humidified air in the 

laboratory. Samples were exposed to clean 

air, SO2 (200 ppb), SO2 and NO2 (each 

200 ppb), or SO2 and O3 (each 200 ppb). The 

same exposure conditions were used for iron 

samples with NaCl deposited on the surface. 

No corrosion products were detected on 

samples exposed to humidified air alone. 

The addition of SO2 alone was not enough to 

initiate a change in corrosion behavior of the 

samples. When an oxidant (NO2 or O3) was 

added to the humidified air/SO2 system, a 

significant increase in corrosion rate was 

observed. SO2 was found to inhibit the NaCl 

induced corrosion of iron, but the 

combination of SO2 and NO2 was found to 

accelerate NaCl induced corrosion. 

Weissenrieder 

et al. (2004) 

 E-8
Materials Exposure Conditions Findings Reference 

Aluminum Aluminum samples were exposed to SO2

(96 ppb) either alone or in the presence of 

other pollutants (NaCl, NO2, or O3). 

SO2 alone resulted in the loss of metallic 

luster. 50% of the surface had developed 

corrosion products after 672 h. Samples 

exposed to NaCl alone showed significantly 

larger mass gain than samples exposed to 

only SO2. The combination of SO2 and NaCl 

resulted in the largest mass gain (fastest 

corrosion rate). While the rate of mass gain 

was highest with a combination of SO2 and 

NaCl, the pitting observed in the presence of 

NaCl alone was significantly reduced. O3

was found to slightly increase the deposition 

rate of SO2, no effect on SO2 deposition rate 

was observed for NO2. 

Blucher et al. 

(2005) 

Copper 

Zinc 

Steel 

Samples were exposed to HNO3 (50–180 ppb) 

in a laboratory exposure chamber. Tests were 

conducted at 65% and 85% relative humidity. 

The corrosion effects of HNO3 on carbon 

steel were larger than on zinc or copper. The 

corrosion effect of HNO3 was found to be 

larger than corrosion from SO2 alone or a 

mixture of SO2 with O3 or NO2. No increase 

in corrosion was observed at 85% relative 

humidity compared to 65% relative humidity. 

Samie et al. 

(2007) 

E.5.1. Role of NOY, NHX, and SOX in the Corrosion Process 

In the atmosphere the NOY/NHX/SOX pollutants occur together, along with other pollutants such as 

O3 or chloride salts. While wetting of metals surfaces is the single greatest factor promoting corrosion, an 

important observation is the enhanced damage that occurs due to interactions among this mixture of 

pollutants. It must be noted that in many studies the various NOY/NHX/SOX species have not been 

adequately separated or quantified, and this may be the cause of conflicting observations from some 

studies. However, some generalizations can be made. Sulfur and chloride pollutants are generally more 

important at causing metals corrosion than N pollutants, however NOX (or NOY) and SO2 together have 

been shown to be more damaging than SO2 alone. The combination of NO2 and SO2 has been shown to 

result in a synergistic effect where the total damage from the mixture is greater than the additive damage 

from the two pollutants separately (Svensson and Johansson, 1993a). This effect may be due to enhanced 

wetting of the surfaces caused by NOY pollutants, resulting in corrosion at lower relative humidities than 

would otherwise be the case. This enhancement has been attributed to the formation of hygroscopic 

nitrate salts, but may also be caused directly by the deposition of gaseous HNO3 onto the surface 

(Dubowski et al., 2004). The corrosion effect of HNO3 on zinc, copper, and steel is larger than that of SO2

alone or a mixture of SO2 and NO2 (Samie et al., 2007). 

Although deposition of NOY/NHX/SOX species in particulate matter can soil metal surfaces, such 

deposition does not directly result in substantial metals damage. However, under wet conditions these 

soluble species form an electrolytic solution that can cause corrosion. Corrosion of steel and zinc has been 

found to depend on the surface electrolyte irrespective of the presence of particles (Askey et al., 1993). 

Temperature has been found to have a complex effect on metals corrosion. Lower temperatures 

tend to increase surface wetness, but decrease the diffusivity of gaseous pollutants, and may reduce the 

 E-9
rates of some reactions that convert SO2 and NOX to sulfuric and nitric acids. Thus, the effect of 

temperature changes on long-term corrosion rates can be hard to predict. 

E.5.2. Effect on Economically Important Metals 

Steel is the most common and economically important structural metal, and is often used in 

galvanized form (i.e., with a protective coating of zinc). SO2 is generally reported to be more corrosive 

than NOX, however for well-protected steel the effects of ambient air pollutants are usually a small 

increment on top of the natural weathering process. The N pollutants can have an enhancing effect on the 

corrosion caused by the S pollutants. This is attributed to the increased wetting that can result from the 

presence of hygroscopic NO3 salts. Relative humidity has been shown to be very important in the 

corrosion of steels by SO2 with much slower corrosion rates observed when the relative humidity is below 

70% (Dehri et al., 1994). The presence of SO2 has been shown to reduce the corrosion pitting of iron 

induced by sodium chloride (NaCl) but there may be an overall synergistic effect among SO2, NO2, and 

NaCl (Weissenrieder et al., 2004). Steel corrosion rates have been shown to decrease over time (Almeida 

et al., 2000; Damian and Fako, 2000) approaching steady state rates after approximately 4000 days 

(Damian and Fako, 2000). 

Zinc corrosion has been shown to be inversely dependent on temperature (Svensson and Johansson, 

1996). Corrosion products formed on zinc in polluted environments are less water soluble, and therefore 

more protective against further corrosion, than corrosion products formed in clean environments (Vilche 

et al., 1995). The combination of SO2 and NO2 showed synergistic (i.e., greater than simply additive) 

corrosive effects on zinc (Svensson and Johansson, 1993a). SO2 slowed the NaCl−

induced corrosion of 

zinc while NO2 accelerated that corrosion (Svensson and Johansson, 1993b). 

Aluminum is naturally protected from corrosion by a formation of a durable surface film, but some 

effects of the NOY/NHX/SOX pollutants have been observed. Minimal damage is caused to Al by NOX. 

The mixture of SO2 and NOX is variously said to be either more or less corrosive to Al than SO2 alone. 

The deposition rate of SO2 to Al was shown to increase in the presence of O3, but no effect on SO2

deposition rate was found for NO2 (Blucher et al., 2005). Interaction between SO2 and NaCl results in an 

increased corrosion rate but decreased pitting of Al compared to NaCl alone (Blucher et al., 2005). Oesch 

and Faller (1997) found that SO2 is more corrosive to Al than NO2 and that there is no difference in Al 

corrosion rate when exposed to NO or clean air. 

Mixtures of NOX and SO2 are more corrosive to copper than either pollutant alone. When hydrogen 

sulfide (H2S) and O3 were also evaluated for damage to copper, they also were found to be more 

damaging to copper than NOX. The corrosion rate of copper exposed to SO2 or NO2 has been shown to 

slow over time (Leuenberger-Minger et al., 2002; Oesch and Faller, 1997). The corrosion rate of copper in 

the presence of NO2 is greater than in clean air. In the first 24 h of exposure to NO2, an acidic electrolyte 

is formed on the surface that dissolves copper oxides and results in an increased corrosion rate (Dante and 

Kelly, 1993). Synergistic effects have been seen between SO2 and O3 (strong) and NO2 (weak) (Aastrup 

et al., 2000). High corrosion rates were observed for field exposures of copper at sites with a combination 

of SO2 and O3. After four years of exposure, 90% of the corrosion products formed on copper remained 

on the surface (Leuenberger-Minger et al., 2002). The copper hydroxy sulfates brochantite and antlerite 

are stable copper corrosion products formed in the presence of SO2, O3, and NO2 (Strandberg, 1998). 

Nickel is also damaged more severely by SO2 or chloride salts than by NOX. Nickel samples 

deployed at urban, industrial, and rural sites showed that corrosion rates increase with SO2

concentrations. Soluble hydrated nickel sulfates were the main corrosion products and are easily removed 

from the surface by rainfall events, thereby exposing the underlying surface (Jouen et al., 2004). 

 E-10
Kim et al. (2004) conducted a study of the effects of ambient SO2 and NO2 on steel, bronze, 

copper, and marble at sites in China, Korea, and Japan. Both sheltered and unsheltered samples were 

exposed with the corrosion rates of the unsheltered samples higher in all cases. The corrosion rate of steel 

was the highest, followed by marble, bronze, and copper. Higher corrosion rates (especially for 

unsheltered samples) were found to be correlated with high SO2 concentrations. 

E.5.3. Effects on Electronics 

The increasingly wide penetration of electronic devices into daily life offers greater opportunities 

for environmental damage to sensitive components. The hardware of communication systems may be 

exposed to pollutants in outdoor air, and the ubiquitous cell phones may be exposed in both indoor and 

outdoor environments. Sulfur and N oxides have been shown to corrode the metallic contacts in electronic 

equipment, which are often made of copper or brass coated with a precious metal such as gold, palladium, 

or nickel. Such materials are corroded more by NO2 than by SO2, but a mixture of these two pollutants is 

more corrosive than either alone. The combination of SO2 and H2S is also less damaging than either NO2

alone or a combination of NO2 and these pollutants. NO2 is also moderately corrosive to solder in 

electronic components. 

E.6. Effects on Paints 

Painted surfaces are extremely common as a means of preventing damage to other materials, and 

may be categorized as architectural coatings (e.g., house paint), product coatings (e.g., automobile 

finishes), and special-purpose coatings (e.g., bridge paint). Environmental damage to painted surfaces is 

expected, and periodic repainting is normal, but any factor that causes more rapid degradation or 

discoloration of paints will require more frequent repainting and thus result in higher costs. Paint 

formulations may differ widely for different applications, so the extent of air pollution damage in a given 

application cannot necessarily be predicted from published information. Previous work at elevated 

pollutant concentrations has shown that oil-based house paint is readily damaged by SO2 and moisture, 

and is more subject to damage by SO2 than by NO2. Sample weights increased with increasing NO2, but it 

is not clear if this indicates direct reaction of NO2 or an enhancement of the effects of SO2 and moisture 

by NO2. The effect of SO2 may be due to reaction with CaCO3 and zinc oxide (ZnO) present in the paint. 

Tests with various paints showed that NOX becomes incorporated into the paint surface upon long 

exposure, apparently by reaction with polymers that make up the cured paint. In other tests HNO3 was 

found to produce substantially more damage to paints containing both low and high levels of carbonate 

(CO3

−

) than did an equal mixing ratio of NO2. 

Grosjean et al. (1994) studied the fading of colorants on cellulose paper. Twelve colorants were 

exposed to various atmospheres including purified air, NO2, SO2, and a mixture of oxidants (O3, NO2, and 

peroxyacetyl nitrate [PAN]). Not all colorants were tested in each atmosphere. The mixture of oxidants 

resulted in the largest color change for each tested colorant. Only one colorant was exposed to SO2, NO2, 

and the mixture. For that colorant, the color change induced by the mixture of oxidants was 

approximately three times the color change with NO2 alone; the color change with SO2 was 

approximately 70% of the color change with NO2. An increase in relative humidity results in increased 

fading of colorants (Grosjean et al., 1993, 1994). Of 35 colorants exposed to a mixture of O3, NO2, and 

PAN, nine exhibited substantial color changes and three exhibited moderate color changes (Grosjean 

et al., 1993). 

 E-11
Paint samples were exposed to UV light, NOX, SO2, and a combination of the treatments by 

Colombini et al. (2002). The exposure conditions were chosen to produce accelerated aging of paint 

samples. Non-pigmented paints were chosen to isolate degradation of the paint binder from synergistic 

effects with pigments. Exposure to the combination of treatments resulted in increased cross-linking in 

the paint binder as well as formation of organic acids. An examination of paint samples taken from 

naturally aged paintings confirmed the presence of organic acids as degradation products. 

E.7. Effects on Stone and Concrete 

The effects of NOY/NHX/SOX air pollutants on stone and concrete are undoubtedly the most widely 

studied because of their impact on historic buildings, monuments, and archeological treasures. Table E-2 

summarizes studies of the effects of NOY/NHX/SOX on stone, concrete, and mortars. Calcareous stone 

(i.e., that consisting of CaCO3, such as marble, limestone, and cement) is most susceptible to damage. 

Mortar used in stone construction is often more porous than calcareous stone, and therefore more subject 

to damage. The damage to such materials is attributed primarily to the effect of SO2 in forming gypsum 

(CaSO4≅2H2O): 

223 24 2 2 OHSOCaCO ⎯++ ⎯→ 02 +⋅ COHCaSO

The gypsum thus formed occupies a larger volume than the original carbonate, so the stone surface 

becomes pitted and damaged. Gypsum is also more soluble than the carbonate, so it can be removed by 

precipitation, exposing the surface to further reaction and damage. As a result, dry deposition of SO2 to 

the stone surface between rain events is important, as it causes continued damage. The reaction of SO2

with calcareous stone is more energetically favorable than the reaction of N oxides, and thus SO2 is the 

primary cause of damage to stone, however the combination of SO2 and NOX is more damaging than SO2

alone. This effect may be due to enhanced wetting of the stone, to oxidation of SO2 by the N oxides, or to 

formation of calcium nitrate (Ca(NO3)2), which is much more soluble than CaCO3 and is easily washed 

off the stone surface by precipitation. Removal of the nitrate salts in this way may result in 

underestimation of the role of N oxides in stone damage when surface layers are analyzed for chemical 

composition. 

 E-12
Table E-2. Studies on corrosive effects of NOY/NH3/SOX on stone. 

Materials Exposure Conditions Comments Reference 

Marble Laboratory exposure to HNO3 ranging from 54 to 

4174 μg/m3 (21 to 1603 ppb). Field exposures 

were conducted in several stages in Greece. Avg 

concentrations for field exposures were 1.41 μg/m3

(0.5 ppb) HNO3, 2.39 μg/m3 (3 ppb) NH3, 4.84 

μg/m3 NO3

−, 14.61 μg/m3 SO42–, and 5.01 μg/m3

NH4+. 

Marble was found to be a very good sink for HNO3. The 

extent of corrosion by sulfates and nitrates were found to be 

of the same order of magnitude. Corrosion was found to be 

caused by acid and salt species (HNO3, H2SO4, etc.) on the 

surface rather than the oxides (SO2, NOX). 

Sikiotis and 

Kirkitsos 

(1995) 

Limestone Samples of differing thickness were exposed in the 

field and runoff water collected for 5 mos. The avg 

SO2 concentration was 60 μg/m3 (23 ppb). 

Damage functions were developed to try to determine the 

ionic sulfate content in runoff water from the ambient SO2

concentration. It was determined that the ambient SO2 concentration alone does not determine the sulfate concentration in runoff water. 

Torfs and van 

Grieken (1996)

Marble Laboratory exposure to 10 ppm of SO2 and NO2. 

Field exposure to either dry deposition or dry and 

wet deposition in Louisville, Kentucky. Avg concentrations of 10 ppb SO2 and 25 ppb NO2 for field 

exposures. 

Gypsum crust thickness of 1.9 μm for rain sheltered samples after 1 year of exposure. SO2 was found to be the 

dominant factor in crust formation. Surface recession due to 

rain washing was 14.5 μm/yr and due to dissolution of the 

gypsum crust as well as dissolution of the original marble. 

Yerrapragada 

et al. (1996) 

Marble 

Brick 

Samples exposed in the field for 8 days with multiple fog episodes. Over 3 measurement campaigns, 

the mean pollutant concentrations were 17.2 μg/m3

(7 ppb) SO2, 265 μg/m3 (139 ppb as NO2) NOX, 

and 131 μg/m3 suspended particles. 

For all samples, gypsum was the only stable mineral formed 

following exposure to fog water in a polluted environment. 

Exposure to fog water may be a significant cause of corrosion for materials sheltered from rainwater, but is of lesser 

importance if a material is exposed to rain. 

Del Monte and 

Rossi (1997) 

Marble 

Limestone 

Mortars 

Samples were exposed to urban environments in 

both sheltered and unsheltered configurations. 

Pollutant concentrations were not reported. 

Sulfation was the primary damage mechanism and was 

more intense on mortars than on stones due to higher 

porosity. Higher concentrations of degradation products 

were found on samples sheltered from rain than on samples 

exposed to rain. 

Zappia et al. 

(1998) 

Jaumont 

limestone 

Samples were exposed to 340 μg/m3 (125 ppb) 

SO2 and 98 μg/m3 (50 ppb) NO2 in the laboratory. 

Samples were either exposed naked or sprinkled 

with fly-ash particles. Field exposure was conducted for 1 year with samples sheltered from 

rainwater. Avg SO2 concentration during the field 

exposure was 107 μg/m3 (40 ppb). 

Sulfation (gypsum formation) was found to proceed with 

greater intensity for samples sprinkled with fly-ash than for 

naked samples. The growth of gypsum crystals fixed the flyash to the surface of the limestone. Fly-ash was found to be 

an important factor in crust formation by facilitating gypsum 

crystal formation. Fly-ash particles also darken gypsum 

crusts from gray to black. 

Ausset et al. 

(1999) 

Calcium 

carbonate 

Powdered calcium carbonate was exposed to 

10 ppm SO2 and 90% relative humidity for 

124 days. 

Reaction between calcium carbonate and SO2 was found to 

take place in a liquid film on the calcium carbonate surface. 

The presence of several different types of airborne particles 

was found to increase the extent of sulfation by 20%. The 

SO2 concentration used is unrealistic for ambient conditions. 

Boke et al. 

(1999) 

Mortars A wide range of mortar and plaster samples were 

collected from sites throughout Europe. Pollutant 

concentrations over the life of the buildings were 

not reported. 

Sulfation was the primary damage mechanism observed. 

Sulfite was found as an intermediate damage product in the 

sulfation process. Ettringite was also found as a secondary 

damage product due to a reaction between gypsum and 

calcium aluminum hydrates. 

Sabbioni et al. 

(2001) 

 E-13
Materials Exposure Conditions Comments Reference 

Mortars Plaster and mortar samples were collected from 

buildings in the Old Venice Arsenal. Pollutant concentrations over the life of the building were not 

reported. 

Gypsum was found to be the primary damage product on all 

of the mortars sampled. A secondary damage mechanism 

was found where gypsum reacts with calcium aluminum 

hydrates to form ettringite, an insoluble sulfate. The presence of sulfur in the damage products indicates SO2 as the 

most aggressive atmospheric pollutant toward mortars. 

Sabbioni et al. 

(2002) 

Concrete Samples were collected from the interior of a tunnel 

in Italy. The tunnel formerly held a railway and 

currently houses a road with heavy automobile 

traffic. SO2 levels have declined from 350 μg/m3

(132 ppb) in 1970 to ~10 μg/m3 (4 ppb) in 2002. 

NOX concentrations have remained relatively constant around 100 μg/m3 (52 ppb as NO2) over the 

same time period. 

The urban mixture of pollutants (SO2, NOX, CO2, and particles) results in formation of dendritic crusts on concrete. 

Nitrates were found to be present in the largely gypsum 

crusts. Soot particles were found embedded in the crusts as 

well. Low quality starting materials provide a more porous 

media that is more susceptible to degradation by atmospheric pollutants. The degradation of concrete is more similar to that of sandstone than of limestone. 

Marinoni et al. 

(2003) 

Marble Samples exposed to atmosphere and sheltered 

from rain at 4 sites. SO2 concentrations ranged 

from ~2 to 20 ppb across the sites. 

Sulfation was the primary damage mechanism observed. 

Marble containing dolomite was less sensitive to SO2 than 

calcite marble. For relative humidity greater than 72%, 

humidity was an important factor in determining the sulfation 

rate. 

Lan et al. 

(2005) 

Limestone 

Sandstone 

Samples were collected from 3 facades of a historical building in Spain. Samples were collected from 

the surface as well as 5 mm below the surface to 

determine degradation and original compositions, 

respectively. Pollutant concentrations over the life 

of the building were not reported. 

The main decay products on the surface were found to be 

nitrate compounds. Samples with black crusts on the surface were found to have predominantly gypsum and soot, 

but nitrate compounds were identified in the crusts as well. 

Sandstone samples were much more damaged than limestone samples due to their higher porosity. 

Martinez-Arkarazo et al. 

(2007) 

Concrete is more susceptible to damage from N oxides than are the calcareous stones, because 

concrete contains calcium hydroxide (Ca(OH)2), which can react to form calcium nitrate (Ca(NO3)2). This 

product is soluble and can be washed out of the concrete, weakening the material. 

Deposition of particulate matter onto stone primarily results in soiling of the stone, due to the 

elemental carbon and organic compound content of the deposited particles. The orientation of the surfaces 

and size of the particles affect deposition: vertical surfaces are more affected by deposition of fine 

particles, whereas horizontal surfaces are more affected by large particles. The fine particles carry the 

bulk of the carbon and organic material. Metal oxides present in deposited particles may enhance the 

reaction of SO2 to form gypsum. 

Gypsum crusts form more readily in rain-sheltered environments than on rain-washed stone 

surfaces (Zappia et al., 1998). The presence of fog water has been shown to increase the rate of gypsum 

formation on surfaces sheltered from rain washing (del Monte and Rossi, 1997). Gypsum crust formation 

has been shown to proceed at a faster rate when the stone surface is sprinkled with fly-ash particles. In 

addition, fly-ash (or other carbonaceous particles) can become entrained in the gypsum matrix and affixed 

to the stone surface. Normally, gypsum crusts are gray in color, but when carbon containing particles are 

entrained, they become black (Ausset et al., 1999). While gypsum crusts are composed primarily of 

sulfates, they have been found to contain nitrate compounds as well (Marinoni et al., 2003; MartinezArkarazo et al., 1999). The inclusion of nitrates in gypsum crusts suggests that N oxides, as well as SO2, 

play a role in the degradation of stone exposed to the atmosphere. The presence of particulate matter, in 

addition to SO2, has been shown to increase gypsum formation by 20% (Böke et al., 1999). Ambient SO2

concentrations alone are not adequate to predict the degree of damage to stone samples (Torfs and Van 

Grieken, 1996). 

 E-14
Dolomite has been shown to be less sensitive to sulfation than calcite (Lan et al., 2005). Corrosion 

of marble due to S species has been found to be of the same order of magnitude as that caused by N 

species. Damage is caused not by the gas phase oxides (SO2, NO2, NO) but by acid (H2SO4, HNO3) and 

salt (SO4

2−

, NO3

−

) species present in the electrolyte which forms on the marble surface (Sikiotis and 

Kirkitsos, 1995). The rate of marble surface recession by rain washing is faster than the rate of gypsum 

crust formation due to dry deposition of S and N containing pollutants. Marble is damaged by rain 

washing through two mechanisms, dissolution of the gypsum crust and dissolution of the underlying 

marble. The gypsum crust is more soluble in water than marble and is rapidly dissolved in rain. The 

naturally occurring acidity in rainwater from the dissolution of carbon dioxide (CO2) is an important 

mechanism by which stone samples are degraded, but additional acidity from the dissolution of SO2 and 

NO2 in rainwater does not greatly increase the solubility of marble in rainwater (Yerrapragada et al., 

1996). 

While gypsum is the primary degradation product found on stone, mortar, and concrete samples, 

other damage products do occur. Sulfite species have been found on mortars as intermediate damage 

products. On mortars, a secondary damage mechanism exists in which gypsum reacts with calcium Al 

hydrates present in the mortar to produce ettringite (3CaO ≅ Al2O3 ≅ 3CaSO4 ≅ 32H2O). Ettringite is an 

insoluble sulfate that may cause damage by expansion and lead to cracking of mortars (Sabbioni et al., 

2001, 2002). 

E.8. Effects of NOX on Paper and Archival Materials 

The cellulose fibers that make up paper are reactive with NO2 and other NOY species, and storage 

condition standards have been set regarding accepTable levels of NOX for archives, libraries, and 

museums. Exposure of archival materials to NOY species in such facilities can arise from normal outdoor 

or indoor sources, but also from generation of such NOY species from the materials themselves. 

Specifically, stored materials that include cellulose nitrate, e.g., in the form of photographic film, 

adhesives, or recording media, can slowly decompose to release NOX and product species such as HNO3. 

These emissions can degrade archival materials, and even be a safety hazard if allowed to accumulate. In 

terms of outdoor air pollutants, it is likely that HNO3 is a key reactant in the degradation of paper 

archives. The rapid deposition velocity of HNO3 and the numerous surfaces in archival facilities provide 

opportunity for attack by HNO3, and probably result in the effects of HNO3 being underestimated, relative 

to those of NOX, based on indoor air measurements. Artists’ pigments can also be damaged by extended 

exposure to ambient atmospheric NO2. 

The effects of SO2 and O3 on paper were studied by Johansson and Lennholm (2000). The 

deposition rate of SO2 to fresh paper was found to decrease rapidly with time and approached steady state 

after ten hours. The deposition rate of SO2 to fresh paper in the presence of O3 was found to be elevated 

compared to SO2 alone. The deposition rates to aged paper were much lower and there was no effect on 

the SO2 deposition rate observed in the presence of O3. The decrease in deposition rate with time is 

thought to be due to protonation of all available carboxylate ions to carboxylic acid. 

Pigments in works of art can be degraded or discolored by atmospheric pollutants. H2S has been 

shown to react with both copper and lead pigments, but only lead white has been seen to darken over time 

(Smith and Clark, 2002). A synergistic effect has been detected between NO2 and both benzene and 

toluene resulting in an increased rate of attack on pigment oxides (Agelakopoulou et al., 2007). 

Deposition of S to the surface of paintings, either as SO2 or ammonium sulfate ((NH4)2SO4) particles, can 

damage, varnish, or cause discoloring of paint (Gysels et al., 2004). Paint models subjected to accelerated 

aging in SO2 (10 ppm) and NOX (10 ppm) as well as UV radiation for 15 days exhibited a variety of 

 E-15
 E-16 

damage markers. Both nitrate and sulfate damage mechanisms were observed with sulfation sometimes 

masking other processes (Arbizzani et al., 2004). 

E.9. Costs of Materials Damage from NOY, NHX, and SOX

Materials exposed to the ambient atmosphere are degraded and damaged through a number of 

mechanisms. Damage associated with air pollutants result in effects such as decreased usable lifetime, 

increased maintenance frequency, and loss of aesthetic appeal. It is difficult to separate the costs 

associated with air pollutants from costs associated with other damage mechanisms. Some estimates of 

cost have been based on empirically derived dose-response functions for specific materials. Other 

estimates have been developed using inspection of actual materials damage and maintenance guidelines 

for the materials (Cowell and Apsimon, 1996). Estimation of costs over large geographic areas is subject 

to considerable uncertainty due to unknown distribution of materials at risk and spatial variations in 

pollutant concentrations. A cost estimate for material cost savings from SO2 emission reductions in 

Europe was performed by Cowell and Apsimon (1996). In this study, the cost savings of theoretical future 

emission reductions across Europe was modeled using cost data extrapolated from a study conducted in 

three Norwegian cities. Total theoretical SO2 reductions of 15,904 kilotons per year resulted in modeled 

annual cost savings of $9,504 million total for Europe (Apsimon and Cowell, 1996). 

E.10. Summary 

Many types of materials, including metals, electronics, plastics, paints, stone, and paper may be 

damaged by atmospheric NOY/NHX/SOX species. Damage occurs due to dry and/or wet deposition of the 

pollutants onto the surface of a material and subsequent formation of an electrolytic solution in water 

present on the surface. At low relative humidity, when little water is present on surfaces, damage rates 

have been observed to be much lower, and in some cases, no damage has been observed. Both SO2 and 

NO2 have been implicated in damage processes for different materials. In general, damage to materials by 

SO2 is greater than by NO2. Little work has been conducted to investigate the effects of NO on material 

damage. What work has been conducted shows no damage, or very minor damage for NO containing 

environments compared to clean air. Synergistic effects between SO2 and NO2 lead to increased damage 

rates for the gases in combination. Other species such as O3, NaCl, organics, or particulate matter have 

also been shown to have synergistic effects with SO2 and NO2. The corrosive effects of nitric acid have 

been found to be stronger than effects of other NOY/NHX/SOX species. Costs associated with damage to 

materials by atmospheric pollutants are difficult to estimate because of the many sources of uncertainty in 

the estimation process. For heavily polluted environments, the cost savings due to decreased rates of 

material degradation could offset a significant portion of the costs to reduce emissions. In general, for 

polluted environments, reductions in SO2 or HNO3 concentrations will reduce damage rates more than 

reductions in NO2, NO, or NH3. In areas with low SO2 concentrations, reductions in NO2, O3, or 

particulate matter concentrations may reduce damage rates. 
Annex F. Valuation of the Environmental 

Effects of N and S (non-materials) 

F.1. Introduction 

The monetary valuation of ecological effects associated with NOX and SOX emissions starts with 

natural science endpoints. These may be things that people value directly, such as loss of a particular 

species, or some remote effect on a resource that is not clearly understood by the general public or not 

valued by the public for itself, such as forest soils. Of course, damage to forest soils will affect the 

terrestrial ecosystem in ways that may be valuable to humans, such as tree growth, habitat, and even the 

aesthetics of the forest. This Annex is a review of the literature that estimates such values for various 

ecosystem endpoints or that provide values for effects that can be reasonably inferred from what is 

provided. 

The purpose of this Annex is to provide an assessment of the economics literature on the effects of 

NOX and SOX emissions on terrestrial, transitional, and aquatic ecosystems. 

F.1.1. Valuation in the Context of NOX and SOX

Figure F-1 provides a schematic representation of how economic valuation is derived from changes 

to NOX and SOX secondary standards. Starting at the upper left-hand side, the NOX and SOX standards are 

set and emissions reductions occur to change the ambient concentrations of NOX and SOX. Reading down 

from “Change in Ambient Concentrations,” these reductions will lead to changes in a variety of ecological 

endpoints (as identified in the ISA) in terrestrial, transitional, and aquatic ecosystems. The box below, 

“Change in Economic Endpoints,” refers to physical endpoints that people care about, in which changes 

can be valued (at least in principle) in monetary terms. Many times, these are referred to as ecosystem 

services. In a few cases, such as agricultural crop growth and yield, ecological and economic endpoints 

are nearly the same. Finally, at the bottom of this diagram is a box labeled “Valuation Methods,” which 

notes alternative approaches for placing monetary values on these economic endpoints. As endpoints are 

discussed in detail in the ISA, this Annex focuses solely on valuation. 

F.1.1.1. Ecosystem Services 

Broadly defined, ecosystem services are the benefits that people obtain from ecosystems 

(Millennium Ecosystem Assessment, 2003). In the Millennium Ecosystem Assessment (MA), ecosystem 

services are classified into provisioning, regulating, supporting, and cultural services. Provisioning 

services denote the products people obtain from ecosystems; regulating services are associated with the 

ecosystem functions that regulate climate, nutrient cycle, water filtration, and so forth; supporting services 

are ecosystem functions, such as primary productivity and production of O2, that support the provision of 

ecosystem services; and cultural services are the non-material benefits ecosystems provide to people 

through spiritual enrichment, cognitive development, reflection, recreation, and aesthetic experiences. 

Ecosystems are productive systems in which various biological and physical factors, as well as 

their interactions, serve various functions in the production of ecosystem services. However, economic 

valuation of the environment has focused mostly on the contributions of individual goods and services to 

 F-1
human well-being. Alternately, ecosystem services valuation is based on the various benefits generated by 

the ecosystem (Polasky et al., 2005). In this case, benefits include both marketed and non-marketed 

services, and their valuation considers the environment as a natural capital asset that generates returns on 

investment in ecosystem protection and management. 

Figure F-1. Illustration chart of the assessment. 

For example, wetlands constitute a form of natural capital. They serve as flood barriers, soaking up 

excess water and slowing and preventing floodwaters from spreading uncontrollably. Wetlands help 

replenish groundwater and improve both ground and surface water quality by slowing down the flow of 

water, and absorbing and filtering out sediments and contaminants. They also provide spawning habitat 

for fish, supporting the regeneration of fisheries. In addition, wetlands provide habitat for many wildlife 

species and support commercial and sport fishing, as well as hunting and other forms of recreation. 

Though different functions and processes of ecosystems, such as water filtration, may be 

economically important, they need to be viewed as inputs of or mechanisms for the production of 

economically valuable services, such as drinking water, timber, or recreational benefits. The end products, 

not the elements of the production process, ultimately generate economic well-being. Along these lines, 

Boyd and Banzhaf (2007) advocate defining ecosystem services as “components of nature, directly 

enjoyed, consumed, or used to yield human well-being.” In other words, ecosystem services are the end 

products of nature to which ecosystems contribute as intermediate inputs or production technologies. 

Though this distinction may at first seem unimportant, it is crucial for the accurate valuation of ecosystem 

 F-2
services. Regarding the incorporation of ecosystem services into the measurements of national income 

and the value of goods and services produced in an economy, such as gross domestic product (GDP) 

accounts, Boyd and Banzhaf (2007) note that if intermediate and final goods are not distinguished, the 

value of intermediate goods is double-counted because the value of intermediate goods is embodied in the 

value of final goods. For example, clean drinking water, which is consumed directly by a household, is 

dependent on a range of intermediate ecological goods, but these intermediate goods should not be 

counted in an ecosystem service welfare account. Also important is that ecosystem services are attributed 

only the incremental value they contribute to the production of valuable end products. Using the above 

example, the value of ecosystem services associated with drinking water denotes the marginal 

contribution of ecosystems in the production of drinking water, not the full value of the final product. 

If intermediate and final goods are not distinguished, the value of intermediate goods is doublecounted because the value of intermediate goods is embodied in the value of final goods. For example, 

clean drinking water, which is consumed directly by a household, is dependent on a range of intermediate 

ecological goods, but these intermediate goods should not be counted in an ecosystem service welfare 

account. Also important is that ecosystem services are attributed only the incremental value they 

contribute to the production of valuable end products. Using the above example, the value of ecosystem 

services associated with drinking water denotes the marginal contribution of ecosystems in the production 

of drinking water, not the full value of the final product. 

Given the complexity and variety of ecosystems and their services, their valuation poses several 

challenges. According to the National Academy of Sciences’ Committee on the Valuation of Ecosystem 

Services, the importance of ecosystem functions and services is often taken for granted and overlooked in 

environmental decision making. Moreover, the key challenge in the valuation of ecosystem services lies 

in the difficult integration of economic valuation and ecological production theory. This is no 

straightforward task, because many ecosystem goods and services are not quantifiable using available 

methods, and the application of economic valuation methods may be subject to judgment, uncertainty, and 

bias (Heal et al., 2005). 

A study by Costanza et al. (1997), seeking to determine the value of global ecosystem services, 

exemplifies the problems and pitfalls in the valuation of ecosystem services. Deriving and summing value 

estimates from the existing literature for a wide range of ecosystem attributes and services, this study 

suggested that the total value of global ecosystem services likely ranges from $16 to $54 trillion annually, 

or roughly one to three times global GDP. The study has been influential and widely quoted and used, 

especially among scientists and environmentalists. Economists consider it fundamentally problematic 

both conceptually and methodologically, preferring to focus on the value of changes to ecosystem 

services, which is relevant for policy, or what is termed the marginal value of ecosystem services. The 

estimate of the value of global ecosystem services by Costanza et al. (1997) has therefore been 

characterized as a “serious underestimate of infinity” (cf. Smith, 2007; Toman, 1998). 

F.1.1.2. Use of the Valuation Literature to Define Adversity 

A secondary standard, as defined in Section 109(b)(2) of the CAA, must “specify a level of air 

quality the attainment and maintenance of which, in the judgment of the Administrator, based on such 

criteria, is required to protect the public welfare from any known or anticipated adverse effects associated 

with the presence of [the] pollutant in the ambient air.” One way to quantify adverse effects is through 

monetary valuation. 

Adversity is difficult to quantify and measure, and there are several challenges to using a monetary 

valuation approach. A major effect that is geographically extensive might be considered to be more 

adverse than a more severe effect limited to one geographic location. Another problem is aggregation. 

Any change in pollution may have multiple effects (i.e. effects on many types of ecosystem services) 

leading to difficulty in aggregating in a consistent way. 

 F-3
Monetary values on any service or resource degradation reflect human preferences about what is a 

severe effect. Larger unit values correlate with more severe effects, other things equal. Also, more 

extensive effects, will contribute to larger welfare loss (or gain). In addition, since monetary units can be 

added, the aggregation issue can be addressed by “simply” summing the welfare losses (or gains). 

Although this is not strictly true (e.g., values for improvements in water quality and fish populations may 

not be additive), in principle the differences in how people conceptualize ecosystem improvements can be 

captured in the way resource improvements are valued in monetary terms. 

Clearly, there are many practical problems associated with using monetary value as a way of 

defining adversity. First, many resources and services have not been valued and efforts to credibly transfer 

the results of valuation studies to other areas and resources have been minimal. Second, studies 

addressing multiple effects are particularly difficult to transfer and few in number. Finally, even with the 

first two problems addressed, a judgment would still need to be made on whether the air quality standard 

was the contributing factor for eliminating adverse (highly monetarily valued) effects. 

F.1.1.3. Methods for Selecting Literature for this Assessment 

Assessing the economics literature on the effects of NOX and SOX emissions on terrestrial, 

transitional, and aquatic ecosystems requires identifying and reviewing relevant studies addressing these 

effects. Multiple methods were used for this Annex: searching existing databases of this valuation 

literature; conducting systematic searches of the economics literature; reviewing a large number of key 

articles, reports, authors, and journals; and identifying studies based on the expertise and familiarity with 

the relevant literature of lead researchers. 

Two existing databases on environmental valuation studies – the Environmental Valuation 

Reference Inventory (EVRI) and the Beneficial Use Values Database (BUVD) – were particularly useful 

for this assessment. The EVRI database, which includes nearly 1,900 valuation articles/studies on 

environmental and human health effects, was screened according to criteria regarding the potential 

relevancy of geographical location (U.S.), types of environmental goods and services valued (ecological 

functions, extractive uses, non-extractive uses, passive uses); and environmental stressor. This resulted in 

over 200 articles/studies of interest. BUVD is a relatively small database (131 articles/studies), so it was 

imported into the literature review database in its entirety, with unrelated articles/studies later excluded on 

an individual basis. 

A large number of additional journals and literature databases were identified that publish and 

cover research potentially relevant to this assessment. The selected peer-reviewed journals1

 and library 

databases2

 were then reviewed using search engines and a range of key words developed to find studies 

addressing relevant ecological endpoints (aquatic, transitional, terrestrial) and their economic values. The 

tables of contents of those journals that could not be searched electronically were reviewed in hard copy 

and relevant articles were added to the literature review database. These searches were augmented by 

reviews of the bibliographies of the following EPA reports: EPA Report to Congress: The Benefits and 

Costs of the CAA 1990–2010 (U.S. EPA 1999b); Air Quality Criteria for Ozone and Related 

Photochemical Oxidants (U.S. EPA 2006b); Air Quality Criteria for Particulate Matter (U.S. EPA 2004). 

 

1

 American Economic Review, American Journal of Agricultural Economics, Canadian Journal of Economics, Canadian Journal of Forestry, 

Contemporary Economic Policy, Ecological Economics, Environment and Development Economics, Environmental and Resource Economics, 

Environmental Science and Technology, Forest Science, Forestry Chronicle, Journal of Agricultural and Applied Economics, Journal of Applied 

Econometrics, Journal of Agricultural and Resource Economics, Journal of Agricultural Economics, Journal of Environmental Economics and 

Management, Journal of Forest Economics, Journal of Forestry, Journal of Political Economy, Journal of Risk and Uncertainty, Land Economics, 

Marine Resource Economics, Resource and Energy Economics, Review of Agricultural Economics, Review of Economics and Statistics, Water 

Resources Research. 

2

 AgEcon Search, Agricola, BioOne, CSA Illumina, EconLit, GeoRef, Google Scholar, SciSearch/Science Citation Index (Web of Science), 

SCOPUS, Sportfishing Values Database, SSRN. 

 F-4
The results from these searches were checked for duplicates and clearly irrelevant studies, after 

which over 500 potentially relevant articles/studies were identified for initial assessment. Relevancy of 

each study for this assessment was determined according to the following key criteria. 

 Does the study address an ecological endpoint sensitive to reductions in NOX and SOX emissions? 

 Does the study value quality changes in the ecological endpoint, which is actually or potentially 

attributed to reductions in NOX and SOX emissions? 

 Is the study peer-reviewed and preferably, published in an academic journal? 

 Very few, if any studies fully satisfy all above criteria. For this reason, studies that at least 

partially satisfy these criteria were deemed potentially relevant for this project. Finally, reviews 

and meta-analyses were included in the assessment whenever they were available and dealt with 

potentially relevant ecological endpoints. 

In the initial assessment, each record’s potential relevancy to the assessment was rated on a scale of 

1 to 4, with 1 indicating that the record appears directly relevant to this assessment (the study addresses 

quality change of an ecological endpoint, which is actually or potentially attributable to NOX/SOX). 

Records rated 2 only partially satisfied the “relevancy criteria,” but were considered important to be 

referenced in this report. Records rated 3 were to be reviewed more closely to determine their usefulness, 

and those rated 4 were found not relevant for the purposes of this assessment. Because the goal in the 

initial assessment was to avoid missing potentially relevant studies, borderline cases have been classified 

to the lower number category. 

Figure F-2. Reviewed studies by ecosystem addressed. 

All studies rated 1 through 3 were next reviewed using several attributes, including ecological 

endpoints, valuation techniques, geographical area, use vs. non-use value category, and other details of 

interest. Of those studies, about half addressed aquatic ecosystems (Figure F-2). The reviewed studies 

addressed many different ecological endpoints, such as sport fishing, commercial fisheries, aquatic 

recreation (e.g., swimming and boating), general water quality, ecosystems services provided by aquatic 

ecosystems, and coral reefs (Figure F-3). Nearly one third of the studies addressed terrestrial ecosystems 

(e.g., forestry/commercial timber, outdoor recreation, and agriculture); the rest dealt with transitional 

ecosystems (e.g., ecosystems services provided by wetlands and wetlands recreation). 

 F-5
F.2. Conceptual Framework 

F.2.1. Taxonomy of Values for Environmental Goods and Services 

The economic value derived by society from these ecological goods and services can be 

categorized as either “use” or “non-use” values. Use values comprise values for those goods and services 

that can be used either directly or indirectly. Non-use values denote the characteristics of the ecological 

goods and services that are not used at all but still hold economic value. The schematic in Figure F-4 

illustrates these divisions of values for environmental goods and services. 

Direct use values are held for those goods and services which can be directly consumed or utilized 

by individuals or society. Some direct use goods, such as fish caught or timber cut are sold in markets and 

thus valued using market data. Other direct uses, such as recreational use of ecosystems for fishing, 

hunting, and sightseeing, are usually not bought or sold through a market and are therefore more difficult 

to value. Similarly, changes in their value are more difficult to quantify. Thus, direct use can be 

subdivided into directly used market and non-market goods or services. 

Figure F-3. Reviewed studies by ecological endpoint. 

 F-6
Source: U.S. EPA (2002a). 

Figure F-4. Taxonomy of values for environmental goods and services. 

Examples of direct, market uses of ecological goods and services include commercially sold food 

sources (fish, crops, other animals); building materials (wood, stone); fuel sources (wood, coal, oil); 

drinking water (groundwater, surface water); chemicals; and minerals (U.S. EPA, 2002a). Examples of 

direct, non-market uses of ecological goods and services include recreational fishing and hunting; beach 

use (sunbathing, swimming, and walking); boating; hiking; camping; wildlife watching; and sightseeing 

(U.S. EPA, 2002a). 

Indirect use values capture those ecological services that are not used directly but still provide 

benefits of economic value to society. These services include flood control, storm water treatment, ground 

water recharge, climate control, pollution mitigation, wave buffering, soil generation, nutrient cycling, 

habitat value, and biodiversity (U.S. EPA, 2002a). 

Non-use values denote the characteristics of the ecological goods and services that are not used at 

all but still hold economic value. These non-use values include what are known as existence and bequest 

values–the value of simply knowing that certain ecosystems exist and of ensuring that they continue to 

exist for future generations, respectively. These services are also not traded in a market and quantifying 

their value is a challenge. 

Importantly, empirical methods for addressing non-use value generally estimate the total value of a 

resource. Distinguishing between the use and non-use values often is not possible, and the valuation 

results including non-use values should therefore generally be considered total valuation rather than nonuse valuation studies. Examples of non-use values of ecological goods and services include existence 

value, cultural/historical value, intrinsic value, bequest value, and altruistic value (U.S. EPA, 2002a). 

In this Annex, the focus is on the potential incremental benefit that might be realized from reducing 

levels of SOX or NOX by a relatively small amount, rather than the total value of the affected ecosystems. 

Therefore, the term “total value” in this Annex generally denotes total marginal benefits that contain both 

use and non-use values, not the total value of the entire resource. This notation is consistent with the 

principles of economic valuation of the environment, which generally focus on predicting damages or 

benefits from marginal changes in environmental conditions. 

F.2.2. Welfare Economics 

Any environmental goods or services that somehow contribute to human well-being are 

economically valuable. Their economic value reflects the capacity of the environment to satisfy different 

human needs, which is related to the direct consumption of different goods and services derived from the 

 F-7
environment. It also includes different indirect, passive, and non-use values for environmental goods and 

services, such as recreational benefits, enjoyment of natural landscape, purity of air and water, and 

provision of habitat for species other than humans. 

Economic valuation is rooted in the basic principle of consumer sovereignty. Rather than judging 

whether an individual’s choices are right or wrong, each person is considered able to make rational 

choices that advance his or her well-being, given the possibilities available. The principle of consumer 

sovereignty extends also to the valuation of environmental goods and services. Even in the absence of 

markets for environmental benefits, each individual is considered able to assess the importance of 

changes in environmental quality on personal well-being or, as economists commonly refer to it, utility. 

Consumer (CS) and producer surpluses (PS) are the basic monetary measures of well-being (or 

welfare) in economics. They denote the “excess utility” consumers and producers enjoy when consuming 

or producing specific goods or services after paying for them. Producer surplus is measured by profit, the 

value of production after accounting for all costs. Consumer surplus, which is a more subtle concept, can 

be thought of as the difference between the price and the maximum value that an individual holds for a 

good or service. However, it generally is not an exact measure of changes in welfare because CS does not 

fix the baseline level of utility, thereby ignoring the income effects of a changing baseline 

CS has two exact (Hicksian) measures: willingness-to-pay (WTP) and willingness-to-accept 

(WTA). In the context of environmental valuation, WTP denotes an individual’s maximum willingness to 

pay for an environmental good or service. WTA, on the other hand, stands for the minimum compensation 

an individual is willing to accept to forgo an environmental good or service. Though WTP and WTA both 

are exact measures of CS, they generally are not equal, meaning that the CS in general does not have a 

unique measure. WTP and WTA for usual marketed goods and services are similar and within narrow 

bounds of income effects (Willig, 1976). Similar findings can be expected with environmental goods and 

services only when they have close substitutes (Hanemann, 1991; Shogren et al., 1994). 

Since many environmental goods and services cannot be easily substituted, WTP and WTA are 

expected to differ, sometimes substantially. In fact, the WTP-WTA divergence can range from zero to 

infinity, depending on the substitutability of an environmental good and other market or non-market 

goods (Hanley et al., 1997). Using WTA to measure welfare changes might often be justified on the 

grounds of economic theory and property rights, but many studies choose to estimate WTP for its 

practicality. In addition, WTP generally is lower than WTA and therefore is conservative, providing 

another justification for using WTP for valuing the environment. Finally, both WTP and WTA can be 

difficult to measure, and valuation studies therefore sometimes estimate the ordinary CS. It approximates 

WTP and WTA and is between these two exact measures of welfare. In the special case of no income 

effects, all different measures of consumer welfare coincide. 

Various methods have been developed to determine the value of different ecological goods and 

services by estimating the change in social welfare or WTP for changes in the quantity or quality of a 

given environmental resource (see Table F-1). Some valuation techniques obtain WTP from observing 

people’s actions (revealed preferences or RP) while others rely on people’s responses to hypothetical 

situations (stated preferences or SP). Yet another set of valuation methods relies on other studies, either by 

transferring their estimates into another context (benefits transfers) or by conducting statistical metaanalyses of earlier studies to examine their systematic findings. See Section F.2.3 for details about these 

approaches. 

As noted by Kramer et al. (2003), several forest protection valuation studies also considered the 

sensitivity of contingent valuation estimates to various preference elicitation methods, including 

dichotomous choice, payment card, and open-ended techniques. Haefele et al. (1992) used payment card 

and dichotomous choice techniques in a contingent valuation survey measuring the WTP of Southern 

Appalachian residents for protecting high-elevation spruce-fir trees from exotic insects and air pollution. 

Estimates for mean WTP were $20.86 per year using a payment card method, and $99.57 per year using a 

discrete choice method. Sample sizes for each method were relatively small (232 and 236 respondents, 

respectively). Another limitation concerns the wide span between the two valuation statistics, which 

 F-8
despite a large difference in the mean estimates showed no overlap of the 95% confidence intervals. 

Similar studies have been performed by Loomis et al. (1996) and Kramer and Mercer (1997). 

F.2.3. Benefit Estimation Approaches 

Table F-1 introduces different valuation techniques and indicated which type of value (direct use, 

indirect use, total value) each can be used to estimate. Studies addressing non-use values are referred to in 

this Annex as total valuation studies. This is because non-use valuation methods in fact generally estimate 

the total value of a resource, including both its use and non-use components. 

Travel cost (including site choice models) and hedonic pricing methods are perhaps the most 

regularly applied revealed preference (RP) methods for valuing the environment, whereas contingent 

valuation and choice experiments are the most popular stated preference (SP) methods. One of the key 

differences between the RP and SP methods is that RP methods can only fully address direct and indirect 

use values, whereas SP methods are required for the estimation of total values of environmental resources, 

including their non-use value component. 

Travel cost studies predict use values for ecological resources, such as natural parks, by examining 

individuals’ travel expenditures to utilize that resource (most often at a park or some other recreational 

site), including the opportunity cost of work time missed while traveling to and utilizing the resource. 

Travel cost studies commonly use a random utility framework, which infers individuals’ WTP for an 

ecological resource (again usually a recreational site) by observing their choice from among one or more 

alternatives. While the travel cost method uses changes in the quality of one resource to ascertain its 

value, the random utility model uses individuals’ choices among various options of various qualities at 

various prices to do the same. 

Hedonic price studies predict the value of ecological resources by examining their effect on 

property values. Assuming that all the benefits from living in a specific location and house are capitalized 

into the market value of the property, hedonic models estimate the independent effects of different 

housing characteristics on housing prices. Controlling for all observable housing and location 

characteristics, hedonic pricing models examine environmental values for, for example, proximity to 

forests or particular watersheds by estimating the implicit incremental price people are willing to pay for 

that proximity. Hedonic pricing relies on assumptions such as efficiently functioning housing market and 

perfect information and mobility by individuals. However, because WTP is not necessarily tied to 

ecosystem changes, this Annex does not consider property values as a method of valuation. 

Other market or RP-based approaches to valuing changes in ecological goods and services include 

the alternative/replacement cost method; avoidance expenditures/averting behavior method; referendum 

method; user fee method; market price and market simulation method; and a host of different variations of 

these and other valuation approaches. See, for example, Freeman (2003) for the theory and applications of 

different methods for valuing environmental quality. 

The RP methods have the advantage of gleaning their value estimates from individuals’ real world 

actions. However, because they do not include the non-use value of ecological resources, none of them 

capture total value. This problem has given rise to the development of a variety of non-market valuation 

methods that use surveys to elicit preferences for public goods. Because these methods are generally 

based on eliciting “stated” rather than “revealed” preferences, they are broadly categorized as SP 

methods. 

The contingent valuation method is the most common SP method. It involves developing and 

administering surveys, in which respondents are presented with a scenario or a program with specified 

environmental outcomes and costs. Each respondent is asked to indicate approval or disapproval of the 

proposed environmental scenario and its monetary cost. Researchers vary the proposed costs across 

different survey respondents and use their choices to estimate how much people on average are willing to 

pay for different scenarios to improve the environment. Because some of the respondents may use a 

 F-9
public good also for direct enjoyment, say viewing an endangered bird, the surveys actually capture total 

value for the improvements, rather than just their non-use value. 

Choice experiment methods (including contingent ranking, contingent choice, and conjoint 

analysis) separate an environmental good into its constituent attributes, recombine those attributes into 

different bundles, and elicit respondents’ preferences for those bundles. Often a monetary value can be 

assigned to those attributes and thus the process allows researchers to determine WTP for the bundle and 

each attribute. Conjoint analysis usually is performed by choosing the most preferred attribute bundle 

from a group (choice experiment, contingent choice) or via ranking a series of attribute bundles 

(contingent ranking). Using conjoint analysis, researchers may be able to simultaneously value various 

relevant goods or services that an environmental resource provides. For example, improving a public 

body of water provides improved recreational opportunities, drinking water, and support of aquatic 

ecosystems. 

Valuing the environment as a factor of production monetizes the incremental benefits from using the 

environment as an input to production. In other words, this method treats the environment as a production 

input comparable to other raw materials and infrastructure, such as land, capital, labor, and so forth. This 

method is appropriate for valuing environmental effects that have direct value as a factor of production. 

Examples of such cases include the effects of water quality on the productivity of commercial fisheries, or 

the impact of soil characteristics on agricultural productivity. However, this method is limited to market 

goods and services and can only address their role as part of the production process. This method does not 

address non-market environmental goods and services or non-use values. 

Benefits transfer approaches translate the entire estimated demand function from one application to 

another. Sometimes the function is adjusted to meet the specific criteria of the target site, and then new 

WTP value estimates are generated for the environmental good/service at the new site using the demand 

function. Using the transferred demand function, both changes in the level of use and the unit value 

benefits for the new site can be estimated (U.S. EPA, 2002a). There are four general types of benefits 

transfer technique: mean unit value transfer/ adjusted unit value transfer, benefit/demand function or 

model transfer, meta-function transfer, and structural benefits transfer. The first three methods dominate 

the literature (Smith et al., 2006b). 

Mean unit value transfer/adjusted unit value transfer entails taking the value of a specific 

environmental good or service (such as recreational hunting), sometimes from a single study of the same 

good and sometimes estimated by averaging a range of value estimates from various primary studies, and 

transferring that value to the same good or service at a new site. 

Benefit/demand function or model transfer is the translation of the entire estimated demand function 

from one site to another. Sometimes the function is adjusted to meet the specific criteria of the target site, 

and then new WTP value estimates are generated for the environmental good/service at the new site using 

the demand function. Using the transferred demand function, both changes in the level of use and the unit 

value benefits for the new site can be estimated (U.S. EPA, 2002a). Meta-function transfer involves the 

use of meta-regressions to combine the results of numerous valuation studies and allows researchers to 

account for influencing factors, thus enabling them to create value estimates for new policy sites. 

Structural benefits transfer, also known as preference calibration, requires selection of a preference model 

which can describe individual choices over a set of market and associated non-market goods to maximize 

utility when faced with budget constraints (Rosenberger and Loomis, 2001; Smith et al., 2006b). 

Meta-study review provides a useful way of summarizing the literature on the valuation of 

ecological endpoints from reductions in NOX and SOX, though there are few studies exactly on this topic. 

But there are studies summarizing monetary valuation efforts for particular sets of endpoints, such as the 

economic valuation of fresh water ecosystem services. Where such studies are available, they are 

summarized in the appropriate section of this annex. 

Even more valuable are meta-analyses, which perform statistical analyses of the results of original 

studies. Such studies explain variation in monetary value estimated for various endpoints using features of 

the original studies' methodologies as well as the characteristics of the site being studied and other factors. 

 F-10
Smith and Pattanayak (2002) defined meta-analysis as the practice of using a collection of formal and 

informal statistical methods to synthesize the results found in a well-defined class of empirical studies. 

In general, the three uses of meta-analysis are: synthesize or “take stock” of the literature on a 

particular valuation topic; test hypotheses with respect to the effects of explanatory variables on the value 

construct of interest; and use the estimated meta-analysis model to predict estimates of the value construct 

across time and space (Bergstrom and Taylor, 2006). Bergstrom and Taylor (2006) provided a review of 

the techniques and theory behind the use of meta-analysis for benefits transfer. They noted that to conduct 

a successful meta-analysis for benefits transfer, it is important to be as comprehensive as possible in terms 

of the studies to be included. Excluding a study would be equivalent to applying a zero weight to the 

information in that study. The authors list some additional criteria to be considered when identifying 

studies for inclusion in a meta-analysis, including controlling for the valuation method and estimated 

welfare measure, as well as addressing the temporal and spatial scales of the valued commodity. 

Extensive evaluation of the relative merits of, and issues with, different environmental valuation 

methods is not within the scope of this Annex. Vast amounts of research have been conducted to develop 

and evaluate alternative environmental valuation methods. For example, the Handbook of Environmental 

Economics recently dedicated an entire 1,100–page volume to addressing methods for valuing 

environmental changes (Mäler and Vincent, 2005). 

The validity of environmental valuation methods is sometimes questioned, in particular that of SP 

methods. Because survey-based valuation methods are based on what people say rather than what they do, 

there is a tendency to question the credibility of the results. For this reason, nonmarket valuation 

researchers, following the lead of the National Oceanic and Atmospheric Administration (NOAA) Expert 

Panel that reviewed the highly publicized studies valuing damages from the Exxon Valdez oil spill, often 

build into their surveys a series of validity tests, such as testing for the sensitivity of WTP to the scope of 

resource being valued (Federal Register, 1993). Additionally, SP surveys are vulnerable to a variety of 

issues dealing with the design and administration of surveys, as well as analyzing their data (e.g., Carson 

and Hanemann, 2005). 

Though examining actual choices lends credence to RP methods, they are not free of problems. For 

example, hedonic pricing studies of housing markets rest on the assumptions that the housing market is at 

equilibrium and that housing choices accurately reflect the attributes of interest, such as air pollution or 

environmental amenities associated with the residential location. Hedonic studies and other RP studies, 

such as recreation trip demand analysis, are also susceptible to potential biases. These include the 

omission of important variables, which may thwart the efforts to accurately value environmental quality. 

A number of studies have compared SP analyses with RP analyses, such as hedonic property value 

studies. Generally, these comparisons have suggested that when similar environmental values are 

examined, RP methods generally yield somewhat higher value estimates than SP methods. For example, 

Carson et al. (1996) reviewed over 80 studies which included comparisons of SP and RP methods, and 

concluded that SP methods are on average about 75% to 90% of corresponding RP values. 

F.3. Valuation of Forests and Terrestrial Ecosystems 

F.3.1. Use Values 

The impacts of NOX and SOX can occur over many different terrestrial ecosystems and use-value 

efforts must look at each of these ecosystems individually. One serious threat to agriculture from NOX

emissions comes from ambient ozone (O3), which is a byproduct of atmospheric reactions between 

Volatile Organic Compounds (VOCs) and NOX. In commercial forests, air pollution effects have not been 

addressed in economics beyond evaluating the potential effects of O3. Unlike in agriculture, the scientific 

 F-11
understanding of O3 effects on trees is more limited and related mostly to visual injury to leaves in young 

saplings, an indicator that is difficult to link to tree growth in mature forests. However, the valuation of O3

has already been evaluated in detail in the 2006 O3 AQCD (U.S. EPA, 2006b), and will not be covered in 

this Annex or the ISA (see also Tables F-2 and F-3). 

Some studies have attempted to specifically measure use values associated with forest quality, 

without regard to the factor that is altering forest quality. For example, an early study by Leuschner and 

Young (1978) considered the effects of crown density changes due to insect infestation around 19 lakeside 

campgrounds in east Texas. A travel-cost model estimated the CS losses from a 10% reduction in crown 

density to fall between 0.69% and 6.5%, depending on the number of substitute recreation sites available. 

More studies that look at how forest quality affects various uses are listed in Table F-4. Also, Table F-5 

lists value estimates from a meta-analysis but does not reflect the considerable variation of value 

estimates across underlying studies. 

Figure F-5 depicts the linkages between aesthetic welfare benefits and air pollution. One such 

linkage that is shown is the visual quality of forests. Changes in woodland appearances are monitored 

using scientific indicators of ecosystem change, such as crown condition, mortality, foliar damage, 

vegetation structure, and plant diversity (McLaughlin and Percy, 1999). However, in comparison to 

valuation of changes in commercial timber, measuring the economic value of aesthetic changes in forests 

and natural ecosystems can be more directly based on scientific information regarding the effects of 

pollution on forest health. Although the scientific linkages between air pollution and visual forest quality 

include large degrees of uncertainty, historic examples of air pollutant effects on forest aesthetics facilitate 

their empirical valuation. Figure F-6 shows the regions and species that have been identified as 

historically affected by air pollutants. 

 F-12
Source: Exhibit 1 in IEc (1999c). 

Figure F-5. Linkages from emissions to forest aesthetics. 

 F-13
Source: Figure 2 in IEc (1999a). 

Figure F-6. Geographic distribution of forested areas historically affected by air pollution. 

When the aesthetic quality of forests is important to people, visible injuries to trees can affect 

consumer welfare. Loss of or damage to foliage, changes in tree density, changes in species composition, 

and changes in vegetation structure can all affect the enjoyment that people derive from forests, and are 

therefore appropriate for economic assessment. Estimation of the aesthetic value of forest condition uses 

RP or SP methods and often focuses on long-term outcome stressors, rather than current intermediate 

situations. Nevertheless, there are few scientific studies that relate changes in pollution concentrations to 

these endpoints, and that make a direct linkage between science and economics. A memorandum by 

Industrial Economics, Inc. (IEc) (1999c) produced for Benefit-Cost Analysis of the CAA 1999, provided a 

comprehensive review of early forest aesthetics valuation literature. A recent literature review by Kramer 

et al. (2003) also summarized several key studies described in this report. Table F-5 lists and describes 

studies which have estimated monetary value for the types of non-consumptive use and non-use values 

for forests and natural ecosystems which may be affected by N and sulfur emissions. 

F.3.2. Total Values 

Several studies were reviewed by Kramer et al. (2003), and measured the total WTP of the general 

public for forest aesthetics and attempted to separate contributions of use values and non-use values. Most 

of these studies indicated that in the aggregate, non-use values (including option, bequest, and existence 

value) were significantly greater than use values (recreation). 

Walsh et al. (1990) employed a contingent valuation study using an iterative bidding technique to 

estimate WTP of Colorado residents for protecting Ponderosa pine (Pinus ponderosa) forests from the 

mountain pine beetle (Dendroctonus ponderosae). Presented with pictures of mid-level forest quality 

representing current conditions (100 to 125 live trees measuring more than 6–inches in diameter per acre), 

respondents were asked their WTP to prevent the lowest forest quality (0 to 50 trees per acre) and to attain 

 F-14
the highest forest quality (125 to 175 trees per acre). Overall mean WTP for changes in forest quality was 

estimated at $47 per household. In addition, respondents decomposed total value into four categories of 

value (recreational use, option, existence, and bequest). Results showed that use values accounted for 

27.4% of total value, and non-use values accounted for 72.6% of total value. The study was limited by 

small sample size (200 respondents) and possible bias from framing the scenario as “one of the most 

important issues facing Colorado residents.” 

A study by Haefele et al. (1992) that measured WTP to protect Southern Appalachian spruce-fir 

forests, used a decomposition approach to determine dominate influences on total values. The authors 

found that non-use values (bequest and existence values) overshadowed use values as reasons to protect 

forests. Holmes and Kramer (1996) relied on results from Haefele et al. (1992) to compare WTP between 

recreational forest users and members of the general public. Mean household estimates for forest-users 

($36.22) were substantially larger than estimates for nonusers ($10.37). 

Kramer et al. (2003) used a dichotomous-choice, contingent-valuation format to determine WTP of 

Southern Appalachian residents for protecting spruce-fir forests from insects and air pollution. The study 

measured incremental levels of forest protection using two scenarios: the first increment occurred along 

road and trail corridors, a scenario that would appeal to people valuing the ecosystem primarily for 

recreational use; the second level was for the entire ecosystem, a scenario that would appeal to people 

valuing the continued existence of the entire threatened ecosystem. Using randomly assigned values 

between $2 and $500 dollars, respondents were asked if they would pay a certain tax amount for each 

scenario. Results suggested that preferences for forest ecosystem protection created a well-behaved 

demand curve, with incremental WTP increasing at a decreasing rate. 

F.3.3. National-Scale Valuation 

An EPA-contracted valuation study by IEc (1999b) developed a benefits-transfer model using 

results from Holmes and Kramer (1996), Peterson et al. (1987), and Walsh et al. (1990) to estimate 

national benefits from the 1990 CAAA. Calculations applied WTP values from these previous studies 

across all households in the affected states. (See Table F-6 for more information on the underlying 

original studies, and Table F-7 for benefits transfer results.) Estimates for the total value of avoiding air 

pollution-induced damage to forest aesthetics in the U.S. during the period 1990–2010 were found to 

range from $3 to $17 billion. Due to limitations in the original studies and the simplicity of the benefitstransfer model, the authors warned that the estimates should only be used to consider the general 

magnitude of benefits to forest aesthetics (in the range of billions of dollars) rather than as precise values. 

F.3.4. Valuation of Degrees of Injury 

A few studies—Hollenhorst et al. (1993); Ruddell et al. (1989); Hammitt et al. (1994); Buhyoff 

et al. (1982)—measured general preferences for forest aesthetics without estimating changes in welfare. 

Hollenhorst et al. (1993) considered the effect of tree mortality on perceived forest aesthetics. On a 1 to 

10 scale for visual and recreational appeal, 400 respondents ranked pictures of 25 sites with tree mortality 

ranging from 6% to 98% from gypsy moth (Lymantria dispar) damage. The results produced a hillshaped function, whereby site appeal increased with mortality at levels as high as 40% but then declined. 

The authors speculated that respondents had a general distaste for tree mortality but valued light 

penetration to the forest understory, which allows for the growth of wild flowers and lower-level 

vegetation. 

Other studies confirmed this visual preference for light penetration, including Ruddell et al. (1989) 

and Hammitt et al. (1994), who observed positive responses to forest edges and open middle grounds 

without light-obstructing tree canopies. Buhyoff et al. (1982) tested how awareness of environmental 

 F-15
damages affects perception of aesthetic losses. The study compared aesthetic rankings of photographs 

between a control group and a group that is informed about the cause of forest damage before the ranking 

session. Results show a heightened level of sensitivity to forest damage by the “informed” group of 

subjects. 

Several other studies investigated the notion of aesthetic thresholds, or discontinuous jumps in 

aesthetic preferences across small changes in visual injuries. Of particular interest to early studies were 

thresholds at the lower limit of visual perceptibility. 

Contingent valuation studies by Vaux et al. (1984) and Flowers et al. (1985) assessed aesthetic 

preferences at recreational areas with fire damage and found that small differences in site appearance can 

produce large changes in recreation preferences. This finding is generally consistent with the results from 

two early studies by Buhyoff et al. (1979) and Buhyoff and Wellman (1980), which asked respondents to 

rate various levels of insect damage. The authors found that preferences seemed to be most affected by 

the presence or absence of insect damage, as opposed to the degree of damage. 

Crocker (1985) surveyed 100 recreationists for their WTP for recreational experiences at forested 

sites with slight injury, moderate injury, or severe injury. Mean WTP estimates for the environment with 

slight injury were three times higher than WTP for environments with moderate and severe injury, 

suggesting that people are willing to pay a premium for recreational access below the lower limit of 

perceptibility. As a group, respondents did not indicate a clear preference ordering between the moderate 

and severe injury environments. 

Holmes et al. (2006) use a hedonic method to predict the impact of forest damage due to the 

hemlock woolly adelgid (Adelges tsugae), an invasive insect, on the value of residential properties. 

Examining 3,379 residential property sales in New Jersey between 1992 and 2002, the study analyzed 

how the appearance/health of the forest on the home’s parcel and within 0.1, 0.5, and 1 km buffers around 

it affects the housing market. Controlling for other relevant variables, the study estimated that hemlock 

health status had a statistically significant effect on property values. The estimation results suggest, for 

example, that a 1-point increase (e.g., from 10% to 11%) in the percentage of healthy hemlocks (less than 

25% foliar damage) of all forests on the home’s parcel was associated with a 0.66% sales price increase. 

Similar changes in the hemlock forests and their health status in the home’s near proximity are predicted 

to be associated with yet larger increments in the sales price. 

At least one study seems to provide evidence against the concept of thresholds at the lower limit of 

perceptibility. A referendum-type contingent valuation survey by Jenkins et al. (2002) used two sample 

populations to value a forest protection program. The initial forest condition was described as “pristine” 

for one group and already “somewhat damaged” for another group. The degree of forest damage incurred 

in the absence of a forest protection program was the same for both sample groups. Regressions for the 

entire sample population showed no statistically (Jenkins et al., 2002) significant difference in WTP for 

forest protection between the two groups. 

Further analysis of Jenkins et al. (2002) suggested that aesthetic preferences and thresholds differ 

between recreational groups. Comparisons among recreational groups revealed that consumptive forest 

users (hunters and anglers) held values that were sensitive to change in forest condition, while nonconsumptive forest users (hikers and campers) held values that were insensitive to the same amount of 

change. Overall, however, non-consumptive forest users expressed higher values than consumptive forest 

users. Aesthetic thresholds also seem apparent for recovering forests. Paquet and Belanger (1997) found 

that the aesthetic effects of clear cutting were largely removed once re-growth reached a height of 4 m. 

Finally, there is substantial literature related to the amenity value of urban/suburban forests and 

open space. McConnell and Walls (2005) recently reviewed this literature, evaluating more than 60 

published articles that have attempted to estimate the value of different types of open space. Two lines of 

research emerge in this literature: studies that estimate the hedonic value of open space proximity to 

residential properties, and studies that use SP methods to value preservation of open space. Unfortunately, 

neither line of research generally comprises studies that provide information on the WTP for quality 

changes to open space that might be caused by changes in NOX and SOX emissions. In most cases, the 

proximity or preservation of open space or urban/suburban forest is valued without reference to the 

 F-16
quality or even the type of open space or forest. Although this literature demonstrates the value of the 

availability and preservation of open space, its relevancy for the purposes of this assessment is at best 

limited. 

F.3.5. Limitations and Uncertainties 

Shortcomings in the air pollution terrestrial valuation literature have been well documented in 

recent reviews (Adams et al., 2003; U.S. EPA, 2006b). In existing economics literature, the effects of O3

on terrestrial endpoints overshadow assessments of other effects of NOX and SOX pollution, such as acid 

deposition and N fertilization. The latter effects have been well documented in the scientific literature, but 

the lack of valuation studies related to them limits full assessments of terrestrial damages from NOX and 

SOX pollution. Incomplete scientific understanding of the effects of air pollutants on ecosystems and 

economic endpoints extends to many valuation studies. Even in the valuation of O3 effects, which have 

been relatively thoroughly studied, improvements in noneconomic input data could play an important role 

in evaluating the magnitude of vegetation damages from O3. 

Economic models should incorporate more specific temporal (dynamic) and spatial data regarding 

terrestrial effects of air pollution to reflect more realistic situations. For example, the O3 effects valuation 

literature analyzes air pollution effects across a period of time using only two or three scenarios that 

represent large, static changes. In the real world, however, these changes would occur gradually and 

incrementally. Future studies should try to consider more dynamic models that can describe effects of 

marginal air quality changes. From a spatial perspective, previous studies often assumed that producer 

responses are similar across large geographic areas. However, regional factors may be important, 

suggesting a possible need for finer-scale agricultural and forestry data that would allow models to 

consider micro-level physical and economic factors (Adams et al., 2003). 

With regards to the effects of air pollution on agricultural crops, most economics studies date from 

the 1980s and many focus on O3. Therefore, there is a general need for updated valuation studies that 

consider agricultural damages from air pollution, especially with regards to the roles played by NOX and 

SOX. Specific issues include the need to develop new exposure-response functions for sensitive crops and 

the effects of air pollution on crop yields under actual farm conditions. 

Assessments of welfare losses in commercial forestry from air pollution are mainly limited by 

scientific uncertainty regarding the extent of ecological damage. Restrained by the large size and slow 

growth of trees, scientific research has primarily considered O3 effects on seedlings and has little 

transferability to mature trees. 

Existing literature on valuation of changes in forest aesthetics has focused primarily on historical 

cases of acute air pollution or insect infestation and may be overly simplistic. Although these valuations 

are useful, no existing assessments have examined the effects of reduced air pollution on forest aesthetics. 

Understanding the effects of marginal changes in air pollution would require the collection of long-term 

high-quality data about forest health, as well as improved casual linkages between air pollutants and 

visible injuries to trees. Scientific advancements at this scale would require a sophisticated monitoring 

network operating over several decades. Until the impacts of pollution on the foundational services of 

terrestrial ecosystems are better understood, valuation assessments may be premature. Additionally, most 

studies also fail to distinguish between marginal values of forest health and average values of forest 

health, an oversight that may create bias in final estimates. 

A major limitation of both non-economic and economic forest health assessments concerns the 

limited extent of documented ecosystem-level changes. Most forest health surveys focus on average forest 

conditions that allow for modeling of near-term trends in economic value, but fail to detect fundamental 

changes in ecosystem processes that sustain natural capital in the long run (McLaughlin and Percy, 1999). 

Quantifications of impacts on species effectively conform to existing valuation methods; however, these 

effects may be overshadowed by long-term or irreversible reductions in ecosystem structure and function 

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(U.S. EPA, 1999b). Assessing ecosystem-level changes presents new challenges to both science and 

economics as they include a large degree of uncertainty in the scale and nature of effects. In particular, 

economic assessments of ecosystem impacts may require regional- to national-scale modeling of 

numerous ecosystem functions rather than analyses of specific service flows that directly contribute to 

human welfare (U.S. EPA, 1999b). In spite of these challenges, the relationship between air pollution and 

forest health has enormous implications for policy development and should be addressed in future 

research. 

More advanced methods to address uncertainty and irreversibility are essential to future modeling 

of complex ecosystem-level impacts. Several alternative methods to address ecological uncertainties are 

currently being developed and should play a central role in future models (U.S. EPA, 1999b). 

F.4. Valuation of Transitional Ecosystems 

The economic analysis of the benefits on wetlands (transitional ecosystems) from reduced 

emissions are limited in this Annex to three recent and comprehensive meta-analyses. Brander et al. 

(2006) provided a comprehensive review and meta-analysis of the wetland valuation literature. This study 

improved upon previous similar studies (Brouwer et al., 1999; Woodward and Wui, 2001) by including 

tropical wetlands estimates from other valuation methodologies, other wetland services, and estimates 

from more countries. 

It is well known that wetlands serve a variety of potentially valuable ecological functions, 

including flood and flow control, storm buffering, sediment retention, groundwater discharge, and habitat 

for plant and animal species. Wetlands also contribute to climate stabilization, carbon sequestration, and 

the overall quality of the natural environment. Brander et al. (2006) (Table F-8) described these functions 

and their associated goods and services, as well as their values as determined by common valuation 

methods. 

F.4.1. Use and Non-Use Values 

The Brander et al. (2006) meta-analysis included 191 different wetland valuation studies from 25 

different countries. Eighty of those studies, half of which were from North America, provided useful 

empirical data for the meta-analysis. A total of 215 individual observations were gleaned from those 

studies for use in meta-regressions. The study reviewed the literature, discussed valuation techniques, 

calculated average wetland values using the results from the combined data set, and then performed a 

meta-regression of the data to determine which explanatory variables had the greatest effect on wetland 

value. Non-market use values of wetlands, as well as non-use/existence values, were also included in the 

analysis. Because not all of the studies generated WTP value estimates, wetland size and population 

statistics were used to convert estimates from the diverse valuation methodologies to 1995 dollar values 

per hectare per year, following the example of Woodward and Wui (2001), which is described below. 

The results of the Brander et al. (2006) meta-analysis showed an average value for wetlands of 

$2800/ha/yr (1995 dollars), and median value of $150/ha/yr. Individual values were calculated by wetland 

type, the wetland service provided, contingent, and valuation method used. The meta-regression also 

revealed that studies using the contingent valuation method consistently returned the highest wetland 

values. It is noted that this may have been due to the type of wetland values that this method was applied 

to, rather than something intrinsic to the methodology. 

To look at the value differences from a reduction specifically in N emissions, there is a focus on 

Brander et al.’s (2006) analysis of wetland value changes that resulted from a small change in water 

quality or some other ecosystem service. These results are valuable because they may be related to 

 F-18
changes in acid deposition as a result of changes in the concentrations of NOX or SOX. In the metaregression results of Brander et al. (2006), the only such ecosystem service which was shown to have a 

statistically significant effect on wetland value was hunting. Hunting had an unexpected negative 

influence on wetland value. Although not statistically significant, water quality, habitat and nursery 

service (specifically support for commercial fisheries and hunting), fishing, and biodiversity all had 

positive effects on wetland value. The accuracy of the value transfer exercise in Brander et al. (2006) 

seems to be in line with other similar efforts, with an average transfer error of 74%. Overall, the value 

transfer function from Brander et al. (2006) produced higher-value transfer error for less valuable 

wetlands (and systematically over-predicted their value) and lower value transfer error for more valuable 

wetlands (and slightly under-predicted their value). 

Woodward and Wui (2001) found 39 studies that contained sufficient data for inter-study 

comparison, including peer-reviewed and gray literature. Similar to the approach of Brander et al. (2006), 

Woodward and Wui (2001) assumed that a wetland’s value is a function of its ecological characteristics 

and socio-economic environment, and that there exists a true public WTP at a given moment for a 

particular wetland. With these assumptions, they attributed variability in wetland value to two principal 

sources: differing characteristics of the wetland and error in the estimation of the true value (Woodward 

and Wui, 2001). 

Brouwer et al. (1999) analyzed the WTP of 30 CV studies addressing ecosystem functions of 

wetlands, providing a total of 103 estimates. The studies were mostly conducted in the U.S., but were not 

in any way comprehensive spatially. They ranged from 1981 to 1997 and covered a wide range of 

commodity definitions, including preserving wetlands threatened by flooding, maintaining or improving 

current catch levels and fish populations, saving the bald eagle (Haliaeetus leucocephalus), 

improving/maintaining habitat, water quality ladder changes, preventing shellfish bed closures, increasing 

the number of protected rivers, and explicitly preserving a measure of water quality. No attempt was made 

to standardize the degree of change being valued. Instead, the authors attempted to address differences in 

the studies through the use of dummy variables and some degree of sub-setting. 

Based on all of the included studies, Brouwer et al. (1999) estimated that the average WTP for the 

addressed ecosystem functions was about $90 per household per year. These studies covered types of 

wetlands (primarily fresh water systems), ecosystem functions (water quality and biodiversity, and to a 

lesser extent water quantity and flood control), and wetlands size. Cutting across types of systems, 

location, and function enhanced, the WTP for salt and fresh water improvements was about the same. 

Within fresh water wetland systems, riverine wetland improvements appeared to be more highly valued 

than palustrine wetland improvements. Across ecosystem function, flood control was more highly valued 

than biodiversity, water quality and water generation, in that order. Improvements to larger wetlands 

appeared to be somewhat more highly valued than to improvements to smaller wetlands. Improvements in 

use values averaged slightly more than $100 per household per year, whereas improvements in non-use 

values only showed values about half this size. Spatially, values were higher in California, followed by 

Georgia and Louisiana. 

The review of outdoor recreation valuation studies by Rosenberger and Loomis (2001) included 13 

studies and 59 value estimates associated with waterfowl hunting, a popular activity in transitional 

ecosystems. On average, these studies estimated that the value of waterfowl hunting is about $32 per 

person per day (1996 dollars). However, different value estimates ranged from about $2 to over $160 per 

person per day. In their meta-analysis, Rosenberger and Loomis (2001) predicted a $40 value per person 

for a day for waterfowl hunting (Table F-4). These results demonstrate potential benefits from waterfowl 

hunting, but do not address changes in the quality of transitional ecosystems that may possibly be 

associated with reduced NOX/SOX emissions. 

 F-19
F.4.2. Limitations and Uncertainties 

Meta-analyses of wetland value (and the underlying original studies) are not directly useful because 

they do not measure changes in ecosystem services that would follow from some tightening or loosening 

of the standard for NOX or SOX. Nevertheless, they have indirect use by pointing out the importance of 

various methodologies, the overall values commonly assigned to wetland improvement, and types of 

ecosystem services that might be affected by air pollutants. For example, both Brander et al. (2006) and 

Woodward and Wui (2001) found that CV studies generally yielded greater value per acre than travel cost 

studies. This is not surprising, as the former would generally include non-use values. Furthermore, 

Woodward and Wui’s (2001) meta-analysis showed that the availability of bird hunting, bird watching, 

and amenity services affect wetland value. For example, bird watching (which could be affected by bird 

populations which, in turn, could be affected by nutrient enrichment) contributes more value to an 

ecosystem than any of the other ecosystem services. This is mainly due to the popularity of bird watching 

and the large numbers of people who engage in this recreational activity. 

F.5. Valuation of Aquatic Ecosystems 

As discussed in the ISA and the preceding annexes, acidification and eutrophication are the two 

main effects of NOX and SOX on aquatic ecosystems. In economic valuation of the effects of NOX and 

SOX deposition on aquatic ecosystems, these effects are reflected partly in the WTP for recreational 

fishing (with effects on the catch rate and fishing quality in a particular aquatic ecosystem) and partly on 

aesthetic and non-use (and total) values. Valuation methods used to assess the value of aquatic ecosystems 

include contingent valuation (CV) (open-ended, discrete choice, etc.), choice experiments, hedonicpricing valuation, travel cost (TC) (hedonic travel cost and traditional travel cost), cost-effectiveness 

analysis, avoided-damage cost, replacement cost, market analysis, and so forth. The CV method has 

gained popularity for total (use plus non-use) value estimation, whereas the travel cost method plays an 

important role in deriving use values. Currently, there is also a tendency to use stated and RP techniques 

together for the valuation of recreational activities. Supplemental to the below discussions about valuation 

of acidification and eutrophication on aquatic ecosystems, Table F-9 describes different aquatic valuation 

studies according to the ecological effect examined (acidification, eutrophication) and lists their relevant 

details. 

An extensive literature review by Wilson and Carpenter (1999) provides an excellent starting point 

to the valuation literature on aquatic ecosystems. This paper examined 30 studies published between 1971 

and 1997 that estimated the value of nonmarket ecosystem services provided by fresh water bodies in the 

U.S. Sixteen were SP studies, with the rest split between travel cost and hedonic pricing approaches. The 

travel cost studies covered very specific water bodies and stressors, such as a boaTable to swimmable 

change in water quality for 13 recreation sites along the Monongahela River in Pennsylvania (Smith and 

Desvousges, 1986), and a change in water quality measured by the Uttormark’s Lake Condition Index for 

recreational use of Pike Lake in Wisconsin (Bouwes and Schneider, 1979). Beyond spatial and stressor 

differences, the studies differ in their units for expressing value. The Monongahela River study used 

benefits per household, with the largest value expressed for a change from boatable to swimmable ($51). 

The Bouwes and Schneider (1979) study concluded that the total (aggregate) annual mean CS was almost 

$86,000. 

There are several additional meta-analyses that describe methods of valuation for aquatic 

ecosystems, but not specifically relating to effects caused by NOX or SOX. Van Houtven et al. (2007) 

analyzed WTP for fresh water quality improvements, covering 131 valuation estimates from 18 SP studies 

that used or could be transformed to use the water quality ladder (boatable, fishable, swimmable) 

modified into a 10–point scale. The analysis started with 90 studies but found that most were not 

 F-20
sufficiently comparable for statistical analyses. The studies ranged from 1977 to 2003 and covered rivers, 

lakes and estuaries. 

A meta-analysis by Johnston et al. (2005) examined the value of fish catch based on a total of 48 

studies published in the U.S. and Canada between 1977 and 2001. The study statistically examined WTP 

for fish relative to variations in resource, context, angler, and policy attributes, as well as methodological 

attributes of the studies themselves. The attributes examined included species targeted, geographic region, 

water body type, catch rate, angler demographics, and fishing method. Among 391 WTP observations, 

122 were estimated using CV methods, 59 using TC methods and the remaining from discrete choice 

models. 

Johnston et al. (2005) found that SP studies have generally resulted in lower WTP estimates than 

RP studies, which is consistent with earlier multiple study reviews and meta-analyses. Dichotomous 

choice CV also has produced lower WTP estimates than choice experiment or conjoint surveys, compared 

to the default of open-ended surveys (including payment cards and iterative bidding). However, these 

findings were not robust across all regression model specifications. The study supported previous findings 

(Poe et al., 2000) that WTP is systematically associated with variations in resource, context, and angler 

attributes. The study also concluded that WTP per fish varies systematically across study methods, but the 

variation due to methodology accounted for a relatively small proportion of total WTP variation. Again, 

these results demonstrate the value of recreational fishing, but do not address changes in water quality 

that may be associated with reduced NOX/SOX emissions. 

F.5.1. Acidification 

The Adirondack Park in New York is the best documented of all areas affected by acidic deposition 

in the U.S. Due to air pollution that largely originated from power plants and other sources to the west 

and southwest of the park, many watersheds in the park have experienced slow acidification of water and 

soil since the late 18th century. This park has been the subject of numerous valuation studies in recent 

decades. 

Morey and Shaw (1990) applied the travel cost model and estimated recreational fishing values 

associated with water quality change resulting from acidic deposition in the Adirondack Park area. The 

results showed that the aggregate expected conditional compensating variation for a 50% increase in catch 

rate of two trout species at four sites was $3,863 (1977 dollars; $13,175 in 2007 dollars), based on 607 

survey respondents with an estimated standard deviation of $89 ($304 in 2007 dollars). 

A travel cost model was also applied by Mullen and Menz (1985) to link acidic deposition and 

recreational fishing in the Adirondack Park. A net average economic value per angler day was calculated 

to be $20 (1976 dollars; $73 in 2007 dollars) for lakes, ponds, and streams in the Adirondacks, with an 

estimated total loss in net economic value of $1.07 million (1976 dollars; $3.89 million in 2007 dollars) 

per year for a 5% reduction in fishable area. These estimates excluded streams because limited knowledge 

existed to assess the effect of pH fluctuations on aquatic life in streams. Shaw (1989) questioned the 

reliability of these results, arguing that the demand equation was not compatible with economic theory 

and that aggregating separate CS measures from different equations was inappropriate. 

Englin et al. (1991) assessed the economic impact on recreational fishing in four upper northeastern 

states resulting from acidic deposition control. Biological effects of acidification were linked with 

hedonic travel cost and random utility models through the acid stress index (ASI) and fish catch per unit 

effort (CPUE). The analysis was based on multiple data sources and yielded positive social welfare for 

2030 under a scenario of reducing by 50% the ambient deposition in 1989. This level of decline in acidic 

deposition was expected to occur on the basis of analyses conducted by the National Acid Precipitation 

Assessment Program (NAPAP). 

Cameron and Englin (1997) measured welfare under an assumption that people are not certain 

about participation in fishing. Based on WTP results from a survey and a random utility model, surpluses 

 F-21
were estimated for preventing a 20% loss in the availability of high-elevation lakes for fishing in 

northeastern states. 

Randall and Kriesel (1990) employed a SP method to value a National Pollution Control Program, 

which led to improved air and water quality by 25% within 5 years. A nationwide survey conducted in 

multiple modes found an estimated annual willingness to pay of $694 ($1,098 in 2007 dollars) per 

household. The researchers concluded that the estimated value was lower than their multimarket hedonic 

estimates. 

Banzhaf et al. (2006) conducted a CV survey, which was designed to estimate the total value (use 

plus non-use values) of reducing acidification by liming the lakes and forests in the Adirondack Park to a 

degree matching recent SOX and NOX reductions under emissions trading programs. The mean WTP for 

improving 600 lakes “of concern,” and small increases in populations of two bird species and one tree 

species was $48 (2003 dollars; $54 in 2007 dollars) per year per New York household. The mean WTP for 

improving 900 lakes “of concern,” four bird species and three tree species was $54 (2003 dollars; $61 in 

2007 dollars) per year per New York household. 

Sequential implementation of several regulations, including in particular the CAA, has 

substantially reduced acidic deposition, especially in the eastern part of the U.S. Burtraw et al. (1997) 

conducted a benefit-cost analysis for the NAPAP program. Besides improving public health and visibility, 

emissions controls contributed to decreased lake acidification which was projected to have economic 

benefit associated with improved recreational fishing in the Adirondack Park. The ASI and CPUE 

approaches were used in the Tracking and Analysis Framework (TAF) model to capture the response of 

three recreationally fished species to water quality improvement in 33 statistically selected Adirondack 

lakes. The per capita recreational fishing benefits in 2010 were estimated to be $0.62 (1990 dollars; $0.98 

in 2007 dollars) annually per angler fishing in the Adirondack area. 

F.5.2. Eutrophication 

As is the case for acidification, the endpoints most studied in the valuation literature for nutrient 

enrichment concern fishing and non-use (or total) values. Change in aquatic recreational behavior is 

another endpoint for looking at valuation of such ecosystems, although it is difficult to link those changes 

due to ambient NOX or SOX concentrations. 

F.5.2.1. Recreation 

Bockstael et al. (1989b) measured the benefits of improvements in water quality of the Chesapeake 

Bay using travel cost and SP methods. A telephone survey with 959 respondents revealed an aggregate 

WTP for water quality improvement from current level to a level acceptable for swimming and/or other 

water activity of about $91 million (1987 dollars; $166 million in 2007 dollars) for households in 

Maryland. Based on three other surveys, a travel cost model yielded aggregate use values of about $34.7, 

$4.7, and $1.4 million ($63.1, $8.6, and $2.6 million in 2007 dollars) for a 20% water quality 

improvement for beach use, boating, and sport fishing, respectively. 

Morgan and Owens (2001) simulated water quality change under a baseline (without additional 

pollution control) and a scenario with pollution control in the Chesapeake Bay watershed and then 

calculated aggregate benefits for beach use, boating, and striped bass sport fishing by transferring the 

benefits from Bockstael et al. (1989b). Lower bound aggregate benefits for beach use, boating, and sport 

fishing were $288.8, $6.7, and $288.8 million (1996 dollars; $380.4, $8.8, and $380.4 million in 2007 

dollars), respectively for a 60% improvement in Chesapeake Bay water quality. 

Lipton (2003) mailed out an open-ended contingent valuation survey to 2,510 randomly selected 

Maryland boaters regarding willingness to pay for one unit improvement in the water quality of the 

Chesapeake Bay. The boaters ranked their perception of water quality on a scale of one to five. The 

 F-22
median WTP for a one step improvement in water quality was $17.50 ($19.66 in 2007 dollars) per year 

and the mean was $63 ($71 in 2007 dollars), with 38% of respondents expressing a zero WTP. It was 

found that the boaters who keep their boats in the water would pay more than those who keep them on 

trailers. Individuals who ranked ambient water quality lower and had more concern for the health effects 

from water quality degradation would likely pay more for water quality improvement. The aggregated 

WTP for the Chesapeake Bay boaters in Maryland was approximately $7.3 million ($8.2 million in 2007 

dollars) per year with a $146 million ($164 million in 2007 dollars) present value (a 5% discount rate). 

Smith et al. (1991) estimated the recreational fishing value of Albemarle-Pamlico Estuary in North 

Carolina using a hedonic travel cost model. The analysis was based on an intercept survey with 1,012 

interviews at 35 boat sites and 44 bank sites in 1981 and 1982. The estimated benefit derived from the 

application of a conventional demand model to the boat site sample ranged from $1.49 to $2.58 (1982 

dollars; $3.19 to $5.53 in 2007 dollar), and for the bank site sample ranged from $0.65 to $1.11 ($1.39 to 

$2.38 in 2007 dollars) for an increase in catch rate of one fish per hour per person. 

Four years later, Kaoru (1995) and Kaoru et al. (1995) used the information collected from the 

same survey to estimate the recreation value from improvements in Albemarle and Pamlico Sounds using 

a household production model and a random utility model. They linked the effluent loading and quality of 

sport fishing with the fisherman’s decision on site selection. Instead of fish availability being estimated as 

the average number of fish caught per person per hour for each entry point, Kaoru et al. (1995) used total 

catch per trip per person as their key measure of fishing success. They included not only the estimated N 

loadings but also the effect on biochemical O2 demand as an influence on fishing quality. The results 

suggested that depending on aggregation methods, CS ranged between $7.05 and $36.19 ($9.56 and 

$49.08 in 2007 dollars) for a 5% increase in total catch. The CS was $1.27 to $6.52 ($1.72 to $8.84 in 

2007 dollars) for a 36% decrease in N loadings at all sites. 

Whitehead et al. (2000) used a joint stated and RP model to estimate recreation value of 

improvements to Albemarle and Pamlico Sounds in terms of CS per trip and per season. A telephone 

survey was conducted in 1995 to learn how North Carolina residents would value improvements of water 

quality resulting in a 60% increase in fish catch and a 25% increase in open shellfish beds. The analysis 

was based on 765 completed responses. The CS per trip was estimated at $64 (1995 dollars; $87 in 2007 

dollars) for current quality and $85 ($115 in 2007 dollars) for improved quality and the CS per season 

was $121 ($164 in 2007 dollars) for current and $155 ($210 in 2007 dollars) for improved quality. 

The Catawba River in North and South Carolina is used for electric power generation, recreation, 

drinking water, and wastewater assimilation. With population growth and land use change, the health and 

vitality of the river system are declining as the river flows downstream. Through a mail-phone survey of 

1,085 residents randomly selected from 16 counties of Catawba River Basin, Kramer and Eisen-Hecht 

(2002) presented a management plan designed to maintain the water quality at the current level over time. 

The water quality in the entire watershed was classified as good, fair or poor on maps to illustrate the 

distribution of the tributaries with different water quality. The mean WTP was estimated to be $139 ($160 

in 2007 dollars) for protecting current water quality from deterioration. 

A site choice model applied jointly with a trip frequency model was used by Needelman and Kealy 

(1995) to assess the relative benefits of eliminating eutrophication, fecal bacteria, and oil and grease in 

New Hampshire lakes. The site choices analysis was based on 53 individuals and 1,021 trips, while the 

trip frequency model was based on a total of 519 individuals including the responses for the site choices 

model. The mean seasonal benefits were valued at $1.40 ($1.90 in 2007 dollars) for eliminating 

eutrophication from all sources ($1.33; $1.80 in 2007 dollars) for eliminating nonpoint source pollution 

alone and $0.09 ($0.12 in 2007 dollars) for eliminating point source pollution alone) with an aggregate 

seasonal benefit of $1.16 million ($1.57 million in 2007 dollars), with $1.11 million for nonpoint source 

and $0.08 million for point source pollution. However, the economic benefit estimates were exclusively 

for swimming and day trips. The measures of water quality were not from the year of the survey (1989) 

but from a range of years from 1976 to 1991. 

The Tar-Pamlico River has experienced declining fish catches, disease in fish and shellfish beds, 

algae blooms, and aquatic grass losses. More than half of the pollution impairing one third of the river 

 F-23
was estimated to come from agricultural nonpoint sources. Whitehead and Groothuis (1992) proposed a 

management program in which farmers used best management practices (BMPs) to significantly improve 

the water quality of the Tar-Pamlico River such that recreational anglers would be able to catch twice as 

many fish per trip. A mail survey was sent to 179 households in four counties in Tar-Pamlico River basin 

(with a 61% response rate). The mean WTP for doubling fish catch was estimated at $25 ($38 in 2007 

dollars), which was bounded from above by a $35 use value ($53 in 2007 dollars) and from below by a 

$21 ($32 in 2007 dollars) non-use value. The benefits of water quality improvement in the study area 

were aggregated at $1.62 million (1991 dollars; $2.46 million in 2007 dollars) each year. The researchers 

believed that non-use value could account for as high as 84% of the estimate. However, the study suffered 

from small sample size and relatively high non-response rate (11%) to the WTP questionnaire. 

Some portions of the coastal coral reefs in the Florida Keys are projected to disappear within 10 to 

25 years. In 2000, the U.S. government announced a long-term plan to save coral reefs proposing that 

20% of all coral reefs in American-controlled waters would become ecological preserves by 2010. Park 

et al. (2002) investigated the WTP to preserve current water quality and health of the coral reefs in the 

Florida Keys. Based on 460 responses to a CV survey and 4,035 respondents to a travel cost survey (460 

responses used in the analysis), the annual use value was estimated at $481 per person per snorkeling trip 

($553 in 2007 dollars). The mean predicted WTP per trip from a Tobit model was $735 ($844 in 2007 

dollars). Over 85% of the predicted WTP value was within plus or minus $50 ($57 in 2007 dollars) of the 

total trip expenses from the contingent valuation scenario. 

F.5.2.2. Commercial Fisheries 

As one of six background studies for the National Science and Technology Council (NSTC), Diaz 

and Solow (1992) used a time series estimation approach to examine the effects from hypoxia1

 in the Gulf 

of Mexico. The study failed to confirm the relationship between the annual occurrence of hypoxia and 

commercial fishery health, based on catch rate per unit effort of three major species from the 1960s to 

1990s. Although the benefit assessment did not detect effects attributable to hypoxia, this does not 

necessarily mean that the economic effects would not occur. NSTC (2000) identified alternatives for 

reducing the adverse effects of hypoxia and examined the costs associated with reduction of N and P 

inputs. A net cost estimated by the U.S. Mathematical Programming Model for Agriculture was about 

$0.8 per kilogram ($0.36 per pound; $0.96 per kilogram/$0.36 per pound in 2007 dollars) for a 20% edgeof-field N loss reduction on agricultural lands, whereas restoring 5 million acres of wetland would have a 

net cost of $1.00 per kilogram ($0.45 per pound) ($1.2 per kilogram/$0.54 per pound in 2007 dollars) of 

N removal. 

The Neuse River in North Carolina is important to the commercial blue crab (Callinectes sapidus) 

fishery in the eastern U.S. It accounted for about a quarter of the blue crab harvest from 1994 to 2002. 

Smith and Crowder (2005) simulated the progress of eutrophication in the Neuse River using a series of 

ordinary differential equations, which linked changes in the quantity of nutrients, algal growth, spatial 

population distribution of blue crab and its prey species with fishing efforts. Results suggested that a 30% 

reduction in N loading in the Neuse River watershed over a 50–yr period would result in about a $2.56 

million ($2.71 million in 2007 dollars) discounted present value generated in fishery rent (the difference 

between fishing revenues and costs including fixed and opportunity cost). 

 

1

 Hypoxia (DO depletion) is a phenomenon resulting from the overloading of nutrients (usually N or P) in water. 

Most fresh waters are P-limited, and therefore added N from atmospheric deposition does not have a substantial 

effect on productivity. In contrast, marine and estuarine waters tend to be N-limited, and are therefore expected to 

respond to additional N inputs by increasing algal productivity. Excessive production of algae can deplete the water 

of oxygen when those algae die and are decomposed by oxygen-consuming microorganisms. If the concentration of 

DO decreases to very low levels, fish, and other life forms can die. 

 F-24
F.5.2.3. Water Clarity 

In addition to effects on fish populations, eutrophication reduces water clarity due to excessive 

growth of algae. Boyle et al. (1999) studied the social welfare related to water clarity of four lakes in 

Maine employing a two-stage hedonic demand model. Based on data from 1990 to 1995 on property sales 

for 25 lakes, tax records, mail survey, and water clarity data from the Maine Department of 

Environmental Protection (DEP), the CS for water clarity improvement from the average ambient level 

(3.78 m) to 5.15 m was estimated to be $3,677, $3,765, and $12,870 ($4,562, $4,671, and $15,967 in 

2007 dollars) for differently specified demand systems (Cobb-Douglas, semilog, and linear demand 

models, respectively). Social welfare loss for visibility deterioration from an average level to 2.41 m 

ranged from $25,388 to $46,750 ($31,497 to $57,989 in 2007 dollars). One of the interesting findings in 

the study was that the slope of the Cobb-Douglas demand model increased to 3.0 m, a threshold used by 

the Maine Department of Environmental Protection to indicate improved water quality and public 

preferences for it. 

Poor et al. (2006) evaluated water quality in a small watershed of Maryland using a hedonic 

property valuation model. The watershed is located in a peninsula surrounded by the Potomac and 

Patuxent Rivers and the Chesapeake Bay. Due to nonpoint source pollution runoff, water quality has 

deteriorated, which might have negative impacts on residential housing prices, especially for those close 

to the river. To estimate the possible extent of the impact, total suspended solids (TSS) and dissolved 

inorganic nitrogen (DIN) were averaged by year and included in the model as indicators of ambient water 

quality. The estimates showed that a marginal increase in TSS reduced average housing prices by $1,086 

($1,113 in 2007 dollars), and a marginal increase in DIN decreased housing prices by $17,642 ($18,087 in 

2007 dollars). 

F.5.3. Avoided Costs 

Based on a model projection, IEc (1999c) estimated the benefits of decreased N deposition to the 

estuaries in the eastern U.S. using the avoided cost method. According to their results, the annual avoided 

cost in 2010 would range from $26 to $102 million if 12.8 million pounds of N loading was reduced 

annually in Long Island Sound. An annual reduction of 58 million pounds of N loading into Chesapeake 

Bay would avoid an annual cost of $349 to $1,278 million. Uncertainty associated with model 

assumptions and the inappropriate use of avoided cost estimates to value benefits or damages are two 

major sources of potential error in generating such estimates. In addition, the avoided costs method 

generally does not measure values conceptually accurately, and these values also are not linked to benefits 

from reduced pollution. 

F.5.4. Limitations and Uncertainties 

Several general limitations apply to the valuation of water quality changes. First, the definition of 

water quality is too ambiguous to quantify comparisons across studies. Second, the degree of water 

quality improvement is not often clear. A common obstacle in any environmental economic valuation is 

the availability of data. Problems stemming from lack of data extend from biological data on the 

populations of target species to limitations of the available economic data on the value of commercial and 

recreational fisheries to small samples of survey respondents (Smith and Crowder, 2005). 

The geographical focus of valuation studies is another limitation. The studies reviewed here largely 

focused on the eastern part of the U.S., represented by the Adirondacks, Chesapeake Bay, and Albemarle 

and Pamlico Sounds. Several studies investigated the total value of aquatic ecosystems and most the 

studies calculated recreational values of water quality improvement. Only a few studies addressed the 

 F-25
economic value of commercial fisheries. There is considerable uncertainty in the estimates of benefits as 

they vary significantly even for studies in the same area, with similar changes in the commodity, and use 

of the same valuation methodology. 

Almost every study reviewed here mentioned the problem of uncertainties about the natural science 

(Diaz and Solow, 1992; National Science and Technology Council, 2000). For example, the processes 

involved in the development of hypoxia are not fully known. There is also no clear connection between a 

decrease in pollution level and an increase in catch rate (Bockstael et al., 1989b). Additionally, 

uncertainty arises from the selection of parameter values (Smith and Crowder, 2005) and models (Burtraw 

et al., 1997). Due to the uncertainty about water quality improvement in tributaries of the Chesapeake 

Bay, the estimates provided by Morgan and Owens (2001) excluded benefits from water recovery in the 

tributaries. 

Study coverage also affects benefit estimates. For example, Morey and Shaw (1990) only included 

those fishermen who could make day trips to one or more of the study sites, fishermen with complete 

records, and fishermen whose distance to the farthest site was less than 200 miles. Morey and Shaw 

(1990) evaluated four sites and two kinds of trout in the Adirondacks while Englin et al. (1991) valued 

only one trout species and lakes in four states. 

Many studies relied on information from surveys, which may suffer from various biases. For 

example, several studies reviewed here used intercept surveys, which are not necessarily representative of 

the target population (Bockstael et al., 1989b; Smith et al., 1991). Double counting of values may also 

introduce errors into value estimation. For example, people who are boating may also go fishing 

(Bockstael et al., 1989b). 

F.6. Summary 

Previous ecosystem valuations presented in AQCDs were very limited because they considered 

only studies that could directly attribute monetary values to changes in emissions. The assessment of the 

literature in this Annex has a different approach: the voluminous literature that values various ecosystem 

services affected by emissions is included, whether or not the actual linkages all the way from emissions 

to those effects have been quantified. This approach nevertheless necessitates that the natural science 

underpinnings be examined in the context of preferences, that is, descriptions of damages (or benefits) 

from NOX and SOX emissions (or reductions in those emissions) from the natural sciences that translate 

into things that the public cares about. These include, for example, whether the water is boatable or 

swimmable, the marketable yield associated with changes in a forest or a crop, and effects on aesthetics of 

wetlands. 

The physical endpoints and their corresponding valuation studies are divided into different 

ecological and value endpoints applicable to terrestrial, transitional, and aquatic ecosystems. There is 

significant valuation literature on the effects of O3 on crops (and to a far lesser degree on forest yield), but 

this topic is beyond of the scope of this assessment. Beyond the O3 effects, there is little quantification of 

the science describing the effects of pollution related to NOX and SOX on ecosystems. Valuation studies 

are themselves classified into meta-analyses and original studies, the latter into market studies (e.g., 

commercial fishing), RP studies (e.g., those related to recreation behavior, property values, etc.), and SP 

studies (those that ask people survey questions about their WTP for hypothetical ecosystem 

improvements). 

For valuation of terrestrial effects, survey methods are most common. Supplemented by travel cost 

approaches, this literature leads to a variety of estimates of WTP for improved forest quality that could 

prove useful for estimating benefits of N and S reductions (provided a number of linkages can be made), 

even though some of the endpoints valued are related to insect damage. One meta-analysis is available 

that summarizes this valuation literature. 

 F-26
For wetland valuation, three meta-analyses are available that summarized the valuation literature. 

This literature is problematic because it focuses on values per wetland acre rather than WTP for changes 

in services provided by a wetland affected by deposition of N and S pollutants. However, some of the 

available studies provide WTP for discreet ecosystem services, which is helpful in matching these values 

to services affected by reductions in N and S deposition. Nevertheless, because there is a very poor 

understanding of the scientific basis for linking NOX and SOX emissions to ecosystem health endpoints, 

the ability to make the necessary linkages to estimate benefits of pollution reductions is very limited. 

The aquatic service valuation literature is the most voluminous of the three categories reviewed. It 

contains many recreation value studies, a number of property value studies, some total (use plus non-use) 

value studies, some studies on commercial fishery damages and several good meta-analyses of primary 

valuation studies, which provide estimates of the WTP of households for improvements in the aquatic 

ecosystem related to N and S changes. Again, however, there is little focus on NOX or SOX as the cause of 

alterations to the aquatic ecosystem. 

Overall, there is a robust literature valuing a variety of ecosystem services that could be related to 

reductions in N and S emissions. Therein addition, issues affecting the credibility of any individual study 

and even the studies grouped by technique, such studies can only be used for general valuation purposes. 

The most important limitation is establishment of the linkages between the physical, chemical, and 

biological effects of air pollutants on natural ecosystems and changes in exposure to NOX and SOX. 

Table F-1. Commonly adopted environmental valuation methods based on revealed or state preferences. 

Methods Description Direct 

Use 

Indirect 

Use 

Total 

Value 

Revealed Preference 

Methods 

Observing individual choices and behavior to predict their preferences for environmental 

goods and services. 

Avoided expenditure 

method 

Predicting the cost of mitigating the effects of reduced environmental quality. X X 

Derived demand 

functions 

Estimate the value of environmental goods and services by deriving the demand 

functions of households or firms for them (e.g., water use) 

X X 

Hedonic valuation 

method 

Estimating an implicit price for the environmental quality attributes of marketable goods, 

such as housing. 

X X 

Market analysis Used for valuing market goods using data on prices and quantities of outputs and 

inputs. May use prices of close substitutes, methods of deriving shadow prices, or 

simulation of changes in market conditions. 

X X 

Referenda Examining voting results related to environmental resources to predict values for them. X X 

Replacement cost Measures expenditures incurred in replacing or restoring environmental good or service 

lost (e.g., water filtration). Provides accurate valuation only under strict assumptions 

usually not met. 

X X 

Travel cost method Values the environmental attributes of recreational sites by examining visitation 

frequency and cost differential incurred in reaching site with different attributes. 

X X 

User fee Examine user fees paid to gain access to an ecological resource such as a park to 

estimate the lower bound of society’s value for that resource. 

X X 

Stated Preferences 

Approach 

Directly surveying individuals to predict their preferences for environmental goods and 

services. 

 F-27
Methods Description Direct 

Use 

Indirect 

Use 

Total 

Value 

Choice experiments, 

conjoint analysis 

Elicits individuals’ choices from several alternatives associated with different 

environmental and cost outcomes. Elicits data on WTP indirectly (by eliciting costenvironmental outcome tradeoffs, similar to contingent ranking). 

X X X 

Contingent valuation Elicits data on individuals’ willingness to pay for environmental goods and services 

(hypothetical setting, limited by potential biases). Often implemented using a 

hypothetical referenda. 

X X X 

Contingent ranking Elicits data on the ranking of several alternatives associated with different 

environmental and cost outcomes. Elicits data on WTP indirectly (by eliciting costenvironmental outcome tradeoffs, similar to choice experiments, conjoint analysis). 

X X X 

Table F-2. Economic effects of ozone and other pollutants on agriculture, as reported in the 1996 ozone 

criteria document. 

Model Features Results (Annual 1980 U.S. Dollar) 

Study Region Pollutant and 

Concentration Price 

Changes

Output 

Substitutions 

Input 

Substitutions

Quality 

Changes Crops Consumer 

Benefits 

Producer 

Benefits 

Total 

Benefits 

(Costs) 

Adams 

et al. 

(1986a, 

1986b) 

U.S. Ozone, 25% reduction from 1980 level 

for each statec 

Yes Yes Yes No Corn, soybeans, cotton, wheat, 

sorghum, 

barley 

1160×106 550×106 1700×106 

Kopp 

et al. 

(1985)b 

U.S. Ozone, universal 

reduction from 53 ppb 

to 40 ppba 

Yes Yes Yes No Corn, soybeans, 

wheat, cotton 

Not reported Not reported Not reported 

Adams 

et al. 

(1986b) 

U.S. Acid deposition, 50% 

reduction in wet 

acidic deposition 

Yes Yes Yes No Soybeans 172×106 –30×106 142×106 

Adams 

and 

Crocker 

(1989) 

U.S. Ozone, seasonal 

standard of 50 ppb 

with 95% complianced; Includes 

adjustments for 1985 

Farm Bill 

Yes Yes Yes No Corn, soybeans, cotton, wheat, 

sorghum, 

rice, hay, 

barley 

905×106 769×106 1674×106 

a Seven hour growing season geometric mean. Given a long-normal distribution of air pollution events, a 7-h seasonal ozone level of 40 ppb is approximately equal to an hourly 

standard of 80 ppb, not to be exceeded more than once a year (Heck et al., 1983). b Reported in 1986 criteria document. c

 Reported All studies except Garcia et al. use NCLAN data to 

generate yield changes due to ozone. d Seven- and twelve-h growing season geometric mean. Analysis includes both fixed rollbacks (e.g., 25% and seasonal standards (with variable 

compliance rates). Source: Adams and Horst (2003). 

 F-28
Table F-3. Economic effects of ozone on marketable benefits from forests. 

Reference Pollutant/Coverage Response and Air 

Quality Data Economic Model Annual Damages or Benefits of Control 

Callaway 

et al. (1986a) 

All pollutants. Forest 

products (hardwood and 

softwood) in the eastern U.S. 

Assumes three arbitrary growth 

reductions (10%, 15%, and 20%) 

for hardwood and softwood tree 

species. 

Spatial equilibrium 

models of softwood 

and hardwood 

stumpage and forest 

products industries in 

the U.S. 

$340 to $510 million; 

damage in 1984 dollars for 

assumed reductions in 

growth levels. 

Crocker 

(1985) 

Acid deposition. 

Forest products and forest 

ecosystem service flows for 

eastern U.S. 

Assumes a 5% reduction in 

products due to acid deposition: 

assumes a pristine background pH 

of approximately 5.2. 

Naive; assumed 

changes in output 

multiplied by avg 

value of those goods 

or services. 

$1.75 billion damage in 1978 

dollars from current levels of 

acid deposition. 

Crocker and 

Forster 

(1986) 

Acid deposition. Forest 

products and forest 

ecosystem services for 

eastern Canada. 

Assumes 5% reduction in forest 

productivity for all eastern 

Canadian forests receiving $10 

kg/ha/yr sulphate deposition. 

Naive; assumed 

changes in output 

multiplied by avg 

value of goods or 

services. 

$1.5 billion damage in 1981 

Canadian dollars from 

current levels of acid 

deposition. 

Haynes and 

Adams 

(1992) 

Air pollutants, including acid 

precipitation. Losses 

estimated for eastern U.S. 

softwoods. 

None; paper demonstrates a 

methodology for assessing 

economic effects of yield (growth 

and inventory) reductions due to 

any course. Assumes losses from 

6% to 21% for softwoods. 

Econometric model 

of U.S. timber sector 

(TAMM). 

Damages of $1.5 to $9.0 

billion in 1988 dollars.b 

Bentley and 

Horst (1998) 

Ozone. All hardwoods and 

softwoods except western 

hardwoods. 

Dose-response based on survey of 

experts scaled to partial attainment 

of secondary standard relative to 

the current primary standard. 

SUM06 exposure metric is based 

on a cumulative daytime exposure 

during the growing season. 

Econometric model 

of U.S. timber sector 

(TAMM). 

Benefits of $14 million in 

2010 (1990 dollars) for 

partial attainment of 0.08 

ppb 3rd max secondary 

standard. This standard was 

considered during the 

standard development 

process. 

aWe used the updated version of Crocker (1986), while Adams and Horst (2003) cited a prior version, from Crocker (1985). bWe drew different numbers from the Crocker and Forster (1986) study than those reported by Adams and Horst (2003). 

Source: Adams and Horst (2003), exceptions: Table 2 in Bentley and Horst (1998). 

 F-29
Table F-4. Forecasted average values for select activities, per day per person in 1996. 

Activity Northeast Southeast Intermountain Pacific Coast Alaska USA 

Swimming 14 9 19 9 14 14 

General recreation* 30 25 34 25 30 30 

Fishing 37 32 41 32 37 37 

Waterfowl hunting 40 35 44 34 40 39 

Big game hunting 45 40 50 40 45 45 

*The activity category includes camping, picnicking, sightseeing, hiking, small game hunting, wildlife viewing, and other general recreation. In the meta-analysis, values for these activity 

categories do not differ statistically significantly. Source: from the meta-analysis benefit function by Rosenberger and Loomis (2001), Table 6. 

Table F-5. Typical impacts of specific pollutants on the visual quality of forests. 

Pollutant Geographic 

Extent 

Injury 

Type Major Types of Visual Injuries Notes 

Direct 

injuries 

Foliar injuries (e.g., pigmented stipple), increased 

needle/leaf abscission, premature senescence of 

leaves. Pattern, size, location, and shape of foliar 

injuries to indicator species can be specific for ozone. 

Ozone Area or 

regional effects 

Indirect 

Injuries 

Increased susceptibility to visual injuries may result 

from other adverse environmental factors, such as 

insect attacks. For example, increased needle/leaf 

abscission, elevated mortality rates, and/or changes in 

species composition. 

Acidic 

Deposition 

Area or 

regional effects 

Indirect 

Injuries 

Increased susceptibility to visual injuries may result 

from other adverse environmental factors, such as 

climatic factors. For example, increased needle/leaf 

abscission, elevated mortality rates, and/or changes in 

species composition. 

Acidic deposition can also cause 

direct foliar injuries. Acids are, 

however, more likely to indirectly 

affect the visual appearance of forest 

trees, unless exposure levels are 

very high. 

Sulfur 

Dioxide 

Point source 

pollution 

Direct 

Injuries 

Foliar injuries including leaf/needle discoloration and 

necrosis. Pattern, size, location, and shape of foliar 

injuries to indicator species can be specific for sulfur 

dioxide. At high concentrations, elevated mortality rates 

of sensitive species and changes in species 

composition may occur. 

Sulfur dioxide may also cause 

indirect injuries. Indirect injuries, 

however, are not well documented. 

Hydrogen 

Fluoride 

Point source 

pollution 

Direct 

Injuries 

Foliar injuries including leaf/needle discoloration and 

necrosis. Pattern, size, location, and shape of foliar 

injuries to indicator species can be specific for sulfur 

dioxide. At high concentrations, elevated mortality rates 

of sensitive species and changes in species 

composition may occur. 

Hydrogen fluoride may also cause 

indirect injuries. Indirect injuries, 

however, are not well documented. 

Source: Exhibit 2 Industrial Economics (IEc) (1999a) 

 F-30
Table F-6. Economic valuation studies related to forest aesthetics. 

Study Method Study Area Description Economic 

Endpoint Economic Value 

Crocker 

(1985) 

CV (open 

ended) 

Southern 

California 

Evaluating visits to 

recreational sites with 

slight, moderate, and 

severe O3 induced 

damages to Ponderosa 

and Jeffery pine stands. 

Household 

WTP 

(per trip) 

Slight damage: $2.09 

Moderate damage: $0.66 

Severe damage: $0.74 

Haefele 

et al. (1992) 

CV (payment 

card, dichotomous choice) 

Southern 

Appalachian 

Mountains 

Protect high-elevation 

spruce firs from insect and 

air pollution 

Household 

WTP (per 

year) 

PC: $21 

DC: $100 

Holmes and 

Kramer 

(1996) 

CV (payment 

card, 

dichotomous 

choice) 

Southern 

Appalachian 

Mountains 

Protect high-elevation 

spruce firs from insect and 

air pollution. Analysis 

compared forest users and 

non-users. 

Household 

WTP (per 

year) 

Forest users: $36 

Non-users: $10 

Holmes 

et al. (1996) 

Hedonic pricing New Jersey Evaluate the effects of 

hemlock forest health 

status on housing prices. 

Hemlock health status is 

potentially deprived by 

hemlock woolly adelgid; an 

exotic forest insect. 

Housing 

price 

Hemlock forest health status is positively 

associated with housing prices. For 

example, a 1–point increment in (e.g., 

from 10% to 11%) in the % of healthy 

hemlocks of all forests on the home 

parcel was associated with a 0.66% sales 

price increase. Similar changes in home’s 

near proximity are associated with yet 

larger price increments. 

IEc 

(1999b)b

Benefits 

transfer 

National Protect trees from various 

different types of damage 

(see description of source 

studies) 

Welfare Loss 

(1990–2010) 

$3 to $17 billion 

Jakus and 

Smith 

(1991) 

CV 

(dichotomous 

choice) 

Maryland and 

Pennsylvania 

Protect private homeowner 

property and surrounding 

public lands from gypsy 

moth damages (reduce 

tree defoliation by 25%) 

Household 

WTP 

(for entire 

program) 

Private Property Prgm (only): $254 to 

$420 

Private and Public Prgm: 

$314 to $527 

Jenkins 

(2002) 

CV (open 

ended) 

Southern 

Appalachian 

Mountains 

Protect high-elevation 

spruce firs from insects 

and air pollution along 

roads and throughout 

ecosystem 

Household 

WTP (per 

year) 

$153 

Kramer 

et al. (2003) 

CV 

(dichotomous 

choice) 

Southern 

Appalachian 

Mountains 

Protect high-elevation 

spruce firs from insects 

and air pollution along 

roads and throughout the 

entire ecosystem 

Household 

WTP (per 

year) 

Road side only: $18 

Entire ecosystem: $28 

Non-use: 87% of total value 

Use: 13% of total value 

Kramer and 

Mercer 

(1997) 

CV (payment 

card and dichotomous choice) 

National (USA) Protect 10% of tropical 

rainforests as national 

parks or forest reserves. 

Household 

WTP (per 

year) 

PC: $31 

DC: $21 

 F-31
Study Method Study Area Description Economic 

Endpoint Economic Value 

Loomis 

et al. (1996) 

CV (dichotomous choice, 

open ended) 

Oregon Protect old growth forests 

of Pacific Northwest from 

fires. 

Household 

WTP (per 

year) 

DC: $98 

OE: $33 

Leuschner 

and Young 

(1978) 

TCM Texas Reduce crown density in 

recreational areas. 

Consumer 

Surplus 

(losses) 

–0.69 to –6.5% (depending on level of 

substitute sites) 

Miller and 

Lindsey 

(1993) 

CV 

(dichotomous 

choice) 

New 

Hampshire 

Protect private homeowner 

property from gypsy moth 

damages (reduce tree 

defoliation by various 

amounts) 

Household 

WTP (per 

year) 

$55 to $86 (depending on level of 

reduction in tree defoliation) 

Peterson 

et al. (1987) 

CV, hedonic 

property 

Los Angeles 

area 

Avoid O3 induced 

damages to trees in local 

national forests for 

recreationists (in greater 

LA area) and homeowners 

(with property bordering 

forest). 

Household 

WTP 

(per year) 

Consumer 

Welfare 

Recreationist: $43 

Homeowner: $131 

$31 to $161 million 

Treiman 

(2006) 

CV 

(dichotomous 

choice) 

Missouri Improve residential tree 

care and maintenance in 

different sized cities. 

Household 

WTP (per 

year) 

Urban areas: $14 to $16 

Walsh et al. 

(1989) 

TCM Colorado 

Rockies 

Reduce tree density in 

recreational areas. 

Consumer 

Surplus 

(losses) 

–8.5 to –23.2% (for reductions in tree 

density ranging between 10 to 30%) 

Walsh et al. 

(1990) 

CV (iterative 

bidding) 

Colorado Protect Ponderosa pine 

forests from damages 

caused by the mountain 

pine beetle. 

Household 

WTP (per 

year) 

$47 

Non-use: 73% of total value 

Use: 27% of total value 

aHolmes and Kramer study applies same results found in Haefele et al. (1991). bBenefits transfer was based on results from Holmes and Kramer (1996), Peterson et al. (1987), Walsh 

et al. (1990). 

 F-32
Table F-7. Summary of the monetized estimates of the annual value of forest quality changes 

Reference Aesthetic Change 

Valued 

Value of Change 

per Household 

(Current Dollars) 

Value of Change 

per Household 

(1990 Dollars)i 

Total Value of 

Change for 

Region (Current 

Dollars) 

Total Value of 

Change for 

Region (1990 

Dollars)i 

Peterson et al. 

(1987a) 

Ozone damage to San 

Bernardino and 

Angeles National 

Forests 

$6.31 - $32.70ii $7.26 - $37.62 $27 - $140 million $31 - $161million 

Walsh et al. 

(1990) 

Visual damage to 

Colorado’s Front 

Range 

$47 $61.68 $55.7 million $73.09 million 

Holmes and 

Kramer (1996) 

Visual damage to 

spruce-fir forests in 

southern Appalachia 

$10.81 nonusers 

$36.22 users 

$10.37 nonusers 

$34.76 users 

NA NA 

Source: Exhibit 4 Industrial Economics (IEc) (1999c). 

Table F-8. Estimated value of avoiding forest damage in the U.S. 

Affected System States Included Value per 

Household Householdsi Total Annual Value 

Cumulative 

Value 

(1990–2010)ii 

Sierra Nevada and Los 

Angeles Basin 

CA $7.26 - $37.62 10.4 million $75.5 million - 

$391.2 million 

$1.02 billion - 

$5.27 billion 

Eastern Spruce Fir and 

Selected Eastern Hardwood 

ME, VT, NH, MA, NY, PA, 

WV, TN, KY, NC, VA 

$7.26 - $37.62 23.2 million $168 million - 

$872.8 million 

$2.27 billion - 

$11.75 billion 

Source: Exhibit 5 Industrial Economics (IEc) (1999c) 

 F-33
Table F-9. Ecological wetland functions, economic goods and services, types of value, and applicable 

valuation methods. 

Ecological Function Economic Goods and Services Value 

Type 

Commonly Used Valuation 

Method(s)a

Flood and flow control Flood protection Indirect use Replacement cost 

Market prices 

Opportunity cost 

Storm buffering Storm protection Indirect use Replacement cost 

Production function 

Sediment retention Storm protection Indirect use Replacement cost 

Production function 

Groundwater recharge/discharge Water supply Indirect use Production function 

NFI Replacement cost 

Maintenance/Nutrient retention Improved water quality 

Waste disposal 

Indirect use 

Direct use 

CVM 

Replacement cost 

Habitat and nursery for plant and animal 

species 

Commercial fishing and hunting 

Recreational fishing and hunting 

Harvesting of natural materials 

Energy resources 

Direct use 

Direct use 

Direct use 

Direct use 

Market prices, NFI 

TCM, CVM 

Market prices 

Market prices 

Biological diversity Appreciation of species existence Non-use CVM 

Micro-climate stabilization Climate stabilization Indirect use Production function 

Carbon sequestration Reduced global warming Indirect use Replacement cost 

Ecological Function Economic Goods and Services Value Type Commonly Used Valuation Method(s)a

Natural environment Amenity 

Recreational activities 

Appreciation of uniqueness to culture/ 

heritage 

Direct use 

Direct use 

Non-use 

HP, CVM 

CVM, TCM 

CVM 

a Acronyms refer to the contingent valuation method (CVM), hedonic pricing (HP), net factor income (NFI), and the travel cost method (TCM). 

Source: Table 1, Brander et al. (2006). Modifications adapted from Barbier (1991); Barbier et al. (1997); Brouwer et al. (1999); and Woodward and Wui (2001). 

 F-34
Table F-10. Economic valuation studies related to acidification and eutrophication in aquatic ecosystems. 

Reference Study Area Method Ecological 

Endpoint 

Valuation 

Endpoint Economic Value Note 

ACIDIFICATION 

Banzhaf 

et al. (2006) 

Injured lakes 

in 

Adirondacks, 

NY 

CV Fish, bird 

species, 

and tree 

species 

WTP (total 

value) 

1. The mean WTP for 

base version was $48 

per year per household 

in New York State while 

the mean WTP for 

scope version was $54 

per year per household 

in New York State 

(discounting rate =3%) 

1. Base version: 

improvement in fish 

population of 600 

lakes, small 

improvements in the 

populations of two 

bird and one tree 

species. 

2. Scope version: 

improvement in fish 

population of 900 

lakes, improvement 

in the population of 

four bird and three 

tree species. 

Bockstael 

et al. 

(1989b) 

Chesapeake 

Bay, MA and 

DC 

CV and TC Fish Aggregate 

benefit/ 

consumer 

surplus 

(recreational 

value) 

1. 20%water quality 

improvement resulting in 

aggregate benefits for 

beach use, boating, and 

sportfishing were about 

$34.7, 4.7, and 1.4 

millions. 

2. aggregate WTP for 

water quality 

improvement from 

current to a level 

acceptable for water 

activity was about $91 

million (1987 dollars) 

1. TNP, product of N 

and P, was included 

in the model. 

2. WTP was 

aggregated for 

households in 

Maryland. 

Cameron 

and Englin 

(1997) 

Northeast 

U.S. 

CV and RUM Trout fishing 

(uncertain 

about use) 

Consumer 

surplus 

(recreational 

value, 

existence 

value) 

1. For passive user, 

surplus for preventing a 

20% loss in currently 

fishable high altitude 

lakes in the Northeast 

was $218 for S model 

and $215 for OP model. 

2. For active user, 

surplus for preventing a 

20% loss in currently 

fishable high altitude 

lakes in the Northeast 

was $283 for S model 

and $439 for OP-I model 

and $436 

for OP-D. 

1. S model: Surplus 

interpretation of the 

WTP responses 

2. OP model: Option 

price interpretation 

of the WTP 

responses. 

3. I: complete 

independence 

between year-toyear decisions 

4. D: complete 

dependence on 

previous decisions 

 F-35
Reference Study Area Method Ecological 

Endpoint 

Valuation 

Endpoint Economic Value Note 

Englin et al. 

(1991) 

ME, NH, NY 

(excluding 

New York 

City) and VT 

HTC and RUM Catch per 

unit effort 

(number of 

fish caught 

in an h) 

Consumer 

surplus 

(recreational 

value) 

1. Under baseline 

scenario (1989), welfare 

loss was estimated at 

$0.27 m (HTC) and 

$1.75 (RUM) 

2. Under NAPAP 

scenario 1, welfare loss 

was occurred in 2010 at 

$13.7 m (HTC) and $1.2 

m (RUM) but social 

welfare would gain in 

2030 at $3.5 m (HTC) 

and $5.5 m (TCM) 

3. Under NAPAP 

scenario 4, welfare 

would gain in 2010 at $3 

m (HTC) and $7.4 m 

(TCM) and also gain in 

2030 at $4.4 m (HTC) 

and $9.7 m (TCM) 

1. Acidic stress 

index (ASI) 

measuring fish 

biological tolerance 

to acidity was 

included in the 

model. 

2. The benefits were 

valued in 1989 

dollars 

3. Multiple datasets 

were used in the 

analysis. 

4. Only trout species 

fishing was 

included. 

5. The benefit 

estimates were 

aggregated for four 

states by accounting 

income increase 

and population 

change resulting 

from baby boom. 

Kaoru et al. 

(1995) 

Albemarle 

and Pamlico 

Sounds, NC 

CV and RUM Fish Consumer 

surplus 

(recreational 

value) 

1. 35–site model: $36.19 

for 5% increase of total 

catch at all sites (fullwage) 

2. 35–site model: $6.52 

for 36% decrease in N 

loadings at all sites (fullwage) 

1. Estimated N 

loadings and 

biochemical O2

demand, were used 

in household 

production models 

to measure fishing 

quality. 

2. The paper also 

estimated benefit 

based on 

opportunity cost at 

1/3 wage for 23−site 

and 11–site models. 

Morey and 

Shaw (1990) 

Four fishing 

sites in 

Adirondacks, 

NY 

CV and TC Brook and 

Lake Trout 

Consumer 

surplus 

(conditional 

compensating 

variations) 

(recreational 

value) 

1. $475.87 for 5% 

increase in catch rates 

for trout (1977 dollars) 

2. $2162.04 for 25% 

increase in catch rates 

for trout (1977 dollars) 

3. $3862.94 for 50% 

increase in catch rates 

for trout (1977 dollars) 

1. Economic values 

were aggregate 

CCV for 607 survey 

respondents. 

2. Catch rate, as an 

indicator of acid 

deposition, was 

included as a 

variable in the 

model. 

 F-36
Reference Study Area Method Ecological 

Endpoint 

Valuation 

Endpoint Economic Value Note 

Morgan and 

Owens 

(Morgan and 

Owens) 

Chesapeake 

Bay 

Water quality 

model and 

Benefit transfer 

Water 

quality 

Aggregate 

benefit 

(recreational 

value) 

1. Lower bound total 

benefit for beach use 

was $288.8m from a 

60% improvement in 

Chesapeake Bay water 

quality. 

2. Lower bound total 

benefit for trailered 

boating was $6.7m from 

a 60% improvement in 

Chesapeake Bay water 

quality. 

3. Lower bound total 

benefit for striped bass 

sport fishing was 

$288.8m from a 60% 

improvement in 

Chesapeake Bay water 

quality. 

1. The study also 

provided aggregate 

benefits at avg and 

high level for three 

recreational uses. 

2. Benefit transfer 

was based on the 

results in Bockstael 

et al. (1988). 

Mullen and 

Menz (1985) 

Adirondack, 

NY 

TC Fish Net economic 

value per 

angler day/ 

Consumer 

surplus 

(recreational 

value) 

1. Net economic value 

per angler day was avgd 

at $19.90 for entire 

waterbody including 

lakes, ponds, and 

streams. 

2. Total angler value was 

estimated $31.3 million 

for entire water body. 

3. The loss in net 

economic value was 

estimated at $1.07 

million for lakes and 

ponds due to reduction 

in angler visitation. 

1. The values were 

in 1976 dollars. 

2. Total value was 

aggregated by 

number of trips. 

Randall and 

Kriesel 

(1990) 

Nationwide, 

U.S. 

CV Water/air 

quality 

WTP (total 

value) 

1. The estimated annual 

willingness to pay was 

$694.42 per household. 

1. The study valued 

a National Pollution 

Control Program, 

which improved air 

and water quality by 

25 percent in 5 

years. 

 F-37
Reference Study Area Method Ecological 

Endpoint 

Valuation 

Endpoint Economic Value Note 

Smith et al. 

(1991) 

Albemarle 

and Pamlico 

Sounds, NC 

HTC and 

demand 

function 

Fish Benefit/ 

consumer 

surplus 

(recreational 

value) 

1. Conventional demand 

model: $2.58 for boat, 

$1.11 for bank fishing 

2. Simple inverse 

demand model: $0.79 

for boat, $0.62 for bank 

fishing 

3. Detailed inverse 

demand model: $0.88 

for boat, $0.79 for bank 

fishing 

1. Benefit was 

calculated for an 

one-fish increase in 

catch rate per hour 

per person 

2. Catch rate was 

included in the 

model. 

3. The paper also 

estimated benefit 

based on 

opportunity cost at 

1/3 wage. 

Whitehead 

et al. (2000) 

Albemarle 

and Pamlico 

Sounds, NC 

CV and TCM Fish 

catches and 

shellfish 

beds 

Consumer 

surplus per 

season/ per trip 

(recreational 

value) 

1. The consumer surplus 

per trip is $64 for current 

quality and $85 for 60% 

increase in fish catch 

and 25% more open 

shellfish beds. 

2. The consumer surplus 

per season is $121 for 

current quality and $155 

for 60% increase in fish 

catch and 25% more 

open shellfish beds. 

1. The consumer 

surplus per trip 

difference between 

two baseline and 

scenario was not 

significant at the 

0.10 level while the 

consumer surplus 

per season 

difference between 

two situations was. 

2. The studies also 

estimated annual 

aggregate 

consumer surplus 

per season for 41 

counties within the 

watershed. 

EUTROPHICATION 

Boyle, et al. 

(1999) 

Four lakes, 

ME 

HTC (two 

stages, hedonic 

demand model 

Water clarity 

(visibility) 

Consumer 

surplus 

(recreational 

value) 

1. The consumer surplus 

(+) for an avg visibility 

improvement from 

3.78m to 5.15m was 

$3,765 and $3,677 for 

semi-log and CobbDouglas model. 

2. The welfare losses (–) 

for an avg visibility 

decrease from 3.78m to 

2.41m was $25,388 and 

$46,750 for semi-log 

and Cobb-Douglas 

model. 

1. The welfare was 

also measured in 

linear demand 

model. However, the 

own price of water 

clarity is not 

significant. 

Diaz and 

Solow 

(1992) 

Gulf of 

Mexico 

Time series Brown 

shrimp, 

white 

shrimp, and 

Menhaden 

Mean catch 

rate per unit 

effort 

1. The study failed to 

quantify economic 

effects of hypoxia based 

on past data. 

1. Hypoxia was 

measured in terms 

of area (zone) and 

index in the model 

to calculate 

correlation. 

 F-38
Reference Study Area Method Ecological 

Endpoint 

Valuation 

Endpoint Economic Value Note 

Kramer and 

Eisen-Hecht 

(2002) 

Catawba 

River, NC and 

SC 

CV Water 

quality 

WTP (total 

value) 

1. A mean willingness to 

pay was revealed at 

$139 for maintaining 

current water quality 

over time. 

1. Phone-mailphone and mailphone survey 

formats applied. 

Lipton 

(2003) 

Chesapeake 

Bay, MA 

CV Water 

quality 

WTP (use 

value) 

1. The median WTP for 

a one scale 

improvement in water 

quality was $17.50 per 

boater per year and the 

mean was $63, with 

38% expressing a zero 

WTP. 

1. The aggregate 

WTP for the 

Chesapeake Bay 

boaters in Maryland 

was about $7.3 

million per year, 

total improvement 

gets a $146 million 

present value. 

Needelman 

and Kealy 

(1995) 

Lakes, NH Site choice 

model and a 

trip frequency 

model 

Water 

quality 

Benefit 

(recreational 

value) 

1. The mean seasonal 

benefits estimates were 

$1.4 for eliminating 

eutrophication from all 

sources ($1.33 for 

nonpoint source and 

$0.09 for point source) 

and the aggregate 

seasonal benefits were 

estimated at $1,163,000 

for eliminating 

eutrophication from all 

sources ($1,105,000 for 

nonpoint source and 

$75,000 for point 

source). 

1. The study also 

reported mean and 

aggregate seasonal 

benefits for 

eliminating bacteria, 

oil and grease, 

eutrophication 

problems, and all 

pollution from all 

sources. 

2. The benefit 

estimates address 

exclusively 

swimming and day 

trips. 

vNSTC 

(2000) 

Gulf of 

Mexico, 

MississippiAtchafalay 

River Basin 

Cost effective, 

simulation 

model (US 

Mathematic 

Programming 

Model in 

Agriculture) 

Fish, 

shrimp, and 

marine 

ecosystem 

Social cost for 

reduction/ 

restoration 

1 For 20% edge-of-field 

N-loss reduction, the net 

cost was estimated at 

$0.8/kg ($0.36/pound), 

while restoring 5 million 

acres of wetland would 

have net cost of 

$1.00/kg ($0.45/pound). 

1. The benefit to 

reduce N loadings 

to the Gulf was 

difficult to calculate 

because economic 

analysis failed to 

show direct effects 

on Gulf fisheries, 

which was a 

background study 

conducted by Diaz 

and Solow (1992). 

Park et al. 

(2002) 

Florida Keys, 

FL 

CV Water 

quality and 

health of 

coral reefs 

WTP per trip 

expenses 

(recreational 

value) 

1. The annual avg use 

value was $481.15 per 

person per snorkeling 

trip. 

 F-39
 F-40

Reference Study Area Method Ecological 

Endpoint 

Valuation 

Endpoint Economic Value Note 

Poor et al. 

(2006) 

St. Mary’s 

River 

watershed, 

MA 

HPVM Ambient 

water 

quality 

Avg housing 

price within the 

watershed 

(commercial 

value) 

1. One unit (mg/L) 

increase in TSS resulted 

in a $1,086 loss on avg 

housing prices within the 

watershed. 

2. One unit increase in 

the dissolved inorganic 

N resulted in a $17,642 

loss on avg housing 

prices. 

1. Total suspended 

solids and dissolved 

inorganic N included 

as indicators of 

ambient water 

quality. 

Smith and 

Crowder 

(2005) 

Neuse River, 

NC 

Bio-economic 

model 

Blue crab Fish rent 

(commercial 

value) 

1. A 30% reduction in N 

loadings over a 50–yr 

time period increases 

present value rents by 

2.56 million, total catch 

by 12.4 million pounds, 

and total trips by 91,000. 

1. Discounting rate 

was 2.5%. 

Whitehead 

and 

Groothuis 

(1992) 

Tar-Pamlico 

River, NC 

CV Catch rate WTP (total 

value, use, and 

non-use value) 

1. The mean WTP 

resulting from a 61% 

response rate was 

estimated at $25, which 

was bounded from 

above by a $35 use 

value, and from below a 

$21 nonuse value. 

2. The aggregate 

benefits from water 

quality improvement 

estimated at $1.62 

million each year. 

1. The study 

proposed a program 

in which farmers are 

required to use 

BMPs to improve 

the water quality of 

the Tar-Pamlico 

River so that 

anglers would catch 

twice as many fish 

per trip. 

2. A mail survey was 

sent to 179 

households in four 

counties in TarPamlico River basin. 

VARIOUS EFFECTS 

Johnston 

et al. (2005) 

U.S. and 

Canada 

Meta-analysis Catch rate WTP 

(recreational 

use) 

1. WTP per fish over the 

sample ranged from 

$.048 to $612.79, with a 

mean of $16.82 

1. This study 

supports previous 

findings that WTP is 

systematically 

associated with 

resource, context, 

and angler 

attributes. 
G-1 

Glossary 

Acid Neutralizing Capacity (ANC) 

A key indicator of the ability of water to neutralize acidifying inputs. This ability depends largely on 

associated biogeochemical characteristics. 

Acidification 

The process of decreasing the pH of a system. Systems that can be acidified by atmospheric deposition of 

acidic or acidifying compounds include lakes, streams, and forest soils. 

Algae 

Photosynthetic, often microscopic and planktonic, organisms occurring in aquatic ecosystems. 

Algal bloom 

A rapid and extreme increase of an algae population in a lake, river, or ocean. 

Alpine 

The biogeographic zone made up of slopes above the tree line, and characterized by the presence of rosetteforming herbaceous plants and low, shrubby, slow-growing woody plants. 

Anthropogenic 

Resulting from human activity or produced by human beings. 

Arid region 

An area receiving <250 mm precipitation per year. 

Atmosphere 

The gaseous envelope surrounding the Earth. The dry atmosphere consists almost entirely of nitrogen and 

O2, together with trace gases including carbon dioxide and ozone. 

Base cation saturation 

The degree to which soil cation exchange sites are occupied with base cations (e.g., Ca2+, Mg2+, K+) as 

opposed to Al3

+

 and H+. Base cation saturation is a measure of soil acidification, with lower values being 

more acidic. A marked increase in the sensitivity of soils to changes in base saturation occurs at a threshold 

of approximately 20%. 

Bioaccumulation 

The gradual increase in accumulation of some compounds in organisms with increasingly higher trophic 

levels. 

Biodiversity 

The total diversity of all organisms and ecosystems at various spatial scales (from genes to biomes). 

Buffering capacity 

The ability of a body of water and its watershed to resist changes in pH. 

Carbon sequestration 

The process of increasing the carbon content of a reservoir other than the atmosphere. 
G-2 

Catchment 

An area that collects and drains rainwater. 

Climate 

Climate in a narrow sense is usually defined as the ‘average weather’, or more rigorously, as the statistical 

description in terms of the mean and variability of relevant quantities over a period of time ranging from 

months to thousands or millions of years. These quantities are most often surface variables such as 

temperature, precipitation, and wind. Climate in a wider sense is the state, including a statistical 

description, of the climate system. The generally accepted period of time is 30 years, as defined by the 

World Meteorological Organization (WMO). 

Critical load 

A quantitative estimate of an exposure to one or more pollutants below which significant harmful effects on 

specified sensitive elements of the environment do not occur according to present knowledge. 

Denitrification 

The anaerobic reduction of oxidized nitrogen (e.g., nitrate or nitrite) to gaseous nitrogen (e.g., N2O or N2), 

normally accomplished by denitrifying bacteria. 

Dry deposition 

The movement of gases and particles from the atmosphere to surfaces in the absence of precipitation (e.g., 

rain or snow) or occult deposition. 

Ecological community 

An assemblage of populations of different species, interacting with one another 

Ecosystem services 

Ecological processes or functions having monetary or non-monetary value to individuals or society at large. 

They may be classified as (i) supporting services such as productivity or biodiversity maintenance; ii) 

provisioning services such as food, fibre, or fish; iii) regulating services such as climate regulation or 

carbon sequestration; and (iv) cultural services such as tourism or spiritual and aesthetic appreciation. 

Ecosystem 

The interactive system formed from all living organisms and their abiotic (physical and chemical) 

environment within a given area. Ecosystems cover a hierarchy of spatial scales and can comprise the entire 

globe, biomes at the continental scale, or small, well-circumscribed systems such as a small pond. 

Eutrophication 

The enrichment of a waterbody with nutrients, resulting in increased productivity (of algae or aquatic 

plants), and sometimes also decreased dissolved O2 levels. 

Eutrophy 

Eutrophy generally refers to a state of nutrient enrichment, but it is commonly used to refer to condition of 

increased algal biomass and productivity, presence of nuisance algal populations, and a decrease in 

dissolved O2 concentrations. 

Evapotranspiration 

The combined process of water evaporation from the Earth’s surface and transpiration from vegetation. 

Fen 

A phase in the development of the natural succession from open lake, through reedbed, fen and carr, to 

woodland as the peat develops and its surface rises. 
Freshet 

A great rise or overflowing of a stream caused by heavy rains or melted snow. 

Greenhouse gas 

Those atmospheric gasses that absorb and emit radiation emitted by the Earth’s surface, the atmosphere, 

and clouds within the infrared portion of the spectrum. This property causes the greenhouse effect. Water 

vapour (H2O), carbon dioxide (CO2), nitrous oxide (N2O), methane (CH4), and ozone (O3) are the primary 

greenhouse gases in the Earth’s atmosphere. Besides these, the Kyoto Protocol also deals with the 

greenhouse gases sulphur hexafluoride (SF6), hydrofluorocarbons (HFCs), and perfluorocarbons (PFCs). 

Gross primary production 

The total carbon fixfixed by plant through photosynthesis. 

Heathland 

A wide-open landscape dominated by low-growing woody vegetations such as heathers and heathland 

grasses. Heathlands generally occur on acidic, nutrient-poor, and often sandy and well-draining soils. 

Hypoxic 

Events that lead to a deficiency of O2. 

Invasive species and invasive alien species 

A species aggressively expanding its range and population density into a region in which it is not native, 

often through outcompeting or otherwise dominating native species. 

Leaching 

The removal of soil elements or chemicals by water movement through the soil. 

Lowland 

In physical geography, lowland is any relatively flat area in the lower levels of regional elevation. The term 

can be applied to the landward portion of the upward slope from oceanic depths to continental highlands, to 

a region of depression in the interior of a mountainous region, to a plain of denudation, or to any region in 

contrast to a highland. 

Net ecosystem exchange (NEE) 

The net flux of carbon between the land and the atmosphere, typically measured using eddy covariance 

techniques. Positive values of NEE usually refer to carbon released to the atmosphere (i.e., a source), and 

negative values refer to carbon uptake (i.e., a sink) 

Net ecosystem production (NEP) 

The difference between net primary production (NPP) and heterotrophic respiration (mostly decomposition 

of dead organic matter) of that ecosystem over the same area. NEP = -NEE, with positive values indicating 

a sequestration of atmospheric carbon in to biosphere. 

Net primary production (NPP) 

The gross primary production minus autotrophic respiration, i.e., the sum of metabolic processes for plant 

growth and maintenance, over the same area. 

Nitrification 

The biological oxidation of ammonia to nitrite and then to nitrate. This process is primarily accomplished 

by autotrophic nitrifying bacteria that obtain energy by reducing ammonium and/or nitrite to nitrate. 

G-3
Nitrogen mineralization 

The conversion of organic nitrogen into plant-available inorganic forms (e.g., NH3 or NH4

+

NH4

+

) by 

microorganisms. 

Nitrogen-retention capacity 

The length of time that an ecosystem can retain nitrogen in plants, microbes and soil-organic matter. 

Nitrogen-retention capacity is highly affected by soil, vegetative, topographic, and land-use factors. 

Nitrogen saturation 

The condition in which nitrogen inputs from atmospheric deposition and other sources exceed the 

biological requirements of the ecosystem. 

Occult deposition 

The transmission of gases and particles from the atmosphere to surfaces by fog or mist. 

Ombrotrophic bog 

An acidic peat-accumulating wetland that is fed by rainwater (instead of groundwater) and, thus, especially 

poor in nutrients. 

pH 

A measure of the relative concentration of hydrogen ions in a solution. The formula for calculating pH is: 

pH = -log10[H+

], where [H+] represents the hydrogen ion concentration in moles per liter. The pH scale 

ranges from 0 to 14. A pH of 7 is neutral. A pH less than 7 is acidic and a pH greater than 7 is basic. 

Phytoplankton 

The plant forms of plankton. Phytoplankton are the dominant plants in the sea and are the basis of the 

marine food web. These single-celled organisms are the principal agents of photosynthetic carbon 

fixfixation in the ocean. 

Primary Production 

All forms of production accomplished by plants, also called primary producers. See GPP, NPP, and NEP. 

Semi-arid regions 

Regions of moderately low rainfall (100– and 250–mm precipitation per year), which are not highly 

productive and are usually classified as rangelands.

Sensitivity 

The degree to which a system responds to pollution (e.g., acidification, n-nutrient enrichment, etc.). The 

response may be direct (e.g., a change in growth following a change in the mean, range, or variability of N 

deposition) or indirect (e.g., changes in growth due to alterations in competitive dynamics between species 

or decreased biodiversity , themselves following N deposition). 

Streamflow 

Water flow within a river channel. A synonym for river discharge. 

Surface runoff 

The water that travels over the land surface to the nearest surface stream; runoff of a drainage basin that has 

not passed beneath the surface since precipitation. 

Throughfall 

The precipitation falling through the canopy of a forest and reaching the forest floor. 

 G-4
 G-5

Trophic level 

The position that an organism occupies in a food web. 

Tundra 

A treeless, level, or gently undulating plain characteristic of the Arctic, sub-Arctic regions and some alpine 

regions characterized by low temperatures and short growing seasons. 

Upland terrestrial ecosystem 

Generally considered to be the ecosystems located at higher elevations directly above riparian zones and 

wetlands. Vegetation in an upland ecosystem is not in contact with groundwater or other permanent water 

sources. 

Valuation 

The economic or non-economic process of determining either the value of maintaining a given ecosystem 

type, state, or condition or the value of a change in an ecosystem, its components, or the services it 

provides. 

Vulnerability 

Susceptibility to degradation or damage from adverse factors or influences. Vulnerability is a function the 

exposure and its sensitivity. 

Welfare effects 

Effects on soils, water, crops, vegetation, man-made materials, animals, wildlife, weather, visibility and 

climate, damage to and deterioration of property, and hazards to transportation, as well as effects on 

economic values and on personal comfort and well-being, whether caused by transformation, conversion, 

or combination with other air pollutants (CAA 302(h)). 

Wet deposition 

The transmission of gases and particles from the atmosphere to surfaces by rain or other precipitation. 

Wetland

Those areas that are inundated or saturated by surface or ground water at a frequency and duration 

sufficient to support a prevalence of vegetation adapted to water-saturated soil conditions. Wetlands 

include swamps, marshes, bogs, and similar areas. 

Zooplankton 

The animal forms of plankton. They consume phytoplankton or other zooplankton. 
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