Review of the Secondary Standards for 

Ecological Effects of Oxides of Nitrogen, 

Oxides of Sulfur, and Particulate Matter: Risk 

and Exposure Assessment Planning Document

EPA-452/D-18-001

August 2018

Review of the Secondary Standards for Ecological Effects of Oxides of Nitrogen, Oxides of 

Sulfur, and Particulate Matter: Risk and Exposure Assessment Planning Document

U.S. Environmental Protection Agency

Office of Air Quality Planning and Standards

Health and Environmental Impacts Division

Research Triangle Park, NC
i

DISCLAIMER

This document has been prepared by staff in the Health and Environmental Impacts 

Division, Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency 

(EPA). Any findings and conclusions are those of the authors and do not necessarily reflect the 

views of the Agency. This document is being circulated to facilitate discussion with the Clean 

Air Scientific Advisory Committee and for public comment to inform the EPA’s consideration of 

the primary national ambient air quality standard for oxides of nitrogen, oxides of sulfur and 

particulate matter. This information is distributed for the purposes of pre-dissemination peer 

review under applicable information quality guidelines. It does not represent and should not be 

construed to represent any Agency determination or policy.

Questions or comments related to this document should be addressed to Dr. Travis Smith, 

U.S. Environmental Protection Agency, Office of Air Quality Planning and Standards, C539-07, 

Research Triangle Park, North Carolina 27711 (email: smith.jtravis@epa.gov) and Ms. Ginger 

Tennant, U.S. Environmental Protection Agency, Office of Air Quality Planning and Standards, 

C504-06, Research Triangle Park, North Carolina 27711 (email: tennant.ginger@epa.gov). 
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TABLE OF CONTENTS

DISCLAIMER................................................................................................................................. i

TABLE OF CONTENTS................................................................................................................ ii

LIST OF FIGURES ........................................................................................................................ v

LIST OF TABLES.......................................................................................................................... v

LIST OF ACRONYMS AND ABBREVIATIONS ...................................................................... vi

EXECUTIVE SUMMARY ............................................................................................................ 1

1 INTRODUCTION................................................................................................................ 1-1

1.1 Background ................................................................................................................... 1-3

1.2 Exposure and Risk Analyses for Oxides of Nitrogen, Oxides of Sulfur and PM ......... 1-5

1.2.1 Context for Analyses.............................................................................................. 1-5

1.2.2 Key Technical Issues ............................................................................................. 1-6

1.2.3 Conceptual Model.................................................................................................. 1-8

1.3 Organization of this Document ..................................................................................... 1-9

2 OVERVIEW OF PREVIOUS ASSESSMENTS ................................................................. 2-1

2.1 Air Quality..................................................................................................................... 2-1

2.2 Direct Effects................................................................................................................. 2-4

2.3 Non-N and S Effects of PM .......................................................................................... 2-4

2.4 Deposition-Related Effects of N and S ......................................................................... 2-5

2.4.1 Aquatic Acidification............................................................................................. 2-5

2.4.2 Aquatic Nitrogen Enrichment.............................................................................. 2-10

2.4.3 Terrestrial Acidification....................................................................................... 2-12

2.4.4 Terrestrial N Enrichment ..................................................................................... 2-14

2.4.5 Mercury Methylation ........................................................................................... 2-15

3 CONSIDERATION OF NEWLY AVAILABLE INFORMATION ................................... 3-1

3.1 Key Considerations....................................................................................................... 3-1

3.2 Air Quality Information ................................................................................................ 3-2

3.2.1 Emissions, Atmospheric Concentrations and Deposition...................................... 3-2

3.2.2 Linking Atmospheric Concentration Changes to Changes in Deposition ............. 3-9

3.3 Direct Effects............................................................................................................... 3-10

3.4 Non-Nitrogen and Non-Sulfur Effects of PM............................................................. 3-11

3.5 Freshwater Acidification and Nitrogen Enrichment ................................................... 3-11
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3.5.1 Freshwater Acidification...................................................................................... 3-11

3.5.2 Freshwater Nitrogen Enrichment......................................................................... 3-14

3.6 Terrestrial Acidification and Nitrogen Enrichment .................................................... 3-16

3.6.1 Forests.................................................................................................................. 3-17

3.6.2 Lichens and Mycorrhizal Fungi........................................................................... 3-18

3.6.3 Herbs and Shrubs................................................................................................. 3-19

3.6.4 Summary.............................................................................................................. 3-20

3.7 Other Nitrogen and Sulfur Effects .............................................................................. 3-20

3.7.1 Estuarine Nitrogen Enrichment............................................................................ 3-20

3.7.2 Wetlands Nitrogen Enrichment............................................................................ 3-21

3.7.3 Coastal Acidification ........................................................................................... 3-22

3.7.4 S Enrichment in Freshwater and Wetland Ecosystems........................................ 3-23

3.8 Ecosystem Services..................................................................................................... 3-25

3.9 Conclusions................................................................................................................. 3-27

4 PLAN FOR QUANTITATIVE ASSESSMENT.................................................................. 4-1

4.1 Analytical Framework................................................................................................... 4-1

4.1.1 Ambient Air Quality and Atmospheric Deposition ............................................... 4-2

4.1.2 Critical Loads and Exceedances ............................................................................ 4-3

4.1.3 Changes in Ecological Responses.......................................................................... 4-4

4.2 Air Quality Analyses..................................................................................................... 4-4

4.2.1 Consideration of Current Conditions for National-scale Air Quality Concentrations 

and Deposition...................................................................................................................... 4-4

4.2.2 Consideration of Air Quality Scenarios................................................................. 4-5

4.3 Ecological Effects Assessment.................................................................................... 4-13

4.3.1 Aquatic Acidification and Nitrogen Enrichment ................................................. 4-13

4.3.2 Terrestrial Effects................................................................................................. 4-16

4.4 Case Study Area Selection .......................................................................................... 4-20

4.5 Consideration of Variability and Uncertainty ............................................................. 4-22

4.5.1 Air Quality ........................................................................................................... 4-23

4.5.2 Critical Loads Data .............................................................................................. 4-24

4.5.3 Exposure-Response Functions............................................................................. 4-29

5 REFERENCES ..................................................................................................................... 5-1

6 APPENDIX .......................................................................................................................... 6-1
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v

LIST OF FIGURES

Figure 1-1. General NAAQS Review Process.......................................................................... 1-5

Figure 1-2. Conceptual Model.................................................................................................. 1-9

Figure 3-1. Key considerations for updated or new quantitative analyses............................... 3-2

Figure 3-2. Annual total deposition of sulfur, kg S ha-1 in 2002.............................................. 3-6

Figure 3-3. Annual total deposition of sulfur, kg S ha-1 in 2015.............................................. 3-7

Figure 3-4. Change in annual total sulfur deposition, 2015 – 2002 (kg S ha-1

)........................ 3-7

Figure 3-5. Annual total deposition of nitrogen, kg N ha-1 in 2002. ........................................ 3-8

Figure 3-6. Annual total deposition of nitrogen, kg N ha-1 in 2015. ........................................ 3-8

Figure 3-7. Change in annual total nitrogen deposition, 2015 – 2002 (kg N ha-1

)................... 3-9

LIST OF TABLES

Table 2-1. Noted Uncertainties from the REA and PA in the Prior Review........................... 2-3

Table 4-1. Current National Ambient Air Quality Standards for PM, NO2, and SO2............. 4-6

Table 4-2. Initial Assessment of Available Deposition Response Factors............................ 4-11

Table 4-3. Major Sources of Uncertainty for the CLs Considered for the REA................... 4-25
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LIST OF ACRONYMS AND ABBREVIATIONS

AAI Aquatic Acidification Index 

Al Aluminum

AMoN Ammonia Monitoring Network

ANC Acid Neutralizing Capacity

AQ Air quality

ASSETS EI Assessment of Estuarine Trophic Status Eutrophication Index

Bc/Al Base cation/Aluminum

BCS Base Cation Surplus

BCw Base Cation weathering

C Carbon

CAA Clean Air Act

CALFIRE State of California fire occurrence, prevention and fire-fighting expenditures

CAMx Comprehensive Air Quality Model with eXtensions

CASAC Clean Air Scientific Advisory Committee

CASTNet Clean Air Status and Trends Network

CH4 Methane

CL Critical Load

CLAD Critical Loads of Atmospheric Deposition Science Committee

CMAQ Community Multiscale Air Quality Model

CONUS Contiguous United States

CO2 Carbon dioxide

CSN Chemical Speciation Networks

CSS Coastal Sage Scrub

CV Coefficient of Variation

DOC Dissolved Organic Carbon

EMAP Environmental Monitoring and Assessment Program

EPA Environmental Protection Agency

FAB First-order Acid Balance

FASOM Forest and Agriculture Sector Optimization Model

FIA Forest Inventory and Analysis 

FR Federal Register

FRM Federal Reference Method

GIS Geographic Information Systems

GLNC Georeferenced Lake Nutrient Chemistry database

HNO3 nitric acid
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IMPROVE Interagency Monitoring of Protected Visual Environments

IRIS Internet Research Information Series

IRP Integrated Review Plan

ISA Integrated Science Assessment

LTM Long-Term Monitoring 

MAGIC Model of Acidification of Groundwater In Catchments

MeHg Methylmercury

MCF Mixed Conifer Forest

μg/m3 micrograms per cubic meter

N Nitrogen

NAAQS National Ambient Air Quality Standard

NADP National Atmospheric Deposition Program

NARS National Aquatic Resource Surveys

NCCA National Coastal Condition Assessment

NCLD National Critical Loads Database

NCore National Core network

NEI National Emissions Inventory

NH3 Ammonia

NH4

+ Ammonium

NH4NO3 Ammonium nitrate

NLA National Lakes Assessment

NOAA National Oceanic and Atmospheric Administration

NO Nitric Oxide

NO2 Nitrogen Dioxide

NO3

- Nitrate

NOx Oxides of Nitrogen

NOy Sum of oxidized N in the gas phase and particle nitrate

N2O Nitrous Oxide

NPS National Park Service

NRSA National River and Stream Assessment

NTN National Trends Network

NWCA National Wetland Condition Assessment

OAQPS Office of Air Quality Planning and Standards

ppb parts per billion

ppm parts per million

PA Policy Assessment
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PAN Peroxyacetyl Nitrate

PM Particulate Matter

PM2.5 particles with a diameter less than or equal to 2.5 μg 

PM10 particles with a diameter less than or equal to 10 μg 

PRISM Parameter-elevation Regression on Independent Slopes Model

REA Risk and Exposure Assessment

S Sulfur

SAV Submerged Aquatic Vegetation

SLT State and Local agencies and Tribes

SMB Simple Mass Balance

SO2 Sulfur dioxide

SO3 Sulfur trioxide

SOx Oxides of Sulfur

SO4

2- Sulfate

SPARROW SPAtially Referenced Regressions on Watershed 

SRB Sulfur-reducing bacteria

SRP Sulfur-reducing prokaryotes

SSWC Steady State Water Chemistry 

TDEP Total Deposition 

TIME Temporally Integrated Monitoring of Ecosystems

USFS U.S. Forest Service

USGS U.S. Geological Survey

VOC Volatile organic compounds

WHO World Health Organization 
ES-1

EXECUTIVE SUMMARY

The U.S. Environmental Protection Agency (EPA) is conducting a review of the air 

quality criteria and the associated secondary (welfare-based) national ambient air quality 

standards (NAAQS) for oxides of nitrogen, oxides of sulfur and particulate matter (PM). This 

review is focused on the contribution of oxides of nitrogen, oxides of sulfur, and PM to 

ecological effects and particularly the contribution of these pollutants to such effects through 

atmospheric deposition. Based on analysis of the information available in this review regarding

support for a quantitative risk and exposure assessment (REA) to inform the review, this 

document outlines a plan, including scope and methods, for conducting a REA, and is intended 

to facilitate consultation with the Clean Air Scientific Advisory Committee (CASAC), as well as 

an opportunity for public participation. The information considered includes newly available 

scientific evidence, new and/or improved data, methods, and tools and other information or data 

supporting a quantitative REA particularly those assessed in the second draft Integrated Science 

Assessment for Oxides of Nitrogen, Oxides of Sulfur and Particulate Matter – Ecological 

Criteria (ISA).

The current review of the secondary standards for oxides of nitrogen, oxides of sulfur and 

PM considers secondary standards for these three pollutants together with regard to protection 

against adverse ecological effects on public welfare. This review differs from the review 

completed in 2012 in that the current review includes consideration of the secondary PM 

standards, in addition to the secondary standards for oxides of nitrogen and sulfur. Given the 

contribution of nitrogen compounds to PM, including but not limited to those related to oxides of 

nitrogen, the current review provides for an expanded and more integrated consideration of N 

deposition and the current related air quality information.

Since the last review, the scientific evidence for all effect categories has been expanded, 

especially for terrestrial nitrogen enrichment effects. New critical loads data and exposureresponse functions for nitrogen enrichment and acidification effects on terrestrial and freshwater 

ecosystems are now available that together provide a basis for analysis of ecological effects of 

atmospheric deposition across the nation. This new information contributes to the stronger 

weight of evidence and expanded causality determinations in the second draft ISA and enables

an evaluation of growth and mortality effects, as well as species or community composition 

changes, that was not available at the time of the last review. In addition, new air quality data

suggest that the spatial variability and distribution of atmospheric deposition has changed in 

recent years. New techniques are also available for combining these measurements and modeling 

outputs to estimate total deposition with lower uncertainty. Thus, given the new scientific 
ES-2

information and data available since the last review, we believe there is support for a new and

updated assessment of risk and exposure to inform the current review.

EPA’s proposed plans for the risk and exposure assessment include quantitative analyses 

of terrestrial and aquatic effects using critical loads and exposure-response functions. These 

analyses will be conducted at a national-scale and within select case study areas. To estimate 

deposition for this assessment, we intend to develop new estimates of total N and S deposition 

that will utilize the methodologies developed by the National Atmospheric Deposition Program 

(NADP) Total Deposition (TDEP) Science Committee and combine the most recently available 

measured ambient air concentration and wet deposition data with modeled deposition velocity 

and dry deposition data. To assess ecological risk under current conditions at a national scale, we 

intend to compare these TDEP estimates to national critical loads and exposure-response datasets 

for aquatic and terrestrial acidification and N enrichment. This will inform our understanding of 

sensitivity and risk for acidification and nitrogen enrichment on a national scale for multiple 

species. 

In addition, to assess ecological risk when just meeting the current standards and any 

potential alternative standards, as appropriate, we propose to evaluate the impacts of changing 

ambient concentrations and atmospheric deposition on aquatic and terrestrial acidification and N 

enrichment in selected study area locations. To do this, we intend to evaluate the relationship 

between emissions, ambient concentrations and atmospheric deposition of N and S based on data 

from air quality models and ambient measurements and to use this information to adjust air 

quality in the study area locations to reflect just meeting the appropriate air quality scenarios. 

These atmospheric deposition estimates would then be compared to critical loads and exposureresponse datasets for aquatic and terrestrial acidification and N enrichment that are most 

applicable for the individual study area locations. Together, the national-scale and study area 

assessment information would be used to inform decisions regarding potential adverse effects to 

public welfare. 
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1 INTRODUCTION 

The U.S. Environmental Protection Agency (EPA) is conducting a review of the air 

quality criteria and the associated secondary (welfare-based) national ambient air quality 

standards (NAAQS) for oxides of nitrogen, oxides of sulfur, and particulate matter (PM). The 

purpose of this planning document (titled Review of the Secondary National Ambient Air Quality 

Standards for Ecological Effects of Oxides of Nitrogen, Oxides of Sulfur and Particulate Matter: 

Risk and Exposure Assessment Planning Document – hereafter referred to as the REA Planning 

Document) is to describe EPA’s consideration of the extent to which newly available scientific 

evidence, tools or methodologies, and/or information warrant the conduct of a quantitative risk 

and exposure assessment (REA) that might inform this review, and may expand our 

characterization of exposure and risk estimates provided by the assessments conducted for the 

last review. Based on these considerations, and as described below, we plan to develop a new 

REA to inform the current review of the secondary NAAQS for oxides of nitrogen, oxides of 

sulfur, and PM. Accordingly, this document’s additional purpose is to describe the general plan, 

including scope and methods for conducting the REA. 

This review of the secondary (welfare-based) NAAQS for oxides of nitrogen, 1 oxides of 

sulfur2 and PM,3 is focused on the contribution of these pollutants to ecological effects and 

particularly the contribution of these pollutants to such effects through atmospheric deposition.4

In so doing, we recognize that oxides of nitrogen, oxides of sulfur, and PM contribute to 

 1 In this document, the term oxides of nitrogen refers to all forms of oxidized nitrogen (N) compounds, including 

nitric oxide (NO), nitrogen dioxide (NO2), and all other oxidized N-containing compounds formed from NO and 

NO2. This follows usages 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.” By contrast, within 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 community for the sum 

of all forms of oxidized nitrogen compounds including those listed in Section 108(c) is total oxidized nitrogen 

(NOy). 

2 Oxides of sulfur are defined here to include sulfur monoxide (SO), sulfur dioxide (SO2), sulfur trioxide (SO3), 

disulfur monoxide (S2O), and sulfate (SO4

2−), however, SO, SO3, and S2O are present at much lower ambient 

levels than SO2 and SO4

2− .

3 PM is the generic term for a broad class of chemically and physically diverse substances that exist as discrete 

liquid and/or solid particles over a wide range of sizes. Particles may be emitted directly from anthropogenic and 

natural sources, or formed in the atmosphere by transformations of gaseous emissions such as SO2, nitrogen 

oxides (NOx), ammonia (NH3) and volatile organic compounds (VOC). The chemical and physical properties of 

PM vary greatly with time, region, meteorology, and source category.

4 In addition, these air pollutants contribute to effects on vegetation, soils, and biota both through direct exposure to 

the pollutant in air, and indirect exposure after deposition.
1-2

ecological effects together, through deposition of N and/or S to the surface of vegetation, soils or 

water bodies. In recognition of these linkages, addressing the pollutants together to assess 

ecological effects enables a comprehensive look at the nature and interactions of the pollutants.

The current secondary NAAQS for oxides of nitrogen and oxides of sulfur were 

established to protect against direct damage to vegetation by exposure to gas-phase oxides of 

nitrogen and oxides of sulfur. The secondary standard for oxides of nitrogen is an annual average 

not to exceed 0.053 parts per million (ppm) NO2. The secondary standard for oxides of sulfur is a 

3-hour average of 0.5 ppm SO2, not to be exceeded more than once per year. The secondary PM

standards were established to provide protection against a variety of PM-associated welfare 

effects, including effects on vegetation as well as visibility impairment and materials damage 

(e.g., soiling, corrosion).5 The annual secondary PM2.5 standard is set at a level of 15 μg/m3, with 

an annual arithmetic mean averaged over three years, and a secondary 24-hour PM2.5 standard is 

set at a level of 35 μg/m3, as the 98th percentile of the 24-hour average, averaged over 3 years. 

The annual secondary PM10 standard is an annual arithmetic mean, averaged over three years 

with a level of 50 μg/m3, and the secondary 24-hour PM10 standard is a 24-hour average of 150 

μg/m3, not to be exceeded more than once per year on average over a three-year period.

Welfare effects associated with PM that are not ecologically related (and therefore not 

included in this review), such as visibility impairment, climate effects and materials damage, and 

the health effects of PM (including particulate transformation products of oxides of nitrogen and 

oxides of sulfur) are being considered as part of a separate review of the NAAQS for PM (U.S. 

EPA, 2016). The health effects of oxides of nitrogen were considered in a separate assessment 

that was completed recently as part of the review of the primary (health-based) NAAQS for 

oxides of nitrogen (83 FR 17226). Similarly, the health effects of oxides of sulfur are currently 

being considered in a separate assessment as part of the review of the primary NAAQS for 

oxides of sulfur (83 FR 26752). In addition, NH3 is not a criteria pollutant but is a precursor to 

PM (ammonium sulfate (NH4SO4) and ammonium nitrate (NH4NO3)) and is considered in this 

review to the extent that it contributes to atmospheric transformations and loading to 

ecosystems.

6

This REA Planning Document presents an evaluation of information related to ecological 

effects that is newly available in the second draft of the Integrated Science Assessment for 

Oxides of Nitrogen, Oxides of Sulfur, and Particulate Matter― Ecological Criteria (U.S. EPA, 

 5 The secondary PM standards were most recently reviewed in 2012 (78 FR 3086, January 15, 2018).

6 The scientific and technical information and analyses in this review are expected to inform our understanding of

the contribution of NH3 to total N deposition and N-related ecological effects, as well as on the role of NH3 in the 

formation of PM (which is a criteria pollutant).
1-3

2017a; hereafter referred to as the second draft ISA). Advances in modeling tools and techniques 

and air quality data that have become available since the last review are also considered. This 

document is intended to facilitate consultation with the Clean Air Scientific Advisory Committee 

(CASAC), as well as an opportunity for public participation, on the evaluation of the potential 

support in the newly available scientific evidence (including existing and historical air quality 

patterns and trends), new and/or improved data, methods, and tools, for conducting updated 

quantitative assessments, and on the plan for such analyses, as warranted. Additionally, this 

document evaluates the extent to which new information, tools or methodologies, will address or 

improve our consideration of important limitations or uncertainties associated with the analyses 

from the last review (summarized in chapter 2). Based on these considerations and our 

preliminary conclusions on the extent to which updated quantitative analyses of ecological risks 

and/or exposures are warranted in the current review, this document presents general plans for 

such analyses. 

1.1 BACKGROUND

Sections 108 and 109 of the CAA govern the establishment and periodic review of the 

NAAQS. Section 108 [42 U.S.C. 7408] directs the Administrator to identify and list certain air 

pollutants and then to issue air quality criteria for those pollutants. The Administrator is to list 

those air pollutants that in his/her “judgment, cause or contribute to air pollution which may 

reasonably be anticipated to endanger public health or welfare,” “the presence of which in the 

ambient air results from numerous or diverse mobile or stationary sources;” and “for which... 

[the Administrator] plans to issue air quality criteria...” CAA section 108(a)(1). The NAAQS are 

established for these pollutants. The CAA requires that NAAQS are to be based on air quality 

criteria, which are intended to “accurately reflect the latest scientific knowledge useful in 

indicating the kind and extent of all identifiable effects on public health or welfare that may be 

expected from the presence of [the] pollutant in the ambient air...” CAA section 108(a)(2). 

Under CAA section 109 [42 U.S.C. 7409], the EPA Administrator is to propose, promulgate, and 

periodically review, at five-year intervals, “primary” (health-based) and “secondary” (welfarebased)7 NAAQS for such pollutants for which air quality criteria are issued. Section 109(b)(2) of 

the CAA directs that a secondary standard is to “specify a level of air quality the attainment and 

maintenance of which, in the judgment of the Administrator, based on such criteria, is requisite 

 7 Section 302(h) of the CAA provides that all language referring to effects on welfare includes but is not limited to, 

“...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...”
1-4

to protect the public welfare from any known or anticipated adverse effects associated with the 

presence of [the] pollutant in the ambient air.”8 Based on periodic reviews of the air quality 

criteria and standards, the Administrator is to make revisions in the criteria and standards, and 

promulgate any new standards, as may be appropriate. The CAA also requires that an 

independent scientific review committee review the air quality criteria and standards and 

recommend to the Administrator any new standards and revisions of existing air quality criteria 

and standards as may be appropriate, a function now performed by the CASAC. 

The overall plan for this review was presented in the Integrated Review Plan (IRP) (U.S. 

EPA, 2017b), which discusses the preparation of key documents in the NAAQS review process 

including an ISA, an REA, as warranted, and a Policy Assessment (PA). In general terms, the 

ISA provides a critical assessment of the latest available scientific information upon which the 

NAAQS are to be based. The purpose of the REA in a secondary standards review is to estimate 

risk and exposure to public welfare associated with the current standards and, if appropriate, 

evaluate potential improvements in public welfare that could be achieved from meeting potential 

alternate standard(s). The PA evaluates the policy implications of the information contained in 

the ISA and of any policy-relevant quantitative analyses, such as a quantitative REA, that were 

performed for the review. Based on that evaluation, the PA presents conclusions regarding 

standard-setting options for the Administrator to consider in reaching decisions on the NAAQS.9

The general NAAQS review process is illustrated in Figure 1-1, below.

 8 Section 109(b)(1) [42 U.S.C. 7409] of the CAA defines a primary standard as one “the attainment and maintenance 

of which in the judgment of the Administrator, based on such criteria and allowing an adequate margin of safety, 

are requisite to protect the public health.” 

9 Review of the NAAQS involves consideration of the four basic elements of a standard: indicator, averaging time, 

form, and level. The indicator defines the pollutant to be measured in the ambient air in order to determine

compliance with the standard. The averaging time defines the time-period over which air quality measurements 

are to be obtained and averaged or cumulated. The form of a standard defines the air quality statistic that is to be 

compared to the level of the standard in determining whether an area attains the standard. The level of a standard 

defines the air quality concentration used (i.e., an ambient air concentration of the indicator pollutant).
1-5

Figure 1-1. General NAAQS Review Process

1.2 EXPOSURE AND RISK ANALYSES FOR OXIDES OF NITROGEN, 

OXIDES OF SULFUR AND PM

This REA Planning Document serves to provide support for conducting any new or 

updated quantitative assessments in the current review. Conclusions regarding such support are 

based on our consideration of the available scientific evidence; the available technical 

information, tools, and methods; and judgments as to the likelihood that quantitative assessments 

will add substantially to our understanding of risk or exposure related to ecological effects, 

beyond the insights gained from the assessments conducted in the last review. Specifically, this 

consideration also includes an evaluation of the newly available data, tools and methods and 

whether they would be expected to reduce previously identified uncertainties or limitations from 

the last review.

1.2.1 Context for Analyses

The most recent review of the secondary NO2 and SO2 NAAQS was completed in 2012. 

Technical analyses for that review focused on two general types of effects (1) direct effects on 

vegetation of exposure to gaseous oxides of nitrogen and sulfur, which are the type of effects that 

the current secondary NO2 and SO2 standards were developed to protect against, and (2) effects 

associated with nitrogen and sulfur deposition to sensitive aquatic and terrestrial ecosystems (77 
1-6

FR 20218, April 3, 2012). The REA for the 2012 review presented assessments of aquatic and 

terrestrial acidification-related and nutrient enrichment-related effects. Based on the REA 

findings, the PA gave primary attention to aquatic acidification effects from deposition of 

nitrogen and sulfur, and developed an aquatic acidification index (AAI)— an equation that relied 

upon ecosystem and air quality modeling to estimate the ecosystem’s natural ability to buffer 

acidic deposition from ambient nitrogen and sulfur deposition. 

In the Agency’s consideration of the scientific and technical information and analyses 

with regard to the potential use in establishing a new standard based on aquatic acidification,

several important uncertainties and data limitations were recognized, both those related to 

characterizing the relationships between N and S deposition and ecological effects generally, and 

those specifically relating to the development of an AAI. Thus, while recognizing the 

scientifically-supported conceptual basis for an AAI-based standard,10 the EPA also recognized 

current limitations in relevant data and uncertainties that, together, were concluded to be too 

great to support a standard that would meet the requirements of the Act (77 FR 20218, April 3, 

2012). 

The current review of the secondary NO2 and SO2 standards differs from the review 

completed in 2012 in that the current review also includes consideration of the secondary PM 

standards. Specifically, the Agency is considering secondary standards for these three pollutants 

together with regard to protection against adverse ecological effects on public welfare and 

particularly such effects related to atmospheric deposition. Given the contribution of nitrogen 

compounds to PM, including but not limited to those related to oxides of nitrogen, the current 

review provides for an expanded and more integrated consideration of N deposition and the 

current related air quality information.

1.2.2 Key Technical Issues

Because NOx, SOx and PM are deposited from the ambient air into ecosystems where 

they have the potential to affect individual organisms and communities, considerations in this 

review will include potential impacts on the public welfare from alterations in structure and 

function of ecosystems. Important considerations for the review, which the REA will inform, 

include:

• Source contributions and loading. It is important to consider the emissions sources, 

chemical species and contributions to overall loading to ecosystems. The ability to 

evaluate modeled data as well as the ability to characterize components of ambient air 

 

10 The Administrator recognized that while an AAI-based standard was innovative and unique, the structure of the 

proposed standard was well-grounded in the science underlying the relationships between ambient concentrations 

of oxides of nitrogen and sulfur and the aquatic acidification related to deposition of nitrogen and sulfur 

associated with such ambient concentrations (77 FR 20251, April 3, 2012).
1-7

and atmospheric deposition are important to understanding the potential effects of the 

pollutants on ecosystems. In some systems atmospheric deposition is not the only source 

of loadings and N and S can be introduced into the system from other sources (e.g., 

surface water runoff). Additionally, the composition of emissions in a given area will

have an effect on what types of chemical species are contributing to atmospheric 

deposition. Using analytical tools and data to help understand what pollutants are 

contributing to the overall effect will be important, particularly in the case of reduced 

nitrogen impacts11. 

• Historic air quality impacts on deposition-related effects. Historic air quality and 

associated deposition can have an appreciable impact on ecosystems in many areas and 

will be an important consideration in understanding the role of current air quality versus 

historic air quality on deposition-related effects. For example, in areas where deposition 

levels have been historically high there may be terrestrial systems where the soil is 

saturated or aquatic systems where improvements to water quality are impeded by the 

area’s geology (e.g., underlying bedrock). In such cases, the continued role of this 

historic deposition in the area’s deposition-related impacts may mean that we would not 

expect the deposition-related risk and exposures to be as responsive to changes in 

atmospheric deposition, and that there would be a substantial delay prior to any such 

response. 

• Timescale of effects and potential for recovery. Another consideration for this review is 

to understand the timescale for the effects being considered. For example, the response 

time for trees to stressors is very different than for fish populations or herbaceous plants. 

This affects both the exposure period necessary for effects to become adverse, as well as 

the uncertainty associated with the ability of the REA to assess these impacts on public 

welfare. In addition, while information may be limited, understanding the scientific 

evidence indicating the potential for recovery or improvement is also important to 

consider.

• Spatial scale of effects. It is important to consider the spatial scale, magnitude, and 

associated variability, of some ecological effects. The effects of N, S and PM deposition 

on aquatic and terrestrial ecosystems occur over time and are a result of atmospheric 

reactions and transport. The evaluation of environmental responses to these pollutants, 

accordingly, will need to consider the variability of environmental characteristics of 

ecosystems across the nation, including those related to ecosystem susceptibility and to 

the relative importance of individual effects (such as acidification or N enrichment). 

• Impacts to public welfare. Importance to the public welfare is an important 

consideration to the review. For example, potential impacts in areas with special federal 

protections, and lands set aside by states, tribes, and public interest groups to provide 

similar benefits to the public welfare, may be of particular importance. The relevance of 

such areas to consideration of effects on the public welfare has been recognized in past 

NAAQS decisions (e.g., 80 FR 65292, October 26, 2015). Such areas include Class I 

 11 NH3 is not a criteria pollutant but is a precursor to PM and is considered in this review to the extent that it 

contributes to atmospheric transformations and loading to ecosystems.
1-8

areas12 which are federally mandated to preserve certain air quality related values. Class 

I areas and other parks have been afforded special federal protection to preserve services 

that provide for the enjoyment of these resources for current and future generations.

1.2.3 Conceptual Model

In each NAAQS review, selection of the approach most appropriate for the 

characterization of risks is influenced by the nature and strength of the evidence for the subject 

pollutants. Depending on the type of evidence available, analyses may include quantitative risk 

assessments based on exposure-response, or ambient air concentration/deposition-response 

relationships. Figure 1-2 below illustrates our conceptual model for evaluating ecological risk 

and exposure of oxides of nitrogen, oxides of sulfur and PM in this review, including exposure 

from direct pathways and through atmospheric deposition. Included in Figure 1-2, below is a 

basic conceptual diagram highlighting the relevant pathways of exposure for the pollutants in 

this review. 

As described in chapter 4, the REA for this review will include an evaluation of the

relationship between emissions of the three criteria pollutants (and their precursors) and ambient 

air concentrations, as well as their contribution to direct as well as deposition-related exposures 

to biota, soils, sediments and surface waters. While the review focuses on atmospheric inputs, 

non-atmospheric inputs of the pollutants that may occur through runoff of the pollutants from 

other human activities, such as agriculture and industrial processes, will also be considered as 

part of the total loading of the pollutant to the ecosystem. The ecological effects of these inputs 

(e.g., nutrient enrichment) will then be evaluated based on the biogeochemical and/or ecological 

responses of “receptors” 13 (e.g., plants, wildlife) within ecosystems. 

 12 Areas designated as Class I include all national parks, national wilderness areas which exceed 5,000 acres in size, 

national memorial parks which exceed 5,000 acres in size, and national parks which exceed six thousand acres in 

size, provided the park or wilderness area was in existence on August 7, 1977. Other areas may also be Class I if 

designated as Class I consistent with the Act.

13 A receptor is defined as a biological element that is impacted by the conditions created by atmospheric deposition.
1-9

Figure 1-2. Conceptual Model.

1.3 ORGANIZATION OF THIS DOCUMENT

The remainder of this document presents our evaluations and preliminary conclusions 

regarding the degree to which available evidence and information address important 

uncertainties and the support for updated or new quantitative analyses in the current review.

Chapter 2 provides background information regarding the assessments conducted in the prior 

reviews, evaluating deposition-related effects of oxides of nitrogen, oxides of sulfur, and PM as 

well as direct effects of oxides of nitrogen and oxides of sulfur. Chapter 2 also includes 

information regarding any uncertainties that were identified in the last reviews. Chapter 3 

includes consideration of information newly available in this review, including scientific 

evidence and data for various ecological effects. This chapter also includes consideration and 

conclusions regarding whether this new information is expected to reduce uncertainties or 

limitations from the prior review and inform updated or new quantitative assessments for this 

review. Chapter 4 presents an overview of the REA analysis approach, summarizes approaches 

for characterizing air quality, and describes the plan for quantitatively evaluating ecological

effects in various ecosystems.
2-1

2 OVERVIEW OF PREVIOUS ASSESSMENTS 

This chapter summarizes the analyses and limitations and/or uncertainties related to 

ecological risk and exposure as assessed in the two previous reviews: the secondary NAAQS 

review of oxides of nitrogen and sulfur; and the review of particulate matter. More detailed 

information on the body of evidence and causal determinations for these reviews can be found in 

the Integrated Science Assessment for Oxides of Nitrogen and Sulfur - Ecological Criteria (U.S. 

EPA, 2008; hereafter referred to as the 2008 NOx/SOx ISA) as well as the Integrated Science 

Assessment for Particulate Matter (U.S. EPA, 2009; hereafter referred to as the 2009 PM ISA).

More detailed descriptions of the assessment approaches in the previous oxides of nitrogen and 

sulfur joint review can be found in the Risk and Exposure Assessment for Review of the 

Secondary National Ambient Air Quality Standards for Oxides of Nitrogen and Oxides of Sulfur: 

Final (U.S. EPA, 2009; hereafter referred to as 2009 NOx/SOx REA) as well as the Policy 

Assessment for the Review of the Secondary National Ambient Air Quality Standards for Oxides 

of Nitrogen and Oxides of Sulfur: Final (U.S. EPA, 2011a; hereafter referred to as the 2011 

NOx/SOx PA). More details about the previous PM review can be found in the Particulate 

Matter Urban-Focused Visibility Assessment - Final Document (U.S. EPA, 2010a)

14 and in the 

Policy Assessment for the Review of the Particulate Matter National Ambient Air Quality 

Standards (U.S. EPA, 2011b). 

In each NAAQS review, selection of the approach most appropriate for the 

characterization of risk is influenced by the nature and strength of the evidence for the subject 

pollutants. This chapter summarizes the assessment approach used in the last review of NOx and 

SOx, which focused on evaluating the protection provided by secondary standards for oxides of 

nitrogen and sulfur for two general types of effects: (1) direct effects on vegetation associated 

with exposure to gaseous oxides of nitrogen and sulfur in the ambient air, which are the effects 

that the current NO2 and SO2 secondary standards were set to protect against; and, (2) effects 

associated with the deposition of nitrogen (N) and sulfur (S) to aquatic and terrestrial 

ecosystems. This chapter also summarizes the assessment approach used in the last PM review to 

evaluate direct and indirect effects of non-N and non-S deposited particles (including metals and 

organics).

2.1 AIR QUALITY

The 2009 NOx/SOx REA examined the path from emissions to air concentrations, 

providing characterizations of (1) major emissions sources of NOx, NH3 and SO2, (2) 

 14 Note that this review does not cover visibility.
2-2

atmospheric concentrations of NOy and SO2, and (3) estimates of deposition of total N and S

across the U.S. and for specific case study areas (see 2009 NOx/SOx REA, sections 3.2 and 3.3). 

Data from the 2002 National Emission Inventory (U.S. EPA, 2006) were assessed in the 2009 

NOx/SOx REA to understand the major emissions sources of N and S across the U.S., and used 

as inputs to the Community Multiscale Air Quality (CMAQ) model to provide estimates of 

atmospheric concentrations and deposition of N and S in the U.S. Spatial fields representing 

2002 annual average N and S deposition were also created by extracting the dry deposition 

predictions from the CMAQ model simulations and combining them with the wet deposition 

measurements from the National Atmospheric Deposition Program (NADP) National Trends 

Network (NTN). 

Based on these analyses, the REA concluded that there was significant variation both 

spatially and seasonally across the country with the eastern U.S. receiving much greater 

deposition of both N and S than the states west of the Mississippi river. CMAQ simulations of 

scenarios with emission reductions of NOx, SOx, and NH3 were used to examine the impact of 

these emissions to specific ecosystems. At the time of the REA analysis, oxidized N was the 

largest contributor to deposition, and the 2009 NOx/SOx REA found that a 50% reduction in 

NOx emissions led to a 30% - 40% reduction in N deposition across most of the Eastern US. In 

locations with more NH3 emissions, such as portions of the Potomac River Estuary and the 

Neuse River Estuary, the 50% NH3 emission reduction led to a 40% - 50% reduction in N 

deposition. For SO2 emissions, a 50% reduction resulted in a nearly 50% reduction in S 

deposition. The 2009 NOx/SOx REA concluded that most of the N and S deposition can be 

attributed to emissions of NOx, NH3, and SO2. However, the REA also recognized that not all 

loadings of N and S compounds to freshwater, estuarine, and wetland ecosystems are due to 

atmospheric deposition. Other inputs, such as runoff from agricultural soils and point-source 

discharges, also contribute to acidification and N enrichment. Thus, some ecosystems may be 

solely impacted by atmospheric deposition (e.g., high elevation lakes), while ecological effects 

attributed to N and S in other systems might be largely due to non-atmospheric sources (e.g., 

high order streams). Sources to total loading of N and S was discussed more in the 2011 

NOx/SOx PA in consideration of the standard-setting process.

Building upon these 2009 NOx/SOx REA analyses, the 2011 NOx/SOx PA further 

explored the relationship between atmospheric concentrations and deposition of N and S. In 

doing so, the 2011 NOx/SOx PA introduced the concept of a “transference ratio” and defined it 

as the ratio of deposition to ambient atmospheric concentrations. The transference ratios were 

calculated in the PA by dividing the 2002 CMAQ model outputs of the annual average of 

deposition of NOy or SOx by the annual average of ambient air concentrations of NOy or SOx, 

respectively. These annual average deposition and ambient air concentration values were 
2-3

estimated for every CMAQ model grid cell (e.g., 12 km by 12 km) and then averaged across 

areas of ecological similarity (i.e., spanning 100 km or more, as defined by level III ecoregions). 

The transference ratios were then used to characterize how changes in air quality concentrations 

of NOy or SOx related to changes in deposition of NOy or SOx in each of these ecological areas.

Limitations and uncertainties related to linking atmospheric concentrations to deposition

are summarized in the 2011 NOx/SOx PA in Table 7-3, and the 2009 NOx/SOx REA in Section 

3.6. Frequently identified issues include lack of spatial coverage and reference methods for 

concentration measurements, CMAQ modeling uncertainties, especially with respect to emission 

estimates, and the inherent difficulty in measuring dry deposition. 

Table 2-1. Noted Uncertainties from the REA and PA in the Prior Review

Source of uncertainty Description of uncertainty from the previous NOx/SOx review

Measurements of atmospheric 

concentrations of NOy

A Federal Reference Method (FRM) for NOy instruments was not 

available and existing techniques were known to have a negative 

bias. The spatial coverage of existing NOy measurements was not 

adequate for assessing effects outside of urban areas.

Measurements of atmospheric 

concentrations of SOx

A lack of adequate spatial coverage was the primary concern for 

SO2 + SO4

2- observations.

Atmospheric deposition of NOy, derived 

from NTN measurements of wet 

deposition and CMAQ estimates of dry 

deposition

Quantifying uncertainty in CMAQ estimates of dry deposition was 

hampered by a lack of dry deposition measurements.

Atmospheric deposition of SOx, derived 

from NTN measurements of wet 

deposition and CMAQ estimates of dry 

deposition

There was general consensus that the overall mass balance of S 

was treated well in CMAQ. As with all dry deposition estimates, 

technologies for direct measurements were not available.

Atmospheric deposition of NHx, derived 

from NTN measurements of wet 

deposition and CMAQ estimates of dry 

deposition

A lack of both NH3 and ammonium (NH4

+) ambient observations 

made it difficult to characterize uncertainty in NHx deposition. As 

with all dry deposition estimates, technologies for direct 

measurements were not available routinely.

Deposition Transference Ratios, 

calculated using CMAQ

Transference ratios were not evaluated in a traditional model to 

observation context. Uncertainty was attributed to the information 

(e,g., NH3 emissions) driving these calculations and availability of 

observations to evaluate model behavior.

Emissions of NOx, SOx, and NH3 Uncertainty depends on the emission source. Emissions from 

electricity generating facilities are directly measured and have low 

uncertainty; however, emission estimates of livestock and fertilized 

fields do not capture all of the variability in these sources. 
2-4

2.2 DIRECT EFFECTS

The 2008 NOx/SOx ISA determined that there was a causal relationship between 

exposure to nitric acid (HNO3) and changes in vegetation, and a causal relationship between 

exposure to NO, NO2, peroxyacetyl nitrate (PAN), and SO2 and injury to vegetation. It was also 

noted that oxides of N and S gases have different degrees of phytotoxic effects on vegetation. In 

addition, while there was relatively little information about the direct effects of HNO3 vapor on 

vegetation in previous reviews, research on the decline of sensitive lichen species was 

highlighted in the 2008 NOx/SOx ISA. In the mixed conifer forest of the San Bernardino 

Mountains, HNO3 has been estimated to provide 60% of all dry deposited nitrogen, and it has 

been suspected as the cause of a dramatic decline in lichen species (see 2009 NOx/SOx REA, 

Section 6.4.3). At high concentrations over the short-term, HNO3 can damage vascular plants 

such as seedlings of ponderosa pine (Pinus ponderosa) and California black oak (Quercus 

kelloggii) (see 2009 NOx/SOx REA, Section 6.4.3). The 2009 NOx/SOx REA noted that more 

research was needed to determine long-term exposure effects at lower concentrations. 

Additionally, at the time of the last review, there were no tools available for use in quantitative 

assessments of direct gaseous effects. For these reasons, the 2009 NOx/SOx REA included a 

qualitative discussion of direct gaseous effects but no quantitative analyses were conducted.

2.3 NON-N AND S EFFECTS OF PM

Direct and indirect ecological effects associated with deposited PM components 

(including metals and organics) were evaluated in the 2009 PM ISA. Direct deposition-related

effects include alteration of leaf processes from deposition of PM (“dust”) to vegetative surfaces. 

Indirect deposition-related effects encompass physiological responses associated with uptake of 

PM components and alterations to ecosystem structure and function. Evidence reviewed in the 

2009 PM ISA was sufficient to determine that there is a likely causal relationship between PM 

deposition and a variety of effects on individual organisms and ecosystems.

In the REA for the prior PM review, issued in 2009,15 the EPA determined that despite 

this likely causal relationship data was insufficient to support quantitative assessments for non-N 

and S deposition-related PM effects. After a careful evaluation of the evidence, the EPA 

determined that data needed to conduct quantitative assessments for ecological welfare effects in 

the last review were not available. A qualitative assessment was included in an Appendix of the 

REA (see 2009 PM REA, Appendix A).

 15 U.S. EPA, Risk and Exposure Assessment for Review of the Secondary National Ambient Air Quality Standards 

for Oxides of Nitrogen and Oxides of Sulfur, September 2009, EPA-452/R-09-008a.
2-5

2.4 DEPOSITION-RELATED EFFECTS OF N AND S

Deposition-related ecological effects are those related to the deposition of mixtures of Nand S-containing compounds onto aquatic or terrestrial surfaces (e.g., vegetation, soil, or 

waterbodies). The ways in which ambient air pollution gets assimilated into ecosystems is a 

complex process, with many geochemical and biological factors contributing to the resulting 

deposition loading. The 2009 NOx/SOx REA assessed the ecological effects associated with 

deposition of N and S, focusing on four main targeted ecosystem effects on terrestrial and 

aquatic systems: (1) aquatic acidification16; (2) terrestrial acidification; (3) aquatic nitrogen 

enrichment17; and (4) terrestrial nitrogen enrichment18,19. In conducting these analyses, the 2009 

NOx/SOx REA evaluated the relationships between atmospheric concentrations, deposition (wet 

and dry), biologically relevant exposures, targeted ecosystem effects, and, to the extent possible, 

associated ecosystem services. In doing so, the 2009 NOx/SOx REA recognized a lack of broadscale data and that deposition-related effects were not evenly distributed across the U.S. 

Accordingly, the 2009 NOx/SOx REA used a case study approach for the quantitative 

assessments, building from the scientific information presented in the 2008 NOx/SOx ISA as 

well as the identification of ecosystems that are sensitive to N and/or S deposition.

To show the impacts of ecological effects on public welfare, the 2009 NOx/SOx REA 

and 2011 NOx/SOx PA qualitatively and quantitatively associated changes in ecological effects 

with changes in their ecological benefits and ecosystem services or welfare effects.

The sections that follow summarize the analysis of deposition-related effects as presented 

in the prior reviews, including (1) the relationship between emissions, air concentrations, and 

deposition; (2) the qualitative and quantitative approaches used to assess deposition-related 

ecological effects and related ecosystem services, where relevant; and (3) key limitations or

uncertainties. 

2.4.1 Aquatic Acidification

With respect to aquatic acidification, the 2008 NOx/SOx ISA for the prior review of 

oxides of nitrogen and sulfur determined that the evidence was sufficient to infer a causal 

 16 These analyses were then expanded upon in the 2011 NOx/SOx PA which focused on outlining an approach for a 

standard which focused on the ecological effects associated with acidifying deposition of oxides of nitrogen and 

sulfur in aquatic ecosystems. (see 2011 NOx/SOx PA, Chapter 7).

17 Referred to as “nutrient enrichment” in the 2009 NOx/SOx REA. This effect includes eutrophication.

18 Referred to as “nutrient enrichment” in the 2009 NOx/SOx REA. 

19 The REA also qualitatively addressed the influence of sulfur oxide deposition on MeHg production; nitrous oxide 

effects on climate; nitrogen effects on primary productivity and biogenic greenhouse gas fluxes; and phytotoxic 

effects on plants. 
2-6

relationship between N and S deposition and changes in biogeochemistry and changes in biota in 

freshwater ecosystems.

The 2009 NOx/SOx REA conducted a case study evaluating acidifying effects on streams 

in the Shenandoah region of the southern Appalachian Mountains of Virginia (hereafter referred 

to as the Shenandoah), and lakes in the Adirondack Mountains of New York (hereafter referred 

to as the Adirondack). These areas were chosen because they were among the most severely 

acid-impacted regions in North America at the time the case study was conducted and had 

substantially high levels of N and S deposition known to have acidified a large number of 

waterbodies in both regions.

2.4.1.1 Assessment Approach

For the case studies, the impacts of acidification on waterbodies were assessed by (1) 

examining the amount and trends of N and S deposition using NADP data, (2) relating water 

quality condition to known biological impacts from acidification, (3) examining past, present, 

and future water quality conditions using long-term monitoring data and the Model of 

Acidification of Groundwaters in Catchments (MAGIC) biogeochemical model,20 and (4) 

calculating critical loads (CLs) that relate deposition levels with water quality conditions. N and 

S deposition from all NADP monitors within the case study areas showed acidifying deposition 

had declined substantially since 1990, but still remained high relative to other regions in the 

United States and historical levels.

Both case studies used sulfate (SO4

2-

) concentrations, nitrate (NO3

-

) concentrations, and 

acid neutralizing capacity (ANC)21 as the chemical indicators of deposition-driven acidification. 

ANC was particularly of interest, given its well understood relationship between deposition, 

water quality, and biological conditions, as well as its common use in modeling platforms (see 

2009 NOx/SOx REA, section 4.2.1). Status of current conditions and trends in SO42-, NO3

-

, 

ANC concentrations measured in surface water were used to characterize links to the effects of 

acidifying deposition on the acid-base chemistry of the studied waterbodies, and to determine if 

conditions of the waterbodies were improving and recovering or were still degrading. These 

trends were analyzed along with monitoring data from the EPA-administered Temporally 

Integrated Monitoring of Ecosystems (TIME)/Long-Term Monitoring (LTM) programs along 

with survey data from EPA’s Environmental Monitoring and Assessment Program (EMAP) for 

 20 MAGIC is a lumped-parameter model of intermediate complexity, developed to predict the long-term effects of 

acidic deposition on surface water chemistry (Cosby et al. 1985a,b,c, 2001). The model simulates soil solution 

and surface water chemistry to predict average concentrations of the major ions.

21 ANC was defined in the 2009 NOx/SOx REA as “a key indicator of the ability of water to neutralize the acid or 

acidifying inputs it receives. This ability depends largely on associated biogeophysical characteristics, such as 

underlying geology, base cation concentrations, and weathering rates.” (see “Key Terms” in U.S. EPA 2009)
2-7

the years 1990 to 2006. At the time the case studies were completed, the trends analysis found 

that the SO4

2- concentrations were strongly declining in nearly all lakes in the Adirondack while 

ANC in many lakes were improving. The results of NO3

- concentrations were mixed. For the 

Shenandoah case study, SO4

2-

, NO3

-

, and ANC concentrations were all variable with some sites 

improving while many were still declining.

MAGIC was used to produce estimates of past, present, and future water chemistry in 60 

streams in the Shenandoah case study area and 44 lakes in the Adirondack case study area. 

Furthermore, MAGIC was used to evaluate the associated risk and uncertainty of the current 

levels of acidification given the pre-acidification water quality and the levels of uncertainty in 

the input parameters to the model. The MAGIC model output for each waterbody was 

summarized into five ANC levels that relate to biological impacts of acidification. The five 

ANC levels correspond to the aquatic status categories: Acute Concern, Severe Concern, 

Elevated Concern, Moderate Concern, and Low Concern. For each of these levels, the expected 

ecological effects were identified (see 2009 NOx/SOx REA, Table 4.2-1). 

Additionally, after considering the five ANC levels that correspond to aquatic status 

categories, ANC “limits” of biological protection were selected: 0 μeq/L (acidic), 20 μeq/L 

(minimal protection), 50 μeq/L (moderate protection), and 100 μeq/L (full protection). The 

MAGIC modeling results showed that SO4

2- and NO3- concentrations in the waterbodies at the 

time of the case studies were still well above pre-historical conditions. In addition, ANC levels 

were much lower, as compared to pre-historical levels, fostering ecological impacts. Lastly, the 

MAGIC model indicated that recovery of ANC was unlikely, assuming no changes in emissions 

between 2002 and 2050. 

The 2009 NOx/SOx REA also calculated CLs22 for waterbodies in each of the case study 

areas in order to assess whether current deposition of N and S was high enough to cause 

ecological effects. For these case studies, the calculated CLs used ANC limits of 0, 20, 50, and 

100 μeq/L to define the biological risk to biota. From the 169 modeled lakes and 60 streams in 

the Shenandoah and Adirondack case study areas, respectively, the number and percentage of 

waterbodies that receive acidifying deposition above their CLs for a given ANC limit of 0, 20, 

50, and 100 μeq/L were determined. These case studies indicated that large number of 

waterbodies were still being impacted by N and S deposition. Between 18 to 58 percent of 

modeled lakes in the Adirondacks and 52 to 93 percent of modeled streams in Shenandoah had 

exceeded their critical load in 2002 for a given ANC level of 0 to 100. (see 2009 NOx/SOx REA, 

section 4.2.4.2).

 22 A critical load is formally defined as a quantitative estimate of 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 (Nilsson and Grennfelt 1988, UNECE 2004). 
2-8

Building from the 2009 NOx/SOx REA, the 2011 NOx/SOx PA focused on a potential 

new standard that could protect against ecological effects associated with acidifying deposition 

of N and S in aquatic ecosystems. As mentioned in Chapter 1 of this document, the 2011 

NOx/SOx PA developed an approach to account for the geographical differences in sensitivity to 

acidification across the United States, called the Aquatic Acidification Index (AAI). In general, 

the AAI was meant to allow for consistent protection of ecosystems across the U.S. by selecting 

a single ANC value for each ecoregions in the U.S., and calculating how much acidifying 

deposition from N and S an ecosystem could accept before crossing the selected ANC limit, 

accounting for differences in ecosystem sensitivity.

The PA recognized that both dynamic (e.g., MAGIC) and steady state [e.g., Steady state 

water chemistry (SSWC)] models calculate ANC and can be used to calculate CLs, and 

discussed these models in the context of answering the question as to which modelling approach 

would be appropriate for development of a nationally applicable standard (see 2001 PA, section 

7.2.2). The 2011 NOx/SOx PA additionally noted that information provided by steady state 

modeling of aquatic acidification would be sufficient to develop and analyze alternative NAAQS 

and the kind of protection they would afford. In addition, the 2011 NOx/SOx PA noted that the 

First-order Acid Balance (FAB) model includes more explicit modeling of N processes including 

soil immobilization, denitrification, in-lake retention of N and S, as well as lake size. Hence, the 

PA used a combination of the SSWC and the FAB model to inform development of the form of 

the standard (the AAI). 

Recognizing the spatial variability across the U.S. of the factors in the AAI equation, the 

PA suggested that AAI values were meant to be calculated specifically for each ecologically 

relevant region (i.e., Ecoregion III (Omernik, 1987)). With regard to a level for the AAI, the PA 

concluded consideration should be given to a ANC threshold within the range of 20 to 75 μeq/L 

noting that a target ANC value of 20 μeq/L would be a reasonable lower end of this range, so as 

to protect against chronic acidification-related adverse impacts which have been characterized as 

severe on fish populations at ANC values below this level. Further, a target ANC value of 75 

μeq/L would be a reasonable upper end of this range in recognition that the potential for 

additional protection at higher ANC values is substantially more uncertain in light of evidence 

that acidification-related effects are far less responsive to increases in ANC above this value (see 

2011 NOx/SOx PA, section 7.7).

Ecosystem services related to aquatic acidification were discussed in the 2009 NOx/SOx

REA and the 2011 NOx/SOx PA. The 2009 NOx/SOx REA included qualitative discussions of 

recreational fishing, which was identified as the service most relevant to aquatic acidification by 

freshwater lakes and streams (see 2009 NOx/SOx REA, section 4.2.1). The 2011 NOx/SOx PA 

included a quantitative assessment of the recreational fishing benefits to New York residents of 
2-9

reducing deposition of anthropogenic N and S for the Adirondacks case study area, but also 

recognized that there are numerous other ecosystem services that may be related to the ecological 

effects of acidification (see 2011 NOx/SOx PA, section 4.4). 

2.4.1.2 Key Uncertainties/Limitations 

An evaluation of the uncertainty in the parameters for the steady state critical load model 

was performed using a Monte Carlo approach in the 2009 NOx/SOx REA.

23 This probabilistic 

framework specifically assessed the degree of confidence in the exceedance values and the 

coefficient of variation (CV) of the critical load and exceedance values. The results suggested 

that the estimates of CLs and exceedances for the case study areas were robust. It was noted in 

the 2009 NOx/SOx REA, however, that this analysis may understate the actual uncertainty 

because some of the range and distribution types of critical load model parameters were not well 

understood for aquatic systems in the U.S. at the time. 

An evaluation of the uncertainty for the water quality estimates and parameters used in 

the MAGIC model was also performed in the 2009 NOx/SOx REA. These uncertainty estimates 

were derived by running multiple calibrations of each site using the “optimization” tool and 

procedure as part of the MAGIC model. Direct comparison of simulated versus observed water 

chemistry values were compared to determine the uncertainty and variability in the MAGIC 

model output. Average water chemistry (SO4

2-

, NO3

-

, and ANC) simulated versus observed 

values during the calibration period (i.e., reference year) were compared for all modeled sites. 

The 2009 NOx/SOx REA found that the simulated and observed water quality values were in 

close agreement. (see 2009 NOx/SOx REA, section 4.2.8).

No formal analysis of the uncertainty in the AAI was performed in the 2011 NOx/SOx

PA. However, the 2011 NOx/SOx PA noted that uncertainty and natural variability existed in all 

of the components of the AAI (for uncertainty analyses conducted for the 2011 NOx/SOx PA, 

see Appendix G). In addition, the 2011 NOx/SOx PA noted that there was no apparent 

directional bias in the uncertainty regarding the biological, chemical and physical processes 

incorporated in the AAI. Lastly, the 2011 NOx/SOx PA noted that the estimates for ecosystem 

services generally were believed to be biased low, meaning the monetary and non-monetary 

value of reaching a target level of protection is underestimated. However, quantification of these 

values was recognized as perhaps the most uncertain of all aspects considered.

Additionally, no formal analysis of uncertainty was performed for ecosystem services in 

the 2011 NOx/SOx/PA.

 23 For a summary of the Monte Carlo approach used in the 2009 NOx/SOx REA, see section 4.2.8.1.
2-10

2.4.2 Aquatic Nitrogen Enrichment

With respect to aquatic N enrichment, the 2008 NOx/SOx ISA determined that the 

evidence was sufficient to infer a causal relationship between N deposition and the following:

• Biogeochemical cycling of N in freshwater and estuarine ecosystems

• Alteration to the biogeochemical cycling of carbon (C) in freshwater, estuarine, and near 

coastal marine ecosystems; and

• Alteration of species richness, species composition and biodiversity in freshwater and 

estuarine ecosystems

The 2009 NOx/SOx REA conducted a case study for estuaries in the Chesapeake Bay and 

the Pamlico Sound areas. Both areas were selected primarily based on the availability of data. 

Because the Chesapeake Bay and Pamlico Sound are fed by multiple river systems, the case 

study was scaled to one main stem river for each system: the Potomac River/Potomac Estuary (in 

the Washington, DC metropolitan area) and the Neuse River/Neuse River Estuary (in North 

Carolina). 

2.4.2.1 Assessment Approach

The 2009 NOx/SOx REA focused the case study on aquatic nitrogen enrichment of 

estuaries and used the National Oceanic and Atmospheric Administration (NOAA)’s Assessment 

of Estuarine Trophic Status Eutrophication Index, commonly referred to as ASSETS EI (Bricker 

et al., 2007), as the ecological indicator for the case study. ASSETS EI is an estimation of the 

likelihood that an estuary is experiencing eutrophication or will experience eutrophication in the 

future based on five chemical and/or biological indicators: chlorophyll a, macroalgae, dissolved 

oxygen, nuisance/toxic algal blooms, and submerged aquatic vegetation (SAV) (Bricker et al., 

2007). 

Specifically, the analysis in this case study sought to determine the change in N loading

required to improve the ASSETS EI from its current level set in the 2002 current condition 

analysis.24 To create response curves for the ASSETS EI based on changes in N loads to an 

estuary, the case study used the U.S. Geological Survey’s (USGS’s) SPAtially Referenced 

Regressions on Watershed attributes (SPARROW) model to calculate the N loads.25

The case study found that a decrease of 78 percent of atmospheric N deposition would be 

required to improve the eutrophication index category ASSETS EI Score for the Potomac 

 24 Current conditions were evaluated using 2002 CMAQ model year and NADP monitoring data.

25 SPARROW relies on a nonlinear regression formulation to relate water quality measurements throughout the 

watershed of interest to attributes of the watershed. SPARROW can be used to predict total N loads at the outlet 

of the watershed that result from changes in the total N atmospheric deposition loads. 
2-11

River/Potomac Estuary from Bad to Poor (see 2009 NOx/SOx REA, section 5.2.7.1), and that 

the Neuse River/Neuse River Estuary ASSETS EI score could not be improved from Bad to 

Poor with only decreases in the 2002 atmospheric N deposition load to the watershed (see 2009 

NOx/SOx REA, section 5.2.7.2). The 2009 NOx/SOx REA found that even if all atmospheric N

deposition inputs were eliminated (100% decrease), the total annual N load to the Neuse River 

Estuary would only decrease by 4%. This small effect is because atmospheric deposition of 

nitrogen to the Neuse River watershed are small relative to other sources, such as run-off and 

agricultural sources. 

The 2009 NOx/SOx REA included qualitative and quantitative assessments of ecosystem 

services related to aquatic nutrient enrichment in the Potomac River/Potomac Estuary and Neuse 

River/Neuse River Estuary (see 2009 NOx/SOx REA, section 5.2.1). The 2009 NOx/SOx REA 

evaluated several cultural ecosystem services, including recreational fishing, boating, and beach 

use. In addition, aesthetic and nonuse values were evaluated; the impacts on recreational fishing 

(e.g., closings, decreased species richness) to eutrophication symptoms through monitoring data 

were quantitatively linked; other recreational activities and aesthetic and non-use services to 

eutrophication symptoms were quantitatively related through user surveys and valuation 

literature; and the current commercial fishing markets were described. The 2011 NOx/SOx PA 

projected the quantitative change in the provision of these services based on the changes in water 

quality related to a policy scenario that eliminated the deposition of anthropogenic N to the 

Chesapeake Bay. The PA also included a hedonic study for aesthetic benefits related to 

improved water quality for near-shore residents. (see 2011 NOx/SOx PA, section 4.4.5)

2.4.2.2 Key Uncertainties/Limitations

The 2009 NOx/SOx REA noted potential uncertainties in the inputs and outputs of the 

SPARROW model, as well as sensitivity of the SPARROW model. The 2009 NOx/SOx REA 

also noted uncertainties in the inputs to the ASSETS EI, given the numerous data requirements 

and sources required to conduct a full ASSETS EI analysis. 

The 2009 NOx/SOx REA determined that the small effect of decreasing atmospheric 

deposition in the Neuse River watershed is because the other N sources within the watershed are 

more influential than atmospheric deposition to the total nitrogen loadings to the Neuse River 

Estuary as estimated with the SPARROW model. The 2009 NOx/SOx REA noted that future 

application of the methods to case study areas where atmospheric deposition plays a larger role 

in the N loading to an estuary will likely provide more tangible results. 

The 2011 NOx/SOx PA noted that the relative lack of empirical models and valuation 

studies imposed obstacles to the estimation of ecosystem services affected by N deposition
2-12

resulting in underestimation of the monetary and non-monetary value of changes in ecosystem 

service provision. (see 2011 NOx/SOx PA, section 4.4.5)

2.4.3 Terrestrial Acidification

With respect to terrestrial acidification, the 2008 NOx/SOx ISA determined that the 

evidence was sufficient to infer a causal relationship between N and S deposition and changes in 

biogeochemistry and biota. 

At the time of the 2009 NOx/SOx REA, Picea rubens (red spruce) and Acer saccharum

(sugar maple) were the best studied species in North America with regard to impacts of 

acidification from N and S deposition to terrestrial systems. Hence, the 2009 NOx/SOx REA 

conducted a case study to evaluate the effects of acidifying deposition on sugar maple and red 

spruce physiological condition leading to impacts on tree growth and/or mortality in the Kane 

Experimental Forest and Hubbard Brook Experimental Forest, located in the Allegheny Plateau 

region in Pennsylvania and New Hampshire’s White Mountains, respectively. 

2.4.3.1 Assessment Approach

In the case study areas, critical load calculations were applied to multiple areas within 24 

states for sugar maple and in 8 states for red spruce—site locations within each state were 

determined by the U.S. Forest Service’s Forest Inventory and Analysis (FIA) database permanent 

sampling plots. The 2009 NOx/SOx REA noted that the Simple Mass Balance (SMB) model 

was a commonly used tool for evaluating soil acidification. The SMB model specifically 

examines a long-term, steady-state balance of base cation, chloride, and nutrient inputs, “sinks,” 

and outputs within an ecosystem, and base cation equilibrium is assumed to equal the system’s 

critical load for ecological effects. The 2009 NOx/SOx REA also noted that the base 

cation/aluminum (Bc/Al) ratio is a good indicator for soil acidification and relates well to the 

calcium/aluminum (Ca/Al) ratio in the soil solution. (see 2009 NOx/SOx REA, section 4.3.1.1) 

Hence, the 2009 NOx/SOx REA quantitative assessment included the use of the SMB model to 

calculate CLs for soil acidification, with the Bc/Al ratio as the indicator.

It should be noted that in order to properly use the SMB model, which is expressed as an 

equation, the user must first choose a critical level or levels for the Bc/Al ratio. For purposes of 

the 2009 NOx/SOx REA, data published by Sverdrup and Warfvringe (1993) was used to make 

decisions regarding the Bc/Al ratio as well as percent estimates for negative tree responses. 

Bc/Al values of 0.6 and 1.2 were selected based on a 50% and 75% chance of negative tree 

response (i.e., >20% reduced growth) for sugar maple and red spruce. A Bc/Al level of 10 was 

also chosen to represent the lowest impact (greatest level of protection) to tree growth. At the 

time of the 2009 NOx/SOx REA, it was considered the most conservative value used in studies 

that had calculated CLs in the U.S. and Canada. (see 2009 NOx/SOx REA, section 4.3.4).
2-13

One important input into the SMB model is base cation weathering (BCw) rates, which 

the 2009 NOx/SOx REA estimated using a clay-substrate method. At the time of the REA, this 

method was one of the most-commonly used methods for estimating this parameter.

Case study results suggested that the health of at least a portion of the sugar maple and 

red spruce growing in the U.S. may have been compromised with acidifying total N and S

deposition in 2002. We concluded that the pattern of case study results suggests that N and S

acidifying deposition in the sugar maple and red spruce forest areas studied were very close to, if 

not greater than, the CLs for those areas and both ecosystems are likely to be sensitive to any 

future changes in the levels of deposition.

The 2009 NOx/SOx REA also identified several ecosystem services that a deterioration 

of sugar maple and red spruce tree health could negatively impact. These include provisioning 

(use of trees for timber and maple syrup), cultural (endangered and threatened species habitat), 

regulating (soil stabilization and erosion control, water regulation, and climate regulation) and 

recreational ecosystem services (fall color viewing). The 2009 NOx/SOx REA used preference 

studies for the southern Appalachians to capture willingness to pay (WTP) for forest 

improvements that specifically addressed non-use values. Most services were qualitatively 

evaluated due to lack of data. However, the 2009 NOx/SOx REA did include a pilot study using 

the Forest and Agriculture Sector Optimization Model (FASOM) to estimate changes in timber 

harvest for red spruce and sugar maple. Uncertainties in model parameterization, however, were 

deemed too high to allow utilization of this model (see 2009 NOx/SOx REA, section 4.3.1).

2.4.3.2 Key Uncertainties/Limitations

As mentioned earlier, the 2009 NOx/SOx REA used the SMB model to estimate CLs for 

soil acidification, and used BCw as an input into the model. The 2009 NOx/SOx REA noted that 

one limitation of the SMB model is that it is a steady-state model and therefore does not capture 

the cumulative changes in ecosystem conditions. Additionally, the 2009 NOx/SOx REA also 

noted that the estimates for BCw rates and forest soil ANC input parameters were the main 

sources of uncertainty since these parameters are rarely measured and require researchers to use 

default values. The 2009 NOx/SOx REA also stated that the BCw value is strongly influenced by 

the classified acidity of the soil parent material, and is poorly measured in non-glaciated soils. 

(see 2009 NOx/SOx REA section 4.3.9). The 2009 NOx/SOx REA determined that the BCw 

rates contributed 49% to the total variability in the critical load estimates, and forest soil ANC 

contributed 46% to the total variability. (see 2009 NOx/SOx REA, section 4.4)
2-14

2.4.4 Terrestrial N Enrichment 

With respect to terrestrial N enrichment, the 2008 NOx/SOx ISA determined that the 

evidence was sufficient to infer a causal relationship between N deposition and the alteration of 

the following:

1. Biogeochemical cycling of N and C;

2. Biogeochemical flux of methane (CH4) and nitrous oxide (N2O); and 

3. Species richness, species composition, and biodiversity 

At the time of the last review, there was a large body of evidence showing that some of 

the highest N deposition has occurred in Southern California, where researchers have 

documented measurable ecological changes related to atmospheric deposition. These changes 

include increases in nonnative grasses and fire susceptibility for Coastal Sage Scrub (CSS), as 

well as tree mortality, increased fire intensity, and a change in nutrient cycling in mixed conifer 

forests (U.S. EPA, 2008). 

2.4.4.1 Assessment Approach

The 2009 NOx/SOx REA included a case study for CSS and Mixed Conifer Forest 

(MCF) communities in the Sierra Nevada Range and San Bernardino Mountains of California. 

No quantitative assessment was conducted for this case study. Rather, geographic information 

systems (GIS) analysis supported a qualitative review of past field research to identify ecological 

benchmarks associated with CSS and mycorrhizal communities, as well as MCF’s nutrientsensitive acidophyte lichen communities, fine-root biomass in Ponderosa pine, and leached NO3

-

in receiving waters. The benchmarks were identified from empirical studies, including CLs, in 

the southern California region. These benchmarks, ranging from 3.1 to 17 kg N/ha/yr for CSS 

and MCF, were compared to 2002 CMAQ/NADP data to discern any associations between 

atmospheric deposition and changing communities. Evidence supported the finding that N alters 

CSS and MCF communities. 

The 2009 NOx/SOx REA also used data from Rocky Mountain National Park to examine 

the sensitivity and effects of nutrient enrichment on aquatic and terrestrial ecosystems, and found 

that exposure levels at which negative effects were observed appeared to be generally 

comparable to levels identified in other sensitive U.S. ecosystems (benchmarks range from 1.5 to 

30.5 kg N/ha/yr). In addition, it included a qualitative assessment of ecosystem services, 

focusing on cultural services, including: habitat for endangered and threatened species, 

recreational (e.g., hiking, fishing, and hunting), aesthetic (view of the landscape), non-use 

(existence value), and regulation (e.g., water, regulation of fire intensity). 

To assess the impact of fire risk, the 2009 NOx/SOx REA used GIS to overlay N

deposition with the locations of MCF and CSS, and CALFIRE data (State of California fire 
2-15

occurrence, prevention and fire-fighting expenditures) to describe the potential benefit of 

reducing N enrichment to these fire-prone areas. This was accompanied by a discussion of the 

hedonic benefits to homeowners and aesthetic benefits to general public for reductions in fire 

damages. (see 2009 NOx/SOx REA, section 5.3.1)

The 2009 NOx/SOx REA provided a qualitative discussion of the services offered by 

CSS and MCF and a sense of the scale of benefits associated with these services. Specifically, 

the 2009 NOx/SOx REA stated the following: CSS and MCF are an integral part of the 

California landscape, and together the ranges of these habitats include the densely populated and 

valuable coastline and mountain areas of the state. Through recreation and scenic value, these 

habitats affect the lives of millions of California residents and tourists. Numerous threatened and 

endangered species at both the state and federal levels reside in CSS and MCF. Both habitats 

may play an important role in wildfire frequency and intensity, an extremely important problem 

for California. The potentially high value of the ecosystem services provided by CSS and MCF 

justify careful attention to the long-term viability of these habitats. (see 2009 NOx/SOx REA, p. 

5-60).

2.4.4.2 Key Uncertainties/Limitations

The 2009 NOx/SOx REA noted that the exact relationship between atmospheric N

loadings, fire frequency and intensity, and nonnative plants, particularly in the CSS ecosystem, 

had not been quantified in the scientific literature. The 2009 NOx/SOx REA noted that although 

various conceptual models linking these factors had been developed, an understanding of cause 

and effect, seasonal influences, and thresholds remained undeveloped.

Overall, the REA concluded that although the available data used for the targeted effect 

of terrestrial N enrichment were considered high quality, there was a limited ability to 

extrapolate these data to a larger regional area. 

2.4.5 Mercury Methylation

With respect to mercury methylation, the 2008 NOx/SOx ISA determined that the 

evidence was sufficient to infer a causal relationship between S deposition and increased 

methylation of mercury in aquatic environments, where the value of other factors is within 

adequate range for methylation.

Information available at the time of the last review demonstrated that methylmercury 

(MeHg) production is mediated primarily by sulfur-reducing bacteria (SRB), and changes in 

SO4

2- deposition result in changes in both Hg methylation rates and Hg concentration in fish. It 

was also shown that watersheds with conditions known to be conducive to Hg methylation could 

be found in the northeastern U.S. and southeastern Canada, though biotic Hg accumulation had 
2-16

been widely observed in other regions that had not been studied as extensively, and where a 

different set of conditions may exist.

The 2009 NOx/SOx REA acknowledged that a number of factors influence or modify the 

relationship between SO4

2- and the methylation of Hg. It noted that while there appears to be a 

relationship between SO4

2- deposition and mercury methylation, the rate of mercury methylation 

varies according to several spatial and biogeochemical factors whose influence has not been fully 

quantified (2009 NOx/SOx REA, section 6.2.1).

2.4.5.1 Assessment Approach

Given the factors considered, the 2009 NOx/SOx REA included a qualitative assessment 

of mercury methylation effects, focusing on Little Rock Lake in Wisconsin. It noted that 

decreases in SO4

2- deposition were linked to observed decreases in MeHg fish tissue 

concentrations in the lake.

The 2009 NOx/SOx REA also discussed qualitatively the provisioning and cultural 

services potentially impacted by mercury methylation. The REA referenced commercial and 

sport fishing, shell fishing, fishing for subsistence, and the cultural and spiritual significance 

derived from fishing and consuming local fish or shellfish (particularly for Native Americans 

and Alaska Native villagers).

2.4.5.2 Key Uncertainties/Limitations

Overall, the REA noted that decreases in SO4

2- deposition will likely result in decreases 

in MeHg concentrations, but that the rate of methylation varies spatially and with 

biogeochemical factors so that the correlation of SO4

2- deposition and MeHg could not be 

quantified for the purpose of interpolating the association across waterbodies or regions (see 

2009 NOx/SOx REA, section 6.2.2.2). This limitation hindered the ability to establish large scale 

dose-response relationships. 
3-1

3 CONSIDERATION OF NEWLY AVAILABLE 

INFORMATION

In this chapter, we consider the extent to which our characterization of risk and exposure

in the last review remains appropriately informative to the key questions in the current review, as 

summarized in the IRP. As noted in that document, the ISA, REA (if warranted) and PA 

developed in this review will provide the basis for addressing the key policy-relevant questions 

and will inform the Administrator’s judgment as to the adequacy of the secondary NAAQS for 

oxides of nitrogen, oxides of sulfur, and PM. Accordingly, this chapter considers the extent to 

which any new assessment of risk and exposure is warranted to inform decisions regarding 

potential adverse effects to public welfare. Based on these considerations, as described below, 

we plan to develop a new REA to inform the current review.

3.1 KEY CONSIDERATIONS

In any NAAQS review, considerations contributing to a decision to conduct a new risk 

and exposure assessment include the role played by the risk and exposure information in the 

EPA’s previous decisions on the existing standards and the role of risk and exposure information 

expected in the current review. Another important consideration is the robustness of the risk and 

exposure estimates for the existing standards that are available from the last review. In reaching a 

decision on conducting a new REA, we also consider the extent to which results of a new 

quantitative risk and exposure assessment are expected to appreciably change our understanding 

of risk and exposures beyond the insights gained from the assessments conducted for the last 

review. More specifically, we consider questions such as the following, which are summarized in 

Figure 3-1:

• Is appropriate scientific and technical information available to support quantitative 

assessments? 

• Is the scientific and/or technical information that could inform updated quantitative 

assessments substantially different from that available in the previous reviews?

• Would the new information appreciably reduce the uncertainties and limitations identified 

in previous reviews?

• Would updated quantitative assessments likely inform decision making in the current 

review by adding substantially to our understanding of pollutant exposures or pollutantattributable risks, beyond the insights gained from previous reviews and assessments?

Regarding these questions, section 3.2 below considers the newly available air quality 

information with a focus on current air quality concentrations and deposition and whether those 

levels across the U.S. have changed significantly since the last review. Additional consideration 
3-2

is also given to new tools, data and information that could inform improvements in the 

quantification of the relationship between ambient air quality concentrations and deposition, a 

key uncertainty in the previous review. Sections 3.3 through 3.7 then consider the scientific 

evidence and data on ecological effects available in the second draft ISA that play critical roles 

in our characterization of risk and exposure. Particular attention is given to consideration of new 

information pertaining to those aspects, and the extent to which it might be expected to address 

key areas of uncertainty identified in the last review. Based on these considerations, section 3.9

then provides preliminary conclusions on the extent to which updated quantitative analyses of 

ecological risk and exposure are warranted in the current review. This document then presents 

our proposed plan for performing such analyses in Chapter 4.

Figure 3-1. Key considerations for updated or new quantitative analyses

3.2 AIR QUALITY INFORMATION

3.2.1 Emissions, Atmospheric Concentrations and Deposition

Sulfur oxides are emitted into the air from specific sources (e.g., fuel combustion 

processes) and are also formed in the atmosphere from other atmospheric compounds (e.g., as an 

oxidation product of reduced sulfur compounds, such as sulfides). Sulfur oxides are also 

transformed in the atmosphere to particulate sulfur compounds, such as SO4

2-

. The most 

prevalent sulfur oxide in the atmosphere is SO2. Emissions of SO2 in the U.S. are largely due to 

coal-fired power plants and diesel fuel combustion (second draft ISA, Appendix 2, section 2.2.1).
3-3

Oxidized nitrogen species considered here range from NO and NO2, collectively referred 

to as NOx, to higher order organic and inorganic oxidation products, collectively referred to as 

NOz (e.g., pNO3, HNO3, HONO, PAN, other organic nitrates). The largest sources of NOx

emissions are related to combustion sources, which includes power plants, industrial facilities, 

vehicles, and wood burning stoves. Non-controllable sources of NOx include wildfires, biological 

soil processes, and lightning (second draft ISA, Appendix 2, section 2.2.1).

Reduced nitrogen species (i.e., NH3 + NH4

+ = NHX) are considered in this review. While, 

NH3 is not a criteria pollutant, it is considered to the extent that it contributes to atmospheric 

transformations and loading to ecosystems. NH3 is a precursor for atmospheric PM, reacting with 

gas phase nitric acid (HNO3) to form ammonium nitrate (NH4NO3), a major contributor to N

deposition. Livestock and fertilized fields are the largest sources of NH3, but there are

combustion related sources as well. Particles containing SO4

2-

, NO3

-

, and NH4

+ are also directly 

emitted into the atmosphere from sources such as wind-blown dust or sea-salt spray, but in total, 

direct particulate emissions are a small contribution to total emissions of N and S containing 

compounds (second draft ISA, Appendix 2, section 2.2.1). 

The geographic distributions of NOx and SOx emissions reported in the National 

Emissions Inventory (NEI) reflect the fact that transportation and power generation source 

sectors dominate NOx and SOx emissions, respectively. In the last review, the density of 

emissions sources of NOx and SOx was highest in the eastern U.S., and around population centers 

and transportation corridors in both the east and west. This remains true for this review, though 

these areas of the country have also experienced some of the largest reductions in emissions of 

NOx and SOx (second draft ISA, Appendix 2, section 2.2.1). National average SO2 emissions are 

estimated to have declined by 81% and NOx emissions have declined by 53% over the period 

from 2002 to 2016. Such declines in emissions are likely related to the implementation of 

national control programs developed under the Clean Air Act Amendments of 1990, including 

Phase I and II of the Acid Rain Program, the Clean Air Interstate Rule, the Cross-State Air 

Pollution Rule, and the Mercury Air Toxic Standards, as well as changes in market conditions, 

e.g., reduction in energy generation by coal (U.S. EIA, 2018).

The largest sources of NH3 emissions are agricultural: livestock, including confined 

animal feeding operations, and soils after addition of N containing fertilizers. Motor vehicles can 

also be a substantial contributor to total NH3 emissions in urban areas. In contrast to SOx and 

NOx, Xing et al. (2013) estimated that the national emissions of NH3 increased between 1990 to 
3-4

2010. Because of this, in many places in the U.S., reduced forms of N are now the largest 

contributor to N deposition26 (second draft ISA, Appendix 2, section 2.6.2).

In the atmosphere, NOx, SO2, and NH3 undergo chemical transformations and the 

resulting compounds are removed from the atmosphere by dry deposition and wet deposition at 

different rates. For example, NO2 can oxidize to form nitric acid (HNO3), which dry deposits 

rapidly. However, in the presence of NH3, particulate NH4NO3, which deposits via dry 

deposition slower than both HNO3 and NH3. Wet deposition, the scavenging of gases and 

particles by cloud droplets and precipitation, is the most important removal process for NH4NO3. 

The deposition rates of oxidized sulfur also depend on the chemical form; particulate sulfate does 

not dry deposit as rapidly as freshly emitted SO2. Since the chemical form is important to 

determining the rate of dry and wet deposition, as well as the relationship between air 

concentrations and deposition, we use process-based models and quality-assured ambient 

measurements to understand the transformation from emissions to concentrations to deposition. 

Both measurements, models, and techniques to develop merged model-measurement datasets

have been advanced since the previous review (see second draft ISA, Appendix 2, sections 2.5 

and 2.6).

Several monitoring networks measure the atmospheric concentrations and wet deposition 

of NOx, SOx, and PM. The most relevant routinely operating networks measuring ambient air 

concentrations include the Interagency Monitoring of Protected Visual Environments 

(IMPROVE), the National Core network (NCore) and EPA Chemical Speciation Networks 

(CSN), Clean Air Status and Trends Network (CASTNet), and the ammonia monitoring network 

(AMoN). The National Trends Network (NTN) is part of the National Atmospheric Deposition 

Program and measures wet deposition of NO3

-

, SO4

2-

, NH4

+

, and other ions. Several new 

monitoring efforts are underway since the previous review. Gas phase NH3 has expanded and as 

of May 2018, is measured at 98 sites as part of the AMoN network. The NCore network includes 

measurements of NO, NOy, SO2 and PM chemical composition at 63 urban and 17 rural sites. 

The near-road network includes NO2 measurements at 80 monitoring sites located near 

roadways. Of these monitoring networks, the co-located CASTNet, AMoN and NTN sites are 

of particular interest because they measure both air concentrations and deposition of N and S 

containing compounds.

However, some limitations remain. Particulate NH4

+ measurements from CASTNet and 

IMPROVE networks are thought to have biases that depend on meteorological conditions and 

require careful consideration when used for analysis. While methods for directly measuring dry 

 26 A portion of the reduced N deposition is from NH4

+ bound in PM. The NH4+ fraction changes according to the 

relative levels of sulfate and NO3

- available to form particles. 
3-5

deposition in routine networks have been deployed in Europe (Skiba et al., 2009), direct 

measurements of dry deposition in the U.S. are sparse and infrequent. The spatial coverage of the 

monitoring networks is representative of some, but not all, of the variability in ecosystems and 

concentrations nationally. To help address the limitations in the available observations of air 

concentration and deposition, a combination of observations and computational models is often 

employed (see second draft ISA, section 2.6). 

The National Atmospheric Deposition Program’s total deposition science committee has 

been advancing the state of the science to create datasets that estimate annual average deposition. 

The Total deposition (TDEP) dataset combines measurements and models to estimate annual 

average deposition for years 2002 – 2016 (Schwede and Lear, 2014). Spatial coverage includes 

the continental U.S. at 4-km horizontal resolution. Wet deposition is estimated by spatial 

interpolation of NTN measurements and dry deposition is calculated by the Community 

Multiscale Air Quality (CMAQ) model. CMAQ is a chemical transport model that simulates the 

fate and transport of gases and particles by using physically-based, numerical models and 

explicitly including the processes of advection, dispersion, chemistry, aerosol physics, cloud 

processes, dry deposition, and wet deposition. The use of modeled estimates is necessary to 

account for dry deposition, which is not measured on a broad enough scale to inform national 

applications. Using the TDEP dataset to analyze ecosystem effects could potentially reduce 

uncertainties associated with the use of deposition values estimated by CMAQ alone, as was 

done in the last review. In addition, we note the representation of chemical and physical 

processes in CMAQ has been further improved since the last review, with updates that include 

gas-phase chemistry relevant to the oxidation of NOx, dry deposition of particles by gravitational 

settling, interactions between meteorology and particles, and the influence of temperature, wind, 

and precipitation on NH3 emissions (see second draft ISA, Appendix 2, section 2.5).

To further assess how deposition has been estimated to have changed since the last 

review, Figures 3-2 and 3-3 are included to illustrate TDEP estimates of S deposition across the 

U.S. for the years of 2002 and 2015, respectively, with Figure 3-4 showing the estimated 

changes in S deposition between these two years. Similarly, the TDEP estimates of N deposition 

for 2002 and 2015 are shown in Figures 3-5 and 3-6, with changes between these two years 

shown in Figure 3-7. These figures mimic the same general patterns, discussed above, for the 

changes in emissions since the last review. Generally, substantial changes in S deposition has 

occurred across most of the U.S., with the largest decreases occurring in the eastern U.S. 

Similarly, N deposition has also generally decreased across the entire U.S., with increases in 

areas of the country generally dominated by agricultural sources, due to an increase in reduced 

nitrogen deposition in these areas.
3-6

Given that emissions, concentrations and deposition levels of N and S species have 

changed substantially across the U.S. since the last review and given the availability of new and 

improved tools and methods for estimating this deposition (e.g., TDEP), we judge that new 

analyses are supported for this review. Assessment of the most recently available emissions, 

concentration and deposition datasets are likely to influence important differences in any new or 

updated quantitative assessments and inform our understanding of changes in pollutant 

exposures and pollutant-attributable risks since the last review. In addition, application of the 

improved tools and methods will likely reduce uncertainties and limitations in the 

characterization of air concentrations and deposition from the last review.

Figure 3-2. Annual total deposition of sulfur, kg S ha-1 in 2002.
3-7

Figure 3-3. Annual total deposition of sulfur, kg S ha-1 in 2015.

Figure 3-4. Change in annual total sulfur deposition, 2015 – 2002 (kg S ha-1)
3-8

Figure 3-5. Annual total deposition of nitrogen, kg N ha-1 in 2002.

Figure 3-6. Annual total deposition of nitrogen, kg N ha-1 in 2015.
3-9

Figure 3-7. Change in annual total nitrogen deposition, 2015 – 2002 (kg N ha-1)

3.2.2 Linking Atmospheric Concentration Changes to Changes in Deposition

The new modeling approaches, additional years of measurements, and combined modelmeasurement TDEP estimates described in the previous section are also relevant for linking 

atmospheric concentration changes to changes in deposition. In the last review, the “transference 

ratio” approach was used to estimate the relationship between air quality concentrations and 

deposition of N and S. The uncertainties relevant to this transference ratio approach are 

summarized in Chapter 2. Also, since the last review, several studies have examined the 

transference ratio using measurements and modeling, as described in more detail in the second 

draft ISA, Appendix section 2.5.2.4. Specific areas of concern are the drivers of the spatial 

variability of the computed transference ratio, differences in the transference ratio when 

calculated by different chemical-transport models, and errors associated with using the air 

concentration of NOy as a predictor of nitrogen deposition. These recent findings and 

uncertainties, listed in Chapter 2, will be used to inform the updated approach to relating changes 

in ambient concentrations to changes in atmospheric deposition, which is described in Chapter 4. 

Briefly, the updated approach analyzes 20-years of concentration and deposition measurements

and CMAQ model results, includes consideration of uncertainty and variability using multiple 

chemical-transport models, and carefully evaluates how the concentration/deposition relationship

may be different near sources versus in remote areas. 
3-10

3.3 DIRECT EFFECTS

New scientific evidence supports the causality determinations made in the 2008 

NOx/SOx ISA regarding the gas-phase effects of NO, NO2, PAN, SO2. and HNO3 on vegetation; 

there are no new causal determinations.27

At the time of the 2008 NOx/SOx ISA, it was known that NO, NO2, and PAN can have 

phytotoxic effects on plants by decreasing photosynthesis and inducing visible foliar injury. 

Since that time, very little new research has been performed on these phytotoxic effects at 

concentrations currently observed in the U.S. (see second draft ISA, Appendix section 3.6.2). 

Additionally, according to the second draft ISA, although PAN continues to persist as an 

important component of photochemical pollutant episodes, there is little evidence in recent years 

to suggest that PAN poses a significant risk to vegetation in the U.S. (see second draft ISA, 

Appendix section 3.3)

At the time of the 2008 NOx/SOx ISA, it was known that gaseous SO2 caused foliar 

injury as well as decreased photosynthesis, growth, and yield in plants. Additionally, it was 

known that SO2 caused mortality in lichens. Due to declines in SO2 emissions, few additional 

studies evaluating direct gaseous effects of SO2 have been conducted in the U.S, and these 

studies generally focused on recovery (see second draft ISA, Appendix section 3.2). 

Additionally, per the second draft ISA, there is no clear evidence of acute foliar injury below the 

level of the current SO2 standard (see second draft ISA, Appendix section 3.6.1).

The 2008 NOx/SOx ISA reported that experimental exposure of HNO3 resulted in 

damage to the leaf cuticle of pine and oak seedlings, which could predispose those plants to other 

stressors such as drought, pathogens, and other air pollutants. Since the 2008 ISA, one study 

(Padgett et al., 2009) investigated dry deposition of HNO3 on the foliage, with findings that 

supported the earlier research. The 2008 ISA also reported several lines of evidence in lichen 

studies, including transplant and controlled exposure studies, indicating that HNO3

concentrations contributed to the decline in lichen species in the Los Angeles basin. Since that 

time, several new studies have been published that continue to support this evidence (see second 

draft ISA, Appendix section 3.4). 

Limited new evidence regarding direct effects of NO, NO2, PAN, and SO2 has become 

available since the 2008 NOx/SOx ISA. Additionally, there are no data and/or tools available to 

support a quantitative assessment of direct gaseous effects. Hence, we do not intend to conduct a 

quantitative assessment for these effects in the REA.

 27 The causality statements in the second draft ISA relate to gas phase SO2, NO, NO2, PAN, and HNO3 and injury to 

vegetation. 
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3.4 NON-NITROGEN AND NON-SULFUR EFFECTS OF PM

Since publication of the 2009 PM ISA, there has been some new evidence to support the 

understanding of ecological effects associated with PM components, particularly for metals and 

organics. The causality determination from the last review remains unchanged, finding that there 

is a likely causal relationship between PM deposition and a variety of effects on individual 

organisms and ecosystems. While the new studies do provide some additional evidence for 

community-level responses to PM deposition, the second draft ISA notes that uncertainties 

remain due to the difficulty in quantifying relationships between ambient concentrations of PM 

and ecosystem response (see second draft ISA, Chapter 1, section 1.10). Additionally, there are 

currently no data and/or tools available for evaluating these effects. Hence, we do not intend to 

conduct a quantitative assessment for these effects in the REA.

3.5 FRESHWATER ACIDIFICATION AND NITROGEN ENRICHMENT 

Since the 2008 NOx/SOx ISA, new studies have been published on the effects of N and S 

deposition on freshwater ecosystems. This evidence includes new CLs for freshwater ecosystems 

and has led to the development of expanded causality determinations in the second draft ISA 

from the last review. These CLs provide national coverage and could significantly expand the 

scope for analyses since the last review, and for N enrichment, to fill a major gap from the last 

review. The sections that follow provide more details about the new scientific and technical 

information pertaining to freshwater acidification and N enrichment that are available for 

consideration in the REA. 

3.5.1 Freshwater Acidification

While the causality determination for biogeochemistry changes remains largely 

unchanged,28 the causality determination for changes in biota have been expanded in the second 

draft ISA, having found that the body of evidence is sufficient to infer a causal relationship 

between acidifying atmospheric deposition and changes in biota, including physiological 

impairment and alteration of species richness, community composition, and biodiversity in 

freshwater ecosystems (rivers, lakes, and streams).

At the time of the 2008 NOx/SOx ISA, it was known that atmospheric N deposition can 

alter the pools and fluxes of the C and N cycles, causing nitrification and denitrification and 

NO3

- leaching to surface waters, increasing acidity. Atmospheric deposition of S directly adds 

SO4

2- to soil leachate and surface waters, increasing acidity. The processes of acidification and 

 28 See Table 1-1 of the second draft ISA for a side-by-side comparison of causality statements from the last review 

and this review. 
3-12

the chemical indicators that describe it were well characterized at the time of the 2008 NOx/SOx

ISA, and newer studies have only further described and quantified some of these relationships

(second draft ISA, Appendix section 7.1.7). Effects of N and/or S deposition on changes in biota 

are linked to chemical indicators in surface water, which include water pH, ANC, and the 

concentrations of SO4

2-

, dissolved inorganic aluminum (Al), and base cations. The strongest 

evidence for a causal relationship between acidifying deposition and aquatic biogeochemistry 

comes from studies of changes in surface water chemistry (second draft ISA, Chapter 1, section

1.6.1). Consistent evidence from multiple studies spanning several decades shows that in 

acidified waters, changes in surface water chemistry can cause the loss of acid-sensitive 

biological species. Biological effects are primarily attributable to low pH and elevated 

concentrations of inorganic Al (see second draft ISA, Appendix section 8.1). ANC integrates 

chemical components of acidification, and surface water acidification models calculate ANC as a 

proxy that relates to pH and inorganic Al concentrations. However, ANC does not directly alter 

the health of biota (see second draft ISA, Chapter 1, section 1.6.3).

Since the last review, there is new evidence for chemical recovery from acidification in 

freshwater ecosystems (see second draft ISA, Appendix section 7.1.5.1), but evidence for 

biological recovery has been more limited. Biological recovery in a freshwater ecosystem can 

occur only if chemical recovery is sufficient to allow reproduction, growth, and survival of acidsensitive plants and animals to occur (see second draft ISA, Appendix 8, Section 8.4). Also, 

chemical recovery of ANC or pH may not necessarily lead to recovery of the ecosystem to its 

previous condition before acidification, due to changes in relationships among ANC, pH, DOC, 

and Al; depletion of soil base cation pools; and/or partially reversible (or irreversible) S 

adsorption on soils. In the 2008 NOx/SOx ISA, studies of biological recovery generally indicated 

that the time required for full biological recovery is uncertain and that responses in biota lag 

behind chemical recovery and may take decades from the onset of chemical recovery. New 

studies documented in the second draft ISA of multiple trophic levels continue to support these 

findings. 

As mentioned earlier in Chapter 2, the 2009 NOx/SOx REA related ANC (as an indicator 

that relates well to pH) to fish species richness and species-level responses to acidification in 

lakes and streams in two case study areas, and calculated CLs for those areas. Since that time, 

new CLs using ANC as a chemical indicator for freshwater acidification have become available 

in the peer reviewed literature. Some of these new data are based on studies that used SMB 

models to calculate CLs (see second draft ISA, Appendix section 8.5.4). McDonnell et al. 

(2014), for example, developed a regional dataset that provides CLs for continuous coverage of 

streams for the southern Appalachian Mountains region from Georgia to southern Pennsylvania, 

and from eastern Kentucky and Tennessee to central Virginia and western North Carolina. In 
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addition, Shaw et al. (2014) developed steady-state CLs for lakes in the Sierra Nevada Mountain, 

where N deposition is the stronger driver of acidification. New CLs have also been determined 

through the use of biogeochemical watershed models such as MAGIC and the photosynthesis 

evapotranspiration biogeochemical (Pnet-BGC) model (e.g., second draft ISA, Appendix section 

8.5.4; Lawrence et al., 2015; Fakhraei et al., 2014; Fakhraei et al., 2016) for the Adirondacks 

Mountains, Appalachian Mountains, and for the Great Smoky Mountains National Park. When 

these CLs, as well others in the National Critical Loads Database of Sulfur and Nitrogen (NCLD 

v3.0)29, are considered together, they approximate broad, national coverage since sites are 

represented across the contiguous United States (CONUS).

In addition to new CLs, new information has become available through the National 

Aquatic Resource Surveys (NARS) program.

30 Through the National Rivers and Streams 

Assessment (NRSA), 1.2 million miles of rivers and streams were assessed for their ecological 

condition and through the National Lakes Assessment (NLA), more than 1,000 lakes, ponds, and 

reservoirs (representing nearly 50,000 water bodies) were sampled for their water quality and 

biological and habitat conditions using comparable field and laboratory protocols. Although the 

surveys were not specifically focused on acid-base chemistry, ANC and pH were among the 

chosen chemical indicators used to assess biological integrity. However, the NLA and NRSA

were designed to be representative of all lakes and streams, so many of the lakes and streams that 

were sampled are not sensitive to acidification. 

The NLA and NRSA datasets are robust and comprehensive. There is potential to use 

these datasets to evaluate risk of acidification to lakes and streams from exposure to deposition 

from oxides of nitrogen, oxides of sulfur and particulate matter. However, air quality metrics 

were not collected as part of the NARS program which limits their use in NAAQS 

applications. Data from the 2007 and 2012 NLA have been incorporated into a database of lake 

nutrient chemistry data for the western United States (Williams and Labou, 2017). These data 

 29 Since the last review, the National Atmospheric Deposition Program (NADP) Executive Committee formed the 

CLs of Atmospheric Deposition Science Committee (CLAD) in April 2010. The CLAD has developed the 

NCLD, which is a compilation of CLs data and supporting information from many regional- and national-scale 

projects within the US. The focus of this database is on CL of N and S deposition and the effects on terrestrial and 

aquatic environments. The database is updated through periodic “calls for data” and corrections. The most recent 

version of the database, version 3.0, was released in October 2017. We note that studies in the NCLDv.3 are 

included in the second draft ISA.

30 This program is designed to assess the status of and changes in quality of the nation’s coastal waters, lakes and 

reservoirs, rivers and streams, and wetlands. These are statistical surveys first conducted in 2007 and the data 

were not available at the time of the last review. The NARS are made up of four individual surveys that are 

implemented on a rotating basis; National Lake Assessment (NLA), National River and Stream Assessment 

(NRSA), National Coastal Condition Assessment (NCCA), and National Wetland Condition Assessment 

(NWCA). The NLA and NRSA are discussed in this section while the NWCA and NCCA are discussed in 

Section 3.7.
3-14

were used by Williams et al. (2017) along with data from the 1985 EPA Western Lake Survey, a 

precursor to the NLA used by Nanus et al. (2017) to develop the CLs discussed below. However, 

no studies are currently available linking the NLA and NRSA (nor the NWCA and NCCA) to 

atmospheric deposition to develop CLs or exposure-response functions. Further analysis of the

NARS data for purposes of this NAAQS review would require regression-based approaches, 

linking ambient air quality data to the biological or chemical metrics in the data to develop new 

CLs or exposure-response functions. Such new, deposition-based analyses are an area of interest 

for future research, but fall outside the scope of this REA.

Given the newly available CLs related to changes in pH and ANC, as well as the

important changes in N and S deposition since the last review, we judge that an updated analysis 

of freshwater acidification would inform our understanding of how the associated risks have 

potentially changed since the last review. In addition, the availability of new datasets and 

improvements in assessment tools will likely reduce some of the uncertainties and limitations 

identified in our analysis of freshwater acidification in the last review.

3.5.2 Freshwater Nitrogen Enrichment 

Nitrogen enrichment from atmospheric deposition of N to freshwater ecosystems leads to 

increased productivity of algae and aquatic plants, altered nutrient ratios, and sometimes 

decreased oxygen levels (see second draft ISA, Chapter 1, section 1.6). The previous review 

found the relationship to be causal for changes in biogeochemistry and it remains largely 

unchanged for this review.31 However, based on new scientific evidence since the last review, 

the causality determination for changes in biota, which included species richness, community 

composition, and biodiversity, has been expanded to include altered growth as an endpoint. 

As discussed in Chapter 2, the 2009 NOx/SOx REA did not include a quantitative 

assessment of freshwater N enrichment effects due to lack of data. For this review, several of the 

new studies available include CLs related to biogeochemical changes as well as altered growth, 

consistent with the expansion of the causality determination in the second draft ISA. Baron et al. 

(2011) assessed lakes in the Sierra Nevada Mountains, Rocky Mountains, and the northeastern 

U.S. CL values were estimated by comparing total N deposition (estimated from a combination 

of the Parameter-elevation Regression on Independent Slopes Model (PRISM) and NADP data 

for wet deposition and CMAQ modelled dry deposition) to NO3

- concentrations in the 3 regions. 

The CL was estimated as the point within the data where NO3

- concentrations begin to increase, 

indicating that deposition is exceeding the N uptake of the watershed and leading to N 

enrichment in the lake. In this study, NO3

- concentrations were not related to a specific biological 

 31 See Table 1-1 of the second draft ISA for a side-by-side comparison of causality statements from the last review 

and this review. 
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endpoint, but N enrichment is connected to a wide range of ecological effects (see second draft 

ISA, Chapter 1, section 1.6). This analysis resulted in non-lake specific CL estimates of 2, 3 and 

6 kg N/ha/yr for the Sierra Nevada’s, Rocky Mountains and northeast, respectively.

Two additional studies used regression models to link N deposition with changes in water 

chemistry that can then provide some insight into potential ecological effects. Williams et al. 

(2017) assessed lakes in the Georeferenced Lake Nutrient Chemistry (GLNC) database

(Williams and Labou, 2017). The Lakes included in the study were located at elevations above 

1200 meters and on federally protected lands (National Park Service (NPS) and U.S. Forest 

Service (USFS). The CL was estimated to be a point at which there was a fundamental change in 

the lake biogeochemical cycle and used a logistic regression approach to relate total N deposition 

to the NO3

- concentration above which there is > 50% chance of the phytoplankton growth in the 

lake having shifted from N to P nutrient limitation. This resulted in a CL value of 4.1 kg N/ha/yr 

(with a range of 2.8 – 5.2 kg N/ha/yr), and a lower estimate of 2 kg N/ha/yr to protect against 

false positive (lakes where deposition does not exceed the CL, but lake chemistry data indicate 

there has been a shift to P limitation).

Nanus et al. (2017) determined CLs for lakes in the greater Yellowstone area (including 2 

National Parks - Yellowstone and Grand Teton) which refined CLs estimates from earlier work 

by Nanus et al. (2012) for the broader Rocky Mountains area. CLs were estimated using a multilinear regression approach to predict surface water NO3- concentrations, using basin 

characteristics and TN deposition. CLs ranged from <1.5 + 1.0 kg N/ha/yr to >4 + 1.0 kg 

N/ha/yr. These values were based on a NO3- threshold of 1.0 μmol/L based on growth 

characteristics of indicator diatom species, which are indicative of shifts from oligotrophic to 

mesotrophic or eutrophic conditions (Saros et al., 2005). CLs for NO3- thresholds of 0.5 μmol/L, 

1.6 μmol/L and 2.0 μmol/L were also reported. 

The availability of new CLs in this review for freshwater N enrichment potentially fills a 

data gap from the last review and informs an assessment of how changes in deposition could 

impact changes in water chemistry. We also note that the availability of the new data through the 

NARS program could possibly be used in this assessment to identify, and prioritize for further 

assessment, impacted lakes and streams. At this time, however, we are unsure whether a new 

quantitative assessment of freshwater N enrichment will be included in the REA since there is 

uncertainty as to whether it would provide additional insight into potential ecological effects, be 

broadly applicable in their application, and/or improve our understanding of pollutantattributable risks since the last review, and as such, substantively inform our ability to assess 

national standards for freshwater N enrichment.
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3.6 TERRESTRIAL ACIDIFICATION AND NITROGEN ENRICHMENT

In terrestrial ecosystems, the acidifying effects from N and S, and the enrichment effects 

from N can occur simultaneously. However, the strength of one process over the other (i.e., 

acidification versus N enrichment) can vary depending upon several factors, including soil pH 

and buffering capacity, the presence and abundance of sensitive species, and the degree of 

nitrogen limitation on primary production. 

Since the 2008 NOx/SOx ISA, more recent research has confirmed and strengthened our 

understanding of terrestrial acidification (see second draft ISA, Appendix section 5.7). While the 

causal determinations for changes in biogeochemistry have generally remained unchanged for 

the second draft ISA, the causal determinations for changes in biota has been expanded for this 

review to include alteration of species richness, community composition, and biodiversity as 

endpoints. Additionally, the second draft ISA includes a new causality determination, finding 

that there is a causal relationship between atmospheric N and S deposition and the alteration of 

the physiology and growth of terrestrial organisms, as well as the productivity of terrestrial 

ecosystems. 

Similar to acidification, the causality determination for biogeochemical changes from N 

enrichment has remained largely unchanged since the last review.32 However, the largest 

increase in scientific evidence, over any other effect category, is for terrestrial N enrichment 

ecological effects (see second draft ISA, Chapter 1, section 1.2.3). This new evidence has 

confirmed and strengthened our understanding of the mechanistic links that inform causal 

determinations between atmospheric N deposition, biogeochemistry, and biota in terrestrial 

ecosystems. The second draft ISA includes a new causal determination for N enrichment, having 

determined that there is a causal relationship between atmospheric N deposition and the 

alteration of the physiology and growth of terrestrial organisms and the productivity of 

terrestrial ecosystems. The new data also improves our ability to quantify dose response 

relationships between N deposition and ecological response. New studies include investigations 

of plant and microbial physiology, long-term ecosystem-scale N addition experiments, regional 

and continental-scale monitoring studies, and syntheses. Additionally, since the last review, new 

research techniques have been developed to understand community composition, a larger number 

of communities have been surveyed, and new regional and continental-scale studies been 

conducted (see second draft ISA, Chapter 1, section 1.2.3). 

The sections that follow provide more details about the new scientific and technical 

information available for consideration in the REA.

 32 See Table 1-1 of the second draft ISA for a side-by-side comparison of causality statements from the last review 

and this review.
3-17

3.6.1 Forests

New studies since the 2008 NOx/SOx ISA indicate that terrestrial acidification is an 

ongoing and widespread issue in areas of elevated acidic deposition. For example, new studies 

have observed the ongoing depletion of exchangeable base cations in forest soils of the 

northeastern U.S. (see second draft ISA, Appendix section 4.3.4). There is considerable evidence 

that N and/or S-driven acidification in forests likely contributes to the mobilization of toxic 

forms of Al and decreases the availability of nutrients to plants due to leaching of base cations 

from soil and interference with uptake (see second draft ISA, section 5.2.1). These soil changes

can affect tree regeneration (e.g., Sullivan et al., 2013 sugar maple study; Lieb et al., 2011). 

Since the 2008 NOx/SOx ISA, there is new evidence of species-specific effects of 

atmospheric N deposition on tree growth and mortality in the U.S. (e.g., Thomas et al., 2010; 

Dietze and Moorcroft et al., 2011); but it is uncertain whether changes in growth or mortality are 

driven by N-enrichment or terrestrial acidification effects. Additionally, Simkin et. al (2016), 

suggests that species richness of forest understory plant communities generally has unimodal 

relationships with atmospheric N deposition. This unimodal relationship was found to be pHdependent, with Simkin et al. (2016) reporting that plant species richness was more likely to 

decline with increasing atmospheric N deposition if the herb community occurred on more acidic 

soils. 

The 2009 NOx/SOx REA calculated CLs for soil acidification in two forest ecosystems 

using the SMB model and estimates of base cation weathering from the clay substrate method. 

Since that document was completed, new CLs have become available from studies that used the 

SMB model with the clay substrate method for estimating base cation weathering (BCw) rates, 

and relied on a Bc/Al ratio of either 1 or 10, based on the precedent for use of these numbers by 

the scientific community.

33 (For a summary of these studies, see second draft ISA, Appendix 

section 5.5.3).

Most new studies explicitly examining acidification from N and S since the last review 

have built upon earlier approaches that modeled soil acidification CLs using one of several 

different models (i.e., SMB, STA, MAGIC, and ForSAFE-VEG)34,which are summarized in the 

second draft of the ISA (see second draft ISA, Appendix sections 4.5 and 4.6). In addition, there 

are several studies (whose CLs are included in the NCLD v3.0) that have evaluated the use of 

other models besides the clay substrate method for purposes of estimating BCw rates. For 

 33 Bc/Al ratios of 1 and 10 have been used as average numbers to represent different forest types in different parts of 

the U.S. (e.g., Eastern versus Western, or coniferous versus deciduous forests).

34 For a summary of these models and related considerations, see second draft ISA, Appendix section 4.5.
3-18

example, because of the known limitations of the clay substrate method,

35 Phelan et al. (2014) 

evaluated the use of the dynamic model, PROFILE, for estimating BCw. Phelan et al. (2014) 

paired PROFILE with national datasets36 as a method to estimate BCw rates for forests in the 

U.S., applying it to 51 forested sites across Pennsylvania. 

In the 2008 NOx/SOx ISA, there was consistent evidence that N additions stimulated 

forest productivity. However, responses included both neutral and negative effects of N additions 

on tree growth (second draft ISA, Appendix section 6.3.2.1). Since the 2008 NOx/SOx ISA, 

there is considerable new evidence from deposition gradient studies, forest modeling, and longterm N addition experiments that atmospheric N deposition broadly stimulates tree growth and 

the productivity of forested ecosystems (see second draft ISA, Appendix section 6.3.2.1); 

however, the effects can vary by species. For example, studies have shown that conifer species, 

particularly at high elevations, are more likely to exhibit negative growth responses or mortality 

in response to added N (second draft ISA, Appendix section 6.2.3.1). One particular study, 

Thomas et al (2010) used exposure-response functions to relate N deposition to growth and 

mortality impacts on 23 individual tree species in the Northeast and mid-Atlantic.37 Leveraging 

forest inventory data from the early 1980s through the mid-1990s, Thomas et al. (2010) reported 

declines in growth for three conifer species, consistent with other observational studies (see 

second draft ISA, section 6.2.3.1). The study also reported higher mortality rates for 8 tree 

species with increasing atmospheric N deposition while only three species showed a positive 

relationship. Overall, this analysis was consistent with other multi-variate analyses that explored 

the key drivers of tree mortality in the region (e.g., Dietze and Moorcroft, 2011).

3.6.2 Lichens and Mycorrhizal Fungi

Since the 2008 NOx/SOx ISA, there is new evidence of decreases in lichen species 

richness as the result of atmospheric N deposition in the U.S., and there are now ambient 

observations that atmospheric N deposition in the U.S. is changing mycorrhizal community 

composition. (see second draft ISA, Appendix section 6.6.2). 

As mentioned in Chapter 2 of this document, the 2009 NOx/SOx REA included a 

qualitative assessment of N enrichment effects through a case study for coastal sage scrub and 

mixed conifer forests. No quantitative assessment was conducted due to lack of data. Since the 

 35 See section 2.5 for a summary of these limitations. 

36 See Table 2 in Phelan et. al., 2014, for the list of national datasets.

37 We anticipate that this study will be expanded to include 94 species nationally, relating growth and mortality 

impacts to N and S deposition (Horn et al., 2018 in review).
3-19

last review, an extensive amount of new data with national coverage is available for terrestrial N 

enrichment CLs for lichens and mycorrhizal fungi.

38

New lichen CLs and response curves are available through two studies conducted in the 

Pacific Northwest, for the portions of the North American West Coast Marine Ecoregion in 

Oregon and Washington (Geiser et al., 2010) and for the portions of the Northwestern Forested 

Mountains Ecoregion in Northern California, Oregon, Washington, and parts of northern Idaho 

and Montana (Root et al., 2015). For the lichens dataset, researchers collected information on 

two responses - lichen community composition and lichen N concentrations - and related those 

responses to atmospheric concentrations of N (Root et al., 2015) and/or measures of atmospheric 

deposition (Geiser et al., 2010). The resulting equations constitute exposure-response curves and 

were used to derive CLs for lichen community composition and lichen N concentrations. These 

data have been extrapolated for application to the CONUS for the NCLDv3.0. 

The mycorrhizal fungi dataset is a collection of empirical CLs for total atmospheric N 

deposition from various studies included in Pardo et al. (2011). This dataset includes minimum 

and maximum CLs for community composition.

3.6.3 Herbs and Shrubs

A national CLs dataset is available for the species richness of herbs and shrubs based on 

the Simkin et al. (2016) study. As mentioned earlier, the Simkin et al. (2016) study analyzed 

relationships between plant species richness and atmospheric N deposition involving interactions 

with soil pH (along with precipitation and temperature). Therefore, the dataset relates to both N 

enrichment and acidification effects. The dataset covers nearly 4,000 herb/shrub species over 

more than 15,000 plots. This dataset includes a collection of total atmospheric N deposition CLs

from Simkin et al. (2016), with CLs calculated separately for “open canopy” (grasslands, shrub 

lands, and woodlands) and “closed canopy” (forested understory plants) ecosystems. 

The CLs in Simkin et al. (2016) were derived using regression analyses relating 

atmospheric N deposition to species richness. These underlying regression models are equations 

that constitute an exposure-response relationship (i.e., for both open and closed canopy systems, 

species richness is related to atmospheric N deposition, soil pH, temperature, and precipitation; 

see Table 1 in Simkin et al. (2016)). These equations can be used to evaluate potential changes in 

effects based on varying levels of atmospheric deposition.

 38 For descriptions of these data, see NADP CLAD report “2017 Summary of Critical Load Maps,” 

http://nadp.slh.wisc.edu/committees/clad/db/NCLDMapSummary_2016.pdf
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3.6.4 Summary

For this review, new or refined national CLs as well as new national exposure response 

curves are available for various terrestrial ecosystems and receptors, which will expand and 

enhance our ability to evaluate sensitivity and risk for acidification and N enrichment nationally 

for multiple species. This new information also represents many of the new endpoints that have 

contributed to the stronger weight of evidence and expanded causality determinations in the 

second draft ISA, enabling an evaluation of growth and mortality effects as well as species or 

community composition changes that was not available at the time of the last review. 

Additionally, several studies have produced refined CLs and/or refined methods for deriving 

CLs, which may reduce identified uncertainties from the last review. Given the extent to which 

new scientific and technical information are expected to fill the data gaps/limitations from the 

last review and add to our understanding of pollutant-driven risk and exposure, we judge that 

quantitative assessments evaluating terrestrial acidification and N enrichment effects will provide 

insight into potential ecological effects and improve our understanding of pollutant-attributable 

risks since the last review.

3.7 OTHER NITROGEN AND SULFUR EFFECTS 

3.7.1 Estuarine Nitrogen Enrichment

The causality determination for atmospheric N deposition and biogeochemical changes in 

estuarine and near coastal marine ecosystems remains unchanged, with new evidence continuing 

to support the findings of a causal relationship in the last review. However, since the last review, 

new paleontological studies, observational studies, and experiments have further characterized 

the effects of N on phytoplankton growth and community dynamics, macroinvertebrate response, 

and other indices of biodiversity. Hence, the causality determination pertaining to atmospheric N 

deposition and alteration of species richness, species composition and biodiversity has been 

expanded to include altered growth, total primary production, and total algal community 

biomass as endpoints. 

The 2009 NOx/SOx REA included a case study for the Potomac River and Potomac 

Estuary in the Chesapeake Bay and the Neuse River and Neuse River Estuary in Pamlico Sound, 

using ASSETS EI and the SPARROW model. The 2009 NOx/SOx REA noted uncertainties in 

the inputs and outputs of the SPARROW model, and noted the importance in choosing case 

study areas where atmospheric deposition plays a large role in N loading to an estuary. Since the 

last review, several modeling studies have estimated the amount and proportion of current and 

future N loading expected to result from atmospheric deposition (see second draft ISA, Table 7-

9). 
3-21

At the time of the last review, CLs were not available. Several new CLs pertaining to salt 

marsh community structure, microbial activity, biogeochemistry, and loss of eel grass are now 

available from Greaver et al. (2011) based on a review of three studies (Caffrey et al., 2007;

Wigand et al., 2003; and Latimer and Rego, 2010). There are three new studies with threshold 

values for eel grasses in estuaries now available (see second draft ISA, Appendix section 10.2.5, 

Table 10.4). These new studies are based on total N loading to the estuary. To assess 

exceedances of CLs (or threshold levels) related to atmospheric N deposition, watershed-level 

estimates of total N loading would need to be calculated from modelling or other watershed-level 

estimates.

While new studies are now available to determine which estuaries are predominantly 

affected by atmospheric deposition, we note that any modelling to evaluate impacts of N 

deposition on ecological effects in an estuary would need to be conducted on an estuary-byestuary basis. Additionally, the CLs are limited in geographic scope, with studies conducted 

primarily in New England, so would be of limited use in assessing national standards.

New data are also available through the NCCA which provide information on overall 

estuarine conditions, but as described in section 3.5.1, these conditions are not specifically 

related to atmospheric deposition, but rather to total nutrient loading. This, and the lack of 

representativeness of any individual estuarine modelling approach, limits the usefulness of 

conducting a quantitative assessment. Given these limitations, such information would be of 

limited usefulness in informing decisions on national-scale standards, therefore we do not intend 

to conduct quantitative assessments for this effect category.

3.7.2 Wetlands Nitrogen Enrichment

The causality determination for atmospheric N deposition and biogeochemical changes in 

wetland ecosystems remains unchanged, with new evidence continuing to support the findings in 

the last review. New research on wetland biogeochemistry since 2008, includes a synthesis of 

wetland improvements to water quality through denitrification and biological uptake, a metaanalysis of N addition effects on methane (CH4) and nitrous oxide fluxes, and multiple 

observations of changes in belowground C cycling in response to added N. These 

biogeochemical shifts may diminish the wetland ecosystem services of long-term carbon storage 

and flood protection, as well as reduce the stability and persistence of wetlands on the landscape. 

Nitrogen loading effects upon productivity are uneven across species, which may affect wetland 

biodiversity and the wetland ecosystem service of provisioning. 

New evidence published from observational studies, experimental N addition studies in 

the field and in mesocosms, and re-analysis of large data sets supports and extends the 

conclusions of the 2008 NOx/SOx ISA. Additionally, since the last review, there is newly 
3-22

published evidence of atmospheric N deposition effects on endpoints not covered in the 2008 

ISA, including alterations to plant physiology and plant architecture (see second draft ISA, 

Appendix section 11.5). Given this newly available information, the causality determination for 

atmospheric N deposition and alteration of species richness, community composition, and 

biodiversity has been expanded to include alteration of growth and productivity as well as 

species physiology as endpoints. 

The 2009 NOx/SOx REA did not include a quantitative assessment for wetlands due to 

lack of available data. Since the last review, new CLs have become available for ombrotrophic 

bogs and the purple pitcher plant (Sarracenia purpurea L.) (Greaver et al., 2011). The CLs in 

Greaver et al. (2011) were based on 6 studies (two studies of purple pitcher plant, and four 

studies related to net primary production and peat accumulation in ombrotrophic bogs). One 

additional study, Crumley et al. (2016), found threshold levels of deposition for purple pitcher 

plant consistent with the CLs estimated by Greaver et al. (2011). 

To determine the utility of these CLs for purposes of the REA, we considered the 

geographic coverage, representativeness and uncertainty tied to these data. We have determined 

that application of these CLs has the highest level of certainty at the individual sites included in 

the 7 studies. However, only four of the studies (Aldous et al., 2002; Gotelli and Ellison, 2002, 

2006; Crumley et al., 2016) included sites within the United States (the remaining studies 

included only sites in Canada). This limits the geographic scope of potential analyses to 15 study 

sites located in the northeastern U.S. The geographic scope of the CLs estimates could be 

expanded by aggregating to Ecoregions, similar to other CLs estimated in Pardo et al. (2011), 

which would include some of the study sites located in Canada. However, this would only 

expand the coverage to include Ecoregions in the northeast and north central U.S. while 

increasing the uncertainty associated with the CLs as values are extrapolated from individual 

points to larger areas. 

Additionally, data is available through the NWCA. The overall scope of the NARS 

datasets is described in section 3.5. Similar to the other NARS datasets, the NWCA did not 

include measurements of ambient air quality or atmospheric deposition. While new CLs are 

available, given the level of uncertainty and limited geographic representation of these studies 

and the reasons discussed in section 3.5.1 regarding the NARS data, we do not intend to conduct 

a quantitative assessment for this effect category.

3.7.3 Coastal Acidification

The 2008 NOx/SOx ISA did not address nutrient enhanced coastal acidification. Since 

the 2008 ISA, N enrichment has been recognized as a possible contributing factor to increasing 

acidification of marine environments. Specifically, N has been recognized as a possible 
3-23

contributing factor to coastal acidification because the CO2 produced by organic matter 

decomposition in eutrophic waters can contribute CO2 to the water column along with the 

dissolution of atmospheric anthropogenic CO2, decreasing the pH (see second draft ISA,

Appendix section 10.5). Given the new scientific information available supporting this effect, 

the second draft ISA found that the relationship between atmospheric N deposition and 

increased nutrient-enhanced coastal acidification is likely causal. Additionally, the second 

draft ISA found that the evidence is suggestive of, but not sufficient to infer, a causal relationship 

between atmospheric N deposition and changes in biota including altered physiology, species 

richness, community composition, and biodiversity due to nutrient enhanced coastal 

acidification. Despite newly available scientific evidence for this effect category, there are 

currently no tools or data available to quantitatively assess the risks due to coastal acidification 

that are associated with atmospheric N and/or S deposition. Because of this, no quantitative 

analyses are supported for this REA.

3.7.4 S Enrichment in Freshwater and Wetland Ecosystems

3.7.4.1 Mercury Methylation

Recent research since the last review continues to strengthen and inform our scientific 

understanding of the relationship between atmospheric S deposition and freshwater MeHg

production. This understanding has expanded since the last review to include the identification 

of: 1) additional types of organisms that play a role in the methylation process, 2) additional 

macro- and micro-environments in which methylating organisms are found, and 3) additional 

areas within the U.S. containing habitats with conditions suitable for methylation. Building on 

the body of available science, including that available in 2008, the second draft ISA finds a 

causal relationship between S deposition and the alteration of mercury (Hg) methylation in 

surface waters, sediment, and soils in wetland and freshwater ecosystems (see second draft ISA, 

Appendix section 12.1). 

Specifically, this recent research has demonstrated that in addition to sulfur-reducing 

bacteria (SRB), certain strains of archaea found in wetland sediments are also active in S 

reduction. The current review therefore uses the broader term of sulfur-reducing prokaryotes 

(SRP), when appropriate, to reflect the joint role of both groups of organisms in certain wetland 

environments (see second draft ISA, Appendix sections 12.1 and 12.3). Further, additional 

organisms that possess the ability to methylate mercury have also been identified, due in part to 

the discovery of the genes associated with this ability (see second draft ISA, Appendix section 

12.3). New evidence has broadened our understanding of where methylation occurs, both in 

terms of types of wetland and freshwater ecosystems, as well as the specific areas within these 

ecosystems where methylation is most likely to occur. For example, it is now known that 
3-24

methylation occurs in agricultural wetlands, rivers and streams and that within aquatic 

ecosystems, periphyton can play an important role as a host for SRPs. Furthermore, the microbial 

communities embedded within the periphyton can be quite complex and diverse, are more 

efficient in the methylation of Hg than are SRPs in sediments, and boost Hg methylation rates 

within the oxygenated water column of freshwater and wetland ecosystems (see second draft 

ISA, Appendix section 12.3). 

These scientific advances confirm and highlight the case, as described in the second draft 

ISA (second draft ISA, Appendix section 12.3.3) and mentioned in Chapter 2 of this document, 

that the relationship between atmospheric S deposition and measured increases in MeHg in 

associated wetland and freshwater aquatic systems and biota is complex, in part because it is 

mediated by the activity of SRP which are influenced by multiple interacting physical, chemical 

and biological variables such as oxygen content, temperature, pH, and labile carbon supply. 

There are several controlling factors that can influence the rates of mercury methylation (see 

second draft ISA, Appendix 12, Figure 12-9). Given the complexity, temporal and seasonal 

variability, and multiple drivers of the MeHg process across the national landscape, no doseresponse functions have been established and there is currently a lack of CLs and assessment 

tools available by which to assess the risks of MeHg enhancement from atmospheric deposition 

of S in North American ecosystems (see second draft ISA, Appendix 12 sections 12.6 and 

12.3.3).

Based on the information presented in the second draft ISA and summarized above, we 

note that there are remaining uncertainties associated with the linkages connecting atmospheric S 

deposition to aquatic SO4

2- concentrations to MeHg production in wetland and freshwater aquatic 

ecosystems. Additionally, there are no known tools or data available for quantitative 

assessments. Therefore, we do not intend to conduct a quantitative assessment for this effect. 

3.7.4.2 Sulfide Phytotoxicity

The 2008 NOx/SOx ISA only included information regarding sulfide phytotoxicity in 

European systems, and in mesocosm studies that showed sulfide toxicity reduced biomass of 

wetland plants and aquatic macrophytes under exposure levels higher than those that occur in 

U.S. regions with high atmospheric S deposition. Newer research, however, shows sulfide 

toxicity occurring in the field within multiple wetland ecosystems under ambient exposure 

conditions in North America (see second draft ISA, Appendix sections 12.2.3 and 12.7.3). This 

new information is sufficient to support a new causal determination in the second draft ISA, 

finding that the body of evidence is sufficient to infer a causal relationship between S deposition 

and changes in biota due to sulfide phytotoxicity, including alteration of growth and 

productivity, species physiology, species richness, community composition, and biodiversity in 
3-25

wetland and freshwater ecosystems. Sulfide interferes with nutrient uptake in plant roots and 

studies show elevated sulfide can result in decreased seed mass, seed viability, seedling 

emergence rates, decreased seedling height, decreased seedling survival rates, and reductions in 

total plant cover, all which can lead to shifts in plant community composition (see second draft 

ISA, Appendix section 12.2.3). 

The relationship between atmospheric S deposition and sulfide toxicity is complex and 

influenced by multiple factors. Since sulfide is the product of microbial SO4

2- reduction, its 

concentration in water (including sediment pore water) is heavily dependent upon environmental 

factors that influence microbial activity, particularly the availability of dissolved organic carbon 

(DOC). In addition, the degree to which aquatic vegetation is exposed to phytotoxic 

concentrations of sulfide depends on its residence time as a free ion in water. When sulfide 

binds with iron it precipitates out of solution and is no longer available to plants. Thus, the 

phytotoxicity of sulfide is regulated in part by the availability of iron in the wetland or freshwater 

system. 

The largest uncertainty that remains regarding sulfide effects, as with methymercury 

discussed in 3.7.4.1. above, is that associated with the linkages connecting atmospheric S 

deposition to aquatic sulfate concentrations in wetland and freshwater aquatic ecosystems. We 

are currently unable to characterize how deposition amounts or changes in deposition affect 

aquatic concentrations of SO4

2-

. Because of this fundamental uncertainty, we do not intend to 

conduct a quantitative assessment for this effect.

3.8 ECOSYSTEM SERVICES

There are several ways in which ecological effects can be related to public welfare. The 

most comprehensive model is that of ecosystem services, which can provide information on the 

linkages between changes in ecological effects and known or anticipated effects to public 

welfare. Ecosystem services can be generally defined as the benefits that individuals and 

organizations obtain from ecosystems. The EPA has defined ecological goods and services as the 

outputs of ecological functions or processes that directly or indirectly contribute to social welfare 

or have the potential to do so in the future. Conceptually, changes in ecosystem services may be 

used to aid in characterizing a known or anticipated adverse effect on public welfare. Ecosystem 

services, those related to Class I areas and endangered species, and analyses of both non-use and 

use values across larger areas and in the human economy, including commercial uses, can be 

used separately or together to help inform public welfare decisions.

Appendix 14 of the second draft ISA includes summaries of several new papers 

published since the 2008 NOx/SOx ISA that connect the effects of N and S deposition to 

ecosystem services. Some of these studies present valuation of costs and benefits of nitrogen 
3-26

loading across the nation for several ecosystem services such as coastal fish harvests, 

recreational uses of waterbodies, and lakefront hedonic values. The second draft ISA also 

includes descriptions of five papers that resulted from a joint workshop between the NPS, EPA, 

the USFS, and members of academia. The papers present a concise conceptual model linking 

CLs to final ecosystem services and a framework for assessing the scientific strength of those 

linkages (Bell et al., 2017; Clark et al., 2017; Irvine et al.; 2017, O’Dea et al., 2017; Rhodes et 

al., 2017). The second draft ISA also presents an evaluation of studies conducted in Europe and a 

global-scale analysis of N cycling and impacts on ecosystem services. Additionally, the second 

draft ISA includes profiles of several threatened or endangered species and their related 

ecosystem services. 

Since the prior review, the NPS has published a list of threatened or endangered species 

in each of the National Parks.39 A national study has also been conducted, which identifies 78 

threatened and endangered species for which N deposition is a contributing stressor (Hernández 

et al., 2016). Many of these species are located in National Parks. The second draft ISA includes 

case studies for several National Parks (including Rocky Mountain, Acadia, Great Smoky 

Mountains, Sequoia and Kings Canyon National Parks), where new scientific evidence for 

deposition-related effects is highlighted.

A separate literature search, from 2009 to 2017, and review for ecosystem services was 

conducted for this REA plan to identify papers related to ecosystem services analysis that were 

not included in the second draft ISA because they do not always directly relate nitrogen and or 

sulfur deposition to changes in ecosystem services. The papers can be loosely binned into 

categories including: freshwater acidification, freshwater nitrogen enrichment, coastal/estuary, 

coral reefs, wetlands, forests and wilderness, grasslands and deserts, fires, lichens, and 

biodiversity. A list of these papers is included in the Appendix. These papers provide linkages 

from deposition to ecological effects and finally to ecosystem services, and provide methods that 

either describe the potential qualitative risk to the services covered or possible avenues for 

quantitative evaluation of the changes in services related to N and/or S deposition. Since the last 

review, information in the recent literature identified by the search increases the number of 

ecosystem services and ecosystems potentially available for analysis and/or expands and 

improves previously available methods. 

In addition to the results of the literature search new information is available from 

updated databases such as the Recreational Values Database,40 government reports such as the

 39 See https://irma.nps.gov/NPSpecies/

40 See http://recvaluation.forestry.oregonstate.edu/
3-27

Internet Research Information Series 41(IRIS) reports from the USFS, and new analyses done by 

EPA’s National Center for Environmental Economics for the Chesapeake Bay Program.

42 These 

databases and reports expand and improve upon analyses included in the previous review which 

could be updated for the current review.

There are a limited number of papers and methods available that provide national-scale 

estimates of ecosystem services affected by atmospheric deposition. The majority of relevant 

work has been place-based studies that are useful for case studies although there are some few 

that could be amenable to a benefit transfer approach to applying the study results to larger 

geographic areas. Given the limited geographical scope of the available information, as well as 

the inherent uncertainties associated with quantifying specific ecosystem service effects using 

the available methods, we do not intend to conduct any specific quantitative analyses of 

ecosystem services in the REA. Instead, we plan to use the information in the available literature 

and databases to describe how the public values specific ecosystem services and to link that 

information qualitatively to the policy relevance of the assessed changes in ecological risks.

Availability of this type of information can also help prioritize the selection of case study areas 

for quantitative assessment (see section 4.4 for more information).

3.9 CONCLUSIONS

The discussion above reflects the EPA staff assessment of the degree to which currently 

available information, including newly available information since the last review (e.g., as 

summarized in the second draft ISA), might be expected to appreciably change our 

understanding of risk and exposures beyond the insights gained from the assessments from the 

last review. A critical consideration is the extent to which use of newly available information or 

approaches in new or updated quantitative assessments would provide risk and exposure 

estimates that are appreciably different or have the potential to reduce uncertainty or limitations

in the previous review, and that indicates that a new assessment of risk and exposure is 

warranted to provide an adequate characterization of ecological risk and exposure, particularly 

with regard to the current standards.

43

Based on these considerations, we have concluded that an REA is warranted to 

quantitatively evaluate acidification and nitrogen enrichment effects within freshwater and 

 41 See https://www.srs.fs.usda.gov/trends/iris/recstats.html

42 See https://www.epa.gov/environmental-economics/research-environmental-economics-ncee-working-paperseries

43 In considering this point, the EPA staff additionally recognize that such a characterization of risk and exposure, in 

addition to the currently available evidence, will be considered in the PA in terms of both evidence-based 

considerations and risk/exposure-based considerations.
3-28

terrestrial ecosystems. Given the lack of information and/or concerns about limitations or 

uncertainties as described in the sections above, we do not intend to quantitatively evaluate the 

other effect categories discussed in this chapter. More specifically, we conclude that it is 

appropriate to focus the REA on the following analyses, for which important new information is 

available in this review:

• Air Quality Information. Since the previous review, advances in scientific methods and 

the changing levels and spatial distribution of N and S deposition have appreciably 

changed our understanding of the linkages between ambient air concentrations, 

atmospheric deposition, and ecosystem exposures. New techniques are now available for 

combining these measurements and modeling outputs to estimate total deposition with 

lower uncertainty. The spatial variability and distribution of deposition has changed in 

recent years, as reduced forms of nitrogen deposition are now the largest source of N 

deposition in many places. Finally, because concentrations and deposition of oxidized N 

and sulfur have declined in recent years, there are new opportunities to better assess the 

linkage between a change in concentration and a change in deposition, while also better 

quantifying the uncertainties. 

• Freshwater Acidification and N Enrichment. Since the 2008 NOx/SOx ISA, new 

studies have been published on the effects of N and S deposition on freshwater 

ecosystems. This evidence includes new CLs for freshwater ecosystems and has created a 

greater weight of evidence and led to the development of expanded causality 

determinations in the second draft ISA from the previous review. These CLs provide 

national coverage and are expected to significantly expand the scope for analyses since 

the last review, and for N enrichment, to fill a major gap from the last review. Therefore, 

we intend to conduct quantitative assessments evaluating freshwater acidification and N 

enrichment effects using CLs.

• Terrestrial Acidification and Nitrogen Enrichment. A substantial body of new 

information is available for terrestrial acidification and N enrichment for this review. 

New information pertaining to acidification and N enrichment of terrestrial ecosystems 

includes studies that evaluate endpoints (herb/shrub and mycorrhizal community 

composition, lichen species richness, and tree growth and mortality) through CLs and/or 

response curves that were not available at the time of the last review. Many of these CLs

and response curves are national in scope and therefore expected to significantly expand

and enhance our ability to evaluate sensitivity and risk for acidification and N 

enrichment. The availability of this information fills a gap in the last review. 

Additionally, several new studies are now available that evaluate potential methods for 

deriving CLs and in particular, for estimating BCw rates in soils. These studies are 

expected to inform updated CLs assessment in the REA, potentially reducing

uncertainties from the prior review. Therefore, we intend to conduct quantitative 

assessments evaluating terrestrial acidification and N enrichment effects in this review. 

In summary, a new REA that utilizes the new information and approaches summarized 

above to provide a more precise characterization of exposure and risks, and one with reduced 

uncertainty, would inform the current review. Therefore, we conclude that it is appropriate to 
3-29

develop a risk and exposure assessment, based on the newly available air quality and ecological 

information, to inform the current review. More information regarding our plans for analyses is 

included in Chapter 4.
4-1

4 PLAN FOR QUANTITATIVE ASSESSMENT

This Chapter describes the proposed quantitative analysis and approach for the REA to 

characterize ecological risk and exposure associated with NOx, SOx, and PM for current air 

quality conditions, as well as for when air quality is just meeting the current standards, and if 

appropriate, when meeting potential alternative air quality standards. The REA will focus 

particularly on the contribution of these pollutants to deposition-driven ecological effects on 

freshwater and terrestrial ecosystems based on the conclusions in Chapter 3. These ecosystems 

warrant further assessment to evaluate information newly available since the last review that 

might be expected to appreciably change our understanding of risk and exposures or have the 

potential to reduce uncertainty or limitations in the previous review. Given the lack of 

information and/or concerns about limitations or uncertainties, we do not intend to quantitatively 

evaluate the other effect categories described in Chapter 3.

4.1 ANALYTICAL FRAMEWORK

The proposed analysis approach for the REA is shown in Figure 4-1 and discussed in 

more detail below. In general, we intend to use air quality data from monitors and chemical 

transport models to estimate atmospheric deposition of N and S associated with ambient 

concentrations of NOx, SOx, and PM. This information will also be used to estimate deposition 

response factors that predict the relationship between ambient concentrations and atmospheric 

deposition in certain areas of the country and can be used to adjust deposition to represent just 

meeting the current standards, as well as potential alternative standards, as appropriate. This air 

quality information can then be used to assess ecological effects at a national scale for current 

conditions, as well as within selected case study areas for air quality conditions that just meet 

current and potential alternative standards. Assessment of these effects for freshwater and 

terrestrial ecosystems can generally be grouped into two main categories: (1) assessment of 

exceedances using CLs; and (2) assessment of changes in biological and chemical responses 

using exposure-response curves. In these analyses, CLs and exposure-response curves would be 

used to relate N and S deposition to changes in biogeochemistry and changes in species-level or 

community-level ecological or biological responses. The resulting information would then be 

used in the consideration of the overall ecological risk for the review. Included below is a brief 

discussion of some of the main components of the analytical framework, including air quality 

and exposure, CLs, and exposure-response curves, as well as the general application of these 

components in the REA. More specific information about the assessments for the REA is 

included in the sections that follow.
4-2

Figure 4-1. Analytical Framework for the REA

4.1.1 Ambient Air Quality and Atmospheric Deposition

The objective for the REA is to characterize risk and exposure associated with just 

meeting the current standards, as well as any potential alternative standards under consideration. 

For this review, we plan to consider both the secondary and primary standards as part of the suite 

of current standards. In the REA, we plan to include a national-scale assessment of ecological 

risk that will be based on estimates of current air quality concentrations and deposition in the 

U.S. Applying the same methodology as used to create the TDEP datasets (Schwede and Lear, 

2014), recent air quality information would be used to create a national-scale gridded surface of 

N and S deposition, with separate estimates of the reduced and oxidized nitrogen contributions to 
4-3

total N deposition.44 This surface will represent average deposition over three years to 

incorporate emissions and meteorological variations across recent years, with the individual 

years also available for use. Because much of the U.S. is already meeting the secondary and 

primary standards for NO2, SO2 and PM, this surface can be used to assess ecological risk in 

most areas for air quality conditions that are at or below the current standards. To further assess 

ecological effects for air quality conditions under different air quality scenarios, we propose to 

adjust air quality and create new gridded surfaces of N and S deposition for each of the study 

areas that reflect deposition levels when just meeting the current standards, and any potential 

alternative standards, as appropriate. The focus on air quality adjustments in smaller areas of the 

country will help focus the analyses on specific air quality conditions and reduce uncertainty in 

the adjustment methodology being applied. 

4.1.2 Critical Loads and Exceedances

The second draft ISA uses the CL concept as an organizing principle to relate 

atmospheric deposition to ecological endpoints that indicate impairment (see second draft ISA, 

Section 1.2.2.3). “A critical load is formally defined as a quantitative estimate of 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” (Nilsson and Grennfelt, 1988, 

UNECE, 2004). CL estimates reflect the current state of knowledge and the selected indicators 

and responses (see second draft ISA, section 1.2.2.3). It is important to recognize that there is no 

single “definitive” CL for an ecological effect, nor is there a single definition of “harm” across 

CLs datasets. Given the heterogeneity of ecosystems affected by N and S deposition, published 

CL values for locations in the U.S. vary depending on both biological and physical factors. In 

fact, it is not uncommon for there to be multiple CLs available for a given pollutant at a single 

location due to the nested sequence of disturbances, receptors, and biological indicators 

considered for a given pollutant. (see second draft ISA, Chapter 1, section 1.2.2.3).

CLs are point estimates of when harmful effects begin to occur. Given the breadth of CL 

data available through new studies, we intend to use the CLs to provide a national-scale picture 

of sensitivity45 and risk46 (when used in conjunction with atmospheric deposition estimates to 

calculate exceedances). The CLs can also be useful in identifying risks of concern under 

different levels of atmospheric deposition and at different spatial scales. Therefore, we intend to 

use CLs and exceedances as tools for identifying potential case study areas for quantitative 

 44 This information allows for better understanding of the portion of deposition that is controllable under the CAA.

44 Sensitivity is as the degree to which an ecosystem is affected by atmospheric N and/or S deposition.

45 Risk is defined as the potential that adverse ecological effects may occur, or are occurring, as a result of exposure 

to one or more stressors
4-4

assessments, including designated areas that are considered important to the public (e.g., Class I 

areas). 

To calculate exceedances of CLs in the REA, we plan to compare CLs to the estimated 

levels of deposition in the national-scale gridded surface for current air quality. Put simply, this 

means: Dep – CL = X, if X >0, then it is exceeding. This concept will be applied to the nationalscale surface of gridded deposition estimates. While this is a straight-forward comparison, 

further work is required to understand the potential effect of an estimated exceedance. Additional 

considerations may also include estimates of the contribution of N and S in the system from air 

deposition, estimates of the time scale of the effect and potential changes in the effect, associated 

uncertainties in both the computation of the CL and the analytical application of the CL in the 

REA, and judgements of the potential adversity of the impact on public welfare. 

4.1.3 Changes in Ecological Responses

Exposure-response curves will be a key component of the assessments in the REA. These 

curves can show predicted ecological responses to specific levels of N or S deposition, as well as 

demonstrate the responsiveness of individual species and/or communities to changes in 

atmospheric deposition. These curves are primarily generated by observations of ecological 

response to experimental additions (e.g., N and S addition studies) or by observation of 

ecological response along a deposition gradient. We intend to use exposure-response curves to 

understand impacts under current conditions at a national-scale. In addition, we also plan to use 

the exposure-response curves to relate different air quality conditions, and associated deposition 

levels, in case study locations to potential changes in ecological effects. 

4.2 AIR QUALITY ANALYSES 

The sections that follow include information regarding the air quality and deposition 

datasets and analytical approaches that we intend to use to evaluate ecological effects in this 

REA.

4.2.1 Consideration of Current Conditions for National-scale Air Quality Concentrations 

and Deposition

Since the previous review, N and S deposition has changed and it is important to develop 

the most up-to-date datasets for the assessment of atmospheric deposition to capture these 

changes. We propose to rely on measurements of atmospheric concentration and deposition 

where available, and chemical transport model simulations to provide data for chemical species 

and locations where measurements are not available.

Accordingly, this review proposes to calculate air concentrations and deposition using the 

most recent CMAQ chemical transport model (version 5.2.1), with the most up to date 
4-5

meteorological and emission datasets, which will likely be for the calendar years 2014-2016. 

While there are other photochemical models available, CMAQ has been shown to have relatively 

low bias in estimating annual average wet deposition, with normalized mean bias reported as 

7.9% for SO4

2-

, -12.8% for NH4

+, and -15% for NO3- when compared with NADP measurements 

(Appel et al., 2011). In addition, while historically the CMAQ chemical transport model has 

reported dry deposition of NH3 and particulate NH4

+ separately, as these happen via different 

physical processes, the model has been recently augmented to be able to track the relative 

contribution of particulate NH4

+ and gas-phase NH3 to wet deposition of reduced nitrogen. This 

recent augmentation can provide data that will be useful in informing the contribution of 

particulate NH4

+ to deposition for this review. For further analyses, we propose to evaluate the 

CMAQ simulations using the NTN observations of wet deposition as well as the CASTNet, 

CSN, IMPROVE, and AMoN observations of oxidized nitrogen, particulate chemical 

composition, and NH3, respectively. The comparison of CMAQ results with these measurements 

can then be used to inform the uncertainty analysis.

The best available assessment of atmospheric deposition generally requires combining 

data from ambient measurements and computer model outputs. To do this, we propose to use the 

process described in Schwede and Lear (2014) to develop TDEP datasets for 2014, 2015 and 

2016 that cover the continental U.S. at 4-km horizontal resolution. These gridded surfaces will 

also be combined to provide an estimate of average deposition for 2014-2016. The TDEP 

method estimates wet deposition by spatially interpolating NTN wet deposition measurements 

and PRISM precipitation observations. These estimates would then be combined with the dry 

deposition estimates from the 2014-2016 CMAQ simulations, projected from the 12-km CMAQ 

model resolution onto the 4-km TDEP grid. For further analyses of the potential error and 

uncertainty in the wet deposition dataset, we intend to use cross validation methods, such as 

reserving some of the wet deposition observations for evaluation.

The result of this analysis will be a spatially complete data set of 3-yr average deposition 

across the continental U.S. for 2014-2016, including nitrogen and sulfur deposition, wet and dry 

deposition, as well as the relative contribution from gas-phase NH3 and particulate-phase NH4

+.

These data will be used to assess ecosystem effects at a national-scale and under current

conditions, where much of the U.S. is meeting the current NO2, SO2 and PM standards.

4.2.2 Consideration of Air Quality Scenarios

The goal of the REA is to consider the level of risk under air quality conditions that just 

meet the current standards, as well as any potential alternative standards under consideration. To 

do so, we plan to conduct quantitative analyses in study area locations with air quality adjusted 

to reflect just meeting the current NAAQS, and just meeting other air quality scenarios, as 
4-6

needed. Accordingly, the sections below describe the proposed methodology for adjusting 

ambient concentrations to reflect specific air quality scenarios and statistically relating those 

levels of ambient concentrations to deposition levels of N and S. Table 4-1 lists the current

NAAQS for PM, NO2, and SO2.

Table 4-1. Current National Ambient Air Quality Standards for PM, NO2, and SO2

Pollutant Primary / 

secondary

Averaging 

time

Level Form

NO2 Primary 1 hour 100 ppb 98th percentile of 1-hour daily maximum 

concentration, averaged over 3 years

Primary & 

secondary

1 year 53 ppb Annual mean

PM2.5 Primary 1 year 12 μg/m3 Annual mean, averaged over 3 years

Secondary 1 year 15 μg/m3 Annual mean, averaged over 3 years

Primary & 

secondary

24 hours 35 μg/m3 98th percentile, averaged over 3 years

PM10 Primary & 

secondary

24 hours 150 μg/m3 Not to be exceeded more than once per year 

on average over 3 years

SO2 Primary 1 hour 75 ppb 98th percentile of 1-hour daily maximum 

concentration, averaged over 3 years

Secondary 3 hours 0.5 ppm Not to be exceeded more than once per year

4.2.2.1 Estimating Ambient Concentrations

The goal of this analysis is to create spatially consistent datasets that represent scenarios 

where, in well-defined case study areas, concentrations are adjusted to just meet the current 

standards. A similar methodology would likely be followed if adjustments are needed to reflect 

just meeting potential alternative standards. These air quality scenarios will be used to estimate 

N and S deposition in the study area and to quantify ecological effects associated with the 

atmospheric deposition. 

There are a number of unique technical challenges in adjusting air quality to reflect just 

meeting multiple standards, particularly given that the indicator pollutants for those standards are 
4-7

related differently through emissions, chemistry and transport. The first challenge is that, SO2, 

NO2, and PM, all have different levels of spatial variability. SO2 and NOx are directly emitted 

from sources. As SO2 and NOx are transported from the emission sources, they are diluted by 

atmospheric mixing, are chemically oxidized to other forms, including particles, and removed 

from the atmosphere by deposition. Locations further from sources have low SO2 and NOx 

concentrations and higher concentrations of their oxidation products, like particulate sulfate and 

particulate nitrate. Because the sulfate and nitrate components of PM2.5 are not directly emitted 

but are instead formed in the atmosphere, these components of PM2.5 are often more spatially 

widespread. A scenario that adjusts SO2 or NOx must consider downwind impacts to sulfate and 

nitrate PM2.5, and a scenario that adjusts PM2.5 must consider if such changes would require SO2

or NOx concentrations that are greater than the standards in upwind areas.

Second, many sensitive ecological areas are located far from large emission sources and 

are affected by emissions in a large area. A recent study by Lee et al. (2016) examined eight 

Class I areas in the U.S., and found that 50% of the nitrogen deposition could be attributed to 

emission sources within 500 km and 90% could be attributed to emission sources as far away as

1500 km. The scenarios should not only consider concentration changes in an isolated emission 

sources area, but also in the upwind areas whose emissions contribute to deposition.

Finally, a related challenge is that there are additional components of PM that are not 

contributors to sulfate and nitrate deposition. For example, concentrations in the South Coast Air 

Basin are greater than the primary PM2.5 NAAQS, but not the primary SO2 NAAQS. Only a 

small portion of PM2.5 in Los Angeles is comprised of particulate sulfate. If SO2 concentrations 

were increased, sulfate would also increase, pushing PM2.5 concentrations further beyond “just 

meeting” the standard. Because of the mix of emission sources and contributing components to 

PM2.5, at many locations, there is not a physically realistic way to adjust SO2, NO2, and PM2.5 

concentrations where concentrations are just meeting all the relevant standards at the same time.

Instead, the REA will need to identify the controlling standard, and associated pollutant, for the 

study area. In this analysis, we define the controlling standard as one where, when it is met, any 

increases in the concentrations of other N and S pollutants would cause the controlling standard 

to not be met.

Noting these challenges, we propose to first identify the area of influence for a particular 

study area location based on available information. The study area location is likely to be around 

100 km in diameter. As a default, we may assume an area of influence with a radius of 1500 km, 

based on the findings of Lee et al. (2016). Within this area of influence, we will identify the 

controlling standard and associated pollutant (i.e. SO2, NOx, or PM) by evaluating information 

from ambient measurements and air quality modeling and considering current levels, as well as 

historical relationships between emissions and ambient concentrations. Then, using this 
4-8

information, we intend to develop an approach to adjust air quality in the study area location to 

reflect just meeting the identified controlling standard under consideration. Based on the 

analytical evidence in the recent SO2, NO2 and PM NAAQS reviews (U.S. EPA, 2018; U.S. 

EPA, 2017c; U.S. EPA, 2010b), we note the successful application of various statistical 

adjustment approaches in those reviews to estimate realistic changes in air quality concentration 

to reflect changes in emissions of SO2, NO2 and PM. Similar approaches will also be considered 

for this review, though in some areas we may also need additional information from air quality 

modeling to better quantify the contribution from an important emissions source. Using the 

approach developed, we intend to adjust air quality concentrations in the study area to develop an 

air quality scenario that just meets the current standards at the locations most relevant for the 

case study area. A similar approach would also be used to adjust air quality in the study area 

location to reflect just meeting any potential alternative standards under consideration. Using the 

approaches described in the next section, the changes in air concentration would be related to a 

change in deposition. 

4.2.2.2 Relating Changing Levels of Atmospheric Concentration to Deposition

For our proposed analyses, relating air concentrations to welfare effects requires that a

change in ambient concentration be related to a change in atmospheric deposition for input into 

an equation or model that then relates a deposition of N and/or S to an ecosystem effect. This 

section discusses the approach under consideration to estimate “deposition response factors,” or 

factors that can be used to relate changes in ambient concentrations of measured N and S species

to changes in atmospheric deposition of N and S in areas across the U.S. 

The previous review introduced the transference ratio, TR, where: Deposition = TR × 

concentration. In that review, the TR was estimated by dividing the annual average deposition by 

concentration in a single CMAQ simulation and averaging over an Ecoregion. There were also 

noted several uncertainties as described in Chapters 2 and 3, including:

• averaging over an ecoregion introduces uncertainty since the transference ratio is lower 

near emissions sources and larger far from emission sources, especially as NOx is 

oxidized to form compounds that deposit more quickly;

• different chemical transport models report similar estimates of wet deposition but report 

very different transference ratios; and

• no quantitative assessment of uncertainty was conducted.

We propose to use an updated approach in this review to estimate deposition response 

factors for each form of N and S deposition, including both dry and wet deposition. For this 

approach, we note that in most areas of the U.S., concentrations and deposition of both oxidized 

nitrogen and sulfur have declined since the last review and that measurements at dozens of co-
4-9

located CASTNet and NTN sites over the last 25 years are available to provide an observational 

record of how a change in ambient concentration is related to a change in atmospheric wet 

deposition. In addition, a 20-year CMAQ chemical transport model simulation (Zhang et al., 

2018) provides a modeled estimate of the relationship between ambient concentrations and both 

wet and dry deposition. Taking advantage of these available modeled and measured 

concentration and deposition data, we propose using statistical models to estimate deposition 

response factors that relate a change in air concentration to a change in deposition and 

quantitatively assess the uncertainty in various study locations that will be selected for this 

analysis. These deposition response factors would then be used in conjunction with the adjusted 

air quality scenarios (section 4.2.2.1) to estimate the N and S deposition associated with just 

meeting the current standards, as well as any potential alternative standards under consideration.

In doing this, we propose to identify several pollutants or combinations of pollutants that 

can be measured and used to relate ambient concentrations to atmospheric deposition. We will 

refer to these as “deposition response factors.” Criteria for a robust deposition predictor are (i) it 

can be measured in ambient air with known accuracy and (ii) it can be used to predict 

atmospheric deposition with low error. We note that these deposition response factors may be 

different than the indicators for the current standards (i.e., NO2, SO2, PM2.5 and PM10) and when 

this is true, we plan to also assess the relationship between the deposition predictor and the 

indicator for the current standards (including consideration of form, averaging time and level of 

the standards). 

We have listed our potential deposition response factors in Table 4-1. For some 

deposition response factors, co-located measurements of ambient concentration and wet 

deposition from 1990 to the present day exist and can provide a dataset to estimate the change in 

wet deposition related to a change in air quality concentration. For dry deposition measurements

that are not routinely available, the CMAQ modeling information can be used to estimate the 

relationship between dry deposition and air quality concentrations. Using these datasets, the 

annual average concentrations and deposition calculated by a 20-year CMAQ chemical transport 

model simulation will be used to fit a statistical model for each deposition predictor at the gridlevel scale (36 km grid cells), as well as at larger spatial scales. Measurements of ambient

concentration and wet deposition and their 20-year trends will be used to evaluate the chemical 

transport model and statistical model results. Each of the steps in this analysis is listed below. 

(1) Using a CMAQ simulation for 1990-2010 (Zhang et al., 2018), calculate the annual 

average deposition (wet, dry, and total) for oxidized N, reduced N, and sulfur at each grid 

cell location. Also calculate the annual average air concentration of NO2, NOy, HNO3, 

particulate NO3

-

, NH3, particulate NH4

+, SO2, and particulate SO42- at each grid cell 

location.
4-10

(2) For a specific grid cell, use the 20 years of concentration and deposition data to estimate a 

statistical model that uses annual average atmospheric concentration -- one of the 

proposed deposition response factors -- to predict the annual average dry deposition. For 

example, the sum of particulate SO4

2- and SO2 is a proposed deposition predictor for dry 

deposition of sulfur. 

(3) Record the residual – the difference between the deposition predicted by the statistical 

model and the deposition calculated by CMAQ – for each of the predicted annual 

deposition. Calculate the average residual. This is used as an assessment of the magnitude 

of error when using this deposition predictor.

(4) Repeat (3) for each grid cell and create a map of the average residual and best fit 

coefficient. Examine how these vary.

(5) Repeat (3-4) for each deposition predictor listed in Table 4-1. For example, for oxidized 

N, this could include HNO3, NOy, or total nitrate (HNO3 + particulate nitrate). Compare 

the residual for each different deposition predictor. Compare both the national residual 

average as well as use the map to make sure there are no areas where the error is 

exceptionally large. 

(6) Repeat (3-5), but instead fit a statistical model to predict wet deposition and total (sum of 

wet and dry) deposition. Where possible, compare with historical measurements of wet 

deposition and atmospheric concentration at co-located NADP and CASTNet sites. 

Examine if the residual lower when estimating wet and dry deposition individually or 

when added together as total deposition. 

(7) Repeat (3-6), but rather than fit a different model for each grid cell, use all the grid cells, 

excluding those over oceans, to fit a statistical model. Create a map of the average 

residual for each grid cell. Explore different averaging areas for use in case studies to find 

a balance that is representative and does not obscure variability in the transference ratio.

(8) Compare the residuals and select a deposition predictor that has low average residual and 

is nationally relevant. Use the distribution of residuals as an estimate of uncertainty.
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Table 4-2. Initial Assessment of Available Deposition Response Factors

Deposition 

predictor

Description Available measurements Reliably related to deposition?

Total nitrate: 

HNO3(g) + 

PM2.5 NO3

-

The sum of gasphase nitric acid 

and particulate 

nitrate

Measured at CASTNet at 

many locations since 1990

Co-located NTN and CASTNet monitors 

show oxidized N wet deposition can be 

reliably predicted with total NO3

- air 

concentration measurements (Sickles 

and Shadwick, 2013)

Total sulfate: 

SO2(g) + PM2.5

SO4

2-

The sum of gasphase SO2 and 

particulate SO4

2-

Measured at CASTNet at 

many locations since 1990.

Co-located NTN and CASTNet monitors 

show S wet deposition can be reliably 

predicted with total sulfate air 

concentration measurements

NOy Sum of oxidized N 

in the gas phase 

and particulate 

nitrate

Measured at NCore sites, 

although most are in urban 

areas.

Most NOy measurements are not colocated with deposition measurements, 

which makes it difficult to evaluate. 

Sickles and Shadwick (2013) 

demonstrated that this sometimes has 

(and sometimes does not have) low 

error for predicting deposition.

PM2.5 NH4

+ Particulate 

ammonium

Measured at CASTNet and 

CSN but measurements are 

biased and known to be 

lower than atmospheric 

concentrations.

Co-located NTN and CASTNet monitors 

show reduced N deposition cannot be 

reliably predicted using PM2.5 NH4

+. 

This could be explained partially by bias 

in CASTNet NH4

+ measurements. 

Another consideration is most of the US 

is thought to have excess NH3 relative to 

sulfate and nitrate, which suggests that 

NH4

+ concentrations are more controlled 

by sulfate and nitrate levels, rather than 

NH3 levels. 

NHx The sum of gasphase NH3 and 

particulate NH4

+

Co-located CASTNet, NTN, 

and AMoN sites measure 

gas phase NH3 and 

particulate NH4

+. New 

approaches to measure NHx

more robustly are under 

evaluation.

Co-located CASTNet, NTN, and AMoN 

sites suggest that NH4

+ wet deposition 

can be reliably predicted using NHx

measurements. However, NH3 is not 

part of this review and therefore the 

scope of this review would need to be 

expanded before this deposition 

predictor could be considered.

NO2, SO2, 

PM2.5, PM10

Existing primary 

NAAQS are based 

on these 

compounds

Nation-wide measurement 

networks, but mostly located 

near urban areas or large 

emission sources. Few are 

co-located with deposition 

measurements.

The analysis described above will 

examine how reliably these compounds 

can be used to predict deposition. 
4-12

Based on preliminary analyses, total nitrate (defined as the sum of nitric acid and 

particulate nitrate) could be the most promising deposition predictor for oxidized nitrogen 

deposition. Analysis of co-located CASTNet and NTN measurements have found total nitrate to 

be well correlated with oxidized nitrogen wet deposition (Figure 4-2) in the Eastern U.S. where 

year-to-year variability in precipitation is relatively low. Analyses also suggest that the sum of 

SO2 and particulate sulfur is well correlated with sulfur wet deposition. However, preliminary 

results suggest that in many locations, particulate NH4

+ is not a good predictor of reduced 

nitrogen deposition, as recent measured trends show declining particulate NH4

+ concentrations

with increasing or stable reduced-nitrogen deposition. 

This approach addresses several of the uncertainties inherent in the approach used to 

relate concentrations and deposition in the previous review. By using a statistical approach and 

the 20-year time series, we can quantify and compare the level of uncertainty for different 

deposition response factors. Rather than average over ecoregions, we will assess the spatial 

variability in the transference ratio and select an averaging area best suited to the ecological 

effect of interest. As described in Section 4.5, we will conduct comparisons with two chemicaltransport models. More analysis (discussed in Section 4.5) is planned to identify sources of 

variability and quantitatively assess the uncertainty in using each of these deposition response 

factors.

Figure 4-2. NADP National Trends Network wet deposition (blue, left axis, kg/ha N) and 

CASTNet air concentration measurements (orange, right axis, μg/m3 N) for Acadia 

National Park from 1999-2016 annual average. CASTNet measurement of particulate 

NH4

+ is not well correlated with reduced N wet deposition (r = 0.08, left figure), while 

total nitrate (gas-phase nitric acid plus particulate nitrate) air concentration is well 

correlated (r = 0.76) with oxidized N wet deposition (right figure).
4-13

4.3 ECOLOGICAL EFFECTS ASSESSMENT

4.3.1 Aquatic Acidification and Nitrogen Enrichment

4.3.1.1 Freshwater Acidification

The connection between SO2 and NOx emissions, atmospheric deposition of N and/or S, 

and the acidification of surface waters is well documented with several decades of evidence for

the eastern U.S. (see second draft ISA, Appendix section 7.1.1.2 and Driscoll et al., 2016). As 

mentioned in Chapter 2, the 2009 NOx/SOx REA evaluated freshwater acidification in the 

Adirondacks and Shenandoah, using ANC as the chemical indicator to derive CLs. In the REA

for this review, we intend to use a similar approach to evaluate freshwater acidification on a 

national scale, and to better understand the linkages between chemical changes (changes in 

ANC) in freshwater ecosystems attributable to acidifying deposition of N and/or S and changes 

in biological effects on receptors, such as fish. To accomplish this, we intend to use a 

combination of water quality measurements, geology, and surface water steady-state CLs data to 

define the spatial distribution of acid-sensitive ecosystems across the U.S. 

Biogeochemical dynamic models have also been used to assess impacts on water quality 

(e.g., pH and ANC) compared with pre-acidification (i.e., pre-industrial) water quality conditions 

(see second draft ISA, Appendix section 7.1.5.1, and Sullivan et al., 2011). Atmospheric N and 

S deposition in recent decades have shown marked decreases due to significant power sector, 

industrial, and mobile emissions reductions, allowing for the chemical and biological recovery of 

water bodies (see Section 1.11.1 of the second draft ISA for more information on recovery). 

Therefore, the evaluation of current ecological conditions will need to be done in the context that 

aquatic ecosystems are on a recovery trajectory in response to decreases in atmospheric N and/or 

S deposition. We intend to evaluate current conditions of aquatic ecosystems and their response 

to atmospheric N and/or S deposition using steady-state and target CLs and model output from 

biogeochemical models. We intend to use CLs for aquatic acidification from the NCLD v3.0 as 

well as any relevant new peer-reviewed publications (e.g., Blett et al., 2014; Sullivan et al., 2015; 

Fakhraei et al., 2014; Fakhraei et al., 2016; Shaw et al., 2014; Lawrence et al., 2016), where the 

data are not yet included in the NCLD v3.0. In developing the REA, consideration will also be 

given to any new CLs in scientific literature, but a comparison of all CLs will be completed to 

more fully understand the differences. 

ANC is an important chemical indicator, defined as the total amount of strong base ions 

minus the total amount of strong acid anions:

ANC = (Ca2+ + Mg2+ + K+ + Na+ + NH4

+) – (SO42- + NO3- + Cl-

)
4-14

While ANC is not directly linked to the physiological impacts of aquatic biota, it is 

commonly used because it integrates overall acid status. Unlike pH, it is not affected by 

dissolved carbon dioxide (CO2), and water quality models are generally better at modeling ANC 

than other indicators (see second draft ISA, Appendix, section 8.1). In addition, ANC has been 

extensively related to the health of biota and other surface water constituents like pH and Al (see 

second draft ISA, Appendix, sections 8.3.6.3). In the case where DOC plays an important role in 

acid-base balance of the surface water, the measure of base cation surplus (BCS) that adjusts for 

the organic acid status of surface waters (Lawrence, 2007) will be considered. BCS is a metric 

like ANC, but accounts for the charge balance natural organic acidity contributes to the ionic 

concentration of the surface water (see second draft ISA, Appendix section 8.1).

Given the connection between atmospheric N and S deposition to biogeochemical 

changes that may induce harmful biological effects, we intend to use ecological literature 

included in the second draft ISA, Appendix section 8.3 and survey data from various sources to 

explore revising the aquatic status ANC categories of potential effects that were used in the 2009 

NOx/SOx REA (e.g., Table 4.2-1 from the 2009 NOx/SOx REA). We will also work to refine 

the relationship between ANC and the biological responses. Compiled information for major 

taxonomic groups and other biogeochemical and ecological information (e.g. pH, habitat 

condition) will be used to inform the aquatic status ANC categories of potential effects described 

in the second draft ISA (see second draft ISA, Appendix section 8.3). Where data are available, 

we intend to develop exposure-response relationships (total species-richness vs. pH or ANC, 

biomass vs. pH or ANC, etc.) by building on the research of Layer et al. (2013), Baldigo et al. 

(2018), and other studies listed in the second draft ISA, Appendix section 8.3. Additionally, we 

intend to evaluate the CLs with respect to range of ANC thresholds of 20 to 100, which represent 

the range of ecosystem protection for biota as described by the aquatic status ANC categories. 

This falls within the range that is most commonly used in the NCLD v3.0 and among CL 

assessments in the U.S. (see second draft ISA, Appendix Table 8-8). 

For the case study areas, we intend to use the MAGIC model to evaluate potential 

ecological and biological changes due to changes in atmospheric deposition. Knowing the preacidification water quality conditions provides a benchmark to measure how near chemical 

recovery is to the pre-acidification level. The time it takes to reach chemical recovery will also 

be determined using the MAGIC model. A host of environmental factors and the severity of 

atmospheric deposition-driven acidification influence how fast aquatic ecosystems respond to 

reduction in atmospheric N and/or S deposition. In sensitive ecosystems that are strongly 

impacted chemical recovery varies significantly, but can take decades. The selection of case 

study areas will aim to include a subset of aquatic ecosystems from various sensitive regions 
4-15

with long-term water quality records with supporting environmental data to be modeled using the 

biogeochemical models. 

4.3.1.2 Freshwater Nitrogen Enrichment

We intend to further consider the extent of the analyses for the REA that use new CLs

data from Baron et al. (2011), Williams et al. (2017), and Nanus et al. (2017), as described in 

Chapter 3, to evaluate aquatic N enrichment effects in freshwater lakes and streams in the REA. 

These CLs use NO3 concentrations as an indicator of N enrichment, associated with varying 

biological and chemical criteria as discussed in section 3.5.2 and the second draft ISA Appendix 

8, section 8.5.4.147. The potential analyses are described below but, as we develop the REA, we 

plan to further consider which, if any, of these analyses will be included in the REA with the 

judgement being based on whether the results can substantively inform our ability to assess 

national standards for freshwater N enrichment.

The Baron et al. (2011) and Williams et al. (2017) studies provide CLs with broad 

geographic scope, which apply to all lakes within a given study region. We could potentially 

apply these CLs in the specific regions covered under each of the studies as follows: apply the 

CLs from Baron et al. (2011) to lakes included in the study in the Sierra Nevada Mountains, 

Rocky Mountains, and the northeastern U.S. to assess risk of elevated NO3 in surface waters48; 

apply the CLs from the Williams et al. (2017) study to western lakes that fall within the sampled 

criteria (elevation > 1200m), with a higher level of confidence attributed to lakes that were 

included in GLNC to assess risk of shifts from N to P-limitation of phytoplankton growth. The 

CLs from the Nanus et al. (2017) study provide CLs that are specific to each lake within the 

greater Yellowstone area. We could apply these CLs to the greater Yellowstone area as defined 

in the study to potentially assess the risk of changes to diatom growth and community 

composition. We could also explore the application of these CLs to the larger Rocky Mountain 

area assessed by Nanus et al. (2012) and potentially to other areas of the western U.S. In 

addition, the regression parameters in Nanus et al. (2017) constitute exposure-response functions 

that potentially relate the NO3 levels in individual lakes to atmospheric N deposition as well as 

other watershed level factors, including slope and amount of barren ground. These functions 

could be used to calculate expected NO3 levels based on different atmospheric deposition 

patterns. The resulting NO3 levels could be compared the categories of potential effects for NO3

described above. The combination of these response functions and categories of potential effects 

 47 We note that elevated NO3 concentrations in lakes and streams are also indicative of possible N saturation in 

adjacent terrestrial ecosystems leading to leaching of NO3 into the waterbodies (see second draft ISA Appendix 4, 

section 4.3.2 and Appendix 7, section 7.1.2).

48 Lake data in Baron et al. (2011) were taken from Linthurst et al. (1986) for eastern lakes and Eilers et al. (1987) 

for western lakes
4-16

could potentially be used to assess the risk posed by different levels of atmospheric N deposition 

to freshwater lakes. Depending on the study being applied, risk may be assessed at individual 

lakes and/or aggregated at larger areas.

4.3.2 Terrestrial Effects

The connections between emissions, atmospheric deposition of N and/or S, and the 

alteration of plant community composition and species richness in terrestrial ecosystems is well 

documented (see second draft ISA, Appendix section 5). For terrestrial ecosystems, the main 

findings of the current ISA are that N and S deposition cause alteration of soil biogeochemistry 

and effects on multiple levels of biological organization ranging from physiological processes to 

shifts in biodiversity and ecological function (ISA ES, p. lxxii).

Thus, this section describes potential quantitative analyses for soil biogeochemistry and 

groups of organisms. Some data are available at the level of biogeochemical effects while other 

datasets reflect growth and mortality effects on individual species of plants or effects to 

community composition and biodiversity. The sections below provide details on the studies that 

we intend to use, as well as any specific modifications and decisions that will be made or 

considered, when conducting the quantitative assessments. 

It is important to note that in terrestrial ecosystems, the acidifying effects from N and S, 

and the enrichment effects from N can occur simultaneously within the geographic boundaries of 

an ecosystem. However, the dominance of one process over the other (i.e., acidification versus N 

enrichment) can vary depending upon several factors, including soil pH and buffering capacity, 

the presence and abundance of sensitive species, and the influence of nitrogen limitation on 

primary production. Because acidification and N enrichment effects are intertwined, it is often 

difficult in large scale gradient studies to isolate mechanisms (i.e., acidification versus N 

enrichment), as both are co-occurring across the landscape. When possible, the mechanism of 

effect will be noted in the description of the datasets.

4.3.2.1 Biogeochemical Effects in Soils

As mentioned in Chapter 2, the 2009 NOx/SOx REA used a case study approach for 

sugar maple and red spruce, deriving CLs using Bc/Al as the chemical indicator for acidification 

of forested soil that related to tree health. In the REA for this review, we intend to use a similar 

approach, but now will expand the CLs nationally. We intend to use the Bc/Al ratio as a soil 

chemical indicator, given that it is commonly used, relates well to the Ca/Al ratio, and serves as 

an important input into the SMB model for calculating soil CLs (see second draft ISA, Appendix 

section 5.2.1). 

There is precedent for using Bc/Al levels between 1 and 10, because these values are 

documented to cause deleterious effects to trees, as the chemical limit in CLs calculated using 
4-17

the SMB model. For example, McNulty et al. (2007) chose Bc/Al levels of 1 (for coniferous 

forests) and 10 (for deciduous forests). Additionally, Duarte et. al (2013) and Phelan et. al (2014) 

conducted studies in the Northeastern U.S., each using a Bc/Al level of 10. For the REA, we 

intend to calculate national CLs using the SMB model and Bc/Al levels of 1 and 10 to represent 

different forest types in different parts of the U.S.. We will also explore in the REA whether 

there are levels between 1 and 10 that should be applied, considering any new scientific evidence 

for relating Bc/Al to categories of ecological effects. This approach is similar to the approach 

proposed for the use of ANC as a chemical indicator for freshwater acidification and NO3 as a 

chemical indicator of freshwater N enrichment. 

BCw is one of the most influential yet difficult to estimate parameters for deriving the 

forest soil acidification CLs (see second draft ISA, Appendix section 4.5.1). There are two 

primary methods available for estimating BCw: the clay-substrate method and the PROFILE 

model. The clay-substrate method has been applied nationally, but as noted in the 2009 

NOx/SOx REA, has known issues especially when estimating BCw in unglaciated soils. Given 

the results of Phelan et al. (2014) in the Pennsylvania case study, we intend to explore the use of 

PROFILE modelling to obtain more accurate national estimates of BCw rates for the REA, 

considering uncertainties identified in Whitfield et al. (2018). Additionally, researchers have 

recommended applying PROFILE in different areas of the U.S. to improve our understanding of 

how the model performs and to compare the BCw rates estimated by PROFILE to those 

estimated using the clay correlation-substrate model. (see second draft ISA, Appendix section 

5.5). We will use the results from Phelan et al. (2014) to explicitly quantify the relationship 

between PROFILE estimated BCw and clay-substrate-method quantified BCw.

4.3.2.2 Species-Level Effects

4.3.2.2.1 Trees

We intend to conduct quantitative analyses to understand growth and mortality effects on 

individual tree species across the contiguous U.S under current air quality conditions using the 

exposure-response curves described in Horn et al. (in review).

49 These curves are national in 

scope and represent statistical equations that relate growth and mortality responses to N 

deposition, S deposition, and other factors including temperature, precipitation, tree size, stand 

basal area. 

There are 156 species assessed in Horn et al. (in review). Additionally, the dataset 

includes benchmarks for the points on the curve where a species experiences various levels of 

reductions in growth and survival — 1%, 2%, 5%, 10%. However, the equations themselves can 

 49 In developing the REA, if Horn et al. (in review) is not published, we plan to revisit the proposed plan for the 

REA and consider analyses that focus on other published literature (e.g., Thomas et al., 2010).
4-18

be used to calculate any percentage desired. Trees were sampled at United States Forest Service 

(USFS) FIA plots, with at least 2,000 trees sampled for each species constituting a dataset of 

over 50,000 plots and more than 1.2 million trees. There were 94 species that met the N>2,000 

sample size cutoff for both growth and survival (highlighted in the publication) and an additional 

14 species that met the cutoff for survival only50. TDEP data years used for the deposition 

estimates overlap with the period of re-measurement for each plot. Samples at each plot were 

collected every 5 years on average. Data years for the dataset span from 2000-2016, with each 

plot linked with the average deposition experienced between the first and last measurement 

period. 

There are two methods under consideration for spatially associating the response curves 

from Horn et al. (in review) to the specific locations of the tree species being evaluated. One 

approach considers analytics based on the USFS FIA plot data, and the other based on the USFS 

continuous surface maps (Wilson et al., 2013). Both methods have their advantages and 

disadvantages, which are summarized more below.

• USFS FIA plot data. Our confidence is high in this data because it is based on empirical 

field measurements. However, although the density of plots is high when viewed at the 

continental scale, at smaller scales the density may be low enough that inferences across 

areas are limited. Thus, because there is one sample taken every 6,000 acres across the 

landscape according to the FIA plot layout, there are several areas that are important to 

public welfare, such as Class I Wilderness Areas and National Parks, that are composed 

of only a few FIA plots. 

• Wilson et al. (2013) USFS maps: These maps are based on the FIA data that provides 

continuous surface maps of predicted abundances for 322 species in 250 m grid cells 

across the contiguous U.S. (including all 94 tree species included in Horn et al. (in 

review)). These abundances are based on statistical relationships with various factors 

(i.e., phenology, temperature, precipitation, topography, and ecoregion). Abundance in 

Wilson et al. (2013) is based on basal area, which is a common metric in forestry that 

represents the area of tree trunks within an area of land (ft2 ha-1). The accuracy of these 

predictions varies by species and the scale of inference, with higher accuracy for more 

abundant species and larger areas. Wilson et al. (2013) and Riemann et al. (2010) provide 

several metrics to assess the accuracy of projections for all 322 tree species. All tree 

species in Horn et al. (in review) are included in Wilson et al. (2013). 

For the analyses, we will determine the most appropriate methodology for applying 

response curves across the landscape in the REA by considering the benefits and limitations of 

both datasets. These data will be compared to the N and sulfur deposition values from the 

national-scale TDEP surface for current air quality conditions. This information will help to 

 50 Note the study focused on the 94 species that met the criteria for both, though all results are provided in the 

Appendix of the paper.
4-19

further identify sensitive areas of the U.S. that are impacted by recent deposition levels and may 

be good candidates for the case study approach.

4.3.2.3 Community-Level Effects

4.3.2.3.1 Mycorrhizal Fungi 

The mycorrhizal fungi dataset is a collection of empirical CLs for total atmospheric N 

deposition from various studies included in Pardo et al. (2011) as well as newer studies reviewed 

by the ISA (e.g. Allen et al., 2016). This dataset includes minimum and maximum CLs for 

changes in community composition. CLs for mycorrhizal fungi will be used to assess the 

potential for current levels of deposition to affect mycorrhizal community composition in forests.

4.3.2.3.2 Lichen Community 

The underlying analyses in Geiser et al. (2010) and Root et al. (2015) include responsefunctions relating N deposition to lichen community composition. The functions described are 

currently based on data from the Pacific Northwest, so extrapolation to other areas of the country 

would have higher levels of uncertainty. While the NCLD v.3.0 includes other lichens CLs from 

Pardo et al. (2010) based on individual site-specific studies, we intend to use only the CLs from 

Root et al. (2015) and Geiser et al. (2010) for assessing risk because they use more consistent 

methodologies. Furthermore, we note that new work is underway to extend the work of Root et 

al. (2015) and Geiser et al. (2010) using similar methods on a national scale (personal 

communications with Linda Geiser, 2018). If this work becomes available, it may be used to 

broaden the geographic scope of the lichen data considered in the REA.

Because of the spatially restricted nature of the lichen study from Geiser et al. (2010) and 

Root et al. (2015), we will likely restrict the application of these exposure-response curves to the 

Western U.S. (specifically, to the two Ecoregions in the west where data were collected: North 

American West Coast Marine Ecoregion in Oregon and Washington and the Northwestern 

Forested Mountains Ecoregion in Northern California, Oregon, Washington, and parts of 

northern Idaho and Montana). We will consider extrapolation to other portions of these 

Ecoregions, but not nationally. Using the relationships in Geiser et al. (2010) and Root et al. 

(2015), we will calculate sensitivity and risk to lichen communities using the CLs and the 

exposure-response curve. 

4.3.2.3.3 Herbs/Shrubs

Given their national coverage, we intend to use the CLs from Simkin et al. (2016) to 

evaluate changes in species richness for herbs/shrubs communities. We note that while the 

NCLD v.3.0 also includes a collection of herbs/shrubs empirical CLs from Pardo et al. (2011), 

we have chosen to use those derived from Simkin et al. (2016) because the study is national in 
4-20

coverage, has explicitly quantified uncertainty, and used a common methodology in contrast with 

those from Pardo et al. (2011). The statistical equations in Simkin et al. (2016) constitute 

exposure-response curves that relate the species richness of herbs/shrubs at a site to N deposition 

as well as other factors, including soil pH, temperature, and precipitation. We will use these 

response curves to assess the risk posed by N deposition to herb/shrub community species 

richness. Risk will be assessed at individual sites or aggregated at larger areas. We will also 

explore the use of these curves to examine loadings that would minimize reductions in species 

richness at individual points/sites and across the country. However, avoiding reductions in total 

numbers of species does not avoid changes in composition and losses of individual species (e.g., 

a gain for 2 species and loss for 2 with no change in total species richness). Thus, if additional 

information becomes available at the species level, we will incorporate it into the analysis in the 

REA.

4.3.2.3.4 Forests and Grasslands

While we intend to conduct quantitative analyses to understand growth and mortality 

effects on individual tree species across the contiguous U.S under current air quality conditions 

using the exposure-response curves described in Horn et al. (in review), we also plan to apply the 

exposure-response curves to consider changes in the growth and survival of multiple species in 

the selected study areas for various air quality scenarios that include just meeting the current 

standards, as well as any potential alternative standards, as appropriate. In doing so, we intend to 

evaluate potential effects on tree species within a forested or grassland area and, as such, 

potential changes in the species composition for that area. We note that in our evaluation that, in 

some areas, we may find that some species are especially abundant and responsive to N and S 

deposition, and drive much of the changes in forest or grassland growth and survival rates. On 

the other hand, another area may be dominated by unresponsive species, and only locally rare 

species are estimated to be impacted. Therefore, the analyses will also provide information 

regarding the extent to which there are differences in effects for various species within each 

study area and between the set of study area locations. 

4.4 CASE STUDY AREA SELECTION

A case study approach is being proposed in the REA to provide the ability to include 

more refined analyses than can be done at the national scale and that can reflect various 

combinations of changing air quality and deposition, as needed and as appropriate. For the case 

study component of this REA, we intend to characterize ecological risk and exposure in 5-10 

study area locations for when air quality is just meeting the current standards, and if appropriate, 

when meeting potential alternative air quality standards. While we do not intend for the study 

area analyses to provide a comprehensive national assessment, they are intended to provide 
4-21

assessment for a small varied set of study areas that will be informative to EPA’s consideration 

of potential risks and exposures that may be associated with various air quality scenarios. 

In selecting the case study locations, we intend to consider specific criteria, with the goal 

of including case studies in the REA that are particularly informative to the review. Based on 

currently available data, tools and information and considering the proposed approaches to the 

analyses, as discussed above, we propose the following set of criteria for consideration in the 

selection of the study area locations:

• Influence of current air quality on deposition-related effects. As recognized above, 

historic air quality (and associated deposition) has had appreciable impacts on 

ecosystems in many areas. In such areas, the continued role of this historic deposition in 

the area’s deposition-related impacts poses challenges to us in assessing the effects of 

current deposition or deposition associated with the current standards. Therefore, it is 

particularly important for study area selection to understand, as well as possible, the 

influence of current air quality (compared to historic air quality) on deposition-related 

effects. In areas with relatively greater influence of current air quality, we would expect 

the deposition-related risk and exposures to be responsive to changes in deposition. The 

impacts for various air quality scenarios in such areas would be expected to be 

particularly informative to consideration of the adequacy of the current standards. Thus, 

case study locations in which the deposition-related effects are not dominated by the 

influence of historic air quality would be good case study candidates. Accordingly, 

features of an area to be considered will include geology, historical deposition levels, 

recovery potential, and has the area experienced historically high deposition levels such 

that, for example, a terrestrial system is N saturated.

• Sensitivity. Related to the prior criterion is one that considers the extent of ecosystem 

sensitivity. To insure consideration of the depositional impacts on at-risk ecosystems, it 

will be important to consider including areas for which ecosystems or receptors are 

sensitive to N and/or S deposition, at risk due to such deposition, and specifically 

sensitive to changes in deposition.

• Importance to public welfare. To assess impacts on public welfare (in order to inform 

consideration of the adequacy of public welfare protection afforded by the current 

standards), good candidate areas will be areas for which there is a connection of potential 

impacts to ecosystems and effects on public welfare. For example, pertinent 

considerations may include: is the area in a National Park or Class I area where there is a 

clear connection to public welfare? Does the area include endangered or threatened 

species? Are there culturally or economically significant species present?

• Diversity and occurrence. A good set of candidate areas would encompass a set of 

diverse conditions occurring across the country. Given this, we propose to consider 

selecting a set of areas comprising a broad array of NOx/SOx/PM-related chemical 

environments, while also ensuring inclusion of areas that reflect current atmospheric 

deposition patterns (e.g. areas dominated by oxidized as opposed to reduced forms of N). 

Similarly, we propose to consider areas where the resident ecological receptors also occur 

more broadly (e.g. tree species in the area are widespread and abundant in locations 

outside of the study area) as well as areas that encompass several different ecosystems.
4-22

• Data availability and adequacy. It is important for the case study locations to have 

adequate data available, thus reducing uncertainty in the risk and exposure analyses. 

− Air quality data: Given that our objective relates to the relationship between air 

quality concentrations and deposition, a good candidate study area would be one 

in which: air concentration and wet deposition measurements for multiple N and S 

species are available for recent years and the monitors are either co-located or 

located close to each other; CMAQ model performance in predicting 

concentrations and deposition of N and S species is reasonable; and the statistical 

model fit to predict wet deposition and total deposition has reasonably low 

uncertainty. An additional related consideration is the magnitude of air quality 

adjustment that would be required for the area’s air quality to just meet the current 

standards, with the understanding that smaller air quality adjustments will likely 

be less uncertain. 

− Freshwater and terrestrial ecosystem data: Given the reliance on CLs and 

exposure-response function data in our proposed analyses, a good candidate area 

would have: CLs and exposure-response function data available; CLs data 

available for multiple endpoints or effects (sections 4.1.2 and 4.1.3); and 

exposure-response function data available for multiple receptors (sections 4.2.4 

and 4.2.5) and for multiple tree species (section 4.2.5.2). Additionally, in a good 

candidate area, CLs data would have sufficient individual site measurements and 

tree species with exposure-response functions to allow robust quantitative 

analyses.

• Availability of additional information. An additional criterion for consideration is the 

availability of other pertinent data. For example, the second draft ISA included six case 

studies, chosen because they were areas for which there was substantial amount of 

published work on ecological response to N and/or S deposition available (second draft 

ISA, Appendix 16) 51. A consideration may be whether the qualitative scientific and air 

quality information in one or more of the case studies in the second draft ISA be used 

when considering the criteria above. Other information may also be available that makes 

an area a good candidate study location. For example, we may want to consider whether 

there is any other qualitative information that is useful (e.g., ecosystem services 

information or survey data).

4.5 CONSIDERATION OF VARIABILITY AND UNCERTAINTY

To characterize ecological risks, exposure and risk assessors commonly use an iterative 

process of gathering data, developing models, and estimating exposures and risks, given the 

goals of the assessment, scale of the assessment performed, and limitations of the input data 

available. However, significant uncertainty often remains and emphasis is then placed on 

characterizing the nature of that uncertainty and its impact on exposure and risk estimates.

 51 The locations included were the northeastern U.S., Adirondack State Park, southeastern Appalachia, Tampa Bay, 

Rocky Mountain National Park, and southern California. The case studies identified current acidification and 

nutrient status, as well as empirical and modelled CLs.
4-23

Below we summarize the most important uncertainties potentially affecting the risk and 

exposure estimates and how we can address some of them using newly available information. 

Conclusions drawn from these characterizations based on new information and any new 

evaluations performed in the current REA will be synthesized following the approach outlined 

by the World Health Organization (WHO, 2008). This synthesis will be used to identify, 

evaluate, and prioritize the most important uncertainties relevant to the estimated exposure and 

risk outcomes. We intend to use both a qualitative approach along with quantitative approaches 

where possible in the REA as discussed below.

The approach to be used here varies from that described by WHO (2008) in that a greater 

focus will be placed on evaluating the direction and the magnitude52 of the uncertainty; that is, 

qualitatively rating how the source of uncertainty, in the presence of alternative information, may 

affect the estimated exposures and ecological risk results. Following the identification of key 

uncertainties, staff will subjectively scale the overall impact of the uncertainty by considering the 

relationship between the source of uncertainty and the exposure concentrations (e.g., low, 

moderate, or high potential impact). Also to the extent possible, staff will also include an 

assessment of the direction of influence, indicating how the source of uncertainty could affect 

estimated exposures or risk estimates (e.g., the uncertainty could lead to over- or 

underestimates). Further, and consistent with the WHO (2008) guidance, staff will discuss the 

uncertainty in the knowledge base (e.g., the accuracy of the data used, acknowledgement of data 

gaps) and decisions made where possible (e.g., selection of model forms). The output of the 

uncertainty characterization will be a summary describing, for each identified source of 

uncertainty, the magnitude of the impact and the direction of influence the uncertainty may have 

on the exposure and risk characterization results. The discussion below illustrates some of the 

main considerations for uncertainty and variability in air quality, CLs datasets broadly, and 

specific uncertainty considerations for specific effect categories.

4.5.1 Air Quality

The review intends to examine uncertainty and variability in (i) our approach to assess 

current conditions and (ii) our approach for estimating the deposition response factors as the 

change in deposition related to a change in air concentration. To assess uncertainty in our 

estimate of current conditions, specifically, the merged model and measurement datasets of air 

concentrations and deposition from 2014 to 2016, we intend to examine the difference between 

modeled values and measurements. The spatially-interpolated wet deposition dataset will be 

assessed using cross validation, such as “leave-p-out” methods where a subset of the 

 52 This is synonymous with the “level of uncertainty” discussed in WHO (2008), section 5.1.2.2.
4-24

observations is reserved to assess the uncertainty in the interpolation. The CMAQ-calculated dry 

deposition could also be evaluated with measurements where possible, but given the scarcity of 

dry deposition measurements, we propose examining three sensitivity studies. First, we could

conduct Comprehensive Air Quality Model with eXtensions (CAMx) simulations with similar 

model configurations and the same emissions and meteorological inputs as the 2016 CMAQ

simulations. Earlier work and CASAC committee have noted differences between CMAQ and 

CAMx deposition estimates from previous model versions, and a large fraction of the air quality 

modeling community (including Federal Land Managers) use CAMx in their work. Second, we 

could conduct simulations using CMAQ augmented to include the Zhang dry deposition scheme 

used in CAMx. This simulation is to understand uncertainty arising specifically from alternative 

model configurations for dry deposition. Third, we could conduct simulations using 

CMAQv5.2.1 with mosaic land cover dry deposition to quantify the variability in deposition for 

different land cover types. Forests, grasslands, and lakes each support ecosystems with differing 

sensitivity to nitrogen and sulfur. Each of the land cover types are also physically different 

surfaces, which influences the micrometeorological conditions that govern dry deposition. For 

example, the dry deposition velocity of nitric acid over rougher forest areas can be a factor of 4 

larger than over water areas. The purpose of this simulation would be to examine the effect of 

considering deposition specific to each land cover type.

We propose to also pursue multiple approaches to assess uncertainty in our estimate of the 

deposition response factors – the change in deposition related to a change in air concentration. 

First, because we are estimating the deposition response factors by fitting a statistical model, we 

can assess the range of errors expected by analyzing the residuals, or the difference between the 

statistical model predictions and the data used to fit the statistical model. Second, since the 

statistical model is fit using CMAQ model results, we can also fit a statistical model to colocated measurements of air concentration and wet deposition. We have greater certainty when 

our estimate of the deposition response factors is similar when using either CMAQ model data or 

co-located measurements to fit the statistical model. Next, we can examine different approaches 

to spatial averaging of the air concentration and deposition data before fitting the statistical 

model, in order to develop an approach with lower uncertainty that can still capture the spatial 

variability needed to assess ecosystem impacts. Finally, we can examine our findings collectively 

from these uncertainty assessments and develop a framework to integrate them into a 

quantitative assessment of the total uncertainty in our estimate of the deposition response factors. 

4.5.2 Critical Loads Data 

Potential uncertainty/limitations for the CLs datasets depend upon several factors: 1) 

whether the dataset includes CLs from one study (i.e., that used a consistent methodology) 
4-25

versus multiple studies (i.e., where different methods may have been applied); 2) whether CLs

were extrapolated from study areas to broader geographical areas; 3) whether there is statistical 

uncertainty in inputs or calculations within modeled CLs.

CLs are also built using different air quality surfaces and projections and this inherently 

introduces a degree of uncertainty in the CL estimates. This uncertainty manifests in several 

ways. If the air quality estimates have quantified uncertainties, but do not have a directional bias, 

then the CL should be considered an average estimate, with some range of uncertainty. However, 

if uncertainty in the air quality estimates is known to be biased, then the CL may be considered 

either a high or low estimate based on the directionality of the bias. For example, a CL based on 

air quality estimates that are known to be low should be considered the low end of the CL 

estimates, with a range of uncertainty that is greater than the estimate. An important component 

of uncertainty related to air quality with respect to CLs will be in the comparison of deposition 

surfaces developed in the REA to CLs developed with different air quality and how the 

uncertainties inherent in both the CL and the deposition surface relate to each other. 

The following table (Table 4-2) summarizes the major sources of uncertainty for the CLs 

included in this plan based on the considerations outlined above.

Table 4-3. Major Sources of Uncertainty for the CLs Considered for the REA.

Critical Load Consistent 

Methodology?

Extrapolation? Quantified Statistical 

Uncertainty

Air Quality

Mycorrhizal 

fungi

No (multiple studies) Yes No Multiple AQ inputs

Lichens Yes within Root et al. 

(2015) and Geiser et 

al. (2010), some 

differences between. 

Yes No (in the NCLD) or 

Yes (source 

publications)

IMPROVE + CMAQ + 

NADP (Geiser et al. 

2010), IMPROVE (Root et 

al. 2015)

Herbaceous 

species 

richness

Yes No (point based) 

or Yes 

(ecoregionbased).

Yes CMAQ + NADP

Forest soil 

acidification

Yes No No (in the NCLD) or 

Yes (source 

publications)

Not an input

Freshwater 

acidification

Yes No No (in the NCLD) Not an Input

Freshwater N 

enrichment

Yes within Baron et al. 

(2011), Williams et al. 

(2017) and Nanus et 

al. (2017), different 

between. 

Yes within Baron 

et al. (2011) and 

Williams et al. 

(2017). No within

Nanus et al. 

(2017)

Yes within Williams et 

al. (2017) and Nanus et 

al. (2017). No within 

Baron et al. (2011)

PRISM+NADP+CMAQ 

(Baron et al. 2011), 

CMAQ (Williams et al. 

2017), 

NADP+RMS+PRISM 

(wet) + TDEP (dry) 

(Nanus et al. 2017)
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Given these considerations, we expect uncertainty for the mycorrhizal fungi dataset to be 

high as compared to the other datasets given that the data was built by combining the results 

from multiple studies and approaches, it includes extrapolation, and there is no explicit 

quantification of uncertainty included in the NCLD. Additionally, we expect uncertainty for the 

lichens dataset to be moderate given that data from two studies in Washington and Oregon were 

extrapolated nationally, and at least in the NCLD v.3 there is no inclusion of uncertainty. Within 

the areas/ecoregions of study, the uncertainty in the lichen CLs will be lower. For herbs/shrubs, 

we intend to use CLs from the Simkin et al (2016) study, which included an explicit 

quantification of uncertainty that was found to be low. In addition, researchers reported that the 

CLs from Simkin et al. (2016) were consistent with CLs derived from other studies. Therefore,

there is high confidence in the data. 

The CLs for freshwater N enrichment have differing uncertainties. We intend to apply the 

CLS for Baron et al. (2011) and Williams et al. (2017) across the study areas, which includes 

higher uncertainty due to extrapolation. In both datasets, we intend to also apply the CLs only to 

lakes included in the calculation of the CLs, which will reduce the uncertainty. Additionally, the 

Williams et al. (2017) study included quantification of uncertainty. We intend to apply the CLs 

from Nanus et al. (2017) across the study area. Similar to the Simkin et al. (2016) study, Nanus 

et al. (2017) included quantification of uncertainty as well as being consistent with CLs derived 

from other studies. 

The CLs for freshwater and terrestrial acidification were modelled and empirical values 

which did not include air quality as a model input. Both datasets have lower levels of uncertainty 

based on the criteria discussed above, however, other uncertainties apply to these CLs which are 

discussed below. 

An important component of uncertainty analysis is sensitivity analysis. Sensitivity 

analysis has been used extensively to look at critical load model parameters uncertainty ranges 

(Skeffington, 2006; Heywood et al., 2007; Fakhraei et al., 2016; Fakhraei et al., 2017). For 

example, Skeffington (2006) and Heywood et al. (2007) used a Monte Carlo probabilistic 

method to assess the degree of confidence in the CL exceedance and the coefficient of variation 

of the CL. They concluded that CL uncertainties are typically surprisingly small because of a 

“compensation of errors,” despite the input parameters often being poorly known. While natural 

variation and uncertainty in model parameters are important considerations, existing Monte 

Carlo sensitivity methods/tools are widely available, with many case study examples that can to 

be used in the REA to provide CL confidence, uncertainty, and CL exceedance probability to 

assess the risk to ecosystems from atmospheric N and/or S deposition. We propose using a 

probabilistic Monte Carlo approach to describe uncertainty and to describe the likelihood of the 

CL to be exceeded.
4-27

4.5.2.1 Freshwater and Terrestrial Acidification CLs

4.5.2.1.1 Freshwater

Freshwater ecosystems are complex and dynamic environmental systems and the impacts 

of ambient air pollution and the resulting deposition is influenced by a host of environmental 

processes that interact with the spatial variability of soils, geology, and biota. Given this 

complexity, CL models and even more complex biogeochemical models are an inevitable 

simplification of the natural system, which leads to inherent uncertainty in these assessments 

(Morgan and Henrion, 1990). The most noted simplification is the steady-state assumption for 

such dynamic ecosystems. Steady-state CLs are often derived from mathematical mass-balance 

models under assumed or modeled equilibrium conditions based on present water quality 

measurements. While steady-state CL models vary in complexity about the processes 

represented, a fundamental aspect of this approach is the steady-state assumption which rarely 

occurs in natural ecosystems since the ambient environment and atmosphere are continuously 

changing. Future weather modification may compound this limitation given that recovery of 

aquatic ecosystems from acidification will be affected by physical, chemical, and biological 

modification as annual mean temperature and precipitation levels lead to changes in runoff and 

bedrock and soil weathering (Greaver, 2016). Nevertheless, the steady-state assumption still 

provides an approximation that allows a quantitative risk assessment of atmospheric N and/S 

deposition impacts on aquatic ecosystems. 

The models and their parameters used to determine aquatic CLs have been extensively 

examined over the past 30 years (e.g., Skeffington, 2006; Rapp and Bishop, 2009). In all cases, 

the strength of the aquatic CL estimate and the exceedance calculation relies on the ability of the 

method to estimate the watershed-average base cation supply (i.e., input of base cations from 

weathering of bedrock, soil and deposition), runoff volume, base cation and acidifying 

deposition, and NO3

- leaching. Runoff volume and base cation deposition make smaller 

contributions to uncertainty as compared to base cation supply. It is the base cation supply that 

buffers the inputs of the acid anions from atmospheric deposition and is the factor that has the 

most influence on the CL calculation and the largest uncertainty (Skeffington, 2006; Li and 

McNulty, 2007; Fakrhaei et al., 2017). 

Compounding the difficulty in estimating the base cation supply is the lack of methods to 

directly measure bedrock and soil weathering rates. In most cases, estimates of watershedaverage base cation supply is determined indirectly by simple mass-balance differencing using 

present watershed water chemistry measurements or by more complex biogeochemical 

watershed models that still use water chemistry measurements as the bases for the determination. 

The CL is based on the present-day water chemistry and a model that drives the pre-industrial 

condition. 
4-28

A host of different methods have been used to determine base cation supply, from more 

complex biogeochemical models (e.g., MAGIC, PnET-BGC, PROFILE) to simple models, such 

as the F-factor as part of the SSWC model (Hernriksen et al., 1990). While the importance of 

base cation supply to the estimate of CLs is well recognized, limited comparisons across models 

have been completed with the focus being on comparing dynamic biogeochemical models 

(Tominage et al., 2010). However, the use of dynamic biogeochemical models is more difficult 

because they require substantial input data, whereas simple models like the F-factor along with 

the SSWC model are often more widely used given the fewer inputs needed. Although the 

SSWC/F-factor critical load model has been widely published around the world (e.g., Henriksen 

and Posch et al., 2001; Dupont et al., 2005; Shaw et al., 2014), many model limitations have 

been noted. Rapp and Bishop (2009) extensively examined the F-factor approach and found key 

limitations related to the use of present-day water chemistry data to parameterize the F-factor. 

They found that water chemistry data from the period where the watershed is recovering from 

acidification can lead to uncertain estimates of the base cation supply. In addition, the F-factor 

was sensitive to short-term variation in the water chemistry data that reduced the confidence in 

the CL. However, the use of long-term averaged water chemistry data help to minimize this 

impact. Another limitation is the influence of increasing DOC concentrations in lakes and 

streams observed in recent water quality data (Monteith et al., 2007) and its impact on the 

accuracy of the F-factor value on the CL estimate, because DOC as a strong influence on the 

acid/base status of the water. The models used to derive the CLs in the NCLD v.3.0 vary from 

simple (e.g., SSWC or FAB) to more complex models (e.g., biogeochemical models); hence, we 

intend to complete a comprehensive comparison of CL models to account for their differences 

and biases.

4.5.2.1.2 Terrestrial

Given that we intend to update the soils acidification dataset and explore the use of 

PROFILE for purposes of estimating BCw rates, the level of uncertainty for this dataset it not yet 

known. However, we provide a summary below of the uncertainties that will be considered 

further in the REA. There are two main sources of uncertainty in the forest soil acidification 

CLs: the Bc/Al ratio as a chemical indicator and the BCw rates as an input into the SMB model. 

As mentioned earlier, the Bc/Al ratio is commonly used as an indicator because it relates well to 

the Ca/Al ratio. It should be noted, however, that while soil Bc/Al and Ca/Al ratios are 

recognized as good indicators of soil acidity risk in most U.S. forest ecosystems, these metrics 

are not static and the responses of sensitive receptors to such changes are variable depending on 

many outside factors. For example, the ratios themselves change depending on spatial scale and 

seasonal fluctuations in soil conditions. In addition, the methods for both sampling and 
4-29

laboratory analyses of base cations in soils can be inconsistent between studies as reported in 

Appendix 5 of the second draft ISA. For example, Bc/Al or Ca/Al ratios reported in studies are 

often based on seedling studies in controlled environments, and these responses are often less 

consistent for seedlings or trees growing outside such controlled environments.

As mentioned in Chapter 2, the 2009 NOx/SOx REA included the use of the clay 

substrate method for calculating BCw rates. The estimated BCw rates were identified in that 

review as a major source of uncertainty. Given the results of Phelan et al. (2014) in the 

Pennsylvania case study, we intend to explore the use of PROFILE modelling to obtain more 

accurate national estimates of BCw rates for the REA, considering uncertainties identified in 

Whitfield et al. (2018).

4.5.3 Exposure-Response Functions

The studies that have developed exposure-response functions (Horn et al. in review, 

Nanus et al. 2017, Geiser et al. 2010, Root et al. 2015, Simkin et al. 2016) all include some 

analysis of uncertainty and variability. For example, the functions were developed using air 

quality estimates averaged across several years. This potentially misses inter-annual variability in 

atmospheric deposition and introduces some uncertainty into the exposure -response function. 

Also, the functions in Horn et al (in review) were developed across the entire range of a given 

tree species. This potentially misses within species variability, which we intend to assess for a 

few important species. Finally, like CLs, an important component of uncertainty related to air 

quality with respect to exposure-response functions will be in the comparison of deposition 

surfaces developed in the REA to exposure-response functions developed with different air 

quality and how the uncertainties inherent in both the exposure-response function and the 

deposition surface relate to each other. Given these uncertainties and limitations for the 

exposure-response datasets, we will further consider their potential impact on the risk and 

exposure results in the REA.
5-1

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6-1

6 APPENDIX

Table 6-1. Results of Literature Search for Ecosystem Services Articles 2009 - 2016

Author Short Title Location Notes

Amiraslany et al. (2012)

Economic value of fire conditions and the effects of wildfire on 

hiking NM

Beier et al (2017) Loss of ecosystem services due to chronic pollution Adirondacks

Caputo et al. (2017)

Impacts of acidification and potential recovery recreational 

fisheries Adirondacks

Caputo et al. (2016)

Effects of soil acidification on long-term ecological and economic 

outcomes Adirondacks

Christman et al. (2014)

Willingness to Pay to Reduce Wild Fire Risk in Wildland-Urban 

Interface NV

Englin et al (2008) Wildfire and the Economic Value of Wilderness Recreation. CA

Holmes et al. (2013)

The effects of personal experience on preferences for wildfire 

protection FL

Huang et al. (2013)

Toward full economic valuation of forest fuels-reduction 

treatments. AZ

Kaval et al. (2008) Using GIS to test homeowner willingness to pay to reduce wildfire CO

Loomis et al. (2008) Contingent valuation of fuel hazard reduction treatments. CA, FL

Loomis et al. (2009)

Willingness to pay function for fuel treatments to reduce wildfire 

acreage CA, FL

Loomis et al. (2010) Forest Service Use of Nonmarket Valuation in Fire Economics US Wide

Loomis et al. (2015) Are WTP Estimates for Wildfire Risk Reductions Transferrable CA, FL

Mueller et al. (2009)

Hedonic Analysis of the Short and Long-Term Effects of Repeated 

Wildfires Southern CA

Sanchez et al. (2016) Valuing hypothetical wildfire impacts on recreation demand. Southern CA

Stetler et al. (2010)

The effects of wildfire and environmental amenities on property 

values MT

Azevedo et al. (2015) Willingness to pay for Clear Lake cleanup. Clear Lake, IA

Banzhaf et al. (2016)

Policy Analysis: Valuation of Ecosystem Services in the S. 

Appalachian Mnts Appalachians

Bin et al. (2013)

Impact of Measures of Water Quality on Coastal Waterfront 

Property Values South FL

Carter et al. (2012)

The economic value of catching and keeping or releasing 

saltwater sport fish Southeast US

Cho et al. (2011)

Negative externalities on property values resulting from water 

impairment NC

Egan et al. (2009) Valuing Water Quality as a Function of Water Quality Measures IA

Foster (2008) Valuing preferences for water quality improvement FL

Freeman et al (2008) Statistical analysis of drinking water treatment plant costs Worldwide

Guignet et al. (2014) The Implicit Price of Aquatic Grasses Chesapeake Bay

Hindlesy et al. (2011) Addressing onsite sampling in recreation site choice models Southeastern US

Huang et al. (2010)

Quantifying the economic effects of hypoxia on a fishery for brown 

shrimp NC
6-2

Author Short Title Location Notes

Huang et al. (2011)

Management of an annual fishery in the presence of ecological 

stress Southeast

Jackson et al. (2012)

Economic value of stream degradation across the central 

Appalachians MD, PA,VA,WV

Jacobsen et al. (2009)

Income Effects on Global Willingness to Pay for Biodiversity 

Conservation Worldwide

Ji et al. (2016)

Water-based Recreation/Water Quality Indices Revealed 

Preference Approach IA

Keeler et al. (2013) Advances in measuring the value of water quality to people MN, IA

Keeler et al. (2015) Recreational demand for clean water MN, IA

Liao et al. (2016)

The Effects of Ambient Water Quality and watermilfoil on Property 

Values ID

Liu et al. (2014)

Estimating the impact of water quality on surrounding property 

values OH

Martín-López (2008)

Economic Valuation of Biodiversity Conservation: the Meaning of 

Numbers Worldwide

Melstrom et al. (2013) Valuing recreational fishing in the Great Lakes Great Lakes

Meyer (2012) Intertemporal valuation of river restoration MN

Moore et al. (2015)

A stated preference study of the Chesapeake Bay and watershed 

lakes Chesapeake Bay

Morgan et al. (2010)

Water Quality and Residential Property Values: A Natural 

Experiment VA

Nelson et al. (2015) Estimate the total economic value of improving water quality UT

Netusil et al. (2014) Valuing water quality in urban watersheds OR and WA

Ramachandran et al. 

(2015) Validating Spatial Hedonic Modeling with a Behavioral Approach Cape Cod, MA

Richardson et al. (2009)

The total economic value of threatened, endangered and rare 

species Worldwide

Roberts et al. (2008) Preferences for environmental quality under uncertainty Tulsa, OK

Tuttle et al. (2011)

Hedonic Studies: Valuing Environmental Amenities in Northern 

New York. Northern NY

Tuttle et al. (2015) A hedonic analysis of lake water quality in the Adirondacks Adirondacks

Van Houtven et al. 

(2014)

Expert elicitation and stated preference methods to value 

ecosystem service VA

Viscusi et al. (2008) The economic value of water quality US Wide

Walsh et al. (2010) Spatial Extent of Water Quality Benefits in Urban Housing Markets FL

Walsh et al. (2011)

The spatial extent of water quality benefits in urban housing 

markets FL

Walsh et al. (2015b) Modeling the Property Price Impact of Water Quality Chesapeake Bay

Welle et al. (2008) Property owners' willingness to pay for restoring impaired waters MN

Welle et al. (2011)

Property owners' willingness to pay for water quality 

improvements MN

Phaneuf et al. (2013) Measuring nutrient reduction benefits for policy analysis Southeastern US
6-3

FULL CITATIONS FROM Table 6-1

Amiraslany, A., et al. (2012). "Comparing the economic value of fire conditions and the effects 

of wildfire on hiking in New-Mexico recreation sites using contingent the valuation 

method and travel cost method." American-Eurasian Journal of Agricultural & 

Environmental Sciences 12(9): 1196-1204.

Azevedo, C.D., Herriges Sr, J.A. and Kling, C.L., 2015. Willingness to pay for Clear Lake 

cleanup. Iowa Ag Review, 7(3), p.2.

Banzhaf, H.S., Burtraw, D., Criscimagna, S.C., Cosby, B.J., Evans, D.A., Krupnick, A.J. and 

Siikamäki, J.V., 2016. Policy Analysis: Valuation of Ecosystem Services in the Southern 

Appalachian Mountains. Environmental science & technology, 50(6), pp.2830-2836.

Beier, C.M., Caputo, J., Lawrence, G.B. and Sullivan, T.J., 2017. Loss of ecosystem services due 

to chronic pollution of forests and surface waters in the Adirondack region (USA). 

Journal of Environmental Management, 191, pp.19-27.

Bin, O., and Czajkowski, J.. 2013. The Impact of Technical and Non-technical Measures of 

Water Quality on Coastal Waterfront Property Values in South Florida. Marine Resource 

Economics, 28(1): 43-63.

Caputo, J., Beier, C.M., Fakhraei, H. and Driscoll, C.T., 2016. Impacts of acidification and 

potential recovery on the expected value of recreational fisheries in Adirondack lakes 

(USA). Environmental Science & Technology.

Caputo, J., Beier, C.M., Sullivan, T.J. and Lawrence, G.B., 2016. Modeled effects of soil 

acidification on long-term ecological and economic outcomes for managed forests in the 

Adirondack region (USA). Science of the Total Environment, 565, pp.401-411.

Carter, D.W. and Liese, C., 2012. The economic value of catching and keeping or releasing 

saltwater sport fish in the Southeast USA. North American Journal of Fisheries 

Management, 32(4), pp.613-625.

Cho, S.-H., Roberts, R.K., Kim S. G. 2011. Negative externalities on property values resulting 

from water impairment: The case of the Pigeon River Watershed, Ecological Economics, 

Volume 70, Issue 12, 15 October 2011, Pages 2390-2399, ISSN 0921-8009.

Christman, L., et al. (2014). "Willingness to Pay to Reduce Wild Fire Risk in Wild land-Urban 

Interface: A Comparative Analysis of Public Programs and Private Actions." Agricultural 

and Applied Economics Association>2014 Annual Meeting, July 27-29, 2014, 

Minneapolis, Minnesota http://purl.umn.edu/170703: 23p.

Egan, K. J., Herriges, J. A., Kling, C. L. and Downing, .n Journal of Agricultural Economics, 

Vol. 91, No. 1, pp. 106-123.

Englin, J., et al. (2008). Wildfire and the Economic Value of Wilderness Recreation. The 

Economics of Forest Disturbances: Wildfires, Storms, and Invasive Species. T. P. 
6-4

Holmes, J. P. Prestemon and K. L. Abt, Forestry Sciences series, vol. 79. New York: 

Springer: 191-208.

Foster C. (2008). "Valuing preferences for water quality improvement in the Ichetucknee 

Springs system: a case study from Columbia county, FL." Dissertation

Freeman et al. (2008). "Statistical analysis of drinking water treatment plant costs, source water 

quality and land cover characteristics." United States Trust for Public Land

Guignet, D., Griffiths, C., Klemick, H. and Walsh, P., 2014. The Implicit Price of Aquatic 

Grasses. US EPA National Center for Environmental Economics Working paper 

Series, 6.

Hindsley, P., Landry, C.E. and Gentner, B., 2011. Addressing onsite sampling in recreation site 

choice models. Journal of Environmental Economics and Management, 62(1), pp.95-110.

Holmes, T. P., et al. (2013). "The effects of personal experience on choice-based preferences for 

wildfire protection programs." International Journal of Wildland Fire 22(2): 234-245.

Huang L., Smith M.D. (2011) Management of an annual fishery in the presence of ecological 

stress: The case of shrimp and hypoxia. Ecol Econ 70: 688-697.

Huang L., Smith M.D., Craig J.K. (2010) Quantifying the economic effects of hypoxia on a 

fishery for brown shrimp Farfantepenaeus aztecus. Marine and Coastal Fisheries: 

Dynamics, Management, and Ecosystem Science 2: 232-248.

Huang, C. H., et al. (2013). "Toward full economic valuation of forest fuels-reduction 

treatments." Journal of Environmental Management 130: 221-231.

Jackson, L. E., Rashleigh, B., and McDonald, M. E. (2012). "Economic value of stream 

degradation across the central Appalachians." The Journal of Regional Analysis & 

Policy 42(3): 188-197.

Jacobsen, J. B., Hanley, N. (2009). "Are There Income Effects on Global Willingness to Pay for 

Biodiversity Conservation? ." Environmental and resource economics 43(2): 137-160.

Ji, Y., Keiser, D. Water-based Recreation and Water Quality Indices: A Revealed Preference 

Approach. No. 235886. Agricultural and Applied Economics Association, 2016.

Kaval, P., Loomis, J. (2008). Using GIS to test the relationship between homeowner willingness 

to pay to reduce wildfire and landscape characteristics. Proceedings of the second 

international symposium on fire economics, planning, and policy: a global view. Gen. 

Tech. Rep. PSW-208. Albany, CA: US Department of Agriculture, Forest Service, 

Pacific Southwest Research Station 

Keeler, B.L., Wood, S.A., Polasky, S., Kling, C., Filstrup, C.T., Downing, J.A., 2015. 

Recreational demand for clean water: evidence from geotagged photographs by visitors 

to lakes. Frontiers in Ecology and the Environment, 13(2), pp.76-81.
6-5

Keeler, B. L. Water and well-being: Advances in measuring the value of water quality to people. 

Diss. University of Minnesota, 2013.

Liao, F.H., Wilhelm, F.M., Solomon, M., 2016. The Effects of Ambient Water Quality and 

Eurasian Watermilfoil on Lakefront Property Values in the Coeur d’Alene Area of 

Northern Idaho, USA. Sustainability, 8(1), p.44.

Liu, H., Gopalakrishnan, S., Browning, D., Herak, P., Sivandran, G. 2014. Estimating the impact 

of water quality on surrounding property values in Upper Big Walnut Creek Watershed in 

Ohio for dynamic optimal control. Agricultural and Applied Economics Association, 

2014 Annual Meeting, July 27-29, 2014, Minneapolis, Minnesota.

Loomis, J., Gonzalez-Caban A. (2010). "Forest Service Use of Nonmarket Valuation in Fire 

Economics: Past, Present, and Future." Journal of Forestry 108(8): 389-396.

Loomis, J., Gonzalez-Caban A (2008). Contingent valuation of fuel hazard reduction treatments. 

The economics of forest disturbances: wildfires, storms and invasive species / edited by 

Thomas P. Holmes, Jeffrey P. Prestemon, Karen L. Abt, Dordrecht ; London : Springer, 

2008: 229-243.

Loomis, J. B., et al. (2009). "Willingness to pay function for two fuel treatments to reduce 

wildfire acreage burned: A scope test and comparison of White and Hispanic 

households." Forest Policy and Economics 11(3): 155-160.

Loomis, J., et al. (2015). "Are WTP Estimates for Wildfire Risk Reductions Transferrable from 

Coast to Coast? Results of a Choice Experiment in California and Florida." Agricultural 

and Applied Economics Association 2015 AAEA & WAEA Joint Annual Meeting, July 

26-28, San Francisco, California http://purl.umn.edu/202661: 25p.

Martín-López, B., et al. (2008). "Economic Valuation of Biodiversity Conservation: the Meaning 

of Numbers " Conservation biology the journal of the Society for Conservation Biology 

22(3): 624-635.

Melstrom, Richard T., Lupi, F. (2013) "Valuing recreational fishing in the Great Lakes." North 

American Journal of Fisheries Management 33.6: 1184-1193.

Meyer, A. (2012). "Intertemporal valuation of river restoration." Environmental Resource 

Economics. DOI 10.1007/s10640-012-9580-4

Moore et al. (2015). "A stated preference study of the Chesapeake Bay and watershed lakes." 

National Center for Environmental Economics Working Paper Series 15-06.

Morgan, O. A., Hamilton, S. E., Chung,V.. 2010. Water Quality and Residential Property 

Values: A Natural Experiment Approach. Appalachian State University, Department of 

Economics Working Paper.

Mueller, J., et al. (2009). "Do Repeated Wildfires Change Homebuyers' Demand for Homes in 

High-Risk Areas? A Hedonic Analysis of the Short and Long-Term Effects of Repeated 
6-6

Wildfires on House Prices in Southern California." Journal of Real Estate Finance and 

Economics 38(2): 155-172.

Nelson, N.M., Loomis, J.B., Jakus, P.M., Kealy, M.J., von Stackelburg, N., Ostermiller, J., 2015. 

Linking ecological data and economics to estimate the total economic value of improving 

water quality by reducing nutrients. Ecological Economics, 118, pp.1-9.

Netusil, N. R., Kincaid, M, Chang, H. (2014). “Valuing water quality in urban watersheds: A 

comparative analysis of Johnson Creek, Oregon, and Burnt Bridge Creek, Washington.” 

Water Resources Research 50(5): 4254-4268.

Phaneuf, D. J., von Haefen, R. H., Mansfield, C. A., Van Houtven, G. L. (2013). Measuring 

nutrient reduction benefits for policy analysis using linked non-market valuation and 

environmental assessment models: Final report on stated preference surveys. Prepared for 

U.S. Environmental Protection Agency, Office of Water.

Ramachandran, M. (2015). Validating Spatial Hedonic Modeling with a Behavioral Approach: 

Measuring the Impact of Water Quality Degradation on Coastal Housing Markets.

Richardson, L. A., et al. (2012). "The hidden cost of wildfires: Economic valuation of health 

effects of wildfire smoke exposure in Southern California." Journal of Forest Economics 

18(1): 14-35.

Richardson, L., Loomis, J. (2009). The total economic value of threatened, endangered and rare 

species: an updated meta-analysis. Ecological Economics, 68(5), pp.1535-1548.

Richardson, L., et al. (2013). "Valuing Morbidity from Wildfire Smoke Exposure: A Comparison 

of Revealed and Stated Preference Techniques." Land Economics 89(1): 76-100.

Roberts, D.C., Boyer, T.A., Lusk, J.L. (20080. Preferences for environmental quality under 

uncertainty. Ecological Economics, 66(4), pp.584-593.

Sanchez, J. J., et al. (2016). "Valuing hypothetical wildfire impacts with a Kuhn-Tucker model 

of recreation demand." Forest Policy and Economics 71: 63-70.

Stetler, K. M., et al. (2010). "The effects of wildfire and environmental amenities on property 

values in northwest Montana, USA." Ecological Economics 69(11): 2233-2243.

Tuttle, C.M. and Heintzelman, M.D. (2015). A loon on every lake: A hedonic analysis of lake 

water quality in the Adirondacks. Resource and Energy Economics, 39, pp.1-15.

Tuttle, C.M., 2011. Hedonic Studies: Valuing Environmental Amenities in Northern New York.

Van Houtven, G., Mansfield, C., Phaneuf, D. J., von Haefen, R., Milstead, B., Kenney, M. A., 

Reckhow, K. H. (2014). Combining expert elicitation and stated preference methods to 

value ecosystem services from improved lake water quality. Ecological Economics, 99, 

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Viscusi W.K., Huber J., Bell J. (2008) The economic value of water quality. Environ Resour 

Econ 41: 169-187.

Walsh P.J., Milon J.W., Scrogin D.O. (2011) The spatial extent of water quality benefits in urban 

housing markets. Land Econ 87: 628-644.

Walsh, P, et al. (2010). The Spatial Extent of Water Quality Benefits in Urban Housing Markets. 

Working Paper Series. U.S. Environmental Protection Agency: National Center for 

Environmental Economics, Washington, DC: 35.

Walsh, P., Griffiths, C., Guignet, D., Klemick, H. (2015). Modeling the Property Price Impact of 

Water Quality in 14 Chesapeake Bay Counties Patrick Walsh, Charles Griffiths.

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improvements: contingent valuation estimates in two central Minnesota watersheds." 

Journal of Applied Business and Economics 12(1): 81-94.
United States

Environmental Protection

Agency

Office of Air Quality Planning and Standards

Health and Environmental Impact Division

Research Triangle Park, NC

Publication No. EPA-452/D-18-001

August 2018