Risk and Exposure Assessment for Review 

of the Secondary National Ambient Air Quality 

Standards for Oxides of Nitrogen and Oxides 

of Sulfur

First Draft, Chapters 1–6
[This page intentionally left blank.] 
EPA-452/P-08-005a

August 2008 

RISK AND EXPOSURE ASSESSMENT FOR REVIEW OF THE SECONDARY

NATIONAL AMBIENT AIR QUALITY STANDARDS FOR OXIDES OF NITROGEN 

AND OXIDES OF SULFUR 

FIRST DRAFT, CHAPTERS 1–6 

U.S. Environmental Protection Agency 

Office of Air Quality Planning and Standards 

Research Triangle Park, NC 
Disclaimer

DRAFT ii August 2008 

DISCLAIMER

This draft document has been prepared by staff from the Health and Environmental 

Impacts and Air Quality Analysis Divisions of the Office of Air Quality Planning and Standards, 

and the Clean Air Markets Division, Office of Air Programs, U.S. Environmental Protection 

Agency. Any opinions, findings, conclusions, or recommendations are those of the authors and 

do not necessarily reflect the views of EPA. This document is being circulated to obtain review 

and comment from the Clean Air Scientific Advisory Committee (CASAC) and the general 

public. Comments on this draft document should be addressed to Dr. Anne Rea, U.S. 

Environmental Protection Agency, Office of Air Quality Planning and Standards, C539-02, 

Research Triangle Park, North Carolina 27711 (email: rea.anne@epa.gov).
Table of Contents 

DRAFT iii August 2008 

TABLE OF CONTENTS 

Disclaimer....................................................................................................................................... ii 

Acronyms and Abbreviations ........................................................................................................ ix 

Key Terms...................................................................................................................................... xi 

1. Introduction.............................................................................................................................. 1-1 

1.1 Rationale and Background for joint review.................................................................. 1-1 

1.2 History........................................................................................................................... 1-5 

1.2.1 History of the Secondary NO2 NAAQS............................................................ 1-5 

1.2.2 History of the Secondary SO2 NAAQS ............................................................1-6 

1.2.3 History of Related Assessments and Agency Actions...................................... 1-7 

1.3 Scope of the Risk and Exposure Assessment for the Current Review ....................... 1-10 

1.3.1 Species of Nitrogen Included in Analyses...................................................... 1-10 

1.3.2 Species of Sulfur Included in the Analyses .................................................... 1-12 

1.3.3 Overview of Nitrogen- and Sulfur-related Ecological Effects ....................... 1-13 

1.4 Policy Relevant Questions ..........................................................................................1-17 

1.5 References................................................................................................................... 1-20 

2. Overview of Risk and Exposure Assessment .......................................................................... 2-1 

2.1 Introduction................................................................................................................... 2-1 

2.2 Seven-Step Approach....................................................................................................2-4 

2.3 Ecosystem services ....................................................................................................... 2-5

2.3.1 Aquatic Acidification........................................................................................ 2-9 

2.3.2 Terrestrial Acidification.................................................................................... 2-9 

2.3.3 Aquatic Nutrient Enrichment............................................................................2-9 

2.3.4 Terrestrial Nutrient Enrichment........................................................................2-9 

2.3.5 Sulfur and Mercury Methylation .................................................................... 2-10 

2.4 Uncertainty.................................................................................................................. 2-13 

2.5 References................................................................................................................... 2-14 

3. Sources, Ambient Concentrations, and Deposition ................................................................. 3-1 

3.1 Science Overview ......................................................................................................... 3-1

3.1.1 Sources of Nitrogen and Sulfur......................................................................... 3-1 

3.1.1.1 NOx ....................................................................................................3-1 

3.1.1.2 NHx ....................................................................................................3-3 

3.1.1.3 SOx ....................................................................................................3-4 

3.1.2 Ambient Concentrations and Policy-Relevant Background ............................. 3-7 

3.1.3 Non-ambient Loadings of Nitrogen and Sulfur ................................................ 3-7 

3.1.4 Deposition......................................................................................................... 3-8 

3.1.4.1 Nitrogen ............................................................................................. 3-8 

3.1.4.2 Sulfur................................................................................................ 3-11 

3.2 Datasets....................................................................................................................... 3-12 

3.2.1 Spatial and Temporal Characterization of Concentrations and Deposition 

for Case Study Areas ...................................................................................... 3-13 

3.2.1.1 Purpose and Intent............................................................................3-13 

3.2.1.2 Data and Tools ................................................................................. 3-14 
Table of Contents 

DRAFT iv August 2008 

3.2.1.3 Analytical Techniques ..................................................................... 3-17 

3.2.1.4 Results and Findings........................................................................ 3-20 

3.2.1.5 Uncertainty....................................................................................... 3-54 

3.2.2 Contributions of Emissions of NOx and NH3 to Deposition of Nitrogen ....... 3-54 

3.2.2.1 Purpose and Intent............................................................................3-54 

3.2.2.2 Data and Tools ................................................................................. 3-55 

3.2.2.3 Analytical Techniques ..................................................................... 3-57 

3.2.2.4 Results and Findings........................................................................ 3-69 

3.2.2.5 Uncertainty..................................................................................... 3-112 

3.3 References................................................................................................................. 3-113

4. Acidification ............................................................................................................................ 4-1 

5. Nutrient Enrichment................................................................................................................. 5-1 

6. Additional Effects .................................................................................................................... 6-1 

6.1 Sulfur and Mercury Methylation .................................................................................. 6-1 

6.1.1 Science Background.......................................................................................... 6-1 

6.1.2 Qualitative Analysis.......................................................................................... 6-3 

6.1.2.1 Watershed Influences......................................................................... 6-6 

6.1.2.2 Conclusions........................................................................................ 6-6 

6.2 Nitrous Oxide (N2O)................................................................................................... 6-10 

6.2.1 Science Overview ........................................................................................... 6-10 

6.2.2 Qualitative Analysis........................................................................................ 6-13 

6.3 Carbon Sequestration.................................................................................................. 6-13 

6.3.1 Terrestrial Ecosystems.................................................................................... 6-13 

6.3.1.1 Forests.............................................................................................. 6-14 

6.3.1.2 Arctic Tundra................................................................................... 6-17 

6.3.1.3 Grasslands........................................................................................ 6-18 

6.3.2 Aquatic Ecosystems........................................................................................ 6-19 

6.3.2.1 Wetlands ..........................................................................................6-19 

6.3.2.2 Freshwater Aquatic Ecosystems ...................................................... 6-20 

6.4 References................................................................................................................... 6-22 

7. Synthesis and Integration of Case Study Results to Influence the Standard Setting 

Process ................................................................................................................................... 7-1 

8. Considerations in the Structure of the NOx/SOx Secondary Standard(s)................................. 8-1 

9. Analyses for Second Draft Risk Assessment........................................................................... 9-1 

Attachment 1 – Working Outline 

Attachment 2 – National Sensitive Areas Analysis 

Attachment 3 – Aquatic Acidification Case Study 

Attachment 4 – Terrestrial Acidification Case Study 

Attachment 5 – Aquatic Nutrient Enrichment Case Study 

Attachment 6 – Terrestrial Nutrient Enrichment Case Study 
Table of Contents 

DRAFT v August 2008 

LIST OF FIGURES 

Figure 1.3-2 Schematic diagram of the cycle of sulfur species in the atmosphere. ............... 1-13 

Figure 1.3-3 Nitrogen and sulfur cycling and interactions in the environment...................... 1-16 

Figure 1.4-1 Possible structure of a secondary NAAQS for NOx and SOx based on an 

ecological indicator............................................................................................1-19 

Figure 2.3-1 Millennium ecosystem assessment categorization of ecosystem services 

and their links to human well-being..................................................................... 2-6

Figure 2.3-2 Pathway from nitrogen deposition to valuation for an aquatic system................ 2-8 

Figure 3.1-1 Annual NOx emissions by state for 2002............................................................. 3-2 

Figure 3.1-2 The distribution of NOx emissions across major source categories in 2002........ 3-3 

Figure 3.1-3 Annual emissions of NH3 by state during 2002................................................... 3-4 

Figure 3.1-4 Annual SO2 emissions in 2006 for acid rain program cooperating facilities....... 3-5 

Figure 3.1-5 The distribution of SO2 emissions across major source categories in 2002.........3-6 

Figure 3.1-6 Total wet and dry nitrogen deposition in the United States in 2002.................... 3-9 

Figure 3.1-7 Total wet and dry oxidized nitrogen deposition in the United States in 

2002.................................................................................................................... 3-10 

Figure 3.1-8 Total wet and dry reduced nitrogen deposition in the United States in 

2002.................................................................................................................... 3-11 

Figure 3.1-9 Total wet and dry sulfur deposition in the United States in 2002......................3-12 

Figure 3.2-1 CMAQ 12-km eastern and western modeling domains..................................... 3-15 

Figure 3.2-2 Case study areas in the eastern United States. ................................................... 3-19 

Figure 3.2-3 Adirondacks Case Study Area. ..........................................................................3-22 

Figure 3.2-4 Contribution to annual total 2002 modeled deposition for the Adirondack 

Case Study Area................................................................................................. 3-24 

Figure 3.2-5 Contribution from wet/dry reduced and oxidized nitrogen to modeled 2002 

annual total nitrogen deposition in the Adirondack Case Study Area............... 3-25 

Figure 3.2-6 Modeled 2002 annual total nitrogen deposition across the Adirondack 

Case Study Area................................................................................................. 3-26 

Figure 3.2-7 Modeled 2002 annual wet deposition of nitrogen across the Adirondack 

Case Study Area................................................................................................. 3-27 

Figure 3.2-8 Modeled 2002 annual dry deposition of nitrogen across the Adirondack 

Case Study Area................................................................................................. 3-28 

Figure 3.2-9 Modeled 2002 annual total nitrogen deposition across the Northeast. .............. 3-29 

Figure 3.2-10a Modeled 2002 annual wet oxidized nitrogen deposition across the 

Adirondack Case Study Area............................................................................. 3-30 

Figure 3.2-10b Modeled 2002 annual dry oxidized nitrogen deposition across the 

Adirondack Case Study Area............................................................................. 3-31 

Figure 3.2-10c Modeled 2002 annual wet reduced nitrogen deposition across the 

Adirondack Case Study Area............................................................................. 3-32 

Figure 3.2-10d Modeled 2002 annual dry reduced nitrogen deposition across the 

Adirondack Case Study Area............................................................................. 3-33 

Figure 3.2-11 Percent of annual total nitrogen deposition by season for the Adirondack 

Region, based on 2002 CMAQ modeling..........................................................3-34 

Figure 3.2-12 Percent of annual precipitation and reduced nitrogen deposition by quarter 

for the Adirondack Region, based on 2002 CMAQ modeling. ......................... 3-35 
Table of Contents 

DRAFT vi August 2008 

Figure 3.2-13 Percent of annual precipitation and oxidized nitrogen deposition by 

quarter for the Adirondack Region, based on 2002 CMAQ modeling.............. 3-35 

Figure 3.2-14 Percent of 2002 annual precipitation and oxidized nitrogen deposition by 

month for the Adirondack Region, based on 2002 CMAQ modeling. .............. 3-36 

Figure 3.2-15 Percent of 2002 annual precipitation and reduced nitrogen deposition by 

month for the Adirondack Region, based on 2002 CMAQ modeling. .............. 3-36 

Figure 3.2-16 Percent by month of 2002 annual wet oxidized nitrogen deposition for 

selected sites in the Adirondack Case Study Area............................................. 3-38 

Figure 3.2-17 Percent by month of 2002 annual wet reduced nitrogen deposition for 

selected sites in the Adirondack Case Study Area............................................. 3-39 

Figure 3.2-18 Percent by month of 2002 annual dry oxidized nitrogen deposition for 

selected sites in the Adirondack Case Study Area............................................. 3-41 

Figure 3.2-19 Percent by month of 2002 annual dry reduced nitrogen deposition for 

selected sites in the Adirondack Case Study Area............................................. 3-42 

Figure 3.2-20 Percentages by component of 2002 annual sulfur deposition for the 

Adirondack Region. ...........................................................................................3-43 

Figure 3.2-21 Contribution to 2002 modeled annual total sulfur deposition. .........................3-44 

Figure 3.2-22 Modeled 2002 annual total sulfur deposition across the Adirondack Case 

Study Area. ........................................................................................................ 3-45 

Figure 3.2-23 Modeled 2002 annual wet sulfur deposition across the Adirondack Case 

Study Area. ........................................................................................................ 3-46 

Figure 3.2-24 Modeled 2002 annual dry sulfur deposition across the Adirondack Case 

Study Area. ........................................................................................................ 3-47 

Figure 3.2-25 Modeled 2002 annual total sulfur deposition across the Northeast. .................. 3-48 

Figure 3.2-26 Percent of annual total sulfur deposition by season for the Adirondack 

Region, based on 2002 CMAQ modeling..........................................................3-49 

Figure 3.2-27 Percent of annual precipitation and wet and dry sulfur deposition by 

quarter for the Adirondack Region, based on 2002 CMAQ modeling.............. 3-49 

Figure 3.2-28 Percent of 2002 annual precipitation and sulfur deposition by month for 

the Adirondack Region, based on 2002 CMAQ modeling................................3-50 

Figure 3.2-29 Percent by month of 2002 annual wet sulfur deposition for selected sites in 

the Adirondack Case Study Area....................................................................... 3-52 

Figure 3.2-30 Percent by month of 2002 annual dry sulfur deposition for selected sites in 

the Adirondack Case Study Area....................................................................... 3-53 

Figure 3.2-31 CMAQ 36 km grids in Neuse River Case Study Area. ..................................... 3-58 

Figure 3.2-32 CMAQ 36-km grids in Potomac Case Study Area. ........................................... 3-59 

Figure 3.2-33 CMAQ 36-km grids in Shenandoah Case Study Area....................................... 3-59 

Figure 3.2-34 CMAQ 36-km grids in Adirondack Case Study Area. ...................................... 3-60 

Figure 3.2-35 CMAQ 36-km grids in Red Spruce Case Study Area........................................ 3-60 

Figure 3.2-36 CMAQ 36-km grids in Sugar Maple Case Study Area...................................... 3-61 

Figure 3.2-37 CMAQ 36-km grids in Coastal Sage Case Study Area. .................................... 3-61 

Figure 3.2-38 CMAQ 36-km grids in Estuaries Case Study Area. ..........................................3-62 

Figure 3.2-39 Percent impact of zero-out of NOx emissions on total nitrogen deposition....... 3-63 

Figure 3.2-40 Percent impact of zero-out of NOx emissions on oxidized nitrogen 

deposition........................................................................................................... 3-64 
Table of Contents 

DRAFT vii August 2008 

Figure 3.2-41 Percent impact of zero-out of NOx emissions on reduced nitrogen 

deposition........................................................................................................... 3-65 

Figure 3.2-42 Percent impact of zero-out of NH3 emissions on total nitrogen deposition....... 3-66 

Figure 3.2-43 Percent impact of zero-out of NH3 emissions on reduced nitrogen................... 3-67 

Figure 3.2-44 Percent impact of zero-out of NH3 emissions on oxidized nitrogen 

deposition........................................................................................................... 3-68 

Figure 3.2-45 Percent impact of NOx anthropogenic United States emissions zero-out on 

total nitrogen deposition in the Sugar Maple Case Study Area.........................3-73 

Figure 3.2-46 Percent impact of NH3 anthropogenic United States emissions zero-out on 

total nitrogen deposition in the Sugar Maple Case Study Area.........................3-74 

Figure 3.2-47 Percent impact of NOx anthropogenic United States emissions zero-out on 

oxidized nitrogen deposition in the Sugar Maple Case Study Area. ................. 3-75 

Figure 3.2-48 Percent impact of NH3 anthropogenic United States emissions zero-out on 

reduced nitrogen deposition in the Sugar Maple Case Study Area. .................. 3-76 

Figure 3.2-49 Percent impact of NOx anthropogenic United States emissions zero-out on 

total nitrogen deposition in the Red Spruce Case Study Area........................... 3-78 

Figure 3.2-50 Percent impact of NH3 anthropogenic United States emissions zero-out on 

total nitrogen deposition in the Red Spruce Case Study Area........................... 3-79 

Figure 3.2-51 Percent impact of NOx anthropogenic United States emissions zero-out on 

oxidized nitrogen deposition in the Red Spruce Case Study Area. ................... 3-80 

Figure 3.2-52 Percent impact of NH3 anthropogenic United States emissions zero-out on 

reduced nitrogen deposition in the Red Spruce Case Study Area. .................... 3-81 

Figure 3.2-53 Percent impact of NOx anthropogenic United States emissions zero-out on 

total nitrogen deposition in the Adirondacks Case Study Area. ........................3-83 

Figure 3.2-54 Percent impact of NH3 anthropogenic United States emissions zero-out on 

total nitrogen deposition in the Adirondacks Case Study Area. ........................3-84 

Figure 3.2-55 Percent impact of NOx anthropogenic United States emissions zero-out on 

oxidized nitrogen deposition in the Adirondacks Case Study Area. ................. 3-85 

Figure 3.2-56 Percent impact of NH3 anthropogenic United States emissions zero-out on 

reduced nitrogen deposition in the Adirondacks Case Study Area.................... 3-86 

Figure 3.2-57 Percent impact of NOx anthropogenic United States emissions zero-out on 

total nitrogen deposition in the Potomac Case Study Area................................ 3-88 

Figure 3.2-58 Percent impact of NH3 anthropogenic United States emissions zero-out on 

total nitrogen deposition in the Potomac Case Study Area................................ 3-89 

Figure 3.2-59 Percent impact of NOx anthropogenic United States emissions zero-out on 

oxidized nitrogen deposition in the Potomac Case Study Area......................... 3-90 

Figure 3.2-60 Percent impact of NH3 anthropogenic United States emissions zero-out on 

reduced nitrogen deposition in the Potomac Case Study Area.......................... 3-91 

Figure 3.2-61 Percent impact of NOx anthropogenic United States emissions zero-out on 

total nitrogen deposition in the Shenandoah Case Study Area..........................3-93 

Figure 3.2-62 Percent impact of NH3 anthropogenic United States emissions zero-out on 

total nitrogen deposition in the Shenandoah Case Study Area..........................3-94 

Figure 3.2-63 Percent impact of NOx anthropogenic United States emissions zero-out on 

oxidized nitrogen deposition in the Shenandoah Case Study Area. .................. 3-95 

Figure 3.2-64 Percent impact of NH3 Anthropogenic United States emissions zero-out on 

reduced nitrogen deposition in the Shenandoah Case Study Area. ................... 3-96 
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DRAFT viii August 2008 

Figure 3.2-65 Percent impact of NOx anthropogenic United States emissions zero-out on 

total nitrogen deposition in the Neuse Case Study Area.................................... 3-98 

Figure 3.2-66 Percent impact of NH3 anthropogenic United States emissions zero-out on 

total nitrogen deposition in the Neuse Case Study Area.................................... 3-99 

Figure 3.2-67 Percent impact of NOx anthropogenic United States emissions zero-out on 

oxidized nitrogen deposition in the Neuse Case Study Area........................... 3-100 

Figure 3.2-68 Percent Impact of NH3 Anthropogenic United States Emissions Zero-out 

on Reduced Nitrogen Deposition in the Neuse Case Study Area.................... 3-101 

Figure 3.2-69 Percent impact of NOx Anthropogenic United States emissions zero-out on 

total nitrogen deposition in the Coastal Sage Case Study Area....................... 3-103 

Figure 3.2-70 Percent impact of NH3 anthropogenic United States emissions zero-out on 

total nitrogen deposition in the Coastal Sage Case Study Area....................... 3-104 

Figure 3.2-71 Percent impact of NOx anthropogenic United States emissions zero-out on 

oxidized nitrogen deposition in the Coastal Sage Case Study Area. ............... 3-105 

Figure 3.2-72 Percent impact of NOx anthropogenic United States emissions zero-out on 

oxidized nitrogen deposition in the Coastal Sage Case Study Area. ............... 3-106 

Figure 3.2-73 Percent impact of NOx anthropogenic United States emissions zero-out on 

total nitrogen deposition in the Estuaries Case Study Area............................. 3-108 

Figure 3.2-74 Percent impact of NH3 anthropogenic United States emissions zero-out on 

total nitrogen deposition in the Estuaries Case Study Area............................. 3-109 

Figure 3.2-75 Percent impact of NOx anthropogenic United States emissions zero-out on 

oxidized nitrogen deposition in the Estuaries Case Study Area. .....................3-110 

Figure 3.2-76 Percent Impact of NH3 anthropogenic United States emissions zero-out on 

reduced nitrogen deposition in the Estuaries Case Study Area. ...................... 3-111 

Figure 6.1-1 The mercury cycle in an ecosystem..................................................................... 6-3 

Figure 6.1-2 Biogeochemical process of mercury methylation................................................ 6-5 

Figure 6.1-3 Preliminary USGS map of mercury methylation-sensitive watersheds, 

derived from more than 55,000 water-quality sites and 2,500 watershed ........... 6-7 

Figure 6.1-4 Spatial and biogeochemical factors influencing methylmercury 

production. ...........................................................................................................6-9 

Figure 6.2-1 Percent of total U.S. emissions of greenhouse gases in CO2 equivalents.......... 6-11 

Figure 6.3-1 Interactions between the carbon and nitrogen cycles. .......................................6-15 

Figure 6.3-2 Nitrogen cycle in a freshwater ecosystem showing links to the 

phosphorous and carbon cycles. ........................................................................6-21 

LIST OF TABLES 

Table 2.1-1 Summary of Sensitive Characteristics, Indicators, Effects, and Ecosystem 

Services Impacted for Each Case Study Evaluated in This Review.................... 2-2 

Table 2.3-1 Ecological Impacts Associated with Acidification, Nutrient Enrichment, 

and Increased Mercury Methylation and Their Associated Ecosystem 

Services.............................................................................................................. 2-11 

Table 3.2-1 CMAQ Nitrogen and Sulfur Deposition Species............................................... 3-16 

Table 3.2-2 Formulas for Calculating Nitrogen and Sulfur Deposition................................ 3-16 
Key Terms 

DRAFT ix August 2008 

1 ACRONYMS AND ABBREVIATIONS 

2

3 ADR Adirondack Mountains of New York 

4 ANC acid neutralizing capacity 

5 AQCD Air Quality Criteria Document 

6 CAA Clean Air Act 

7 CAFO confined animal feeding operation 

8 CAIR Clean Air Interstate Rule 

9 CASAC Clean Air Scientific Advisory Committee 

10 CASTNet Clean Air Status and Trends Network 

11 CH4 methane 

12 CMAQ Community Multiscale Air Quality model. 

13 CO2 carbon dioxide 

14 CSS coastal sage scrub 

15 DIN dissolved inorganic nitrogen 

16 DOI U.S. Department of Interior 

17 EGU Electric Generating Units 

18 EPA U.S. Environmental Protection Agency 

19 FIA Forest Inventory and Analysis National Program 

20 GHG greenhouse gases 

21 GIS geographic information systems 

22 GPP gross primary productivity 

23 H2O water vapor 

24 H2SO4 sulfuric acid 

25 ha hectare 

26 HFC hydrofluorocarbon 

Hg+2 27 reactive mercury 

Hg028 elemental mercury 

29 HNO3 nitric acid

30 HONO nitrous acid 

31 HUC hydrologic unit code 

32 IDW inverse distance weighted 

33 IPM Integrated Planning Model 

34 ISA Integrated Science Assessment 

35 km kilometer 

36 LTER Long Term Ecological Monitoring and Research 

37 MEA Millennium Ecosystem Assessment 

38 (NH4)2SO4 ammonium sulfate 

39 N2 gaseous nitrogen 

40 N2O nitrous oxide 

41 N2O3 nitrogen trioxide

42 N2O4 nitrogen tetroxide

43 N2O5 dinitrogen pentoxide

44 NAAQS National Ambient Air Quality Standards 
Key Terms 

DRAFT x August 2008 

1 NADP National Atmospheric Deposition Program 

2 NAPAP National Acid Precipitation Assessment Program 

3 NEI National Emissions Inventory 

4 NEP net ecosystem productivity 

5 NH3 ammonia gas

NH4

+

6 ammonium ion 

7 NO nitric oxide 

8 NO2 nitrogen dioxide

NO2

-

9 reduced nitrite 

NO3

-

10 reduced nitrate 

11 NOx nitrogen oxides 

12 NOy total oxidized nitrogen 

13 NPP net primary productivity 

14 NRC National Research Council 

15 NTN National Trends Network 

16 NTR organic nitrate 

17 O3 ozone

18 OAQPS Office of Air Quality Planning and Standards 

19 PAN peroxyacyl nitrates 

20 PFC perfluorocarbons 

21 ppb parts per billion 

22 ppm parts per million 

23 ppt parts per trillion 

24 REA Risk and Exposure Assessment 

25 RSM response-surface model 

26 S2O3 thiosulfate 

27 S2O7 heptoxide

28 SAV submerged aquatic vegetation 

29 SF6 sulfur hexafluoride 

30 SO sulfur monoxide 

31 SO2 sulfur dioxide

32 SO3 sulfur trioxide 

33 SO4 wet sulfate 

34 SOM soil organic matter 

35 SOx sulfur oxides 

36 SRB sulfate-reducing bacteria 

37 TP total phosphorus 

38 USGS U.S. Geological Survey 

39 VOC volatile organic carbon 

μg/m3

40 micrograms per cubic meter 

41

42
Key Terms 

DRAFT xi August 2008 

1 KEY TERMS 

2 Acidification: The process of increasing the acidity of a system (e.g., lake, stream, forest soil). 

3 Atmospheric deposition of acidic or acidifying compounds can acidify lakes, streams, 

4 and forest soils. 

5 Adverse Effect: The response or component of an ecosystem that is deemed harmful in its 

6 function.

7 Air Quality Indicator: The substance or set of substances (e.g., PM2.5, NO2, SO2) occurring in 

8 the ambient air for which the National Ambient Air Quality Standards set a standard level 

9 and monitoring occurs. 

10 Alpine: The biogeographic zone made up of slopes above the tree line, characterized by the 

11 presence of rosette-forming herbaceous plants and low, shrubby, slow-growing woody 

12 plants.

13 Acid Neutralizing Capacity: A key indicator of the ability of water to neutralize the acid or 

14 acidifying inputs it receives. This ability depends largely on associated biogeophysical 

15 characteristics, such as underlying geology, base cation concentrations, and weathering 

16 rates.

17 Arid Region: A land region of low rainfall, where “low” is widely accepted to be less than 

18 250 mm precipitation per year. 

19 Assessment Endpoint: An ecological entity and its attributes that are considered welfare effects, 

20 as defined in Clean Air Act Section 302(h), and that are analyzed in the assessment. 

21 Base Cation Saturation: The degree to which soil cation exchange sites are occupied with base 

cations (e.g., Ca2+, Mg2+, K+

) as opposed to Al3+ and H+22 . Base cation saturation is a 

23 measure of soil acidification, with lower values being more acidic. There is a threshold 

24 whereby soils with base saturations less than 20% (especially between 10%–20%) are 

25 extremely sensitive to change. 
Key Terms 

DRAFT xii August 2008 

1 Biologically Relevant Indicator: A physical, chemical, or biological entity/feature that 

2 demonstrates a consistent degree of response to a given level of stressor exposure and 

3 that is easily measured/quantified to make it a useful predictor of biological, 

4 environmental, or ecological risk. 

5 Critical Load: A quantitative estimate of an exposure to one or more pollutants, below which 

6 significant harmful effects on specified sensitive elements of the environment do not 

7 occur, according to present knowledge. 

8 Denitrification: The anaerobic reduction of oxidized nitrogen (e.g., nitrate or nitrite) to gaseous 

9 nitrogen (e.g., N2O or N2) by denitrifying bacteria. 

10 Dry Deposition: The removal of gases and particles from the atmosphere to surfaces in the 

11 absence of precipitation (e.g., rain, snow) or occult deposition (e.g., fog). 

12 Ecological Dose: The concentration of a toxicant that inhibits a microbe-mediated ecological 

13 process by a designated percentage; for example, ED50 inhibits 50%. 

14 Ecological Exposure: The exposure of a nonhuman organism to an environmental stressor. 

15 Ecological Risk: The likelihood that adverse ecological effects may occur or are occurring as a 

16 result of exposure to one or more stressors (U.S. EPA, 1992). 

17 Ecological Risk Assessment: A process that evaluates the likelihood that adverse ecological 

18 effects may occur or are occurring as a result of exposure to one or more stressors (U.S. 

19 EPA, 1992). 

20 Ecosystem: The interactive system formed from all living organisms and their abiotic (i.e., 

21 physical and chemical) environment within a given area. Ecosystems cover a hierarchy of 

22 spatial scales and can comprise the entire globe, biomes at the continental scale, or small, 

23 well-circumscribed systems such as a small pond. 

24 Ecosystem Benefit: The value, expressed qualitatively, quantitatively, and/or in economic terms, 

25 where possible, associated with changes in ecosystem services that result either directly 

26 or indirectly in improved human health and/or welfare. Examples of ecosystem benefits 
Key Terms 

DRAFT xiii August 2008 

1 that derive from improved air quality include improvements in habitats for sport fish 

2 species, the quality of drinking water and recreational areas, and visibility. 

3 Ecosystem Function: The processes and interactions that operate within an ecosystem. 

4 Ecosystem Services: The ecological processes or functions having monetary or non-monetary 

5 value to individuals or society at large. These are (1) supporting services, such as 

6 productivity or biodiversity maintenance; (2) provisioning services, such as food, fiber, or 

7 fish; (3) regulating services, such as climate regulation or carbon sequestration; and (4) 

8 cultural services, such as tourism or spiritual and aesthetic appreciation. 

9 Elasticity: The percentage of change in the response variable for a 1% change in the input 

10 physical or meteorological characteristic. 

11 Eutrophication: The process by which nitrogen additions stimulate the growth of autotrophic 

12 biota, usually resulting in the depletion of dissolved oxygen.

13 Greenhouse Gas: Those gaseous constituents of the atmosphere, both natural and 

14 anthropogenic, that absorb and emit radiation at specific wavelengths within the spectrum 

15 of infrared radiation emitted by the earth’s surface, the atmosphere, and clouds. This 

16 property causes the greenhouse effect. Water vapor (H2O), carbon dioxide (CO2), nitrous 

17 oxide (N2O), methane (CH4), and ozone (O3) are the primary greenhouse gases in the 

18 earth’s atmosphere. In addition to CO2, N2O, and CH4, the Kyoto Protocol deals with the 

19 greenhouse gases sulfur hexafluoride (SF6), hydrofluorocarbons (HFCs), and 

20 perfluorocarbons (PFCs).

21 Key Elements of a Secondary National Ambient Air Quality Standard 

22 (a) Indicator 

23 (1) Atmospheric indicator (for a secondary NAAQS): The air pollutant(s) whose 

24 concentration(s) in the ambient air is (are) measured for purposes of determining 

25 compliance with the standard. This indicator may either be the actual criteria air pollutant 

26 listed in the Clean Air Act or an appropriate surrogate. For example, NO2 is the current 

27 indicator for the primary and secondary NOx NAAQS and represents all oxides of 
Key Terms 

DRAFT xiv August 2008 

1 nitrogen, while the current indicator for the primary and secondary SOx NAAQS is SO2,

2 representing all oxides of sulfur. 

3 (2) Ecological Indicator: A characteristic of an ecosystem that can provide quantitative 

4 information on its ecological condition. An indicator can be or contribute to a measure of 

5 integrity and sustainability. For example, one indicator of increasing acidification effects 

6 in an aquatic ecosystem is a decrease in acid neutralizing capacity (ANC). As a result, a 

7 reduction in ANC can lead to acidification of stream water and thereby changes to fish 

8 community structure, a good indicator of overall stream health.

9 (b) Level (of a secondary NAAQS): The specified value of the indicator or metric (see 

10 definition below) that is judged requisite to protect the public welfare from any known or 

11 anticipated adverse effects associated with the presence of the criteria pollutant in 

12 ambient air. The current level of the secondary NO2 NAAQS indicator is 0.053 ppm 

13 (same as primary). The current level of the secondary SO2 NAAQS indicator is 0.5 ppm. 

14 The level of the W126 metric proposed in the 2007 O3 Secondary NAAQS proposal was 

15 21 ppm-hrs. 

16 (c) Averaging Time (for a secondary NAAQS): The period of time over which 

17 exposure to metric values at or above the level of the standard is considered relevant. 

18 Over that time period, concentrations are averaged or cumulated to determine whether the 

19 level of the standard has been met. Examples include 3-hour, 8-hour, 24-hour, seasonal, 

20 or annual averages. The current averaging time for the secondary NO2 NAAQS is a year. 

21 The current averaging time for the secondary SO2 NAAQS is 3 hours. 

22 (d) Form (of a secondary NAAQS): The statistical characteristics of a standard that 

23 determine the stringency, stability, and robustness of that standard when implemented. 

24 For example, the current secondary O3 standard is set at the level of 0.075 ppm averaged 

25 over an 8-hour period. To attain this standard, however, only the 3-year average of the 

26 fourth highest daily maximum (rather than the maximum itself) 8-hour average O3

27 concentrations measured at each monitor within an area over each year is compared to the 

28 level of the standard and must not exceed 0.075 ppm. The current form of the secondary 
Key Terms 

DRAFT xv August 2008 

1 NO2 NAAQS is the annual arithmetic mean. The current form of the secondary SO2

2 NAAQS is not to be exceeded more than once per year. 

3 Nitrogen Enrichment: The process by which a terrestrial system becomes enhanced by nutrient 

4 additions to a degree that stimulates the growth of plant or other terrestrial biota, usually 

5 resulting in an increase in productivity. 

6 Nitrogen Saturation: The point at which nitrogen inputs from atmospheric deposition and other 

7 sources exceed the biological requirements of the ecosystem; a level beyond nitrogen 

8 enrichment. 

9 Occult Deposition: The removal of gases and particles from the atmosphere to surfaces by fog 

10 or mist. 

11 Semi-arid Regions: Regions of moderately low rainfall, which are not highly productive and are 

12 usually classified as rangelands. “Moderately low” is widely accepted as between 100- 

13 and 250-mm precipitation per year.

14 Sensitivity: The degree to which a system is affected, either adversely or beneficially, by an 

15 effect of NOx and/or SOx pollution (e.g., acidification, nutrient enrichment). The effect 

16 may be direct (e.g., a change in growth in response to a change in the mean, range, or 

17 variability of nitrogen deposition) or indirect (e.g., changes in growth due to the direct 

18 effect of nitrogen consequently altering competitive dynamics between species and 

19 decreased biodiversity).

20 Target Load: A policy-based metric that takes into consideration such factors as economic costs 

21 and time frame for emissions reduction. This can be lower than the critical load if a very 

22 sensitive area is to be protected in the short term, especially if deposition rates exceed 

23 critical loads. 

24 Total Reactive Nitrogen: This includes all biologically, chemically, and radiatively active 

nitrogen compounds in the atmosphere and biosphere, such as NH3, NH4

+

25 , NO, NO2,

HNO3, N2O, NO3

–

26 , and organic compounds (e.g., urea, amines, nucleic acids). 
Key Terms 

DRAFT xvi August 2008 

1 Valuation: The economic or non-economic process of determining either the value of 

2 maintaining a given ecosystem type, state, or condition, or the value of a change in an 

3 ecosystem, its components, or the services it provides. 

4 Variable Factors: Influences which by themselves or in combination with other factors may 

5 alter the effects on public welfare of an air pollutant (section 108 (a)(2)) 

6 (a) Atmospheric Factors: Atmospheric conditions that may influence transformation, 

7 conversion, transport, and deposition, and thereby, the effects of an air pollutant on 

8 public welfare, such as precipitation, relative humidity, oxidation state, and co-pollutants 

9 present in the atmosphere.

10 (b) Ecological Factors: Ecological conditions that may influence the effects of an air 

11 pollutant on public welfare once it is introduced into an ecosystem, such as soil base 

12 saturation, soil thickness, runoff rate, land use conditions, bedrock geology, and 

13 weathering rates. 

14 Vulnerability: The degree to which a system is susceptible to, and unable to cope with, the 

15 adverse effects of NOx and/or SOx air pollution.

16 Welfare Effects: The effects on soils, water, crops, vegetation, man-made materials, animals, 

17 wildlife, weather, visibility, and climate; as well as damage to and deterioration of 

18 property, hazards to transportation, and the effects on economic values and on personal 

19 comfort and well-being, whether caused by transformation, conversion, or combination 

20 with other air pollutants (Clean Air Act Section 302[h]). 

21 Wet Deposition: The removal of gases and particles from the atmosphere to surfaces by rain or 

22 other precipitation. 
Chapter 1 – Introduction 

DRAFT 1-1 August 2008 

1 1. INTRODUCTION 

2 1.1 RATIONALE AND BACKGROUND FOR JOINT REVIEW 

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

4 existing secondary (welfare-based) National Ambient Air Quality Standards (NAAQS) for 

5 nitrogen oxides (NOx) and sulfur oxides (SOx), which are currently defined in terms of nitrogen 

dioxide (NO2) and sulfur dioxide (SO2), respectively.1 6 Sections 108 and 109 of the Clean Air Act 

7 (CAA or the Act) govern the establishment and periodic review of the NAAQS and of the air 

8 quality criteria upon which the standards are based. The NAAQS are established for pollutants 

9 that may reasonably be anticipated to endanger public health or welfare and whose presence in 

10 the ambient air results from numerous or diverse mobile or stationary sources. The NAAQS are 

11 based on air quality criteria that reflect the latest scientific knowledge, useful in indicating the 

12 kind and extent of identifiable effects on public health or welfare that may be expected from the 

13 presence of the pollutant in ambient air. Based on periodic reviews of the air quality criteria and 

14 standards, EPA makes revisions to the criteria and standards and promulgates any new standards 

15 as may be appropriate. The Act also requires that an independent scientific review committee 

16 advise the Administrator as part of this NAAQS review process, a function now performed by 

17 the Clean Air Scientific Advisory Committee (CASAC). 

18 In conducting this periodic review of the NO2 and SO2 secondary NAAQS, EPA has 

19 decided to jointly assess the scientific information, associated risks, and standards relevant to 

20 protecting the public welfare from adverse effects associated with NOx and SOx. As noted below 

21 in Section 1.2, EPA has historically defined the NAAQS for these pollutants in terms of the 

22 specific compounds NO2 and SO2, which serve as the indicators of the broader set of compounds 

23 that comprise NOx and SOx, respectively. The species of nitrogen and sulfur compounds and the 

24 types of related ecological effects that are being considered within the scope of this review are 

25 discussed below in Section 1.3. A joint review of these pollutants is being conducted because 

26 NOx, SOx, and their associated transformation products are linked from an atmospheric 

27 chemistry perspective, as well as from an environmental effects perspective, and because the 

28 National Research Council (NRC) has recommended that EPA consider multiple pollutants, as 

29 appropriate, in forming the scientific basis for the NAAQS (NRC, 2004). This is the first time 

1

 EPA is also conducting separate reviews of the primary (health-based) NAAQS for NOx and SOx.
Chapter 1 – Introduction 

DRAFT 1-2 August 2008 

1 since NAAQS were established in 1971 that a joint review of these two pollutants has been 

2 conducted. There is a strong basis for considering these pollutants together at this time, building 

3 upon EPA’s and CASAC’s past recognition of the interactions of these pollutants and on the 

4 growing body of scientific information that is now available related to these interactions and 

5 associated ecological effects. A series of policy-relevant questions that help to frame this review 

6 are presented below in Section 1.4, together with an overview of how secondary NAAQS for 

7 NOx and SOx might be structured to reflect the complex interactions among relevant species of 

8 these pollutants in an ecologically meaningful way. 

9 As discussed in the Act (section 109(b)(2)), the purpose of a secondary NAAQS is to 

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

11 presence of such air pollutants in the ambient air. An adverse public welfare effect from a policy 

12 perspective may not be the same as an adverse ecological effect from a scientific perspective. 

13 While adversity to ecological systems from a scientific perspective will be used to inform the 

14 Administrator’s decision, the degree of change in an ecological indicator that corresponds to an 

15 adverse public welfare effect is ultimately decided by the Administrator. For example, levels of 

16 acid neutralizing capacity (ANC) below 100 may be a useful scientific indicator of ecological 

17 adversity for an array of ecological endpoints of concern. By considering this complete array of 

18 information on impacts, the Administrator makes the final determination on the specific level of 

19 ANC that will protect the public welfare from any known or anticipated adverse effects. Adverse 

20 public welfare effects are based on an assessment of how ecologically adverse impacts translate 

21 into adverse impacts on the public welfare. Adversity is not explicitly defined in the CAA: 

22 however, it can be inferred that adverse ecological impacts must have some corresponding 

23 impact on the well-being of human populations, through reductions in ecosystems services that 

24 might include direct services to humans (e.g., flood control) or indirect services (e.g., provision 

25 of habitat for endangered species). 

26 This joint review is organized according to the Agency’s current NAAQS review process, 

27 which consists of four major components and related documents: an Integrated Review Plan, an 

28 Integrated Science Assessment (ISA), a Risk and Exposure Assessment (REA), and a policy 

29 assessment and rulemaking notices. The Integrated Review Plan (EPA, 2007) provides the 

30 framework and schedule for this review and identifies policy-relevant questions to be addressed 

31 in the other components of the review. The second draft ISA (EPA, 2008), released for CASAC 
Chapter 1 – Introduction 

DRAFT 1-3 August 2008 

1 and public review on August 11, 2008, provides an integrative assessment of the relevant 

2 scientific information and forms the scientific basis for the assessments presented in this draft 

3 REA document. This first draft REA describes the progress to date on the assessments being 

4 conducted as part of the third component of the review process. To view related documents 

5 developed as part of the planning and science assessment phases of this review (e.g., integrated 

6 review plan, draft ISA), see http://www.epa.gov/ttn/naaqs/standards/no2so2sec/index.html. 

7 This risk and exposure assessment, when complete, will evaluate the exposures of 

8 ecological receptors to both ambient and deposited species of NOx and SOx as well as their 

9 transformation products (including reduced forms of ambient nitrogen), and assess, both 

10 quantitatively and qualitatively, the risks associated with these exposures. Where possible, we 

11 will characterize the contributions of various sources and forms of atmospheric nitrogen to these 

12 risks. The final risk and exposure assessment will be organized as follows (See Attachment 1 for 

13 a more detailed working outline), which, to the degree possible, is also reflected in this 

14 document: 

15  Chapter 1 provides an overview of this review; a history of past reviews and other 

16 relevant scientific assessments and Agency actions; a discussion of the scope of this joint 

17 NOx and SOx review; and a series of policy-relevant questions, together with an overview 

18 of how secondary NAAQS for NOx and SOx might be structured. 

19  Chapter 2 provides an overview of the risk and exposure assessment, including the scope 

20 and approach to assessing current conditions for a targeted effect, a summary of case 

21 study locations, initial progress on identifying ecosystem services, and a discussion on 

22 addressing uncertainly throughout the review. 

23  Chapter 3 addresses the relevant air quality issues associated with this review, including 

24 the sources, emissions, and deposition of total reactive nitrogen and sulfur, and the 

25 current contributions to ambient conditions. Both spatial and temporal characterizations 

26 of ambient concentrations of nitrogen and sulfur and the contributions of ambient 

27 concentrations of nitrogen and sulfur to deposition are explored in select case study areas. 

28  Chapter 4 focuses on acidification, with an overview of the relevant science and 

29 progress on assessing current conditions in select case study locations for both aquatic 

30 and terrestrial acidification. (Note: For this draft, this information is included in 

31 Attachments 2, 3, and 4) 
Chapter 1 – Introduction 

DRAFT 1-4 August 2008 

1  Chapter 5 focuses on nitrogen nutrient enrichment, with an overview of the relevant 

2 science and progress on assessing current conditions in select case study locations for 

3 both aquatic and terrestrial nitrogen nutrient enrichment. (Note: For this draft, this 

4 information is included in Attachments 2, 5, and 6)

5  Chapter 6 addresses additional effects, including a qualitative discussion on the 

6 interactions between sulfur and methylmercury production; nitrous oxide; and carbon 

7 sequestration.

8  Chapter 7 synthesizes the case study results and presents them in the context of a 

9 scientific structure that links fundamental scientific elements needed for a secondary 

10 standard based on a suite of ecological indicators. 

11  Chapter 8 explores more specifically how a secondary NAAQS might be structured to 

12 address the targeted ecological effects discussed in the risk assessment. 

13  Chapter 9 includes a brief list of ongoing analyses for the second draft of this risk 

14 assessment. 

15 Due to the very tight schedule under which this review is being conducted, some of the 

16 analyses we anticipated for this first draft risk and exposure assessment are as yet incomplete. 

17 Currently, the case study analyses have been initiated, but not completed. Our progress on 

18 identifying sensitive areas for each targeted effect and the case study analyses are included as 

19 Attachments 2 through 6. As the analyses progress, summaries of the case study analyses will be 

20 presented in Chapters 4 and 5 of the second draft risk and exposure assessment. This first draft 

21 also includes in Chapter 9 a brief list of ongoing analyses to be presented in the second draft risk 

22 and exposure assessment. Please note that Chapters 7 and 8 are not available for public review at 

23 this time. We anticipate that, if available, Chapters 7 and 8 will be released no later than the 

24 week of September 15, 2008. Otherwise, they will be included in the second draft risk and 

25 exposure assessment. The second draft of this document will also describe the assessment of 

26 risks and exposures associated with just meeting potential alternative standards. 
Chapter 1 – Introduction 

DRAFT 1-5 August 2008 

1 1.2 HISTORY 

2 1.2.1 History of the Secondary NO2 NAAQS

3 On April 30, 1971, EPA promulgated identical primary and secondary NAAQS for NO2

4 under Section 109 of the CAA. The standards were set at 0.053 parts per million (ppm), annual 

5 average (36 FR 8186). In 1982, EPA published Air Quality Criteria for Oxides of Nitrogen (U.S. 

6 EPA, 1982), which updated the scientific criteria for NOx, upon which the initial NO2 standards 

7 were based. On February 23, 1984, EPA proposed to retain these standards (49 FR 6866). After 

8 taking into account public comments, EPA published the final decision to retain these standards 

9 on June 19, 1985 (50 FR 25532).

10 On July 22, 1987, EPA announced that it was undertaking plans to revise the 1982 air 

11 quality criteria (52 FR 27580). In November 1991, EPA released an updated draft air quality 

12 criteria document for CASAC and public review and comment (56 FR 59285). The draft 

13 document provided a comprehensive assessment of the available scientific and technical 

14 information on health and welfare effects associated with NO2 and other oxides of nitrogen. 

15 CASAC reviewed the draft document at a meeting held on July 1, 1993, and concluded in a 

16 closure letter to the Administrator that the document “provides a scientifically balanced and 

17 defensible summary of current knowledge of the effects of this pollutant and provides an 

18 adequate basis for EPA to make a decision as to the appropriate NAAQS for NO2” (Wolff, 

19 1993). The Air Quality Criteria Document (AQCD) for the Oxides of Nitrogen was then finalized 

20 (U.S. EPA, 1993).

21 EPA also prepared a Staff Paper that summarized an air quality assessment for NO2

22 conducted by the Agency (McCurdy, 1994). This Staff Paper summarized and integrated the key 

23 studies and scientific evidence contained in the revised air quality criteria document and 

24 identified the critical elements to be considered in the review of the NO2 NAAQS. CASAC 

25 reviewed two drafts of the Staff Paper and concluded in a closure letter to the Administrator that 

26 the document provided a “scientifically adequate basis for regulatory decisions on nitrogen 

27 dioxide” (Wolff, 1995). In September of 1995, EPA finalized the Staff Paper, entitled Review of 

28 the National Ambient Air Quality Standards for Nitrogen Dioxide: Assessment of Scientific and 

29 Technical Information (U.S. EPA, 1995). 
Chapter 1 – Introduction 

DRAFT 1-6 August 2008 

1 In October 1995, the Administrator announced her proposed decision not to revise either 

2 the primary or secondary NAAQS for NO2 (60 FR 52874; October 11, 1995). A year later, the 

3 Administrator made a final determination not to revise the NAAQS for NO2 after careful 

4 evaluation of the comments received on the proposal (61 FR 52852; October 8, 1996). The level 

5 for both the existing primary and secondary NAAQS for NO2 is 0.053 ppm (100 micrograms per 

cubic meter [μg/m36 ] of air), annual arithmetic average, calculated as the arithmetic mean of the 

7 1-hour NO2 concentrations. 

8 1.2.2 History of the Secondary SO2 NAAQS 

9 Based on the 1970 SOx criteria document (DHEW, 1970), EPA promulgated primary and 

10 secondary NAAQS for SO2 under Section 109 of the CAA on April 30, 1971 (36 FR 8186). The 

11 secondary standards included a standard at 0.02 ppm in an annual arithmetic mean and a 3-hour 

12 average of 0.5 ppm, not to be exceeded more than once per year. These secondary standards 

13 were established solely on the basis of vegetation effects evidence. In 1973, revisions made to 

14 Chapter 5 (“Effects of Sulfur Oxide in the Atmosphere on Vegetation”) of Air Quality Criteria 

15 for Sulfur Oxides (U.S. EPA, 1973) indicated that it could not properly be concluded that the 

16 vegetation injury reported resulted from the average SO2 exposure over the growing season, 

17 rather than from short-term peak concentrations. Therefore, EPA proposed (38 FR 11355) and 

18 then finalized (38 FR 25678) a revocation of the annual mean secondary standard. At that time, 

19 EPA was aware that SOx have other public welfare effects, including effects on materials, 

20 visibility, soils, and water. However, the available data were considered insufficient to establish 

21 a quantitative relationship between specific SOx concentrations and effects needed for setting a 

22 standard (38 FR 25679). 

23 In 1979, EPA announced that it was revising the Air Quality Criteria Document (AQCD) 

24 for sulfur oxides concurrently with that for particulate matter and would produce a combined 

25 particulate matter and sulfur oxides criteria document. Following its review of a draft revised 

26 criteria document in August 1980, CASAC concluded that acid deposition was a topic of 

27 extreme scientific complexity because of the difficulty in establishing firm quantitative 

28 relationships among (1) emissions of relevant pollutants (e.g., SO2 and oxides of nitrogen), (2) 

29 formation of acidic wet and dry deposition products, and (3) effects on terrestrial and aquatic 

30 ecosystems. CASAC also noted that acid deposition involves, at a minimum, several different 
Chapter 1 – Introduction 

DRAFT 1-7 August 2008 

1 criteria pollutants: oxides of sulfur, oxides of nitrogen, and the fine particulate fraction of 

2 suspended particles. CASAC felt that any document on this subject should address both wet and 

3 dry deposition, since dry deposition was believed to account for at least one half of the total acid 

4 deposition problem. 

5 For these reasons, CASAC recommended that a separate, comprehensive document on 

6 acid deposition be prepared prior to any consideration of using the NAAQS as a regulatory 

7 mechanism for the control of acid deposition. CASAC also suggested that a discussion of acid 

8 deposition be included in the AQCDs for nitrogen oxides and PM and SOx. Following CASAC 

9 closure on the criteria document for SO2 in December 1981, EPA’s Office of Air Quality 

10 Planning and Standards (OAQPS) published a Staff Paper in November 1982, but the paper did 

11 not directly assess the issue of acid deposition. Instead, EPA subsequently prepared the 

12 following documents: The Acidic Deposition Phenomenon and Its Effects: Critical Assessment 

13 Review Papers, Volumes I and II (U.S. EPA, 1984a, b), and The Acidic Deposition Phenomenon 

14 and Its Effects: Critical Assessment Document (U.S. EPA, 1985) (53 FR 14935 -14936). These 

15 documents, though they were not considered criteria documents and did not undergo CASAC 

16 review, represented the most comprehensive summary of relevant scientific information 

17 completed by EPA at that point. 

18 On April 26, 1988 (53 FR 14926), EPA proposed not to revise the existing primary and 

19 secondary standards. This proposal regarding the secondary SO2 NAAQS was due to the 

20 Administrator’s conclusions that (1) based upon the then-current scientific understanding of the 

21 acid deposition problem, it would be premature and unwise to prescribe any regulatory control 

22 program at that time, and (2) when the fundamental scientific uncertainties had been reduced 

23 through ongoing research efforts, EPA would draft and support an appropriate set of control 

24 measures. 

25 1.2.3 History of Related Assessments and Agency Actions 

26 In 1980, the Congress created the National Acid Precipitation Assessment Program 

27 (NAPAP) in response to growing public concern about acidic deposition. The NAPAP was given 

28 a broad 10-year mandate to examine the causes and effects of acidic deposition and to explore 

29 alternative control options to alleviate acidic deposition and its effects. During the course of the 
Chapter 1 – Introduction 

DRAFT 1-8 August 2008 

1 program, the NAPAP issued a series of publicly available interim reports prior to the completion 

2 of a final report in 1990 (NAPAP, 1990). 

3 In spite of the complexities and significant remaining uncertainties associated with the 

4 acid deposition problem, it soon became clear that a program to address acid deposition was 

5 needed. The Amendments to the CAA passed by Congress and signed into law by the President 

6 on November 15, 1990, included numerous separate provisions related to the acid deposition 

7 problem that reflect a comprehensive approach envisioned by Congress. The primary and most 

8 important of the provisions, Title IV of these Amendments, established the Acid Rain Program to 

9 reduce emissions of SO2 by 10 million tons and NOx emissions by 2 million tons from 1980 

10 emission levels in order to achieve reductions over broad geographic regions. In this provision, 

11 Congress included a statement of findings that led them to take action, concluding that (1) the 

12 presence of acid compounds and their precursors in the atmosphere and in deposition from the 

13 atmosphere represents a threat to natural resources, ecosystems, materials, visibility, and public 

14 health; (2) the problem of acid deposition is of national and international significance; and (3) 

15 current and future generations of Americans will be adversely affected by delaying measures to 

16 remedy the problem. 

17 Second, Congress authorized the continuation of the NAPAP in order to assure that the 

18 research and monitoring efforts already undertaken would continue to be coordinated and would 

19 provide the basis for an impartial assessment of the effectiveness of the Title IV program. 

20 Third, Congress, clearly envisioning that further action might be necessary in the long 

21 term to address any problems remaining after implementation of the Title IV program and, 

22 reserving judgment on the form that action could take, included Section 404 of the 1990 

23 Amendments (Clean Air Act Amendments of 1990, Pub. L. 101-549, § 404) requiring EPA to 

24 conduct a study on the feasibility and effectiveness of an acid deposition standard or standards to 

25 protect “sensitive and critically sensitive aquatic and terrestrial resources.” At the conclusion of 

26 the study, EPA was to submit a report to Congress. Five years later, EPA submitted its report, 

27 entitled Acid Deposition Standard Feasibility Study: Report to Congress (U.S. EPA, 1995) in 

28 fulfillment of this requirement. The Report concluded that establishing acid deposition standards 

29 for sulfur and nitrogen deposition may at some point in the future be technically feasible, 

30 although appropriate deposition loads for these acidifying chemicals could not be defined with 

31 reasonable certainty at that time. 
Chapter 1 – Introduction 

DRAFT 1-9 August 2008 

1 Fourth, the 1990 Amendments also added new language to sections of the CAA 

2 pertaining to the scope and application of the secondary NAAQS designed to protect the public 

3 welfare. Specifically, the definition of “public welfare” in Section 302(h) was expanded to state

4 that the welfare effects identified should be protected from adverse effects associated with 

5 criteria air pollutants “...whether caused by transformation, conversion, or combination with 

6 other air pollutants.” That change has particular relevance to this review because the 

7 transformation products of NOx and SOx are associated with environmental impacts. 

8 In 1999, seven Northeastern states cited this amended language in Section 302(h) in a 

9 petition asking EPA to use its authority under the NAAQS program to promulgate secondary 

10 NAAQS for the criteria pollutants associated with the formation of acid rain. The petition stated 

11 that this language “clearly references the transformation of pollutants resulting in the inevitable 

12 formation of sulfate and nitrate aerosols and/or their ultimate environmental impacts as wet and 

13 dry deposition, clearly signaling Congressional intent that the welfare damage occasioned by 

14 sulfur and nitrogen oxides be addressed through the secondary standard provisions of Section 

15 109 of the Act.” The petition further stated that “recent federal studies, including the NAPAP 

16 Biennial Report to Congress: An Integrated Assessment, document the continued-and increasing17 damage being inflicted by acid deposition to the lakes and forests of New York, New England 

18 and other parts of our nation, demonstrating that the Title IV program had proven insufficient.” 

19 The petition also listed other adverse welfare effects associated with the transformation of these 

20 criteria pollutants, including impaired visibility, eutrophication of coastal estuaries, global 

21 warming, and tropospheric ozone and stratospheric ozone depletion. 

22 In a related matter, the Office of the Secretary of the U.S. Department of Interior (DOI) 

23 requested in 2000 that EPA initiate a rulemaking proceeding to enhance the air quality in 

24 national parks and wilderness areas in order to protect resources and values that are being 

25 adversely affected by air pollution. Included among the effects of concern identified in the 

26 request were the acidification of streams, surface waters, and/or soils; eutrophication of coastal 

27 waters; visibility impairment; and foliar injury from ozone. 

28 In a Federal Register notice in 2001, EPA announced receipt of these requests and asked 

29 for comment on the issues raised in them. EPA stated that it would consider any relevant 

30 comments and information submitted, along with the information provided by the petitioners and 
Chapter 1 – Introduction 

DRAFT 1-10 August 2008 

1 DOI, before making any decision concerning a response to these requests for rulemaking (65 FR 

2 48699).

3 The most recent 2005 NAPAP report states that “... scientific studies indicate that the 

4 emission reductions achieved by Title IV are not sufficient to allow recovery of acid-sensitive 

5 ecosystems. Estimates from the literature of the scope of additional emission reductions that are 

6 necessary in order to protect acid-sensitive ecosystems range from approximately 40-80% 

7 beyond full implementation of Title IV.... The results of the modeling presented in this Report to 

8 Congress indicate that broader recovery is not predicted without additional emission reductions” 

9 (NAPAP, 2005). 

10 Given the state of the science as described in the ISA and in other recent reports, such as 

11 the NAPAP’s above, EPA believes it is appropriate, in the context of evaluating the adequacy of 

12 the current NO2 and SO2 secondary standards in this review, to revisit the question of the 

13 appropriateness and the feasibility of setting a secondary NAAQS to address remaining known 

14 or anticipated adverse public welfare effects resulting from the acidic and nutrient deposition of 

15 these criteria pollutants and their transformation products. This document comprises the risk and 

16 exposure assessment portion of the review. 

17 1.3 SCOPE OF THE RISK AND EXPOSURE ASSESSMENT FOR THE 

18 CURRENT REVIEW 

19 1.3.1 Species of Nitrogen Included in Analyses 

20 The sum of mono-nitrogen oxides, NO2 and NO, typically are referred to as nitrogen 

21 oxides (NOx) in the atmospheric science community. More formally, the family of nitrogen 

22 oxides includes any gaseous combination of nitrogen and oxygen, e.g., NO2, NO, nitrogen 

23 dioxide (N2O), nitrogen trioxide (N2O3), nitrogen tetroxide (N2O4), and dinitrogen pentoxide 

24 (N2O5).

25 With regard to NOx, it is also necessary in this review to distinguish between the 

26 definition of “nitrogen oxides” as it appears in the enabling legislation related to the NAAQS and 

27 the definition commonly used in the air pollution research and management community. In this 

28 document, the terms “oxides of nitrogen” and “nitrogen oxides” refer to all forms of oxidized 

29 nitrogen compounds, including nitric oxide (NO), NO2, and all other oxidized nitrogen30 containing compounds transformed from NO and NO2. This definition is supported by Section 
Chapter 1 – Introduction 

DRAFT 1-11 August 2008 

1 108(c) of the CAA, which states that “Such criteria [for oxides of nitrogen] shall include a 

2 discussion of nitric and nitrous acids, nitrites, nitrates, nitrosamines, and other carcinogenic and 

3 potentially carcinogenic derivatives of oxides of nitrogen.” 

4 By contrast, within the scientific community, the terms “oxides of nitrogen” and 

5 “nitrogen oxides” typically refer only to the mono-nitrogen oxides, NO and NO2, and their sum 

6 is commonly abbreviated as NOx. The term used by the scientific community to represent the 

7 complete set of oxidized nitrogen compounds, including those listed in CAA Section 108(c), is 

8 total oxidized nitrogen (NOy). NOy includes all nitrogen oxides, as well as gaseous and 

9 particulate nitrate species such as nitric acid (HNO3), peroxyacyl nitrates (PAN), and particle10 phase ammonium nitrates. 

11 In addition to oxidized forms of nitrogen, reduced forms of nitrogen also contribute to the 

12 atmospheric chemistry that leads to the deposition of ambient nitrogen species to the 

13 environment. Reduced atmospheric nitrogen species include ammonia gas (NH3) and ammonium 

ion (NH4

+

14 ), the sum of which is referred to as NHx. Total reactive nitrogen is recognized as the 

15 combination of both oxidized and reduced forms of nitrogen that are biologically available; i.e., 

16 forms other than the stable form of gaseous nitrogen (N2). Atmospheric nitrogen deposition often 

17 is delineated further as dry (gas and particulate phases) or as wet (precipitation-derived ion 

18 phase) (see Figure 1.3-1).

19 In many areas, multiple forms of nitrogen from a variety of atmospheric and other 

20 sources enter ecosystems. The scientific community has long recognized that reactive nitrogen 

21 can impact ecosystems; it is the total amount of reactive nitrogen entering the ecosystem that is 

22 most relevant when assessing these impacts. That is, the effects from atmospheric deposition of 

23 nitrogen to ecosystems are due to both oxidized and reduced forms, rather than to one form 

24 alone. As a result, much of the published research on ecological response to nitrogen does not 

25 differentiate between the various sources of nitrogen, but instead reports only total reactive 

26 nitrogen inputs to the ecosystem. 
Chapter 1 – Introduction 

DRAFT 1-12 August 2008 

1

2 Figure 1.3-1. Schematic diagram of the cycle of reactive, oxidized nitrogen species in the 

atmosphere. (IN refers to inorganic particulate species [e.g., Na+

,Ca++ 3 ], MPP to 

4 multiphase processes, hv to a solar photon, and R to an organic radical. Particulate-phase 

5 organic nitrates are also formed from the species on the right side of the figure) (U.S. 

6 EPA, 2007.) 

7 1.3.2 Species of Sulfur Included in the Analyses 

8 SO2 is one of a group of substances known as oxides of sulfur, or SOx, which include 

9 multiple gaseous (e.g., SO2, sulfur monoxide [SO], sulfur trioxide [SO3], thiosulfate 

10 (S2O3),heptoxide (S2O7), and particulate (e.g., ammonium sulfate (NH4)2SO4) species (Figure

11 1.3-2). Acidification can result from the atmospheric deposition of SOx and NOx; in acid 

12 deposition, these species combine with water in the atmosphere to form sulfuric acid (H2SO4)

13 and HNO3. Acidification is an environmental effect primarily due to sulfur in which acid 

14 precipitation lowers the natural pH of waterbodies and damages terrestrial ecosystems. Over the 

15 past few decades, acidification of waterbodies has been recognized as an environmental issue 

16 throughout Europe and North America, and steps have been taken to control SOx and NOx

17 emissions and to identify the recovery of the impacted ecosystems. Due to known acute effects 

18 on plants, SO2 served as the chemical indicator for SOx species in previous NAAQS reviews. 
Chapter 1 – Introduction 

DRAFT 1-13 August 2008 

1

2 Figure 1.3-2. Schematic diagram of the cycle of sulfur species in the atmosphere. 

3 1.3.3 Overview of Nitrogen- and Sulfur-related Ecological Effects 

4 Nitrogen and sulfur interactions in the environment are highly complex. Both are 

5 essential, and sometimes limiting, nutrients needed for growth and productivity. Excess nitrogen 

6 or sulfur can lead to acidification, nutrient enrichment, and eutrophication. The current 

7 secondary NAAQS were set to protect against direct damage to vegetation by exposure to gas8 phase NOx or SOx. Acute and chronic exposures to SO2 can have phytotoxic effects on 

9 vegetation, such as foliar injury, decreased photosynthesis, and decreased growth. Similarly, 

10 exposure to sufficient concentrations of NO2, NO, PAN, and HNO3 can cause foliar injury, 

11 decreased photosynthesis, and decreased growth. In addition, these gas-phase NOx species may 

12 contribute to nitrogen saturation in some areas of the United States. The second draft ISA 

13 indicates there is little new evidence for direct effects of exposure to gas-phase NOx or SOx on 

14 vegetation at current concentrations in the United States (U.S. EPA, 2008).

15 Emissions of NOx and SOx compounds into the air react through a complex series of gas16 phase and heterogeneous reactions to produce additional intermediate compounds and final 

17 products. These reactions with NOx and SOx often occur under the same meteorological 

18 influences as those acting on formation of ozone (O3) and secondary aerosols. These nitrogen19 and sulfur-containing compounds are removed from the air by deposition—wet (rain, snow), 
Chapter 1 – Introduction 

DRAFT 1-14 August 2008 

1 cloud and fog, and dry (gases and particles)—onto surfaces. Prevailing winds can transport these 

2 compounds hundreds of miles and across state and national borders.

3 Deposition of chemical species derived from NOx and SOx to the environment can affect 

4 ecosystem biogeochemistry, structure, and function. This can result from acidification that 

5 occurs when NOx and SOx emissions react in the atmosphere to form strong acids (i.e., sulfuric 

6 (H2SO4) and nitric (HNO3) acid), which are then deposited onto the landscape. Acidification 

7 causes a cascade of effects that alter both terrestrial and aquatic ecosystems. These effects 

8 include slower growth, the injury or death of forest vegetation, and localized extinction of fish 

9 and other aquatic species.

10 The second draft ISA highlights evidence from two well-studied areas to provide more 

11 detail on how acidification affects ecosystems: the Adirondacks (New York) and Shenandoah 

12 National Park (Virginia) (U.S., EPA, 2008, Section 3.2) In the Adirondacks, the current rates of 

13 nitrogen and sulfur deposition exceed the amount that would allow recovery of the most acid 

14 sensitive lakes. In the Shenandoah National Park, past sulfate has accumulated in the soil and is 

15 slowly released from the soil into stream water, where it causes acidification and makes this 

16 region sensitive to current loading. Models suggest that the number of acidic streams will 

17 increase under the current deposition rates, but that re-acidification can be prevented if 

deposition is kept between 9–15 kg N ha1

 yr1 and 0–6 kg S ha1 yr118 (U.S. EPA, 2008; Section 

19 3.2). The second draft ISA highlights forests in the Adirondack Mountains of New York, Green 

20 Mountains of Vermont, White Mountains of New Hampshire, the Allegheny Plateau of 

21 Pennsylvania, and high-elevation forest ecosystems in the southern Appalachians as the regions 

22 most sensitive to terrestrial acidification effects from acidifying deposition (U.S. EPA, 2008; 

23 Section 3.2). In the risk and exposure assessment, we target these areas for the air quality 

24 modeling presented in Chapter 3 and the case study analyses in Chapter 4. 

25 In addition to acidification, NOx acts with other forms of reactive nitrogen (including 

26 ammonia-based nitrogen) to increase the total amount of nitrogen available in terrestrial 

27 ecosystems and high elevation lakes. Reactive nitrogen deposition may contribute to the total 

28 reactive nitrogen load of some wetland and aquatic ecosystems that receive reactive nitrogen 

29 through multiple pathways (i.e., agricultural land runoff and wastewater effluent) (U.S. EPA, 

30 2008; Section 3.3). Nitrogen deposition alters primary productivity that leads to changes in 

31 community composition and eutrophication. In aquatic ecosystems, deposition loads of 
Chapter 1 – Introduction 

DRAFT 1-15 August 2008 

approximately 1.5–2 kg N ha1 yr11 are reported to cause alterations in diatom communities of 

2 freshwater lakes and impair water quality in the western United States (U.S. EPA, 2008; Section 

3 3.3). In estuarine ecosystems, additional nitrogen from atmospheric and non-atmospheric sources 

4 contributes to increased phytoplankton and algal productivity, which leads to eutrophication. 

5 Estuary eutrophication is a detrimental ecological problem indicated by water quality 

6 deterioration, resulting in numerous adverse effects, including hypoxic zones, species mortality, 

7 and harmful algal blooms (HABs). The second draft ISA indicates that the contribution of 

8 atmospheric deposition to total nitrogen loads can be greater than 40% in highly eutrophic 

9 estuaries. The Chesapeake Bay is an example of a large, well-studied estuary that receives 21%–

10 30% of its total nitrogen load from the atmosphere (U.S. EPA, 2008; Section 3.3). 

11 In terrestrial ecosystems, there are multiple chemical indicators for the alteration of the 

12 biogeochemical cycling of nitrogen that is caused by reactive nitrogen deposition. Nitrate 

13 leaching is a well-documented effect that indicates the ecosystem is receiving more nitrogen than 

it uses; the onset of leaching is calculated to be between 5.6 and 10 kg ha-1 yr-1 14 for Eastern 

15 forests (U.S. EPA, 2008; Section 3.3). Nitrogen deposition can result in impacts prior to the 

16 onset of nitrate leaching. For example, nitrogen deposition affects primary productivity thereby 

17 altering terrestrial carbon cycling. This may result in shifts in population dynamics, species 

18 composition, community structure and, in extreme instances, ecosystem type. Lichen are the 

most sensitive terrestrial taxa, with documented adverse effects occurring at 3 kg N ha1

 yr119

(Pacific Northwest and southern California); 5 kg N ha1

 yr120 correlates to the onset of declining 

biodiversity within grasslands (Minnesota and the E.U.), and at 10 kg N ha1 yr121 causes 

22 community composition of Alpine ecosystems and forest encroachment into temperate 

23 grasslands (U.S. EPA, 2008; Section 3.3). In the risk and exposure assessment, we target some of 

24 the aquatic and terrestrial ecosystems highlighted in the ISA for the air quality modeling 

25 presented in Chapter 3 and the case study analyses in Chapter 5.

26 In terrestrial and wetland ecosystems, reactive nitrogen deposition alters biogenic sources 

27 and sinks of N2O and methane, two potent greenhouse gases, resulting in a higher emission to the 

28 atmosphere of these gases. Terrestrial soil is the largest source of N2O, accounting for 60% of 

29 global emission. Reactive nitrogen deposition increases the flux of N2O in coniferous forests, 

30 deciduous forests, grasslands, and wetlands. Nitrogen deposition significantly reduces methane 

31 uptake in coniferous and deciduous forests, with a reduction of 28% and 45%, respectively. In 
Chapter 1 – Introduction 

DRAFT 1-16 August 2008 

1 wetlands, nitrogen addition increases methane production, but has no significant effect on 

2 methane uptake (U.S. EPA, 2008; Section 3.4). These effects are addressed qualitatively in 

3 Chapter 6. 

4 There is increasing evidence on the relationship between sulfur deposition and increased 

5 methylation of mercury in aquatic environments; this effect occurs only where other factors are 

6 present at levels within a range to allow methylation. The production of methylmercury requires 

7 the presence of sulfate and mercury, but the amount of methylmercury produced varies with 

8 oxygen content, temperature, pH, and supply of labile organic carbon (U.S. EPA, 2008; Section 

9 3.4). In watersheds where changes in sulfate deposition did not produced an effect, one or several 

10 of those interacting factors were not in the range required for meaningful methylation to occur 

11 (U.S. EPA, 2008; Section 3.4). Watersheds with conditions known to be conducive to mercury 

12 methylation can be found in the northeastern United States and southeastern Canada. The 

13 relationship between sulfur and methylmercury production is also addressed qualitatively in 

14 chapter 6. 

15 A summary illustration of NOx and SOx effects on the environment is presented in Figure

16 1.3-3.

17

18 Figure 1.3-3. Nitrogen and sulfur cycling and interactions in the environment.
Chapter 1 – Introduction 

DRAFT 1-17 August 2008 

1 1.4 POLICY RELEVANT QUESTIONS 

2 For this secondary NAAQS review of NOx/SOx, the main policy-relevant questions 

3 include the following: 

4  What are the known or anticipated welfare effects influenced by ambient NOx and SOx,

5 and for which effects is there sufficient information available to be useful as a basis for 

6 considering distinct secondary standard(s)? 

7  What is the nature and magnitude of ecosystem responses to NOx and SOx that are 

8 understood to have known or anticipated adverse effects, and what is the variability 

9 associated with those responses (including ecosystem type, climatic conditions, 

10 environmental effects, and interactions with other environmental factors and pollutants)? 

11  To what extent do receptor surfaces influence the deposition of gases and particles (dry 

12 deposition), since dry deposition can contribute significantly to total deposition? 

13  Can effects from NOx be distinguished from effects due to total reactive nitrogen? 

14  What ecologically relevant metrics adequately capture the relationships between 

15 ecosystem exposures and responses for the known or anticipated welfare effects we are 

16 trying to protect against? 

17  To what extent do the current standards provide protection from the public welfare 

18 effects associated with NOx and SOx?

19 To the extent the evidence suggests that the current standards do not provide appropriate 

20 protection from known or anticipated adverse welfare effects associated with NOx and SOx, we 

21 will consider ecologically meaningful revisions to the current standards. Recognizing the high 

22 degree of complexity that exists in relationships between ambient air concentrations of NOx and 

23 SOx, deposition of nitrogen and sulfur into sensitive aquatic and terrestrial ecosystems, and 

24 associated potential adverse ecological effects, we anticipate that ecologically meaningful 

25 NAAQS would need to be structured so as to take into account such complexity. To provide 

26 some context for addressing key policy relevant questions that are salient in this review, we have 

27 developed a possible structure for standards that could be based on meaningful ecological 

28 indicators that would provide for protection against the range of potentially adverse ecological 

29 effects that are associated with the deposition of NOx and SOx. In so doing, we have considered 
Chapter 1 – Introduction 

DRAFT 1-18 August 2008 

1 how the basic elements of NAAQS standards—indicator, averaging time, form, and level— 

2 would be reflected in such a structure. 

3 Figure 1.4-1 depicts an example of a possible structure for an ecological effects based 

4 secondary NAAQS for NOx and SOx, together with the various elements that in combination 

5 would serve to define such a standard. While presented here for purposes of providing a context 

6 for understanding the additional policy-relevant questions, outlined below, that will help to frame 

7 our consideration of potential revisions to the current standards, the scientific foundations for this 

8 figure are more fully presented and discussed in the chapters below.

9 Chapter 3 provides information on current ambient levels of NOx and SOx and current 

10 deposition of nitrogen and sulfur, focusing on spatial and temporal patterns of deposition and 

11 impacts of ambient emissions on deposition. Chapters 4, 5, and 6 provide information on 

12 ecological effects and relevant ecological indicators of those effects. Chapter 7 synthesizes 

13 information across different endpoints and identifies impacts linked to ecosystem services that 

14 can help to inform the decision as to what levels of ecological indicators are protective against 

15 adverse public welfare effects. Linkages between ambient concentrations and deposition and 

16 between deposition and ecological indicators are discussed in Chapter 8, and the overall 

17 framework for linking atmospheric concentrations to ecological indicators through deposition is 

18 illustrated using the Adirondacks case study results for aquatic acidification effects. We 

19 anticipate that in the next draft, we will restructure the chapters such that Chapter 3 will discuss 

20 the linkages between ambient concentrations and deposition, while Chapters 4 and 5 will discuss 

21 the linkages between ecological indicators and deposition. In the next draft, Chapter 8 will focus 

22 on the legal support for a jointly structured, ecological effects based standard, as well as 

23 illustrations of the standard associated with different levels of protection against ecological 

24 impacts. 

25 As shown in Figure 1.4-1, this secondary NAAQS structure accounts for variable 

26 atmospheric and ecological factors that are critical aspects of the complex relationships that need 

27 to be reflected in ecologically meaningful standards. We anticipate that the deposition and 

28 ecological effect functions shown in Figure 1.4-1 may be based on complex formulas that 

29 incorporate factors related to atmospheric transformations, climate conditions, land uses, and 

30 ecosystem characteristics. A discussion of these formulas, along with an example, is provided in 

31 Chapter 8. 
Chapter 1 – Introduction 

DRAFT 1-19 August 2008 

1

2

3 Figure 1.4-1. Possible structure of a secondary NAAQS for NOx and SOx based on an 

4 ecological indicator. 

5 In considering potential alternative standards that may be structured as illustrated above, 

6 the following questions should be addressed: 

7  Does the available information provide support for considering different air quality 

8 indicators for NOx and SOx?

9  Does the available information provide support for the development of appropriate 

10 deposition functions, and what atmospheric and environmental factors are most relevant 

11 for such a function? 

12  Does the available information provide a basis for identifying relevant ecological 

13 indicators for the range of ecological effect endpoints being considered in the review? 

14  Does the available information provide support for the development of appropriate 

15 ecological effect functions that meaningfully relate to the ecological effect endpoints 

16 being considered, and what ecological factors are most relevant for such functions? 

17  For which ecological effect endpoints being considered is a joint NOx/SOx standard most 

18 appropriate, and for which endpoints would separate standards be more appropriate? 
Chapter 1 – Introduction 

DRAFT 1-20 August 2008 

1  Taking into consideration factors related to defining adversity for various ecological 

2 endpoints being considered, what range of levels, averaging times, and forms of 

3 alternative ecological indicators are supported by the information, and what are the 

4 uncertainties and limitations in that information? 

5  To what extent do specific levels, averaging times, and forms of alternative ecological 

6 indicators reduce adverse impacts attributable to NOx/SOx, and what are the uncertainties 

7 in the estimated reductions? 

8 In order to be able to answer these questions, we believe that the relevant scientific and 

9 policy issues that need to be addressed in the science, risk and exposure, and policy assessment 

10 portions of this review include the following: 

11  Identifying important chemical species in the atmosphere 

12  Identifying the atmospheric pathways that govern the chemical transformation, transport, 

13 and deposition of NOx and SOx to the environment 

14  Identifying the attributes of ecosystem receptors that govern their susceptibility to effects 

15 from deposition of nitrogen and sulfur compounds 

16  Identifying the relationships between ambient air quality indicators and ecological 

17 indicators of effects (through deposition) 

18  Identifying relationships between ecosystem services and ecological indicators. 

19 Evaluating alternative approaches to assess the adversity of effects on ecosystem 

20 services, including, but not limited to, economic valuation 

21  Evaluating environmental impacts and sensitivities to varying meteorological scenarios 

22 and climate conditions 

23  Evaluating the relationship between NOx and total deposition of reactive nitrogen, and 

24 between NOx and total nitrogen loadings that are related to ecological effects. 

25 These issues are addressed below in the discussions presented in Chapters 2 through 6. 

26 1.5 REFERENCES 

27 DHEW (U.S. Department of Health, Education, and Welfare). 1970. Air Quality Criteria for 

28 Sulfur Oxides. U.S. Government Printing Office, Washington, DC, AP-50. 
Chapter 1 – Introduction 

DRAFT 1-21 August 2008 

1 McCurdy, T.R. 1994. Analysis of High 1-Hour NO2 Values and Associated Annual Averages 

2 Using 1988-1992 Data. Report of the U.S. Environmental Protection Agency, Office of 

3 Air Quality Planning and Standards, Durham, NC. 

4 NAPAP (National Acid Precipitation Assessment Program). 2005. Report to Congress: An 

5 Integrated Assessment. National Acid Precipitation Assessment Program, Washington, 

6 DC.

7 NAPAP (National Acid Precipitation Assessment Program).1990. Acidic Deposition: State of 

8 Science and Technology, Volumes I-IV. National Acid Precipitation Assessment Program, 

9 Washington, DC. 

10 NRC (National Research Council). 2004. Air Quality Management in the United States.

11 Washington, DC: National Academies Press. 

12 U.S. EPA (Environmental Protection Agency). 1973. Effects of sulfur oxides in the atmosphere 

13 on vegetation; revised chapter 5 for air quality criteria for sulfur oxides. EPA/R3-73-

14 030. U.S. Environmental Protection Agency, Office of Research and Development, 

15 Research Triangle Park, NC.

16 U.S. EPA (Environmental Protection Agency). 1982. Air quality criteria for oxides of nitrogen.

17 EPA-600/8-82-026. U.S. Environmental Protection Agency, Office of Health and 

18 Environmental Assessment, Environmental Criteria and Assessment Office, Research 

19 Triangle Park, NC. 

20 U.S. EPA (Environmental Protection Agency). 1984a. The acidic deposition phenomenon and its 

21 effects: critical assessment review papers. Volume I: atmospheric sciences. EPA-600/8-

22 83-016-AF. U.S. Environmental Protection Agency, Office of Research and 

23 Development, Washington, DC. 

24 U.S. EPA (Environmental Protection Agency). 1984b. The acidic deposition phenomenon and its 

25 effects: critical assessment review papers. Volume II: effects sciences. EPA-600/8-83-

26 016-BF. U.S. Environmental Protection Agency, Office of Research and Development, 

27 Washington, DC.
Chapter 1 – Introduction 

DRAFT 1-22 August 2008 

1 U.S. EPA (Environmental Protection Agency). 1985. The acidic deposition phenomenon and its 

2 effects: critical assessment document. EPA/600/8-85/001. U.S. Environmental Protection 

3 Agency, Office of Acid Deposition, Environmental Monitoring, and Quality Assurance, 

4 Washington, DC.

5 U.S. EPA (Environmental Protection Agency). 1992. Framework for Ecological Risk 

6 Assessment. EPA/630/R-92/001. U.S. Environmental Protection Agency, Risk 

7 Assessment Forum, Washington, DC. February. 

8 U.S. EPA (Environmental Protection Agency). 1993. Air quality criteria for oxides of nitrogen.

9 EPA/600/8-91/049aF-cF. 3v. U.S. Environmental Protection Agency, Office of Health 

10 and Environmental Assessment, Environmental Criteria and Assessment Office, Research 

11 Triangle Park, NC.

12 U.S. EPA (Environmental Protection Agency). 1995. Review of the National Ambient Air Quality 

13 Standards for Nitrogen Dioxide: Assessment of Scientific and Technical Information.

14 OAQPS Staff Paper. EPA-452/R-95-005. U.S. Environmental Protection Agency, Office 

15 of Air Quality Planning and Standards, Research Triangle Park, NC. September. 

16 U.S. EPA (Environmental Protection Agency). 2007. Integrated Science Assessment for Oxides 

17 of Nitrogen and Sulfur Environmental Criteria (First External Review Draft).

18 EPA/600/R-07/145A. U.S. Environmental Protection Agency, Office of Research and 

19 Development, National Center for Environmental Assessment, Research Triangle Park, 

20 NC. December.

21 U.S. EPA (Environmental Protection Agency). 2008. Integrated Science Assessment for Oxides 

22 of Nitrogen and Sulfur – Environmental Criteria (Second External Review Draft).

23 Prepared by U.S. Environmental Protection Agency, National Center for Environmental 

24 Assessment-RTP Division, Office of Research and Development, Research Triangle 

25 Park, NC. EPA/600/R-08-082. August. 

26 Wolff, G.T. 1993. On a NOx-focused control strategy to reduce O3. Journal of the Air and Waste 

27 Management Association 43:1593–1596.
Chapter 1 – Introduction 

DRAFT 1-23 August 2008 

1 Wolff, G.T. 1995. CASAC closure letter for the 1995 OAQPS Staff Paper. Letter sent to Carol 

2 M. Browner, U.S. EPA Administrator. August 22. 
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Chapter 2 – Overview of Risk and Exposure Assessment 

DRAFT 2-1 August 2008 

1 2. OVERVIEW OF RISK AND EXPOSURE ASSESSMENT 

2 2.1 INTRODUCTION 

3 This REA focuses on ecosystem welfare effects that result from the deposition of total 

4 reactive nitrogen and sulfur. Because ecosystems are diverse in biota, climate, geochemistry, and 

5 hydrology, response to pollutant exposures can vary greatly. In addition, these diverse 

6 ecosystems are not distributed evenly across the United States. To target acidification and 

7 nitrogen and sulfur enrichment, this risk and exposure assessment addresses four main ecosystem 

8 effects on terrestrial and aquatic systems identified in the ISA: 

9  Aquatic acidification due to nitrogen and sulfur

10  Terrestrial acidification due to nitrogen and sulfur 

11  Aquatic nitrogen enrichment, including eutrophication 

12  Terrestrial nitrogen enrichment. 

13 In addition to these four effects, we have qualitatively addressed the influence of sulfur 

14 enrichment on methylmercury production and the effects associated with N2O and carbon 

15 sequestration.

16 Because these ecosystem effects are not evenly distributed across the United States, we 

17 have identified case studies for these analyses based on ecosystems identified as sensitive to 

18 nitrogen and/or sulfur deposition effects. This assessment builds upon the scientific information 

19 presented in the 2007 draft ISA (U.S. EPA, 2007). Taking into account the recommendations of 

20 the ISA authors, we have selected ecological indicator(s) and case study locations (U.S. EPA, 

21 2007). The choice of case study locations are summarized in Table 2.1-1 based on ecosystem 

22 characteristics, indicators, and ecosystem service information developed as part of the risk and 

23 exposure assessment. Detailed explanations of this information are presented in Attachments 2 

24 through 6. In the second draft risk and exposure assessment, we will evaluate the case study 

25 results for use in a broader characterization of national conditions to represent key components 

26 of our nation’s ecology, recognizing that some ecosystems, and the effects on them, may be too 

27 unique to be characterized broadly.
Chapter 2 – Overview of Risk and 

DRAFT 2-2 Table 2.1-1. Summary of Sensitive Characteristics, Indicators, Effects, and Ecosystem Services Impacted for Eac

Evaluated in This Review 

Targeted Effect Area 

Characteristics of Sensitivity (Variable Ecological Factors) 

Biological/ Chemical Indicator 

Ecological Indicator Ecological Effects 

Ecosystem ServiceImpacted 

Aquatic Acidification 

Geology, surface water flow, soil depth, weathering rates

[Al] pHANC

Species richness, abundance, composition, ANC

Species losses of fish, phytoplankton, zooplankton; changed community composition, ecosystem structure and function 

Fisheries, recreationtourism 

Terrestrial Acidification 

Geology, surface water flow, soil depth, weathering rates

Soil base saturation [Al] [Ca] C:N ratio 

Tree health Red spruce, sugar maple, ANC

Decreased tree growth, Increased susceptibility to stress, episodic dieback; changed community composition, ecosystem structure and function 

Food, natural habitat, tourism 

Aquatic Nutrient Enrichment 

N-limited systems, presence of nitrogen in surface water, eutrophication status, nutrient criteria,

Chlorophyll a,

macroalgae,dissolved oxygen, nuisance/toxic algal blooms, submerged aquatic vegetation (SAV) 

Eutrophication Index (EI) 

Habitat degradation, algal blooms, toxicity, hypoxia, anoxia, fish kills, decreases in biodiversity 

Fish populations, water quality, and habitat quality 
Chapter 2 – Overview of Risk and 

DRAFT 2-3 

Targeted Effect Area 

Characteristics of Sensitivity (Variable Ecological Factors) 

Biological/ Chemical Indicator 

Ecological Indicator Ecological Effects 

Ecosystem ServiceImpacted 

Terrestrial Nutrient Enrichment 

Presence of Acidophytic Lichens, anthropogenic land cover 

Cation Exchange Capacity, C:N ratios, Ca:Al ratios, NO3- leaching and export 

Species composition Species changes, 

nitrogen enrichment of soil, changes in fire regime, changes in nutrient cycling 

Loss of habitat, loss of biodiversity, recreation, water quality 

Sulfur and Mercury Methylation Potential 

Wetland type, presence of sulfate reducing bacteria, water pH, dissolved organic carbon, suspended particulate matter, 

Interaction between: dissolved organic carbon, temperature, anoxia, and sulfide land cover, precipitation response, and limnography 

MeHg concentrations in fish and shell fish 

Neurotoxic effects in fish and throughout food web 

Fishing, shell fishing, sports fishing, food, recreation,biodiversity 
Chapter 2 – Overview of Risk and Exposure Assessment 

DRAFT 2-4 August 2008 

1 To address the policy-relevant questions that guide the scope of this review, the risk and 

2 exposure assessment evaluates the relationships between atmospheric concentrations, deposition, 

3 biologically relevant exposures, ecosystem effects, and ecosystem services. To evaluate the 

4 nature and magnitude of ecosystem responses associated with adverse effects, this risk and 

5 exposure assessment examines various ways to quantify the relationship between air quality 

6 indicators, deposition of biologically available forms of nitrogen and sulfur, ecologically relevant 

7 indicators relating to deposition, exposure and effects on sensitive receptors, and related effects 

8 on ecosystem change and services. The intent of the assessment is to determine the exposure 

9 metrics that incorporate temporal considerations (i.e., biologically relevant timescales), 

10 pathways, and ecologically relevant indicators necessary to maintain the functioning of these 

11 ecosystems. To the extent feasible, we also evaluate the overall load to the system for nitrogen 

12 and sulfur, as well as the variability in ecosystem responses to these pollutants. In addition, we 

13 evaluate the contributions of atmospherically deposited nitrogen and sulfur relative to total 

14 loadings in the environment. Since oxidized nitrogen is the listed criteria pollutant, for the 

15 atmospheric contribution to total nitrogen, we examine the contribution of NOx to total reactive 

16 nitrogen in the atmosphere relative to the contributions of reduced forms of nitrogen (e.g., 

17 ammonia, ammonium) to ultimately assess how a meaningful secondary NAAQS might be 

18 structured.

19 The Risk and Exposure Assessment for the Secondary NAAQS Review for NOx and SOx

20 will aid the Administrator in judging whether the current secondary standards are requisite to 

21 protect public welfare from any known or anticipated adverse effects, or whether these standards 

22 should be retained, revised, revoked, and/or replaced with alternative standard(s) having different 

23 ambient air indicators to provide the required protection. 

24 2.2 SEVEN-STEP APPROACH 

25 The seven basic steps guiding the risk and exposure assessment and the assessments for 

26 each case study area of interest are highlighted below. These steps were initially presented in the 

27 scope and methods plan for this review, which received CASAC approval. Therefore, we are 

28 carrying this approach forward for the risk and exposure assessment. The seven steps address the 

29 selection of effects, indicators, and ecosystem services measured for exposure via atmospheric 

30 deposition of total reactive nitrogen and sulfur from ambient air. The initial step of identifying 
Chapter 2 – Overview of Risk and Exposure Assessment 

DRAFT 2-5 August 2008 

1 effects, sensitive ecosystems, and potential indicators are documented in the ISA. In addition, the 

2 ISA identifies and reviews candidate multimedia models available for fate and transport analyses 

3 of a variety of ecosystems. The science documented in the ISA provides critical inputs into the 

4 risk and exposure assessment. For some of the desired case study areas, data were not abundant 

5 enough to perform a quantitative assessment for each of the steps; in those cases, we have chosen 

6 to execute some of these steps in a qualitative or semi-quantitative fashion. Our progress towards 

7 characterizing current conditions (complete Steps 1 through 4) for each targeted effect area and 

8 case study analysis is presented in Attachments 2 through 6. 

9 The details of these seven steps will be addressed in each case study description. The 

10 steps are as follows: 

11  Step 1. Plan for assessment using documented effects: biological, chemical, and 

12 ecological indicators, and potential ecosystem services. 

13  Step 2. Map sensitive areas that show responses using research findings and geographic 

14 information systems (GIS) mapping. 

15  Step 3. Select risk and exposure case study assessment area(s) within a sensitive area. 

16  Step 4. Evaluate current loads and effects to case study assessment areas, including 

17 ecosystem services, where possible. 

18  Step 5. Where feasible, scale-up case study assessment area findings to sensitive areas. 

19  Step 6. Assess the current ecological conditions for those sensitive areas. 

20  Step 7. Assess alternative levels of protection under different scenarios of deposition 

21 from ambient sources. 

22 2.3 ECOSYSTEM SERVICES 

23 Humankind benefits from a multitude of resources and processes that are supplied by 

24 ecosystems. Collectively, these benefits are known as ecosystem services and include products, 

25 such as clean drinking water, and processes, such as the decomposition of wastes. Ecosystem 

26 services are distinct from other ecosystem products and functions because there is human 

27 demand for them. Ecosystem services are generally defined as the benefits individuals and 

28 organizations obtain from ecosystems. In the Millennium Ecosystem Assessment (MEA), 

29 ecosystem services are classified into four main categories: 
Chapter 2 – Overview of Risk and Exposure Assessment 

DRAFT 2-6 August 2008 

1  Provisioning. Includes products obtained from ecosystems, such as the production of 

2 food and water. 

3  Regulating. Includes benefits obtained from the regulation of ecosystem processes, such 

4 as the control of climate and disease. 

5  Cultural. Includes the nonmaterial benefits people obtain from ecosystems through 

6 spiritual enrichment, cognitive development, reflection, recreation, and aesthetic 

7 experiences. 

8  Supporting. Includes those services necessary for the production of all other ecosystem 

9 services, such as nutrient cycles and crop pollination (MEA, 2005).

10 Figure 2.3-1 is the World Resources Institute’s schematic demonstrating the connections 

11 between the categories of ecosystem services and human well-being. 

12

13 Figure 2.3-1. Millennium ecosystem assessment categorization of ecosystem services 

14 and their links to human well-being (MEA, 2005a). 

15 The interrelatedness of these categories means that any one ecosystem may provide 

16 multiple services. Changes in these services can impact human well-being by affecting our 

17 security, health, social relationships, and access to basic material goods (MEA, 2005b). 

18 Historically, ecosystem services have been undervalued and overlooked. More recently, 

19 degradation and destruction of ecosystems has piqued interest in assessing the value of their 
Chapter 2 – Overview of Risk and Exposure Assessment 

DRAFT 2-7 August 2008 

1 services. The economic approach to valuation is laid out in EPA’s Ecological Benefits 

2 Assessment Strategic Plan, “Economists generally attempt to estimate the value of ecological 

3 goods and services based on what people are willing to pay (WTP) to increase ecological 

4 services or by what people are willing to accept (WTA) in compensation for reductions in them” 

5 (U.S. EPA, 2006). There are three primary approaches for estimating these values: market-based 

6 approaches, revealed preference methods, and stated preference methods (U.S. EPA, 2006). 

7 Because economic valuation of services can be difficult, non-monetary valuation using 

8 biophysical measurements and concepts can also be used to value services. One non-monetary 

9 valuation methodology uses relative value indicators (such as a flow chart indicating uses of a 

10 waterbody - boatable, fishable, swimmable); another assigns values to ecosystem goods and 

11 services through the use of the common currency of energy. Valuation may be an important step 

12 from a policy perspective because it can be used to compare the costs and benefits of altering 

13 versus maintaining an ecosystem (i.e., it may be easier to protect than repair ecosystem effects). 

14 In this review, valuation will be used where possible based on available data in the case study 

15 locations.

16 The ecosystems of interest in this risk assessment are heavily impacted by anthropogenic 

17 air pollution. These effects may alter the services provided by the ecosystems in question. For 

18 example, changes in forest health as a result of soil acidification from NOx and SOx deposition 

19 may affect supporting services (e.g., nutrient cycling), provisioning service (e.g., timber 

20 production), and regulating services (e.g., climate regulation). Eutrophication caused by NOx

21 deposition may affect supporting services such as primary production, provisioning services such 

22 as food, and cultural services such as recreation and ecotourism.

23 We plan to develop, where possible, for each area of interest linkages to ecosystem 

24 services from indicators of each effect (i.e., biological, chemical, ecological) identified in Step 1 

25 of the risk and exposure assessment. This link will be developed based on existing literature and 

26 will focus on the services identified in the peer-reviewed literature. These linkages are essential 

27 to any attempt to evaluate air pollution-induced changes in the quantity and/or quality of 

28 ecosystem services provided. According to EPA’s Science Advisory Board Committee on 

29 Valuing the Protection of Ecological Systems and Services, this linkage is one of the critical 

30 elements that will allow for valuation of benefits of EPA-regulated air pollutants (SAB C31 VPESS, 2007). Figure 2.3-2 illustrates an example of a path from nitrogen deposition in an 
Chapter 2 – Overview of Risk and Exposure Assessment 

DRAFT 2-8 August 2008 

1 ecosystem to valuation that links ecological endpoints to changes in services and finally to 

2 valuation.

3

4 Figure 2.3-2. Pathway from nitrogen deposition to valuation for an aquatic system. 
Chapter 2 – Overview of Risk and Exposure Assessment 

DRAFT 2-9 August 2008 

1 We have begun identifying the primary ecosystem service(s) for both acidification and 

2 enrichment and for major ecosystem types and components (i.e., terrestrial ecosystems, soils, 

3 aquatic ecosystems) under consideration in this risk and exposure assessment (see Table 2.1-1). 

4 The impacts associated with various ecosystem services for each targeted effect area are 

5 summarized in Table 2.3-1. Some of the potential linkages between impacts and effects in 

6 relation to specific ecosystem services are summarized for each targeted effect area below. This 

7 information will be more fully addressed in the second draft risk and exposure assessment. 

8 2.3.1 Aquatic Acidification 

9 The Aquatic Acidification Case Study will focus on ecosystem services such as fisheries,

10 recreation, and tourism. Fisheries (decreased species richness) will be quantitatively linked to 

11 acidification through monitoring data and modeling of ANC, and recreation activities will likely 

12 be qualitatively related to acidification symptoms through user surveys. 

13 2.3.2 Terrestrial Acidification 

14 The Terrestrial Acidification Case Study will focus on ecosystem services such as food, 

15 natural habitat, and tourism. Sugar maple and red spruce abundance and growth (i.e., crown 

16 vigor, biomass, and geographic extent) will be quantitatively linked to acidification symptoms 

17 through the Forest Inventory and Analysis National Program (FIA) database analyses and 

18 analysis of estimated sales of maple sugar products. 

19 2.3.3 Aquatic Nutrient Enrichment 

20 The Aquatic Nutrient Enrichment Case Study will focus on ecosystem services such as 

21 fisheries, recreation, and tourism. Fisheries (closings, decreased species richness) will likely be 

22 quantitatively linked to eutrophication symptoms through monitoring data, and recreation 

23 activities will likely be qualitatively related to eutrophication symptoms through user surveys. 

24 2.3.4 Terrestrial Nutrient Enrichment 

25 The Terrestrial Nutrient Enrichment Case Study for Coastal Sage Scrub (CSS) will focus 

26 on ecosystem services such as biodiversity; threatened and endangered species and rare species 

27 (both national and state); landscape view; water quality; and fire hazard mitigation. Linkage 

28 methods from endpoint to services may include measurement of changes in biodiversity and 
Chapter 2 – Overview of Risk and Exposure Assessment 

DRAFT 2-10 August 2008 

1 abundance and distribution of threatened and endangered species, comparison of past and present 

2 photography, and measurement of the distribution of soil moisture with depth and possible 

3 nitrate leakage. 

4 The Terrestrial Nutrient Enrichment Case Study for Mixed Conifer Forests will focus on 

5 ecosystem services such as visual and recreational aesthetics provided by the community and 

6 water quality. Linkage methods from endpoint to services may include measurement of the 

7 densification of stands, shifts in tree dominance, shifts in lichen communities, foliar nitrogen 

8 increases, and increasing nitrate concentrations in streams. 

9 2.3.5 Sulfur and Mercury Methylation 

10 The major ecosystem services potentially impacted by mercury methylation are 

11 provisioning and cultural services. Fishing and shellfishing can involve both commercial 

12 operations and sport fishing, which provide food for human populations. For some socio13 economic groups (especially involving groups with low incomes), fishing is a subsistence 

14 activity that makes a very significant contribution to household food intake. Sport fishing often 

15 involves important recreational services, and for many groups (e.g., Native Americans and 

16 Alaska native Villagers), fishing and consuming local fish or shellfish is of cultural and spiritual 

17 significance. A synthesis of the ecological service and valuation aspects of fishing and 

18 shellfishing activities, with a focus on issues related to mercury pollution issues affecting human 

19 health and well being, is found in the Regulatory Impact Analysis of the Clean Air Mercury Rule 

20 (U.S. EPA, 2005) and in the Mercury Study Report to Congress (U.S. EPA, 1997). 
Chapter 2 – Overview of Risk and 

DRAFT 2-11 Table 2.3-1. Ecological Impacts Associated with Acidification, Nutrient Enrichment, and Increased Mercury Me

Associated Ecosystem Services 

Targeted Effect Area Provisioning Services Regulating Services Cultural Services Supp

Aquatic Acidification  Fish kills 

 Decline in fish population  Decline in aquatic species 

richness, abundance, and health

Decline in habitat Fish kills 

Decline in fish populationDecline in aquatic species richness, abundance, and health 

Terrestrial Acidification 

 Decline in forest 

productivity

 Increase forest soil 

erosion

 low water retention 

 Decline in forest 

aesthetics 

 Increase forest soil 

erosion

 low water retention 

Aquatic Nutrient Enrichment 

 Fish kills 

 Fish/water contamination  Decline in fish population

 Decline in shoreline 

quality (erosion) 

 Fish kills 

 Fish/water

contamination 

 Decline in fish 

population

 Decline in shoreline 

quality (erosion) 

 Poor water clarity 

and color 

 Unpleasant odors 

 Sur

Terrestrial Nutrient Enrichment 
Chapter 2 – Overview of Risk and 

DRAFT 2-12 

Targeted Effect Area Provisioning Services Regulating Services Cultural Services SuppCoastal Sage Scrub (CSS)

 Decline in habitat, 

shrub abundance, species of concern 

 Increase abundance 

of non-natives 

 Increase in wildfires 

 Decline in habitat, 

shrub abundance, species of concern 

 Increase abundance 

of non-natives 

 Increase in wildfires 

Mixed Conifer Forest  Change in habitat 

suitability 

 Increased tree 

mortality

 Increase in fire 

intensity 

 Decline in surface 

water quality 

 Change in habitat 

suitability 

 Increased tree 

mortality

 Decline in mixed 

conifer forest aesthetics 

 Cha

nutcauto b

Sulfur and Mercury Methylation 

 Fish kills 

 Fish/water contamination  Decline in fish population

 Fish kills 

 Fish/water

contamination 

 Decline in fish 

population
Chapter 2 – Overview of Risk and Exposure Assessment 

DRAFT 2-13 August 2008 

1 2.4 UNCERTAINTY 

2 A risk assessment of this scope, with four targeted effect areas, includes several 

3 components that rely on numerous analytical tools and techniques, data sources, and analyses, 

4 each containing some degree of uncertainty. The environmental effects of nitrogen and sulfur 

5 deposition vary widely in both terrestrial and aquatic ecosystems and may be either direct or 

6 indirect. The natural resistance of an ecosystem will also affect the severity of its response. In 

7 addition to the natural variability in any given ecosystem, there are likely to be sources of 

8 uncertainty related to the input parameters necessary to evaluate current conditions associated 

9 with nitrogen and sulfur deposition. For example, empirical data will contain uncertainties 

10 associated with measurements and analyses, whereas modeling results propagate uncertainties 

11 due to the scale and representativeness of the model input data. Due to the inherent complexity 

12 of the environmental processes involved with nitrogen and sulfur, uncertainty is difficult to 

13 define and capture quantitatively, especially within the scope of this review. 

14 Some of the categories of uncertainty include (1) air quality/deposition and ecological 

15 modeling (with their associated parameterizations and input data), (2) characterization of 

16 sensitive ecosystems, and (3) the case study selection process along with the applicability of case 

17 study results to larger geographic areas. The magnitude of these uncertainties will vary 

18 depending on the associated data quality and availability. Each aspect and component of the risk 

19 assessment may be uncertain and, depending on its position in the analytical chain, may cascade 

20 through subsequent steps in the analysis and thus have a multiplicative effect on the overall 

21 uncertainty in final risk estimates. 

22 Some key sources of uncertainty in each stage of the risk assessment are the following: 

23  Gaps in scientific knowledge of physical, chemical, atmospheric, and ecological 

24 processes

25  Variability in estimated relationships between atmospheric concentrations and deposition 

26 that is not captured by existing models and analytical techniques 

27  Insufficient measurements in time and space to properly characterize ambient conditions 

28 for variables such as deposition, soil chemistry, and species composition 

29  Errors in measurements 

30  Use of surrogate variables and simplification of complex functions 
Chapter 2 – Overview of Risk and Exposure Assessment 

DRAFT 2-14 August 2008 

1  Biases due to omissions or other research limitations. 

2 The various sources of uncertainty will be discussed, as appropriate, within each section 

3 of the risk assessment. 

4 2.5 REFERENCES 

5 MEA (Millennium Ecosystem Assessment Board). 2005a. Ecosystems and Human Well-being: 

6 Current State and Trends, Volume 1. Edited by R. Hassan, R. Scholes, and N. Ash. 

7 Washington: Island Press. Available at http://www.millenniumassessment.org/ 

8 documents/document.766.aspx.pdf. 

9 MEA (Millennium Ecosystem Assessment Board). 2005b. Millennium Ecosystem Assessment 

10 Reports. Washington: Island Press. Available at 

11 http://www.millenniumassessment.org/en/index.aspx. 

12 SAB CVPESS (Science Advisory Board Committee on Valuing the Protection of Ecological 

13 Systems and Services). 2007. Valuing the Protection of Ecological Systems and Services.

14 SAB CVPESS Draft Report, September 24. 

15 U.S. EPA (Environmental Protection Agency) 1997. Mercury Study Report to Congress. EPA16 452/R-97-003. Office of Air Quality Planning & Standards and Office of Research and 

17 Development, Washington, DC. December. 

18 U.S. EPA (Environmental Protection Agency) 2005. Regulatory Impact Analysis of the Final 

19 Clean Air Mercury Rule. U.S. Environmental Protection Agency, Office of Air Quality 

20 Planning and Standards Air Quality Strategies and Standards Division. Research Triangle 

21 Park, NC. EPA-452/R-05-003. March 2005 

22 U.S. EPA (Environmental Protection Agency). 2006. Wadeable Streams Assessment.

23 EPA/600/R-07/045. U.S. Environmental Protection Agency, Office of Research and 

24 Development, Washington, DC. May. 

25 U.S. EPA (Environmental Protection Agency). 2007. Integrated Science Assessment for Oxides 

26 of Nitrogen and Sulfur Environmental Criteria (First External Review Draft).
Chapter 2 – Overview of Risk and Exposure Assessment 

DRAFT 2-15 August 2008 

1 EPA/600/R-07/145A. U.S. Environmental Protection Agency, Office of Research and 

2 Development, National Center for Environmental Assessment, Research Triangle Park, 

3 NC. December.

4
Chapter 2 – Overview of Risk and Exposure Assessment 

DRAFT 2-16 August 2008 

1

2

3

4

5

6

7

8

9 [This page intentionally left blank.] 
Chapter 3 – Sources, Ambient Concentrations, and Deposition 

DRAFT 3-1 August 2008 

1 3. SOURCES, AMBIENT CONCENTRATIONS, AND 

2 DEPOSITION

3 This chapter discusses current emissions sources of nitrogen and sulfur, as well as 

4 atmospheric concentrations, policy-relevant background, non-ambient loadings to ecosystems, 

5 and estimates of deposition for nitrogen and sulfur nationwide. Both measured and modeled data 

6 are used to evaluate current contributions of nitrogen and sulfur compounds to the case study 

7 locations described in detail in Attachments 2 through 6. The impacts of spatial and temporal 

8 parameters on ambient concentrations and their associated deposition are evaluated in Section 

9 3.2.1. The relative contributions of ambient concentrations on deposition are evaluated in Section 

10 3.2.2 using a response surface model analysis. The deposition fields described here will be used 

11 as modeling input for the individual case study modeling described in Attachments 2 through 6. 

12 3.1 SCIENCE OVERVIEW 

13 Prior to analyzing the effects of nitrogen and sulfur deposition to the environment, we 

14 must first evaluate the ambient emissions, transformations, and transport of nitrogen and sulfur 

15 in the atmosphere. As noted in the introduction, the terms “oxides of nitrogen” and “nitrogen

16 oxides” (NOx) refer to all forms of oxidized nitrogen compounds, including nitric oxide (NO), 

17 nitrogen dioxide (NO2), and all other oxidized nitrogen-containing compounds transformed from 

NO and NO2. Additionally, reduced forms of nitrogen (e.g., NH3 and NH4

+

18 , collectively termed 

19 NHx) can also play an important role in the emission, transformation, and deposition and are 

20 included in this review. Much like NOx, additional NHx can lead to increased acidification and 

21 nutrient enrichment in ecosystems. Sulfur oxides (SOx) refer to all oxides of sulfur, including 

22 SO, SO2, SO3, and disulfur monoxide (S2O); however, only SO2 is present in concentrations 

23 relevant for atmospheric chemistry and ecological exposures. 

24 3.1.1 Sources of Nitrogen and Sulfur 

25 3.1.1.1 NOx

26 The total amount of NOx emitted is 20.8 million tons/yr; NOx emissions by state are 

27 shown in Figure 3.1-1.
Chapter 3 – Sources, Ambient Concentrations, and Deposition 

DRAFT 3-2 August 2008 

1

2 Figure 3.1-1. Annual NOx emissions by state for 2002 (U.S. EPA, 2002). 

3 Combustion sources are the primary emitters of NOx; their main emissions are in the 

4 form of NO and NO2. The major combustion sources of NOx are on-road motor vehicles and 

5 electrical utilities, with contributions from stationary engines, off-road vehicles, and industrial 

6 facilities. Nationally, anthropogenic sources account for approximately 87% of total NOx

7 emissions. Mobile sources (both on-road and off-road) account for about 60% of total 

8 anthropogenic emissions of NOx, whereas stationary sources (e.g., electrical utilities and 

9 industry) account for the remainder (2007 ISA Annex2 Table 2-1). Highway vehicles represent 

10 the major mobile source component. In the United States, approximately half the mobile source 

11 emissions are contributed by diesel engines and half are emitted by gasoline-fueled vehicles and 

12 other sources (2007 ISA Annex2 Section 2.1.1 and Table 2-1). Apart from these anthropogenic 

13 sources, there are also natural sources of NOx, including lightning, wildfires, and microbial 

14 activity in soils (2007 ISA Annex2 Section 2.1.2).

15 The distribution of NOx emissions across major source categories is shown in the pie 

16 charts in Figure 3.1-2. Charts are provided to show the distribution of emissions on a national 

17 total basis, as well as for the eastern and western United States, due to differences in source 

18 emissions profiles. For this display, we have defined the eastern United States to include Texas, 

19 Oklahoma, Kansas, Nebraska, the Dakotas, and all states to the east. All other states are included 
Chapter 3 – Sources, Ambient Concentrations, and Deposition 

DRAFT 3-3 August 2008 

1 as part of the western United States. Note that emissions from Alaska and Hawaii are not 

2 included in any of these charts. 

3 In both the East and West, a number of emissions sectors contribute relatively large 

4 amounts to the overall NOx inventory. In general, NOx emissions in the East are far greater than 

5 in the West. Most of the NOx in the West is emitted from sources in California (not shown). The 

6 on-road sector is the largest contributor, followed by emissions from utilities (Electric 

7 Generating Units [EGUs]). The non-road, aircraft/locomotive/marine, and non-EGU point 

8 emissions contribute generally similar amounts to the overall NOx inventory. Although NOx

9 emissions from fires are a relatively small fraction of the annual total emissions in the West, fires 

10 are episodic events, and thus, emissions can be quite high during those events.

nonroad

10%

onroad

36%

EGU

24%

aircraft/

locomotive/

marine

10%

stationary area

8%

fires

0%

non-EGU point

12%

onroad

38%

stationary area

7%

EGU

23%

non-EGU point

7% nonroad

10%

aircraft/

locomotive/

marine

11%

fires

1%

2002 NOx Nationwide: 

20.8 million tons/yr 2002 NOx Eastern U.S.: 

17.2 million tons/yr 

onroad

42%

EGU

15%

aircraft/

locomotive/

marine

13%

nonroad

13%

stationary area

6%

fires

2%

non-EGU point

9%

Source: U.S. EPA, 

2002 NOx Western U.S.: 

3.6 million tons/yr 11

12 Figure 3.1-2. The distribution of NOx emissions across major source categories in 2002. 

13 3.1.1.2 NHx

14 Total emissions of NHx are 4.0 million tons/year; Figure 3.1-3 shows annual ammonia 

15 emissions by state during 2002. 
Chapter 3 – Sources, Ambient Concentrations, and Deposition 

DRAFT 3-4 August 2008 

1

2 Figure 3.1-3. Annual emissions of NH3 by state during 2002 (U.S. EPA, 2002).

3 The primary anthropogenic sources of NHx emissions are fertilized soils and livestock. 

4 Confined animal feeding operations (CAFOs) and other intensified agricultural production 

5 methods have resulted in greatly increased volumes of animal wastes, of which 30%–70% may 

6 be emitted as NH3. Motor vehicles and stationary combustion are small emitters of NHx. Some 

7 NH3 is emitted as a by-product of NOx reduction in motor vehicle catalysts. 

8 Where possible, our analyses will separate oxidized from reduced forms of nitrogen to 

9 show the impact from each component, as well as the overall impact from total reactive nitrogen. 

10 This will play an important role in the standard-setting process, as discussed in Chapter 8. 

11 3.1.1.3 SOx

12 Total emissions of SO2 are 14.7 million tons/yr; Figure 3.1-4 shows annual SO2

13 emissions by state during 2002. 
Chapter 3 – Sources, Ambient Concentrations, and Deposition 

DRAFT 3-5 August 2008 

1

2 Figure 3.1-4. Annual SO2 emissions in 2006 for acid rain program cooperating facilities 

3 (U.S. EPA, 2008).

4 Industrial emissions of SO2 in the United States are mainly due to the combustion of 

5 fossil fuels by electrical utilities (~66 %) and industry (~29%); transportation-related sources 

6 contribute minimally (~5%) (2002 statistics) (U.S. EPA, 2006d). Thus, most SO2 emissions 

7 originate from point sources. Almost all the sulfur in fuel is released as volatile components (SO2

8 or SO3) during combustion. The higher sulfur content of coal compared to other types of fossil 

9 fuels results in higher SO2 emissions from electrical utilities using this type of fuel.

10 The largest natural sources of SO2 are volcanoes and wildfires. Although SO2 constitutes 

11 a relatively minor fraction (0.005% by volume) of total volcanic emissions (Holland, 1978), 

12 concentrations in volcanic plumes can be up to tens of ppm. Volcanic sources of SO2 in the 

13 United States are limited to the Pacific Northwest, Alaska, and Hawaii. Sulfur is a component of 

14 amino acids in vegetation and is released during combustion. Gaseous sulfur emissions from this 

15 source are mainly in the form of SO2.
Chapter 3 – Sources, Ambient Concentrations, and Deposition 

DRAFT 3-6 August 2008 

1 Emissions of SO2 from burning vegetation are generally in the range of 1%–2% of the 

2 biomass burned (Levine and Pinto, 1998). 

3 The distribution of SO2 emissions across major source categories are shown in the pie 

4 charts in Figure 3.1-5. As with the pie charts for NOx, charts are provided to show the 

5 distribution of emissions on a national total basis, as well as for the eastern and western United 

6 States. Note that emissions from Alaska and Hawaii are not included in any of these charts. 

EGU

72%

aircraft/

locomotive/

marine

2%

fires

0% onroad

2%

nonroad

1%

non-EGU point

15%

stationary area

8%

EGU

47%

aircraft/

locomotive/

marine

onroad 8%

3%

fires

0%

nonroad

3%

non-EGU point

21%

stationary area

14% Source: U.S. EPA, 

2002 SO2 Western U.S.: 920,000 

tons/yr

EGU

71%

aircraft/

locomotive/

marine

2%

nonroad

fires 1%

0% onroad

2%

stationary area

9%

non-EGU point

7%

2002 SO2 National Annual: 

14.7 million tons/yr

2002 SO2 Eastern U.S.: 

13.8 million tons/yr

EGU

72%

aircraft/

locomotive/

marine

2%

fires

0% onroad

2%

nonroad

1%

non-EGU point

15%

stationary area

8%

EGU

47%

aircraft/

locomotive/

marine

onroad 8%

3%

fires

0%

nonroad

3%

non-EGU point

21%

stationary area

14% Source: U.S. EPA, 

2002 SO2 Western U.S.: 920,000 

tons/yr

EGU

71%

aircraft/

locomotive/

marine

2%

nonroad

fires 1%

0% onroad

2%

stationary area

9%

non-EGU point

7%

2002 SO2 National Annual: 

14.7 million tons/yr

2002 SO2 Eastern U.S.: 

13.8 million tons/yr

7

8 Figure 3.1-5. The distribution of SO2 emissions across major source categories in 2002 

9 (U.S. EPA, 2002).

10 Similar to emissions of NOx, emissions of SO2 are much greater in the East than in the 

11 West. The breakout of SO2 emissions by source sector indicates that EGU emissions dominate in 

12 both the East and the West, but they are a much greater fraction of the inventory in the East 

13 (72%) compared to the West (47%). In the West, stationary area sources and non-EGU point 

14 sources also have a greater contribution to SO2 than in the East. Note that SO2 emissions from 

15 fires are understated in the National Emissions Inventory (NEI) due to an error in the emissions 

16 calculations. 
Chapter 3 – Sources, Ambient Concentrations, and Deposition 

DRAFT 3-7 August 2008 

1 3.1.2 Ambient Concentrations and Policy-Relevant Background 

2 Policy-relevant background concentrations are those concentrations that would occur in 

3 the United States in the absence of anthropogenic emissions in continental North America 

4 (defined here as the United States, Canada, and Mexico). For NO2, policy-relevant background 

5 concentrations are less than 300 parts per trillion (ppt) over most of the continental United States 

6 and less than 100 ppt in the eastern United States on an annual average basis (U.S. EPA, 2008). 

7 In urban areas near monitoring locations, 24-hour ambient NO2 concentrations averaged less 

8 than 20 parts per billion (ppb), with a 99 percentile value of less than 50 ppb. Annual average 

9 NO2 concentrations over the continental United States are less than 5 ppb for nearly all urban, 

10 rural, and remote sites. According to the ISA (U.S. EPA, 2008), background SO2 concentrations 

11 are less than 10 ppt throughout most of the continental United States, except in areas of the 

12 Pacific Northwest, where natural SO2 sources are particularly strong due to volcanic activity. 

13 Maximum policy-relevant background SO2 concentrations are 30 ppt. In general, policy-relevant 

14 background concentrations of SO2 contribute less than 1% of current concentrations, except in 

15 the Pacific Northwest, where policy-relevant background concentrations can contribute up to 

16 80% (U.S. EPA, 2008). 

17 The analyses for the REA examine the contribution of total reactive nitrogen and sulfur 

18 above the policy-relevant background concentrations. 

19 3.1.3 Non-ambient Loadings of Nitrogen and Sulfur 

20 Not all loadings of nitrogen and sulfur compounds to ecosystems are due to atmospheric 

21 deposition. Other inputs, such as run-off from agricultural soils to waterbodies and point-source 

22 discharges, also contribute to acidification and nutrient enrichment. In this assessment, we 

23 examine the atmospheric contribution due to total reactive nitrogen and sulfur, recognizing that 

24 some systems may be solely impacted by atmospheric deposition, while effects in other systems 

25 might be largely due to non-atmospheric sources. This source distinction will play an important 

26 role in the standard-setting process. 
Chapter 3 – Sources, Ambient Concentrations, and Deposition 

DRAFT 3-8 August 2008 

1 3.1.4 Deposition 

2 3.1.4.1 Nitrogen 

3 As noted in the ISA (U.S. EPA, 2008), increasing trends in urbanization, agricultural 

4 intensity, and industrial expansion during the previous 100 years have produced a nearly ten-fold 

5 increase in atmospherically deposited nitrogen. Increased deposition of reduced nitrogen in the 

United States, measured as NH4

+

6 deposition, correlates well with the local and regional increases 

7 in agricultural intensity (U.S. EPA, 2008).

8 From 2004–2006, mean nitrogen deposition was greatest in the Ohio River Valley, 

specifically in Indiana and Ohio, which had values as high as 9.2 and 9.6 kg haҐ

-1

 y

Ґ-1 9 ,

10 respectively. Nitrogen deposition was lower in other parts of the East, including the Southeast, 

11 and in northern New England. The greatest deposition in the central United States occurred in 

Kansas and Oklahoma, which reported 7.0 and 6.5 kg haҐ

-1

 y

Ґ-1 12 , respectively. Figure 3.1-6 shows 

13 the total nitrogen deposition for 2002; Figures 3.1-7 and 3.1-8 show the total oxidized and 

14 reduced nitrogen deposition in the United States in 2002, respectively. 

In most regions of the United States, wet deposition of nitrate (NO3) and NH4

+

15 are the 

16 primary pathways of nitrogen deposition. Next most common is deposition in dry forms, as dry 

HNO3, NH4

+

17 , and nitrate ions. This varies regionally as some of the arid Western areas have 

18 higher rates of dry deposition than the more humid East.

19 Atmospheric nitrogen loads to the Great Waters and estuaries in the United States are 

20 estimated to range from approximately 2%–38% of total atmospheric deposition. In the 

21 Chesapeake Bay, where nitrogen deposition and its ecological effects have been extensively 

22 studied, direct deposition of atmospheric nitrate is estimated to contribute from 20%–30% of 

23 total nitrogen and to 14% of the ammonium loadings. 
Chapter 3 – Sources, Ambient Concentrations, and Deposition 

DRAFT 3-9 August 2008 

1

2 Figure 3.1-6. Total wet and dry nitrogen deposition in the United States in 2002.
Chapter 3 – Sources, Ambient Concentrations, and Deposition 

DRAFT 3-10 August 2008 

1

2 Figure 3.1-7. Total wet and dry oxidized nitrogen deposition in the United States in 2002. 
Chapter 3 – Sources, Ambient Concentrations, and Deposition 

DRAFT 3-11 August 2008 

1

2 Figure 3.1-8. Total wet and dry reduced nitrogen deposition in the United States in 2002.

3 3.1.4.2 Sulfur 

4 Average sulfur deposition was highest in the eastern United States during 2004–2006, 

5 with the maximum in the Ohio River Valley. In this region, measured sulfur deposition was 21.3 

kg haҐ

-1

 y

Ґ-1 6 at one monitoring station; most recording stations reported 3-year averages greater 

than 10 kg haҐ

-1

 y

Ґ-1 7 (U.S. EPA, 2008) Total sulfur deposition in the United States west of the 

100th meridian was relatively low, with all recording stations reporting less than 2 kg haҐ

-1

 y

Ґ-1 8 and

many reporting less than 1.0 kg haҐ

-1

 y

Ґ-1 9 . Total wet and dry sulfur deposition for 2002 are shown 

10 in Figure 3.1-9.

11 The primary form of sulfur deposited is wet sulfate (SO4); smaller contributions to 

12 deposition are made by dry sulfur dioxide and dry sulfate. 
Chapter 3 – Sources, Ambient Concentrations, and Deposition 

DRAFT 3-12 August 2008 

1

2 Figure 3.1-9. Total wet and dry sulfur deposition in the United States in 2002. 

3 3.2 DATASETS 

4 To create composite nitrogen and sulfur deposition datasets of both wet and dry 

5 constituents, two data sources were used: 

6  2002 measured wet deposition from the National Atmospheric Deposition Program 

7 (NADP) National Trends Network (NTN). 

8  2002 estimated dry deposition from the Community Multiscale Air Quality (CMAQ) 

9 model.

10 The NADP data is collected at several hundred point locations across the contiguous 

11 United States. From these points, analysts at the NADP network generated continuous surfaces at 

12 a 2.5-kilometer (km) grid cell resolution by using an inverse distance weighted (IDW) algorithm. 

13 The species of sulfur collected was SO4, while for nitrogen it was NO3 for oxidized nitrogen and 

14 NH4 for reduced nitrogen.
Chapter 3 – Sources, Ambient Concentrations, and Deposition 

DRAFT 3-13 August 2008 

1 The CMAQ data was generated at a 12-km grid cell size and consisted of many estimated 

2 deposition values, including total dry sulfur, total dry nitrogen, total dry oxidized nitrogen, and 

3 total dry reduced nitrogen. For total dry oxidized nitrogen, the species were NO3, HNO3, NO, 

4 NO2, N2O5, PAN, nitrous acid (HONO), and organic nitrate (NTR), while for total dry reduced 

5 nitrogen, the species were NH3 and NH4.

6 Both input datasets contained deposition values in kilograms per hectare (kg/ha)/yr. The 

7 NADP data was at a finer spatial resolution, and in order to add the two gridded datasets 

8 together, the finer NADP dataset was resampled up to the 12-km scale of the CMAQ data. Once 

9 both datasets were at the same spatial resolution, the deposition values could be added together 

10 on a grid cell by grid cell basis. In order to calculate total nitrogen, the two chemical species 

11 from the NADP (i.e., NO3 and NH4) were added together and then added to the total dry nitrogen 

12 estimated values from CMAQ. 

13 3.2.1 Spatial and Temporal Characterization of Concentrations and Deposition for 

14 Case Study Areas 

15 3.2.1.1 Purpose and Intent 

16 The purpose of this section of Chapter 3 is to describe the spatial and temporal patterns of 

nitrogen and sulfur deposition and NOx and SOx concentrations2 17 in and near five of the case 

study areas.3 18 In this analysis, we characterize and compare the magnitude, spatial gradients, and 

19 intra-annual and inter-annual variation in nitrogen and sulfur deposition and NOx and SOx

20 concentrations for each case study area. In addition to improving our overall understanding of 

21 the behavior of nitrogen and sulfur deposition, the results and findings of this analysis are 

22 intended to provide information on the following: 

23  The relative portion of oxidized nitrogen versus reduced nitrogen 

24  The relative amounts of wet and dry deposition of nitrogen and sulfur

25  The magnitude of NOx and SOx concentrations. 

2

 For the purpose of this analysis, NOx is defined to be NOy , which includes the following species: NO, NO2, HNO3,

and PAN. SOx is defined as SO2.

3

 The case study areas are identified as case study locations in Chapter 2, Table 2.1-1. 
Chapter 3 – Sources, Ambient Concentrations, and Deposition 

DRAFT 3-14 August 2008 

1 We refer to wet/dry oxidized/reduced nitrogen and wet/dry sulfur deposition as the 

2 components of total reactive nitrogen and total sulfur deposition, respectively. 

3 The inter-relationships of physical, chemical, and meteorological processes and land use 

4 that affect the spatial and temporal patterns of deposition and concentration are complex. The 

5 state of the science regarding these processes is described in the ISA. The main goal of chapter 3 

6 is to help readers understand the characteristic patterns of deposition in the case study areas and 

7 how these patterns might influence the overall levels of adverse effects under current conditions. 

8 It is beyond the scope of this chapter to fully explain the characteristics revealed by the analysis 

9 of modeled and measured deposition and concentrations. Further exploration of these behaviors 

10 should be the subject of future research efforts. 

11 3.2.1.2 Data and Tools 

Both air quality model predictions and ambient measurements4 12 are used in this analysis. 

13 The modeled data were obtained from annual simulations of the CMAQ model (Byun and Ching, 

14 1999; Byun and Schere, 2006; Dennis et al., 1996) version 4.6.1. The measured data include wet 

15 deposition of SO4, NO3, and NH4 from the NADP network and SO2 measurements from Clean 

16 Air Status and Trends Network (CASTNet). We chose to use both measured and modeled data 

17 since each dataset provides information and value not fully captured by the other. The relative 

18 strengths and limitations of these datasets are described as part of the uncertainty discussion in 

19 Section 3.2.1.5. 

20 Modeled Data 

21 The CMAQ model is a comprehensive, three-dimensional grid-based Eulerian air quality 

22 model designed to simulate the formation and fate of gaseous and particle (PM) species, 

23 including ozone, oxidant precursors, and primary and secondary PM concentrations and 

24 deposition over urban, regional, and larger spatial scales. CMAQ is run for user-defined input 

25 sets of meteorological conditions and emissions. For this analysis, we are using results from 

26 several existing CMAQ runs. These runs were made for modeling regions (i.e., modeling 

27 domains) covering the eastern and western United States, as shown in Figure 3.2-1. The 

28 horizontal spatial resolution of the CMAQ grid cells in these domains is approximately 12 x 12 

4

 We use the “ modeled data” to refer to the model predictions and “measured data” to refer to ambient

measurements. 
Chapter 3 – Sources, Ambient Concentrations, and Deposition 

DRAFT 3-15 August 2008 

1 km. For the eastern domain, we have model outputs from annual CMAQ runs using meteorology 

2 and emissions for each of the 5 years from 2002 through 2006. (Note that the current analysis is 

3 based on a 2002 CMAQ model run; in the second draft risk and exposure assessment, we will 

4 present results based on 2002–2006 model runs.) We also have 12-km CMAQ data from the 

5 western domain for 2002. These annual CMAQ runs feature year-specific meteorology, as well 

6 as year-specific inventories for key emissions source sectors such as utilities, on-road vehicles, 

7 non-road vehicles, wildfires, and natural biogenic sources. Emissions for other sectors of the 

8 inventory for each of the years modeled rely on inventories for 2002. The inputs for these 

9 CMAQ runs were developed based on the data, procedures, and tools in the 2002 Multi-Pollutant 

10 Air Quality Modeling Platform. Details on the development and evaluation of this platform are 

described elsewhere.5 11

12

13 Figure 3.2-1. CMAQ 12-km eastern and western modeling domains.

14 The CMAQ runs produce hourly concentrations and wet and dry deposition of individual 

pollutant species in each grid cell within the domain. Concentration predictions for NOx as NOy 15 6

16 and SOx as SO2, both in units of ppb, are produced as part of our standard model output. The 

5 Placeholder for citation for the 2002 Platform Report.

6

 NOy is defined as the sum of CMAQ predictions for NO, NO2, HNO3, and PAN. 
Chapter 3 – Sources, Ambient Concentrations, and Deposition 

DRAFT 3-16 August 2008 

1 CMAQ deposition data for nitrogen and sulfur species are used to calculate oxidized and reduced 

2 wet and dry nitrogen deposition, wet and dry sulfur deposition, and total reactive nitrogen and 

3 total sulfur deposition. These composite deposition variables are derived from the species 

4 identified in Table 3.2-1, as applied in the formulas shown in Table 3.2-2. The CMAQ 

5 deposition data are in units of kg/ha. We are also including in the analysis gridded precipitation 

6 data that were input to the CMAQ runs to help understand the temporal and spatial behavior of 

7 wet deposition. 

Table 3.2-1. CMAQ Nitrogen and Sulfur Deposition Species 

CMAQ Species Chemical Name 

ANO3 Particle Nitrate 

HNO3 Nitric Acid 

N2O5 Nitrogen Pentoxide 

HONO Nitrous Acid 

NO Nitric Oxide 

NO2 Nitrogen Dioxide 

PAN Peroxyacyl Nitrate 

NTR Organic Nitrate 

ASO4 Particle Sulfate 

SO2 Sulfur Dioxide 

Table 3.2-2. Formulas for Calculating Nitrogen and Sulfur Deposition 

Deposition

Type Formula 

Oxidized

Nitrogen

0.2258*ANO3 + 0.2222*HNO3 + 0.4667*NO + 0.3043*NO2 + 

0.2592*N2O5 + 0.1157*PAN + 0.2978*HONO + 0.1052*NTR 

Reduced

Nitrogen

0.7777*NH4 + 0.8235*NH3

Sulfur 0.3333*ASO4 + 0.5000*SO2

8 Measured Data 

9 [Placeholder for description of Grimm-Lynch data base containing gridded wet 

10 deposition data for NO3, SO4, and NH4+NH3. (Grimm and Lynch, 2004).] 

11 [Placeholder for description of CASTNet SO2 data base.] 
Chapter 3 – Sources, Ambient Concentrations, and Deposition 

DRAFT 3-17 August 2008 

1 [Placeholder for discussion of why existing NO2 measurements are not appropriate for 

2 this analysis......i.e., no monitors in rural areas.] 

3 3.2.1.3 Analytical Techniques 

4 As noted above, this analysis focuses on five case study areas, four in the East and one in 

5 the West. Two of the eastern Case Study Areas, the Adirondack Mountains of New York (ADR) 

6 and western Virginia (VIR) were selected in order to examine the effects of acidification. The 

7 ADR includes 44 lakes and ponds and the VIR includes 61 streams that are being modeled using 

8 the MAGIC water quality model. The other two eastern case study areas in the East are the 

9 Potomac and Neuse river basins, which were selected to analyze the effects of nutrient 

10 enrichment. 

11 [Placeholder for description of the western case study area] 

12 The characterization of deposition and concentrations for each of these areas is presented 

13 in terms of the following: 

14  The relative amount of oxidized nitrogen versus reduced nitrogen deposition 

15  The relative amount of wet versus dry deposition of nitrogen and sulfur 

16  The magnitude and spatial gradients of annual total nitrogen and sulfur deposition and 

17 each of the component species 

18  The intra-annual variation in nitrogen and sulfur deposition and each of the component 

19 species based on seasonal and monthly deposition data 

20  The inter-annual variation in nitrogen and sulfur deposition 

21  The magnitude, spatial gradients, and intra-annual variation of annual average NOy and 

22 SO2 concentrations. 

23 [(7) Placeholder for analysis of the influence of inter-annual variability in meteorology 

24 on deposition – this analysis will be added in the second draft of this document.] 

25 Approach for Analyzing CMAQ Deposition—Eastern United States Case Study 

26 Areas

27 To analyze the CMAQ data, we developed procedures for mapping the CMAQ 12-km 

28 grid cells to each of the case study areas. The first step in this process was to identify the 
Chapter 3 – Sources, Ambient Concentrations, and Deposition 

DRAFT 3-18 August 2008 

hydrologic unit codes (HUCs)7

, 8, 9 1 within each area. We then used GIS to overlay the CMAQ 

2 grid cells on these HUCs in order to link specific grid cells to each HUC. A grid cell was linked 

3 to a HUC if any part of the grid cell touched a portion of the HUC. Note that a grid cell may be 

4 linked to more than one HUC using this approach. The map in Figure 3.2-2 shows the four 

5 eastern case study areas, along with the HUCs in each area and the outer boundary of the CMAQ 

6 grid cells that cover the area. 

7

 HUCs are used to identify the drainage basins within the United States. See 

http://imnh.isu.edu/digitalatlas/hydr/huc/huctxt.htm for additional information on HUCs. 

8

 We used finest-resolution HUC information available, which was 11-digit HUCs for the ADR, 12-digit HUCs for 

the VIR area, and 8-digit HUCs for the Potomac and Neuse. 

9

 In our analyses for the ADR and VIR areas, we selected the HUCs that contain the lakes/ponds and streams to be 

modeled with MAGIC. For the Potomac and Neuse, we included all the HUCs within the watersheds for each of 

these areas. 
Chapter 3 – Sources, Ambient Concentrations, and Deposition 

DRAFT 3-19 August 2008 

1

2 Figure 3.2-2. Case study areas in the eastern United States. 
Chapter 3 – Sources, Ambient Concentrations, and Deposition 

DRAFT 3-20 August 2008 

Using the set of selected grid cells, we calculated the monthly, seasonal,10 1 and annual 

2 total deposition of wet and dry oxidized nitrogen, reduced nitrogen, and sulfur for each HUC in 

3 each area. This was done by summing the CMAQ deposition data for all the grid cells linked to 

4 the HUC. We also calculated the total deposition for each case study area as a whole using 

5 deposition data for the set of unique grid cells that cover the entire case study area. To analyze 

6 the intra-annual temporal patterns in nitrogen deposition, we computed the percentage of annual 

7 total deposition and precipitation that was predicted in each season and each month. 

8 In addition to the HUC-level aggregations, we also prepared maps showing annual total 

9 deposition based on the gridded modeled data. These maps are used to (1) characterize the spatial 

10 gradients in nitrogen and sulfur deposition across each case study area and (2) compare the 

11 amount of deposition in each case study area to that in other adjacent parts of the region. 

12 Approach for Analyzing CMAQ Deposition – Western United States Case Study 

13 Area

14 [Placeholder for this approach, if different from what we are doing for the East] 

15 Approach for Analyzing Measured Deposition Data 

16 [Placeholder for this approach] 

17 Approach for Analyzing CMAQ Concentration Data

18 [Placeholder for this approach] 

19 Approach for Analyzing Measured Concentration Data 

20 [Placeholder for this approach] 

21 3.2.1.4 Results and Findings 

22 The results for each case study area are presented in the following manner. The 

23 characterization of nitrogen deposition is presented first, followed by the results for sulfur 

24 deposition. For nitrogen deposition, we describe the relative contribution of wet and dry oxidized 

25 and reduced nitrogen to annual total reactive nitrogen deposition in the case study area and 

26 examine how the contribution varies geographically across the area. We have a similar analysis 

10 Seasonal deposition and precipitation were calculated based calendar quarters (e.g., Jan/Feb/Mar is the winter 

season).
Chapter 3 – Sources, Ambient Concentrations, and Deposition 

DRAFT 3-21 August 2008 

1 for wet and dry sulfur deposition. The analysis of contribution is followed by an analysis of 

2 spatial gradients in annual deposition. Next, we look at the seasonal and monthly (i.e., intra3 annual) variation in each component of deposition for the case study area, along with the 

4 geographic variation in temporal patterns. We then investigate the inter-annual variability in 

5 deposition over the period 2002 through 2006 (in the second draft risk and exposure assessment). 

6 [Placeholder: may also include analysis of influence of inter-annual met variability on 

7 deposition] 

8 Adirondack Mountains Case Study Area 

9 A map of the ADR is provided in Figure 3.2.1-3. This map shows the location of the 44 

10 lakes and ponds and the HUCs which include these sites. The sites shaded in yellow represent 15 

11 sites selected for analysis of the geographic variation deposition across the ADR. 
Chapter 3 – Sources, Ambient Concentrations, and Deposition 

DRAFT 3-22 August 2008 

1

2 Figure 3.2-3. Adirondacks Case Study Area. 
Chapter 3 – Sources, Ambient Concentrations, and Deposition 

DRAFT 3-23 August 2008 

1 Relative Contribution of Wet and Dry Oxidized and Reduced Nitrogen Deposition 

2 In Figure 3.2-4, we show the contribution to annual nitrogen deposition from wet and dry 

3 oxidized nitrogen and reduced nitrogen for the ADR as a whole, based on the 2002 CMAQ 

4 modeling data. Deposition of total reactive nitrogen in this case study area is dominated by 

5 oxidized nitrogen (69% oxidized nitrogen vs 31% reduced nitrogen). Oxidized nitrogen 

6 deposition is fairly evenly divided between wet and dry. In contrast, wet deposition is the largest 

7 contributor to reduced nitrogen (25% wet vs 6 %dry). Overall, the predicted total wet deposition 

8 (oxidized and reduced) is greater than dry deposition by 61% vs 39%. Figure 3.2-5 indicates that 

9 the relative proportion of wet/dry oxidized/reduced nitrogen are generally similar across the 

10 ADR. Oxidized nitrogen deposition is greater than reduced nitrogen deposition in all locations 

11 with oxidized nitrogen contributing in the range of approximately 65%–75% of the total reactive 

12 nitrogen deposition. There does appear to be some geographic differences in wet vs dry 

13 deposition. Total wet deposition (oxidized nitrogen + reduced nitrogen) is in the range of 60% to 

14 70% (with dry 30% to 40%) in the western portion of the ADR. In the eastern ADR, the portion 

15 of wet is somewhat less at 50% to 55%. Looking at oxidized nitrogen alone, it appears that wet 

16 oxidized nitrogen is generally a larger fraction of total reactive nitrogen compared to dry 

17 oxidized nitrogen in the southern/western portions of the ADR (35% to 40% wet oxidized 

18 nitrogen vs 25% to 30% dry oxidized nitrogen). However, in other portions of this case study 

19 area, wet and dry oxidized nitrogen are either roughly equivalent or dry deposition is a larger 

20 fraction to total reactive nitrogen deposition. For reduced nitrogen, wet deposition is much larger 

21 than dry reduced nitrogen in all portions of the ADR. 

22 [Placeholder for contribution analysis based on measured data and a comparison 

23 between measured and modeled in terms of wet Ox vs wet Re N dep] 
Chapter 3 – Sources, Ambient Concentrations, and Deposition 

DRAFT 3-24 August 2008 

1

2 Figure 3.2-4. Contribution to annual total 2002 modeled 

3 deposition for the Adirondack Case Study Area.
Chapter 3 – Sources, Ambient Concentrations, and Deposition 

DRAFT 3-25 August 2008 

1

2 Figure 3.2-5. Contribution from wet/dry reduced and oxidized nitrogen to modeled 

3 2002 annual total nitrogen deposition in the Adirondack Case Study Area. 

4 Spatial Gradients in Annual Nitrogen Deposition Across the ADR

5 The annual total 2002 modeled total reactive nitrogen deposition in the ADR, as shown in 

6 Figure 3.2.1-6, reveals a clear spatial gradient in total reactive nitrogen deposition across the 

7 region. For example, total reactive nitrogen deposition is greater than 14 kg/ha in the southwest 

8 ADR compared to less than 8 kg/ha in the east. The spatial gradient in total reactive nitrogen 

9 deposition is largely driven by wet deposition as evident by comparing the wet nitrogen 

10 deposition map in Figure 3.2.1-7 to the dry nitrogen deposition map in Figure 3.2.1-8. The west 

11 to east gradient in wet nitrogen deposition appears to be much stronger than the gradient in dry 
Chapter 3 – Sources, Ambient Concentrations, and Deposition 

DRAFT 3-26 August 2008 

1 deposition. From Figure 3.2-9, it is evident that the relatively high total reactive nitrogen 

2 deposition in the southwestern portion of the ADR is part of a broad area of high deposition that 

3 stretches westward from this case study area along the southern shore of Lake Ontario toward 

4 western Pennsylvania and beyond.

5

6 Figure 3.2-6. Modeled 2002 annual total nitrogen deposition across

7 the Adirondack Case Study Area. 
Chapter 3 – Sources, Ambient Concentrations, and Deposition 

DRAFT 3-27 August 2008 

1

2 Figure 3.2-7. Modeled 2002 annual wet deposition of nitrogen

3 across the Adirondack Case Study Area. 
Chapter 3 – Sources, Ambient Concentrations, and Deposition 

DRAFT 3-28 August 2008 

1

2 Figure 3.2-8. Modeled 2002 annual dry deposition of nitrogen 

3 across the Adirondack Case Study Area.
Chapter 3 – Sources, Ambient Concentrations, and Deposition 

DRAFT 3-29 August 2008 

1

2 Figure 3.2-9. Modeled 2002 annual total nitrogen deposition across the Northeast. 

3 The spatial patterns in wet and dry oxidized and reduced nitrogen are shown in Figures

4 3.2-10a–d. Wet oxidized and wet reduced nitrogen are similar in terms of west to east gradients, 

5 as expected since wet deposition of both oxidized and reduced nitrogen are largely driven by 

6 precipitation. In contrast, dry oxidized nitrogen deposition is largest in a southeast to northwest 

7 band across the mid-portion of the ADR. The amount of dry reduced nitrogen is small compared 

8 to the other components of nitrogen deposition with little spatial variation. 
Chapter 3 – Sources, Ambient Concentrations, and Deposition 

DRAFT 3-30 August 2008 

1

2 Figure 3.2-10a. Modeled 2002 annual wet oxidized nitrogen deposition 

3 across the Adirondack Case Study Area. 
Chapter 3 – Sources, Ambient Concentrations, and Deposition 

DRAFT 3-31 August 2008 

1

2 Figure 3.2-10b. Modeled 2002 annual dry oxidized nitrogen deposition 

3 across the Adirondack Case Study Area. 
Chapter 3 – Sources, Ambient Concentrations, and Deposition 

DRAFT 3-32 August 2008 

1

2 Figure 3.2-10c. Modeled 2002 annual wet reduced nitrogen deposition

3 across the Adirondack Case Study Area. 
Chapter 3 – Sources, Ambient Concentrations, and Deposition 

DRAFT 3-33 August 2008 

1

2 Figure 3.2-10d. Modeled 2002 annual dry reduced nitrogen deposition

3 across the Adirondack Case Study Area. 
Chapter 3 – Sources, Ambient Concentrations, and Deposition 

DRAFT 3-34 August 2008 

1 [Placeholder for spatial patterns in measured vs modeled wet nitrate dep.] 

2 Intra-Annual Variation in Nitrogen Deposition in the ADR 

3 The seasonal variation in modeled 2002 total reactive nitrogen deposition is shown in 

4 Figure 3.2-11. Note that the data in this figure represent the percentage of annual total 

5 deposition that was predicted to occur in each season. For example, 29% of the 2002 modeled 

6 total reactive nitrogen deposition was predicted in the spring, with 27% in winter and 25% in the 

7 fall. The least amount of total reactive nitrogen deposition was in the summer, with 19% of the 

8 annual total. Although there seem to be relatively little seasonal differences in total reactive 

9 nitrogen deposition, this is not the case when broken out by wet and dry deposition for oxidized 

10 nitrogen and reduced nitrogen. The season percent of annual total modeled deposition of wet/dry 

11 oxidized nitrogen and reduced nitrogen, along with precipitation, are shown in Figures 3.2-12

12 and 3.2-13, respectively. The figures indicate that wet deposition of both oxidized and reduced 

13 nitrogen tend to track the temporal pattern in precipitation, with reduced nitrogen a closer match 

14 to precipitation than oxidized nitrogen. The clearest signal in the data is the minimum in wet 

15 deposition of both oxidized nitrogen and reduced nitrogen in the summer of 2002. The seasonal 

16 variations in dry deposition of both oxidized nitrogen and reduced nitrogen are very different 

17 from that of wet deposition. Dry oxidized nitrogen is fairly consistent from season to season, 

18 whereas reduced nitrogen shows a definite seasonal pattern that peaks in the summer. Thus, 

19 although there is relatively little intra-annual variation in total reactive nitrogen deposition, there 

20 are considerable seasonal differences in several of the individual components. 

21

22 Figure 3.2-11. Percent of annual total nitrogen deposition by season for the Adirondack 

23 Region, based on 2002 CMAQ modeling. 
Chapter 3 – Sources, Ambient Concentrations, and Deposition 

DRAFT 3-35 August 2008 

1

2 Figure 3.2-12. Percent of annual precipitation and reduced nitrogen deposition by quarter 

3 for the Adirondack Region, based on 2002 CMAQ modeling.

4

5 Figure 3.2-13. Percent of annual precipitation and oxidized nitrogen deposition by 

6 quarter for the Adirondack Region, based on 2002 CMAQ modeling. 

7 Additional insight into the temporal behavior of nitrogen deposition as modeled for 2002 

8 is revealed by examining the time series of monthly data for the ADR, as shown in Figures

9 3.2-14 and 3.2-15 for oxidized nitrogen and reduced nitrogen, respectively. The monthly data 

10 indicate that both wet and dry reduced nitrogen exhibit clear temporal patterns. Dry reduced 

11 nitrogen increases from January to a peak in July, followed by a steady decline to December. In 

12 contrast, the monthly pattern in dry oxidized nitrogen is fairly flat through most of 2002, as 

13 evident from Figure 3.2-14. The monthly wet reduced nitrogen tracks the monthly precipitation 

14 rather closely, with the highest deposition in late spring from April through June. The monthly 
Chapter 3 – Sources, Ambient Concentrations, and Deposition 

DRAFT 3-36 August 2008 

1 temporal pattern of wet oxidized nitrogen does not follow precipitation to the same degree as wet 

2 reduced nitrogen.

3

4 Figure 3.2-14. Percent of 2002 annual precipitation and oxidized nitrogen deposition by 

5 month for the Adirondack Region, based on 2002 CMAQ modeling. 

6

7 Figure 3.2-15. Percent of 2002 annual precipitation and reduced nitrogen deposition by 

8 month for the Adirondack Region, based on 2002 CMAQ modeling. 

9 The monthly wet oxidized nitrogen and wet reduced nitrogen data at the 15 selected sites 

10 in the ADR are shown in Figures 3.2-16 and 3.2-17, respectively. The highest wet deposition in 

11 nearly all areas occurs in March and April for both wet oxidized nitrogen and reduced nitrogen, 
Chapter 3 – Sources, Ambient Concentrations, and Deposition 

DRAFT 3-37 August 2008 

1 although some sites also showed elevated levels of wet reduced nitrogen deposition extending 

2 into June. A secondary peak in wet oxidized and reduced nitrogen is evident in October and 

3 November at most locations. The minimum wet deposition tends to occur in July or July through 

4 September. 
Chapter 3 – Sources, Ambient Concentrations, and Deposition 

DRAFT 3-38 August 2008 

1

2 Figure 3.2-16. Percent by month of 2002 annual wet oxidized nitrogen 

3 deposition for selected sites in the Adirondack Case Study Area. 
Chapter 3 – Sources, Ambient Concentrations, and Deposition 

DRAFT 3-39 August 2008 

1

2 Figure 3.2-17. Percent by month of 2002 annual wet reduced nitrogen 

3 deposition for selected sites in the Adirondack Case Study Area. 
Chapter 3 – Sources, Ambient Concentrations, and Deposition 

DRAFT 3-40 August 2008 

1 The monthly dry oxidized nitrogen and dry reduced nitrogen data at the 15 selected sites 

2 in the ADR are shown in Figures 3.2-18 and 3.2-19, respectively. The temporal patterns of dry 

3 oxidized nitrogen and dry reduced nitrogen are quite different. The dry oxidized nitrogen 

4 temporal pattern is generally flat, except for notable peaks in January and March. In contrast, dry 

5 reduced nitrogen deposition is at a minimum January and December. Values begin to increase in 

6 March and reach a peak in June and July, followed by a steady month-to-month decline to 

7 December. The dry oxidized nitrogen and dry reduced nitrogen monthly temporal patterns are 

8 each fairly consistent across the ADR. 
Chapter 3 – Sources, Ambient Concentrations, and Deposition 

DRAFT 3-41 August 2008 

1

2 Figure 3.2-18. Percent by month of 2002 annual dry oxidized nitrogen 

3 deposition for selected sites in the Adirondack Case Study Area. 
Chapter 3 – Sources, Ambient Concentrations, and Deposition 

DRAFT 3-42 August 2008 

1

2 Figure 3.2-19. Percent by month of 2002 annual dry reduced nitrogen 

3 deposition for selected sites in the Adirondack Case Study Area.

4 [Placeholder for temporal analysis of wet dep based on measured data and a comparison 

5 between measured and modeled wet Ox vs wet Re N dep] 
Chapter 3 – Sources, Ambient Concentrations, and Deposition 

DRAFT 3-43 August 2008 

1 Relative Contribution of Sulfur Deposition 

2 The contributions of wet and dry sulfur deposition to annual total sulfur deposition are 

3 shown in Figure 3.2-20. The portion of wet sulfur deposition is much greater than dry, with 64% 

4 wet versus 36% dry. The relative amount of wet and dry sulfur deposition is fairly uniform 

5 across the ADR, as shown in Figure 3.2-21.

6

7 Figure 3.2-20. Percentages by component of 2002 annual sulfur 

8 deposition for the Adirondack Region.
Chapter 3 – Sources, Ambient Concentrations, and Deposition 

DRAFT 3-44 August 2008 

1

2 Figure 3.2-21. Contribution to 2002 modeled annual total

3 sulfur deposition. 

4 Spatial Gradients in Annual Sulfur Deposition Across the ADR 

5 The annual total 2002 modeled sulfur deposition across the ADR is shown in Figure

6 3.2-22. The spatial pattern in sulfur deposition is similar to the pattern in nitrogen deposition (see 

7 Figure 3.2-19). Specifically, the highest amount of sulfur deposition is predicted in the southern 

8 and western portions of the case study area. Like nitrogen deposition, sulfur deposition is greater 

9 than 14 kg/ha in the southwest ADR compared to less than 8 kg/ha in the east. The spatial 

10 gradient in total sulfur deposition is largely driven by wet deposition as evident by comparing the 

11 wet sulfur deposition map in Figure 3.2-23 to the dry sulfur deposition map in Figure 3.2-24.

12 The spatial gradient in wet sulfur deposition appears to be much stronger than the gradient in dry 

13 sulfur deposition. Like nitrogen deposition, the relatively high total sulfur deposition in the 

14 southwestern portion of the ADR is part of a broad area of high sulfur deposition that stretches 
Chapter 3 – Sources, Ambient Concentrations, and Deposition 

DRAFT 3-45 August 2008 

1 westward from the case study area along the southern shore of Lake Ontario toward western 

2 Pennsylvania and beyond, as seen in Figure 3.2-25.

3

4 Figure 3.2-22. Modeled 2002 annual total sulfur deposition across the 

5 Adirondack Case Study Area. 
Chapter 3 – Sources, Ambient Concentrations, and Deposition 

DRAFT 3-46 August 2008 

1

2 Figure 3.2-23. Modeled 2002 annual wet sulfur deposition across the

3 Adirondack Case Study Area. 
Chapter 3 – Sources, Ambient Concentrations, and Deposition 

DRAFT 3-47 August 2008 

1

2 Figure 3.2-24. Modeled 2002 annual dry sulfur deposition across the

3 Adirondack Case Study Area.
Chapter 3 – Sources, Ambient Concentrations, and Deposition 

DRAFT 3-48 August 2008 

1

2 Figure 3.2-25. Modeled 2002 annual total sulfur deposition across 

3 the Northeast. 

4 Intra-Annual Variation in Sulfur Deposition in the ADR

5 In Figure 3.2-26, we show the seasonal patterns in modeled 2002 total sulfur deposition 

6 in the ADR. In general, the relative amount of predicted sulfur deposition that falls in each 

7 season is fairly similar during 2002. Like total nitrogen, the greatest portion of annual sulfur 

8 deposition is predicted to occur in the spring (30%). The least amount of sulfur deposition is in 

9 the summer at 20% of the annual total. Figure 3.2-27 provides a breakout of the seasonal 

10 amounts in terms of wet and dry sulfur deposition. The seasonal percentages of precipitation are 

11 provided for reference. The data in Figure 3.2-27 indicate that wet sulfur deposition is greatest 
Chapter 3 – Sources, Ambient Concentrations, and Deposition 

DRAFT 3-49 August 2008 

1 during the spring, which is also the season with the highest predicted precipitation. Over 37% of 

2 the annual wet sulfur deposition occurs in this season. In each of the other seasons, wet sulfur 

3 deposition is in the range of 19%–23%. Dry sulfur deposition is greatest in the winter (43% of 

4 annual total) followed by the fall (28%). The spring and summer have the least amount of dry 

5 sulfur deposition, with about 15% of the annual total in each of these seasons. 

6

7 Figure 3.2-26. Percent of annual total sulfur deposition by season for the Adirondack 

8 Region, based on 2002 CMAQ modeling. 

9

10 Figure 3.2-27. Percent of annual precipitation and wet and dry sulfur deposition by 

11 quarter for the Adirondack Region, based on 2002 CMAQ modeling. 
Chapter 3 – Sources, Ambient Concentrations, and Deposition 

DRAFT 3-50 August 2008 

1 The 2002 modeled monthly wet and dry sulfur deposition data for the ARD, as a whole, 

2 are displayed in Figure 3.2-28. These data show that wet sulfur deposition increases from a low 

3 in January to a peak in May. There is a sharp drop in wet sulfur deposition in July associated 

4 with a similar decline in precipitation. Moderately high amounts are predicted in August, 

5 September, and October followed by a decline toward the end of the year. In contrast to the 

6 temporal pattern exhibited by wet sulfur deposition, dry sulfur deposition is highest in January 

7 through March. There is a sharp decline between March and April with generally low values 

8 (i.e., 5% or less in each month) from May though September. Dry sulfur deposition increases in 

9 October and reaches a secondary peak in November. 

10

11 Figure 3.2-28. Percent of 2002 annual precipitation and sulfur deposition by month for 

12 the Adirondack Region, based on 2002 CMAQ modeling.

13 The monthly modeled dry and wet sulfur deposition data at the 15 selected sites in the 

14 ADR are shown in Figures 3.2-29 and 3.2-30, respectively. The temporal trend during the first 

15 half of the year in wet sulfur deposition at individual sites seems to be fairly consistent with the 

16 overall pattern of low values in the winter and high values in the spring. All sites exhibit the 

17 sharp drop in wet sulfur deposition in July. There are, however, geographic differences in the 

18 temporal patterns of wet sulfur deposition in the second half of the year. At the southern ADR 

19 sites wet sulfur deposition begins gradually increasing in August with a peak in October. In the 

20 northern half of this case study area, wet sulfur deposition shows a sharp increase from July to 
Chapter 3 – Sources, Ambient Concentrations, and Deposition 

DRAFT 3-51 August 2008 

1 August, followed by a steady decline to the end of the year. The temporal trends in the southern 

2 portion of the ADR area during the second half of 2002 are consistent with the trends in 

3 precipitation during this time period. In the northern ADR, the trends in wet sulfur deposition are 

4 not as consistent with the trends in precipitation. The monthly dry sulfur deposition data (see 

5 Figure 3.2.1-30) show a “concave” pattern. The highest amounts are in January through March 

6 followed by a sharp drop in April. Dry deposition continues to decline to a minimum in 

7 June/July followed by a gradual increase to a secondary peak in November. Unlike the temporal 

8 trends in wet sulfur deposition, the temporal behavior for dry sulfur deposition is geographically 

9 fairly consistent across the ADR. The pattern in dry sulfur deposition also differs from the 

10 monthly trend in dry oxidized and reduced nitrogen (see Figures 3.2-18 and 3.2-19). In fact, the 

11 trend in dry sulfur deposition, which is at a minimum in the summer, shows the opposite pattern 

12 of dry reduced nitrogen deposition, which peaks during this season. 
Chapter 3 – Sources, Ambient Concentrations, and Deposition 

DRAFT 3-52 August 2008 

1

2 Figure 3.2-29. Percent by month of 2002 annual wet sulfur 

3 deposition for selected sites in the Adirondack Case Study Area. 
Chapter 3 – Sources, Ambient Concentrations, and Deposition 

DRAFT 3-53 August 2008 

1

2 Figure 3.2-30. Percent by month of 2002 annual dry sulfur 

3 deposition for selected sites in the Adirondack Case Study Area. 

4 Inter-Annual Variation in Nitrogen and Sulfur Deposition 
Chapter 3 – Sources, Ambient Concentrations, and Deposition 

DRAFT 3-54 August 2008 

1 [Placeholder for this section.] 

2 Virginia Case Study Area 

3

4 Potomac Case Study Area 

5

6 Neuse Case Study Area 

7

8 Western Case Study Area 

9

10 3.2.1.5 Uncertainty 

11 [Placeholder for this section] 

12 3.2.2 Contributions of Emissions of NOx and NH3 to Deposition of Nitrogen

13 3.2.2.1 Purpose and Intent 

14 The public welfare effects associated with ambient NOx and SOx do not occur due to 

15 direct exposure to ambient concentrations of NOx and SOx. Instead, ecosystem effects occur due 

16 to ecological exposures to loadings of all forms of nitrogen and sulfur due, in part, to 

17 atmospheric deposition of nitrogen and sulfur. Atmospheric deposition of nitrogen and sulfur is 

18 directly related to the concentrations of NOx, NH3, and SOx in the atmosphere, and thus, 

19 reducing atmospheric emissions of NOx, NH3, and SOx will directly impact deposited nitrogen 

20 and sulfur and the associated ecosystem effects. In order to set ambient standards for NOx and 

21 SOx that are protective of public welfare, it is necessary to understand the contribution of 

22 ambient NOx and SOx to the ecosystem pollutants of concern: sulfur and total reactive nitrogen. 

23 Because the focus of this review is on oxides of nitrogen, rather than total reactive nitrogen, it is 

24 important to understand for that fraction of total nitrogen attributable to atmospheric deposition, 

25 the contribution of NOx relative to reduced forms of nitrogen (NH3 and NH4). This section 

26 describes the analysis of the contribution of NOx relative to reduced forms of nitrogen. The 

27 analysis uses a Response Surface Model (described below) to estimate the percent contribution 
Chapter 3 – Sources, Ambient Concentrations, and Deposition 

DRAFT 3-55 August 2008 

1 of NOx and NH3 emissions to total nitrogen deposition and to the oxidized and reduced forms of 

2 nitrogen deposition. 

3 3.2.2.2 Data and Tools 

4 EPA has recently developed a response-surface model (RSM) representation of the 

5 CMAQ model using multidimensional kriging techniques. CMAQ is a three-dimensional 

6 regional grid-based air quality model designed to simulate PM and O3 concentrations and 

7 deposition over large spatial scales (e.g., over the contiguous United States) over an extended 

8 period of time (e.g., up to a year). It includes state-of-the-science capabilities for conducting 

9 urban to regional scale simulations of multiple air quality issues, including tropospheric ozone, 

10 fine particles, toxics, acid deposition, and visibility degradation. The CMAQ model is a publicly 

11 available (supported by the Community Modeling and Analysis System [CMAS] Center; 

12 http://www.cmascenter.org/), peer reviewed, state-of-the-science model consisting of a number 

13 of science attributes that are critical for simulating the oxidant precursors and nonlinear organic 

14 and inorganic chemical relationships associated with the formation of sulfate, nitrate, and organic 

15 aerosols. It also simulates the transport and removal of directly emitted particles that are 

16 speciated as elemental carbon, crustal material, nitrate, sulfate, and organic aerosols.

17 The RSM is a reduced-form prediction model using statistical correlation structures to 

18 approximate model functions through the design of complex multi-dimension experiments. In 

19 other words, the RSM is a metamodel, or model of a model, representing the outputs of the 

20 CMAQ model using statistical predictions. The RSM technique has been successfully tested and 

21 evaluated for PM2.5 and ozone, respectively (U.S. EPA, 2006a, b) The RSM provides an accurate 

22 representation of the more complex CMAQ atmospheric chemistry model and allows for 

23 instantaneous calculation of the change in ambient PM2.5 resulting from a change in emissions 

24 within a predefined set of sources, locations, and precursor emission types. The RSM allows for 

25 a more complete, systematic evaluation of the relative contribution of emission reductions (e.g., 

26 the percent impact on nitrogen deposition of NOx versus NH3 emissions, across these 

27 dimensions). The RSM includes 12 source/pollutant combinations and allows for application of 

28 emissions reductions in 9 urban areas and a region representing the rest of the continental United 

29 States.
Chapter 3 – Sources, Ambient Concentrations, and Deposition 

DRAFT 3-56 August 2008 

1 The RSM used here is based on air quality modeling using CMAQ version 4.4 with a 36 

2 km horizontal domain (148 x 112 grid cells) and 14 vertical layers. The modeling domain 

3 encompasses the contiguous United States and extends from 126 degrees to 66 degrees west 

4 longitude and from 24 degrees to 52 degrees north latitude. A complete description of CMAQ, 

5 meteorological, emission, and initial and boundary condition inputs used for this analysis are 

6 discussed in the technical support document for the EPA Clean Air Interstate Rule (CAIR) (U.S. 

7 EPA, 2005). The RSM outputs are based on projected 2010 pre-CAIR emissions inventories, and 

8 therefore reflect any uncertainties in those inventories. The range of emissions changes that are 

9 supported by the RSM extends from 0% to 120% of 2010 emissions levels. 

10 The RSM can evaluate air quality changes that result from adjusting each of the 

11 following 12 emissions control factors on a regional basis: 

12 1. NOx EGU = NOx EGU point source emissions based on the Integrated Planning Model 

13 (IPM) (see REF). 

14 2. NOx NonEGU Point and Area = NOx IPM Non-EGU point source, area source, and 

15 agricultural source emissions 

16 3. NOx Mobile = NOx non-road source and mobile source emissions 

17 4. SOx EGU = SOx IPM EGU point source emissions 

18 5. SOx NonEGU Point = SOx IPM Non-EGU point source emissions 

19 6. SOx Area = SOx area source and agricultural source emissions 

20 7. NH3 Area = Ammonia area source and agricultural source emissions 

21 8. NH3 Mobile = Ammonia non-road source and mobile source emissions 

22 9. POC/PEC Point (EGU and NonEGU) = Elemental carbon and organic carbon IPM EGU 

23 point source and IPM Non-EGU point source emissions 

24 10. POC/PEC Mobile = Elemental carbon and organic carbon non-road source and mobile 

25 source emissions 

26 11. POC/PEC area = Elemental carbon and organic carbon area source and agricultural 

27 source emissions 

28 12. Volatile organic compound (VOC) All = IPM EGU point source, IPM non-EGU point 

29 source, area source, agricultural source, non-road source, and mobile source emissions. 

30 Source groupings with small contributions to emissions were grouped with similar larger 

31 source groupings for efficiency. Non-EGU Area NOx and SOx sources were primarily smaller 

32 industrial combustion sources, such as coal, oil, and natural gas-powered boilers and internal 

33 combustion engines. Agricultural area sources were the only significant contributors to ammonia 

34 emissions. VOC sources were lumped together based on the chemistry incorporated in CMAQ 

35 version 4.4, indicating that VOCs are not expected to influence PM levels significantly.
Chapter 3 – Sources, Ambient Concentrations, and Deposition 

DRAFT 3-57 August 2008 

1 Based on the 12 emissions control factors above, we developed the experimental design 

for these factors using a Latin Hypercube11 2 method, which identified the necessary CMAQ 

3 modeling runs. Latin hypercube designs are very flexible in accommodating restrictions on the 

4 number of runs (as opposed to factorial designs, for example, which are fairly rigid). We 

5 implemented a design with 211 model runs (a base case plus 210 control runs). Any specific run 

6 had different levels of the 12 factors, for example, factor 1 (EGU NOx) might be set at 0.1 (90% 

7 reduction), factor 2 (Non-EGU NOx) at 0.3 (70% reduction), factor 3 (Mobile NOx) at 0.75 (25% 

8 reduction), and so on. The complete list of model runs and corresponding emissions reduction 

9 scenarios (i.e., selection of policy factor controls) are available (U.S. EPA, 2006b). The CMAQ 

10 model was run for 4 months, 1 month from each season in 2002 (?),—February, April, July, 

11 October—to reduce computational time for such a large number of annual model runs. These 

12 months were chosen based on greatest predictability of the quarterly mean. 

13 3.2.2.3 Analytical Techniques 

14 To better inform our understanding of the roles of NOx and NH3 in deposition of 

15 nitrogen, we used the RSM described above to estimate the relative contribution of emissions of 

16 NOx and NH3 to deposition of nitrogen, including total as well as reduced and oxidized nitrogen. 

17 We focus on the percent contribution in the set of eight case study areas that are the focus of the 

18 risk and exposure analysis. All analyses were based on zero-out runs, e.g., setting the emissions 

19 of NOx or NH3 equal to zero and estimating the change in deposition at grid cells within the 

20 CMAQ domain). Note that zeroing out the RSM emissions factor for NOx will not result in zero 

21 emissions of NOx —the remaining emissions will include international sources and non22 anthropogenic sources (e.g., lightning). Likewise, zeroing out the RSM emissions factor for NH3

23 emissions will not result in zero emissions of NH3, with remaining NH3 emissions comprised of 

24 international emissions, non-anthropogenic emissions, and additionally, point sources of NH3,

25 which, while accounting for a low proportion of overall NH3 emissions, can be significant in 

26 some limited locations. 

27 We examine the contribution of NOx, and NH3 emissions to deposition in eight case 

28 study areas, including the Neuse River, Potomac River, Shenandoah National Park, Adirondacks, 

29 red spruce habitat, sugar maple habitat, coastal sage habitat, and in all coastal estuaries. The 

11 A Latin hypercube is the generalization of a Latin square to an arbitrary number of dimensions, whereby each 

sample is the only one in each axis-aligned hyperplane containing it. 
Chapter 3 – Sources, Ambient Concentrations, and Deposition 

DRAFT 3-58 August 2008 

1 CMAQ grid cells that cover each of these case study areas are displayed in Figures 3.2-31 

2 through 3.2-38. For each of the case study areas, we constructed box plots for several metrics 

3 (Figures 3.2-39 through 3.2-44), covering several combinations of emissions and output 

4 variables. Box plots are a graphical method for displaying the central tendency and variability in 

5 a set of values. The box plots are arrayed for the eight case study areas in combined graphs to 

6 allow for comparison across case study areas, as well as illustrate the variability within each case 

7 study area. In each case, we examine the impact of ambient NOx and NH3 on deposition of total 

8 nitrogen, reduced nitrogen, and oxidized nitrogen. The percent impact on deposition was 

9 estimated to provide a more comparable relative metric across locations and seasons. An analysis 

10 of the spatial patterns of responses within each case study area is also presented. 

11

12 Figure 3.2-31. CMAQ 36 km grids in Neuse River Case Study Area. 
Chapter 3 – Sources, Ambient Concentrations, and Deposition 

DRAFT 3-59 August 2008 

1

2 Figure 3.2-32. CMAQ 36-km grids in Potomac Case Study Area. 

3

4 Figure 3.2-33. CMAQ 36-km grids in Shenandoah Case Study Area. 
Chapter 3 – Sources, Ambient Concentrations, and Deposition 

DRAFT 3-60 August 2008 

1

2 Figure 3.2-34. CMAQ 36-km grids in Adirondack Case Study Area. 

3

4 Figure 3.2-35. CMAQ 36-km grids in Red Spruce Case Study Area. 
Chapter 3 – Sources, Ambient Concentrations, and Deposition 

DRAFT 3-61 August 2008 

1

2 Figure 3.2-36. CMAQ 36-km grids in Sugar Maple Case Study Area. 

3

4 Figure 3.2-37. CMAQ 36-km grids in Coastal Sage Case Study Area. 
Chapter 3 – Sources, Ambient Concentrations, and Deposition 

DRAFT 3-62 August 2008 

1

2 Figure 3.2-38. CMAQ 36-km grids in Estuaries Case Study Area. 
Chapter 3 – Sources, Ambient Concentra

DRAFT 3-63 

1

2 Figure 3.2-39. Percent impact of zero-out of NOx emissions on total nitrogen deposition.
Chapter 3 – Sources, Ambient Concentra

DRAFT 3-64 

1

2 Figure 3.2-40. Percent impact of zero-out of NOx emissions on oxidized nitrogen deposition. 
Chapter 3 – Sources, Ambient Concentra

DRAFT 3-65 

1

2 Figure 3.2-41. Percent impact of zero-out of NOx emissions on reduced nitrogen deposition. 
Chapter 3 – Sources, Ambient Concentra

DRAFT 3-66 

1

2 Figure 3.2-42. Percent impact of zero-out of NH3 emissions on total nitrogen deposition. 
Chapter 3 – Sources, Ambient Concentra

DRAFT 3-67 

1

2 Figure 3.2-43. Percent impact of zero-out of NH3 emissions on reduced nitrogen.
Chapter 3 – Sources, Ambient Concentra

DRAFT 3-68 

1

2 Figure 3.2-44. Percent impact of zero-out of NH3 emissions on oxidized nitrogen deposition. 
Chapter 3 – Sources, Ambient Concentrations, and Deposition 

DRAFT 3-69 August 2008 

1 Relative (percent) impacts can be slightly greater than 100% due to the small level of 

2 error in the response surface modeling. The sum of the relative impacts across NOx, NH3 can be 

3 less than 100% due to nonlinearities in the atmospheric chemistry, e.g. reducing all pollutants by 

4 100% would give different results than reducing each individually by 100% and summing the 

5 results. Because of the chemistry governing gas and particle-phase ammonia, SO2 emissions can 

6 also have a small impact on deposition of nitrogen. However, because the focus of this section is 

7 on the relative importance of NOx and NH3, we do not provide results for SO2 here.

8 3.2.2.4 Results and Findings 

9 The first set of results, displayed in Figures 3.2-39 and 3.2-40, examine the relative 

10 impact of emissions of NOx on the deposition of total reactive nitrogen. Figure 3.2-39 shows that 

11 NOx emissions represent a significant contribution to deposition of total reactive nitrogen in each 

12 case study area, although the impact varies by season. The smallest impact of NOx emissions, 

13 22.5%, occurs in the Neuse River Case Study Area in July. The largest impact of NOx emissions, 

14 75.5%, occurs in the Adirondacks Case Study Area in February. In general, across case study 

15 areas, the largest NOx percent impacts on total reactive nitrogen deposition occur in February, 

16 ranging from 44%–75% percent, while the smallest relative impacts, ranging from 22%–54%, 

17 occur in July. With the exception of the Coastal Sage Case Study Area, each area has its highest 

18 relative contribution from NOx in February and lowest relative contribution in July. The Coastal 

19 Sage Case Study Area has the highest relative contribution in July and the lowest relative 

20 contribution in April. This may reflect differences in the climates between the eastern United 

21 States, where most of the other areas (with the exception of western portions of the Estuaries 

22 Case Study Area) are located, and the western California coast, where the Coastal Sage Case 

23 Study Area is located. 

24 Figures 3.2-40 and 3.2-41 explore the relationship between NOx emissions and total 

25 reactive nitrogen deposition in more detail, examining separately the relative impacts of NOx on 

26 oxidized and reduced forms of nitrogen. It was anticipated that NOx emissions will have a larger 

27 relative impact on oxidized nitrogen compared with reduced nitrogen (U.S. EPA, 2008). Figures 

28 3.2-40 and 3.2-41 confirm this expectation. In each case study area and season, the relative 
Chapter 3 – Sources, Ambient Concentrations, and Deposition 

DRAFT 3-70 August 2008 

impact of NOx emissions is over 84%, and in some cases, has a 100% impact,12 1 indicating that all 

2 of the oxidized nitrogen is likely associated with NOx emissions. Also, as expected, Figure 3.2-

3 41 shows that in all case study areas and all seasons, NOx emissions have less than a 20% impact 

4 on reduced nitrogen deposition. And, in most cases, the NOx impact is actually negative, 

5 suggesting that NOx emissions contribute to greater deposition of reduced nitrogen. This 

6 relationship reflects the atmospheric reactions that lead to deposition of reduced nitrogen. One 

7 possibility is that reducing NOx reduces HNO3, which limits ammonium nitrate (NH4NO3)

8 formation (and for existing aerosol, a reduction in HNO3 shifts the equilibrium toward the gas 

phase), thereby increasing the lifetime of NH3. A net increase in NH3 /NH4

+

9 results. Because the 

deposition velocity of NH3 is much higher than the deposition velocity for NH4

+

10 aerosol, dry 

11 deposition of NHx increases. The only instances where NOx emissions contribute to decreased 

12 reduced nitrogen deposition are in the Adirondacks Case Study Area in February and October; 

13 however, these are very small impacts and may reflect statistical imprecision in the modeling. 

14 We will continue to explore these results for the final risk and exposure analysis. 

15 Figure 3.2-42 examines the relative impact of emissions of NH3 on the deposition of total 

16 reactive nitrogen. Figure 3.2-42 shows that NH3 emissions represent a significant contribution to 

17 total reactive nitrogen in most case study areas, although the impact varies by season and by 

18 area. The smallest impact of NH3, 10%, occurs in the Potomac Case Study Area in February. The 

19 largest impact of NH3, 73%, occurs in the Neuse Case Study Area in July. The Neuse Case Study 

20 Area has the largest overall impact from NH3 of any of the case study areas, across all four 

21 seasons. This may be due to the large concentration of CAFOs located in eastern North Carolina. 

22 In general, across case study areas, the largest NH3 relative impacts on total nitrogen deposition 

23 occur in July from 37%–73%, while the smallest relative impacts, ranging from 10%–43%, occur 

24 in February. Each area has its highest relative contribution from NH3 in July and its lowest 

25 relative contribution in February. 

26 Figures 3.2-43 and 3.2-44 explore the relationship between NH3 emissions and nitrogen 

27 deposition in more detail, examining separately the relative impacts of NH3 on oxidized and 

12 In fact, the RSM modeling predicts a greater than 100% impact in some case study areas. This likely reflects that 

fact that the RSM is a statistical approximation to the CMAQ model. As with all statistical models, extrapolations 

to extreme cases can lead to larger than average statistical errors. In this analysis, where we are zeroing out 

emissions of individual pollutants, we are pushing the RSM model to its boundaries, and as such, the findings of 

greater than 100% impact are likely a statistical artifact. In this case, we interpret greater than 100% impacts as 

100% impacts. 
Chapter 3 – Sources, Ambient Concentrations, and Deposition 

DRAFT 3-71 August 2008 

1 reduced forms of nitrogen. It is expected that NH3 emissions will have a larger relative impact on 

2 reduced forms of nitrogen deposition. This modeling exercise, depicted in Figures 3.2-43 and 

3 3.2-44, confirms this expectation. In each case study area and season, the relative impact of NH3

4 emissions is over 85%, and in some cases, has a 100% impact, indicating that all of the reduced 

5 nitrogen is likely associated with NH3 emissions. Also, as expected, Figure 3.2-44 shows that in 

6 all case study areas and all seasons, NH3 has less than a 20% impact on oxidized nitrogen 

7 deposition. And, in most cases, the NH3 impact is actually negative, meaning that NH3 emissions 

8 contribute to greater deposition of oxidized nitrogen deposition. This relationship reflects the 

9 atmospheric reactions that lead to deposition of reduced and oxidized nitrogen. Reducing NH3

limits NH4NO3 aerosol formation, increasing the lifetime of HNO3. The ratio HNO3;NO3

-

10

increases and since the deposition velocity of HNO3 is much larger than that of NO3

-

11 aerosol, dry 

12 deposition of total oxidized nitrogen increases. The only positive impacts of NH3 on oxidized 

13 nitrogen occur in the Adirondack Case Study Area in February; however, these are very small 

14 impacts and may reflect statistical imprecision in the modeling. We will continue to explore 

15 these results for the final risk and exposure analysis. 

16 Spatial Analysis of Results 

17 As noted above, there is a good deal of variability in the impacts of NOx and NH3 within 

18 and between case study areas. In order to explore this variability, the estimated impacts of NOx

19 and NH3 on their deposition counterparts were mapped. For NOx, the percentage impact on 

20 oxidized and total nitrogen deposition was mapped, and for NH3, the percentage impact on 

21 reduced and total nitrogen deposition was mapped. Each of the impact maps uses the same color 

22 scale for ease of comparison across case study areas. Each map has four panels, one for each of 

23 the four months modeled, representing the four seasons. There are four maps for each case study 

24 area, for a total of 32% impact maps. The critical factors to consider in the maps of impacts on 

25 total nitrogen are the spatial uniformity of contribution in each case study area and the 

26 uniformity of contribution across seasons. For the maps displaying the impact of NOx on 

27 oxidized nitrogen and the impact of NH3 on reduced nitrogen, we expect to see most grid cells 

28 with close to 100% impact, reflecting the dominant impact of NOx on oxidized nitrogen 

29 deposition and the dominant impact of NH3 on reduced nitrogen. In some cases, the maps may 

30 show lower-impact percentages due to three types of emissions that are included in the baseline 

31 CMAQ modeling but not included as controllable emissions in the RSM modeling: (1) 
Chapter 3 – Sources, Ambient Concentrations, and Deposition 

DRAFT 3-72 August 2008 

1 international emissions, (2) non anthropogenic emissions, and (3) large point sources of 

2 ammonia.

3 Figures 3.2-45 through 3.2-48 present maps of the results for the Sugar Maple Case 

4 Study Area. With the Sugar Maple Case Study Area (Figure 3.2-45), it is clear that there is 

5 considerable heterogeneity in response to NOx emissions reductions across the case study area, 

6 and between seasons. However, NOx contributions are significant in a large number of grid cells 

7 in all seasons, suggesting that NOx is an important part of overall nitrogen deposition in the 

8 Sugar Maple Case Study Area. Based on this analysis, NOx appears to contribute the most 

9 consistently across the area during the winter and fall months, with lower contributions and more 

10 spatial heterogeneity during the spring and summer months. Likewise, as shown in Figure 

11 3.2-46, the impact of ammonia emissions is greatest during the spring and summer months, with 

12 less impact during fall and winter months. Note that even during the fall and winter months, 

13 ammonia emissions have a large impact in those grid cells closest to major agricultural ammonia 

14 sources (e.g., the high poultry production area in northern Virginia and the high hog production 

15 area in southeastern Pennsylvania). With regard to the impact of NOx emissions on oxidized 

16 nitrogen, as expected, Figure 3.2-47 shows that zeroing out domestic, anthropogenic NOx

17 emissions results in close to100% reduction in oxidized nitrogen deposition in most grid cells in 

18 the area, with the exception of some grid cells on the East Coast and in Canada, which likely 

19 reflects international emissions sources. Likewise, Figure 3.2-48 shows that zeroing out NH3

20 emissions results in close to 100% reductions in reduced nitrogen deposition throughout the area. 

21 In a few grid cells near large point sources of ammonia, there is a less that 100% impact from 

22 zeroing out the area and mobile source NH3 emissions, and off of the United States coast and in 

23 Canada, international emissions appear to contribute a portion of reduced nitrogen deposition. 
Chapter 3 – Sources, Ambient Concentra

DRAFT 3-73 

Percent Impact

0% - 10%10.1% - 20%20.1% - 30%30.1% - 40%40.1% - 50%50.1% - 60%60.1% - 70%70.1% - 80%80.1% - 90%90.1% - 100%

February

April

Percent Impact

0% - 10%10.1% - 20%20.1% - 30%30.1% - 40%40.1% - 50%50.1% - 60%60.1% - 70%70.1% - 80%80.1% - 90%90.1% - 100%

July

October

1

2 Figure 3.2-45. Percent impact of NOx anthropogenic United States emissions zero-out on total nitrogen d3 Maple Case Study Area.
Chapter 3 – Sources, Ambient Concentra

DRAFT 3-74 

Percent Impact

0% - 10%10.1% - 20%20.1% - 30%30.1% - 40%40.1% - 50%50.1% - 60%60.1% - 70%70.1% - 80%80.1% - 90%90.1% - 100%

Percent Impact

<0% - 10%10.1% - 20%20.1% - 30%30.1% - 40%40.1% - 50%50.1% - 60%60.1% - 70%70.1% - 80%80.1% - 90%90.1% - 100%

February April

July October

1

2 Figure 3.2-46. Percent impact of NH3 anthropogenic United States emissions zero-out on total nitrogen d3 Maple Case Study Area.
Chapter 3 – Sources, Ambient Concentra

DRAFT 3-75 

Percent Impact

0% - 10%10.1% - 20%20.1% - 30%30.1% - 40%40.1% - 50%50.1% - 60%60.1% - 70%70.1% - 80%80.1% - 90%90.1% - 100%

Percent Impact

0% - 10%10.1% - 20%20.1% - 30%30.1% - 40%40.1% - 50%50.1% - 60%60.1% - 70%70.1% - 80%80.1% - 90%90.1% - 100%

February April

July October

1

2 Figure 3.2-47. Percent impact of NOx anthropogenic United States emissions zero-out on oxidized nitrog3 Sugar Maple Case Study Area.
Chapter 3 – Sources, Ambient Concentra

DRAFT 3-76 

Percent Impact

0% - 10%10.1% - 20%20.1% - 30%30.1% - 40%40.1% - 50%50.1% - 60%60.1% - 70%70.1% - 80%80.1% - 90%90.1% - 100%

Percent Impact

0% - 10%10.1% - 20%20.1% - 30%30.1% - 40%40.1% - 50%50.1% - 60%60.1% - 70%70.1% - 80%80.1% - 90%90.1% - 100%

February April

July October

These are likely large point NH3 sources, which were not included in the RSM factors

1

2 Figure 3.2-48. Percent impact of NH3 anthropogenic United States emissions zero-out on reduced nitroge3 Sugar Maple Case Study Area. 
Chapter 3 – Sources, Ambient Concentrations, and Deposition 

DRAFT 3-77 August 2008 

1 Figures 3.2-49 through 3.2-52 present maps of the results for the Red Spruce Case 

2 Study Area. For the most part, the Red Spruce Case Study Area overlaps the Sugar Maple Case 

3 Study Area. As such, similar patterns of total nitrogen deposition response can be seen in Figure 

4 3.2-49. With the exception of July, the seasonal pattern of total nitrogen deposition response to 

5 NOx is similar, with a large percent impact from zeroing out domestic, anthropogenic NOx. The 

6 exceptions are in portions of Canada and in the heavy poultry production area of northern 

7 Virginia, where ammonia emissions are very high. In July, NOx impacts are less relative to 

8 ammonia impacts, but are still significant in many grid cells. Examining Figure 3.2-51, as with 

9 the Sugar Maple Case Study Area, almost all of the oxidized nitrogen deposition is due to 

10 domestic NOx emissions, with the exception of some grid cells in Canada and in the United 

11 States bordering Canada. Likewise, Figure 3.2-52 shows that almost all of the reduced nitrogen 

12 deposition is due to domestic NH3 emissions, excepting some grid cells in Canada.
Chapter 3 – Sources, Ambient Concentra

DRAFT 3-78 

Percent Impact

0% - 10%10.1% - 20%20.1% - 30%30.1% - 40%40.1% - 50%50.1% - 60%60.1% - 70%70.1% - 80%80.1% - 90%90.1% - 100%

Percent Impact

0% - 10%10.1% - 20%20.1% - 30%30.1% - 40%40.1% - 50%50.1% - 60%60.1% - 70%70.1% - 80%80.1% - 90%90.1% - 100%

February April

July October

1

2 Figure 3.2-49. Percent impact of NOx anthropogenic United States emissions zero-out on total nitrogen d3 Spruce Case Study Area.
Chapter 3 – Sources, Ambient Concentra

DRAFT 3-79 

Percent Impact

0% - 10%10.1% - 20%20.1% - 30%30.1% - 40%40.1% - 50%50.1% - 60%60.1% - 70%70.1% - 80%80.1% - 90%90.1% - 100%

Percent Impact

<0% - 10%10.1% - 20%20.1% - 30%30.1% - 40%40.1% - 50%50.1% - 60%60.1% - 70%70.1% - 80%80.1% - 90%90.1% - 100%

February April

July October

1

2 Figure 3.2-50. Percent impact of NH3 anthropogenic United States emissions zero-out on total nitrogen d3 Spruce Case Study Area.
Chapter 3 – Sources, Ambient Concentra

DRAFT 3-80 

Percent Impact

0% - 10%10.1% - 20%20.1% - 30%30.1% - 40%40.1% - 50%50.1% - 60%60.1% - 70%70.1% - 80%80.1% - 90%90.1% - 100%

Percent Impact

0% - 10%10.1% - 20%20.1% - 30%30.1% - 40%40.1% - 50%50.1% - 60%60.1% - 70%70.1% - 80%80.1% - 90%90.1% - 100%

February April

July October

1

2 Figure 3.2-51. Percent impact of NOx anthropogenic United States emissions zero-out on oxidized nitrog3 Red Spruce Case Study Area. 
Chapter 3 – Sources, Ambient Concentra

DRAFT 3-81 

Percent Impact

0% - 10%10.1% - 20%20.1% - 30%30.1% - 40%40.1% - 50%50.1% - 60%60.1% - 70%70.1% - 80%80.1% - 90%90.1% - 100%

Percent Impact

0% - 10%10.1% - 20%20.1% - 30%30.1% - 40%40.1% - 50%50.1% - 60%60.1% - 70%70.1% - 80%80.1% - 90%90.1% - 100%

February April

July October

1

2 Figure 3.2-52. Percent impact of NH3 anthropogenic United States emissions zero-out on reduced nitroge3 Red Spruce Case Study Area. 
Chapter 3 – Sources, Ambient Concentrations, and Deposition 

DRAFT 3-82 August 2008 

1 Figures 3.2-53 through 3.2-56 present maps of the results for the Adirondacks Case 

2 Study Area. The Adirondacks Case Study Area is completely contained within the Red Spruce 

3 Case Study Area. Figure 3.2-53 shows that the specific grid cells in the Adirondacks Case Study 

4 Area show strong responses of total nitrogen deposition to domestic, anthropogenic NOx

5 emissions. With the exception of July, NOx impacts are mostly greater than 50% throughout the 

6 case study area. In July, NOx contributes more modestly, but still accounts for 40%–50% percent 

7 of total nitrogen deposition. Figure 3.2-55 shows that NOx emissions account for almost all 

8 oxidized nitrogen deposition in the Adirondacks Case Study Area, while Figure 3.2-56 shows 

9 that NH3 emissions account for almost all reduced nitrogen deposition. 
Chapter 3 – Sources, Ambient Concentra

DRAFT 3-83 

Percent Impact

0% - 10%10.1% - 20%20.1% - 30%30.1% - 40%40.1% - 50%50.1% - 60%60.1% - 70%70.1% - 80%80.1% - 90%90.1% - 100%

Percent Impact

0% - 10%10.1% - 20%20.1% - 30%30.1% - 40%40.1% - 50%50.1% - 60%60.1% - 70%70.1% - 80%80.1% - 90%90.1% - 100%

February April

July October

1

2 Figure 3.2-53. Percent impact of NOx anthropogenic United States emissions zero-out on total nitrogen d3 Adirondacks Case Study Area.
Chapter 3 – Sources, Ambient Concentra

DRAFT 3-84 

Percent Impact

0% - 10%10.1% - 20%20.1% - 30%30.1% - 40%40.1% - 50%50.1% - 60%60.1% - 70%70.1% - 80%80.1% - 90%90.1% - 100%

Percent Impact

0% - 10%10.1% - 20%20.1% - 30%30.1% - 40%40.1% - 50%50.1% - 60%60.1% - 70%70.1% - 80%80.1% - 90%90.1% - 100%

February April

July October

1

2 Figure 3.2-54. Percent impact of NH3 anthropogenic United States emissions zero-out on total nitrogen d3 Adirondacks Case Study Area. 
Chapter 3 – Sources, Ambient Concentra

DRAFT 3-85 

Percent Impact

0% - 10%10.1% - 20%20.1% - 30%30.1% - 40%40.1% - 50%50.1% - 60%60.1% - 70%70.1% - 80%80.1% - 90%90.1% - 100%

Percent Impact

6.1% - 10%10.1% - 20%20.1% - 30%30.1% - 40%40.1% - 50%50.1% - 60%60.1% - 70%70.1% - 80%80.1% - 90%90.1% - 100%

February April

July October

1

2 Figure 3.2-55. Percent impact of NOx anthropogenic United States emissions zero-out on oxidized nitrog3 Adirondacks Case Study Area.
Chapter 3 – Sources, Ambient Concentra

DRAFT 3-86 

Percent Impact

0% - 10%10.1% - 20%20.1% - 30%30.1% - 40%40.1% - 50%50.1% - 60%60.1% - 70%70.1% - 80%80.1% - 90%90.1% - 100%

Percent Impact

0% - 10%10.1% - 20%20.1% - 30%30.1% - 40%40.1% - 50%50.1% - 60%60.1% - 70%70.1% - 80%80.1% - 90%90.1% - 100%

February April

July October

1

2 Figure 3.2-56. Percent impact of NH3 anthropogenic United States emissions zero-out on reduced nitroge3 Adirondacks Case Study Area. 
Chapter 3 – Sources, Ambient Concentrations, and Deposition 

DRAFT 3-87 August 2008 

1 Figures 3.2-57 through 3.2-60 present maps of the results for the Potomac Case Study 

2 Area. Figure 3.2-57 shows that the Potomac Case Study Area has a significant fraction of total 

3 nitrogen deposition from domestic, anthropogenic NOx emissions, but also has a number of grid 

4 cells where NOx emissions have a less than 40% impact. This is likely due to the location of high 

5 NH3 emitting sources in or near Potomac Case Study Area grid cells; for example, poultry 

6 production in northern Virginia and hog and cattle production in southern Pennsylvania. 

7 However, for many of the grid cells nearest to the Chesapeake Bay, NOx emissions contribute 

8 significantly (50% impact or greater) to total nitrogen deposition. As with the Adirondacks Case 

9 Study Area, Figure 3.2-59 shows that almost all of the oxidized nitrogen deposition is associated 

10 with NOx emissions, while Figure 3.2-60 shows that almost all of the reduced nitrogen 

11 deposition is associated with NH3 emissions.
Chapter 3 – Sources, Ambient Concentra

DRAFT 3-88 

Percent Impact

0% - 10%10.1% - 20%20.1% - 30%30.1% - 40%40.1% - 50%50.1% - 60%60.1% - 70%70.1% - 80%80.1% - 90%90.1% - 100%

Percent Impact

0% - 10%10.1% - 20%20.1% - 30%30.1% - 40%40.1% - 50%50.1% - 60%60.1% - 70%70.1% - 80%80.1% - 90%90.1% - 100%

February April

July October

1

2 Figure 3.2-57. Percent impact of NOx anthropogenic United States emissions zero-out on total nitrogen d3 Potomac Case Study Area.
Chapter 3 – Sources, Ambient Concentra

DRAFT 3-89 

Percent Impact

0% - 10%10.1% - 20%20.1% - 30%30.1% - 40%40.1% - 50%50.1% - 60%60.1% - 70%70.1% - 80%80.1% - 90%90.1% - 100%

Percent Impact

<0% - 10%10.1% - 20%20.1% - 30%30.1% - 40%40.1% - 50%50.1% - 60%60.1% - 70%70.1% - 80%80.1% - 90%90.1% - 100%

February April

July October

1

2 Figure 3.2-58. Percent impact of NH3 anthropogenic United States emissions zero-out on total nitrogen d3 Potomac Case Study Area.
Chapter 3 – Sources, Ambient Concentra

DRAFT 3-90 

Percent Impact

0% - 10%10.1% - 20%20.1% - 30%30.1% - 40%40.1% - 50%50.1% - 60%60.1% - 70%70.1% - 80%80.1% - 90%90.1% - 100%

Percent Impact

0% - 10%10.1% - 20%20.1% - 30%30.1% - 40%40.1% - 50%50.1% - 60%60.1% - 70%70.1% - 80%80.1% - 90%90.1% - 100%

February April

July October

1

2 Figure 3.2-59. Percent impact of NOx anthropogenic United States emissions zero-out on oxidized nitrog3 Potomac Case Study Area.
Chapter 3 – Sources, Ambient Concentra

DRAFT 3-91 

Percent Impact

0% - 10%10.1% - 20%20.1% - 30%30.1% - 40%40.1% - 50%50.1% - 60%60.1% - 70%70.1% - 80%80.1% - 90%90.1% - 100%

Percent Impact

0% - 10%10.1% - 20%20.1% - 30%30.1% - 40%40.1% - 50%50.1% - 60%60.1% - 70%70.1% - 80%80.1% - 90%90.1% - 100%

February April

July October

1

2 Figure 3.2-60. Percent impact of NH3 anthropogenic United States emissions zero-out on reduced nitroge3 Potomac Case Study Area. 
Chapter 3 – Sources, Ambient Concentrations, and Deposition 

DRAFT 3-92 August 2008 

1 Figures 3.2-61 through 3.2-64 present maps of the results for the Shenandoah Case 

2 Study Area. The Shenandoah Case Study Area overlaps a portion of the Potomac Case Study 

3 Area, and thus, shares similar characteristics. Figure 3.2-61 shows that there are a number of grid 

4 cells, especially in the northernmost and southernmost portions of the Shenandoah Case Study 

5 Area, that have relatively low percentage impacts on total nitrogen deposition from NOx

6 emissions, reflecting the higher contribution from NH3 sources in northern Virginia and on the 

7 North Carolina/Virginia border. However, NOx emissions still contribute significantly in many 

8 grid cells, especially during the winter and fall. As with the Adirondacks and Potomac areas, 

9 Figure 3.2-63 shows that almost all of the oxidized nitrogen deposition is associated with NOx

10 emissions, while Figure 3.2-64 shows that almost all of the reduced nitrogen deposition is 

11 associated with NH3 emissions.
Chapter 3 – Sources, Ambient Concentra

DRAFT 3-93 

Percent Impact

0% - 10%10.1% - 20%20.1% - 30%30.1% - 40%40.1% - 50%50.1% - 60%60.1% - 70%70.1% - 80%80.1% - 90%90.1% - 100%

Percent Impact

0% - 10%10.1% - 20%20.1% - 30%30.1% - 40%40.1% - 50%50.1% - 60%60.1% - 70%70.1% - 80%80.1% - 90%90.1% - 100%

February April

July October

1

2 Figure 3.2-61. Percent impact of NOx anthropogenic United States emissions zero-out on total nitrogen d3 Shenandoah Case Study Area.
Chapter 3 – Sources, Ambient Concentra

DRAFT 3-94 

Percent Impact

0% - 10%10.1% - 20%20.1% - 30%30.1% - 40%40.1% - 50%50.1% - 60%60.1% - 70%70.1% - 80%80.1% - 90%90.1% - 100%

Percent Impact

<0% - 10%10.1% - 20%20.1% - 30%30.1% - 40%40.1% - 50%50.1% - 60%60.1% - 70%70.1% - 80%80.1% - 90%90.1% - 100%

February April

July October

1

2 Figure 3.2-62. Percent impact of NH3 anthropogenic United States emissions zero-out on total nitrogen d3 Shenandoah Case Study Area.
Chapter 3 – Sources, Ambient Concentra

DRAFT 3-95 

Percent Impact

0% - 10%10.1% - 20%20.1% - 30%30.1% - 40%40.1% - 50%50.1% - 60%60.1% - 70%70.1% - 80%80.1% - 90%90.1% - 100%

Percent Impact

0% - 10%10.1% - 20%20.1% - 30%30.1% - 40%40.1% - 50%50.1% - 60%60.1% - 70%70.1% - 80%80.1% - 90%90.1% - 100%

February April

July October

1

2 Figure 3.2-63. Percent impact of NOx anthropogenic United States emissions zero-out on oxidized nitrog3 Shenandoah Case Study Area.
Chapter 3 – Sources, Ambient Concentra

DRAFT 3-96 

Percent Impact

0% - 10%10.1% - 20%20.1% - 30%30.1% - 40%40.1% - 50%50.1% - 60%60.1% - 70%70.1% - 80%80.1% - 90%90.1% - 100%

Percent Impact

0% - 10%10.1% - 20%20.1% - 30%30.1% - 40%40.1% - 50%50.1% - 60%60.1% - 70%70.1% - 80%80.1% - 90%90.1% - 100%

February April

July October

1

2 Figure 3.2-64. Percent impact of NH3 Anthropogenic United States emissions zero-out on reduced nitrog3 Shenandoah Case Study Area. 
Chapter 3 – Sources, Ambient Concentrations, and Deposition 

DRAFT 3-97 August 2008 

1 Figures 3.2-65 through 3.2-68 present maps of the results for the Neuse Case Study

2 Area. Figure 3.2-65 shows that the Neuse Case Study Area is highly dominated by NH3

3 emissions, especially in the central grid cells, which are located over the counties in North 

4 Carolina with high levels of CAFOs, primarily for hogs and turkeys. NOx still contributes 

5 significantly in the western and eastern portions of this case study area, but the impact of NH3

6 emissions is much more pronounced relative to the other case study areas. As with most of the 

7 other eastern case study areas, Figure 3.2-67 shows that almost all of the oxidized nitrogen 

8 deposition is associated with NOx emissions, while Figure 3.2-68 shows that almost all of the 

9 reduced nitrogen deposition is associated with NH3 emissions.
Chapter 3 – Sources, Ambient Concentra

DRAFT 3-98 

Percent Impact

0% - 10%10.1% - 20%20.1% - 30%30.1% - 40%40.1% - 50%50.1% - 60%60.1% - 70%70.1% - 80%80.1% - 90%90.1% - 100%

Percent Impact

0% - 10%10.1% - 20%20.1% - 30%30.1% - 40%40.1% - 50%50.1% - 60%60.1% - 70%70.1% - 80%80.1% - 90%90.1% - 100%

February April

July October

1

2 Figure 3.2-65. Percent impact of NOx anthropogenic United States emissions zero-out on total nitrogen d3 Neuse Case Study Area.
Chapter 3 – Sources, Ambient Concentra

DRAFT 3-99 

Percent Impact

0% - 10%10.1% - 20%20.1% - 30%30.1% - 40%40.1% - 50%50.1% - 60%60.1% - 70%70.1% - 80%80.1% - 90%90.1% - 100%

Percent Impact

0% - 10%10.1% - 20%20.1% - 30%30.1% - 40%40.1% - 50%50.1% - 60%60.1% - 70%70.1% - 80%80.1% - 90%90.1% - 100%

February April

July October

1

2 Figure 3.2-66. Percent impact of NH3 anthropogenic United States emissions zero-out on total nitrogen d3 Neuse Case Study Area. 
Chapter 3 – Sources, Ambient Concentra

DRAFT 3-100 

Percent Impact

0% - 10%10.1% - 20%20.1% - 30%30.1% - 40%40.1% - 50%50.1% - 60%60.1% - 70%70.1% - 80%80.1% - 90%90.1% - 100%

Percent Impact

0% - 10%10.1% - 20%20.1% - 30%30.1% - 40%40.1% - 50%50.1% - 60%60.1% - 70%70.1% - 80%80.1% - 90%90.1% - 100%

February April

July October

1

2 Figure 3.2-67. Percent impact of NOx anthropogenic United States emissions zero-out on oxidized nitrog3 Neuse Case Study Area.
Chapter 3 – Sources, Ambient Concentra

DRAFT 3-101 

Percent Impact

0% - 10%10.1% - 20%20.1% - 30%30.1% - 40%40.1% - 50%50.1% - 60%60.1% - 70%70.1% - 80%80.1% - 90%90.1% - 100%

Percent Impact

0% - 10%10.1% - 20%20.1% - 30%30.1% - 40%40.1% - 50%50.1% - 60%60.1% - 70%70.1% - 80%80.1% - 90%90.1% - 100%

February April

July October

1

2 Figure 3.2-68 Percent Impact of NH3 Anthropogenic United States Emissions Zero-out on Reduced 3 in the Neuse Case Study Area. 
Chapter 3 – Sources, Ambient Concentrations, and Deposition 

DRAFT 3-102 August 2008 

1 Figures 3.2-69 through 3.2-72 present maps of the results for the Coastal Sage Case 

2 Study Area. Figure 3.2-69 shows the response of total nitrogen to NOx emissions in the Coastal 

3 Sage Case Study Area. The Coastal Sage Case Study Area is the only case study area located 

4 completely in the western United States. As opposed to the eastern case study areas, the most 

5 significant contributions of NOx are during July, rather than during the fall and winter. Domestic, 

6 anthropogenic NOx contributes a significant amount to at least some grid cells in each season, 

7 but there is heterogeneity in response in each season. The northern portion of this case study area 

8 appears less responsive to domestic, anthropogenic NOx than the southern portion. Examining 

9 the responses of oxidized nitrogen deposition in Figure 3.2-71, it appears that international NOx

10 emissions are contributing a small fraction to oxidized nitrogen deposition along the coast of 

11 California. Figure 3.2-72 shows that reduced nitrogen appears to have some international NH3

12 component, but in a few grid cells, it seems to also be impacted by either a point source of NH3

13 or non-anthropogenic sources.
Chapter 3 – Sources, Ambient Concentra

DRAFT 3-103 

Percent Impact

0% - 10%10.1% - 20%20.1% - 30%30.1% - 40%40.1% - 50%50.1% - 60%60.1% - 70%70.1% - 80%80.1% - 90%90.1% - 100%

Percent Impact

0% - 10%10.1% - 20%20.1% - 30%30.1% - 40%40.1% - 50%50.1% - 60%60.1% - 70%70.1% - 80%80.1% - 90%90.1% - 100%

Percent Impact

0% - 10%10.1% - 20%20.1% - 30%30.1% - 40%40.1% - 50%50.1% - 60%60.1% - 70%70.1% - 80%80.1% - 90%90.1% - 100%

Percent Impact

0% - 10%10.1% - 20%20.1% - 30%30.1% - 40%40.1% - 50%50.1% - 60%60.1% - 70%70.1% - 80%80.1% - 90%90.1% - 100%

February April

July October

1

2 Figure 3.2-69. Percent impact of NOx Anthropogenic United States emissions zero-out on total nitrogen d3 Coastal Sage Case Study Area.
Chapter 3 – Sources, Ambient Concentra

DRAFT 3-104 

Percent Impact

0% - 10%10.1% - 20%20.1% - 30%30.1% - 40%40.1% - 50%50.1% - 60%60.1% - 70%70.1% - 80%80.1% - 90%90.1% - 100%

Percent Impact

0% - 10%10.1% - 20%20.1% - 30%30.1% - 40%40.1% - 50%50.1% - 60%60.1% - 70%70.1% - 80%80.1% - 90%90.1% - 100%

Percent Impact

0% - 10%10.1% - 20%20.1% - 30%30.1% - 40%40.1% - 50%50.1% - 60%60.1% - 70%70.1% - 80%80.1% - 90%90.1% - 100%

Percent Impact

0% - 10%10.1% - 20%20.1% - 30%30.1% - 40%40.1% - 50%50.1% - 60%60.1% - 70%70.1% - 80%80.1% - 90%90.1% - 100%

February April

July October

1

2 Figure 3.2-70. Percent impact of NH3 anthropogenic United States emissions zero-out on total nitrogen d3 Coastal Sage Case Study Area. 
Chapter 3 – Sources, Ambient Concentra

DRAFT 3-105 

Percent Impact

0% - 10%10.1% - 20%20.1% - 30%30.1% - 40%40.1% - 50%50.1% - 60%60.1% - 70%70.1% - 80%80.1% - 90%90.1% - 100%

Percent Impact

0% - 10%10.1% - 20%20.1% - 30%30.1% - 40%40.1% - 50%50.1% - 60%60.1% - 70%70.1% - 80%80.1% - 90%90.1% - 100%

Percent Impact

0% - 10%10.1% - 20%20.1% - 30%30.1% - 40%40.1% - 50%50.1% - 60%60.1% - 70%70.1% - 80%80.1% - 90%90.1% - 100%

Percent Impact

0% - 10%10.1% - 20%20.1% - 30%30.1% - 40%40.1% - 50%50.1% - 60%60.1% - 70%70.1% - 80%80.1% - 90%90.1% - 100%

February April

July October

1

2 Figure 3.2-71. Percent impact of NOx anthropogenic United States emissions zero-out on oxidized nitrog3 Coastal Sage Case Study Area. 
Chapter 3 – Sources, Ambient Concentra

DRAFT 3-106 

Percent Impact

0% - 10%10.1% - 20%20.1% - 30%30.1% - 40%40.1% - 50%50.1% - 60%60.1% - 70%70.1% - 80%80.1% - 90%90.1% - 100%

Percent Impact

0% - 10%10.1% - 20%20.1% - 30%30.1% - 40%40.1% - 50%50.1% - 60%60.1% - 70%70.1% - 80%80.1% - 90%90.1% - 100%

Percent Impact

0% - 10%10.1% - 20%20.1% - 30%30.1% - 40%40.1% - 50%50.1% - 60%60.1% - 70%70.1% - 80%80.1% - 90%90.1% - 100%

Percent Impact

0% - 10%10.1% - 20%20.1% - 30%30.1% - 40%40.1% - 50%50.1% - 60%60.1% - 70%70.1% - 80%80.1% - 90%90.1% - 100%

This m any be a large point NH3 source, which was not included in the RSM factors, or it m ay represent nonanthropogenic em issions

This m ight be offshore sources or international transport?

February April

July October

1

2 Figure 3.2-72. Percent impact of NOx anthropogenic United States emissions zero-out on oxidized nitrog3 Coastal Sage Case Study Area. 
Chapter 3 – Sources, Ambient Concentrations, and Deposition 

DRAFT 3-107 August 2008 

1 Figures 3.2-73 through 3.2-76 present maps of the results for the Estuaries Case Study 

2 Area. Figure 3.2-73 shows the response of total nitrogen to domestic, anthropogenic NOx

3 emissions in the Estuaries Case Study Area. The Estuaries Case Study Area covers a wide set of 

4 locations across the United States. In general, domestic, anthropogenic emissions of NOx have a 

5 higher percentage impact on total nitrogen deposition to estuaries in the eastern United States 

6 relative to estuaries in the western United States. Examining the oxidized nitrogen deposition 

7 maps in Figure 3.2-75, part of the reason for this is the larger role of international NOx emissions 

8 on the West Coast. In general, there is a significant impact of NOx emissions on total nitrogen in 

9 most estuaries in at least some months; however, the degree of impact is highly variable. The 

10 majority of oxidized nitrogen deposition is due to domestic anthropogenic emissions, even in 

11 western coastal locations, whereas the majority of reduced nitrogen emissions is due to domestic, 

12 anthropogenic NH3 emissions.
Chapter 3 – Sources, Ambient Concentra

DRAFT 3-108 

Percent Impact

0% - 10%10.1% - 20%20.1% - 30%30.1% - 40%40.1% - 50%50.1% - 60%60.1% - 70%70.1% - 80%80.1% - 90%90.1% - 100%

Percent Impact

0% - 10%10.1% - 20%20.1% - 30%30.1% - 40%40.1% - 50%50.1% - 60%60.1% - 70%70.1% - 80%80.1% - 90%90.1% - 100%

February April

July October

1

2 Figure 3.2-73. Percent impact of NOx anthropogenic United States emissions zero-out on total nitrogen d3 Estuaries Case Study Area. 
Chapter 3 – Sources, Ambient Concentra

DRAFT 3-109 

Percent Impact

0% - 10%10.1% - 20%20.1% - 30%30.1% - 40%40.1% - 50%50.1% - 60%60.1% - 70%70.1% - 80%80.1% - 90%90.1% - 100%

Percent Impact

<0% - 10%10.1% - 20%20.1% - 30%30.1% - 40%40.1% - 50%50.1% - 60%60.1% - 70%70.1% - 80%80.1% - 90%90.1% - 100%

February April

July October

1

2 Figure 3.2-74. Percent impact of NH3 anthropogenic United States emissions zero-out on total nitrogen d3 Estuaries Case Study Area.
Chapter 3 – Sources, Ambient Concentra

DRAFT 3-110 

Percent Impact

0% - 10%10.1% - 20%20.1% - 30%30.1% - 40%40.1% - 50%50.1% - 60%60.1% - 70%70.1% - 80%80.1% - 90%90.1% - 100%

Percent Impact

0% - 10%10.1% - 20%20.1% - 30%30.1% - 40%40.1% - 50%50.1% - 60%60.1% - 70%70.1% - 80%80.1% - 90%90.1% - 100%

February April

July October

1

2 Figure 3.2-75. Percent impact of NOx anthropogenic United States emissions zero-out on oxidized nitrog3 Estuaries Case Study Area.
Chapter 3 – Sources, Ambient Concentr

DRAFT 3-111 

Percent Impact

0% - 10%10.1% - 20%20.1% - 30%30.1% - 40%40.1% - 50%50.1% - 60%60.1% - 70%70.1% - 80%80.1% - 90%90.1% - 100%

Percent Impact

0% - 10%10.1% - 20%20.1% - 30%30.1% - 40%40.1% - 50%50.1% - 60%60.1% - 70%70.1% - 80%80.1% - 90%90.1% - 100%

February April

July October

1

2 Figure 3.2-76. Percent Impact of NH3 anthropogenic United States emissions zero-out on reduced nitroge3 Estuaries Case Study Area. 
Chapter 3 – Sources, Ambient Concentrations and Deposition

DRAFT 3-112 August 2008 

1 Over all of the case study areas, domestic, anthropogenic NOx emissions have significant 

2 impacts on total nitrogen deposition and account for almost all of the oxidized nitrogen 

3 emissions. As such, standards that focus on NOx will, in many locations, reduce both oxidized 

4 nitrogen deposition and the total nitrogen deposition. The separability between the impacts of 

5 NH3 and NOx on the different forms of deposition (e.g., NOx affect mainly oxidized nitrogen 

6 deposition, while NH3 affects mainly reduced nitrogen deposition) indicates the possibility of 

7 using forms of the standard that maintain the separation of oxidized and reduced nitrogen. We 

8 will continue to refine this analysis in the second draft risk and exposure assessment. 

9 3.2.2.5 Uncertainty 

10 To be drafted 
Chapter 3 – Sources, Ambient Concentrations and Deposition

DRAFT 3-113 August 2008 

1 3.3 REFERENCES

2 Byun, D.W., and J.K.S. Ching (eds.). 1999. Science Algorithms of the EPA Models-3 Community 

3 Multi-scale Air Quality (CMAQ) Modeling System. EPA/600/R-99/030. U.S. 

4 Environmental Protection Agency, National Exposure Research Laboratory, Research 

5 Triangle Park, NC. 

6 Byun, D.W., and K.L. Schere. 2006. Review of the governing equations, computational 

7 algorithms, and other components of the Models-3 Community Multiscale Air Quality 

8 (CMAQ) modeling system. Journal of Applied Mechanics Reviews 59(2):51–77.

9 Dennis, R.L., D.W. Byun, J.H. Novak, K.J. Galluppi, C.J. Coats, and M.A. Vouk. 1996. The next 

10 generation of integrated air quality modeling: EPA’s Models-3. Atmospheric

11 Environment 30:1925–1938.

12 Grimm, J.W., and J.A. Lynch. 2004. Enhanced wet deposition estimates using modeled 

13 precipitation inputs. Environmental Monitoring and Assessment 90:243–268.

14 Holland, H.D. 1978. The Chemistry of the Atmosphere and Oceans. New York: John Wiley & 

15 Sons.

16 Levine and Pinto, 1998. TBD 

17 Sacks, J., W.J. Welch, T.J. Mitchell, and H.P. Wynn. 1989. Design and Analysis of Computer 

18 Experiments. Statistical Science 4(4):409–435.

19 Srivastava, A., K. Hacker, K. Lewis, and T.W. Simpson. 2004. A method for using legacy data 

20 for metamodel-based design of large-scale systems. Structural and Multidisciplinary 

21 Optimization 28:146–155.

22 U.S. EPA (Environmental Protection Agency) 2002. 2002 National Emissions Inventory. U.S. 

23 Environmental Protection Agency, Office of Air Quality Planning and Standards, 

24 Technology Transfer Network, Clearinghouse for Inventories and Emission Factors, 

25 Washington, DC. Available at http://www.epa.gov/ttn/chief/net/2002inventory.html. 
Chapter 3 – Sources, Ambient Concentrations and Deposition

DRAFT 3-114 August 2008 

1 U.S. EPA (Environmental Protection Agency). 2005. Technical Support Document for the Clean 

2 Air Interstate Rule: Air Quality Modeling. U.S. Environmental Protection Agency, Office 

3 of Air Quality Planning and Standard, Research Triangle Park, NC. (Docket No. OAR4 2005-0053-2151).

5 U.S. EPA (Environmental Protection Agency). 2006a. Technical Support Document for the 

6 Proposed Mobile Source Air Toxics Rule: Ozone Modeling. U.S. Environmental 

7 Protection Agency, Office of Air Quality Planning and Standards, Research Triangle 

8 Park, NC.

9 U.S. EPA (Environmental Protection Agency). 2006b. Technical Support Document for the 

10 Proposed PM NAAQS Rule: Response Surface Modeling. U.S. Environmental Protection 

11 Agency, Office of Air Quality Planning and Standards, Research Triangle Park, NC. 

12 U.S. EPA (Environmental Protection Agency). 2006c. Final Regulatory Impact Analysis: PM2.5

13 NAAQS. U.S. Environmental Protection Agency, Office of Air Quality Planning and 

14 Standards, Research Triangle Park, NC. Available http://www.epa.gov/ttn/ecas/ria.html 

15 (accessed March 10, 2008). 

16 U.S. EPA (Environmental Protection Agency). 2006d. TBD 

17 U.S. EPA (Environmental Protection Agency). 2008. Integrated Science assessment for Oxides 

18 of Nitrogen and Sulfur – Environmental Criteria (Second External Review Draft).

19 EPA/600/R-08/082. U.S. Environmental Protection Agency, Office of Research and 

20 Development, National Center for Environmental Assessment – RTP Division, Research 

21 Triangle Park, NC.
Chapter 4 – Acidification 

DRAFT 4-1 August 2008 

1 4. ACIDIFICATION 

2 For this first draft of the Risk and Exposure Assessment document, we are including an 

3 outline of the intended content of this chapter for future drafts (see Attachment 1, Working

4 Outline). Because the analyses are incomplete at this time, we refer the reader to Attachment 2 

5 (National Sensitive Areas Analysis) for a discussion of the approach we are undertaking to 

6 identify areas sensitive to acidification caused by nitrogen and sulfur deposition. We have 

7 selected case study areas and have begun the analyses for aquatic and terrestrial acidification. 

8 Attachment 3 (Aquatic Acidification Case Study) and Attachment 4 (Terrestrial Acidification 

9 Case Study) detail the case study selection rationale, analysis approach, and results to date. At 

10 this time, we are requesting review of these four attachments in lieu of a formal Chapter 4. 

11 We recognize that there may be some discrepancies in the use of terms between the case 

12 study reports and the risk assessment document. For example, in the case studies, the word 

13 “indicator” may reflect a biological, chemical, or ecological indicator, or it may be used to 

14 describe the indicator of a standard (typically an atmospheric concentration), whereas in risk 

15 assessment’s described structure of a secondary standard, we attempt to make careful distinctions 

16 between air quality indicators, ecological indicators, and the atmospheric and ecological 

17 variables that affect them. In the second draft risk assessment, the results of the case study 

18 analyses will be synthesized into a common framework, and we will make the terminology 

19 consistent with risk assessment’s standard structure. 

20
Chapter 4 – Acidification 

DRAFT 4-2 August 2008 

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10
Chapter 5 – Nutrient Enrichment 

DRAFT 5-1 August 2008 

1 5. NUTRIENT ENRICHMENT 

2 For this first draft of the Risk and Exposure Assessment document, we are including an 

3 outline of the intended content of Chapter 5 for future drafts (see Attachment 1, Working

4 Outline). Because the analyses are incomplete at this time, we refer the reader to Attachment 2 

5 (National Sensitive Areas Analysis) for a discussion of the approach we are undertaking to 

6 identify areas sensitive to nutrient enrichment caused by nitrogen deposition. We have selected 

7 case study areas and have begun the analyses for aquatic and terrestrial nutrient enrichment. 

8 Attachment 5 (Aquatic Nutrient Enrichment Case Study) and Attachment 6 (Terrestrial Nutrient 

9 Enrichment Case Study) detail the case study selection rationale, analysis approach, and results 

10 to date. At this time, we are requesting review of these four attachments in lieu of a formal 

11 Chapter 5. 

12 We recognize that there may be some discrepancies in the use of terms between the case 

13 study reports and the risk assessment document. For example, in the case studies, the word 

14 “indicator” may reflect a biological, chemical, or ecological indicator, or it may be used to 

15 describe the indicator of a standard (typically an atmospheric concentration), whereas in the risk 

16 assessment’s described structure of a secondary standard, we attempt to make careful distinctions 

17 between air quality indicators, ecological indicators, and the atmospheric and ecological 

18 variables that affect them. In the second draft risk assessment, the results of these case study 

19 analyses will be synthesized into a common framework, and we will make the terminology 

20 consistent with the risk assessment’s standard structure. 

21
Chapter 5 – Nutrient Enrichment 

DRAFT 5-2 August 2008 

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Chapter 6 – Additional Effects 

DRAFT 6-1 August 2008 

1 6. ADDITIONAL EFFECTS 

2 6.1 SULFUR AND MERCURY METHYLATION 

3 The biogeochemical cycle of mercury is closely tied to the sulfur cycle because the 

4 presence of sulfate in wetland and lake sediments is necessary for mercury to be incorporated 

5 into the food web. Adverse effects of mercury, including behavioral, reproductive, 

6 neurochemical, and hormonal effects, have been demonstrated in piscivorous mammals and birds 

7 (U.S. EPA, 1996; Scheuhammer et al., 2007), and methylmercury has been shown to be the form 

8 of mercury that accumulates in the tissues of fish and piscivorous species (Becker and Bigham, 

9 1995; Bloom, 1992; Harris et al., 2003; Scheuhammer et al., 2007). Sulfate-reducing bacteria 

10 (SRB) play a key role in mercury methylation, and changes in sulfate deposition have resulted in 

11 changes in both mercury methylation and mercury concentrations in fish. 

12 6.1.1 Science Background 

13 Sulfur deposition likely increases mercury methylation in regions that receive relatively 

14 high levels of atmospheric sulfur and mercury deposition and that exhibit characteristics 

15 conducive to methylation. These regions include surface waters with low ANC and low pH and

16 with large upstream or adjoining wetlands (Chen et al., 2005; Scheuhammer and Blancher, 1994; 

17 Scheuhammer et al., 2007). These sensitive ecosystems are prevalent in areas of the northeastern 

18 United States and southeastern Canada. Studies of mercury concentrations in feathers, blood, and 

19 eggs of the common loon (Gavia immer) indicate decreasing concentrations from west to east in 

20 this region (Evers et al., 1998, 2003). This pattern is generally consistent with patterns of 

21 deposition of both mercury and sulfur. 

22 Several interrelated factors seem to be related to mercury uptake, including low lake23 water pH, dissolved organic carbon, and suspended PM concentrations in the water column 

24 (Driscoll et al., 1994; Grieb et al., 1990; Kamman et al., 2004; Mierle and Ingram, 1991; Suns 

25 and Hitchin, 1990; U.S. EPA, 1996). In addition, the proportion of upland to wetland land area 

26 within a watershed, as well as wetland type and annual water yield, appear to be important (St. 

27 Louis et al., 1996).
Chapter 6 – Additional Effects 

DRAFT 6-2 August 2008 

1 Mercury in the Environment 

2 Mercury is a naturally occurring element, is very ubiquitous, and cycles through air, 

3 water, soils, and living organisms. Mercury concentrations have increased approximately 2 to 5 

4 times since the onset of the industrial revolution and appear in even the most remote locations on 

5 the Earth (Munthe et al., 2007; U.S. EPA, 2006). In the northeastern United States, where 

6 population growth and industry have heavily influenced the region for a century, mercury 

7 concentrations are approximately four- to six-fold higher than in pre-Industrial Revolution times 

8 (Evers et al., 2007). Additionally, ecosystems with local emissions sources can exhibit mercury 

9 concentrations that exceed 10 times pre-Industrial Revolution levels (Munthe et al., 2007). 

10 In the United States, the primary source of mercury to ecosystems is atmospheric 

11 deposition due to coal combustion (e.g., coal-fired electric utilities). Other sources include 

12 municipal waste combustion, medical waste incineration, chlor-alkali plants, and industrial 

13 boilers. Depending on the particulate association and oxidation state, atmospheric mercury 

14 particles can remain suspended in the atmosphere for more than 2 years (Evers et al., 2007; U.S. 

15 EPA, 2006). 

16 In the atmosphere, mercury is primarily inorganic. Approximately 95%–97% of 

atmospheric mercury is elemental mercury (Hg0

) and relatively nonreactive. Hg017 is the least 

18 soluble of the inorganic mercury species and can be transported readily across long distances 

19 (Driscoll et al., 2007). Atmospheric transport is most likely the process that is responsible for the 

20 presence and accumulation of mercury in remote sites (Watras et al., 2006). Ionic forms of 

21 mercury are more soluble, generally react with water particles, and deposit within short ranges of 

22 emissions (Driscoll et al., 2007). 

23 Atmospheric mercury deposition occurs by wet deposition, dry deposition, and to a lesser 

24 extent, direct stomatal uptake by plants. When deposited into terrestrial and aquatic ecosystems, 

elemental mercury is oxidized to reactive mercury (Hg+2 25 ) (Ambrose et al., 2005; U.S. EPA, 

26 2006). Inorganic mercury species do not directly pose a health threat to humans or animals; 

however, Hg+2 27 is much more likely to undergo transformation processes (Driscoll et al., 2007). 

28 Out of the deposited mercury pool, approximately 1%–2% is reduced and methylated to 

29 methylmercury, an organic lipophilic mercury species that is four times more capable of 

30 bioaccumulating in the tissues of humans, fish, birds, and other biota than is inorganic mercury 
Chapter 6 – Additional Effects 

DRAFT 6-3 August 2008 

1 (Benoit et al., 2003; King et al., 2000; U.S. EPA, 2006). Figure 6.1-1 shows the processes and 

2 oxidation states involved in mercury cycling in the environment. 

3 Although it is clear that the primary source of mercury to most of the United States is of 

4 atmospheric origin and that mercury must be converted to methylmercury to accumulate to 

5 potential risk levels in biotic tissues, the mercury methylation process reflects a wide range of 

6 controlling factors that will differ from one part of the country to another. These site-specific 

7 factors present complications in extrapolating the findings of existing regionally focused risk 

8 assessments to other areas (Driscoll et al., 2007). 

9

10 Figure 6.1-1. The mercury cycle in an ecosystem (USGS, 2006). 

11 6.1.2 Qualitative Analysis 

12 The role of atmospherically deposited sulfur species in mercury methylation varies 

13 greatly across ecosystems. Field studies have determined that the majority of mercury 
Chapter 6 – Additional Effects 

DRAFT 6-4 August 2008 

1 methylation occurs within anoxic waters and sediments (Gilmour et al., 1998; Hammerschmidt et 

2 al., 2004; Watras et al., 1995); however, several studies have observed that the quantifiable 

3 prediction of mercury methylation is confounded by the interdependency of several variables, 

4 including the presence and types of SRB, sulfur species, mercury species, organic matter, and 

5 others (Benoit et al., 2003; Gilmour et al., 1992; Langer et al., 2001; Munthe et al., 2007; Watras 

6 and Morrison, 2008). SRB have been implicated as a significant mercury methylation vector as a 

7 by-product of converting sulfate to sulfide (Benoit et al., 2003; Branfireun et al., 1999; Compeau 

8 and Bartha, 1985; Gilmour et al., 1992). Methylation via iron-reducing bacteria has also been 

9 observed in anoxic, iron-rich sediments; however, this process is not well understood and 

10 appears to be less extensive than the SRB-mediated mercury methylation (Fleming et al., 2006; 

11 Kerin et al., 2006).

12 In general, the rate of methylmercury generation depends on the factors that affect SRB 

13 propagation and activity, the availability of inorganic mercury, and the demethylation of 

mercury. The introduction of sulfate to SRB in the presence of methane and Hg+2 14 , usually in low 

15 oxygen sediments, leads to the following biomediated transformations: 

 Hg+2 ĺ HgS ĺ MeHg+

16

17 Methylmercury concentrations are correlated with the amount of mercury in the 

18 ecosystem. Therefore, the presence of sulfate, inorganic mercury, and SRB are the primary 

19 requirements for the sulfate-reducing, bacterially mediated mercury conversion. Additional 

20 factors affecting conversion include temperature, the presence and types of organic matter, the 

21 presence and types of mercury-binding species, and watershed effects (e.g., watershed type, land 

22 cover, waterbody limnography, and runoff loading). Demethylation involves aerobic and 

23 anaerobic microbial processes, as well as processes involving exposure to sunlight (i.e., 

24 photodemethylation); therefore, increased methylation in natural environments should be 

25 considered as increased net mercury methylation (Benoit et al., 2003). 

26 The role of sulfate in mercury methylation has been confirmed through a series of 

27 independent and interdependent studies. Early studies on Little Rock Lake, WI, first observed the 

28 link between sulfur enrichment, acidification, and methylmercury concentrations (Hrabik and 

29 Watras, 2002). The beneficial effect of decreased sulfate deposition on fish tissue methylmercury 

30 concentrations has also recently been observed in an isolated Lake Superior ecosystem, where 

31 fish tissue concentrations fell below fish consumption advisory levels in the absence of any 
Chapter 6 – Additional Effects 

DRAFT 6-5 August 2008 

1 change in atmospheric mercury deposition (Drevnick et al., 2007). Other studies have focused on 

2 the biogeochemical process of mercury cycling to determine factors that are responsible for the 

3 link between methylmercury and acidification. Early research by Faust and Osman (1981) 

4 estimated that 90%–99% of total mercury concentration in surface waters was associated with 

5 sediment. With regard to methylmercury, the highest concentrations in the environment 

6 generally occur at or near the sedimentary surface, below the oxic-anoxic boundary. The 

7 formation of methylmercury has also been associated with macrophytic vegetation and 

8 periphyton (Mauro et al., 2002). Mercury methylation rate and organic carbon substrates (e.g., 

9 acetate, lactate) may fluctuate when associated with the presence of SRB and environmental 

10 conditions (Mitchell et al., 2008). Figure 6.1-2 illustrates the general SRB methylation process. 

11 Although mercury methylation occurs within the water column, there is generally a 

12 greater contribution of mercury methylation in sediments because of more concentrated 

13 availabilities of SRB, substrate, and sulfate concentration. Therefore, the conditions within and 

14 affecting sediment porewaters may collectively play a key role in mercury methylation. The 

15 relative contribution of methylmercury from porewater in the surficial sediment layer is 

16 dependent on the size of the hypolimnic anoxic zone, the location of the bacterioplankton 

17 activity, and several other factors, such as temperature, organic carbon content, and the presence 

18 of sulfides (Watras et al., 1995). 

19

20 Figure 6.1-2. Biogeochemical process of mercury methylation. 
Chapter 6 – Additional Effects 

DRAFT 6-6 August 2008 

1 6.1.2.1 Watershed Influences 

2 The effect of watersheds on methylmercury production is dependent on many factors 

3 (e.g., dissolved organic carbon, temperature, anoxia, and sulfide); however, watershed influences 

4 also include the conditions and processes that impact these effects (e.g., land cover, precipitation 

5 response, and limnography). Watershed influences may also play a role in the uptake of 

6 methylmercury into fish and other aquatic species. 

7 Land cover and land use affect the transport of chemical species, such as mercury, 

8 nutrients, and dissolved organic carbon. Methylmercury production generally increases with 

9 increasing percentages of contributing wetlands to surface water systems (Benoit et al., 2003; 

10 Watras and Morrison, 2008). In general, wetland environments tend to promote mercury 

11 methylation because of increased anoxic environments, fresh organic matter, moderated 

12 temperature, and macrophytic environments for bacterial activity (Back et al., 2002). 

13 Additionally, increased forest cover and mixed agriculture have been correlated with increased 

14 mercury methylation in downstream surface waters, presumably due to organic matter (Driscoll 

15 et al., 2007; Krabbenhoft et al., 1999). Land disturbance may also contribute to increased 

16 mercury methylation downstream by increasing erosion, and therefore, the mobility of mercury 

17 and organic matter (Driscoll et al., 2007). 

18 6.1.2.2 Conclusions 

19 There appears to be a relationship between sulfate deposition and mercury methylation; 

20 however, the rate of mercury methylation varies according to several factors. Therefore, no 

21 quantifiable correlation between sulfate deposition and methylmercury could be discerned for the 

22 purpose of interpolating the association across waterbodies or regions. Nevertheless, the 

23 association between sulfur and mercury cannot be neglected because of the implications of 

24 changes in methylmercury in ecosystems. 

25 The research summarized here is continually evolving and, in the future, could potentially 

26 allow for more quantitative statements regarding the generation of methylmercury. As the 

27 computational capacity of models expands to meet the complexity of methylmercury in 

28 ecosystems, confounding factors may be parsed out to identify ecosystems or regions that are 

29 more likely to generate higher concentrations of methylmercury. Figure 6.1-3 illustrates the type 

30 of current and forward-looking research being developed by the U.S. Geological Survey (USGS) 
Chapter 6 – Additional Effects 

DRAFT 6-7 August 2008 

1 to synthesize the contributing factors of mercury and to develop a map of sensitive watersheds. 

2 The mercury score referenced in Figure 6.1-3 is based on sulfate concentrations, ANC, dissolved 

3 organic carbon, pH, mercury species concentration, and soil types to gauge the methylation 

4 sensitivity (Myers et al., 2007). 

5

6 Figure 6.1-3. Preliminary USGS map of mercury methylation-sensitive watersheds, 

7 derived from more than 55,000 water-quality sites and 2,500 watershed (Myers et al., 

8 2007).

9 This discussion highlights the interdependency of biogeochemical factors and precludes 

10 the existence of simple sulfate-related mercury-methylation models. However, it is evident that 

11 decreases in sulfate deposition will likely result in decreases in methylmercury concentration. 

12 Future research may allow for the quantification of a sulfate-methylmercury response 

13 curve; however, no regional or classification calculation scale can be created at this time because 

14 of the number of confounding factors. According to the current state of research, associations 

15 with mercury methylation occur between the following: 

16  Total mercury concentrations. Mercury loading to ecosystems is required for the 

17 production of methylmercury. Increases in mercury concentrations are associated with 

18 increases in methylmercury. 
Chapter 6 – Additional Effects 

DRAFT 6-8 August 2008 

1  Sulfate. The majority of U.S. waters are sulfate-limited (Harmon et al., 2007); therefore, 

2 decreases in sulfate are likely to promote decreases in methylmercury. 

3  Wetlands. The presence of wetlands in or upstream of surface water systems is 

4 significantly correlated with methylmercury concentrations. 

5  Average temperature. Warmer temperatures stimulate the activity of sulfate-reducing 

6 bacteria.

7  Sulfide. In sulfate-enriched systems, the rate of methylmercury generation may be 

8 retarded or inhibited by increased sulfide accumulation. 

9  Land, sediment, and water-level disturbance. Land-use changes, water-level 

10 fluctuations, and sediment disturbances can promote unintentional releases or 

11 bioavailability of organic matter, sulfate, and mercury. 

12  Salinity. Freshwater systems appear to yield higher percentages of methylmercury than 

13 salt waters. However, the importance of methylmercury in coastal and marine systems 

14 cannot be discounted because of the human presence in coastal environments and the 

15 abundance of fish and shellfish industries that rely on these systems. Also, salt water 

16 mercury fish consumption advisories demonstrate that methylmercury production in 

17 marine waters is present at levels that may be harmful to humans. 

18 Figure 6.1-4 illustrates the complexity of mercury methylation in ecosystems. 
Chapter 6 – Additional Effects 

DRAFT 6-9 August 2008 

1

2 Figure 6.1-4. Spatial and biogeochemical factors influencing 

3 methylmercury production. 

4 Management strategies for the reduction of methylmercury production are currently 

5 limited to reducing sulfur deposition, reducing mercury deposition, and preventing mercury sink 

6 disturbances. The latter strategy is not discussed here because of the lack of an overall ability to 

7 control these systems on a regional or federal scale and because it is beyond the scope of a 

8 secondary NOx/SOx NAAQS review. 

9 Decreases in sulfate emissions have already shown promising reductions in 

10 methylmercury. Decreases in methylmercury fish tissue concentrations have been observed in 

11 Little Rock Lake, WI, and Isle Royale in Lake Superior, MI, (Hrabik and Watras, 2002; 

12 Drevnick et al., 2007). Although the possibility exists that reductions in sulfate emissions could 

13 generate a pulse in methylmercury production because of decreased sulfide inhibition in sulfate14 saturated waters, the majority of U.S. waters are sulfate-limited (Harmon et al., 2007). Also, 

15 because of the diffusion and outward flow of both mercury-sulfide complexes and sulfate, 

16 increased mercury methylation downstream may still occur in sulfate-enriched ecosystems with 

17 increased organic matter and/or downstream transport capabilities. 
Chapter 6 – Additional Effects 

DRAFT 6-10 August 2008 

1 Remediation of heavily mercury-contaminated sediments has yielded significant 

2 reductions of methylmercury in biotic tissues. Because the biotic responses to methylmercury 

3 levels as a result of atmospheric mercury deposition are much lower, direct associations have 

4 been confounded by all of the factors discussed here. Current research observations show that 

5 percentages of methylmercury and total mercury in ecosystems are positively correlated. If these 

6 observations continue to be confirmed, reductions in mercury deposited into ecosystems would 

7 eventually lead to reductions in methylmercury in biotic tissues. 

8 Ultimately, an integrated approach that involves the reduction of both sulfur and mercury 

9 emissions may be most efficient because of the variability in ecosystem responses. 

10 6.2 NITROUS OXIDE (N2O)

11 6.2.1 Science Overview 

12 Nitrous oxide has not been considered in setting previous NO2 NAAQS. In the first NOx

13 review, N2O was not considered an air contaminant because there was “no evidence to suggest 

14 N2O is involved in photochemical reactions in the lower atmosphere” (U.S. EPA, 1971). Nitrous 

15 oxide was addressed in both the 1982 and 1993 criteria documents. In 1982, it was described as 

16 one of the eight nitrogen oxides that may be present in the ambient air, but “not generally 

17 considered a pollutant.” The effect of N2O on stratospheric ozone was described, and the criteria 

18 document noted that N2O may cause a small decrease in stratospheric ozone (U.S. EPA, 1982). 

19 Finally, the criteria document concluded that N2O significantly contributes to the atmospheric 

20 greenhouse effect by trapping outgoing terrestrial radiation, and that the issue was being 

21 investigated, but that many years of research were still needed to reliably assess the issue. In 

22 1993, the criteria document again identified N2O as an oxidized nitrogen compound that is not 

23 generally considered to be an air pollutant, but does have an impact on stratospheric ozone and is 

24 considered to be among the more significant greenhouse gases (GHGs). These documents clearly 

25 considered N2O to be within the scope of the listed nitrogen oxides’ criteria for pollutants. 

26 The second draft ISA acknowledges N2O as a potent GHG and discusses N2O sources 

27 and emissions in the United States, as well as the biogeochemistry of its microbial-mediated 

28 production via denitrification in natural ecosystems (U.S. EPA, 2008; Section 3.3). Based on the 

29 current U.S. Greenhouse Gas (GHG) Inventory (U.S. EPA, 2007), N2O contributes 

30 approximately 6.5 % to total GHG emissions (in CO2 equivalents) (Figure 6.2-1).
Chapter 6 – Additional Effects 

DRAFT 6-11 August 2008 

1

2 Figure 6.2-1. Percent of total U.S. emissions of greenhouse gases 

3 in CO2 equivalents (U.S. EPA, 2007).

4 Since the definition of “welfare effects” includes effects on climate [CAA Section 

5 302(h)], we will include N2O within the scope of this review. However, it is most appropriate to 

6 analyze the role of N2O in anthropogenic climate change in the context of all of the GHGs. 

7 Because such an analysis is outside the scope of this review, it will not be a quantitative part of 

8 this assessment. 

9 Integrated Science Assessment Summary 

10 Nitrous oxide is a GHG that contributes to global warming. Although the atmospheric 

11 concentration of N2O (319 ppb) is much lower than CO2 (379 ppm), its global warming potential 

12 is 296 times that of CO2. Human activities have increased the atmospheric concentration of N2O

13 by 18% since preindustrial times (IPCC, 2007). The continuing increase of those GHG 

14 concentrations has been shown to threaten human and ecosystem health. 

15 Anthropogenic nitrogen deposition to ecosystems not only changes the global nitrogen 

16 cycle, it also has profound impacts on biogeochemical processes associated with GHG emissions 

17 (Bodelier and Laanbroek, 2004; Dalal et al., 2003; Vitousek et al., 1997). The impacts of 

18 nitrogen addition on N2O emissions were reviewed and quantitatively synthesized by meta19 analysis in the ISA. The publications included in this meta-analysis are in Annex D of the draft 

20 ISA (U.S. EPA, 2008).

21 Biogenic sources are the dominating contributors (>90%) to atmospheric N2O. Terrestrial 

22 soil is the largest source of atmospheric N2O, accounting for 60% of global emissions (IPCC, 

23 2001). Nitrous oxide production in soil is mainly governed by microbial nitrification and 

24 denitrification (Dalal et al., 2003). The contribution of each process to the total N2O production 

varies with environmental conditions. Denitrifying bacteria reduce nitrate (NO3

-

) or nitrite (NO2

-) 25

26 into N2O or N2 under anaerobic conditions. In submerged soils, such as wetland soil, 
Chapter 6 – Additional Effects 

DRAFT 6-12 August 2008 

denitrification should be the dominant process to N2O emission (Conrad, 1996). Increasing NO3

-

1

2 input generally increases the denitrification rate under suitable conditions of temperature and 

organic carbon supply. High soil NO3

-

3 concentrations also inhibit N2O reducing to N2 and result 

4 in a high N2O/N2 ratio (Dalal et al., 2003). Under aerobic environments, autotrophic nitrifying 

bacteria obtain energy by reducing NH4

+

5 . Nitrous oxide is an intermediate product of the 

oxidation of NH4

+

 to NO2

-

 or the decomposition of NO2

-

6 . The increase in N2O emissions 

following NH4

+

7 addition has been observed in many laboratory and field experiments (Aerts and 

8 Caluwe 1999; Aerts and Toet 1997; Keller et al., 2005).

9 The meta-analysis on the effects of nitrogen addition on N2O emissions from non10 agricultural ecosystems includes 99 observations from 30 publications (U.S. EPA, 2008). 

11 Nitrogen addition normally enhanced N2O emissions, with some exceptions (Ambus et al., 2006; 

12 Ambus and Robertson, 2006; Borken et al., 2002; Curtis et al., 2006; Skiba et al., 1999). 

13 Although some natural ecosystems can be a N2O sink (Chapuis-Lardy et al., 2007), very limited 

14 publications assessed the impact of nitrogen addition on N2O uptake. Thus, only changes in N2O

15 production were estimated in this meta-analysis. Overall, the results of the meta-analysis 

16 indicated that nitrogen addition increased N2O emissions by 215%. The response of N2O

17 emissions was influenced by ecosystem type and the form and amount of nitrogen addition. 

18 Compared to other ecosystems, tropical forests emitted more N2O under nitrogen 

19 enrichment conditions (+735%). This greater response may be because tropical forests are often 

20 phosphorus-limited rather than nitrogen-limited (IPCC, 2001). However, climatic conditions, 

21 especially temperature and precipitation, could also be key factors to drive N2O emissions from 

22 tropical forest ecosystems. 

Nitrate caused a higher stimulation (+494%) on N2O emission than did NH4

+

23 (+95%). By 

adding radiolabeled nitrogen-15 (15N), labeled NO3

-

 and NH4

+

24 to soil, Russow and colleagues 

25 (2008) found that N2O was mainly emitted by denitrification, and the contribution of 

26 denitrification to the total N2O production increased from 54% in soil with normal soil organic 

27 matter (SOM) content to 76% in soil with high SOM content. 

28 The ISA concludes that the reviewed evidence is sufficient to infer a causal relationship

29 between reactive nitrogen deposition and the alteration of biogeochemical flux of N2O in 

30 terrestrial ecosystems (U.S. EPA, 2008). Overall, the results of the meta-analysis discussed in 

31 Section 3.3.4 of the ISA indicated that nitrogen addition increased N2O emissions by 215%. The 
Chapter 6 – Additional Effects 

DRAFT 6-13 August 2008 

1 response of N2O emission to nitrogen addition for coniferous forests, deciduous forests, and 

2 grasslands was significant. The ISA also concluded that the evidence reviewed was sufficient to 

3 infer a causal relationship between reactive nitrogen deposition and the alteration of N2O flux in 

4 wetland ecosystems. In the meta-analysis of 19 observations from studies that evaluated the 

effects of nitrogen additions ranging from 15.4 to 300 kg N ha1

 yr15 , nitrogen addition was 

6 shown to increase the production of N2O by 207% (U.S. EPA, 2008) 

7 6.2.2 Qualitative Analysis 

8 The analysis of risk to public welfare from the increased generation of N2O as a GHG is 

9 beyond the scope of this first draft risk and exposure assessment. A more complete analysis of 

10 the effects of increasing GHGs on public welfare should include N2O as one of a suite of gases 

11 that affect global warming trends and would require a much broader treatment than could be 

12 given in the scope of this review. The EPA recently released an Advance Notice of Public 

13 Rulemaking on Regulating Greenhouse Gas Emissions under the Clean Air Act 

14 (http://www.epa.gov/ climatechange/emissions/downloads/ANPRPreamble.pdf), which discusses 

15 these effects in more detail. 

16 6.3 CARBON SEQUESTRATION 

17 This section discusses the mechanisms by which atmospheric nitrogen deposition alters 

18 carbon cycling in terrestrial and aquatic ecosystems. The interactions between increased nitrogen 

19 deposition and carbon sequestration in terrestrial and aquatic ecosystems are summarized in 

20 Sections 6.3.1 and 6.3.2. Although predicted values of atmospheric CO2 concentrations in the 

21 future may alter the interaction between nitrogen and carbon cycling, further analysis on this 

22 topic is beyond the scope of this review. 

23 6.3.1 Terrestrial Ecosystems 

24 Because nitrogen availability often limits rates of net primary production in terrestrial 

25 ecosystems (Vitousek and Howarth, 1991), there is an implicit link between the carbon and 

26 nitrogen cycles (Figure 6.3-1). More than 50% of plant nitrogen is used for photosynthetic 

27 enzymes. Because nitrogen is necessary for photosynthesis, rates of photosynthesis and net 

28 primary productivity (NPP) typically correlate with metrics of nitrogen availability, such as leaf 
Chapter 6 – Additional Effects 

DRAFT 6-14 August 2008 

1 nitrogen content and net nitrogen mineralization rate (Field and Mooney, 1986; Reich et al., 

2 1997a, b; Smith et al., 2002). 

3 Few studies have isolated the effect of chronic nitrogen deposition on plant growth and 

4 ecosystem carbon balances. It is difficult to untangle the effects of climate, disease, and land use 

5 from nitrogen deposition effects. Therefore, to address this question, we rely on fertilization 

6 studies, modeling, gradient studies, and time-trend analyses. 

7 Carbon accumulation in terrestrial ecosystems occurs in the plants and in the soil. Carbon 

8 cycling is a complex process that can be quantified into ecosystem carbon budgets on the basis of 

9 net ecosystem productivity (NEP), defined as gross primary productivity (GPP) after subtracting 

10 the ecosystem respiration (i.e., vegetative + heterotrophic respiration). Factors that may increase 

11 terrestrial CO2 sinks on a regional scale are increased NPP and decreased respiration of CO2

12 from leaf or soil processes. These two mechanisms may be altered by atmospheric deposition of 

13 nitrogen, tropospheric ozone exposure, increased CO2 concentrations, land-use change, and 

14 factors associated with climate warming (Beedlow et al., 2004; Melillo et al., 2002; Myneni 

15 et al., 1997; Schimel et al., 2001). This adds to the uncertainty regarding the sources and sinks of 

16 CO2 in the terrestrial biosphere (Houghton, 2003). It should be noted that it is not known whether 

17 present terrestrial carbon sequestration can be sustained in view of limits of forest regrowth, 

18 nutrient availability, and uncertainty about changes in the frequency of disturbances such as fire 

19 (Schimel et al., 2001; Scholes and Noble, 2001). 

20 6.3.1.1 Forests 

21 Aboveground Processes 

22 There is substantial evidence that nitrogen additions to trees cause increased leaf-level 

23 photosynthetic rates. However, the ISA (U.S. EPA, 2008) evaluated the potential for nitrogen 

24 deposition to increase aboveground carbon biomass and concluded that it is limited for reasons 

25 related to the biogeochemical cycling of nitrogen.
Chapter 6 – Additional Effects 

DRAFT 6-15 August 2008 

1

2 Figure 6.3-1. Interactions between the carbon and nitrogen cycles. 

3 Forest growth enhancement, to the extent that it occurs, can potentially exacerbate other 

4 nutrient deficiencies, such as calcium, magnesium, or potassium. Multiple long-term experiments 

5 have demonstrated transient growth increases followed by increased mortality, especially at 

6 higher rates of fertilization (Elvir et al., 2003; Högberg et al., 2006; Magill et al., 2004; McNulty 

7 et al., 2005).

8 Decreased growth and increased mortality have more commonly been observed in high9 elevation coniferous stands than in lower-elevation hardwood forests, and these differences have 

10 been partially attributed to higher inputs of nitrogen at higher elevations and to response 

11 characteristics of coniferous, as opposed to deciduous, trees (Aber et al., 1998). Conifer forests 

12 that receive high inputs of reactive nitrogen appear to exhibit decreases in productivity and 

13 increases in mortality (Fenn et al., 1998). For example, fertilization experiments at Mount 

14 Ascutney, VT, suggested that nitrogen saturation may lead to the replacement of slow-growing 

15 spruce-fir forest stands by fast-growing deciduous forests that cycle nitrogen more rapidly 

16 (McNulty et al., 1996, 2005). 
Chapter 6 – Additional Effects 

DRAFT 6-16 August 2008 

1 Belowground Processes 

2 Soils contain the largest near-surface reservoir of terrestrial carbon; more than 50% of 

3 carbon captured annually by plants may be allocated below ground (Kubiske and Godbold, 

4 2001). Although there remains considerable uncertainty in the potential response of soil carbon 

5 to increases in reactive nitrogen additions (Neff et al., 2002), a meta-analysis by Johnson and 

6 Curtis (2001) suggested that nitrogen fertilization caused an 18% increase in soil carbon content.

7 There is also evidence of a relationship between nitrogen deposition and root production. 

8 Nadelhoffer (2000) argued that it is likely that nitrogen deposition functions to decrease forest 

9 fine-root biomass, but to stimulate fine-root turnover and production. However, very high levels 

of nitrogen (>100 kg N ha1

 yr110 ) decreased root life span of Pinus ponderosa (Johnson et al., 

11 2000).

12 Litterfall is usually the dominant source of soil organic carbon and a substantial source of 

13 organic nitrogen. Decomposition of litterfall is often facilitated by heterotrophic bacteria and 

14 mycorrhizae. The quantity of litter has been shown to increase with elevated nitrogen deposition 

15 (Schulze et al., 2000), resulting in increased microbial metabolism in soil. It is also well 

16 demonstrated that increased nitrogen availability reduces the ratio of carbon to nitrogen in leaf 

17 tissue. In turn, a lower carbon-to-nitrogen ratio in leaf litter has been shown to cause faster initial 

18 rates of decomposition (Melillo et al., 1982); however, the biochemistry of the leaf tissue is also 

19 important, and higher nitrogen litter can actually decompose more slowly in the long term (Berg, 

20 2000).

21 Soil respiration is the dominant source by which plant-assimilated carbon is returned to 

22 the atmosphere via CO2. Changes in the magnitude of soil CO2 efflux due to changes in 

23 environmental conditions will likely influence the global atmospheric CO2 budget (Schlesinger 

24 and Andrews, 2000). The effects of nitrogen addition on soil respiration are mixed. In the 

25 Harvard Forest Long Term Ecological Monitoring and Research (LTER) Site Chronic Nitrogen 

26 Amendment Study, nitrogen additions increased soil respiration for a hardwood stand, but not for 

27 a pine stand, during the first year of fertilization. However, continued nitrogen additions over a 

28 decade caused a 40% decrease in soil respiration for both stands, and this decrease was attributed 

29 mostly to a decrease in microbial respiration (Bowden et al., 2004). 
Chapter 6 – Additional Effects 

DRAFT 6-17 August 2008 

1 Regional Trends in Net Ecosystem Productivity

2 Analyses of satellite observations of canopy greenness over the past 20 years across 

3 North America suggest enhancement of net ecosystem productivity in some regions, 

4 corresponding to observed changes in climate and forest management. Few such changes were 

5 observed in the northeastern United States (Hicke et al., 2002). In another study, evaluation of 

6 tree growth rates in five states (i.e., Minnesota, Michigan, Virginia, North Carolina, and Florida) 

7 found little evidence for growth enhancement due to any factor examined, including nitrogen 

8 deposition, CO2 fertilization, or climate change (Caspersen et al., 2000). Potential effects of 

9 nitrogen deposition on boreal forests of North America are of concern, in part because of the 

10 large size of this terrestrial biome. Climate warming and nitrogen deposition may increase net 

11 primary productivity and carbon sequestration in the boreal forest, but they may also stimulate 

12 decomposition of soil organic matter, potentially leading to a net loss of carbon from the 

13 ecosystem (Kirschbaum, 1994; Mäkipää et al., 1999). 

14 6.3.1.2 Arctic Tundra 

15 In a long-term fertilization experiment (Mack et al., 2004), plots were fertilized from 

16 1981 to 2000 to receive approximately 5 to 8 times the annual soil nitrogen uptake requirement 

17 for aboveground production in the arctic tundra ecosystem. Carbon storage increased above 

18 ground because of the accumulation of woody shrub biomass and litter, but this was offset by a 

19 larger decrease of carbon in belowground pools because of a pronounced decrease in the carbon 

20 contained in deep organic (>5 cm depth) and upper mineral soil layers (Shaver et al., 2001). This 

21 study clearly showed that increased nutrient availability enhanced decomposition of 

22 belowground carbon pools in deep soil layers more than it increased primary production, leading 

23 to a substantial net loss of carbon from this ecosystem. 

24 Increasing temperatures may amplify these effects and further stimulate carbon losses 

25 from high-latitude systems, causing species shifts in the vegetation community, from tussock to 

26 increased shrub abundance, and leading to decreased ecosystem carbon storage. Finally, the 

27 decreased soil moisture and increased depth of thaw with temperature rise are predicted to have a 

28 positive effect on decomposition (Shaver et al., 2001), releasing more CO2.
Chapter 6 – Additional Effects 

DRAFT 6-18 August 2008 

1 6.3.1.3 Grasslands 

2 Belowground Factors 

3 An investigation by Neff and colleagues (2002) of long-term effects (10 years) of 

nitrogen deposition (10 kg N ha1

 yr14 ) in a dry meadow ecosystem indicated that nitrogen 

5 additions significantly accelerated the decomposition of soil carbon fractions with decadal 

6 turnover times while further stabilizing soil carbon compounds in mineral-associated fractions 

7 with multi-decadal to century lifetimes. Despite these changes in the dynamics of different soil 

8 pools, no significant changes in bulk soil carbon were observed, highlighting a limitation of the 

9 single-pool approach for investigating soil carbon responses to changing environmental 

10 conditions (Neff et al., 2002). The authors noted that it remains to be seen if the effects that were 

11 caused by relatively high, decadal-term fertilizer additions are similar to those that would arise 

12 from lower, longer-term additions of nitrogen to natural ecosystems from atmospheric 

13 deposition.

14 Interactions with Fire 

15 Several lines of evidence suggest that reactive nitrogen deposition may be contributing to 

16 greater fuel loads, thus altering the fire cycle in a variety of ecosystem types (Fenn et al., 2003). 

17 Invasive grasses, which can be favored by high nitrogen deposition, promote a rapid fire cycle in 

18 many locations (D’Antonio and Vitousek, 1992). The increased productivity of flammable 

19 understory grasses increases the spread of fire and has been hypothesized as one mechanism for 

20 the recent conversion of CSS to grassland in California (Minnich and Dezzani, 1998).

21 High grass biomass has also been associated with increased fire frequency in the Mohave 

22 Desert (Brooks, 1999; Brooks and Esque, 2002; Brooks et al., 2004). Fire was relatively rare in 

23 the Mojave Desert until the past two decades, but now occurs frequently in areas that have 

24 experienced invasion of exotic grasses (Brooks, 1999). 
Chapter 6 – Additional Effects 

DRAFT 6-19 August 2008 

1 6.3.2 Aquatic Ecosystems 

2 6.3.2.1 Wetlands 

3 Aboveground Processes 

4 In a literature summary, U.S. EPA (1993) showed that nitrogen applications, ranging 

from 7 to 3120 kg N ha-1yr-1 5 , stimulated standing biomass production by 6%–413%. However, 

6 the magnitude of the changes in primary production depended on soil nitrogen availability and 

7 the limitation of other nutrients. The degree of nitrogen limitation to growth varies among 

8 wetlands across the United States (Bedford, 1999). 

9 Although studies applying fertilizer treatment increase the primary production of plant 

10 species in intertidal wetlands, applications are several orders of magnitude larger than 

11 atmospheric deposition (Mendelssohn, 1979; Wigand et al., 2003). In comparison, nitrogen loads 

brought by tidal water and groundwater (565-668 kg N ha-1yr-1 12 ) are much larger than nitrogen 

13 depositing directly to the surface of coastal marshes, which suggests that direct nitrogen 

14 deposition may have limited impacts on this ecosystem (Morris, 1991). On the other hand, 

15 indirect atmospheric deposition that is nitrogen deposited to the watershed and transported via 

16 surface or groundwater could be the major source of the total nitrogen load to coastal marshes. 

17 For example, model calculation in Chesapeake Bay waters (U.S. EPA, 2000) suggests that 30% 

18 of the nitrogen delivered to wetlands via estuarine tides would originate from atmospheric 

19 deposition.

20 Belowground Processes 

21 Bragazza and colleagues (2006) found that enhanced decomposition rates for material 

22 accumulated under higher atmospheric nitrogen supplies resulted in higher CO2 emissions and 

23 dissolved organic carbon releases. The increased nitrogen availability favored microbial 

24 decomposition (1) by removing nitrogen constraints on microbial metabolism and (2) through a 

25 chemical amelioration of litter peat quality with a positive feedback on microbial enzymatic 

26 activity. Although some uncertainty remains about whether decay-resistant Sphagnum will 

27 continue to dominate litter peat, the data indicated that even without such changes, increased 

28 nitrogen deposition poses a serious risk to the valuable peatland carbon sinks. 
Chapter 6 – Additional Effects 

DRAFT 6-20 August 2008 

1 Reduced vs. Oxidized Nitrogen 

2 The form of added nitrogen may regulate wetland response to nitrogen deposition. 

3 Experimental applications of nitrate (i.e., oxidized nitrogen) appear to have been less effective at 

4 stimulating wetland plant productivity than applications of ammonium ion (i.e., reduced 

5 nitrogen) (U.S. EPA, 1993). However, an important caveat expressed by U.S. EPA (1993) was 

6 that the results of relatively short-term nitrogen fertilization experiments are not necessarily good 

7 predictors of long-term wetland community responses to increased nitrogen inputs.

8 6.3.2.2 Freshwater Aquatic Ecosystems 

9 The biogeochemical cycles of nitrogen, phosphorus, and carbon are linked in freshwater 

10 ecosystems (Figure 6.3-2); therefore, nitrogen additions alter the balance of all three cycles. In 

11 nitrogen-limited aquatic systems, atmospheric inputs of nitrogen increase productivity and alter 

12 biological communities, especially phytoplankton. 

13 Nitrogen Limitation 

14 A freshwater lake or stream must be nitrogen-limited in order to be sensitive to nitrogen15 mediated eutrophication. Recently, a comprehensive study of available data from the northern 

16 hemisphere surveys of lakes along gradients of nitrogen deposition shows increased inorganic 

17 nitrogen concentration and productivity to be correlated with atmospheric nitrogen deposition 

18 (Bergström and Jansson, 2006). These authors suggested that the majority of lakes in the 

19 northern hemisphere may have originally been nitrogen-limited, and that atmospheric nitrogen 

20 deposition has changed the balance of nitrogen and phosphorus in lakes so that phosphorus 

21 limitation is generally observed today. If this is correct, the role of atmospheric nitrogen 

22 deposition as an influence on aquatic primary production may have been underestimated 

23 throughout the entire history of limnology. 
Chapter 6 – Additional Effects 

DRAFT 6-21 August 2008 

1

2 Figure 6.3-2. Nitrogen cycle in a freshwater ecosystem showing links 

3 to the phosphorous and carbon cycles. 

4 Productivity investigations have included gradient studies in which the relationship 

5 between lake nitrogen concentration and primary productivity (reported as chlorophyll a, net 

6 primary productivity, or an index such as the lake chemistry ratio of dissolved inorganic nitrogen 

7 [DIN] to total phosphorus [TP] [DIN:TP]) was surveyed and correlated with atmospheric 

8 nitrogen deposition. Productivity studies have also included lake and stream bioassays in which 

9 nitrogen was added to waters in the field or the laboratory to measure the response. The most 

10 common, and easiest to document, indicators of change in algal productivity are measures of the 

11 concentration of chlorophyll a and water clarity. However, water clarity is also strongly 

12 influenced by the erosion of fine sediment to the lake or stream system. Chlorophyll a

13 concentration is generally more directly tied to algal productivity than is water clarity.

14 Phytoplankton Biomass 

15 Studies have shown an increase in lake phytoplankton biomass with increasing 

16 nitrogen deposition in several regions, including the Snowy Range in Wyoming (Lafrancois 
Chapter 6 – Additional Effects 

DRAFT 6-22 August 2008 

1 et al., 2003), the Sierra Nevada Mountains in California (Sickman et al., 2003), and across 

2 Europe (Bergström and Jansson, 2006). Gradient studies of undisturbed northern temperate, 

3 mountain, or boreal lakes that receive low levels of atmospheric nitrogen deposition found strong 

4 relationships between nitrogen limitation and productivity where nitrogen deposition was low, 

5 and between phosphorus and nitrogen and phosphorus limitations where nitrogen deposition was 

6 higher (Bergström et al., 2005; Bergström and Jansson, 2006; Fenn et al., 2003). 

7 A meta-analysis of enrichment bioassays in 62 freshwater lakes of North America, 

8 including many of the studies described above, found algal growth enhancement from nitrogen 

9 amendments to be common in slightly less than half the studies (Elser et al., 1990). There was a 

10 mean increase in phytoplankton biomass of 79% in response to nitrogen enrichment (average of 

46.3 μeq L-1 11 N) (Elser et al., 1990). This meta-analysis was recently repeated with a much larger 

12 data set and similar results (Elser et al., 2007). 

13 The most widely used index of biological change in response to nutrient addition is the 

14 measurement of chlorophyll a concentration in water. Surveys and fertilization experiments 

15 show increased inorganic nitrogen concentration and aquatic ecosystem productivity (as 

16 indicated by chlorophyll a concentration) to be strongly related.

17 The ISA (U.S. EPA, 2008) provides a broad summary on the interaction between 

18 nitrogen deposition and carbon sequestration.

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19 Russow et al. 2008. TBD

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15 Environmental Criteria and Assessment Office, Research Triangle Park, NC. 

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21 Washington, DC. July.

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24 Office of Water, Washington, DC. 

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27 Programs, Washington, DC. April. 
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2 of Nitrogen and Sulfur – Environmental Criteria (Second External Review Draft).

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6 USGS (U.S. Geological Survey). 2006. Investigations and Monitoring of Mercury in Indiana by 

7 the U.S. Geological Survey. U.S. Department of the Interior, USGS, Indiana Water 

8 Science Center, Indianapolis, IN. Available at http://in.water.usgs.gov/mercury/. 

9 Vitousek, P.M., and R.W. Howarth. 1991. Nitrogen limitation on land and in the sea: how can it 

10 occur? Biogeochemistry 13:87–115.

11 Vitousek, P.M., J.D. Aber, R.W. Howarth, G.E. Likens, P.A. Matson, D.W. Schindler, W.H. 

12 Schlesinger, and D.G. Tilman. 1997. Human alteration of the global nitrogen cycle: 

13 sources and consequences. Ecological Applications 7:737–750.

14 Watras, C.J., and K.A. Morrison. 2008. The response of two remote, temperate lakes to changes 

15 in atmospheric mercury deposition, sulfate, and the water cycle. Canadian Journal of 

16 Fisheries and Aquatic Sciences 65:100–116.

17 Watras, C.J., K.A. Morrison, J.S. Host, and N.S. Bloom. 1995. Concentration of mercury species 

18 in relationship to other site-specific factors in the surface waters of northern Wisconsin 

19 lakes. Limnology and Oceanography 40:556–565.

20 Watras, C.J., K.A. Morrison, O. Regnell, and T.K. Kratz. 2006. The methylmercury cycle in 

21 Little Rock Lake during experimental acidification and recovery. Limnology and 

22 Oceanography 51(1):257–270.

23 Wigand, C., R.A. McKinney, M.A. Charpentier, M.M. Chintala, and G.B. Thursby. 2003. 

24 Relationships of nitrogen loadings, residential development, and physical characteristics 

25 with plant structure in New England salt marshes. Estuaries and Coasts 26:1494–1504.
Chapter 6 – Additional Effects 

DRAFT 6-36 August 2008 

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Chapter 7 – Synthesis and Integration of Case Study Results 

to Influence the Standard Setting Process 

DRAFT 7-1 August 2008 

1 7. SYNTHESIS AND INTEGRATION OF CASE STUDY RESULTS 

2 TO INFLUENCE THE STANDARD SETTING PROCESS 

3 This chapter is currently unavailable for release to CASAC and the public. We anticipate 

4 the release of this chapter on or about the week of September 15, 2008. 

5
Chapter 7 – Synthesis and Integration of Case Study Results 

to Influence the Standard Setting Process 

DRAFT 7-2 August 2008 

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Chapter 8 – Considerations in the Structure of the NOx/SOx Secondary Standard(s) 

DRAFT 8-1 August 2008 

1 8. CONSIDERATIONS IN THE STRUCTURE OF 

2 THE NOX/SOX SECONDARY STANDARD(S) 

3 This chapter is currently unavailable for release to CASAC and the public. We anticipate 

4 the release of this chapter on or about the week of September 15, 2008. 

5
Chapter 8 – Considerations in the Structure of the NOx/SOx Secondary Standard(s) 

DRAFT 8-2 August 2008 

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Chapter 9 – Analyses for Second Draft Risk Assessment 

DRAFT 9-1 August 2008 

1 9. ANALYSES FOR SECOND DRAFT RISK ASSESSMENT 

2 This first draft risk and exposure assessment for the secondary NAAQS review of NOx

3 and SOx has focused on assessing the current conditions for the four targeted effect areas since 

4 the scope and methods plan was reviewed by CASAC in April 2008. For the second draft risk 

5 and exposure assessment, we plan to complete and update the current conditions assessments, as 

6 well as scale up to larger assessment areas where feasible. The following list previews our 

7 current plans for the second draft risk and exposure assessment, organized as currently presented 

8 in this document. 

9  Chapter 3: Sources Ambient Concentrations and Deposition 

10 − Add 2002–2006 CMAQ model year run to analyses to examine variability in 

11 meteorology relative to concentrations —creating dataset for Case Study Analyses 

12 − Create hybrid data set of 2002–2006 CMAQ/NADP data for case study modeling and 

13 scaling to larger assessment areas 

14 − Model 5 years of meteorology with one year of emissions to look year to year at

15 meteorological effects on deposition 

16  Chapter 4 : Acidification 

17 − Use 2002–2006 CMAQ data set for modeling analysis 

18 − Scaling up to larger assessment areas: more Adirondack lakes and Shenandoah 

19 waterbodies

20 − Methods for the Risk Assessment 

21 • Risk Modeling with MAGIC

22 • Model Parameters for evaluating risk 

23 − Results

24 • Adirondack - uncertainty 

25 • Shenandoah - uncertainty 

26 − Characterization of Risks Associated with Alternative Levels of Protection 

27 − Scaling up to Larger Assessment Areas 

28 − Uncertainty for Larger Assessment Areas 
Chapter 9 – Analyses for Second Draft Risk Assessment 

DRAFT 9-2 August 2008 

1 − Present Terrestrial Acidification Case Study results from Simple Mass Balance 

2 Modeling expressed as base cation to Aluminum ratio and binning to soil ANC values 

3 (to provide a range of critical limits) 

4  Chapter 5 : Nutrient Enrichment 

5 − Using 2002–2006 CMAQ data set for modeling analysis 

6 − Scaling up to larger assessment areas 

7 − Characterization of risks 

8  Chapter 6 : Additional Effects 

9 − Further evaluation as needed based on CASAC consultation 

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10 WORKING OUTLINE 
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Working Outline 

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6 Risk and Exposure Assessment to Support the 

7 Joint Review of the NO2 and SO2 Secondary 

8 National Ambient Air Quality Standards: 

9 Preliminary Draft 

10

11 Working Outline 

12

13 Draft

14

15 EPA Contract Number EP-D-06-003 

16 Work Assignment 2-44 

17 Project Number 0209897.002.044 

18

19

20

21 Prepared for 

22

23 U.S. Environmental Protection Agency 

24 Office of Air Quality Planning and Standards 

25 Research Triangle Park, NC 27709 

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DRAFT Attachment 1, pg ii August 2008 

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Working Outline 

DRAFT Attachment 1, pg 1 August 2008 

Risk and Exposure Assessment to Support the Joint Review of the NO2 and SO2 Secondary 

National Ambient Air Quality Standards: Preliminary Draft 

1. Introduction

1.1. Rationale for Background for Joint Review 

1.2. Policy-Relevant Questions 

1.3. History

1.3.1 History of the Secondary NO2 NAAQS 

1.3.2 History of the Secondary SO2 NAAQS 

1.3.3 Conclusions from Previous NAAQS Reviews and alternative assessments (e.g. 

NAPAP, ADSFS, etc).

1.4. Scope of the Risk and Exposure Assessment for the Current Review 

1.4.1 Species of Nitrogen Included in the Analyses 

1.4.2 Species of Sulfur Included in the Analyses 

1.4.3 Science Overview 

2. Overview of Risk and Exposure Assessment 

2.1. Introduction

2.2. Seven Step Approach 

2.3. Ecosystem Services 

2.4. Uncertainty 

3. Sources, Ambient Concentrations and Deposition 

3.1. Science Overview

3.1.1. Sources of Nitrogen and Sulfur (emissions)

3.1.2. Ambient Concentrations and Policy Relevant Background 

3.1.3. Non-ambient Loadings of Nitrogen and Sulfur 

3.1.4. Deposition (CMAQ/NADP maps) 

3.2. Current Contributions to Ambient Conditions 

3.2.1. Spatial and Temporal Characterization of Ambient Concentrations and 

Deposition

3.2.1.1. Purpose and Intent 

3.2.1.2. Data and Tools 

3.2.1.3. Analytical Techniques 

3.2.1.4. Results and Findings 

3.2.1.5. Uncertainty 

3.2.2. Contributions to Ambient Concentrations and Deposition

3.2.2.1. Purpose and Intent 

3.2.2.2. Data and Tools 

3.2.2.3. Analytical Techniques 

3.2.2.4. Results and Findings 

3.2.2.5. Uncertainty 

4. Acidification 

4.1. Science Overview

4.1.1. Aquatic Acidification
Working Outline 

DRAFT Attachment 1, pg 2 August 2008 

4.1.2. Terrestrial Acidification 

4.1.3. Uncertainty 

4.2. Aquatic Acidification

4.2.1. Biological, Chemical, Ecological Indicators 

4.2.2. Characteristics of Sensitive Areas 

4.2.3. Case Study Selection 

4.2.4. Current Conditions Assessment (includes empirical data and evidence of 

effects)

4.2.5. Scaling up to Larger Assessment Areas 

4.2.6. Current Conditions for Assessment Areas 

4.2.7. Characterization of Risks Associated with Alternative Levels of Protection 

4.2.8. Uncertainty 

4.3. Terrestrial Acidification 

4.3.1. Biological, Chemical, Ecological Indicators 

4.3.2. Characteristics of Sensitive Areas 

4.3.3. Case Study Selection 

4.3.4. Current Conditions Assessment (includes empirical data and evidence of 

effects)

4.3.5. Scaling up to Larger Assessment Areas 

4.3.6. Current Conditions for Assessment Areas 

4.3.7. Characterization of Risks Associated with Alternative Levels of Protection 

4.3.8. Uncertainty 

5. Nutrient Enrichment 

5.1. Science Overview (from ISA, what to emphasize) 

5.1.1. Aquatic Nutrient Enrichment 

5.1.2. Terrestrial Nutrient Enrichment 

5.1.3. Uncertainty 

5.2. Aquatic Nutrient Enrichment 

5.2.1. Biological, Chemical, Ecological Indicators 

5.2.2. Characteristics of Sensitive Areas 

5.2.3. Case Study Selection 

5.2.4. Current Conditions Assessment (includes empirical data and evidence of 

effects)

5.2.5. Scaling up to Larger Assessment Areas 

5.2.6. Current Conditions for Assessment Areas 

5.2.7. Characterization of Risks Associated with Alternative Levels of Protection 

5.2.8. Uncertainty 

5.3. Terrestrial Nutrient Enrichment 

5.3.1. Biological, Chemical, Ecological Indicators 

5.3.2. Characteristics of Sensitive Areas 

5.3.3. Case Study Selection 

5.3.4. Current Conditions Assessment (includes empirical data and evidence of 

effects)

5.3.5. Scaling up to Larger Assessment Areas 

5.3.6. Current Conditions for Assessment Areas 
Working Outline 

DRAFT Attachment 1, pg 3 August 2008 

5.3.7. Characterization of Risks Associated with Alternative Levels of Protection 

5.3.8. Uncertainty 

6. Additional Effects (qualitative) 

6.1. Sulfur and Mercury Methylation 

6.1.1. Science background

6.1.2. Qualitative Analysis 

6.2. Nitrous Oxide

6.2.1. Science background 

6.2.2. Qualitative Analysis 

6.3. Carbon Sequestration 

6.3.1. Science background

6.3.2. Qualitative Analysis 

7. Synthesis and Integration of Case Study Results to Influence the Standard Setting Process 

7.1. Summary of Case Study Analyses 

7.2. Integrating Case Study Results to Inform the Standard Setting Process 

7.3. Linkages between Ecological Indicators and Adverse

8. Considerations in the Structure of the NOx/SOx Secondary Standard(s) 

8.1. Statutory and Legal Considerations in the Structure of the Standard

8.2. Linkages for Structuring Ecologically Relevant Standards

8.3. Illustrative Example Focused on Aquatic Acidification 

9. Ongoing Analyses 

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10 NATIONAL SENSITIVE AREAS ANALYSIS 
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10 EPA Contract Number EP-D-06-003 

11 Work Assignment 2-44 

12 Project Number 0209897.002.044 

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23

24 U.S. Environmental Protection Agency 

25 Office of Air Quality Planning and Standards 

26 Research Triangle Park, NC 27709 

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32 RTI International 

33 3040 Cornwallis Road

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DRAFT Attachment 2, pg ii August 2008 

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 National Sensitive Areas Analysis 

DRAFT Attachment 2, pg iii August 2008 

1 CONTENTS 

2 1. Objective..................................................................................................................................... v 

3 2. Selection of Geospatial Datasets................................................................................................. 1 

4 3. Sensitivity to Aquatic Acidification............................................................................................ 2 

5 3.1 Selected Indicator Geospatial Datasets............................................................................ 2 

6 3.1.1 Slope .................................................................................................................... 2

7 3.1.2 Soil pH ................................................................................................................. 2

8 3.1.3 Soil pH ................................................................................................................. 3

9 3.1.4 Soil Depth ............................................................................................................ 3 

10 3.1.5 Surface Water Alkalinity ..................................................................................... 3 

11 3.1.6 Geology................................................................................................................ 4 

12 3.2 Overlay Results................................................................................................................ 4 

13 4. Sensitivity to Terrestrial Acidification........................................................................................ 5 

14 4.1 Selected Indicator Geospatial Datasets............................................................................ 5 

15 4.1.1 Range of Sugar Maple ......................................................................................... 5 

16 4.1.2 Range of Red Spruce ........................................................................................... 6 

17 4.1.3 Geology................................................................................................................ 6 

18 4.1.4 Precipitation ......................................................................................................... 6 

19 4.1.5 Soil pH ................................................................................................................. 7

20 4.1.6 Soil pH ................................................................................................................. 7

4.1.7 Wet Deposition of Sulfur Containing SO4

-2 21 ......................................................... 7 

4.1.8 Wet Deposition of Nitrogen Containing Chemical Species NO3

-

22 and

NH4

+

23 ..................................................................................................................... 7 

24 4.1.9 Total Dry Deposition of Nitrogen Containing Both Oxidized and 

25 Reduced Chemical Species.................................................................................. 8 

26 4.1.10 Total Dry Deposition of Sulfur............................................................................ 8 

27 4.1.11 Soil Depth ............................................................................................................ 8 

28 4.2 Layers Considered, but not Included ............................................................................... 9 

29 4.2.1 Elevation .............................................................................................................. 9 

30 4.3 Overlay Results................................................................................................................ 9 

31 5. Sensitivity to Aquatic Nutrient Enrichment.............................................................................. 10 

32 5.1 Selected Indicator Geospatial Datasets.......................................................................... 10 

33 5.1.1 Nitrogen in Surface Water ................................................................................. 10 

5.1.2 Wet Deposition of Nitrogen Containing Chemical Species NO3

-

34 and

NH4

+

35 ................................................................................................................... 11 

36 5.1.3 Total Dry Deposition of Nitrogen Containing both Oxidized and 

37 Reduced Chemical Species................................................................................ 11 

38 5.1.4 Eutrophic Estuaries ............................................................................................ 12 

39 5.1.5 Nutrient Criteria................................................................................................. 12 

40 5.1.6 Nitrogen-Limited Waters................................................................................... 12 

41 5.2 Indicator Geospatial Datasets Considered, But Not Used ............................................. 13 

42 5.2.1 Presence of Nitrogen Sensitive Species............................................................. 13 

43 5.3 Overlay Results.............................................................................................................. 13 

44 6. Sensitivity to Terrestrial Nutrient Enrichment.......................................................................... 14 

45 6.1 Selected Indicator Geospatial Datasets.......................................................................... 14 

46 6.1.1 Presence of Acidophytic Lichens....................................................................... 14 
 National Sensitive Areas Analysis 

DRAFT Attachment 2, pg iv August 2008 

6.1.2 Wet Deposition of Nitrogen Containing Chemical Species NO3

-

1 and

NH4

+

2 ................................................................................................................... 15 

3 6.1.2 Total Dry Deposition of Nitrogen Containing both Oxidized and 

4 Reduced Chemical Species................................................................................ 15 

5 6.1.3 Anthropogenic Land Cover................................................................................ 15 

6 6.2 Indicator Geospatial Datasets Considered, But Not Used ............................................. 16 

7 6.2.1 Soil Nitrogen Content ........................................................................................ 16 

8 6.2.2 Presence of Nitrogen Sensitive Species Identified in Literature ....................... 16 

9 6.2.3 Presence of Mountains....................................................................................... 17 

10 6.3 Overlay Results.............................................................................................................. 17 

11

12 FIGURES

13 Figure 3.2-1 Area potentially sensitive to aquatic acidification.................................................. 5 

14 Figure 4.3-1 Areas potentially sensitive to terrestrial acidification........................................... 10 

15 Figure 5.3-1 Areas potentially sensitive to aquatic nutrient enrichment................................... 14 

16 Figure 6.3-1 Areas potentially sensitive to terrestrial nutrient enrichment. .............................. 18 

17
 National Sensitive Areas Analysis 

DRAFT Attachment 2, pg v August 2008 

1 ACRONYMS AND ABBREVIATIONS 

2

3 cm centimeters 

4 CMAQ Community Multiscale Air Quality 

5 GIS geographic information systems 

6 Km kilometer 

7 NADP National Atmospheric Deposition Program 

8 USDA U.S. Department of Agriculture 

9 USFS U.S. Forest Service 

10 FIA Forest Inventory and Analysis National Program 

11 EPA U.S. Environmental Protection Agency 

12 USGS U.S. Geological Service 

SO4

-2 13 wet sulfate 
 National Sensitive Areas Analysis 

DRAFT Attachment 2, pg vi August 2008 

1

2

3

4

5

6

7

8

9 [This page intentionally left blank.] 
 National Sensitive Areas Analysis 

DRAFT Attachment 2, pg 1 August 2008 

1 1. OBJECTIVE 

2 The objective of this analysis is to define geographical areas sensitive to aquatic 

3 acidification, terrestrial acidification, aquatic nutrient enrichment, and terrestrial nutrient 

4 enrichment. The first step in this process is to identify national geospatial datasets (or geographic 

5 information systems [GIS] layers) that contain measures of parameters that are known to affect 

6 any of these ecosystems. Each layer will play a role to a varying degree; not all layers contribute 

7 equally to ecosystem sensitivity. Each layer that makes a contribution must be classified so that 

8 categories of varying degrees of sensitivity can be created. These categories can either be defined 

9 by a simple threshold value (i.e., above or below which an area is sensitive), or by several values 

10 of increasing or decreasing sensitivity. When the layers are combined in a GIS system, the 

11 geographic areas that exceed the threshold values in each of the layers can be identified. This 

12 would yield the areas of highest potential sensitivity. 

13 2. SELECTION OF GEOSPATIAL DATASETS 

14 There are several broad criteria for selecting appropriate geospatial datasets, including the 

15 following:

16  Physical characteristics. Physical characteristics are those that pertain to the physical 

17 environment of a given location (e.g., elevation, soil depth). 

18  Chemical characteristics. Chemical characteristics are those that pertain to the 

19 underlying chemical characteristics of the water or soil (e.g., soil pH). 

20  Presence of sensitive receptors. Overall sensitivity can be increased if there are biotas 

21 that are either known to be or depend on plant communities that are sensitive to 

22 acidification or nutrient enrichment. 

23  Spatial resolution. At a national scale of study, most, if not all, datasets will have an 

24 acceptable spatial resolution. The data will not be too coarse to show regional variation. 

25 Care must be used when combining data with different spatial resolutions (i.e., 

26 combining data designed to be used on a national scale with data designed to be used on a 

27 county scale) because the resultant data will only be as accurate as the least accurate of 

28 the inputs. 
 National Sensitive Areas Analysis 

DRAFT Attachment 2, pg 2 August 2008 

1  Spatial extent. The goal of this analysis is to use data that geographically cover the entire 

2 continental United States. Where data only exists on a regional level, they may be 

3 considered if they can still represent the range of sensitivities. 

4  Temporal resolution. Every effort will be made to use data collected during the same 

5 general time period and, preferably as recently as possible, to reflect current conditions. 

6  Completeness of metadata. Only data with well-documented origins and collection 

7 techniques will be considered for inclusion in any analysis. 

8 3. SENSITIVITY TO AQUATIC ACIDIFICATION 

9 3.1 SELECTED INDICATOR GEOSPATIAL DATASETS 

10 The publicly available geospatial datasets outlined in the following subsections have been 

11 identified as important contributors to aquatic acidification and meet the selection criteria. 

12 3.1.1 Slope 

13  Name: Grayscale North America Shaded Relief 

14  Contribution: Streams or rivers tend to be more sensitive is to acidification in areas of 

15 steeper slopes because base cations are leached from soils and washed downstream. 

16  Source: U.S. Geological Service (USGS) National Atlas 

17  Date: September 2006 

18  Spatial Extent: Continental United States 

19  Spatial Resolution: 1 kilometer (km) grid cells 

20  Threshold Value(s): 3% 

21 3.1.2 Soil pH 

22  Name: Statsgo (Conus soils) 

23  Contribution: Areas that have low soil pH tend to also have low surface water pH. 

24  Source: Penn State University 

25  Date: 1998 

26  Spatial Extent: Continental United States 

27  Spatial Resolution: Soil unit (variable size) 

28  Threshold Value(s): pH less than or equal to 5.0
 National Sensitive Areas Analysis 

DRAFT Attachment 2, pg 3 August 2008 

1 3.1.3 Soil pH 

2  Name: U.S. Forest Service Soils Survey 

3  Contribution: Areas that have low soil pH tend to also have low surface water pH. 

4  Source: U.S. Department of Agriculture (USDA) Forest Service (USFS), Forest 

5 Inventory and Analysis National Program (FIA) 

6  Date: 2001–2003

7  Spatial Extent: Continental United States 

8  Spatial Resolution: Forest plot 

9  Threshold Value(s): pH less than or equal to 5.0

10 3.1.4 Soil Depth 

11  Name: Statsgo (Conus soils) 

12  Contribution: Areas that have thin soils tend to also have low surface water pH 

13  Source: Penn State University 

14  Date: 1998 

15  Spatial Extent: Continental United States 

16  Spatial Resolution: Soil unit (variable size) 

17  Threshold Value(s): Soil depth was divided into four quartiles, and the areas with the 

18 lowest soil depth (bottom quartile) were identified. The value defining the break point 

19 between the first and second quartiles was 51 inches in total depth. 

20 3.1.5 Surface Water Alkalinity 

21  Name: Alkus 

22  Contribution: Classifies the continental United States into categories of acid neutralizing 

23 capacity (ȝeq/l). Areas with lowest acid neutralizing capacity are most sensitive to 

24 acidification.

25  Source: U.S. Environmental Protection Agency (EPA), Office of Research and 

26 Development, Corvallis, OR 

27  Date: Pre-1992 

28  Spatial Extent: Continental United States 

29  Spatial Resolution: Unknown 

30  Threshold Value(s): 400 ȝeq/l or less are considered acid sensitive. 
 National Sensitive Areas Analysis 

DRAFT Attachment 2, pg 4 August 2008 

1 3.1.6 Geology 

2  Name: Karst 

3  Contribution: Karst topography is comprised of carbonate rocks, such as limestone and 

4 dolomite, which have a high ANC. This can be used to exclude these areas as being 

5 sensitive to acidification. 

6  Source: USGS National Atlas 

7  Date: 1998 

8  Spatial Extent: Continental United States 

9  Spatial Resolution: Unknown 

10  Threshold Value(s): All areas of karst, with the exception of fissure tubes (volcanic in 

11 origin), are used to exclude areas of acid sensitivity. 

12  Geology: ANC 

13 3.2 OVERLAY RESULTS 

14 The extraction of the areas of greatest acid sensitivity is a relatively simple process within 

15 the GIS. The two soil pH layers were averaged to yield a hybrid value. This hybrid layer was 

16 intersected with the other input layers to create a polygon that defines the area of highest 

17 potential sensitivity. The area can then be displayed in map form, as shown in Figure 3.2-1.
 National Sensitive Areas Analysis 

DRAFT Attachment 2, pg 5 August 2008 

1

2 Figure 3.2-1. Area potentially sensitive to aquatic acidification.

3 4. SENSITIVITY TO TERRESTRIAL ACIDIFICATION 

4 4.1 SELECTED INDICATOR GEOSPATIAL DATASETS 

5 The publicly available geospatial datasets outlined in the following subsections have been 

6 identified as important contributors to terrestrial acidification and meet the selection criteria. 

7 4.1.1 Range of Sugar Maple 

8  Name: Acersacr 

9  Contribution: Sugar maples are known to be sensitive to acidification and have an 

10 economic value, including the production of maple syrup and marketable timber. 

11  Source: USGS 

12  Date: 1971–1977 

13  Spatial Extent: Continental United States; however, only found regionally. 

14  Spatial Resolution: For use at scales of 1:10,000,000 or smaller. 

15  Threshold Value(s): Boundary defines range of the species. 
 National Sensitive Areas Analysis 

DRAFT Attachment 2, pg 6 August 2008 

1 4.1.2 Range of Red Spruce 

2  Name: Picerube 

3  Contribution: Red spruce are known to be sensitive to acidification, especially at higher 

4 elevations, and have economic value, including their use as marketable timber. 

5  Source: USGS 

6  Date: 1971–1977 

7  Spatial Extent: Continental United States; however, only found regionally. 

8  Spatial Resolution: For use at scales of 1:10,000,000 or smaller. 

9  Threshold Value(s): Boundary defines range of the species. 

10 4.1.3 Geology 

11  Name: Karst 

12  Contribution: Karst topography is comprised of carbonate rocks, such as limestone and 

13 dolomite, which have a high ANC. The presence of karst can be used to exclude these 

14 areas as being sensitive to acidification. 

15  Source: USGS National Atlas 

16  Date: 1998 

17  Spatial Extent: Continental United States 

18  Spatial Resolution: Unknown 

19  Threshold Value(s): All areas of karst, with the exception of fissure tubes (volcanic in 

20 origin), are used to exclude areas of acid sensitivity.

21 4.1.4 Precipitation 

22  Name: Precipitation pH 

23  Contribution: Areas receiving acidic (low pH) precipitation are more likely to lose their 

24 buffering capacity over time, thus making them sensitive to acidification. 

25  Source: National Atmospheric Deposition Program (NADP). 

26  Date: 2006 

27  Spatial Extent: 312 monitoring stations variably distributed across the United States 

28  Spatial Resolution: For use on regional or national scale only. 

29  Threshold Value(s): Currently using a pH of less than or equal to 5.0 to define areas of 

30 acidic precipitation (subject to change). 
 National Sensitive Areas Analysis 

DRAFT Attachment 2, pg 7 August 2008 

1 4.1.5 Soil pH 

2  Name: Statsgo (Conus soils) 

3  Contribution: Areas that have low soil pH tend to also have low surface water pH 

4  Source: Penn State University 

5  Date: 1998 

6  Spatial Extent: Continental United States 

7  Spatial Resolution: Soil unit (variable size) 

8  Threshold Value(s): pH less than or equal to 5.0

9 4.1.6 Soil pH 

10  Name: U.S. Forest Service Soils Survey 

11  Contribution: Areas that have low soil pH tend to also have low surface water pH 

12  Source: USFS, Forest Inventory and Analysis National Program 

13  Date: 2001–2003 

14  Spatial Extent: Continental United States 

15  Spatial Resolution: Forest plot 

16  Threshold Value(s): pH less than or equal to 5.0

4.1.7 Wet Deposition of Sulfur Containing SO4

-2 17

 Name: Wet Sulfate (SO4

-2 18 ) Deposition 

19  Contribution: Greater deposition of sulfate in precipitation leads to lower precipitation 

20 pH. Over time this can reduce an area’s buffering capacity. 

21  Source: NADP 

22  Date: 2006 

23  Spatial Extent: 312 monitoring stations variably distributed across the United States 

24  Spatial Resolution: For use on regional or national scale only, continental United States 

25  Threshold Value(s): None currently selected 

4.1.8 Wet Deposition of Nitrogen Containing Chemical Species NO3

-

and NH4

+

26

27  Name: Wet nitrogen (both reduced and oxidized) deposition 

28  Contribution: Greater deposition of nitrate in precipitation leads to lower precipitation 

29 pH; over time this can reduce an area’s buffering capacity 
 National Sensitive Areas Analysis 

DRAFT Attachment 2, pg 8 August 2008 

1  Source: NADP 

2  Date: 2006 

3  Spatial Extent: 312 monitoring stations variably distributed across the United States 

4  Spatial Resolution: For use on regional or national scale only 

5  Threshold Value(s): None currently selected 

6 4.1.9 Total Dry Deposition of Nitrogen Containing Both Oxidized and Reduced 

7 Chemical Species 

8  Name: DDTOTN_1A field from Community Multiscale Air Quality (CMAQ) dataset 

9  Contribution: Greater deposition of nitrogen deposited increases the likelihood of base 

10 cation depletion; over time, this can reduce an area’s buffering capacity 

11  Source: CMAQ model 

12  Date: 2002 

13  Spatial Extent:12 km grid cells of the contiguous United States 

14  Spatial Resolution: For use on regional or national scale only, continental United States 

15  Threshold Value(s): None currently selected. 

16 4.1.10 Total Dry Deposition of Sulfur 

17  Name: DDTOTS_1A field from CMAQ dataset 

18  Contribution: Greater deposition of sulfur increases the likelihood of base cation 

19 depletion; over time, this can reduce an area’s buffering capacity 

20  Source: CMAQ model 

21  Date: 2002 

22  Spatial Extent: 12 km grid cells of the contiguous United States 

23  Spatial Resolution: For use on regional or national scale only. continental United States 

24  Threshold Value(s): None currently selected 

25 4.1.11 Soil Depth 

26  Name: Statsgo (Conus soils) 

27  Contribution: Areas that have thin soils tend to also have low surface water pH 

28  Source: Penn State University 

29  Date: 1998 
 National Sensitive Areas Analysis 

DRAFT Attachment 2, pg 9 August 2008 

1  Spatial Extent: Continental United States 

2  Spatial Resolution: Soil unit (variable size) 

3  Threshold Value(s): RTI divided soil depth into fourth quartiles and used the areas with 

4 the lowest soil depth (bottom quartile) to define the areas of the highest sensitivity to 

5 acidification. The ended up as all measurements less than 51 centimeters (cm) in total 

6 depth.

7 4.2 LAYERS CONSIDERED, BUT NOT INCLUDED 

8 4.2.1 Elevation 

9  Name: Grayscale North America Shaded Relief 

10  Contribution: Certain species, especially red spruce, become sensitive to acidification 

11 above an elevation of 750 meters 

12  Source: USGS National Atlas 

13  Date: September 2006 

14  Spatial Extent: Continental United States 

15  Spatial Resolution: 1 km grid cells 

16  Threshold Value(s) - 750 meters 

17  Exclusion Reason - Not used because we already have range of red spruce 

18 4.3 OVERLAY RESULTS 

19 The areas of greatest terrestrial acidification sensitivity were defined by the following 

20 GIS process. The ranges of sugar maple and red spruce were combined to create a layer that 

21 consisted of either sugar maple or red spruce. The two soil pH layers were averaged to create a 

22 hybrid layer of top layer (20 cm) soil pH. From this hybrid layer, only those areas that had a 

23 surface pH of 5.00 or less were extracted. These layers were combined with the following: 

24  The lowest quartile of soil thickness 

25  The highest quartile of total nitrogen deposition (both wet from NADP and dry from 

26 CMAQ)

27  The highest quartile of total sulfur deposition (both wet from NADP and dry from 

28 CMAQ)
 National Sensitive Areas Analysis 

DRAFT Attachment 2, pg 10 August 2008 

1  Areas of precipitation of pH 5.00 or less. 

2 The area can then be displayed in map form. 

3 Only areas common to all the inputs were retained. From this intermediate layer, areas of 

4 karst geology were removed. Karst geology typically has a high acid buffering capacity. The 

5 resultant layer contains the area of highest potential sensitive to terrestrial acidification and can 

6 be displayed in map form, as shown in Figure 4.3-1.

7

8 Figure 4.3-1. Areas potentially sensitive to terrestrial acidification. 

9 5. SENSITIVITY TO AQUATIC NUTRIENT ENRICHMENT 

10 5.1 SELECTED INDICATOR GEOSPATIAL DATASETS 

11 The publicly available geospatial datasets outlined in the subsections below have been 

12 identified as important contributors to aquatic nutrient enrichment and meet the selection criteria. 

13 5.1.1 Nitrogen in Surface Water 

14  Name: Total nitrogen (Kjeldahl). 
 National Sensitive Areas Analysis 

DRAFT Attachment 2, pg 11 August 2008 

1  Contribution: Elevated nitrogen levels in surface water lead to increases in some aquatic 

2 plant species, resulting in a loss of dissolved oxygen (eutrophication) 

3  Source: EPA National Nutrient Database 

4  Date: Published in1998 

5  Spatial Extent: National 

6  Spatial Resolution: For use at national, regional, or state scales 

7  Threshold Value(s): Not yet determined 

5.1.2 Wet Deposition of Nitrogen Containing Chemical Species NO3

-

and NH4

+

8

9  Name: Wet nitrogen (both reduced and oxidized) deposition. 

 Contribution: Greater deposition of nitrogen (especially NO3

-

10 ) in precipitation leads to 

11 increased nitrogen concentration of receiving water. Nitrogen acts as a nutrient in aquatic 

12 systems. 

13  Source: NADP 

14  Date: 2002 

15  Spatial Extent: 312 monitoring stations variably distributed across the United States 

16  Spatial Resolution: For use on regional or national scale only 

17  Threshold Value(s): None currently selected 

18 5.1.3 Total Dry Deposition of Nitrogen Containing both Oxidized and Reduced 

19 Chemical Species 

20  Name: DDTOTN_1A field from CMAQ dataset 

 Contribution: Greater deposition of nitrogen (especially NO3

-

21 ) in precipitation leads to 

22 increased nitrogen concentration of receiving water. Nitrogen acts as a nutrient in aquatic 

23 systems. 

24  Source: CMAQ model 

25  Date: 2002 

26  Spatial Extent: 12 km grid cells of the contiguous United States 

27  Spatial Resolution: For use on a regional or national scale only 

28  Threshold Value(s): None currently selected 
 National Sensitive Areas Analysis 

DRAFT Attachment 2, pg 12 August 2008 

1 5.1.4 Eutrophic Estuaries 

2  Name: Coastal Assessment Framework 

3  Contribution: Identifies which estuaries are currently eutrophic or have the potential to 

4 become eutrophic 

5  Source: National Oceanic and Atmospheric Administration (NOAA) 

6  Date: 1999 

7  Spatial Extent: Continental United States 

8  Spatial Resolution: For use on regional or national scale only 

9  Threshold Value(s): Boundary defines areas of eutrophication 

10 5.1.5 Nutrient Criteria 

11  Name: Maximum Nutrient Concentrations by Region 

12  Contribution: Defines the maximum amount of nutrient load (total phosphorus, total 

13 nitrogen, chlorophyll a) for waterbodies by Level III ecoregion 

14  Source: EPA Office of Science and Technology 

15  Date: 2002 

16  Spatial Extent: Continental United States 

17  Spatial Resolution: Appropriate for use on a regional or national scale 

18  Threshold Value(s): Variable by region. May be possible to identify areas that exceed 

19 nutrient criteria with results from National Nutrient Database 

20 5.1.6 Nitrogen-Limited Waters 

21  Name: Total nitrogen to total phosphorus ratios taken at the same time and at the same 

22 station

23  Contribution: Nitrogen to phosphorus ratio is a measure of how much a waterbody is 

24 nutrient-limited. If a system is not nitrogen-limited, then it is phosphorus-limited. It is 

25 typically accepted that in water with a nitrogen to phosphorus ratio less than 7.2 nitrogen 

26 is the limiting factor. With higher ratios, phosphorus is the limiting nutrient. 

27  Source: EPA National Nutrient Database 

28  Date: Published in 1998 

29  Spatial Extent: National 

30  Spatial Resolution: For use at national, regional, or state scales 
 National Sensitive Areas Analysis 

DRAFT Attachment 2, pg 13 August 2008 

1  Threshold Value(s): 7.2:1 

2 5.2 INDICATOR GEOSPATIAL DATASETS CONSIDERED, BUT NOT 

3 USED

4 The publicly available spatial datasets outlined in the following subsections were 

5 considered for inclusion in the national sensitivity assessment, but were not used. 

6 5.2.1 Presence of Nitrogen Sensitive Species 

7  Name: Johnson’s Seagrass 

8  Contribution: Nutrient enrichment, caused by inorganic and organic nitrogen and 

9 phosphorus loading via urban and agricultural land run-off, can stimulate increased algal 

10 growth and smother Johnson’s seagrass by shading rooted vegetation and diminishing the 

11 oxygen content of the water. 

12  Source: NOAA 

13  Date: 2000 

14  Spatial Extent: Ten portions of the Indian River Lagoon and Biscayne Bay, FL 

15  Spatial Resolution: For use on a statewide basis 

16  Threshold Value(s): Presence of species 

17  Exclusion Reason: Not a national distribution 

18 5.3 OVERLAY RESULTS 

19 The extraction of the areas of greatest aquatic nutrient enrichment sensitivity is a 

20 relatively simple process within the GIS. A simple intersection of the input layers yields a 

21 polygon that defines this area. The area can then be displayed in map form, as shown in Figure

22 5.3-1. (Note: This overlay is currently in progress; therefore, the map provided in Figure 

23 5.3-1 is a placeholder.) 
 National Sensitive Areas Analysis 

DRAFT Attachment 2, pg 14 August 2008 

1

2 Figure 5.3-1. THIS FIGURE IS A PLACEHOLDER. Areas potentially sensitive to 

3 aquatic nutrient enrichment. 

4 6. SENSITIVITY TO TERRESTRIAL NUTRIENT ENRICHMENT 

5 6.1 SELECTED INDICATOR GEOSPATIAL DATASETS 

6 The publicly available geospatial datasets outlined in the following subsections have been 

7 identified as important contributors to terrestrial nutrient enrichment and meet the selection 

8 criteria.

9 6.1.1 Presence of Acidophytic Lichens 

10  Name: Acidophytic Lichens 

11  Contribution: Lichen species that are known to be sensitive to increased levels of 

12 nitrogen loading will decrease in number. Other species are dependent upon lichens for 

13 both food and habitat. 

14  Source: List of acidophytic species from Fenn et al. (2008), Empirical and simulated 

15 critical loads for nitrogen deposition in California mixed conifer forests. Environmental 

16 Pollution, May. Geospatial data obtained from USFS FIA. 
 National Sensitive Areas Analysis 

DRAFT Attachment 2, pg 15 August 2008 

1  Date: 2001–2006 

2  Spatial Extent: Continental United States 

3  Spatial Resolution: For use at national or regional scales 

4  Threshold Value(s): Point (plot location) defines presence of the species. 

6.1.2 Wet Deposition of Nitrogen Containing Chemical Species NO3

-

and NH4

+

5

6  Name: Wet nitrogen (both reduced and oxidized) deposition 

 Contribution: Greater deposition of nitrogen (especially NO3

-

7 ) in precipitation leads to 

8 increased nitrogen concentration of receiving water. Nitrogen acts as a nutrient in 

9 terrestrial systems. 

10  Source: NADP 

11  Date: 2006 

12  Spatial Extent: 312 stations variably distributed across the United States

13  Spatial Resolution: For use on regional or national scale only 

14  Threshold Value(s): None currently selected 

15 6.1.2 Total Dry Deposition of Nitrogen Containing both Oxidized and Reduced 

16 Chemical Species 

17  Name: DDTOTN_1A field from CMAQ dataset 

 Contribution: Greater deposition of nitrogen (especially NO3

-

18 ) in precipitation leads to 

19 increased nitrogen concentration of receiving water. Nitrogen acts as a nutrient in 

20 terrestrial systems. 

21  Source: CMAQ model 

22  Date: 2002 

23  Spatial Extent: 12 km grids of the contiguous United States 

24  Spatial Resolution: For use on regional or national scale only 

25  Threshold Value(s): None currently selected 

26 6.1.3 Anthropogenic Land Cover 

27  Name: Urban and Agricultural Land Covers 

28  Contribution: Used to exclude areas that are not sensitive to terrestrial nutrient 

29 enrichment, such as agricultural areas and urbanized areas
 National Sensitive Areas Analysis 

DRAFT Attachment 2, pg 16 August 2008 

1  Source: USGS National Atlas 

2  Date: 2006

3  Spatial Extent: Continental United States 

4  Spatial Resolution: 1 km meter grid cells 

5  Threshold Value(s): Select out urban and agricultural land covers. 

6 6.2 INDICATOR GEOSPATIAL DATASETS CONSIDERED, BUT NOT 

7 USED

8 The publicly available spatial datasets outlined in the following subsections were 

9 considered for inclusion in the national sensitivity assessment, but were not used. 

10 6.2.1 Soil Nitrogen Content 

11  Name: Soil nitrogen concentration 

12  Contribution: Areas with a high nitrogen concentration may be at risk for nitrogen 

13 saturation.

14  Source: Oak Ridge National Laboratory (not yet received) 

15  Date: Pre-1980

16  Spatial Extent: Continental United States 

17  Spatial Resolution: Not yet known 

18  Threshold Value(s): Not yet determined 

19  Exclusion Reason: Data not received; quality uncertain 

20 6.2.2 Presence of Nitrogen Sensitive Species Identified in Literature 

21  Name: To be created 

22  Contribution: Since there is not a single nationwide species that displays range loss 

23 because of additional nitrogen, it may be possible to assemble a “patchwork quilt” of 

24 study sites across the United States. 

25  Source: Literature 

26  Date: Recent 

27  Spatial Extent: Continental United States 

28  Spatial Resolution: Site-specific, but it may be possible to define a range 

29  Threshold Value(s): Presence of species 
 National Sensitive Areas Analysis 

DRAFT Attachment 2, pg 17 August 2008 

1  Exclusion Reason: Source of nitrogen-sensitive species distribution not found 

2 6.2.3 Presence of Mountains 

3  Name: Physiographic Provinces of the United States 

4  Contribution: Leeward sides of mountains tend to receive a greater amount of nitrogen 

5 deposition

6  Source: USGS 

7  Date: 1946 

8  Spatial Extent: Continental United States 

9  Spatial Resolution: Published scale of 1:7,000,000; for use on regional or national scale 

10 only

11  Threshold Value(s): Select mountain ranges only 

12  Exclusion Reason: Terrain is already taken into account by the CMAQ modeling 

13 6.3 OVERLAY RESULTS 

14 The extraction of the areas of greatest nutrient enrichment sensitivity involved the 

15 following steps within the GIS. The total nitrogen deposition grid (a sum of dry deposition from 

16 CMAQ and wet deposition from NADP) was reclassified into four quartiles. The quartile of 

17 highest total nitrogen deposition was then extracted. From this, areas of human use (urban and 

18 agricultural land covers) were removed. To this, a layer of acidophytic lichen distribution was 

19 added. The area of highest potential sensitivity can be displayed in map form, as shown in 

20 Figure 6.3-1.
 National Sensitive Areas Analysis 

DRAFT Attachment 2, pg 18 August 2008 

1

2 Figure 6.3-1. Areas potentially sensitive to terrestrial nutrient enrichment. 
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9 ATTACHMENT 3 

10 AQUATIC ACIDIFICATION CASE STUDY 

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6 Aquatic Acidification Case Study 

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8 Draft

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10 EPA Contract Number EP-D-06-003 

11 Work Assignment 2-44 

12 Project Number 0209897.002.044 

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22 Prepared for 

23

24 U.S. Environmental Protection Agency 

25 Office of Air Quality Planning and Standards 

26 Research Triangle Park, NC 27709 

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Aquatic Acidification Case Study 

DRAFT Attachment 3, pg ii August 2008 

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Aquatic Acidification Case Study 

DRAFT Attachment 3, pg iii August 2008 

1 CONTENTS 

2 Acronyms and Abbreviations ......................................................................................................... v 

3 1. Purpose........................................................................................................................................ 1 

4 2. Background................................................................................................................................. 1 

5 2.1 Acidification .................................................................................................................... 1 

6 2.2 Indicators of Acidification ............................................................................................... 2

7 2.3 Surface Waters Acidification in Eastern United States ................................................... 3 

8 3. Case Studies................................................................................................................................ 5 

9 3.1 Adirondack Mountains..................................................................................................... 6 

10 3.1.1 General Description ............................................................................................. 6 

11 3.1.2 Levels of Acidic Deposition ................................................................................ 6 

12 3.2 Shenandoah National Park and Surrounding Areas of Virginia ...................................... 7 

13 3.2.1 General Description ............................................................................................. 7 

14 3.2.2 Levels of Acidic Deposition ................................................................................ 8 

15 4. Approach and Methods............................................................................................................... 8 

16 4.1 Approach and Objectives................................................................................................. 8 

17 4.1.1 Surface water trends............................................................................................. 9 

18 4.1.2 Level of Acidification and Biological Impacts.................................................... 9 

19 4.1.3 ANC and Biological Impacts............................................................................. 11 

20 4.1.3.1 Surface Water Assessment Using Monitoring Data ........................... 14 

21 4.1.3.2 Surface Water Assessment Using Modeled Outputs .......................... 15 

22 4.1.4 Critical Loads..................................................................................................... 16 

23 4.2 Modeling Approach ....................................................................................................... 18 

24 4.2.1 MAGIC .............................................................................................................. 18 

25 4.2.1.1 Input Data and Calibration.................................................................. 19 

26 4.2.1.2 Lake and Stream, and Soil data for Calibration.................................. 19 

27 4.2.1.3 Wet Deposition and Meteorology Data for Calibration...................... 20 

28 4.2.1.4 Wet Deposition Data (Reference Year and Calibration Values) ........ 22 

29 4.2.1.5 Dry and Occult Deposition Data and Historical Deposition 

30 Sequences............................................................................................ 22 

31 4.2.1.6 Protocol for MAGIC Calibration and Simulation at Individual 

32 Sites..................................................................................................... 23 

33 4.2.1.7 Combined Model Calibration and Simulation Uncertainty ................ 25 

34 4.3 Critical Loads................................................................................................................. 26 

35 4.3.1 The Steady-State Water Chemistry Model ........................................................ 26 

36 4.3.2 Pre-industrial Base Cation Concentration.......................................................... 28 

37 4.3.3 F-factor............................................................................................................... 29

38 4.3.4 ANC Limit ......................................................................................................... 29 

39 4.3.5 Sea Salt Corrections........................................................................................... 30 

40 5. Results (NOT COMPLETE)..................................................................................................... 30

41 5.1 Adirondack Region of New York.................................................................................. 30 

42 5.1.1 Surface Water Trends from 1990-2006 ............................................................. 30 

43 5.1.2 Condition of Surface Water Acidity .................................................................. 32 
Aquatic Acidification Case Study 

DRAFT Attachment 3, pg iv August 2008 

1 5.1.2.1 Modeled ANC Conditions .................................................................. 32 

2 5.1.2.2 Critical Load ....................................................................................... 33 

3 5.1.2.3 Uncertainty and Risk........................................................................... 35 

4 5.2 Shenandoah National Park and Surrounding Areas, Virginia ....................................... 35 

5 5.2.1 Surface Water Trends from 1990-2006 ............................................................. 35 

6 5.2.2 Condition of Surface Water Acidity .................................................................. 35 

7 5.2.2.1 Modeled ANC Conditions .................................................................. 35 

8 5.2.2.2 Critical Load ....................................................................................... 35 

9 5.2.2.3 Uncertainty and Risk........................................................................... 36 

10 6. References................................................................................................................................. 36 

11 FIGURES

12 Figure 2.3-1 Sensitive ecosystem to acidic deposition in the eastern United States. .................. 4 

Figure 3.1-1 Annual average trends from 1990 to 2006 in wet SO4

2- 13 (green line) and 

NO3

-

14 (blue line) deposition from nine NADP/NTN sites in the Adirondack 

15 region. ..................................................................................................................... 7 

Figure 3.2-1 Annual average trends from 1990 to 2006 in wet SO4

2- 16 (green line) and 

NO3

-

17 (blue line) deposition from seven NADP/NTN sites in the 

18 Shenandoah National Park region........................................................................... 8 

19 Figure 4.1-1 (a) Number of fish species per lake or stream versus acidity statues, 

20 expressed either as ANC. (b) Number of fish species among 13 streams in 

21 Shenandoah National Park.................................................................................... 32 

22 Figure 5.1-3 Critical loads of surface water acidity for an ANC concentrations of 50 

23 ȝeq/L. .................................................................................................................... 33 

24 Figure 5.1-4 Critical load exceedances for ANC concentration of 50 ȝeq/L............................ 34 

25 Figure 5.1-5 Trend in SO4, NO3, and ANC from LTM network............................................... 35 

26 TABLES

27 Table 4.1-1 Aquatic Status Categories..................................................................................... 14 

28 Table 5.1-1 Number and percentage of lakes that have current N and S deposition 

29 loads that prevent surface water ANC concentration to be below an ANC 

30 of 50, and 20 ug/L for lakes in the Adirondack Mountains.................................. 34 

31
Aquatic Acidification Case Study 

DRAFT Attachment 3, pg v August 2008 

1 ACRONYMS AND ABBREVIATIONS 

2

3 Al(OH)3 aluminum hydroxide 

4 Al aluminum 

5 ANC acid neutralizing capacity 

6 ASTRAP Advanced Statistical Trajectory Regional Air Pollution model 

Ca2+ 7 calcium 

8 CMAQ Community Multiscale Air Quality 

9 CO2 carbon dioxide 

10 EMAP Environmental Monitoring and Assessment 

11 EPA U.S. Environmental Protections Agency 

12 F-. fluoride 

H

+

13 hydrogen 

K

+

14 potassium 

15 kg/ha kilograms/hectare 

16 LTM Long-term Monitoring Program 

17 m meter 

Mg2+ 18 magnesium 

Na+

19 sodium 

20 NAPD National Atmospheric Deposition Program 

21 NEG.ECP New England Governors and Eastern Canadian Premier 

22 NO2 nitrogen dioxide 

23 NO3 nitrate

24 NOx nitrogen oxides 

25 NSWS National Lake/Stream Surveys 

26 Si silicon 

27 SO2 sulfur dioxide 

SO42

-

28 sulfate

29 SOx sulfur oxides 

30 SSWC Steady-State Water Chemistry Model 

31 TIME Temporally Integrated Monitoring of Ecosystems Program 
Aquatic Acidification Case Study 

DRAFT Attachment 3, pg vi August 2008 

1

2

3

4

5

6

7

8

9 [This page intentionally left blank.] 

10
Aquatic Acidification Case Study 

DRAFT Attachment 3, pg 1 August 2008 

1 1. PURPOSE 

2 This case study is intended to estimate the ecological exposure and risk associated to 

3 aquatic ecosystems from acidification effects of the deposition of nitrogen and sulfur for two 

4 sensitive regions of eastern United States: the Adirondack Mountains and Shenandoah National 

5 Park and the surrounding areas of Virginia.

6 2. BACKGROUND 

7 2.1 ACIDIFICATION 

8 Emissions of sulfur dioxide (SO2) and nitrogen dioxide (NO2) compounds into the air 

9 react in the atmosphere through a complex mix of reactions and thermodynamic processes in 

10 gaseous, liquid, and solid phases to form various acidic compounds. These compounds are 

11 removed from the atmosphere through deposition: either wet (e.g., rain, snow), occult (e.g., fog, 

12 mist), or dry (e.g., gases and particles). Deposition of sulfur oxides (SOx) and nitrogen oxides 

13 (NOx) leads to ecosystem exposure. Among other effects on ecosystem structure and function, 

14 deposition of these compounds can lead to acidification of surface waters through the leaching of 

sulfate (SO42

-

) and nitrate (NO3

-

15 ) from soils. The effects depend on the magnitude of deposition, 

16 as well as a host of biogeochemical processes occurring in the soils and waterbodies. 

When sulfur or nitrogen moves from soils to surface waters in the form of SO4

2- or NO3-

17 ,

18 an equivalent amount of cations, or countercharge, is also transported. If the countercharge is 

provided by cations (such as calcium (Ca2+), magnesium (Mg2+), sodium (Na+

19 ) and potassium 

(K+

).) other than hydrogen (H+) and Aln+20 , the base saturation and buffering capacity of the soil is 

reduced as the acidity of the soil water is neutralized. Continued SO4

2- or NO3-

21 leaching can 

22 deplete the base supply of the soil, thereby impairing the soil’s ability to neutralize further acidic 

deposition. Further deposition and leaching of SO4

2- 23 leads to acidification of soil water, and by 

24 connection, surface water as the base cations are removed. Loss of soil base saturation is a 

25 cumulative effect that increases the sensitivity of the watershed to further acidic deposition.

Cumulative effects of sulfur deposition can also result from the adsorption of SO4

2- 26 to soil 

particles, a process that removes SO4

2- 27 from soil solution, and therefore, prevents leaching of 

28 cations and further acidification of soil. However, this potentially reversible process results in an 
Aquatic Acidification Case Study 

DRAFT Attachment 3, pg 2 August 2008 

1 accumulation of sulfur in the soil, which can contribute to soil acidification if, and when, the 

SO4

2- is eventually released back into solution. The degree to which SO42- 2 adsorbs on soil is 

dependent on soil characteristics. Soils in the United States that most effectively adsorb SO4

2- 3

4 occur south of the maximum extent of glaciation that occurred during the most recent ice age 

5 (Rochelle et al., 1987; Rochelle and Church, 1987). Sulfate adsorption is strongly pH dependent, 

6 and a decrease in soil pH resulting from acidic deposition can enhance the ability of soil to 

adsorb SO4

2- 7 .

8 2.2 INDICATORS OF ACIDIFICATION 

9 Surface water chemistry is a primary indicator of acidification and the resulting adverse 

10 effects on the biotic integrity of freshwater ecosystems. There are numerous sensitive chemical 

11 receptors that can be used to assess effects of acidic deposition on lake or stream acid-base 

chemistry. These include surface water pH and concentrations of SO4

2-, NO3-, Al+

, Ca2+ 12 ; the sum 

13 of base cations; and the recently developed base cation surplus. Another widely used water 

14 chemistry indicator for both atmospheric deposition sensitivity and effects is acid neutralizing 

15 capacity, or ANC. Each of these chemical indicators can provide useful information regarding 

16 both sensitivity to surface water acidification and the level of acidification that has occurred. 

17 Acidification effects on aquatic biota are most commonly evaluated using Al, pH, or ANC. 

18 Although ANC does not relate directly to the health of biota, the utility of the ANC criterion lies 

19 in the association between ANC and the surface water constituents that directly contribute to or 

ameliorate acidity-related stress, in particular pH, Ca2+ 20 , and Al. Furthermore, surface water 

21 acidification models do a better job estimating ANC than either pH or Al concentrations. For the 

22 purpose of this case study, ANC will be the focus of the indicator used.

23 ANC of surface waters was used as a metric to quantify the current acidic conditions and 

24 biological impacts of a subset of waterbodies in the study areas, because it provides an acid-base 

25 chemistry that reflects the relative balance between cations and strong acid anions and the 

26 cumulative effects of all of the ionic interactions that occur as atmospheric deposition and 

27 precipitation move from the atmosphere into the soil and drainage water to emerge in a stream or 

28 lake. For the purpose of this case study, ANC of surface waters is simply measured as the total 

29 amount of strong base ions minus the total amount of strong acid anions: 

 ANC = (Ca2+ + Mg2++ K + Na + NH4) – (SO4

2- + NO3-+ Cl30 ) (1) 
Aquatic Acidification Case Study 

DRAFT Attachment 3, pg 3 August 2008 

1 The unit of ANC is usually microequivalents per liter (ȝeq/L). If the sum of the 

2 equivalent concentrations of the base cations exceeds those of the strong acid anions, then the 

3 water will have positive ANC. To the extent that the base cation sum exceeds the strong acid 

anion sum, the ANC will be higher. Higher ANC is generally associated with higher pH and Ca2+ 4

concentrations; lower ANC is generally associated with higher H+

 and Al+5 concentrations and a 

6 greater likelihood of toxicity to biota. This is the buffering capacity, or the ability of the system 

7 to resist acidification. 

8 Field studies often rely upon the Gran titration approach. Process-based models, such as 

9 MAGIC and PnET-BGC, utilize the ANC calculated from the charge balance. For monitoring 

10 and assessment purposes, it is always best to determine both titrated and calculated ANC values. 

11 The difference between the two can be used to quantify uncertainty and reveal the influences of 

12 natural organic acidity and/or dissolved Al on the overall acid-base chemistry of the water. 

13 Surface water pH is a common alternative to ANC as an indicator of acidification. 

14 However, at pH values above about 6.0, pH is not a good indicator of either sensitivity to 

15 acidification or level of effect. In addition, pH measurements (especially at these higher values) 

16 are sensitive to levels of dissolved carbon dioxide (CO2) in the water. In contrast, ANC is more 

17 stable and it reflects sensitivity and effect in a linear fashion across the full range of ANC values. 

18 Therefore, ANC is the preferred indicator variable for surface water acidification. Both titrated 

19 and calculated ANC values are commonly determined in field studies aimed at resource 

20 characterization or long-term monitoring. 

21 2.3 SURFACE WATERS ACIDIFICATION IN EASTERN UNITED 

22 STATES

23 The regions of the United States with low surface water ANC values are the locations 

24 that are sensitive to acidic deposition. The majority of lakes and streams in the United States 

25 have ANC levels above 200 ȝeq/L and are not sensitive to the deposition of NOx and SOx air 

26 pollution at their existing ambient concentration levels. Figure 2.3-1 shows the acid-sensitive 

27 regions of the eastern United States with the potential of low surface water ANC, as determined 

28 by geology and surface water chemistry. 
Aquatic Acidification Case Study 

DRAFT Attachment 3, pg 4 August 2008 

Northern Florida 

Highlands 

Appalalachian

Plateau 

Ridge/Blue Ridge 

Upper Mid-west 

New England 

Adirondacks 

Mountains 

Northern Florida 

Highlands 

Appalalachian

Plateau 

Ridge/Blue Ridge 

Upper Mid-west 

New England 

Adirondacks 

Mountains 

1

2 Figure 2.3-1. Sensitive ecosystem to acidic deposition in the eastern United States. 

3 Freshwater surveys and monitoring in the eastern United States has been conducted by 

4 many program since the mid-1980s, including the U.S. Environmental Protections Agency’s 

5 (EPA’s) Environmental Monitoring and Assessment (EMAP), National Lake/Stream Surveys 

6 (NSWS), Temporally Integrated Monitoring of Ecosystems (TIME) (Stoddard, 1990), and Long7 term Monitoring (LTM) (Ford et al, 1993; Stoddard et al., 1998) programs. The purpose of these 

8 programs is to identify the current state and determine trends in regional populations of lakes or 

9 streams impacted by acidic deposition. Based on surface water data from these programs in the 

10 eastern United States, New England, the Adirondacks, the Appalachian Mountains (northern 

11 Appalachian Plateau and Ridge/Blue Ridge region), northern Florida, and the Upper Midwest 

12 contain the greatest proportion of sensitive lakes and streams (i.e., ANC less than about 50 

13 ȝeq/L) since the 1980s.
Aquatic Acidification Case Study 

DRAFT Attachment 3, pg 5 August 2008 

1 New England, the Adirondacks, the Appalachian Mountains (northern Appalachian 

2 Plateau and Ridge/Blue Ridge region), and the Upper Midwest are estimated to contain 95% of 

3 the lakes and 84% of the streams in the United States that have been anthropogenically acidified 

4 through deposition. The Adirondacks had a large proportion of acidic surface waters (14%) in 

5 the NSWS; from 1984 to 1987, the Adirondack Lakes Survey Corporation sampled 1,469 

6 Adirondack lakes greater than 0.5 hectares (ha) in size and estimated that many more (26%) were 

7 acidic (Driscoll et al., 1991). The proportions of lakes estimated by NSWS to be acidic were 

8 smaller in New England and the Upper Midwest (5% and 3%, respectively), but because of the 

9 large numbers of lakes in these regions, there were several hundred acidic waters in each of these 

10 two regions. The Valley and Ridge Province and Northern Appalachian Plateau had 5.5% and 

11 6% acidic sites, respectively, based on data from the early 1990s. Portions of northern Florida 

12 also contain many acidic and low-ANC lakes and streams, although the role of acidic deposition 

13 in these areas is less clear. In 2002, Stoddard and colleagues (2003) took another comprehensive 

14 look at the level of acidification within all of these regions. Although improvement in ANC 

15 occurred, they still found that about 8% of lakes in the Adirondacks and 6%–8% of streams in 

16 northern Appalachian Plateau and Ridge/Blue Ridge region were acidic at base-flow conditions. 

17 Because they are still receiving substantial NOx/SOx deposition inputs and still contain a large 

18 number of waterbodies that are acidic, areas in New England, the Adirondacks Mountains, the 

19 Northern Appalachian Plateau, and the Ridge/Blue Ridge region provide ideal case study 

20 locations to assess the risk to aquatic ecosystems from NOx/SOx acidic deposition.

21 3. CASE STUDIES 

22 The Adirondacks Mountains in New York and the Ridge/Blue Ridge Mountains in the 

23 Shenandoah National Park and surrounding areas of Virginia were selected for the evaluation of 

24 the risk of ambient NOx/SOx concentrations to aquatic acidification and their biological impacts. 

25 Three main reasons support the selection of these two areas. First, both regions fall within the 

26 areas of the United State known to be sensitive to acidic deposition because of a host of 

27 environmental factors (e.g., geology) that make these regions predisposed to acidification. 

28 Second, these areas are representative of other sensitive areas to acidification, which will allow 

29 the results of this case study to be generalized. Third, a high degree of knowledge, research, and 

30 data have already been collected within these geographic regions (see Section 4 of the Integrated 
Aquatic Acidification Case Study 

DRAFT Attachment 3, pg 6 August 2008 

1 Science Assessment [ISA]). For example, extensive water quality data exists (from monitoring 

2 networks in operation since the 1980s), along with numerous research studies that directly link 

3 the biological harm of individuals, populations, communities, and ecosystems to aquatic 

4 acidification. Sections below describe the case studies areas, past impacts of acidic deposition, 

5 and research linking biological and acidic conditions for each region. 

6 3.1 ADIRONDACK MOUNTAINS 

7 3.1.1 General Description 

8 The Adirondack Mountains region is situated in northeastern New York State. It is 

9 characterized by dense forest cover and abundant surface waters, with 46 peaks that extend up to 

10 1600 meter (m) in elevation. The Adirondack region has long been a nationally important 

11 recreation area for fishing, hiking, boating, and other outdoor activities. The Adirondack region, 

12 and the southwestern Adirondacks in particular, is sensitive to acidic deposition because it 

13 receives high precipitation, has shallow base-poor soils, and is underlain by igneous bedrock 

14 with low weathering rates and buffering capacity (Driscoll et al., 1991; Sullivan et al., 2006). 

15 The Adirondack region is also among the most severely acid-impacted regions in North America 

16 (Driscoll et al., 2003; Landers et al., 1988; Stoddard et al., 2003). It has long been used as an 

17 indicator of the response of forest and aquatic ecosystems to United States policy on atmospheric 

18 emissions of SO2 and NOx (U.S. EPA, 1995; NAPAP, 1998).

19 3.1.2 Levels of Acidic Deposition 

20 Wet deposition in the Adirondacks has been monitored by the National Atmospheric 

21 Deposition Program/National Trends Network (NADP/NTN) since 1978 at two sites (Huntington 

22 Forest and Whiteface Mountain) and seven other sites since 1980s. Since 1990, wet sulfate and 

23 nitrate deposition at these NADP/NTN sites in the Adirondacks has declined by about 45% and 

40%, respectively (Figure 3.1-1). However, deposition is still 15 and 10 kg/ha of SO4

2- and NO3-

24

25 respectively.
Aquatic Acidification Case Study 

DRAFT Attachment 3, pg 7 August 2008 

1

Figure 3.1-1. Annual average trends from 1990 to 2006 in wet SO4

2- 2 (green line) and 

NO3

-

3 (blue line) deposition from nine NADP/NTN sites in the Adirondack region.

4 3.2 SHENANDOAH NATIONAL PARK AND SURROUNDING AREAS 

5 OF VIRGINIA 

6 3.2.1 General Description 

7 Shenandoah National Park is located along the crest of the Blue Ridge Mountains in 

8 Virginia. Air pollution within Shenandoah National Park and surrounding areas, including 

9 concentrations of sulfur, nitrogen, and ozone (O3), is higher than in most other national parks in 

10 the United States. This area is sensitive to acidic deposition because it receives high 

11 precipitation, has shallow base-poor soils, and is underlain by igneous and silicon (Si)-based 

12 bedrock with low weathering rates and poor buffering capacity. Shenandoah National Park 

13 region is also among the most severely acid-impacted regions in North America (Stoddard et al., 

14 2003; Webb et al., 2004). 
Aquatic Acidification Case Study 

DRAFT Attachment 3, pg 8 August 2008 

1 3.2.2 Levels of Acidic Deposition 

2 Wet deposition in the Shenandoah National Park of Virginia has been monitored at 7 sites 

3 by the NADP/NTN since the 1980s. Since 1990, wet sulfate and nitrate deposition has declined 

4 by about 28% and 20%, respectively (Figure 3.2-1). However, deposition is still 15 and 10 

kilograms/hectare (kg/ha) of SO4

2- and NO3-

5 , respectively.

6

Figure 3.2-1. Annual average trends from 1990 to 2006 in wet SO4

2- 7 (green line) and 

NO3

-

8 (blue line) deposition from seven NADP/NTN sites in the Shenandoah National 

9 Park region. 

10 4. APPROACH AND METHODS 

11 4.1 APPROACH AND OBJECTIVES 

12 For each of the case study areas, current conditions of the aquatic ecosystems to 

13 acidification impacts were evaluated by using multiple approaches that rely on monitoring data 

14 and modeled output. Current conditions were evaluated by a three-step process that assessed: 
Aquatic Acidification Case Study 

DRAFT Attachment 3, pg 9 August 2008 

1  The trends in sulfate, nitrate, and ANC concentrations in surface water to establish 

2 current pollution levels and trends that are linked to nitrogen and sulfur deposition,

3  The percent of waterbodies that have different degrees of acidic conditions, 

4  The percent of waterbodies receiving current nitrogen and sulfur deposition loads above 

5 biological harmful levels.

6 4.1.1 Surface water trends 

Trends in SO4

2- and NO3-

7 , and ANC concentrations measured in surface water, were used 

8 to establish the current condition of sensitive chemical receptors that are linked to the effects of 

9 acidic deposition on waterbodies acid-base chemistry. Each provides information regarding both 

10 sensitivity to surface water acidification and the level of acidification that has occurred today and 

11 in the past. Trends in these sensitive chemical receptors allow for the determination of whether 

12 the conditions of the waterbodies are improving and heading towards recovery or if the 

conditions are degrading. Measurements of SO4

2- 13 concentrations in surface waters provide 

important information on the extent of cation leaching in soils and how SO4

2- 14 concentrations 

15 relate to deposition and to the levels of ambient atmospheric sulfur. Assessments of acidic 

deposition effects dating from the 1980s to the present have shown SO4

2- 16 to be the primary anion 

17 in most acid-sensitive waters (Driscoll and Newton, 1985; Driscoll et al., 1988, 2001; Webb et 

al., 2004). Nitrate has the same potential as SO4

2- 18 to acidify drainage waters and leach potentially 

Aln+19 from watershed soils. In most watersheds, however, nitrogen is a limiting factor for plant 

20 growth; therefore, most nitrogen inputs through deposition are quickly incorporated into biomass 

as organic nitrogen with little leaching of NO3

-

21 into surface waters. 

22 To assess surface water trends in sulfate, nitrate, and ANC concentrations, we used 

monitoring samples from the EPA-administered LTM program. Trends in SO4

2-, NO3-

23 , and ANC 

24 concentration were assessed using average yearly values for the period from 1990 to 2006. All 

25 the lakes included in this analysis were sampled weekly. 

26 4.1.2 Level of Acidification and Biological Impacts 

27 Ecological effects occur at four levels of biological organization: (1) the individual, (2) 

28 the population, comprised of many individuals, (3) the biological community, composed of many 

29 species, and (4) the ecosystem. Several metrics have been developed to describe the effects of 
Aquatic Acidification Case Study 

DRAFT Attachment 3, pg 10 August 2008 

1 acidification at each of these levels of organization. For the individual, impacts are assessed in 

2 terms of fitness (i.e., growth, development, and reproduction) or sub-lethal effects on condition. 

3 Low-pH or ANC water can directly influence aquatic organism fitness or mortality by disrupting 

4 ion regulation and mobilizes Al, which is highly toxic to fish under acidic conditions (i.e., pH 

5 below 6 and ANC below 50 ȝeq/L). For example, research showed that as the pH of surface 

6 waters decreased below 6, many aquatic species, including fish, invertebrates, zooplankton, and 

7 diatoms, tended to decline (Schindler, 1988). Van Sickle and colleagues (1996) also found that 

8 blacknose dace (Rhinichthy spp.) were highly sensitive to low pH and could not tolerate 

9 inorganic Al concentrations above about 3.7 ȝM for extended periods of time. After 6 days of 

10 exposure to high inorganic Al, dace mortality increased rapidly to nearly 100%. 

11 At the community level, species richness and community structure can be used to 

12 evaluate effects. Species composition refers to the mix of species that are represented in a 

13 particular ecosystem, while species richness refers to the total number of species in a stream or 

14 lake. Acidification alters species composition and richness in aquatic ecosystems. There are a 

15 number of species common to many oligotrophic waterbodies that are sensitive to and cannot 

16 survive, compete, or reproduce in acidic waters. In response to small to moderate changes in 

17 acidity, acid-sensitive species are often replaced by other more acid-tolerant species, resulting in 

18 changes in community composition and richness, but little or no change in total community 

19 biomass. The effects of acidification are continuous, with more species being affected at higher 

20 degrees of acidification. At a point, typically at a pH below 4.5 and an ANC below 0 ȝeq/L,

21 complete to near loss of many classes of organisms occur, such as fish and aquatic insect 

22 populations, while others are reduced to only a few acidophilic forms. 

23 Decreases in species richness have been observed in the Adirondacks and Catskills of 

24 New York (Baker et al 1993), the upper Midwest of the United States (Schindler et al., 1989), 

25 New England and Pennsylvania (Haines and Baker, 1986), and Virginia (Bulger et al., 2000). 

26 For example, studies in the Adirondack Mountains demonstrated the effect of acidification on 

27 species richness; of the 53 fish species recorded in Adirondack lakes, only 27 species were found 

28 in lakes with pH below 6.0. The 26 species missing from lakes with pH below 6.0 include 

29 important recreational species, such as Atlantic salmon, tiger trout (Salmo trutta X Salvelinus 

30 fontinalis), redbreast sunfish (Lepomis auritus), bluegill (Lepomis macrochirus), tiger musky 

31 (Esox masquinongy X lucius), walleye (Sander vitreus), alewife (Alosa pseudoharengus), and 
Aquatic Acidification Case Study 

DRAFT Attachment 3, pg 11 August 2008 

1 kokanee (Oncorhynchus nerka) (Kretser et al., 1989), plus ecologically important minnows that 

2 are commonly eaten by sport fish. 346 of 1,469 lakes surveyed were devoid of fish. Among lakes 

3 with fish, there was a relationship between the number of fish species and lake pH, ranging from 

4 about one species per lake for lakes having pH less than 4.5 to about six species per lake for 

5 lakes having pH >6.5 (Driscoll et al., 2001; Kretser et al., 1989). 

6 4.1.3 ANC and Biological Impacts 

7 ANC of surface waters was used as a metric to quantify the current acidic conditions and 

8 biological impacts of a subset of waterbodies in the study areas because it has been found in 

9 many studies to be the best single indicator of the biological response and health of aquatic 

10 communities in acid-sensitive systems (Lien et al., 1992; Sullivan et al., 2006). It is a strong 

11 indicator of biological response because acid-base conditions in surface water have been shown 

12 to have direct effects on aquatic systems (i.e., individual species fitness loss or death, reduced 

13 species richness, and altered community structure). At the community level, species richness is 

14 positively correlated with pH and ANC (Kretser et al., 1989; Rago and Wiener, 1986), because 

15 energy cost in maintaining physiological homeostasis, growth, and reproduction is high at low 

16 ANC levels (Schreck, 1981, 1982; Wedemeger et al. 1990). For example, Sullivan et al. (2006) 

17 found a logistic relationship between fish species richness and ANC class for Adirondack lakes 

18 (Figure 4.1-1, a), which indicates the probability of occurrence of an organism for a given value 

19 of ANC. In Shenandoah National Park, a statistically robust relationship between acid-base 

20 status of streams and fish species richness was also documented (Figure 4.1-1, b).
Aquatic Acidification Case Study 

DRAFT Attachment 3, pg 12 August 2008 

Figure 4.1-1. (a) Number of fish species per lake or stream versus acidity statues, expressed 

either as ANC. Adirondack lakes (Sullivan et al., 2006). (b) Number of fish species among 13 

streams in Shenandoah National Park. Values of ANC are means based on quarterly 

measurements, 1987–1994. The regression analysis shoed a highly significant relationship (p < 

0.0001) between mean stream ANC and the number of fish species. Streams having ANC 

consistently <75 ȝeq/L had three or fewer species.

1 However, because there is a continuum in the relationship between ANC levels and 

2 resulting biological effects, a range of ANC concentrations related to specific biological effects 

3 is needed to provide a comprehensive assessment of the current biological condition under 

4 current acidic deposition loads. For this reason, five categories of ANC concentrations were 

5 selected that relate to specific biological health conditions of aquatic communities, ranging from 

6 no impacts to complete loss of fish populations. These five classes are based on the relationships 

7 between ANC/pH and ecological attributes, including richness, diversity, community structure, 

8 and individual fitness of organisms. The below paragraph describes the biological impact given a 

9 range of ANC values and the scientific research that supports the grouping (see Section AX4 of 

10 the Annexes to the ISA for a more in depth description of the biological relationship used in this 

11 grouping).

12 For freshwater systems, ANC chemical levels are best grouped into five major classes: 

13 Acute Concern <0 ȝeq/L, Severe Concern 0–20 ȝeq/L, Elevated Concern 20–50 ȝeq/L, and 

14 Moderate Concern 50–100 ȝeq/L, and Low Concern >100 ȝeq/L, with each range representing a 

15 probability of ecological damage to the community (Table 4.1-1). Biota is generally not harmed 

16 when ANC values are above 100 ȝeq/L. The number of fish species also peak at ANC values

17 above 100 ȝeq/L (Bulger et al., 1999; Driscoll et al., 2001; Kretser et al., 1989; Sullivan et al., 
Aquatic Acidification Case Study 

DRAFT Attachment 3, pg 13 August 2008 

1 2006). Below 100 ȝeq/L, it has been shown that fish fitness and community diversity begin to 

2 decline (Figure 4.1-2). At ANC levels between 100 and 50 ȝeq/L, the fitness of sensitive species 

3 (e.g., brook trout and other zooplankton) begins to decline. However, the overall health of the 

4 community remains high. When ANC concentrations are below 50 ȝeq/L, there are negative 

5 effects on sensitive biota. In Adirondack lakes, Kretser and colleagues (1989) showed a 50% 

6 reduction in the number of fish species below an ANC of 50 ȝeq/L. From 50 to 20 ȝeq/L, it has 

7 been shown that the overall fitness of most fish species are greatly reduced (Dennis and Bulger, 

8 1995). Below 20 ȝeq/L, all biota exhibit some level of negative effects. At these levels, surface 

9 waters are susceptible to episodic acidification and their associated harmful effects. Fish and 

10 plankton diversity and the structure of the communities continue to decline sharply to levels 

11 where acid-tolerant species begin to outnumber all other species (Matuszek and Beggs, 1988; 

12 Driscoll et al., 2001). Below an ANC of 0 ȝeq/L, complete loss of fish populations and 

13 extremely low diversity of planktonic communities occur. Only acidophilic species are present, 

14 but their population numbers are sharply reduced. For example, under average ANC <0 ȝeq/l,

15 lakes is the Adirondack region are generally fishless (Sullivan et al., 2006). A summary of the 

16 five categories of ANC and expected ecological effects can be found in Table 4.1-1.

> 100 ȝeq/L - Fish species and Zooplankton 

communities richness is unaffected. 

50 – 100 ȝeq/L - Fish species richness begins 

to decline as sensitive species are lost, such as 

brook trout. Diversity and distribution of 

zooplankton communities also begin to decline 

as sensitive species to acid deposition are lost. 

20 – 50 ȝeq/L. Fish species richness is 

reduced. Brook trout populations 

experience loss of fitness. Diversity 

and distribution of zooplankton 

communities declines. 

0 – 20 ȝeq/L All 

biota effected. 

Brook trout 

populations 

experience 

lethal effects. 

Diversity 

continues to 

declines. 

<O ȝeq/L - Loss of fish 

populations and extremely low 

diversity

> 100 ȝeq/L - Fish species and Zooplankton 

communities richness is unaffected. 

50 – 100 ȝeq/L - Fish species richness begins 

to decline as sensitive species are lost, such as 

brook trout. Diversity and distribution of 

zooplankton communities also begin to decline 

as sensitive species to acid deposition are lost. 

20 – 50 ȝeq/L. Fish species richness is 

reduced. Brook trout populations 

experience loss of fitness. Diversity 

and distribution of zooplankton 

communities declines. 

0 – 20 ȝeq/L All 

biota effected. 

Brook trout 

populations 

experience 

lethal effects. 

Diversity 

continues to 

declines. 

<O ȝeq/L - Loss of fish 

populations and extremely low 

diversity

17

18 Figure 4.1-2. Number of fish species per lake or stream versus acidity statues, expressed 

19 as ANC in lakes in the Adirondacks of New York (Sullivan et al., 2006). Five classes: <0,

20 0–20, 20–50, and 50–100 ȝeq/L describe the biological effects at a range of ANC 

21 concentrations. See Table 4.1-1. 
Aquatic Acidification Case Study 

DRAFT Attachment 3, pg 14 August 2008 

Table 4.1-1. Aquatic Status Categories 

Category Label ANC Levels* Expected Ecological Effects 

Acute

Concern

<0 micro 

equivalent

per Liter 

(ȝeq/L)

Complete loss of fish populations is expected. Planktonic 

communities have extremely low diversity and are 

dominated by acidophilic forms. The numbers of individuals

in plankton species that are present are greatly reduced. 

Severe

Concern

 0 – 20 

ȝeq/L

Highly sensitive to episodic acidification. During episodes 

of high acid deposition, brook trout populations may 

experience lethal effects. Diversity and distribution of 

zooplankton communities declines sharply. 

Elevated

Concern

20 – 50 

ȝeq/L

Fish species richness is greatly reduced (more than half of 

expected species are missing). On average, brook trout 

populations experience sub-lethal effects, including loss of 

health and reproduction (fitness). Diversity and distribution 

of zooplankton communities declines. 

Moderate

Concern

50 – 100 

ȝeq/L

Fish species richness begins to decline (sensitive species are 

lost from lakes). Brook trout populations are sensitive and

variable, with possible sub-lethal effects. Diversity and 

distribution of zooplankton communities begin to decline as 

species that are sensitive to acid deposition are affected. 

Low

Concern

>100

ȝeq/L

Fish species richness may be unaffected. Reproducing brook 

trout populations are expected where habitat is suitable. 

Zooplankton communities are unaffected and exhibit 

expected diversity and distribution. 

1 4.1.3.1 Surface Water Assessment Using Monitoring Data 

2 Current acid-base condition and the biological status of 175 lakes in the Adirondacks and 

3 60 streams in the Shenandoah National Park and surrounding areas were assessed by grouping 

4 surface water ANC concentrations into the five aquatic status categories. This grouping allows 

5 for the evaluation of the range of current biological conditions under current nitrogen and sulfur 

6 deposition loads for this subpopulation of waterbodies. Surface water chemistry data were used 

7 from two EPA-administered surface water monitoring and survey programs: the TIME and the 

8 LTM programs. The years 2002 and 2006 were evaluated. Average yearly ANC concentrations 

9 were calculated from weekly values for LTM sites and monthly values for TIME sites. In the 

10 Adirondacks region, the 175 lakes consist of 60 LTM lakes and the regional EMAP probability 

11 sample of 115 lakes. The total number of target Adirondack lakes included in the EMAP frame 

12 was 1,829 (SE = 244). Details of the EMAP design were given by Larsen et al. (1994). In the 
Aquatic Acidification Case Study 

DRAFT Attachment 3, pg 15 August 2008 

1 Shenandoah National Park and surrounding areas, the 60 lakes were from LTM program (Figure

2 4.1-3).

3

4 Figure 4.1-3. Site locations. To be included in second draft REA.

5 4.1.3.2 Surface Water Assessment Using Modeled Outputs 

6 The MAGIC model was used to determine the natural conditions of the lakes in each 

7 study areas. MAGIC is a lumped-parameter model of intermediate complexity, developed to 

8 predict the long-term effects of acidic deposition on surface water chemistry (Cosby et al. 

9 1985a). The model simulates soil solution chemistry and surface water chemistry to predict the 

10 monthly and annual average concentrations of the major ions in these waters. MAGIC consists of 

11 (1) a 2–10 submodel in which the concentrations of major ions are assumed to be governed by 

12 simultaneous reactions involving SO42- adsorption, cation exchange, dissolution-precipitation13 speciation of Al, and dissolution-speciation of inorganic carbon; and (2) a mass balance 

14 submodel in which the flux of major ions to and from the soil is assumed to be controlled by 
Aquatic Acidification Case Study 

DRAFT Attachment 3, pg 16 August 2008 

1 atmospheric inputs, chemical weathering, net uptake and loss in biomass, and losses to runoff. At 

2 the heart of MAGIC is the size of the pool of exchangeable base cations in the soil. As the fluxes 

3 to and from this pool change over time owing to changes in atmospheric deposition, the chemical 

4 equilibria between soil and soil solution shift to give changes in surface water chemistry. Thus, 

5 the degree and rate of change of surface water acidity depend both on flux factors and the 

6 inherent characteristics of the affected soils. The advantage of using a model like MAGIC is that 

7 it allows us to directly link known amounts of nitrogen and sulfur deposition to specific surface 

8 water ANC values and biological effects. See Section 4.3.1 for more details regarding the model 

9 MAGIC. .

10 Surface water ANC values for 44 lakes in the Adirondacks and 60 streams in the 

11 Shenandoah National Park and surrounding areas were modeled using 2002 levels of deposition. 

12 The resulting surface water ANC concentrations from the model were grouped accordingly to the 

13 five categories for the years from 2002 through 2007. 

14 4.1.4 Critical Loads 

15 Surface water chemistry data from LTM, TIME, and EMAP programs were used to 

16 calculate the critical load of 175 lakes in the Adirondacks and 60 streams in the Shenandoah 

17 National Park region. A critical load is simply the level of acidic deposition that a watershed can 

18 receive and still maintain an acid-base balance or ANC level that protects the biological 

19 community. In other words, it’s the “buffering” capacity of a watershed to neutralize the addition 

of acidic deposition, such as SO4

2- or NO3

-

20 , to the system and maintain a value of ANC that 

21 provides a level of biological protection. The buffering capacity of a watershed is determined by 

22 a host of biogeophysical factors, including base cation concentrations, base cation weathering 

23 rates, uptake by vegetation, rate of surface water flow, soil depth, and bedrock, which are best 

24 estimated by calculating a critical load. A critical load estimate is analogous to determining the 

25 “susceptibility” of a waterbody to become acidified from the deposition of nitrogen and sulfur. 

Low critical load values (i.e., >50 meq/m2

26 /yr) mean that the watershed has a limited ability to 

27 neutralize the addition of acidic anions, and hence, it is susceptible to acidification. The greater 

28 the critical load value, the greater the ability of the watershed to neutralize the addition acidic 

29 anions and protect aquatic life, making the system less susceptible to acidification.
Aquatic Acidification Case Study 

DRAFT Attachment 3, pg 17 August 2008 

1 ANC was used to link the water chemistry to the relevant community level biological 

2 changes or “protection.” Three levels of biological protection or risk (i.e., ANClimit) were used: 

3 ANC values of above 0 ȝeq/L (low protection), above 20 ȝeq/L (modest protection), and above 

4 50 ȝeq/L (moderate protection). Detrimental effects are noted for waterbodies with these ANC 

5 levels, including decreased fitness and some loss in species diversity, with the effects being more 

6 severe near the 20 ȝeq/L ANC threshold. ANC values above 100 ȝeq/L are generally not 

7 considered because many waterbodies have ANC values naturally below that point and biota are 

8 not often harmed (see Figure 4.1-2). Thus, an ANC above 50 ȝeq/L gives moderate protection 

9 from species loss and fitness decline of aquatic organisms (Table 4.1-1). Below 20 ȝeq/L, all 

10 communities exhibit some negative effects (see Figure 4.1-2, Table 4.1-1), particularly because 

11 surface waters become susceptible to episodic acidification and associated harmful effects 

12 (where ANC goes below zero). An ANC of 0 ȝeq/L protects surface waters from becoming 

13 acidic, but overall, offers little to no protection of the biota (see Figure 4.1-2, Table 4.1-1). 

14 The percent of waterbodies receiving current nitrogen and sulfur deposition loads above 

15 harmful levels were determined by subtracting the current deposition from each of the three 

16 calculated critical loads of ANC (0 ȝeq/L, 20 ȝeq/L, 50 ȝeq/L). Waterbodies with positive values 

17 (i.e., deposition – critical load) are protected, while negative values (i.e., deposition of nitrogen 

18 and sulfur exceed the critical load) are assumed to be adversely harmful to the biological 

19 community. Also, by repeating the assessment with three different threshold levels (above 0 

20 ȝeq/L, above 20 ȝeq/L, and above 50 ȝeq/L), uncertainty can be accounted for in the level of 

21 protection of acidification. For both ANC levels of above 20 ȝeq/L and above 50 ȝeq/L, the 

22 number of waterbodies that maintain ANC conditions above these levels were determined using 

23 the same streams and lakes used to calculate susceptibility. 

24 The Steady-State Water Chemistry Model (SSWC) was used to estimate critical load for 

25 each biological protective level. In order to assess current conditions for each waterbody, the 

26 calculated critical load for the three biological protection levels were compared to 2002 total wet 

27 and dry acidic deposition to determine which sites exceed their biological protection level. 

28 Estimates of wet and dry deposition for 2002 were based on measured values from the National 

29 Atmospheric Deposition Program (NADP) network combined with modeled values based on the 

30 Community Multiscale Air Quality (CMAQ) model, respectively. See Section 54.4 for more 

31 details.
Aquatic Acidification Case Study 

DRAFT Attachment 3, pg 18 August 2008 

1 4.2 MODELING APPROACH 

2 4.2.1 MAGIC 

3 MAGIC is a lumped-parameter model of intermediate complexity, developed to predict 

4 the long-term effects of acidic deposition on surface water chemistry (Cosby et al., 1985a, b). 

5 The model simulates soil solution chemistry and surface water chemistry to predict the monthly 

6 and annual average concentrations of the major ions in these waters. MAGIC consists of (1) a 2–

7 10 submodel in which the concentrations of major ions are assumed to be governed by 

8 simultaneous reactions involving SO42- adsorption, cation exchange, dissolution-precipitation9 speciation of Al, and dissolution-speciation of inorganic carbon; and (2) a mass balance 

10 submodel in which the flux of major ions to and from the soil is assumed to be controlled by 

11 atmospheric inputs, chemical weathering, net uptake and loss in biomass, and losses to runoff. At 

12 the heart of MAGIC is the size of the pool of exchangeable base cations in the soil. As the fluxes 

13 to and from this pool change over time owing to changes in atmospheric deposition, the chemical 

14 equilibria between soil and soil solution shift to give changes in surface water chemistry. Thus, 

15 the degree and rate of change of surface water acidity depend both on flux factors and the 

16 inherent characteristics of the affected soils. 

17 Cation exchange is modeled using equilibrium (Gaines-Thomas) equations with 

18 selectivity coefficients for each base cation and Al. Sulfate adsorption is represented by a 

19 Langmuir isotherm. Aluminum dissolution and precipitation are assumed to be controlled by 

20 equilibrium with a solid phase of aluminum hydroxide (Al(OH)3). Aluminum speciation is 

calculated by considering hydrolysis reactions, as well as complexation with SO4

2- 21 and fluoride 

(F22 ). The effects of CO2 on pH and on the speciation of inorganic carbon are computed from 

23 equilibrium equations. Organic acids are represented in the model as tri-protic analogues. 

24 Weathering and the uptake rate of nitrogen are assumed to be constant. A set of mass balance 

25 equations for base cations and strong acid anions are included. 

26 Given a description of the historical deposition at a site, the model equations are solved 

27 numerically to give long-term reconstructions of surface water chemistry (for complete details of 

28 the model, see Cosby et al., 1985 a, b; 1989). MAGIC was used to reconstruct the history of 

29 acidification and to simulate the future trends on a regional basis and in a large number of 

30 individual catchments in both North America and Europe (e.g., Lepisto et al., 1988; Whitehead 
Aquatic Acidification Case Study 

DRAFT Attachment 3, pg 19 August 2008 

1 et al., 1988; Cosby et al., 1989, 1990, 1996; Hornberger et al., 1989; Jenkins et al., 1990a–c; 

2 Wright et al., 1990, 1994; Norton et al., 1992; Sullivan and Cosby, 1998; Sullivan et al., 2004). 

3 The input data required in this project for aquatic and soils resource modeling with the

4 MAGIC model (i.e., stream water, catchment, soils, and deposition data) were assembled and 

5 maintained in databases for each site modeled (electronic spreadsheets and text-based MAGIC 

6 parameter files). Model outputs for each site were archived as text-based time-series files of 

7 simulated variable values. The outputs were also concatenated across all sites and maintained in 

8 electronic spreadsheets. 

9 4.2.1.1 Input Data and Calibration 

10 The calibration procedure requires that streamwater chemistry, soil chemical and physical 

11 characteristics, and atmospheric deposition data be available for each watershed. The water 

chemistry data needed for calibration are the concentrations of the individual base cations (Ca2+ 12 ,

Mg2+, Na+

, and K+) and acid anions (Cl-, SO4

2-, NO3- 13 ) and the stream pH. The soil data used in the 

14 model include soil depth and bulk density, soil pH, soil cation-exchange capacity, and 

exchangeable bases on the soil (Ca2+, Mg2+, Na+

, and K+ 15 ). The atmospheric deposition inputs to the 

16 model include all major ions and must be estimates of total deposition, not just wet deposition. 

17 The acid-base chemistry modeling for this project was conducted using 2002 as the Base 

18 Year. The effects models were calibrated to the available atmospheric deposition and water 

19 chemistry data and then interpolated or extrapolated to yield Base Year estimates of lake water 

20 chemistry in the year 2002, which served as the starting point for modeling of current water 

21 chemistry (i.e., 2002 to 2100, etc.). 

22 4.2.1.2 Lake and Stream, and Soil data for Calibration 

23 Several water chemistry databases were acquired for use in model calibration. Data were 

24 derived primarily from the EMAP and TIME survey and monitoring efforts. The required lake 

25 water and soil composition data for the modeling efforts included the following measurements: 

 Stream water composition— pH, ANC, Ca2+, Mg2+, K+

, Na+, SO4

2-, NO3-

, and Cl26

 Soil properties— thickness and total cation exchange capacity, exchangeable bases (Ca2+ 27 ,

Mg2+, Na+, and K+28 ) bulk density, porosity, and pH where available; the stream water 
Aquatic Acidification Case Study 

DRAFT Attachment 3, pg 20 August 2008 

1 chemistry database also included dissolved organic and inorganic carbon, H4SiO4, and 

2 inorganic monomeric Al (Ali). 

3 4.2.1.3 Wet Deposition and Meteorology Data for Calibration 

4 MAGIC requires, as atmospheric inputs for each site, estimates of the total annual 

5 deposition (eq/ha/yr) of eight ions, and the annual precipitation volume (m/yr). The eight ions 

6 are: Ca, Mg, Na, K, NH4, SO4, Cl, and NO3. Total deposition of an ion at a particular site for any 

7 year can be represented as combined wet, dry, and occult (cloud and fog) deposition:

8 TotDep = WetDep + DryDep + OccDep (2) 

9 Inputs to the MAGIC model are specified as wet deposition (the annual flux in 

10 meq/m2/yr) and a dry and occult deposition factor (DDF, unitless), which is multiplied by the 

11 wet deposition in order to get total deposition: 

12 TotDep = WetDep * DDF (3) 

13 Given an annual wet deposition flux (WetDep), the ratio of dry deposition to wet 

14 deposition (DryDep/WetDep), and the ratio of occult deposition to wet deposition 

15 (OccDep/WetDep) for a given year at a site, the total deposition for that site and year is uniquely 

16 determined. 

17 In order to calibrate MAGIC, time-series of total deposition are needed for the calibration 

18 year of 2002 and the 140 years preceding the calibration for the historical reconstructions that are 

19 part of the calibration protocol. The procedure for providing a time-series of total deposition 

20 inputs to MAGIC is as follows. 

21 The absolute values of wet deposition and DDF for each ion are provided for a Reference 

22 Year at each site. For this case study, the MAGIC Reference Year was 2002 at all sites. Given 

23 the Reference Year deposition values, the deposition data for the historical and calibration 

24 periods, and potentially any future deposition scenarios, can be calculated using the Reference 

25 Year absolute values and scaled time-series of wet deposition and DDF that give the values for a 

26 given year as a fraction of the Reference Year value. For instance, to calculate the total 

27 deposition of a particular ion in some historical or future year j: 

28 TotDep(j) = [WetDep(0) * WetDepScale(j) ] * [ DDF(0) * DDF Scale(j)] (4) 
Aquatic Acidification Case Study 

DRAFT Attachment 3, pg 21 August 2008 

1 where

2 WetDep(0) = the Reference Year wet deposition (meq/m2/yr) of the ion 

3 WetDepScale(j) = the scaled value of wet deposition in year j (expressed as a fraction of the 

4 wet deposition in the Reference Year) 

5 DDF(0) = the dry and occult deposition factor for the ion for the Reference Year

6 DDFScale(j) = the scaled value of the dry and occult deposition factor in year j (expressed 

7 as a fraction of the DDF in the Reference Year 2002). 

8 The absolute value of wet deposition used for the Reference Year is time and space 

9 specific— varying geographically within the region, varying locally with elevation, and varying 

10 from year to year. It is desirable to have the estimates of wet deposition take into account the 

11 geographic location and elevation of the site, as well as the year for which calibration data are 

12 available. Therefore, estimates of wet deposition used for the Reference Year should be derived 

13 from a procedure (model) that has a high spatial resolution and considers elevation effects. As 

14 described below, the absolute wet deposition values used for the Reference Year in this project 

15 were derived from observed data based on the NADP. 

16 The absolute value of the DDF used for the Reference Year specifies the ratio between 

17 the absolute amounts of wet and total deposition. This ratio is less variable in space and time 

18 than is the estimate of wet deposition. That is, if in a given year, the wet deposition goes up, then 

19 the total deposition usually goes up also (and conversely); and if the elevation or aspect of a 

20 given site results in lower wet deposition, the total deposition also will often be lower (and 

21 conversely). Therefore, estimates of the absolute vales of DDF may be derived from a procedure 

22 (model) that has a relatively low spatial resolution and/or temporally smoothes the data. 

23 Estimates of the absolute values of the DDF for the Reference Year at each site in this project 

24 were derived from the Advanced Statistical Trajectory Regional Air Pollution (ASTRAP) model 

25 (Shannon, 1998), as described below.

26 The long-term scaled sequences used to specify time-series of deposition inputs for 

27 MAGIC simulations usually do not require detailed spatial or temporal resolution. Scaled 

28 sequences of wet deposition or DDF (normalized to the same reference year) at neighboring sites 

29 will be similar, even if the absolute wet deposition or DDF at the sites are different due to local 

30 aspect, elevation, etc. Therefore, if the scaled long-term patterns of any of these do not vary 

31 much from place to place, estimates of the scaled sequences (as for estimates of absolute DDF 
Aquatic Acidification Case Study 

DRAFT Attachment 3, pg 22 August 2008 

1 values) may be derived from a model that has a relatively low spatial resolution. As described in 

2 the following sections, output from the ASTRAP model was used to constrict scaled sequences 

3 of both wet deposition and DDF for this project. 

4 4.2.1.4 Wet Deposition Data (Reference Year and Calibration Values) 

5 The absolute values of wet deposition used for defining the Reference Year and for the 

6 MAGIC calibrations must be highly site-specific. We used estimated wet deposition data for 

7 each site derived from the spatial interpolation model of Grimm and Lynch (2004), referred to 

8 here as the Grimm model. The Grimm model is based on observed wet deposition at NADP 

9 monitoring stations and provides a spatially interpolated value of wet deposition of each of the 

10 eight ions needed for MAGIC. The model also makes a correction for changes in precipitation 

11 volume (and thus wet deposition) based on the elevation at a given site. This correction arises 

12 from a model of orographic effects on precipitation volumes derived from regional 

13 climatological data. 

14 The latitude, longitude, and elevation of the 100+ MAGIC modeling sites were provided 

15 as inputs to the Grimm model. The model outputs were quarterly and annual wet deposition and 

16 precipitation estimates for each modeling site. The annual data were used to define the Reference 

17 Year and for MAGIC calibration and simulation. The NADP data (and thus the estimates 

18 provided by Grimm’s model) cover the period 1983 to 2002. This period includes the MAGIC 

19 Reference Year and the calibration years for all of the modeling sites in this project. 

20 4.2.1.5 Dry and Occult Deposition Data and Historical Deposition Sequences 

21 Absolute values of DDF and the scaled sequences of wet deposition and DDF are derived 

22 for this project from simulations using the ASTRAP model. The ASTRAP model was used to 

23 provide estimates of historical wet, dry, and occult deposition of sulfur and oxidized nitrogen at 

24 modeled sites of the two case studies regions. The ASTRAP sites included 10 existing NADP 

25 deposition. For each of the modeled sites, ASTRAP produced wet, dry, and occult deposition 

26 estimates of sulfur and oxidized nitrogen every 10 years, starting in 1900 and ending in 1990. 

27 The model outputs are smoothed estimates of deposition roughly equivalent to a 10-year moving 

28 average centered on each of the output years. The outputs of ASTRAP were used to estimate the 

29 absolute DDF for each site (using the DryDep/WetDep and OccDep/WetDep ratios from the 
Aquatic Acidification Case Study 

DRAFT Attachment 3, pg 23 August 2008 

1 ASTRAP 19 output) and to set up the scaled sequences of historical wet deposition and historical 

2 DDF for the calibration of each site modeled in this project. 

3 The wet, dry, and occult deposition estimates provided by ASTRAP for each year (for 

4 both sulfur and oxidized nitrogen) at each ASTRAP site were used to calculate the MAGIC DDF 

5 for each year and each site. This provided time series of DDF for sulfur and oxidized nitrogen for 

6 each ASTRAP site extending from 1850 to 1990. The value of DDF for 1990 was used as the 

7 absolute value of DDF for the Reference Year (i.e., no change was assumed for DDF from 1990 

8 to 2002). The resulting time series of DDF values from 1900 to 2002 for each ASTRAP site were 

9 normalized to the 2002 values to provide historical scaled sequences of DDF at each ASTRAP 

10 site.

11 The time series of wet deposition estimates for each ASTRAP site were used to construct 

12 historical scaled sequences of wet deposition. The absolute wet deposition outputs of ASTRAP 

13 were normalized to their 1990 values and converted to scaled sequences of wet deposition from 

14 1850 to 1990 for each ASTRAP site. It was then necessary to couple these historical scaled wet 

15 deposition sequences from 1990 to the MAGIC Reference Year 2002. This coupling was 

16 accomplished using scaled observed changes in wet deposition from 1850 to 2002 derived from 

17 the Grimm model. 

18 4.2.1.6 Protocol for MAGIC Calibration and Simulation at Individual Sites 

19 The aggregated nature of the MAGIC model requires that it be calibrated to observed 

20 data from a system before it can be used to examine potential system response. Calibration is 

21 achieved by setting the values of certain parameters within the model that can be directly 

22 measured or observed in the system of interest (called fixed parameters). The model is then run 

23 (using observed and/or assumed atmospheric and hydrologic inputs), and the outputs

24 (streamwater and soil chemical variables called criterion variables) are compared to observed 

25 values of these variables. If the observed and simulated values differ, the values of another set of 

26 parameters in the model (called optimized parameters) are adjusted to improve the fit. After a 

27 number of iterations adjusting the optimized parameters, the simulated-minus-observed values of 

28 the criterion variables usually converge to zero (within some specified tolerance). The model is 

29 then considered calibrated. 
Aquatic Acidification Case Study 

DRAFT Attachment 3, pg 24 August 2008 

1 There are eight parameters to be optimized in this procedure (the weathering and the 

2 selectivity coefficient of each of the four base cations), and there are eight observations that are 

3 used to drive the estimate (i.e., current soil exchangeable pool size and current output flux of 

4 each of the four base cations). If new assumptions or new values for any of the fixed variables or 

5 inputs to the model are adopted, the model must be re-calibrated by re adjusting the optimized 

6 parameters until the simulated-minus-observed values of the criterion variables again fall within 

7 the specified tolerance. 

8 Estimates of the fixed parameters, the deposition inputs, and the target variable values to 

9 which the model is calibrated all contain uncertainties. A “fuzzy optimization” procedure was 

10 utilized in this project to provide explicit estimates of the effects of these uncertainties. The 

11 procedure consists of multiple calibrations at each site using random values of the fixed 

12 parameters drawn from a range of fixed parameter values (representing uncertainty in knowledge 

13 of these parameters) and random values of Reference Year deposition drawn from a range of

14 total deposition estimates (representing uncertainty in these inputs). The final convergence 

15 (completion) of the calibration is determined when the simulated values of the criterion variables 

16 are within a specified “acceptable window” around the nominal observed value. This “acceptable 

17 window” represents uncertainty in the target variable values being used to calibrate the site. 

18 Each of the multiple calibrations at a site begins with (1) a random selection of values of 

19 fixed parameters and deposition, and (2) a random selection of the starting values of the 

20 adjustable parameters. The adjustable parameters are then optimized using an algorithm seeking 

21 to minimize errors between simulated and observed criterion variable. Calibration success is 

22 judged when all criterion values simultaneously are within their specified “acceptable windows”, 

23 21 (which may occur before the absolute possible minimum error is achieved). This procedure is 

24 repeated ten times for each site. 

25 For this project, the “acceptable windows” for base cation concentrations in streams were 

26 taken as +/- 2 ȝeq/L around the observed values. “Acceptable windows” for soil exchangeable 

27 base cations were taken as +/- 0.2% around the observed values. Fixed parameter uncertainty in 

28 soil depth, bulk density, cation exchange capacity, stream discharge, and stream area were 

29 assumed to be +/- 10% of the estimated values. Uncertainty in total deposition was +/- 10% for 

30 all ions. 
Aquatic Acidification Case Study 

DRAFT Attachment 3, pg 25 August 2008 

1 The final calibrated model at the site is represented by the ensemble of parameter values 

2 of all of the successful calibrations at the site. When performing simulations at a site, all of the 

3 calibrated parameter sets in the ensemble are run for a given historical or future scenario. The 

4 result is multiple simulated values of each variable in each year, all of which are acceptable in 

5 the sense of the calibration constraints applied in the fuzzy optimization procedure. The median 

6 of all the simulated values within a year is the “most likely” response for the site in that year. For 

7 this project, whenever single values for a site are presented or used in an analysis, these values 

8 are the median values derived from running all of the ensemble parameter sets for the site. 

9 An estimate of the uncertainty (or reliability) of a simulated response to a given scenario 

10 can also be derived from the multiple simulated values within a year resulting from the ensemble 

11 simulations. For any year in a given scenario, the largest and smallest values of a simulated 

12 variable define the upper and lower confidence bounds for that site’s response for the scenario 

13 under consideration. Thus, for all variables and all years of the scenario, a band of simulated 

14 values can be produced from the ensemble simulations at a site that encompasses the likely 

15 response (and provides an estimate of the simulation uncertainty) for any point in the scenario. 

16 For this project, whenever uncertainty estimates are presented, the estimate is based on this range 

17 of simulated values in any year arising from the simulations using the ensemble parameter sets. 

18 Three classes of uncertainty were examined for both case study areas that include uncertainty 

19 due to specification of

20  Soils Data for calibration 

21  Stream Water Data calibration 

22  Deposition Data calibration.

23 4.2.1.7 Combined Model Calibration and Simulation Uncertainty 

24 The sensitivity analyses described above were designed to address specific assumptions 

25 or decisions that had to be made in order to assemble the data for the 66 modeled sites in a form 

26 that could be used for calibration of the model. In all cases, the above analyses address the 

27 questions of what the effect would have been if alternate available choices had been taken. These 

28 analyses were undertaken for a subset of sites for which the alternate choices were available at 

29 the same sites. As such, the analyses above are informative, but they provide no direct 

30 information about the uncertainty in calibration or simulation arising from the choices that were 
Aquatic Acidification Case Study 

DRAFT Attachment 3, pg 26 August 2008 

1 31 incorporated into the final modeling protocol for all sites. That is, having made the choices 

2 about soils assignments, high elevation deposition, and stream samples for calibration (and 

3 provided an estimate of their inherent uncertainties), the need arises for a procedure for 

4 estimating uncertainty at each and all of the individual sites using the final selected calibration 

5 and simulation protocol. 

6 These simulation uncertainty estimates were derived from the multiple calibrations at 

7 each site provided by the “fuzzy optimization” procedure employed in this project. For each of 

8 the modeled sites, 10 distinct calibrations were performed with the target values, parameter 

9 values, and deposition inputs for each calibration, reflecting the uncertainty inherent in the 

10 observed data for the individual site. The effects of the uncertainty in the assumptions made in 

11 calibrating the model (and the inherent uncertainties in the data available) can be assessed by 

12 using all successful calibrations for a site when simulating the response to different scenarios of 

13 future deposition. The model then produces an ensemble of simulated values for each site. The 

14 median of all simulated values in a year is considered the most likely response of the site. The 

15 simulated values in the ensemble can also be used to estimate the magnitude of the uncertainty in 

16 the projection. Specifically, the difference in any year between the maximum and minimum 

17 simulated values from the ensemble of calibrated parameter sets can be used to define an 

18 “uncertainty” (or “confidence”) width for the simulation at any point in time. All ten of the 

19 successful model calibrations will lie within this range of values. These uncertainty widths can 

20 be produced for any variable and any year to monitor model performance. 

21 4.3 CRITICAL LOADS 

22 4.3.1 The Steady-State Water Chemistry Model 

23 The critical load of acidity for lakes or streams was derived from present-day water 

24 chemistry using the SSWC model. The SSWC model is based on the principle that excess base 

25 cation production within a catchment area should be equal to or greater than the acid anion input, 

26 thereby maintaining the ANC above a pre-selected level (Reynolds and Norris, 2001). This 

model assumes steady-state conditions and assumes that all SO4

2– 27 in runoff originates from sea 

28 salt spray and anthropogenic deposition. Given a critical ANC protection level, the critical load 

29 of acidity is simply the input flux of acid anions from atmospheric deposition (i.e., natural and 
Aquatic Acidification Case Study 

DRAFT Attachment 3, pg 27 August 2008 

1 anthropogenic) subtracted from the natural (i.e., pre-industrial) inputs of base cations in the 

2 surface water.

3 Critical loads of acidity, CL(A), were calculated for each waterbodies from the principle 

4 that the acid load should not exceed the non-marine, non-anthropogenic base cation input and 

5 sources and sinks in the catchment minus a buffer to protect selected biota from being damaged: 

 CL(A) = BC*

6 dep + BCw – Bcu – ANClimit (5) 

7 where

 BC*

8 dep (BC*=Ca*+Mg*+K*+Na*) = the sea-salt corrected (see section XX) non9 anthropogenic deposition of base cations, 

10 BCw = the average weathering flux, 

11 Bcu (Bc=Ca*+Mg*+K*) = the net long-term average uptake of base cations in the 

12 biomass (i.e., the annual average removal of base 

13 cations due to harvesting) 

 ANClimit 14 = the lowest ANC-flux that protects the biological 

15 communities. 

16 Since the average flux of base cations weathered in a catchment and reaching the lake is 

17 difficult to measure or compute from available information, the average flux of base cations and 

18 the resulting critical load estimation were derived from water quality data (Sverdrup et al., 1990; 

19 Henriksen et al., 1992; Henriksen and Posch, 2001). Weighted annual mean water chemistry 

20 values were used to estimate average base cation fluxes, which were calculated from water 

21 chemistry data collected from the LTM/TIME monitoring networks (see Section 4.1.2.1).

22 The pre-acidification non-marine flux of base cations for each lake or stream, BC*0, is 

 BC*

o = BC*

23 dep + BCw - Bcu (6) 

24 Thus, critical load for acidity can be re-written as 

 CL(A) = BC*

0 – ANClimit = Q.

([BC*25 ]0 – [ANC]limit) (7) 

26 where the second identity expresses the critical load for acidity in terms of catchment 

27 runoff Q (in m/yr) and concentration ([x] = X/Q). 
Aquatic Acidification Case Study 

DRAFT Attachment 3, pg 28 August 2008 

1 4.3.2 Pre-industrial Base Cation Concentration 

2 Present-day surface water concentrations of base cations are elevated above their steady3 state concentrations because of base cation leaching through ion exchange in the soil due to 

anthropogenic inputs of SO4

-

4 to the watershed. For this reason, present-day surface water base 

5 cation concentrations are higher then natural or pre-industrial levels, which if not corrected for, 

6 would result in critical load values not to be in steady-state condition. To estimate the preacidification flux of base cations, we started by calculating the present flux of base cations, BC*

7 t,

8 given by

 BC*

t = BC*

9 dep + BCw – Bcu +BCexc (8) 

10 where

11 BCexc = the release of base cations due to ion-exchange processes.

12 Assuming that deposition, weathering rate, and net uptake have not changed over time, 

13 BCexc we obtained by subtracting Equation 2 from equation 4: 

 BCexc = BC*

t

 – BC*14 0 (9) 

15 This present-day excess production of base cations in the catchment was related to the 

long-term changes in inputs of non-marine acid anions ( ̈SO*

16 2 +  ̈NO3) by the F-factor (see 

17 below):

 BCexc = F .

 ( ̈SO*18 2 +  ̈NO3) (10) 

19 For the pre-acidification base cation flux, we thus get from Equation (5): 

 BC*

0 = BC*

t

 – F . (SO*

4,t - SO*4,0 + NO*3,t - NO*

20 3,0) (11) 

The pre-acidification nitrate concentration, NO*

21 3,0, is was assumed to be zero. Finally, the 

22 present exceedances of the critical load of acidity is defined as

 Ex(A) = S*

23 dep + Nleach – CL(A) (12) 

While sulfate is assumed to be a mobile anion (Sleach = S*

24 dep), nitrogen is to a large extent 

25 retained in the catmint by various processes; therefore, Ndep can not be used directly in the 

26 exceedances calculation. Therefore, only present-day exceedances can be calculated from the 

27 leaching of N, Nleach, which is determined from the sum of measured concentration of nitrate and 

28 ammonia in the stream chemistry. No nitrogen-deposition data are required for exceedances 
Aquatic Acidification Case Study 

DRAFT Attachment 3, pg 29 August 2008 

1 calculations; however, Ex(A) quantifies only the exceedances at present rates of retention of 

2 nitrogen in the catchment. 

3 4.3.3 F-factor 

4 An F-factor was used to correct the concentrations and estimate pre-industrial base 

5 concentrations. An F-factor is a ratio of the change in non-marine base cation concentration due 

6 to changes in strong anion concentrations (Henriksen, 1984; Brakke et al., 1990): 

 F = [BC*

]t - [BC*]0 / [SO4

*

]t- [SO4

*

]0 + [NO3*]t- [NO3*7 ]t (13) 

8 where the subscripts t and 0 refer to present and pre-acidification concentrations, 

9 respectively. If F=1, all incoming protons are neutralized in the catchment (only soil 

10 acidification); at F=0, none of the incoming protons are neutralized in the catchment (only water 

11 acidification). The F-factor was estimated empirically to be in the range 0.2–0.4, based on the

12 analysis of historical data from Norway, Sweden, the United States, and Canada (Henriksen, 

13 1984). Brakke and colleagues (1990) later suggested that the F-factor should be a function of the 

14 base cation concentration: 

 F = sin (pie/2 Q.

[BC*15 ]t/[S] (14) 

16 where

17 Q = the annual runoff (m/yr) 

18 [S] = the base cation concentration at which F=1; and for [BC*]t>[S] F is set to 1.

19 For Norway [S] has been set to 400 meq/m3 (ca. 8 mgCa/L) (Brakke et al., 1990). 

The pre-acidification sulphate concentration in lakes, [SO4*

20 ]0, is assumed to consist of a 

21 constant atmospheric contribution and a geologic contribution proportional to the concentration 

22 of base cations (Brakke et al., 1989). A XX pre-acidification sulfate concentration was used.

23 4.3.4 ANC Limit 

24 Four classes of ANC limited were estimated: Suitable, ANC > 50 ȝeq/L; Indeterminate, 

25 ANC 20–50 ȝeq/L; Marginal, ANC 0–20 ȝeq/L; and Unsuitable, ANC < 0 ȝeq/L.
Aquatic Acidification Case Study 

DRAFT Attachment 3, pg 30 August 2008 

1 4.3.5 Sea Salt Corrections 

2 The model applies a sea salt correction to the water chemistry concentrations. The 

3 equations below were applied to all lakes and streams, which was also applied to all the New 

4 England states and eastern Canadian provinces for the New England Governors and Eastern 

5 Canadian Premier (NEGECP) assessment. The equations correct for sea salt. An asterisk (*) 

6 indicates the value has been corrected for sea salt, Units are in ueq/L. 

 Ca*

 = (Ca – (CL .7 0.0213)) (10) 

 Mg*

 = (Mg – (CL .8 0.0669)) (11) 

 Na* = (Na – (CL .9 0.557)) (12) 

 K*

 = (K – (CL .10 0.0.0206)) (13) 

 SO4

*

 = (SO4 – (CL .11 0.14)) (14) 

12 5. RESULTS (NOT COMPLETE) 

13 5.1 ADIRONDACK REGION OF NEW YORK

14 5.1.1 Surface Water Trends from 1990-2006 

15 Since the mid-1990s, lakes in the Adirondack region have shown signs of recovery from 

16 nitrogen and sulfur deposition and acid rain. Emissions of SO2 and NOx have been reduced 

17 (Figure 3.1-1), and, as a result, sulfate and nitrate concentrations have decreased in surface 

18 waters by approximately 26% and 13%, respectively. This has led to improvement in the acid 

19 ANC of these waterbodies, which helps to neutralize or buffer the acidic deposition (Figure 5.1-

20 1).
Aquatic Acidification Case Study 

DRAFT Attachment 3, pg 31 August 2008 

Figure 5.1-1. Trends in LTM monitored lakes in the Adirondacks of 

New York. Both sulfate and nitrate concentrations have decreased in 

surface waters by approximately 26% and 13%, respectively. This has 

led to improvement in the ANC of these waterbodies. 
Aquatic Acidification Case Study 

DRAFT Attachment 3, pg 32 August 2008 

1 5.1.2 Condition of Surface Water Acidity 

2 5.1.2.1 Modeled ANC Conditions 

0

10

20

30

40

50

60

70

80

90

100

Historic Observed MAGIC MAGIC

Percent

Acute (< 0 ȝeq/L) Severe (0-20 ȝeq/L)

 Elevated (20-50 ȝeq/L) Moderate (50-100 ȝeq/L)

Low (>100 ȝeq/L)

(2002) (2002) (2010)

Figure 5.1-2. Predicted distribution of surface water ANC concentrations of 44 

lakes across five assessment ANC categories based on results from MAGIC 

modeling (Historic and MAGIC 2002, 2010) and TIME/LTM monitoring data 

(Observed, 2002) for the Adirondacks Case Study Area. Individual bar graphs 

represent the percentage of the 44 studied lakes that fall into the five ANC 

categories. Categories of ANC include: Acute = <0 ȝeq/L, Severe = 0–20 ȝeq/L,

Elevated = 20–50 ȝeq/L; Moderate = 50–100 ȝeq/L, Low = >100 ȝeq/L.

Historical conditions represent the surface water ANC concentrations modeled by 

MAGIC before anthropogenic acidic deposition occurred (i.e., before 1860). 

Current condition is assessed as year 2002. Despite improvement in surface water 

ANC concentrations (Figure 5.1-1), both observed and modeled results show a 

higher percent of lakes that have acute and severe acidic conditions compared to 

their historical conditions.
Aquatic Acidification Case Study 

DRAFT Attachment 3, pg 33 August 2008 

1 5.1.2.2 Critical Load 

Figure 5.1-3. Critical loads of surface water acidity for an ANC 

concentrations of 50 ȝeq/L. Each dot represents an estimated 

amount of acidic deposition (i.e., critical load) that each lake’s 

watershed can receive and still maintain an surface water ANC 

concentration of above 50 ȝeq/L. Watersheds with critical load 

values less than 100 meq/m2

/yr (red and orange dots) are most 

sensitive to surface water acidification while watersheds with 

values greater then 100 meq/m2

/yr (yellow and green dots) are 

the least sensitive sites.
Aquatic Acidification Case Study 

DRAFT Attachment 3, pg 34 August 2008 

Figure 5.1-4. Critical load exceedances for ANC concentration of 

50 ȝeq/L. Green dots represent lakes where current nitrogen and 

sulfur deposition is above their critical load and maintain an ANC 

concentration above 50 ȝeq/L. Red dots are lakes where current 

nitrogen and sulfur deposition exceeds their limit and are affected 

by current nitrogen and sulfur deposition load. An ANC limit of 50 

ȝeq/L gives moderate protection from species loss and fitness 

decline of aquatic organisms (Table 4.1-1). 

Table 5.1-1. Number and percentage of lakes that have current nitrogen and sulfur deposition 

loads that prevent surface water ANC concentration to be below an ANC of 50, and 20 ug/L for 

lakes in the Adirondack Mountains. 

 50 ug/L 20 ug/L 

LTM (N=60) 37 (62%) 24 (40%) 

TIME (N=117) 25 (21%) 7 (6%) 

MAGIC (N=44) 21 (48%) 7 (16%) 
Aquatic Acidification Case Study 

DRAFT Attachment 3, pg 35 August 2008 

1 5.1.2.3 Uncertainty and Risk 

2

3 5.2 SHENANDOAH NATIONAL PARK AND SURROUNDING AREAS,

4 VIRGINIA 

5 5.2.1 Surface Water Trends from 1990–2006 

Figure 5.1-5. Trend in SO4, NO3, and ANC from LTM 

network.

6 5.2.2 Condition of Surface Water Acidity 

7 5.2.2.1 Modeled ANC Conditions 

8

9 5.2.2.2 Critical Load 

10
Aquatic Acidification Case Study 

DRAFT Attachment 3, pg 36 August 2008 

1 5.2.2.3 Uncertainty and Risk 

2

3

4 6. REFERENCES 

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13 Finland. Nordic Hydrology 19:99–120.

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21 Congress: An Integrated Assessment. Silver Spring, MD: National Science and 

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25 Maine, USA. Environmental Pollution 77:279–286.
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16 Academic Press. 

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20 Southern Appalachian Mountains Initiative, April. 

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1 Webb, J.R., B.J. Cosby, F.A. Deviney, J.N. Galloway, S.W. Maben, and A.J. Bulger. 2004. Are 

2 brook trout streams in western Virginia and Shenandoah National Park recovering from 

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6 American Fisheries Society; pp. 178–196. 

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8 acidification trends in the Welsh Uplands: A modelling study of the Llyn Brianne 

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13 Sogndal, Norway: results after 8 years. Water, Air, and Soil Pollution 72:297–315.
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10 EPA Contract Number EP-D-06-003 

11 Work Assignment 2-44 

12 Project Number 0209897.002.044 

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22 Prepared for 

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24 U.S. Environmental Protection Agency 

25 Office of Air Quality Planning and Standards 

26 Research Triangle Park, NC 27709 

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32 RTI International 

33 3040 Cornwallis Road

34 Research Triangle Park, NC 27709-2194 

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1 CONTENTS 

2 1. Background............................................................................................................................... v 

3 1.1 Indicators, Endpoints, and Ecosystem Services............................................................... 2 

4 1.1.1 Indicators.............................................................................................................. 2

5 1.1.2 Endpoints ............................................................................................................. 9 

6 1.1.3 Ecosystem Services............................................................................................ 11 

7 1.2 Case Studies................................................................................................................... 11 

8 1.2.1 Sugar Maple....................................................................................................... 18 

9 1.2.2 Red Spruce......................................................................................................... 21 

10 2. Approach and Methods........................................................................................................... 28 

11 2.1 Chosen Method .............................................................................................................. 31

12 2.1.1 Critical Load Analysis Formulation................................................................... 33 

13 2.1.2 Data Requirements............................................................................................. 39 

14 2.1.3 Data Issues and Assumptions with Method....................................................... 41 

15 2.2 Critical Load Assessment Response Curve ................................................................... 42 

16 3. Results..................................................................................................................................... 45 

17 3.1 Current State of Systems................................................................................................ 45 

18 3.1.1 Sugar Maple....................................................................................................... 46 

19 3.1.2 Red Spruce......................................................................................................... 46 

20 3.2 Future Case Study Assessments..................................................................................... 49 

21 4. Implications for Other Systems .............................................................................................. 50

22 5. Uncertainty.............................................................................................................................. 51 

23 6. Conclusions............................................................................................................................. 53 

24 7. References............................................................................................................................... 53 

25

26 FIGURES

27 Figure 1.1-1 Conceptual impacts of acid deposition on ecosystem Ca health and 

28 sustainability ........................................................................................................... 8 

29 Figure 1.1-2 Areal coverages of red spruce and sugar maple tree species within the 

30 continental United States. ..................................................................................... 10 

31 Figure 1.2-1 Kane Experimental Forest..................................................................................... 18 

32 Figure 1.2-2 Hubbard Brook Experimental Forest.................................................................... 21 

33 Figure 1.2-3 Hubbard Brook Vegetation and Experimental Watershed Number 6. ................. 26 

34 Figure 1.2-4 Grid unit representation of Experimental Watershed Number 6. ......................... 27 

35 Figure 2.1-1 A diagram of the SMB method to calculate critical loads for acidity 

36 showing data derivations by model component.................................................... 35 
Terrestrial Acidification Case Study 

DRAFT Attachment 4, pg iv August 2008 

1 Figure 2.1-2 Illustration of the Critical Load Function (CLF) created from the 

2 calculated maximum and minimum levels of nitrogen and sulfur 

deposition (eq ha-1 yr-1 3 )......................................................................................... 37 

4 Figure 2.2-1 An example of the response curves that relate deposition scenarios to 

5 critical loads calculated based on varying levels of expected risk in tree 

6 mortality................................................................................................................ 45 

7 Figure 3.1-1 Average annual atmospheric deposition of sulfur and nitrogen (particle + 

8 SO2 + precipitation + cloud water) to New Hampshire and Vermont 

9 (1999–2003).......................................................................................................... 48 

10 Figure 3.1-2 Critical loads of sulfur and nitrogen for upland forest areas of New 

11 Hampshire and Vermont....................................................................................... 49 

12 TABLES

13 Table 1.1-1 Key Indicators of Acidification Due to NOx and SOx ............................................ 2 

14 Table 1.1-2 Literature Support for Selected Indicators of Acidification ................................... 3 

15 Table 1.1-3 Summary of Linkages between Acid Deposition, Biogeochemical 

16 Processes that Affect Ca, Physiological Processes that are Influenced by 

17 Ca, and Effect on Forest Function .......................................................................... 8 

18 Table 1.2-1 Summary of Indicators, Mapping Layers, and Models for Targeted 

19 Ecosystems............................................................................................................ 12 

20 Table 1.2-2 SAB/EES Listing of Potential Assessment Areas for Evaluation of 

21 Benefits of Reductions in Atmospheric Deposition.............................................. 12 

22 Table 1.2-3 Potential Assessment Areas for Terrestrial Acidification Identified in the 

23 Draft ISA............................................................................................................... 13 

24 Table 1.2-4 Compilation of Study Sites for Red Spruce within the Literature........................ 15 

25 Table 1.2-5 Major Studies at the Kane Experimental Forest ................................................... 20 

26 Table 1.2-6 Major Studies at Hubbard Brook Experimental Forest......................................... 24 

27 Table 2.1-1 Mandatory Data Requirements for Calculating Critical Loads for Nitrogen 

28 and Sulfur for Forest Ecosystems (as described in Duarte, 2005)........................ 39 

29 Table 2.1-2 Optional Data Requirements for Calculating Critical Loads for Nitrogen 

30 and Sulfur for Forest Ecosystems ......................................................................... 40 

31 Table 2.2-1 Summary of Critical Endpoints for Al Effects on Tree Health ............................ 43 

Table 3.1-1 Atmospheric deposition (kg ha-1 yr-1 32 ) of solutes at the Kane Experimental 

33 Forest, PA, from the National Atmospheric Deposition Program/National 

34 Trends Network .................................................................................................... 46 

35 Table 3.1-2 Critical Load Calculations for the Hubbard Brook Experimental Forest ............. 47 
Terrestrial Acidification Case Study 

DRAFT Attachment 4, pg v August 2008 

1 ACRONYMS AND ABBREVIATIONS 

2

3 ANC acid neutralizing capacity 

4 Ca calcium 

5 CAL critical load of acidity 

6 CLF critical load function 

7 CLRTAP Convention on Long-Range Transboundary Air Pollution 

8 cm centimeter 

9 CMAQ Community Multiscale Air Quality model 

10 ESSC Earth System Science Center 

11 FIA Forest Inventory and Analysis National Program 

12 GIS geographic information systems 

13 ha hectare 

14 HBEF Hubbard Brook Experimental Forest 

15 HBES Hubbard Brook Ecosystem Study 

16 ISA Integrated Science Assessment 

17 kg kilogram 

18 km kilometer 

km2

19 square kilometer 

20 LTER Long-Term Ecological Research 

21 m meter 

m

2

22 square meter 

23 MEA Millennium Ecosystem Assessment 

24 Mg magnesium 

25 mm millimeter 

26 mo month 

NO3

-

27 nitrate

28 NOx nitrogen oxides 

29 NRCS National Resources Conservation Service 

30 NSF National Science Foundation 

31 SMB Simple Mass Balance 

SO4

2

32 sulfate

33 SO2 sulfur dioxide

34 SOx sulfur oxides 

35 SSURGO Soil Survey Geographic Database 

36 UNECE United Nations Economic Commission for Europe 

37 USDA U.S. Department of Agriculture 

38 USFS U.S. Forest Service 

39 USGS U.S. Geological Survey 
Terrestrial Acidification Case Study 

DRAFT Attachment 4, pg vi August 2008 

1

2

3

4

5

6

7

8

9 [This page intentionally left blank.] 

10

11
Terrestrial Acidification Case Study 

DRAFT Attachment 4, pg 1 August 2008 

1 1. BACKGROUND 

2 The selection and performance of case studies represent Steps 3 and 4, respectively, of 

3 the 7-step approach to planning and implementing a risk/exposure assessment of nitrogen oxides 

4 (NOx) and sulfur oxides (SOx) deposition on ecosystems, as presented in the April 2008 Scope

5 and Methods Plan for Risk Exposure Assessment (U.S. EPA, 2008). Step 4 entails evaluating the 

6 current nitrogen and sulfur loads and effects to a chosen case study assessment area, including 

7 ecosystem services. In this case study, we will evaluate the current wet and dry nitrogen and 

8 sulfur deposition load to terrestrial ecosystems and the role atmospheric deposition can play in 

9 the acidification of a terrestrial ecosystem. 

10 Deposition of NOx and SOx can result in acidification of certain terrestrial ecosystems. 

11 Because ecosystems may respond differently, it will be necessary to first perform risk exposure 

12 assessment case studies that are unique to the effect and ecosystem type. This report presents a 

13 proposed quantitative approach to analyzing the acidification effects of SOx and NOx deposition 

14 on red spruce and sugar maples. 

15 Acidification 

16 Acidification is the process of increasing the acidity of a system (e.g., lake, stream, forest 

17 soil). Within soils, acidification occurs through increases in hydrogen cations or protons. 

18 Terrestrial acidification occurs as a result of both natural biogeochemical processes and acidic 

19 deposition where mineral acids are added to the soils. Acidic deposition increases concentrations 

of sulfur and nitrogen in soil, which accelerates leaching of sulfate (SO4

2-) and nitrate (NO3-

20 )

21 from soil to drainage water. Under natural conditions (i.e., low atmospheric deposition of sulfur 

22 and nitrogen), the limited mobility of anions in the soil controls the rate of base cation leaching. 

23 However, acidic deposition of sulfur and nitrogen provides anions that are more mobile in the 

24 soil environment than naturally occurring anions in the soil; these mineral acid anions can 

25 accelerate natural rates of base-cation leaching, particularly calcium (Ca) and magnesium (Mg). 

26 If soil base saturation (i.e., the concentration of exchangeable base cations as a percent of the 

27 total cation exchange capacity) is 20% to 25%, or lower, inorganic aluminum (Al) can become 

28 mobilized, leading to the leaching of Al into soil waters and surface waters (Reuss and Johnson, 

29 1985). This is an extremely important effect of acidic deposition because inorganic Al is toxic to 
Terrestrial Acidification Case Study 

DRAFT Attachment 4, pg 2 August 2008 

1 tree roots, fish, algae, and aquatic invertebrates (U.S. EPA, 2007, Sections 4.2.1, 4.2.1.4, and 

2 4.2.1.5).

3 1.1 INDICATORS, ENDPOINTS, AND ECOSYSTEM SERVICES 

4 1.1.1 Indicators 

5 There are a variety of indicators that can be used to measure the effects of acidification. 

6 Table 1.1-1 provides a general summary and description of indicator groups. Within the 

7 Integrated Science Assessment (ISA), several of these indicators were further supported through 

8 references from the literature (Table 1.1-2).

Table 1.1-1. Key Indicators of Acidification Due to NOx and SOx

Key

Indicator

Group

Examples of 

Indicators Description 

Acid anions SO4

2-, NO3-

 Trends in these concentrations 

reflect recent trends in 

atmospheric deposition (especially 

SO4

2-) and in ecosystem responses 

to long-term deposition (notably 

NO3

-

 and desorbed SO4

2-).

Base cations Ca2+, Mg2+,

Σ(Ca2++Mg2+),

K

+

, Na+

These cations are mobilized by 

weathering reactions and cation 

exchange. They respond indirectly 

to decreases in SO4

2- and NO3-

because a reduced input of acids 

will lead to a reduction of 

neutralizing processes in the soil, 

thereby reducing the release of 

base cations to soil water and 

runoff water. (Base saturation is 

included within this category.) 

Acidity pH, (Gran) 

alkalinity, acid 

neutralizing

capacity (ANC) 

These indicators reflect the 

outcomes of interactions between 

the changing concentration of acid 

anions and base cations. 

Carbon carbon:nitrogen 

ratio

The carbon:nitrogen ratio of soil 

indicates alterations to the 

nitrogen biogeochemical cycle 
Terrestrial Acidification Case Study 

DRAFT Attachment 4, pg 3 August 2008 

Key

Indicator

Group

Examples of 

Indicators Description 

Metals Al3+, Fe3+ These metals are mobilized as a 

response to the deposition of SO4

2-

and NO3

-

.

Biological Forest health, 

community

structure, species 

composition, 

taxonomic 

richness, Index of 

Biotic Integrity 

Ecological effects occur at 4 

levels: individual, population, 

community, and ecosystem. 

Metrics have been developed for 

each level to assess the adverse 

effects of acids. 

Table 1.1-2. Literature Support for Selected Indicators of Acidification

Citation Main Finding 

Soil Base Saturation 

Reuss, 1983 If base saturation is less than 15% to 20%,

exchange ion chemistry is dominated by inorganic 

Al.

Cronan and Grigal, 1995 Base saturation below about 15% in the soil Bhorizon could lead to impacts from Al stress. 

Lawrence et al.., 2005 Base saturation declines from 30% to 20% in the 

upper soil B-horizon showed decreases in 

diameter growth of Norway spruce. 

Bailey et al., 2004 Sugar maple mortality found at Ca saturation less 

than 2% and Mg saturation less than 0.5% in the 

upper soil B-horizon. 

Johnson et al., 1991; Joslin and Wolfe, 

1992; Eagar et al., 1996 

In soils with base saturation below about 20%, 

base cations reserves are so low that Al exchange 

dominates. 

Al Concentrations 

Johnson et al.,1991; Joslin and 

Wolfe,1992; Eagar et al.,1996 

See explanation above. 

Cronan and Grigal,1995 There is a 50% risk of adverse effects on tree 

growth if the molar ratio of Ca to Al in soil 

solution was as low as 1.0. 100% risk for adverse 

effects on growth at molar ratio value below 0.2. 

Johnson et al.,1994a, b Ca:Al ratios above 1.0 across 4 years were found 

in a forestland experiencing high mortality. 
Terrestrial Acidification Case Study 

DRAFT Attachment 4, pg 4 August 2008 

Citation Main Finding 

DeWitt et al.,2001 Ca:Al ratios of Norway spruce stand below 0.5 

showed reduced Mg concentrations in needles in 

the third year. 

Carbon:Nitrogen Ratio 

Aber et al.,2003; Ross et al.,2004 Increased effects of nitrification occur only in soil 

with carbon:nitrogen ratio below about 20–25. 

Source: U.S. EPA, 2007 

1 Much of the literature surrounding terrestrial acidification focuses on Ca and Al as the 

2 primary indicators of detrimental effects for trees and other terrestrial vegetation. As such, we 

3 have focused our detailed discussion of indicators of terrestrial acidification on these two 

4 parameters and the interaction between them. The use of these indicators in combination and 

5 through the evaluation framework that will be described within this work ultimately combines all 

6 indicator categories described in Table 1-1 except the carbon category. Ca and Al are the focus 

7 of the analysis because these indicators have been shown to have quantitative links to tree health. 

8 Schaberg and colleagues (2001) provide a more detailed description of the leaching 

9 effects caused by Al: 

10 Decreases in concentrations of exchangeable calcium are generally 

11 attributed to displacement by hydrogen ions, which can originate from either acid 

12 deposition or uptake of cations by roots (Johnson and others, 1994a, Richter and 

13 others, 1994). A regional survey of soils in northeastern red spruce forests in 

14 1992-93 (fig. 2) has revealed that decreases in exchangeable calcium 

15 concentrations in the Oa horizon (a layer within the forest floor, where uptake of 

16 nutrients is greatest) can also result from increased concentrations of 

17 exchangeable aluminum, which originated in the underlying mineral soil 

18 (Lawrence and others, 1995). By lowering the pH of the aluminum-rich mineral 

19 soil, acid deposition can increase aluminum concentrations in soil water through 

20 dissolution and ion-exchange processes. Once in solution, the aluminum 

21 (although not a nutrient) is taken up by roots and transported through the trees to 

22 be eventually deposited on the forest floor in leaves and branches. 
Terrestrial Acidification Case Study 

DRAFT Attachment 4, pg 5 August 2008 

1 A continued buildup of aluminum in the Oa horizon can (1) decrease the 

2 availability of calcium for roots (Lawrence and others, 1995), (2) lower the 

3 efficiency of calcium uptake because aluminum is more readily taken up than 

4 calcium when the ratio of calcium to aluminum in soil water is less than 1 

5 (Cronan and Grigal, 1995), and (3) be toxic to roots at high concentrations 

6 (Lawrence, et al. 1995). 

7 These findings are further summarized in the ISA (U.S. EPA, 2007), as excerpted below. 

8 Aluminum Concentration in Soil Solution: Ca to Al Ratio (U.S. EPA, 2007, Section 

9 4.2.2.1.2)

10 Aluminum is toxic to tree roots. Plants affected by a high Al concentration 

11 in soil solution often have reduced root growth, which restricts the ability of the 

12 plant to take up water and nutrients, especially Ca (Parker et al., 1989). Calcium is 

13 well known as an ameliorant for Al toxicity to roots in soil solution, as well as to 

14 fish in streams. However, because inorganic Al does not become mobilized until 

15 after soil Ca is depleted, elevated concentrations of inorganic Al tend to occur 

16 with low levels of Ca in surface waters. Mg, and to a lesser extent sodium (Na) 

17 and potassium (K), have also been associated with reduced Al toxicity. 

18 Dissolved Al concentrations in soil solution at spruce-fir study sites in the 

19 southern Appalachian Mountains frequently exceed 50 ȝM and sometimes exceed 

20 100 ȝM (Johnson et al., 1991; Joslin and Wolfe, 1992; Eagar et al., 1996). All 

21 studies reviewed by Eagar and colleagues (1996) showed a strong correlation 

between Al concentrations and NO3

-

22 concentrations in soil solution. They 

surmised that the occurrence of periodic large pulses of NO3

-

23 in solution were 

24 important in determining Al chemistry in the soils of spruce-fir forests. 

25 The negative effect of Al mobilization on uptake of Ca by tree roots was 

26 proposed by Shortle and Smith (1988), and substantial evidence of this 

27 relationship has accumulated over the past 2 decades through field studies 

28 (McLaughlin and Tjoelker, 1992; Schlegel et al., 1992; Minocha et al., 1997; 

29 Shortle et al., 1997; Kobe et al., 2002) and laboratory studies (Sverdrup and 
Terrestrial Acidification Case Study 

DRAFT Attachment 4, pg 6 August 2008 

1 Warfvinge, 1993; see also review of Cronan and Grigal, 1995). Based on these 

2 studies, it is clear that high inorganic Al concentration in soil water can be toxic to 

3 plant roots. The toxic response is often related to the concentration of inorganic 

4 Al relative to the concentration of Ca, expressed as the molar ratio of Ca to 

5 inorganic Al in soil solution. As a result, considerable effort has been focused on 

6 determining a threshold value for the ratio of Ca to Al that could be used to 

7 identify soil conditions that put trees under physiological stress.

8 From an exhaustive literature review, Cronan and Grigal (1995) estimated 

9 that there was a 50% risk of adverse effects on tree growth if the molar ratio of Ca 

10 to Al in soil solution was as low as 1.0. They estimated that there was a 100% risk 

11 for adverse effects on growth at a molar ratio value below 0.2 in soil solution.

12 The information available to define levels of risk for the Ca:Al ratio is 

13 complicated by differences in natural soil conditions. As a result of these 

14 complications, the risk levels for the ratio defined in laboratory experiments have 

15 not necessarily been successfully applied to field conditions. For example, 

16 Johnson and colleagues (1994a, 1994b) reported Ca:Al ratios above 1.0 through 

17 most of 4 years in the Oa and B horizons of a high-elevation red spruce stand 

18 experiencing high mortality. In the 3-year study of DeWitt and colleagues (2001), 

19 Al additions lowered molar Ca to inorganic Al ratios in soil solutions of a Norway 

20 spruce stand below 0.5, but the authors found no response other than reduced Mg 

21 concentrations in needles in the third year, which was a possible precursor to 

22 damage. 

23 In summary, a molar ratio of Ca to Al in soil solution can be used as a 

24 general index that suggests an increasing probability of stress to forest ecosystems 

25 as the ratio decreases. The ratio value of 1.0 is proposed as a general damage 

26 threshold, but it cannot be interpreted as a universally applicable threshold in all 

27 natural systems. Tree species vary widely in their sensitivity to Al stress. In 

28 addition, Al concentrations in soil solution often exhibit pronounced spatial and 

29 temporal variability that is difficult to relate to root activity. Finally, the form of 
Terrestrial Acidification Case Study 

DRAFT Attachment 4, pg 7 August 2008 

1 Al present in solution plays an important role in determining toxicity. For 

2 example, organically complexed Al, which predominates in upper, organic-rich 

3 soil horizons, is essentially nontoxic (U.S. EPA, 2007). 

4 Building on the explanation between Ca, Al, and tree health provided in the ISA, 

5 DeHayes and colleagues (1999) depict the relationship between nitrogen and sulfur deposition 

6 through acid rain and Ca within an ecosystem (Figure 1.1-1). The authors used solid lines to 

7 denote known connections and dotted lines to present potential impacts. While the authors did 

8 not specify that increases in Al within the soils will occur with reductions in biologically 

9 available Ca pools, this impact is expected as detailed in the previous paragraphs. The final 

10 process represented in Figure 1.1-1 completes the linkage from the indicator of Ca (and therefore 

11 Al) to the effects on the ecosystem services for the terrestrial area. Continuing the example using 

12 Ca and Al indicators, Fenn and colleagues (2006) provided a description of the assessment 

13 endpoints and ecosystem services that may be impacted through acidification effects measured 

14 using these indicators (Table 1.1-3). 
Terrestrial Acidification Case Study 

DRAFT Attachment 4, pg 8 August 2008 

Acid Rain

Air pollutants

Temperature perturbation 

(high, low, variable)

Insects

Pathogens

Drought

Heavy metals

Disruptions in Forest Ecosystem Health/Stability

Predisposition to Stress-induced Injury

Membrane 

Disruption

Reductions in Biologically 

Important Calcium Pools

Soil Calcium 

Depletion

Disruptions in Stress 

Response Systems

Potential Secondary 

Environmental Stresses

1

2 Figure 1.1-1. Conceptual impacts of acid deposition on ecosystem Ca health

3 and sustainability (recreated from DeHayes et al., 1999). 

Table 1.1-3. Summary of Linkages between Acid Deposition, Biogeochemical Processes that 

Affect Ca, Physiological Processes that are Influenced by Ca, and Effect on Forest Function 

Biogeochemical Response to 

Acid Deposition Physiological Response Effect on Forest Function 

Leach Ca from leaf 

membrane 

Reduce the cold tolerance of 

needles in red spruce 

Loss of current-year needles 

in red spruce 

Reduce the ratio of Ca to Al 

in soil and soil solutions 

Dysfunction in fine roots of 

red spruce blocks uptake of 

Ca

Decreased growth and 

increased susceptibility to 

stress in red spruce 

Reduce the ratio of Ca to Al 

in soil and soil solutions 

More energy is used to 

acquire Ca in soils with low 

Ca:Al ratios 

Decreased growth and 

increased photosynthetic 

allocation to roots 
Terrestrial Acidification Case Study 

DRAFT Attachment 4, pg 9 August 2008 

Biogeochemical Response to 

Acid Deposition Physiological Response Effect on Forest Function 

Reduce the availability of 

nutrient cations in marginal 

soils

Sugar maples on droughtprone or nutrient-poor soils 

are less able to withstand 

stresses

Episodic dieback and growth 

impairment in sugar maple 

Source: Fenn et al., 2006 

1 1.1.2 Endpoints 

2 The tree species most commonly associated with the adverse acidification-related effects 

3 of nitrogen and sulfur deposition include red spruce (a conifer) and sugar maple (a deciduous 

4 tree species). Both species are found in the eastern United States (Figure 1.1-2).

5 Red spruce is found scattered throughout high-elevation sites in the Appalachian 

6 Mountains, including the southern peaks. Noticeable levels of the canopy red spruce died within 

7 the Adirondack, Green, and White mountains in the 1970s and 1980s. Acidic deposition has been 

8 implicated in this decline due to freezing injury (DeHayes et al., 1999). Within the southeastern 

9 United States, periods of red spruce growth decline were turned around after the 1980s, when a 

10 corresponding decrease in sulfur dioxide (SO2) emissions was recorded in the United States 

11 (Webster et al., 2004). Al and Ca ratios in forest floor soil are also important to the overall health 

12 of red spruce trees in the Northeast. Red spruce has been shown to have an increased instance of 

13 foliar winter injury and bud mortality due to imbalanced Al and Ca levels in soils at locations in 

14 Vermont and surrounding states. A decrease in cold and winter weather tolerance leads to an 

15 increase in freezing injuries to red spruce, placing the species at a greater risk of declining 

16 overall forest health. Soil nutrient imbalances and deficiencies can reduce the ability of a tree to 

17 respond to stresses, such as insect defoliation, drought, and cold weather damage (DeHayes et 

18 al., 1999; Driscoll et al., 2003). From the overall research, important factors relating to the high 

19 mortality rates and decreased growth trends of red spruce include depletion of base cations in 

20 upper soil horizons by acidic deposition, Al toxicity to tree roots, and accelerated leaching of 

21 base cations from foliage as a consequence of acidic deposition (U.S. EPA, 2007). 

22 Sugar maple has been on the decline in the eastern United States since the 1950s. This 

23 species is found throughout the northeastern United States and the central Appalachian Mountain 

24 region. Studies on sugar maple have found that decline in growth is related to both acidic 

25 deposition and base-poor soils on geologies dominated by sandstone or other base-poor substrate 
Terrestrial Acidification Case Study 

DRAFT Attachment 4, pg 10 August 2008 

1 (Bailey et al., 2004; Horsley et al., 2000). These site conditions are representative of the kinds 

2 expected to be most susceptible to adverse impacts of acidic deposition because of probable low 

3 initial base cation pools and high base cation leaching losses (U.S. EPA, 2007). The probability 

4 of a decrease in crown vigor or occurrence of tree mortality increases on sites with low Ca and 

5 Mg as a result of leaching caused by acid deposition (Drohan and Sharpe, 1997). Additionally, 

6 plots of sugar maples in decline were found to have lower base cation concentrations and pH

7 values, and Ca:Al ratios less than 1 (Drohan et al., 2002). These indicators have all been shown 

8 to be related to the deposition of nitrogen and sulfur. 

9

10 Figure 1.1-2. Areal coverages of red spruce and sugar maple tree species 

11 within the continental United States (USGS, 1999). 
Terrestrial Acidification Case Study 

DRAFT Attachment 4, pg 11 August 2008 

1 1.1.3 Ecosystem Services 

2 Ecosystem services are generally defined as the benefits individuals and organizations 

3 obtain from ecosystems. In the Millennium Ecosystem Assessment, (MEA) ecosystem services 

4 are classified into four main categories: 

5  Provisioning. Includes products obtained from ecosystems. 

6  Regulating. Includes benefits obtained from the regulation of ecosystem processes. 

7  Cultural. Includes the nonmaterial benefits people obtain from ecosystems through 

8 spiritual enrichment, cognitive development, reflection, recreation, and aesthetic 

9 experiences. 

10  Supporting. Includes those services necessary for the production of all other ecosystem 

11 services (MEA, 2005).

12 A number of impacts on the endpoints of forest health, water quality, and habitat exist, 

13 including the following: 

14  Decline in forest aesthetics – cultural 

15  Decline in forest productivity – provisioning 

16  Increase forest soil erosion and low water retention – cultural and regulating. 

17 The terrestrial acidification case study approach will focus on food, natural habitat, and 

18 tourism. Sugar maple and red spruce abundance and growth (i.e., crown vigor, biomass and 

19 geographic extent) will be quantitatively linked to acidification symptoms through U.S. 

20 Department of Agriculture (USDA) Forest Service (USFS) Forest Inventory and Analysis 

21 National Program (FIA) database analyses and analysis of maple sugar production estimates 

22 sales.

23 1.2 CASE STUDIES 

24 As described in the introduction to these case study assessments, selections of case study 

25 areas specific to terrestrial acidification began with geographic information systems (GIS)

26 mapping. We used GIS analysis on datasets of physical, chemical, and biological properties 

27 indicative of terrestrial acidification potential to identify sensitive areas of the United States. 

28 (Table 1.2-1).
Terrestrial Acidification Case Study 

DRAFT Attachment 4, pg 12 August 2008 

Table 1.2-1. Summary of Indicators, Mapping Layers, and Models for Targeted Ecosystems 

Targeted

Ecosystem

Effect Indicator(s) Mapping Layers Model(s) 

Terrestrial 

Acidification 

Due to 

nitrogen and 

sulfur

Soil ANC 

Soil pH 

CEC

Inorganic Al 

Ca:Al ratio 

Special areas (e.g., Class I areas, 

the Adirondack Mountains) 

CMAQ (nitrogen & sulfur) by 

HUC

Forest soils from USFS 

STATSGO soils 

USFS lichen 

USFS forest types 

MAGIC; ILWAS; 

PnET-BGC

Note: ANC = acid neutralizing capacity, CEC = cation exchange capacity, USFS = U.S. Forest 

Service, HUC = hydrological unit, ILWAS = Integrated Lake-Watershed Acidification Study. 

1 We also considered the potential case study areas identified by the Ecological Effects 

2 Subcommittee (EES) for examining the ecological benefits of reducing atmospheric deposition. 

3 Terrestrial acidification-relevant case study areas suggested by the EES are presented in Table

4 1.2-2. The ISA also recommended case study areas as candidates for risk/exposure assessments. 

5 (Table 1.2-3 contains terrestrial acidification-relevant area.) 

Table 1.2-2. SAB/EES Listing of Potential Assessment Areas for Evaluation 

of Benefits of Reductions in Atmospheric Deposition 

Ecosystem/

Region

Main CAA 

Pollutant(s) 

Percentage(s) of 

Total Nutrient 

Load

Attributable to 

Atmospheric

Deposition

Quantitative 

Ecological and 

Economic

Information EES Comments 

Forested 

Adirondacks Nitrogen; 

Sulfur; 

Mercury

Nearly 100% Yes High priority. Good 

quantitative ecological and 

economic data exist. 

Previous studies can be 

augmented readily. 

Catskills Nitrogen; 

Sulfur; 

Nearly 100% Yes Medium priority. 

Economic data may be 

lacking. Issues similar to 

the Adirondacks. 

Southern Nitrogen; Nearly 100% Yes Medium priority. 
Terrestrial Acidification Case Study 

DRAFT Attachment 4, pg 13 August 2008 

Ecosystem/

Region

Main CAA 

Pollutant(s) 

Percentage(s) of 

Total Nutrient 

Load

Attributable to 

Atmospheric

Deposition

Quantitative 

Ecological and 

Economic

Information EES Comments 

Appalachian

Mountains

Sulfur; Economic data on fisheries 

are available. Issues 

similar to the Adirondacks.

Rocky

Mountains

Nitrogen Nearly 100% Yes Medium priority. Levels of 

nitrogen loading much 

lower than for northeastern 

locations. Economic data 

may be lacking. 

Table 1.2-3. Potential Assessment Areas for Terrestrial Acidification Identified in the Draft ISA 

Area Indicator 

Detailed

Indicator

Area

Studies Models 

References in U.S. EPA, 

2007

Hubbard

Brook,

New

Hampshire 

Terrestrial 

acidification;

aquatic

acidification

Forest

ecosystem; 

soils; 

streams 

Many

studies for 

decades

PnETBGCGbondo-Tugbawa and 

Driscoll, 2002; GbondoTugbawa et al., 2002 

Source: U.S. EPA, 2007 

1 With the potential areas of assessment highlighted and the indicators (Ca and Al) and 

2 endpoints (i.e., tree health decline in sugar maple and red spruce) defined, we reviewed literature 

3 studies, federal reports, and additional sources of information, such as established experimental 

4 forests, to determine specific case study locations. 

5 Selection of a case study location for sugar maples quickly focused on the Allegany 

6 Plateau in Pennsylvania, where a preponderance of the work in the literature has been focused. A 

7 significant amount of the work has been sponsored by the USFS (Horsley et al., 2000; Bailey et 

8 al., 2004; Hallett et al., 2006). Within this literature compilation, several forest sites were 

9 monitored and analyzed (Bailey et al., 2004). For this case study, we have settled on the Kane 

10 Forest study site. Kane was designated as an experimental forest by the USFS; it has been the 

11 focus of several long-term studies since the 1930s. 

12 For red spruce, selection of a case study location involved a much larger geographic area 

13 because there was no overwhelming source of information. Using four studies that examined Ca 

14 and Al relationships to tree health, we compiled a list of forest sites and key information for each 
Terrestrial Acidification Case Study 

DRAFT Attachment 4, pg 14 August 2008 

1 (Table 1.2-4). After review of this information (i.e., tree population characteristics and reported 

2 impacts, as well as monitoring results), we again chose to go with an experimental forest site. 

3 The Hubbard Brook Experimental Forest (HBEF) experienced both high deposition levels and 

4 low Ca to Al ratios, although neither parameter was the extreme value amongst the compiled 

5 study sites. This forest has also been the subject of extensive nutrient investigations and provides 

6 a large data set from which to work. 
Terrestrial Ac

DRAFT Attachment 4, pg 15 Table 1.2-4. Compilation of Study Sites for Red Spruce within the Literature 

Site Name 

Elevation (m) Latitude (degrees N) Longitude (degrees S) Size of Tree Population Availability of Field Data Ecological Importance Reported Impacts 

Reported Ca & Al Ratios

Balsam High Top 

1641 35.6656 –83.1962 Spruce-fir forest+ University study Great Smoky 

Mountains National Park 

Nearly 100% risk of adverse forest health effects 

0.094* 

Clingman’s Dome 

2020 35.5629 –83.4987 Spruce-fir forest+ University study Great Smoky 

Mountains National Park 

Nearly 100% risk of adverse forest health effects 

0.084* 

Double Spring Gap

1678 35.5652 –83.5429 Spruce-fir forest+ University study Great Smoky 

Mountains National Park 

Nearly 100% risk of adverse forest health effects 

0.053* 

Mount LeConte 2010 35.6526 –83.4355 Spruce-fir forest+ University study Great Smoky 

Mountains National Park 

75% risk of adverse forest health effects 

0.567* 

Mount Sterling 1772 35.7024 –83.1224 Spruce-fir forest+ University study Great Smoky 

Mountains National Park 

Nearly 100% risk of adverse forest health effects 

0.07* 

RichlandBalsam Mountain 

1941 35.3676 –82.9904 Spruce-fir forest+ University study Blue Ridge 

Parkway 

Nearly 100% risk of adverse forest health effects 

0.07* 

SpruceMountain 

1695 35.6084 –83.1790 Spruce-fir forest+ University study Great Smoky 

Mountains National Park 

Nearly 100% risk of adverse forest health effects 

0.128* 

Sleepers River, Vermont 

 44.4092 –72.0158 Red spruce 

dominated with low exchangeable Al;Ca ratio

Not selected in studies

 Site did not contain 

sufficient number of healthy, mature red spruce for study 

 

Groton, Vermont 

520 44.2100 –72.2000 Red spruce 

dominated with a gradient of forest floor exchangeable Al;Ca ratios 

USFS study location No specific references 

at this time, but disturbances are known to have occurred 

0.3^ 

Howland, Maine 60 45.2000 –68.7300 Red spruce 

dominated with a gradient of forest floor exchangeable Al;Ca ratios 

USFS study location No specific references 

at this time, but disturbances are known to have occurred 

0.4^ 
Terrestrial Ac

DRAFT Attachment 4, pg 16 

Site Name 

Elevation (m) Latitude (degrees N) Longitude (degrees S) Size of Tree Population Availability of Field Data Ecological Importance Reported Impacts 

Reported Ca & Al Ratios

Bartlett, New Hampshire 

525 44.1100 –71.2900 Red spruce 

dominated with a gradient of forest floor exchangeable Al;Ca ratios 

USFS Experimental Forest (1,052 ha); red spruce covers highest slopes 

Within the White Mountains 

No specific references at this time, but disturbances are known to have occurred 

0.8^ 

Kossuth, Maine 100 45.4000 –67.9000 Red spruce 

dominated with a gradient of forest floor exchangeable Al;Ca ratios 

USFS study location No specific references 

at this time, but disturbances are known to have occurred 

0.8^ 

Hubbard Brook, New Hampshire 

755 43.9400 –71.7500 Red spruce 

dominated with a gradient of forest floor exchangeable Al;Ca ratios 

USFS Experimental Forest (3,138 ha); red spruce abundant at higher elevations and on rock outcrops 

Within the White Mountains 

Acid-extractable Al in the forest floor increased over the past two decades at the HBEF, and ratios of Al to Ca in mineral soil solutions (but not forest floor solutions) were strongly correlated with exchangeable Al content in the forest floor. 

0.8^ 

Whiteface Mountain, New York

950 44.3900 -73.8600 Red spruce 

dominated with a gradient of forest floor exchangeable Al;Ca ratios 

USFS study location Contained neither 

evidence of unusual mortality or current tree decline; winter injury events reported (Lazarus et al., 2004) 

0.8^ 

CrawfordNotch, New Hampshire 

670 44.1590 -71.3617 Red spruce 

dominated with a gradient of forest floor exchangeable Al;Ca ratios 

USFS study location Within the White 

Mountains 

50% risk of adverse forest health effects; mortality of red spruce was significant but most of the remaining trees were in good to fair health

1.1^ 

Big Moose Lake, New York 

550 43.8300 -74.8500 Red spruce 

dominated with a gradient of forest floor exchangeable Al;Ca ratios 

USFS study location 50% risk of adverse 

forest health effects 

1.2^ 
Terrestrial Ac

DRAFT Attachment 4, pg 17 

Site Name 

Elevation (m) Latitude (degrees N) Longitude (degrees S) Size of Tree Population Availability of Field Data Ecological Importance Reported Impacts 

Reported Ca & Al Ratios

Bear Brook, Maine 

400 44.8700 -68.1100 Red spruce 

dominated with a gradient of forest floor exchangeable Al;Ca ratios 

USFS study location 75% risk of adverse 

forest health effects 

1.9^ 

Cone Pond, New Hampshire 

610 43.9000 -71.6000 Red spruce 

dominated with a gradient of forest floor exchangeable Al;Ca ratios 

USFS study location Within the White 

Mountains 

Nearly 100% risk of adverse forest health effects 

5.2^ 

Mt. Abraham, Vermont 

 44.1201 -72.9357 Red spruce 

dominated with a high exchangeable Al;Ca ratio 

Not selected in studies

Within the Green Mountains 

Site did not contain sufficient number of healthy, mature red spruce for study; Forest floor solution Al:Ca ratio above the 50% risk level

7.1^ 

Mt. Ascutney, Vermont 

762 43.4333 -72.4500 Series of high 

elevation spruce-fir forest nitrogen addition plots=

USFS study location Nitrogen 

additions to system 

Reduction in live basal area on the high nitrogen addition plots versus control plots 

 

* Molar Ca/Al Ratio (Bintz and Butcher, 2007). ^ Oa horizon Al/Ca Ratios (Wargo et al., 2003). # Estimated wet nitrogen deposition (Lilleskov et al., 2008). % In addition to ambient nitrogen deposition, paired plots each received 15.7 kg N ha-1 year-1 (low nitrogen addition), 31.4 kg N ha-1 year-1 (high nitrogen addition) o

from 1988 to 2002. 

+ High elevation sites in the Southern Appalachians—The sites are located in the Great Smoky Mountains National Park and Richland Balsam Mountain on the Blue R

selected because of the presence of a spruce-fir forest with a northwest slope aspect within 10 km of a trailhead at elevations between 1650 and 2025 m. 

= Red spruce grew in large patches (> 1 hectare [ha]) at elevations above 725 m. Red spruce comprised > 80% of the total basal area in all plots; the remainder of the o

among balsam fir, red maple, mountain maple, and birch. 
Terrestrial Acidification Case Study 

DRAFT Attachment 4, pg 18 August 2008 

1 1.2.1 Sugar Maple 

2 1.2.1.1 Kane Experimental Forest (USFS, 2008b; USFS, 1999) 

3 The Kane Experimental Forest (Figure 1.2-1) was established on March 23, 1932, 

4 although research there began as early as 1927 or 1928. The forest’s primary mission has been 

5 forest management research, although watershed research was included in the beginning, and 

6 wildlife research is part of the current program. Ongoing long-term studies include individual 

7 tree and understory vegetation measurements; treatments include thinnings, regeneration cuts, 

8 uneven-age cuts, and long-term measurements of unmanaged forest. 

9

10 Figure 1.2-1. Kane Experimental Forest (Horsley et al., 2000). 

11 The Kane Experimental Forest is on the eastern edge of the Allegheny National Forest, 

12 3.5 miles south of Kane, PA. Main access is from Pennsylvania Route 321 or the Highland13 Lamont Road, via Forest Service Road 138. The Experimental Forest is comprised of 1,737 acres 

14 of forestland; it ranges in elevation from about 1,800 to 2,100 feet above sea level, primarily on 

15 flat to gently sloping land. The Wolf Run and Ackerman Run drainages cross Kane Experimental 

16 Forest, as do the Mill Creek and Twin Lakes trails. 

17 The climate of the Kane Experimental Forest is humid temperate. The forest receives 

18 approximately 110 centimeters (cm) of precipitation per year, mostly as rain, including 10 

19 cm/month during the growing season. Precipitation can be quite acidic; Kane Experimental 

Forest receives high levels of both SO4

2- and NO3-

20 deposition. Between 1992 and 1998, the 
Terrestrial Acidification Case Study 

DRAFT Attachment 4, pg 19 August 2008 

average annual atmospheric deposition was 4.57 kilograms/hectare (kg/ha) for NO3

-

1 , 2.33 kg/ha 

for ammonium (NH4

+

2 ), and 9.48 kg/ha for SO4-S (Lewis and Likens 2007). The average annual 

3 temperature is 43° F. Overcast days are frequent, reducing the transpirational demand on plants. 

4 The forest soils on the Allegheny Plateau are derived from shales and sandstones. In 

5 general, these soils are very stony and exist as extremely stony loams and sandy loams. They are 

6 strongly acidic. The major soil series are the well-drained Hazelton series, the moderately well7 drained to somewhat poorly drained Cookport series, and the somewhat poorly drained Cavode 

8 series.

9 The forest stands on the Kane Experimental Forest are typical of the Allegheny Plateau. 

10 They resulted from a series of cuttings made in the original hemlock-beech-maple stands. The 

11 first cutting, made in the mid-to-late 1800s, removed the hemlock and the best hardwood trees to 

12 supply the local tanneries and sawmills. Most of the remaining hardwoods were cut between 

13 1890 and 1925, but a few stands were clear-cut as late as 1937. Trees of nearly all sizes were 

14 removed in the later cuts; large trees were used for sawtimber products, whereas small trees were 

15 destructively distilled for charcoal and wood chemical products. 

16 Currently, the Kane Experimental Forest contains second-growth stands ranging from 60 

17 to about 100 years of age, a few third-growth stands 20- or 40-years-old, and one tract with 

18 remnant old growth. Most stands are even-aged in character, although they may actually contain 

19 several age classes because of the previous sequence of cuttings. The most common tree species 

20 are black cherry, maples, and beech, but many other species are present (e.g., yellow and sweet 

21 birch, eastern hemlock, cucumbertree, yellow poplar, white ash). Beech and striped maple 

22 seedlings dominate the understory of many unmanaged stands, joined by black cherry and black 

23 birch in managed stands. These forest stands represent the Allegheny hardwood or black cherry–

24 maple; the northern hardwood, including the hemlock-hardwood and beech–birch–maple; and 

25 the upland hardwood, or red maple–dominated, forest types. 

26 Several species of ferns, grasses, goldenrod, and aster occur in abundance as ground 

27 covers. Common spring ephemerals include trout lily, dwarf ginseng, and spring beauties. 

28 Wildlife species observed on the Kane Experimental Forest include white-tailed deer, wild 

29 turkey, black-throated green warblers, hermit thrushes, deer mice, chipmunks, red-backed 

30 salamanders, and wood frogs. The wildlife communities are typical of those found in managed 

31 second-growth forests of the Allegheny Plateau region. 
Terrestrial Acidification Case Study 

DRAFT Attachment 4, pg 20 August 2008 

1 Since the establishment of the Kane Experimental Forest, research has been conducted 

2 continuously on the forest; most research consists of relatively long-term studies. During the 

3 Civilian Conservation Corps days of the 1930s, studies of forest growth and development were 

4 initiated at the Kane Experimental Forest. Information from this early work has made important 

5 contributions to the present research program, and many of these long-term study areas are still 

6 yielding valuable information. Other past research includes thinning research, forest stocking, 

7 factors affecting the natural regeneration of Allegheny hardwoods, and the development of 

8 SILVAH, an early computerized decision-support system still widely used for forest 

9 management. Treatment techniques at the Kane Experimental Forest have included cutting, roto10 tilling, irrigation, bending overstory trees, trenching, heating cables, fertilization, and shading.

11 Currently, the Northeastern Forest Experiment Station research team that maintains and 

12 administers the Kane Experimental Forest research conducts research on three problems: 

13 regeneration and forest renewal stand dynamics, silviculture, and sugar maple decline. Most of 

14 the research on the Kane Experimental Forest is focused on the stand dynamics and silviculture 

15 research problems. Table 1.2-5 summarizes major studies at the Kane Experimental Forest 

16 related to the sugar maple and chemical criterion that can be used in calculating critical loads of 

17 nitrogen and sulfur. 

Table 1.2-5. Major Studies at the Kane Experimental Forest 

Authors Year Title Key Finding 

Horsley, S.B., 

R.P. Long, S.W. 

Bailey, R.A. 

Hallett, and T.J. 

Hall

2000 Factors 

Associated with 

the DeclineDisease of Sugar 

Maple on the 

Allegheny

Plateau

The most important factors determining sugar 

maple health were foliar levels of Mg and Mn 

and defoliation history. The decline-disease of 

sugar maple appears to be the result of an 

interaction between Mg (and perhaps Mn) 

nutrition and stress caused by defoliation. 
Terrestrial Acidification Case Study 

DRAFT Attachment 4, pg 21 August 2008 

Authors Year Title Key Finding 

Bailey, S.W., 

S.B. Horsley, 

and R.P. Long 

2005 Thirty Years of 

Change in Forest 

Soils of the 

Allegheny

Plateau,

Pennsylvania

Between 1967 and 1997, there were significant 

decreases in exchangeable Ca and Mg 

concentrations and pH at all depths. 

Exchangeable Al concentrations increased at all 

depths at all sites; however, increases were only 

significant in upper soil horizons. At most of the 

sites, losses of Ca and Mg on a pool basis were 

much larger than could be accounted for in 

biomass accumulation, suggesting the leaching 

of nutrients off-site. 

1 1.2.2 Red Spruce 

2 1.2.2.1 Hubbard Brook Experimental Forest (HBES, 2008; Pardo and Driscoll, 

3 1996; USFS, 2008a) 

4 The HBEF was established in 1955 as a major center for hydrologic research in New 

5 England. Located in White Mountain National Forest in central New Hampshire, the 3,138-ha 

6 bowl-shaped valley has hilly terrain, ranging from 222 to 1,015 meters (m) in altitude. The 

7 Hubbard Brook Ecosystem Study (HBES) was established by a cooperative agreement in 1963. 

8 In 1988, the HBEF was designated as a Long-Term Ecological Research (LTER) site by the 

9 National Science Foundation (NSF). Figure 1.2-2 presents a map of the HBEF, with 

10 identification of four forest transect studies conducted by Siccama and colleagues, 2007. 

11

12 Figure 1.2-2. Hubbard Brook Experimental Forest (Siccama et al., 2007). 
Terrestrial Acidification Case Study 

DRAFT Attachment 4, pg 22 August 2008 

1 The soils, vegetation, and climate at the HBEF are characteristic of the northern 

2 hardwood forest complex, which spans much of the north-central and northeastern United States 

3 and southeastern Canada. Streamflow and chemistry reflect the landscape characteristics of the 

4 drainage area. Consequently, results from the relatively small watersheds at the HBEF are, to a 

5 first approximation, representative of a much larger regional area. 

6 The HBEF is located in the southern part of the White Mountain National Forest in 

7 central New Hampshire (i.e., 43o56’N, 71o45’W; the geographic center of the HBEF). It lies in 

8 the towns of Ellsworth, Thornton, Warren, and Woodstock, all in Grafton County, and is near the 

9 village of West Thornton. The HBEF is an oblong basin about 8 km long by 5 km wide. Hubbard 

10 Brook is the single major stream draining the basin. 

11 Although the climate of the HBEF varies with altitude, some major features include the 

12 following: (1) large and rapid changes in weather, (2) broad ranges in daily and annual air 

13 temperature, and (3) uniform monthly precipitation (i.e., about 100 millimeters/month 

14 [mm/mo]). In spite of the proximity of the HBEF to the ocean (116 km), the climate is 

15 predominantly continental. Annual precipitation at the HBEF averages about 1,400 mm, with 

16 one-third to one-quarter as snow. January averages about –9o C, and the average July 

17 temperature is 18o C. The average number of days without killing frost is 145; however, the 

18 growing season for trees is considered to be from May 15, the approximate time of full leaf 

19 development, to September 15, when the leaves begin to fall. The estimated annual 

20 evapotranspiration is about 500 mm. 

21 Soils at the HBEF are predominantly well-drained Spodosols (Typic Haplorthods) 

22 derived from glacial till, with sandy loam textures. Principal soil series are the sandy loams of 

23 the Berkshire series, along with the Skerry, Becket, and Lyman series. These soils are acidic (i.e., 

24 pH about 4.5 or less) and relatively infertile (i.e., base saturation of mineral soil ~ 10%). Soil 

25 depths, including unweathered till, average about 2.0 m surface to bedrock, although this is 

26 highly variable. Depth to the C horizon averages about 0.6 m. At various places in the HBEF, the 

27 C horizon exists as an impermeable pan. Long-term measurements suggest that the forest floor is 

28 at steady-state. 

29 The HBEF is entirely forested, mainly with deciduous northern hardwoods: sugar maple 

30 (Acer saccharum), beech (Fagus grandifolia), and yellow birch (Betula allegheniensis), and 

31 some white ash (Fraxinus americana) on the lower and middle slopes. Other less abundant
Terrestrial Acidification Case Study 

DRAFT Attachment 4, pg 23 August 2008 

1 species include mountain maple (Acer spicatum), striped maple (Acer pensylvanicum), and 

2 trembling aspen (Populus tremuloides). Red spruce (Picea rubens), balsam fir (Abies balsamea),

3 and white birch (Betula papyrifera var. cordifolia) are abundant at higher elevations and on rock 

4 outcrops. Hemlock (Tsuga canadensis) is found in riparian areas. Pin cherry (Prunus

5 pensylvanica), a shade-intolerant species, dominated all sites for the first decade following a 

6 major forest disturbance. The region was settled by Europeans in the late 1800s; logging 

7 operations ending around 1915 to 1917 removed large portions of the conifers and better quality, 

8 accessible hardwoods. The present second-growth forest is even-aged and composed of about 

9 80% –90% hardwoods and 10%–20% conifers. The total forest biomass has stopped 

accumulating since the early 1980s and is currently about 235 tons ha-1 10 . Present basal area is 

about 26 m2

 ha-1 11 , but varies according to elevation, habitat, and stand history. 

Mean nitrogen loading in bulk deposition for the period 1965 to 1987 was 480 mol ha-1 12

yr-1. Mean total nitrogen loading, including wet and dry deposition, was 570 mol ha-1 yr-1 13 . There 

14 was no significant trend in nitrogen deposition for the period 1965 to 1987. 

15 Research at the HBEF has been in progress for more than 50 years. During the first 8 

16 years following the establishment of the HBEF, the Northeastern Research Station, USFS, 

17 developed a network of precipitation and stream-gauging stations, and weather instrumentation, 

18 and vegetation monitoring sites on small, experimental watersheds. Data from these installations, 

19 combined with several initial studies, formed the hydrometeorologic foundation for much of the 

20 future research at the HBEF. The major emphasis in these early studies was to determine the 

21 impact of forest land management on water yield and quality and on flood flow. 

22 The HBES originated in 1960 with the idea of the small watershed approach to studying 

23 element flux and cycling. Using the small watershed approach, studies of element-hydrologic 

24 interactions were conducted to form a basis for subsequent process-level and experimental 

25 research. In September 1987, the HBEF was awarded an LTER grant through the NSF. The 

26 overall objective of the project is to develop a better understanding of the response of northern 

27 hardwood-ecosystems to large-scale disturbances. Particular emphasis is placed on the areas of 

28 (1) vegetation structure, composition, and productivity; (2) dynamics of dead organic matter; (3) 

29 atmospheric-terrestrial-aquatic linkages; and (4) heterotroph population dynamics. Experimental 

30 manipulation has been used extensively in research at the HBEF. A number of whole watershed, 

31 stream, and lake manipulations have been conducted to test research hypotheses, obtain 
Terrestrial Acidification Case Study 

DRAFT Attachment 4, pg 24 August 2008 

1 quantitative information on pertinent environmental issues, and validate process-related 

2 formulations used in simulation models. Treatments applied at the HBEF include cutting and 

3 application of herbicides and fertilizers. Table 1.2-6 summarizes major studies at the HBEF 

4 related to red spruce that calculated critical loads of nitrogen and sulfur. 

Table 1.2-6. Major Studies at Hubbard Brook Experimental Forest 

Authors Year Title Key Finding 

Driscoll, C.T., 

et al. 

1989 Changes in the chemistry of 

surface waters: 25-year 

results at the Hubbard Brook 

Experimental Forest, NH 

A decline in the sum of basic 

cations in surface water has 

paralleled the sulfate decline in 

atmospheric deposition, preventing 

any long-term decrease in stream 

acidity. There have been no 

significant long-term trends in 

precipitation inputs or stream 

outflow of NO3

-

.

Pardo, L.H. and 

C.T. Driscoll 

1996 Critical loads for nitrogen 

deposition: case studies at 

two northern hardwood 

forests

Critical loads for nitrogen 

deposition with respect to acidity 

ranged from 0–630 eq ha-1 yr-1;

critical loads with respect to effects 

of elevated nitrogen (eutrophication 

and nutrient imbalances) ranged 

from 0–1450 eq ha-1 yr-1

.

Palmer S.M., 

C.T. Driscoll, 

and C.E. 

Johnson

2004 Long-term trends in soil 

solution and stream water 

chemistry at the HBEF; 

relationship with landscape 

position

Significant declines in strong acid 

anion concentrations were 

accompanied by declines in base 

cation concentrations in soil 

solutions draining the Oa and Bs 

soil horizons at all elevations. 

Persistently low Ca2+/Ali ratios (< 1) 

in Bs soil solutions at these sites 

may be evidence of continuing Al 

stress to trees.

Siccama, T.G, 

et al. 

2007 Population and biomass 

dynamics of trees in a 

northern hardwood forest at 

Hubbard Brook Experimental 

Forest

Tree data from 1991–2001, 

including total aboveground 

biomass, in-growth of  10 cm DBH 

trees, mortality, biomass by type, 

aboveground net primary 

productivity, and net ecosystem 

productivity.
Terrestrial Acidification Case Study 

DRAFT Attachment 4, pg 25 August 2008 

1 The specific study area in which we will carry out the critical loads analysis within HBEF 

2 has been narrowed to a portion of experimental Watershed 6. This watershed is maintained as the 

3 biogeochemical watershed for studies within the forest. It is 13.2 ha in area. Watershed 6 consists 

4 of typical northern hardwood species (e.g., sugar maple, beech, yellow birch) on the lower 90% 

5 of its area and by a montane boreal transition forest of red spruce, balsam fir, and white birch on 

6 the highest 10% of its area (Figure 1.2-3). Research within this watershed has provided data

7 concerning throughfall (1989–1992), canopy leaf chemistry, forest inventories (2002), coarse 

8 literfall data (2002 near watershed), and forest floor mass, organic matter, and chemistry (1997) 

9 (www.hubbardbrook.org).

In 1965, a grid system of 208 grid cells, each 25 x 25 square meters (m2

10 ), was instituted 

11 to serve as a plot system for analyses. Using this grid system and the 2002 Forest Inventory for 

12 the watershed, we identified nine grid units within the northeast portion of the watershed that 

13 contain large portions of red spruce trees (Figure 1.2-4). We intend to carry out the initial critical 

14 loads analysis across the area (0.56 ha) defined by these grid units. Further analyses can extend

15 to larger portions of the watershed. 
Terrestrial Acidification Case Study 

DRAFT Attachment 4, pg 26 August 2008 

1

2 Figure 1.2-3. Hubbard Brook Vegetation and Experimental Watershed Number 6. 
Terrestrial Acidification Case Study 

DRAFT Attachment 4, pg 27 August 2008 

1

2 Figure 1.2-4. Grid unit representation of Experimental Watershed Number 6 

3 (www.hubbardbrook.org). The red outline indicates the spruce/fir-dominated forest. The 

4 dotted grid cells represent the study area with high proportions of red spruce.
Terrestrial Acidification Case Study 

DRAFT Attachment 4, pg 28 August 2008 

1 2. APPROACH AND METHODS 

2 The ISA identified a key approach to quantifying the adverse effects of anthropogenic 

3 pollution as using critical loads. A critical load is “a quantitative estimate of ecosystem exposure 

4 to one or more pollutants below which significant harmful effects on specified sensitive elements 

5 of the environment do not occur, according to present knowledge” (McNulty et al., 2007). 

6 Critical loads of sulfur and nitrogen acidity for an ecosystem have been specifically defined as 

7 “the highest deposition of acidifying compounds that will not cause chemical changes leading to 

8 long-term harmful effects on ecosystem structure and function” (Nilsson and Grennfelt, 1988). 

9 “The basic idea of the critical load concept is to balance the depositions that an ecosystem is 

10 exposed to with the capacity of this ecosystem to buffer the input (e.g., the acidity input buffered 

11 by the weathering rate), or to remove it from the system (e.g., nitrogen by harvest) without 

12 harmful effects within or outside the system” (UNECE, 2004). 

13 European countries have been using critical acid loads for many years to assess nitrogen

14 and sulfur deposition in forest ecosystems. These studies have served as the platform for 

15 informing policy related to the control and reduction of emissions of acidifying pollutants. The 

16 International Cooperative Programme on Modelling and Mapping Critical Loads & Levels and 

17 Air Pollution Effects, Risks and Trends has published a series of manuals, the most recent in 

18 2004, to provide guidance on calculating and mapping critical loads. The manuals help Parties to 

19 the United Nations Economic Commission for Europe (UNECE) Convention on Long-Range 

20 Transboundary Air Pollution (CLRTAP) meet their obligations of deriving data for effects and 

21 risk assessments using harmonized methods (UNECE, 2004). Canada has also completed critical 

22 loads studies in support of efforts to design emission-reduction programs (Jeffries and Lam, 

23 1993; RMCC, 1990). Critical loads modeling was included in the 1997 Canadian Acid Rain 

24 Assessment (Jeffries, 1997) for several regions in eastern Canada. 

25 The establishment and analysis of critical loads within the United States is relatively new. 

26 The Conference of New England Governors and Eastern Canadian Premiers (NEG/ECP) funded 

27 studies that used critical loads–based methods to estimate sustainable acidic deposition rates and 

28 exceedences for upland forests representative of the New England states and the eastern 

29 Canadian Provinces in the early 2000s (NEG/ECP Forest Mapping Group, 2001). More recently, 

30 McNulty and colleagues (2007) completed a national critical loads assessment for U.S. forest 

soils at a 1 square kilometer (km231 ) scale.
Terrestrial Acidification Case Study 

DRAFT Attachment 4, pg 29 August 2008 

1 Within the ISA (U.S. EPA, 2007), EPA detailed an 8-step protocol to define the basic 

2 critical loads question in any analysis. Those steps are repeated here: 

3 1. Identify the ecosystem disturbance that is occurring (e.g., acidification, eutrophication). 

4 Not all disturbances will occur in all regions or at all sites, and the degree of disturbance 

5 may vary across landscape areas within a given region or site.

6 2. Identify the landscape receptors that are subjected to the disturbance (e.g., forests, 

7 surface waters, crops). Receptor sensitivity may vary locally and/or regionally, and the 

8 hierarchy of those receptors that are most sensitive to a particular kind of disturbance 

9 may vary as well. 

10 3. Identify the biological indicators within each receptor that are affected by atmospheric 

11 deposition (i.e., individual organism, species, population, or community characteristics). 

12 Indicators will vary geographically and perhaps locally within a given receptor type. 

13 4. Establish the critical biological responses that define “significant harm” to the biological 

14 indicators (e.g., presence/absence, loss of condition, reduced productivity, species shifts). 

15 Significant harm may be defined differently for biological indicators that are already at 

16 risk from other stressors or for indicators that are perceived as “more valued.” 

17 5. Identify the chemical indicators or variables that produce or are otherwise associated 

18 with the harmful responses of the biological indicators (e.g., streamwater pH, lake Al 

19 concentration, soil base saturation). In some cases, the use of relatively easily measured 

20 chemical indicators (e.g., surface water pH or acid neutralizing capacity [ANC]) may be 

21 used as a surrogate for chemical indicators that are more difficult to measure (e.g., Al 

22 concentration).

23 6. Determine the critical chemical limits for the chemical indicators at which the harmful 

24 responses to the biological indicators occur (e.g., pH < 5, base saturation < 5%, inorganic 

25 Al concentration greater than 2 ȝM). Critical limits may be thresholds for indicator

26 responses, such as presence/absence, or may take on a continuous range of values for 

27 continuous indicator responses, such as productivity or species richness. Critical limits 

28 may vary regionally or locally depending on factors such as temperature, existence of 
Terrestrial Acidification Case Study 

DRAFT Attachment 4, pg 30 August 2008 

1 refugia, or compensatory factors (e.g., high Ca concentration mitigates the toxicity of Al 

2 to fish and plant roots).

3 7. Identify the atmospheric pollutants that control (affect) the pertinent chemical indicators 

(e.g., deposition of SO4

2-, NO3-ҟ

, ammonium [NH4

+

4 ], nitric acid [HNO3]). Multiple 

5 pollutants can affect the same chemical variable. The relative importance of each 

6 pollutant in producing a given chemical response can vary spatially and temporally. 

8. Determine the critical pollutant loads (e.g., kg haҐ-1 yr-Ґ1

7 total deposition of sulfur or 

8 nitrogen) at which the chemical indicators reach their critical limits. Critical pollutant 

9 loads usually include both wet and dry forms of pollutant deposition. The critical 

10 pollutant load may vary regionally within a receptor or locally within a site (e.g., as 

11 factors such as elevation or soil depth vary) and may vary temporally at the same location 

12 (e.g., as accumulated deposition alters chemical responses). 

13 As shown in the eight steps above, a variety of indicators and responses can be 

14 incorporated into a critical load. Varying any one of these will result in a different critical load. 

15 As a result, there is no single definitive critical load for an ecosystem. For this case study, we 

16 will focus on forest acidification using the biological indicators of red spruce and sugar maple 

17 stands. We have determined that the chemical indicators will be the Ca to Al ratio in soil 

18 solution. The criteria chemical limits allow for the calculation of multiple critical loads 

19 depending on the risk level of interest. We examine this situation in further detail when 

20 discussing characteristics of the response curve of our analysis. 

21 Several approaches can be taken to derive critical loads. Three of the most common are 

22 empirically derived estimates, mass balances, and dynamic models (Bull et al., 2001; Bobbink et 

23 al., 2003; Jenkins et al., 2003; McNulty et al., 2007; UNECE, 2004). 

24 The UNECE CLRTAP has used empirical loads within their mapping framework. 

25 Empirical critical loads of nitrogen for specific receptor groups for natural and seminatural 

26 terrestrial ecosystems and wetland ecosystems were first presented in a background document for 

27 the 1992 workshop on critical loads held under the UNECE CLRTAP Convention at Lökeberg 

28 (Sweden) (Bobbink et al., 1992). After detailed discussion before and during the meeting, the 

29 proposed values were set at that meeting (Grennfelt and Thörnelöf, 1992). Updates to the 

30 empirical loads were completed for a 2007 update to the 2004 Manual on Methodologies and 
Terrestrial Acidification Case Study 

DRAFT Attachment 4, pg 31 August 2008 

1 Criteria for Modeling and Mapping Critical Loads and Levels and Air Pollution Effects, Risks, 

2 and Trends (henceforth referred to as the ICP Mapping and Modeling Manual) (UNECE, 2004). 

3 The ICP Mapping and Modeling Manual also provides separate critical loads for acidification of 

4 soils based on soil mineralogy and/or chemistry. For these critical loads, the guidance is 

5 presented as a range instead of a single value. Additionally, the guidance specifies modifying 

6 factors that allow the critical load value to be adjusted within the ranges presented (UNECE, 

7 2004).

8 Mass balance methods are a form of simple chemical models that relate chemical criteria 

9 for the biological impact of deposition to the deposition levels going into the ecosystem. Use of a 

10 mass balance provides a simpler form of modeling than deterministic models, but still must rely 

11 on appropriate (soil) chemical criteria (and critical limits) with proven (empirical) relationships 

12 to biological effects. These models use the principles of the mass balance to determine a critical 

13 load on the basis of what is coming into, going out of, and being stored within the ecosystem. 

14 They offer steady-state estimates of critical levels for time frames based on the data used to 

15 evaluate the balance (UNECE, 2004). 

16 Dynamic models simulate the processes of pollutant fate and transport into, out of, and 

17 within a system on a temporally varying basis. They require parameterization and, usually, 

18 calibration. “Since critical loads are steady-state quantities, the use of dynamic models for the 

19 sole purpose of deriving critical loads is somewhat inadequate. However, if dynamic models are 

20 used to simulate the transition to a steady state for the comparison with critical loads, care has to 

21 be taken that the steady-state version of the dynamic model is compatible with the critical load 

22 model” (UNECE, 2004). 

23 2.1 CHOSEN METHOD 

24 The terrestrial acidification case studies will be carried out using a critical loads 

25 assessment based on the critical loads formulation used and outlined most recently by McNulty 

26 and colleagues (2007) and the ICP Mapping and Modeling Manual (UNECE, 2004). This 

27 method, termed the Simple Mass Balance (SMB) method, has been developed and used as one of 

28 the principal methods for calculating critical loads of acidity of forest soils and ecosystems. The

29 SMB method has been applied on a variety of systems and is used widely throughout Europe 

30 (McNulty et al., 2007; Sverdrup and de Vries, 1994; UNECE, 2004). 
Terrestrial Acidification Case Study 

DRAFT Attachment 4, pg 32 August 2008 

1 The SMB model examines a long-term, steady-state balance of nitrogen and sulfur inputs, 

2 sinks, and outputs within an ecosystem. With this model, equilibrium is assumed to equal the 

3 system’s critical load. It is a single-layer model where assumptions stipulate that the soil layer is 

4 a homogeneous unit at least as deep as the rooting zone so that the nutrient cycle can be ignored. 

5 This allows the model to focus directly on growth and uptake processes. There are several 

6 additional assumptions that are included with application of the SMB model: 

7  All evapotranspiration occurs on the top of the soil profile

8  Percolation is constant through the soil profile and occurs only vertically 

9  Physico-chemical constants are assumed to be uniform throughout the whole soil profile 

10  Internal fluxes (e.g., weathering rates, nitrogen immobilization) are independent of soil 

11 chemical conditions (such as pH) (UNECE, 2004). 

12 The SMB relates deposition of nitrogen and sulfur to a critical load by incorporating mass 

13 balances for nitrogen and sulfur within the soils with the charge balance of ions in the soil 

14 leaching flux. With this method, the processes that add and remove nitrogen and sulfur from the 

15 soil, as well as the other charged elemental species, are accounted for. The leaching flux that 

16 drives this analysis provides the opportunity to specify the chemical criterion of importance to 

17 determine ecological effects using the critical loads analysis. More specifically, a critical level of 

18 leaching measured by the ANC is specified within the loading calculation. Details on the 

19 calculation of this critical level are provided below, along with discussion on the linkage to 

20 endpoints.

21 Although this method allows for the analysis of both nitrogen and sulfur deposition loads, 

22 it does not allow for the analysis of effects between the different reactive nitrogen species. 

23 However, this simplification of the nitrogen cycle is acceptable when looking at terrestrial 

24 acidification effects because the research to support the ecological endpoints of the effects due to 

25 each species of nitrogen has not been conducted. As stated by Hall in Chapter 5 of the UNECE 

26 2004 Mapping and Modeling Manual, “the possible differential effects of the deposited nitrogen 

27 species (oxidized nitrogen [NOy] or reduced nitrogen [NHx]) are insufficiently known to make a 

28 differentiation between these nitrogen species for critical load establishment” (UNECE, 2004). 
Terrestrial Acidification Case Study 

DRAFT Attachment 4, pg 33 August 2008 

1 2.1.1 Critical Load Analysis Formulation 

2 Creation of the SMB equation for acidity begins with a charge balance of ions in the soil 

3 leaching flux. Combining this charge balance with basic assumptions and mass balances around 

4 sulfur and nitrogen leaching from soils results in a simplified charge balance for the soil 

5 compartments. Critical loads for nitrogen and sulfur can then be calculated by defining a critical 

6 ANC leaching level (ANCle,crit) within that charge balance (Equation 1). For complete 

7 development of the charge and mass balance equations, please refer to the ICP Mapping and 

8 Modeling Manual (UNECE, 2004). 

The parameters in Equations 1 through 6 are expressed in units of eq ha-1 yr-1 9 except

10 where noted. Equations 1 through 3 are presented as expressed by McNulty and colleagues 

11 (2007) where they specify that calculations are for a critical load of acidity (CAL) and not any 

12 other type of critical load. 

( ) CAL S+ N =BCdep − Cldep +BCw − BCu + Ni + Nu + Nde − ANCle,crit 13 (1) 

14 where

15 CAL(S+N) = forest soil critical acid load for sulfur and nitrogen 

16 BCdep = base cation (i.e., Ca + K + Mg + Na) deposition

17 Cldep = chloride deposition; BCw is base cation weathering 

18 BCu = uptake of base cations (i.e., Ca + K + Mg) in trees 

19 Ni = nitrogen immobilization 

20 Nu = uptake of nitrogen in trees 

21 Nde = denitrification 

22 ANCle,crit = forest soil acid neutralizing capacity of CAL leaching (Gregor et al., 2004). 

Exceedence (eq ha-1 yr-1 23 ) of the critical load is calculated by comparing the CAL to the 

24 current levels of nitrogen and sulfur deposition in Equation 2.

Ex(S N) S N CAL(S N) 25 + dep = dep + dep − + (2) 

26 where

27 Ex = exceedence of the forest soil critical nitrogen and sulfur loads 

28 (S+N)dep = the deposition of S+N. 
Terrestrial Acidification Case Study 

DRAFT Attachment 4, pg 34 August 2008 

1 Higher exceedence values reflect greater exceedence of acidic deposition above the level 

2 associated with an increased likelihood of environmental harm (McNulty et al., 2007). 

3 The specification of the critical chemical criterion for effects on the receptor occurs 

4 within the calculation of ANC(le,crit). Several formulations for ANC(le,crit) exist, depending on 

5 which criterion is being used to examine the critical load for the receptor: sensitivity to pH 

6 conditions or sensitivity to the toxic effects of Al. Generally, using criterion based on hydrogen 

7 ion concentrations are recommended for soils with a high organic matter content, while using 

8 criterion based on Al concentrations are considered most appropriate for mineral soils with a low 

9 organic matter content (UNECE, 2004). For our purposes of examining tree health, most of the 

10 previous research points to Al toxicity in relation to Ca depletion as the main indicator of tree 

11 mortality and decline. Therefore, we have chosen to calculate ANC(le,crit) (Equation 3) by setting 

12 the critical Al concentration through the (BC/Al)crit ratio. Further discussion on the criterion 

13 chosen is provided in the following sections. 

crit

dep w u

1/3

crit

gibb

2/3 dep w u

(le,crit)

Al

BC

BC BC BC

1.5

Al

BC

BC BC BC

ANC 1.5

 ̧

1

·

 ̈

©

§

+ −

− ×

 ̧

 ̧

 ̧

 ̧

 ̧

1

·

 ̈

 ̈

 ̈

 ̈

 ̈

©

§

 ̧

1

·

 ̈

©

§

×

+ −

= − × ×

K

14 Q (3) 

15 where

 Q = annual runoff in m3

 ha-1 yr -1 16

17 BCdep = base cation (i.e., Ca + K + Mg) deposition 

18 BCw = forest soil base cation weathering 

19 BCu = base cation uptake by trees 

20 Kgibb = the gibbsite equilibrium constant, a function of forest soil organic matter 

21 content that affects Al solubility (Gregor et al., 2004)

22 BC/Al = the assumed critical base cation to Al ratio. 

23 A depiction of the data and calculations for Equations 1 through 3 are presented in 

24 Figure 2.1-1. Color-coding reveals those values that are calculated, reported in literature, derived 

25 from monitoring data, or constants or assumed values specified in previous studies. This 

26 multistep process produces a single critical load value and a single value for any exceedence due 

27 to current deposition levels. 
Terrestrial Ac

DRAFT Attachment 4, pg 35 

1

2 Figure 2.1-1. A diagram of the SMB method to calculate critical loads for acidity showing data derivatio3 component.
Terrestrial Acidification Case Study 

DRAFT Attachment 4, pg 36 August 2008 

1 To define a critical load function (CLF) against which all combinations of ambient 

2 nitrogen and sulfur deposition may be compared, you must calculate the maximum and minimum 

3 critical load levels for both nitrogen and sulfur. These maximum and minimum levels are defined 

4 in Equations 4 through 6 and are illustrated in Figure 2.1-2 (UNECE, 2004). With respect to 

5 sulfur, there is no minimum critical level of sulfur deposition; the immobilization, uptake, and 

6 reduction of sulfur are not considered in this critical loads framework because these processes 

7 have been shown to be insignificant contributions to the cycling of nutrients within forests 

8 (Johnson, 1984). This results in no minimum critical level of sulfur deposition. The maximum 

9 critical load of sulfur (CLmax(S)) occurs when nitrogen deposition does not exceed the nitrogen 

10 sinks (Ni + Nu + Nde) within the ecosystem. At these low nitrogen deposition levels, all acidity 

11 from deposition is due to sulfur. As such, the critical load is calculated as previously defined, but 

12 considers only sulfur (Equation 4). The minimum critical deposition load for nitrogen 

13 (CLmin(N)), the load at which the system can no longer absorb nitrogen deposition and the 

14 acidification effects begin to take place, occurs when deposition equals the nitrogen sources and 

15 sinks within the system (Equation 5). Finally, the maximum critical load level for nitrogen 

16 (CLmax(N)) occurs when there is no sulfur deposition and all acidity due to deposition comes 

17 from nitrogen. Translated into an equation, this critical load can be calculated as the sum of 

18 CLmin(N) and CLmax(S) (corrected for denitrification). 

( ) CLmax S =BCdep − Cldep +BCw − BCu − ANCle,crit 19 (4) 

min Ni Nu Nde 20 CL (N) = + + (5) 

de

max

max min 1 f

CL )S(

CL (N) CL (N)

−

21 = + (6) 

22 where

23 fde = denitrification fraction (0 < fde < 1); unitless. 

24 The definitions of these maximum and minimum critical loads levels help define the 

25 combinations of nitrogen and sulfur deposition that will fall below, meet, or exceed the critical 

26 loads. All combinations of nitrogen and sulfur deposition that fall on the function line defined by 

27 the maximum and minimum levels, the Critical Load Function (CLF), (Figure 2.1-2) are at the 

28 critical load level. Any deposition combination that falls within the grey area is below the critical 
Terrestrial Acidification Case Study 

DRAFT Attachment 4, pg 37 August 2008 

1 load level, as defined by the receptor criterion used in calculating the ANCle,crit. The white space 

2 within Figure 2.1-2 defines combinations of nitrogen and sulfur deposition that exceed the 

3 critical load. 

4

5 Figure 2.1-2. Illustration of the Critical Load Function (CLF) created from the calculated 

maximum and minimum levels of nitrogen and sulfur deposition (eq ha-1 yr-1 6 ). The grey 

7 areas show deposition levels in compliance with the established critical loads. The pink 

8 line is the maximum critical level of sulfur deposition (valid only when deposition is less 

9 than the minimum critical level of nitrogen deposition [blue dotted line]). The gold circle 

10 represents the maximum critical level of nitrogen deposition (where there is no sulfur 

11 deposition). When there is no denitrification (e.g., upland forests), the slope of the line is 

12 1.

13 Additional calculations for individual parameters used within the SMB are summarized 

14 and described below.

15 The base cation weathering rate (BCw) can be estimated through a number of different 

16 methods (UNECE, 2004). Here we present the method used by McNulty and colleagues (2007) 

17 in their national analysis (Equations 7 through 9). This method, first developed by Sverdrup and 

18 colleagues (1990), relies on a combination of parent material and clay percentage to determine 

19 the soil weathering rate. This model parameter is one in which a large quantity of uncertainty can 

20 be introduced into the model; site-specific investigations will be conducted to verify that this 

21 method is appropriate before full implementation in the model. 

 Acid Substrate: ( ) ( ) ( )

2

e 22 BC = 56 7. × %clay − .0 32× %clay (7) 

nitrogen

Deposition

S Deposition 

CLmin(N) CLmax(N)

CLmax(S)
Terrestrial Acidification Case Study 

DRAFT Attachment 4, pg 38 August 2008 

 Intermediate Substrate: ( ) ( ) ( )

2

e 1 BC = 500 + 53 6. × %clay − .0 18× %clay (8) 

2 Basic Substrate: BCe = 500 + ( ) 59 2. × %clay (9) 

3 where

 BCe = empirical soil base cation weathering rate (eq ha

-1 yr-1 4 )

5 % clay = the percentage of clay within the soil substrate. 

6 The empirical base cation weathering rate must then be corrected for air temperature and 

7 actual depth of the soil units used to determine the final BCw. Equations 10 and 11 define these 

8 correction factors. 

 ̧

 ̧

1

·

 ̈

 ̈

©

§

 ̧

 ̧

1

·

 ̈

 ̈

©

§

+

 ̧−

1

·

 ̈

©

§

+

= ×

273 Tm

A

6.2 273

A

c e 9 BC (10) BC e

10 BCw = BCc × depth (11) 

11 where

 BCc = base cation weathering rate corrected for air temperature (eq ha-1 yr-1 m

-1 12 )

13 A = Arthenius constant (3600 K) 

14 Tm = mean annual air temperature (oC) 

15 Depth = the depth of the mineral soil (m). 

16 Nitrogen and base cation uptake were calculated in the same manner by McNulty and 

17 colleagues (2007) where differentiation is made in the concentration of either base cations or 

18 nitrogen in bark and bole (Equation 12). These calculations are conducted for each forest cover 

19 type and tree species on the site under investigation. Uptake values are only relevant if wood is 

20 being removed from the forest. 

Uptake (eq ha yr ) AVI NC SG %bark 0.65 1 1

= × × × ×

− −

21 (12) 

22 where

 AVI = average forest volume increment (m3 ha-1 yr-1 23 )

24 NC = base cation or nitrogen nutrient concentration in bark and bole 

 SG = specific gravity of bark and bole wood (g cm-3 25 )

26 %bark = percentage of volume growth that is allotted to bark 
Terrestrial Acidification Case Study 

DRAFT Attachment 4, pg 39 August 2008 

1 65% = tree volume that is removed from the site (Birdsey, 1992; Hall et al., 1998; 

2 Martin et al., 1998). 

Because denitrification (the process by which NO3

-

3 is converted into gaseous nitrogen) 

4 usually occurs within water-saturated soil, an assumption of zero for denitrification in upland 

5 forests is valid. However, to provide a robust analysis, we have included the formula for 

6 denitrification provided by the ICP Mapping and Modeling Manual in Equation 13 (UNECE, 

7 2004).

 ̄

®

­ − − > +

=

0 else

f (N N N ) if N N N

N

de dep i u dep i u

de 8 (13) 

9 where

10 fde = denitrification fraction (0 < fde < 1); unitless 

11 Ndep = total nitrogen deposition. 

12 The remaining model parameters (highlighted in yellow and green in Figure 2.1-1) will 

13 be compiled from site-specific literature and applicable previous critical loads assessments. 

14 Further details are also provided in the next section. 

15 2.1.2 Data Requirements 

16 To satisfy Equations 1 through 9 used to calculate the critical loads, data requirements 

17 must be met (Table 2.1-1). There are also additional data elements that can be used to create a 

18 more robust analysis (Table 2.1-2). Tables 2.1-1 and 2.1-2 also provide information on likely 

19 sources from which to obtain the data. These data sources will be further refined upon 

20 completion of the 2002 base-case scenario. All efforts will be made to develop site-specific data 

21 rather than rely on blanket estimates of parameters from the literature. 

Table 2.1-1. Mandatory Data Requirements for Calculating Critical Loads for Nitrogen and 

Sulfur for Forest Ecosystems (as described in Duarte, 2005) 

Mandatory Data Requirement Data Type Data Source 

Wet deposition (N, S, Ca, Mg, K, Na) Atmospheric/climate data CMAQ 

Dry deposition (N, S, Ca, Mg, K, Na) Atmospheric/climate data CMAQ 

Runoff Atmospheric/climate data GSI; LE 

Stand composition Tree data FIA; GSI; TS 
Terrestrial Acidification Case Study 

DRAFT Attachment 4, pg 40 August 2008 

Mandatory Data Requirement Data Type Data Source 

Number of stems by species Tree data FIA; GSI; TS; HBFI

Nutrient concentration (N, Ca, Mg, K) by 

biomass fraction by species 

Tree data FIA; LE 

Annual biomass removal rate by species Tree data GSI; LE; FIA 

Percent of growth allocated to bark by 

species

Tree data LE 

Specific gravity of bark and bole wood by 

species

Tree data LE 

Mean annual increment by species Tree data FIA; TS; LE 

Mean annual temperature (long-term) Atmospheric/climate data GSI 

Soil depth Soil data SRG; TS 

Soil texture Soil data SRG; TS 

Parent material Soil data SRG; TS 

Soil series Soil data SRG; TS 

Organic matter percent in soil Soil data SRG; TS 

CMAQ = estimates from the Community Multi-scale Air Quality Model; LE = literature 

estimates; GSI = general site information; FIA = the Forest Inventory Analysis; TS = targeted 

study for the site; HBFI = Hubbard Brook Forest Inventory; SRG = Soil Survey Geographic 

(SSURGO) Database soils data 

Table 2.1-2. Optional Data Requirements for Calculating Critical Loads for Nitrogen and Sulfur 

for Forest Ecosystems 

Optional Data Requirement Data Type Data Source 

Latitude and longitude (center point) Site description data GSI 

Elevation Site description data GSI 

Polygon file and/or plot radius Site description data GSI 

Throughfall Atmospheric/climate data LE 

Precipitation volume (long-term) Atmospheric/climate data GSI 

Mean annual evapotranspiration Atmospheric/climate data GSI; LE 

Bulk deposition (N, S, Ca, Mg, K, Na) Atmospheric/climate data CMAQ; TS 

Biomass by species Tree data GSI; LE; FIA 

Diameter at breast height (DBH) by 

species Tree data FIA; GSI; TS; HBFI

Volume by species Tree data FIA; TS; LE 

Number of soil pits per site Soil data GSI; LE 

Soil bulk density Soil data SRG; TS 
Terrestrial Acidification Case Study 

DRAFT Attachment 4, pg 41 August 2008 

Optional Data Requirement Data Type Data Source 

Extractable nutrients (Al, Ca, Mg, K, Na) Soil data SRG; TS 

Cation exchange capacity Soil data SRG; TS 

Mineralogy Soil data SRG; TS 

Base saturation Soil data SRG; TS 

Volumetric soil moisture Soil data SRG; TS 

CMAQ = estimates from the Community Multi-scale Air Quality Model; GSI = general site 

information; LE = literature estimates; FIA = Forest Inventory and Analysis National Program; 

TS = targeted study for the site; SRG = SSURGO soils data. 

1 Each study site has been the subject of a large number of studies since its creation. 

2 Therefore, many of the long-term average parameters needed for the site are available in 

3 summaries of this general site information. In other instances, targeted studies have examined 

4 certain site characteristics. For instance, Drohan and colleagues (2002) completed a targeted 

5 study of the soils in northern Pennsylvania, which included the Kane Experimental Forest. 

6 Literature estimates are available from other critical loads analyses, such as the national analysis 

7 completed by McNulty and colleagues (2007) or previous site-specific critical loads analysis, 

8 such as the work done by Pardo and Driscoll (1996) in the HBEF. The FIA by the USFS collects, 

9 analyzes, and reports information on the status and trends of America’s forests, and, therefore, 

10 can be a great source for tree characteristics. Finally, the Soil Survey Geographic (SSURGO) 

11 Database by the USDA National Resources Conservation Service (NRCS) provides information 

12 on soil units intended for farm, landowner/user, township, or county natural-resource planning 

13 and management (NRCS, 2006). 

14 2.1.3 Data Issues and Assumptions with Method 

15 The use of the SMB critical loads analysis method on a national level has raised several 

16 issues concerning the assumptions and choices in process representation used for estimating 

17 model parameters. These issues are highlighted below to ensure that we adequately address them 

18 at the site-specific level at which we will conduct our case study analyses. 

19  Wet deposition data should be corrected for sea-salt interactions if your study site is 

20 within 70 km of the coast. 

21  Cloud deposition must be accounted for in order to not underestimate the exceedence. 
Terrestrial Acidification Case Study 

DRAFT Attachment 4, pg 42 August 2008 

1  The base cation weathering rate estimation method must be validated for the site of 

2 interest. The mineral weathering rate is very significant for sites where there are concerns 

3 about acidification because of the role it plays in buffering acidic inputs. Underestimating 

4 the weathering rate will cause the critical load to be too low. 

5  Organic matter type should be calculated by soil map unit by combining CONUS-SOIL 

6 layers with STATSGO layers using the Earth System Science Center (ESSC) technique. 

7  The base cation and nitrogen uptake values calculated are only relevant if wood is being 

8 removed from the forest. In the McNulty analysis, for instance, areas designated as 

9 wilderness in the National Wilderness Preservation System were given uptake values set 

10 to zero. In site-specific studies, it may be possible to use county-level data as a crude 

11 estimate of biomass removal. Additionally, for Class I areas, it is necessary to have 

12 information about frequency and intensity of fire. 

13  In the national analysis (McNulty et al., 2007), denitrification was set to zero to represent 

14 upland forests. Denitrification losses are often considered to be negligible and are 

15 excluded from critical load calculations, but they should be included at sites where they 

16 are significant. 

17  In the national analysis (McNulty et al., 2007), nitrogen immobilization was set to 42.86 

eq N ha-1 yr-1 18 based on average latitudes of forests in the United States. This value will be 

19 specified as a site-specific value for each of the case studies. 

20 2.2 CRITICAL LOAD ASSESSMENT RESPONSE CURVE 

21 In determining whether a critical load is exceeded, the key factors going into determining 

22 what the critical load is must be defined. The forest soil ANC is one of the most important 

23 factors in determining the critical load. This factor is determined based on the critical base cation 

24 to Al ratio [(BC/AL)crit]. In most literature studies, this ratio is set to 1.0 for coniferous forests 

25 and 10.0 for deciduous forests (McNulty et al., 2007; NEG/ECP Forest Mapping Group, 2001; 

26 Pardo and Duarte 2007; UNECE, 2004). To provide more specific estimates for the tree species 

27 of interest in these case studies, we have conducted an extensive literature search to refine this 

28 ratio for determination of an appropriate critical load that can be related to tree growth or 

29 nutrition inhibition or tree die-off. A series of studies have been identified that provide the 

30 necessary link between the Ca to Al ratio (Ca:Al) in soils to tree impacts (Table 2.2-1). 
Terrestrial Acidification Case Study 

DRAFT Attachment 4, pg 43 August 2008 

1 A study by Cronan and Grigal (1995) compiled all relevant research up to that point in 

2 time on Al stress. A review of this literature resulted in the estimation of a 50:50 risk of adverse 

3 impacts on tree growth or nutrition when the soil solution Ca:Al ratio is as low as 1.0; a 75% risk 

4 when the soil solution ratio is as low as 0.5; and nearly a 100% risk when the soil solution Ca:Al 

5 molar ratio is as low as 0.2. Many studies since have referenced this study to set chemical 

6 criterion for monitoring comparisons and critical loads calculation. Additionally, other research 

7 has built on these ratios to support the findings. For instance, Shortle and colleagues (1997) 

8 found that as the Al:Ca binding ratio in the root zone of red spruce stands increased from 0.3 to 

9 1.9, the foliar concentration of the biochemical stress marker putrescine also increased from 45 

to 145 nm g-1. This correlation of the putrescine concentration to the Al:Ca binding ratio (adj. r2

10

11 0.68, P < 0.027) suggests that foliar stress may be linked to soil chemistry. 

Table 2.2-1. Summary of Critical Endpoints for Al Effects on Tree Health 

Study Species Ca:Al Ratio Risk Level 

Cronan and Grigal, 

1995

Multiple species 1.0 50% risk 

 0.5 75% risk 

 0.2 100% risk 

Thornton et al., 1987 

(As cited in Cronan and 

Grigal, 1995) 

Red spruce 1 Threshold of 

significant impact on 

tree growth or nutrient 

content

Thornton et al., 1986 

(As cited in Cronan and 

Grigal, 1995) 

Sugar maple 0.42 to 2.5 Threshold of 

significant impact on 

tree growth or nutrient 

content

Sverdrup and 

Warfvinge, 1993 

Multiple species 0.2 to 2.8* Point of 20% 

reduction in root 

growth

Johnson et al., 1994a, b Not specified > 1.0 for 4 years High mortality 

DeWitt et al., 2001 Norway Spruce < 0.5 reduced Mg 

concentrations in 

needles in third year 

Shortle et al., 1997 Red Spruce Correlation between

Al:Ca ratio to 

biological stress marker

Use Cronan and 

Grigal risk levels for 

comparison 

Drohan et al., 2002 Sugar Maple < 1.0 Declining plots 
Terrestrial Acidification Case Study 

DRAFT Attachment 4, pg 44 August 2008 

Study Species Ca:Al Ratio Risk Level 

* Ratio presented is BC:Al, not Ca:Al 

1 For this analysis, we plan to vary the BC:Al critical ratio based on previous research 

2 (Cronan and Grigal, 1995; Drohan et al., 2002) to reflect different risk levels for tree mortality, 

3 thereby producing different ANCle,crit and resulting CLFs. For example, a critical load would be 

4 developed using the level of BC:Al shown to produce a 20% decline in growth in trees. A second 

5 critical load would be developed using the level of BC:Al shown to produce a 50% decline and 

6 so on. Other critical loads analyses have chosen to use set values for the (BC:Al)crit. For instance, 

7 in the national analysis by McNulty and colleagues (2007), values were set at 1.0 for coniferous 

8 forests (Gregor et al., 2004) and 10.0 for deciduous forests (Watmough et al., 2004). By using 

9 various (BC:Al)crit values based on the anticipated level of risk to tree health, we are able to 

10 evaluate different CLFs against the baseline deposition levels and any policy or deposition 

11 reductions scenarios we are provided (Figure 2.2-1).

12 The literature values to this point have provided a mixture of critical levels related to 

13 either the Ca:Al ratio or the BC:Al ratio. As shown in Equations 1 through 3, the critical loads 

14 analysis has been developed in terms of BC:Al. The work by Cronan and Grigal (1995) presents 

15 these risk levels in terms of Ca:Al and not BC:Al. The Ca:Al ratio is not directly transferrable to 

16 BC:Al ratios, although work has been done to present the critical load framework in terms of 

17 Ca:Al (Heywood et al., 2006). These alterations of the basic SMB method require the estimation 

18 of Ca weathering and leaching rates, which introduce further sources of uncertainty to the model 

19 calculations in addition to the estimation of those rates for base cations. For this reason, we have 

20 chosen to proceed with BC:Al ratios related to risk levels. Sverdrup and Warfvinge (1993) have 

21 provided such ratio-risk level relationships. At this time, we present the ratios reported by 

22 Cronan and Grigal in the graphics for illustrative purposes. The actual analysis will be conducted 

23 using critical BC:Al levels derived by Sverdrup and Warfvinge. If the ratios for BC:Al are not to 

24 provide relatable risk levels, we will proceed with the methods developed by Heywood and 

25 colleagues (2006) to use the Ca:Al ratios reported by Cronan and Grigal (1995).
Terrestrial Acidification Case Study 

DRAFT Attachment 4, pg 45 August 2008 

1

2 Figure 2.2-1. An example of the response curves that relate deposition scenarios to 

3 critical loads calculated based on varying levels of expected risk in tree mortality. 

4 3. RESULTS 

5 At this time we do not have current results for the 2002 base-case (“current condition”) 

6 scenario. The intention of this draft report is to lay out the methods that will be used to conduct 

7 this base- case modeling run. We intend to perform the base-case scenario during the summer of 

8 2008. The 2002 base-case scenario and future case study assessments, when combined, will 

9 result in a figure, such as the one in Figure 2.2-1, which can be used to assess the most feasible 

10 and beneficial nitrogen and sulfur reduction scenario.

11 3.1 CURRENT STATE OF SYSTEMS 

12 In future drafts of the report, this section will summarize the results of the 2002 base-case 

13 scenario, which will rely on Community Multi-scale Air Quality Model (CMAQ) output for that 

14 year based on various sources’ measured emissions rates. Critical loads research at each of the 

15 case study sites is either not available in the literature, or exists for a period other than the one of 

16 interest. For now, we present some results on the health of trees, deposition levels, and any 

17 available thresholds that will provide a basis of comparison for the 2002 base-case scenario. 

CLF based on 100% risk 

level

CLF based on 75% risk 

level

CLF based on 50% risk 

level

Current level of nitrogen 

and S deposition

nitrogen

Deposition

S Deposition 

nitrogen and S deposition 

with policy scenario #1

nitrogen and S deposition 

with policy scenario #2
Terrestrial Acidification Case Study 

DRAFT Attachment 4, pg 46 August 2008 

1 3.1.1 Sugar Maple 

2 Horsley et al. (2000) found that the most important factors associated with sugar maple 

3 health were foliar levels of Mg and Mn, as well as defoliation history. They propose that acid 

4 deposition may contribute to the low base cation status on upper slopes, but indicate that the 

5 relative contributions of geologic factors and acidic deposition to low base cation status and 

6 sugar maple decline remain unquantified in northern Pennsylvania. Bailey et al. (2005) found 

7 that between between 1967 and 1997, there were significant decreases in exchangeable Ca and 

8 Mg concentrations and pH at all depths in the soils of the Allegheny Plateau. Atmospheric 

deposition of various solutes, including NO3

-

 and SO4

2- 9 , are presented in Table 3.1-1.

Table 3.1-1. Atmospheric deposition (kg ha-1 yr-1) of solutes at the Kane Experimental Forest, 

PA, from the National Atmospheric Deposition Program/National Trends Network 

Solute 1992 1993 1994 1995 1996 1997 1998 

NO3-N 4.66 4.68 5.31 4.01 4.53 4.62 4.16 

NH4-N 2.54 1.79 2.81 2.17 2.59 2.51 1.89 

Inorganic N 7.20 6.48 8.12 6.19 7.12 7.13 6.05 

SO4-S 11.52 9.06 12.15 7.38 9.12 9.07 8.07 

Ca2+ 1.37 1.16 1.30 0.98 1.27 1.32 0.88 

Mg2+ 0.18 0.39 0.22 0.15 0.20 0.19 0.13 

K

+

 0.20 0.63 0.27 0.14 0.45 0.17 0.13 

Na+ 0.62 0.64 0.54 0.46 0.81 0.52 0.34 

Cl- 1.56 1.98 1.63 1.22 1.73 1.47 1.20 

H

+

 0.72 0.62 0.82 0.53 0.60 0.63 0.59 

Note: Values from 1993 and 1997 do not meet all NADP/NTN criteria for completeness 

(Lewis and Likens, 2007). 

10 3.1.2 Red Spruce 

11 There are two studies that can be used to summarize work at HBEF: one is a site-specific 

12 critical loads study conducted by Pardo and Driscoll (1996), and another is a statewide 

13 assessment of sustainable deposition by the Forest Mapping Group (NEG/ECP Forest Mapping

14 Group, 2005). 

15 In the Pardo study, critical loads were calculated over a time-series spanning 22 years, 

16 using long-term biogeochemical data collected at HBEF. Critical load calculations were made 

17 for both HBEF and Huntington Wildlife Forest using four charge and mass balance equations:
Terrestrial Acidification Case Study 

DRAFT Attachment 4, pg 47 August 2008 

1 steady-state water chemistry method, nitrogen mass balance method, basic cation mass balance 

2 method, and steady-state mass balance method. Data types included stream water and 

3 precipitation chemistry, precipitation volume and stream water flux, biomass increment, soil 

4 pools and increment, and mineral weathering rate. Calculations for HBEF were made for three 

5 periods, 1965 to1976, 1977 to 1981, and 1982 to 1986, each having a different rate of biomass 

6 accumulation. Critical loads of N, with respect to acidity, for Huntington Wildlife Forest and 

HBEF ranged from 0-630 mol ha-1 yr-1 7 . Critical loads of nitrogen, with respect to elevated 

nitrogen (eutrophication and nutrient imbalances) ranged from 0-1450 mol ha-1 yr-1 8 . Table 3.1-2

9 summarizes the critical loads calculated using each method. 

Table 3.1-2. Critical Load Calculations for the Hubbard Brook Experimental Forest 

Model Critical load type 

1965–

1976

1977–

1981

1982–

1986

1965–

1986

Steady-state water 

chemistry 

Critical [NO3

-

] (ȝeq

L

-1)

11 -1 -4 5 

Nitrogen mass 

balance

Critical nitrogen load 

(mol ha-1 yr-1)

1452 923 133 1033 

Basic cation mass 

balance

Critical nitrogen load 

(eq ha-1 yr-1)

62 133 91 84 

Modified basic cation 

mass balance 

Critical nitrogen load 

(eq ha-1 yr-)

1405 770 -45 931 

Critical nitrogen load 

low (eq ha-1 yr-

)

Steady-state balance 498 630 606 552

Critical nitrogen load 

high1 (eq ha-1 yr-)

-433 -240 -236 -334 

1 Negative critical load values were set to 0 for analysis purposes in the original work (Pardo 

and Driscoll, 1996). 

10 The Forest Mapping Group found that a 50% reduction in nitrogen and sulfur deposition 

11 can reverse damaging forest effects by 76% in New Hampshire. They also found that 

12 atmospheric deposition of nitrogen and sulfur during 1999 to 2003 (Figure 3.1-1) exceeded the 

13 critical load in approximately 18% of the forested area of New Hampshire. The critical loads 

14 estimated by the group (Figure 3.1-2) ranged widely in New Hampshire and Vermont (0 – 21 

keq ha-1y

-1 15 ) as a result of the diverse geology and climate of the region. It must be remembered 

16 that the Forest Mapping Group used an alternative form of the critical loads method, relying on 
Terrestrial Acidification Case Study 

DRAFT Attachment 4, pg 48 August 2008 

1 sustainable deposition, and thus, their findings will not be directly comparable to the results that 

2 will be calculated under the methods described for this case study. 

3

4 Figure 3.1-1. Average annual atmospheric deposition of sulfur and nitrogen (particle + 

5 SO2 + precipitation + cloud water) to New Hampshire and Vermont (1999–2003). 

Nitrogen includes both ammonium and NO3

-

6 forms (NEG/ECP Forest Mapping Group, 

7 2005).
Terrestrial Acidification Case Study 

DRAFT Attachment 4, pg 49 August 2008 

1

2 Figure 3.1-2. Critical loads of sulfur and nitrogen for upland forest areas of New 

3 Hampshire and Vermont (NEG/ECP Forest Mapping Group, 2005). 

4 3.2 FUTURE CASE STUDY ASSESSMENTS 

5 The future case study assessments will include the 2002 base-case scenario and the future 

6 policy scenarios designated by alternative CMAQ inputs to the critical loads model. The data 

7 requirements for the case study assessments will all be satisfied upon completion of the 2002 

8 base-case scenario. The actual critical loads functions (shown in Figure 2.2-1) will be completed 

9 with the base-case scenario because the critical loads depend on model parameters for each site 

10 that are independent of the estimates of the nitrogen and sulfur depositions measures that will be 
Terrestrial Acidification Case Study 

DRAFT Attachment 4, pg 50 August 2008 

1 derived from the various CMAQ deposition scenarios (assuming that base cation deposition 

2 values will not be derived from CMAQ data, but rather estimated from site-specific literature, 

3 studies, and monitoring results for the time period of interest). The various deposition policy 

4 scenarios, in addition to the base case, can then be plotted against the CLFs for final analysis, as 

5 depicted in Figure 2.2-1. 

6 If additional information is desired, there are methods presented in the UNECE ICP 

7 Mapping and Modeling Manual that allow for critical loads to be examined on the basis of 

8 decreases in only one of the deposition parameters instead of decreases in both nitrogen and 

9 sulfur. For further details on this type of analysis, please refer to the manual (UNECE, 2004). 

10 4. IMPLICATIONS FOR OTHER SYSTEMS 

11 Critical loads analyses have been widely used across all of Europe and are now required 

12 in some instances. Canada has also used critical loads for deposition policy scenarios. Within the 

13 United States, there has been one national analysis and several targeted analyses using different 

14 critical loads methods in the recent literature. Statewide assessments were conducted by the 

15 Forest Mapping Group within New England using a sustainable deposition approach to critical 

16 loads (NEG/ECP Forest Mapping Group, 2001). These applications illustrate that a critical loads 

17 method can be applied to a wide variety of geographic and climatic terrestrial ecosystems. 

18 The scalability of the analysis can also be assessed through these previous analyses. The 

19 national analysis conducted for the United States (McNulty et al., 2007) required the use of 

20 several simplifying assumptions that left out key points highlighted by others, such as cloud 

21 deposition (NEG/ECP Forest Mapping Group, 2001) and correction for sea-salt interactions. 

22 These issues were listed in Section 2.1.3. The statewide analyses conducted by the Forest 

23 Mapping Group addressed most of the issues listed in that section showing that larger scale 

24 applications are possible. Ultimately, the scalability of a critical loads analysis depends on the 

25 data sources available, the assumptions made within the study design, and the internal scale of 

26 the model calculations. 

27 The question remains on whether the chemical criterion of using the Ca to Al ratio to 

28 provide the link to biological indicators can be used within the same critical loads method in 

29 various systems across the country. The apparent answer is “yes.” This is because a plethora of 

30 studies have examined nutrient imbalances in soil solution and their effects on tree health and 
Terrestrial Acidification Case Study 

DRAFT Attachment 4, pg 51 August 2008 

1 foliar chemistry. Numerous other studies have linked the nutrient imbalances in soil solution to 

2 Al increases in soil solution. The nutrient imbalances and Al increases have also been shown to 

3 stem from acidic deposition. Although the links are not direct in most literature studies, the areas 

4 covered and the nutrients and cations studied provide enough information to discern the same 

5 effects across regions. 

6 5. UNCERTAINTY 

7 Because the SMB model examines a long-term, steady-state balance of nitrogen and 

8 sulfur inputs, sinks, and outputs within an ecosystem and does not represent dynamic soil 

9 processes, the results of the model are general system estimates and not temporally varying cause 

10 and effect results. Another concern with using this method is that the representation of internal 

11 fluxes (e.g., weathering rates, nitrogen immobilization) is independent of soil chemical 

12 conditions (such as pH) (UNECE, 2004). The use of these representations relies on the 

13 assumption that soil conditions do not vary over short periods and can be accurately represented 

14 by longer-term averages. Systems that experience rapid changes in soil and vegetation 

15 characteristics (i.e., due to large storm or erosion events) will be sources of greater uncertainty in 

16 the results than systems that remain relatively stable over a number of years. 

17 Additionally, as widely used as the SMB is, there are still fundamental issues of 

18 uncertainty surrounding the calculation method. The uncertainty comes from the equation’s 

19 dependence on assumptions that the researcher must make, as well as the need to pull data from a 

20 variety of sources. For example, it is difficult to obtain the most accurate estimate of the forest 

21 soil’s weathering rate based on forest system disparities. Also, calculating the forest system’s 

22 ANC relies on multiple variables that can be very difficult to estimate and often introduces a 

23 wide range of critical loads. Li and McNulty (2007) tested SMB’s accuracy and reliability across 

24 a large scale in the United States. The results of the study indicated that uncertainty in using 

25 SMB to assess critical loads of acid deposition came primarily from varying assessments of base 

26 cation weathering and ANC, with each respectively contributing 49% and 46% to the total 

27 variability in CAL estimates. Uncertainty in base cation weathering was dominated by the base 

28 cation weathering base rate (74%), with additional large contributions from soil depth and 

29 temperature. The most important parameters to sensitivity in ANCle,crit were the base cation 

30 weathering base rate, soil depth, growth rate, stem wood density, and base cation weathering 
Terrestrial Acidification Case Study 

DRAFT Attachment 4, pg 52 August 2008 

1 percent. A 20% increase in each of these parameters led to a > 90% increase in ANCle,crit.

2 Overall, base cation weathering base rate, soil depth, and soil temperature were also critical 

3 parameters to the model (Li and McNulty 2007). 

4 Thus, improvements to the model must be made to reduce the amount of error in 

5 obtaining estimates of the two most important factors—base cation weathering rate and the soil’s 

6 ANC—in order to reduce the uncertainty in the range of CALs that are developed. There are a 

7 number of different methods that can be used to estimate the magnitude of weathering. The 

8 seven different approaches used to quantify soil weathering rates presented in the UNECE ICP 

9 Mapping and Modeling Manual (UNECE, 2004) are as follows:

10  The use of soil type and general bedrock geology to approximate the cation release 

11  Assignment according to the proceedings of the Skokloster workshop (Nilsson and 

12 Grennfelt, 1988)

13  Approximation using the Steady-State Water Chemistry model (Henriksen et al., 1992) 

14  The base mineral index correlation model, total analysis correlation model, and 

15 mineralogy correlation model (Equations 7 through 9)

16  Calculation with the regional version of the PROFILE model. 

17 Although the models vary in accuracy (i.e., with the soil type and bedrock geology 

18 method and the Skokloster workshop method requiring the least amount of data), which model 

19 the researcher would use to calculate soil weathering and the subsequent release of base cations 

20 depends on the amount and types of data available. 

21 ANCle,crit can be calculated (Equation 3) by either setting the critical Al and hydrogen ion 

22 concentrations and converting them to critical fluxes or by defining the fluxes in relation to a 

23 critical molar ratio of Ca or base cations to Al. Setting critical Al and hydrogen ion 

24 concentrations is yet another variable that may introduce variability. Critical concentrations can 

25 be set for Al, hydrogen ion, or both, that are related to adverse effects on the chosen receptor. 

26 Different gibbsite equilibrium constant values can also affect the critical load; therefore, it is 

27 important that the value selected is related to the percentage of organic matter in the soil at the 

28 rooting zone of the selected receptor. Different critical molar ratio values affect the critical load,

29 as does the choice of which ratio—(Ca:Al)crit or (BC:Al)crit —is applied.
Terrestrial Acidification Case Study 

DRAFT Attachment 4, pg 53 August 2008 

1 For initial analyses, we will seek to control for and quantify uncertainty by using a 

2 combination of the mineralogy correlation model and site-specific literature estimates for the 

3 weathering rates and through variation of the (BC:Al)crit ratio within the CLF calculations. If use 

4 of these methods provides unacceptable results, the other methods listed above will be 

5 investigated. 

6 6. CONCLUSIONS 

7 The following topics will be developed more fully in later drafts after comment period: 

8  The critical loads method will be based on the SMB equation established 

9  Analyses using the SMB equation will be conducted on two sites dominated by either red 

10 spruce or sugar maples 

11  The SMB relies on estimates of the critical leaching level for ANC. 

12  Imbalances in Ca, Mg, and Al in forest soils have been shown to result from acidic 

13 deposition.

14  Tree species have been shown to be sensitive to levels of Ca and Al in forest soil 

15 solutions. 

16  The base cation to Al or Ca to Al ratio can be used within the SMB method to provide 

17 chemical criteria of importance to the biological indicators of red spruce and sugar 

18 maples. 

19  Base cation to Al ratios of 1.0, 0.5, and 0.2 will be used to show risk levels for adverse 

20 effects of 50%, 75%, and 100%, respectively. 

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9 Palmer, S.M., C.T. Driscoll, and C.E. Johnson. 2004. Long-term trends in soil solution and 

10 stream water chemistry at the Hubbard Brook Experiment Forest: relationship with 

11 landscape position. Biogeochemistry 68:51–70.

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13 Ecosystems in Great Smoky Mountains National Park using Critical Loads for Sulfur and 

14 Nitrogen. U.S. Department of Agriculture, Forest Service. Prepared for Tennessee Valley 

15 Authority, S. Burlington, VT. 

16 Pardo, L.H., and C.T. Driscoll. 1996. Critical loads for nitrogen deposition: case studies at two 

17 northern hardwood forests. Water, Air, and Soil Pollution 89:105–128.

18 Parker, D.R., L.W. Zelazny, and T.B. Kinraide. 1989. Chemical speciation and plant toxicity of 

19 aqueous aluminum. Pp. 117–145 in Environmental Chemistry and Toxicology of 

20 Aluminum. Edited by T.E. Lewis. Washington, DC: American Chemical Society. 

21 Reuss, J.O. 1983. Implications of the calcium-aluminum exchange system for the effect of acid 

22 precipitation on soils. Journal of Environmental Quality 12:591–595.

23 Reuss, J.O., and D.W. Johnson. 1985. Effect of soil processes on the acidification of water by 

24 acid deposition. Journal of Environmental Quality 14:26–31.

25 Richter, D.D., D. Markewitz, C.G. Wells, H.L. Allen, R. April, P.R. Heine, and B. Urrego. 1994. 

26 Soil chemical change during three decades in an old-field loblolly pine (Pinus taeda L.) 

27 ecosystem. Ecology 75:1463–1473.
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1 RMCC (Federal/Provincial Research and Monitoring Coordinating Committee). 1990. The 1990 

2 Canadian Long-Range Transport of Air Pollutants and Acid Deposition Assessment 

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6 eight northeastern USA forested research sites. Forest Ecology Management 188:317–

7 335.

8 Schaberg, P.G., D.H. DeHayes, and G.J. Hawley. 2001. Anthropogenic calcium depletion: a 

9 unique threat to forest ecosystem health? Ecosystem Health 7:214–228.

10 Schlegel, H., R.G. Amundson, and A. Hüttermann. 1992. Element distribution in red spruce 

11 (Picea rubens) fine roots; evidence for aluminum toxicity at Whiteface Mountain. 

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13 Shortle, W.C., and K.T. Smith. 1988. Aluminum-induced calcium deficiency syndrome in 

14 declining red spruce. Science 240:1017–1018.

15 Shortle, W.C., K.T. Smith, R. Minocha, G.B. Lawrence, and M.B. David. 1997. Acidic 

16 deposition, cation mobilization, and biochemical indicators of stress in healthy red 

17 spruce. Journal of Environmental Quality 26:871–876.

18 Siccama, T.G., T.J. Fahey, C.E. Johnson, T.W. Sherry, E.G. Denny, E.B. Girdler. G.E. Likens, 

19 and P.A. Schwarz. 2007. Population and biomass dynamics of trees in a northern 

20 hardwood forest at Hubbard Brook. Canadian Journal of Forest Research 37(4):737–

21 749.

22 Sverdrup, H., and P. Warfvinge. 1993. The effect of soil acidification on the growth of trees, 

23 grass and herbs as expressed by the (Ca+ Mg+ K)/Al ratio. Reports in Ecology and 

24 Environmental Engineering 2. Lund University, Department of Chemical Engineering, 

25 Lund, Sweden. 

26 Sverdrup, H., and W. de Vries. 1994. Calculating critical loads for acidity with the simple mass 

27 balance equation. Water, Air, and Soil Pollution 72:143–162.
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1 Sverdrup, H., W. de Vries, and A. Henriksen. 1990. Mapping Critical Loads: A Guidance to the 

2 Criteria, Calculations, Data Collection and Mapping of the Critical Loads. Miljørapport 

3 14 1990:98. Nordic Council of Ministers, Copenhagen, Denmark. 

4 Thorton, F.C., M. Schaedle, and D.J. Raynal. 1986. Effect of aluminum on the growth of sugar 

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7 in solution culture. Environmental and Experimental Botany 27:489–498.

8 U.S. EPA (Environmental Protection Agency). 2007. Integrated Science Assessment for Oxides 

9 of Nitrogen and Sulfur Environmental Criteria. First External Review Draft. EPA/600/R10 07/145A. U.S. EPA, Office of Research and Development, National Center for 

11 Environmental Assessment, Research Triangle Park, NC. December. 

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14 Research Triangle Park, NC 

15 USFS (U.S. Forest Service). 1999. Kane Experimental Forest. Online information. U.S. 

16 Department of Agriculture, USFS, Kane Experimental Forest, Forestry Sciences 

17 Laboratory, Irvine, PA. Available at http://www.fs.fed.us/ne/warren/kane.html (accessed 

18 June 17, 2008). 

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20 U.S. Department of Agriculture, USFS, Northeastern Research Station, Newtown Square, 

21 PA. Available at http://www.fs.fed.us/ne/newtown_square/research/experimental22 forest/hubbard-brook.shtml (accessed June 17, 2008).

23 USFS (U.S. Forest Service). 2008b. Kane Experimental Forest. Online information. U.S. 

24 Department of Agriculture, USFS, Northeastern Research Station, Newtown Square, PA. 

25 Available at http://www.fs.fed.us/ne/newtown_square/research/experimental26 forest/kane.shtml (accessed June 17, 2008). 
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1 UNECE (United Nations Economic Commission for Europe). 2004. Manual on Methodologies 

2 and Criteria for Modeling and Mapping Critical Loads and Levels and Air Pollution 

3 Effects, Risks, and Trends. Convention on Long-Range Transboundary Air Pollution, 

4 Geneva Switzerland. Available at http://www.icpmapping.org (accessed August 16, 

5 2006).

6 USGS (U.S. Geological Survey). 1999. Digital representation of “Atlas of United States Trees” 

7 by Elbert L. Little, Jr. Digital Version 1.0. U.S. Department of the Interior, U.S. 

8 Geological Survey, Denver, CO. Available at http://esp.cr.usgs.gov/data/atlas/little/

9 (accessed 25 July 25, 2008). 

10 Wargo, P.M., K. Vogt, D. Vogt, Q. Holifield, J. Tilley, G. Lawrence, and M. David. 2003. 

11 Vitality and chemistry of roots of red spruce in forest floors of stands with a gradient of 

12 soil Al/Ca ratios in the northeastern United States. Canadian Journal of Forest Research 

13 33:635–652.

14 Watmough, S.A., M.C. Eimers, J. Aherne, and P.J. Dillon. 2004. Climate effects on nitrate 

15 export from forested catchments in south central Ontario. Environmental Science and 

16 Technology 38:2383–2388.

17 Webster, K.L., I.F. Creed, N.S. Nicholas, H.V. Miegroet. 2004. Exploring interactions between 

18 pollutant emissions and climatic variability in growth of red spruce in the Great Smoky 

19 Mountains National Park. Water, Air, and Soil Pollution 159:225–248.

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9 ATTACHMENT 5 

10 AQUATIC NUTRIENT ENRICHMENT 

11 CASE STUDY 
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11 EPA Contract Number EP-D-06-003 

12 Work Assignment 2-44 

13 Project Number 0209897.002.044 

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23 Prepared for 

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25 U.S. Environmental Protection Agency 

26 Office of Air Quality Planning and Standards 

27 Research Triangle Park, NC 27709 

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33 RTI International 

34 3040 Cornwallis Road

35 Research Triangle Park, NC 27709-2194 

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Aquatic Nutrient Enrichment Case Study 

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1 CONTENTS 

2 1. Background............................................................................................................................... 1 

3 1.1 Indicators, Endpoints, and Ecosystem Services............................................................... 3 

4 1.2 Case Studies..................................................................................................................... 9 

5 1.2.1 Case Study Selection............................................................................................ 9 

6 1.2.2 Potomac River and Estuary................................................................................ 15 

7 1.2.3 Neuse River and Estuary.................................................................................... 18 

8 2. Approach and Methods........................................................................................................... 25 

9 2.1 Modeling........................................................................................................................ 25 

10 2.2 Chosen Method .............................................................................................................. 28

11 2.2.1 SPARROW ........................................................................................................ 29 

12 2.2.1.1 Background and Description............................................................... 29 

13 2.2.1.2 Key Definitions for Understanding SPARROW Modeling................ 34 

14 2.2.1.3 Concepts of Importance to Case Study SPARROW Application....... 36 

15 2.2.1.4 Consideration of Ammonia in Total Reactive Nitrogen Load............ 37 

16 2.2.2 ASSETS Eutrophication Index .......................................................................... 38 

17 2.2.2.1 Background and Description............................................................... 38 

18 2.2.2.2 Applications and Updates ................................................................... 44 

19 2.2.3 Assessments Using Linked SPARROW and EI ................................................ 45 

20 3. Results..................................................................................................................................... 48 

21 3.1 Current State of Systems................................................................................................ 48 

22 3.1.1 Potomac River and Estuary Summary ............................................................... 48 

23 3.1.1.1 SPARROW Assessment ..................................................................... 49 

24 3.1.1.2 ASSETS EI Assessment ..................................................................... 55 

25 3.1.2 Neuse River and Estuary Summary ................................................................... 59 

26 3.1.2.1 SPARROW Assessment ..................................................................... 59 

27 3.1.2.2 ASSETS EI Assessment ..................................................................... 64 

28 3.2 Future Case Study Assessments..................................................................................... 65 

29 3.2.1 Potomac River Watershed.................................................................................. 66 

30 3.2.2 Neuse River Watershed...................................................................................... 68 

31 3.2.3 Discussion on Analysis of Changing Nitrogen Loads....................................... 70 

32 4. Implications for Other Systems .............................................................................................. 71

33 5. Uncertainty.............................................................................................................................. 71 

34 6. Conclusions............................................................................................................................. 73 

35 7. References............................................................................................................................... 74 

36 FIGURES

37 Figure 1.1-1 Descriptions of the five eutrophication indicators used in the NEEA.................... 5 

38 Figure 1.1-2 A simplified schematic of eutrophication effects on an aquatic ecosystem. .......... 6 

39 Figure 1.1-3 An illustrated representation of eutrophication measures through the use of 

40 indicators and influencing factors from the NEEA................................................. 6 
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1 Figure 1.2-1 The Potomac River Watershed and Estuary. ........................................................ 16 

2 Figure 1.2-2 The Neuse River Watershed and Estuary. ............................................................ 21 

3 Figure 2.2-1 Modeling methodology for case study.................................................................. 29 

4 Figure 2.2-2 Mass balance description applied to the SPARROW model formulation. ........... 30 

5 Figure 2.2-3 Conceptual illustration of a reach network. .......................................................... 31 

6 Figure 2.2-4 SPARROW model components............................................................................ 33 

7 Figure 2.2-5 Influencing factors description and decision matrix............................................. 39 

8 Figure 2.2-6 Overall eutrophic condition description and decision matrix............................... 42

9 Figure 2.2-7 Detailed descriptions of primary and secondary indicators of 

10 eutrophication ....................................................................................................... 43 

11 Figure 2.2-8 Future outlook description and decision matrix.................................................... 44 

12 Figure 2.2-9 Conceptual approach to eutrophication management using the ASSETS EI 

13 assessment............................................................................................................. 46 

14 Figure 2.2-10 Example of response for case study analysis........................................................ 46 

15 Figure 3.1-1 Atmospheric deposition inputs based on interpolation of wet nitrate 

16 measures from NADP for the late 1990s’ Chesapeake Bay SPARROW 

17 application............................................................................................................. 54 

18 Figure 3.1-2 Total nitrogen yields from all sources and from atmospheric deposition 

19 only as predicted by the late 1990s’ Chesapeake Bay SPARROW 

20 application............................................................................................................. 55 

21 Figure 3.1-3 The ASSETS EI scores for the Potomac River Estuary ....................................... 56 

22 Figure 3.1-4 Background atmospheric total nitrogen deposition from NADP, 

23 CASTNET, and modeled data over the time period of 1996–2000...................... 60 

24 Figure 3.1-5 The ASSETS assessment summary for the Neuse River Estuary from the 

25 2007 NEEA Update .............................................................................................. 65 

26 Figure 3.2-1 Atmospheric deposition inputs based on CMAQ simulations for 2002 for 

27 the Potomac River watershed and estuary. ........................................................... 67 

28 Figure 3.2-2 Atmospheric deposition inputs based on CMAQ simulations for 2002 for 

29 the Neuse River watershed and estuary. ............................................................... 69 

30 TABLES

31 Table 1.1-1 Key Indicators of Nutrient Enrichment Due to Reactive Nitrogen, 

32 Including NOx ......................................................................................................... 4 

33 Table 1.1-2 Assessment Endpoints for Nutrient Enrichment Due to Deposition of 

34 Reactive Nitrogen, Including NOx .......................................................................... 7 

35 Table 1.1-3 Ecosystem Services for Aquatic Systems Affected by Nutrient Enrichment......... 7 

36 Table 1.2-1 Summary of Indicators, Mapping Layers, and Models for Targeted 

37 Ecosystems.............................................................................................................. 9 

38 Table 1.2-2 Science Advisory Board/EES Listing of Potential Assessment Areas for 

39 Evaluation of Benefits of Reductions in Atmospheric Deposition with 

40 Respect to Aquatic Nutrient Enrichment .............................................................. 11 

41 Table 1.2-3 Potential Assessment Areas for Aquatic Nutrient Enrichment Identified in 

42 the Draft ISA......................................................................................................... 13 

43 Table 1.2-4 Physical Characteristics of the Potomac River Estuary (NEEA Estuaries 

44 Database)............................................................................................................... 16 
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DRAFT Attachment 5, pg v August 2008 

1 Table 1.2-5 Hydrological Characteristics of the Potomac River Estuary (NEEA 

2 Estuaries Database)............................................................................................... 17 

3 Table 1.2-6 Neuse River and Estuary Physical Characteristics (NEEA Estuaries 

4 Database)............................................................................................................... 22 

5 Table 1.2-7 Neuse River Basin Land Use and Population (NEEA Estuaries Database) ......... 22 

6 Table 1.2-8 Neuse River and Estuary Hydrology (NEEA Estuaries Database)....................... 23 

7 Table 2.1-1 Examples of SPARROW Applications................................................................. 33 

8 Table 3.1-1 Key Facts/Aspects of the Potomac Case Study .................................................... 51 

9 Table 3.1-2 Key Facts/Aspects of the Neuse Case Study ........................................................ 61 

10
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1 ACRONYMS AND ABBREVIATIONS 

2 ASSETS Assessment of Estuarine Trophic Status 

3 CAFO confined animal feeding operation 

4 CASTNet Clean Air Status and Trends Network 

5 CMAQ Community Multiscale Air Quality model. 

6 CO2 carbon dioxide 

7 DIN dissolved inorganic nitrogen 

8 EDA Estuarine Drainage Areas 

9 EPA U.S. Environmental Protection Agency 

10 GIS geographic information systems 

11 HAB harmful algal bloom 

12 HSPF Hydrologic Simulation Program-FORTRAN 

13 HUC hydrologic unit code 

14 INCA Integrated Nitrogen in Catchments 

15 ISA Integrated Science Assessment 

16 km kilometer 

km2

17 square kilometer 

18 MAGIC Model of Acidification of Groundwater in Catchments 

19 MD DNR Maryland Department of Natural Resources 

20 MEA Millennium Ecosystem Assessment 

m

i2 21 square mile 

22 NAAQS National Ambient Air Quality Standards 

23 NCCR National Coastal Condition Report 

24 NEEA National Estuarine Eutrophication Assessment 

25 NOAA National Oceanic and Atmospheric Administration 

26 NOx nitrogen oxides 

27 OEC Overall Eutrophic Condition 

28 OHI Overall Human Influence 

29 QA quality assurance 

30 QC quality control 

31 QUAL2K Enhanced Stream Water Quality Model 

32 RCA/ECOMSED Row Column AESOP/Estuary and Coastal Ocean Model with Sediment 

33 Transport

34 RF1 Reach File version 1 

35 RHESSys Regional Hydeo-Economic Simulation System 

36 SAV submerged aquatic vegetation 

37 SOx sulfur oxides 

38 SPARROW Spatially Referenced Regression on Watershed

39 STORET STOrage and RETrieval 

40 USGS U.S. Geological Survey 

41 WASP Water Quality Analysis Simulation Program 

42
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1 1. BACKGROUND 

2 One classification of effects targeted for this risk and exposure assessment is nitrogen and 

3 sulfur enrichment of ecosystems in response to nitrogen oxides (NOx) and sulfur oxides (SOx)

4 deposition. Enrichment effects are caused by nitrogen or sulfur deposition, but are dominated by 

5 nitrogen deposition, which is the focus of this case study. Nitrogen enrichment can result in 

6 eutrophication in aquatic systems (see Section 4.3 of the the U.S. Environmental Protection 

7 Agency’s [EPA’s] draft Integrated Science Assessment [ISA], U.S. EPA, 2007). 

8 Because ecosystems may respond differently to enrichment, it will be necessary to first 

9 perform risk and exposure assessment case studies unique to the effect and ecosystem type. We 

10 will assess the feasibility of consolidating the effects and/or ecosystems in the risk and exposure 

11 assessment and, where feasible, perform a broader characterization. However, some ecosystems 

12 and their effects may be too unique to consolidate into a broad characterization. 

13 Upon completion of all risk and exposure assessment case studies, the results of the 

14 assessments performed for unique combinations of effects and ecosystem types will be presented 

15 together to facilitate decision making on the total effects of nitrogen and sulfur deposition. 

16 Ecosystem services that relate to the effects will be identified and valued, if possible. Ecosystem 

17 services provide an additional way to compare effects across various ecosystems. 

18 The selection and performance of case studies represent Steps 3 and 4, respectively, of 

19 the seven-step approach to planning and implementing a risk and exposure assessment, as 

20 presented in the April 2008 Draft Scope and Methods Plan for Risk/Exposure Assessment:

21 Secondary NAAQS Review for Oxides of Nitrogen and Oxides of Sulfur (U.S. EPA, 2008). Step 4 

22 entails evaluating the current nitrogen and sulfur loads and effects to a chosen case study 

23 assessment area, including ecosystems services. In this case study, we will evaluate the current 

24 nitrogen deposition load to aquatic ecosystems; in particular, estuarine systems and the role 

25 atmospheric deposition can play in the eutrophication of an aquatic ecosystem. 

26 Eutrophication

27 Eutrophication is the process whereby a body of water becomes over-enriched in 

28 nutrients, resulting in increased productivity (e.g., of algae or aquatic plants) and sometimes also 

29 in decreased dissolved oxygen levels. Reactive nitrogen also promotes eutrophication in inland 

30 freshwater ecosystems and estuarine and coastal marine ecosystems, ultimately reducing 
Aquatic Nutrient Enrichment Case Study 

DRAFT Attachment 5, pg 2 August 2008 

1 biodiversity due to a lack of oxygen needed for the survival of many species of aquatic plants 

2 and animals. 

3 Freshwater Aquatic Ecosystems (U.S. EPA, 2007, Section 4.3.2.3.1) 

4 A freshwater lake or stream must be nitrogen-limited to be sensitive to nitrogen-mediated 

5 eutrophication. Although conventional wisdom holds that most lakes and streams in the United 

6 States are limited by phosphorus, recent evidence illustrates examples of lakes and streams that 

7 are limited by nitrogen and show symptoms of eutrophication in response to nitrogen addition. 

8 For example, surveys of lake nitrogen concentrations and trophic status along gradients of 

9 nitrogen deposition show increased inorganic nitrogen concentration and productivity to be 

10 correlated with atmospheric nitrogen deposition (Bergström and Jansson, 2006). Additional 

11 information supporting the connection between nitrogen loading and eutrophication in freshwater 

12 systems is provided in the EPA’s draft ISA (U.S. EPA, 2007). 

13 Estuarine and Coastal Marine Ecosystems

14 Estuarine and coastal marine ecosystems are highly important to human and ecological 

15 welfare through the ecosystem services they provide (e.g., fisheries and recreation). “Because the 

16 productivity of estuarine and nearshore marine ecosystems is generally limited by the availability 

of Nr

, an excessive contribution of Nr 17 from sources of water and air pollution can contribute to 

18 eutrophication” (U.S. EPA, 2007). The National Estuarine Eutrophication Assessment (NEEA) 

19 examined more than 140 estuaries along the coasts of the conterminous United States. The 

20 assessment examined a range of symptoms of eutrophication, including algal blooms, hypoxia, 

21 and vegetation growth. Findings from the study concluded that 65% of the assessed systems had 

22 moderate to high overall eutrophic conditions (Bricker et al., 2007a). Increasingly, individual 

23 estuarine ecosystems have become the center of intensive studies on nutrient 

24 enrichment/eutrophication causes and effects. Within the Chesapeake Bay, studies of the 

25 frequency of phytoplankton blooms and the extent and severity of hypoxia revealed overall 

26 increases in these detrimental effects (Officer et al., 1984). Within the Pamlico Estuary in North 

27 Carolina, similar trends have been observed and studied by Paerl and colleagues (1998). Sources 

28 identified within these assessments range from atmospheric deposition to fertilizer applications 

29 and other land use-based applications. 

30
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DRAFT Attachment 5, pg 3 August 2008 

1 Estuarine and coastal marine ecosystems experience a range of ecological problems 

2 associated with nutrient enrichment. Because the productivity of estuarine and nearshore marine 

3 ecosystems is generally limited by the availability of reactive nitrogen, an excessive contribution 

4 of Nr from sources of water and air pollution can contribute to eutrophication. Some of the most 

5 important environmental effects include increased algal blooms, depletion of dissolved oxygen in 

6 bottom waters, and reduction in fisheries and seagrass habitats (Boynton et al., 1995; Costa, 

7 1988; Howarth et al., 1996; Paerl, 1995, 1997; Valiela et al., 1990).

8 There is broad scientific consensus that nitrogen-driven eutrophication of shallow 

9 estuaries in the United States has increased over the past several decades and that environmental 

10 degradation of coastal ecosystems is now a widespread occurrence (Paerl et al., 2001). For 

11 example, the frequency of phytoplankton blooms and the extent and severity of hypoxia have 

12 increased in the Chesapeake Bay (Officer et al., 1984), the Pamlico Estuary in North Carolina 

13 (Paerl et al., 1998), and along the continental shelf adjacent to the Mississippi and Atchafalaya 

14 river discharges to the Gulf of Mexico (Eadie et al., 1994). A recent national assessment of 

15 eutrophic conditions in estuaries found that 65% of the assessed systems had moderate to high 

16 overall eutrophic conditions (Bricker et al., 2007a). Estuaries with high overall eutrophic 

17 conditions were generally those that received the greatest nitrogen loads from all sources, 

18 including atmospheric and land-based sources (Bricker et al., 2007a).

19 1.1 INDICATORS, ENDPOINTS, AND ECOSYSTEM SERVICES 

20 Major indicators for nutrient enrichment to aquatic systems from atmospheric deposition 

21 of reactive nitrogen require measurements based on available monitoring stations for wet 

22 deposition (National Atmospheric Deposition Program [NADP]/National Trends Network 

23 [NTN]) and limited networks for dry deposition (Clean Air Status and Trends Network 

24 [CASTNet]). Wet deposition monitoring stations can provide more information on an extensive 

25 range of nitrogen species than is possible for dry deposition monitoring stations. This creates 

26 complications in developing estimates for total nitrogen deposition levels because dry deposition 

27 data sources will likely be underestimated due to the use of fixed deposition velocities that do not 

28 reflect local conditions at the time of measurement, under-representation of monitoring sites in 

29 certain landscapes, and omission of some reactive nitrogen species in the measurements (U.S. 

30 EPA, 2007). 
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DRAFT Attachment 5, pg 4 August 2008 

1 For aquatic ecosystems, the indicators for “nutrient enrichment” effects reflect a 

2 combination of inputs from all media (e.g., air, discharges to water, diffuse runoff, groundwater 

3 inputs). Major aquatic system indicators include nutrient loadings (Heinz Center for Science, 

4 2007), indicators of excess algal standing crops or, in larger waterbodies, anoxia (i.e., absence of 

5 dissolved oxygen) and hypoxia (i.e., reduced dissolved oxygen) in bottom waters (see Table

6 1.1-1). For nitrogen, loadings or concentration values related to total nitrogen (a combination of 

7 nitrates, nitrites, organic nitrogen, and total ammonia) are encouraged for inclusion in numeric 

8 criteria as part of EPA-approved state water quality standards (U.S. EPA, 2000). Given the 

9 nature of the major indicators for atmospheric deposition and indicators for aquatic and 

10 terrestrial ecological systems, a data-fusion approach that combines monitoring indicators with 

11 modeling inputs and outputs is often used (Howarth, 2007).

Table 1.1-1. Key Indicators of Nutrient Enrichment Due to Reactive Nitrogen, Including NOx

Key Indicator 

Group Examples of Indicators Description 

Nitrogen deposition Nitrate or ammonia From wet or dry deposition monitoring 

stations and networks 

Nitrogen

throughfall

deposition

Nitrate, ammonia, 

organic nitrogen 

Special measurements in terrestrial 

ecosystem with corrections for nitrogen 

intercepted by plant canopies 

Nitrogen loadings 

and fluxes to 

receiving waters 

Total nitrogen or 

constituent species 

combined with flow data 

from gauged stations 

Reflects a combination of inputs from all 

media (e.g., air, discharges to water, diffuse 

runoff, and groundwater inputs); relative 

role of air deposition should ideally be 

compared with air deposition data and also 

with available (preferably multimedia) 

models

Other indicators of 

aquatic system 

nutrient enrichment 

(eutrophication)

Algal standing crop 

(plankton and 

periphyton);

anoxia/hypoxia for 

estuaries and large rivers 

Reflects a combination of inputs from all 

media (e.g., air, discharges to water, diffuse 

runoff, and groundwater inputs); relative 

role of air deposition should ideally be 

compared with air deposition data and also 

with available (preferably multimedia) 

models

12 Nitrogen is an essential nutrient for estuarine and marine ecosystem fertility and is often 

13 the algal growth-limiting nutrient (U.S. EPA, 2007; Section 4.3.3.4). Excessive nitrogen 

14 contributions can cause habitat degradation, algal blooms, toxicity, hypoxia,, anoxia, fish kills, 
Aquatic Nutrient Enrichment Case Study 

DRAFT Attachment 5, pg 5 August 2008 

1 and a decrease in biodiversity (Paerl, 2002). To evaluate these impacts, five biological indicators 

2 were used in the recent national assessment of estuary trophic condition: chlorophyll a,

3 macroalgae, dissolved oxygen, nuisance/toxic algal blooms, and submerged aquatic vegetation 

4 (SAV) (Bricker et al., 2007a).

5 Figure 1.1-1, excerpted from the National Oceanic and Atmospheric Administration’s 

6 (NOAA’s) NEEA Update, provides a brief description of each of the indicators. Further 

7 interactions between the indicators are described in the following text. For greater detail on each 

8 of the indicators, including previous findings and study areas, refer to the draft ISA and the 

9 NEEA Update. 

10

11 Figure 1.1-1. Descriptions of the five eutrophication indicators used in the NEEA 

12 (Bricker et al., 2007a).

13 Figure 1.1-2 provides a simplified progression of the indicators as the estuarine waters 

14 become more eutrophic. In the NEEA Update (Bricker et al., 2007a), an illustrated relationship 
Aquatic Nutrient Enrichment Case Study 

DRAFT Attachment 5, pg 6 August 2008 

1 between the overall eutrophic condition, water quality and biological indicators, and influencing 

2 factors (nitrogen loads) is presented (Figure 1.1-3).

3

4 Figure 1.1-2. A simplified schematic of eutrophication effects on an aquatic ecosystem.

5

6 Figure 1.1-3. An illustrated representation of eutrophication measures through the use of 

7 indicators and influencing factors from the NEEA (Bricker et al., 2007a). 

Nutrient inputs 

Excessive growth of phytoplankton and macroalgae (grazers cannot 

control growth) 

SAV inhibition Nuisance/toxic algal blooms 

Low dissolved oxygen/hypoxia 

Invertebrates and fish kills 

Decreased water clarity/decreased light penetration/decreased 

dissolved oxygen 
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DRAFT Attachment 5, pg 7 August 2008 

1 Indicators of eutrophication do not provide a direct link to the ecological benefits of the 

2 ecosystem. Because of this, the endpoints of eutrophication impacts and the ecosystem services 

3 affected must be identified and related to the quantifiable indicators. Table 1.1-2 provides some 

4 examples of the endpoints associated with the indicators of eutrophication. As described in the 

5 introduction, the endpoints are ecological entities and their impacts. For instance, an indicator 

6 may be low dissolved oxygen, but the endpoint or impact of having low dissolved oxygen is a 

7 decrease in fish populations that are highly sensitive to dissolved oxygen conditions.

Table 1.1-2. Assessment Endpoints for Nutrient Enrichment 

Due to Deposition of Reactive Nitrogen, Including NOx

Assessment Endpoint 

Fish abundance/population 

Water quality, color, clarity 

Species richness/community structure 

Habitat quality, including benthos and shoreline 

Surface scum, odors 

8 Continuing to link the indicators and endpoints to the ecological processes of value to 

9 society brings us to the ecosystem services related to eutrophication. Examples are provided in 

10 Table 1.1-3. Using the example of dissolved oxygen and the resulting decrease in fish 

11 population, we identify the ecosystem services of fish catch rate and fish kills, which support 

12 both food and materials and recreational uses of the ecosystem. 

Table 1.1-3. Ecosystem Services for Aquatic 

Systems Affected by Nutrient Enrichment 

Ecosystem Service 

Fisheries

 Fish catch rate 

 Fishable area 

 Size/extent of fish kills 

Recreation

 Boating

 Swimming 

 Beach conditions 
Aquatic Nutrient Enrichment Case Study 

DRAFT Attachment 5, pg 8 August 2008 

Ecosystem Service 

Tourism 

 Aesthetics

Risk of illness 

 Drinking water quality 

 Contaminated fish 

1 The methods of connecting the endpoints and ecosystem services related to 

2 eutrophication are beyond the scope of this case study. Rather, the remaining discussion focuses 

3 on determining and detailing the indicator measures as a function of the changing atmospheric 

4 deposition inputs of reactive nitrogen, including NOx.

5 Ecosystem services are generally defined as the benefits individuals and organizations 

6 obtain from ecosystems. In the Millennium Ecosystem Assessment (MEA), ecosystem services 

7 are classified into four main categories 

8  Provisioning. Includes products obtained from ecosystems. 

9  Regulating. Includes benefits obtained from the regulation of ecosystem processes. 

10  Cultural. Includes the nonmaterial benefits people obtain from ecosystems through 

11 spiritual enrichment, cognitive development, reflection, recreation, and aesthetic 

12 experiences. 

13  Supporting. Includes those services necessary for the production of all other ecosystem 

14 services (MEA, 2005).

15 A number of impacts on the endpoints of fish population, water quality, and habitat 

16 quality and the related ecosystem services exist, including the following 

17  Fish kills – provisioning and cultural 

18  Surface scum – cultural 

19  Fish/water contamination – provisioning and cultural 

20  Decline in fish population – provisioning and cultural 

21  Decline in shoreline quality (erosion) cultural and regulating 

22  Poor water clarity and color – cultural 

23  Unpleasant odors - cultural 
Aquatic Nutrient Enrichment Case Study 

DRAFT Attachment 5, pg 9 August 2008 

1 The aquatic enrichment case study approach will focus on fisheries, recreation, and 

2 tourism. Fisheries (closings, decreased species richness) will likely be quantitatively linked to 

3 eutrophication symptoms through monitoring data, and recreation activities will likely be 

4 qualitatively related to eutrophication symptoms through user surveys. 

5 1.2 CASE STUDIES 

6 1.2.1 Case Study Selection 

7 The selection of case study areas specific to eutrophication began with a review of spatial 

8 datasets of physical, chemical, and biological properties indicative of eutrophication potential in 

9 order to identify sensitive areas of the United States (Table 1.2-1).

Table 1.2-1. Summary of Indicators, Mapping Layers, and Models for Targeted Ecosystems 

Targeted

Ecosystem

Effect Indicator(s) Mapping Layers Model(s) 

Aquatic

nitrogen

enrichment 

and

eutrophication

 Nitrate and ammonia, 

total nitrogen (major 

reactive nitrogen species) 

 Al toxicity data 

 Chlorophyll a (e.g., algal 

standing crop) 

 Anoxia/hypoxia

(primarily estuaries and 

tidal rivers) 

 Nitrogen loadings for 

sub-watersheds or larger 

basins and Estuarine 

Drainage Areas (EDAs) 

 EPA National Coastal 

Condition Reports

(NCCR) Water Quality 

Index; and NOAA 

Estuarine Coastal 

Eutrophication Index 

 Diatom data for nitrogenlimited systems 

 STOrage and RETrieval 

(STORET) retrievals 

 U.S. Geological Survey 

(USGS) National Water 

Quality Assessment 

Program information 

 USGS Spatially Referenced 

Regression on Watershed 

(SPARROW) attributes, 

information 

 Water quality standards 

nutrient criteria for rivers 

and lakes 

 EPA, NCCR, and NOAA 

estuarine eutrophication 

indicators

 NOAA EDAs 

 EPA/NOAA airsheds for 

major Atlantic and Gulf 

estuaries Community 

Multiscale Air Quality 

(CMAQ) (nitrogen) by 

hydrological unit code 

 USGS

SPARROW

 PnET-BCG
Aquatic Nutrient Enrichment Case Study 

DRAFT Attachment 5, pg 10 August 2008 

1 We also considered the potential case study areas identified by the Ecological Effects 

2 Subcommittee (EES) of the Advisory Council on Clean Air Compliance Analysis for examining 

3 the ecological benefits of reducing atmospheric deposition. Nutrient enrichment relevant case 

4 study areas suggested by the EES are reproduced in Table 1.2-2. The draft ISA also 

5 recommended case study areas as candidates for risk and exposure assessments; Table 1.2-3

6 contains nutrient enrichment relevant areas. For aquatic nutrient enrichment, special emphasis 

7 was given to the Chesapeake Bay because it has been the focus of many previous studies and 

8 modeling efforts and it is currently one of the few systems within the United States in which 

9 economic-related ecosystem services studies have been conducted. 

10 For purposes of the risk assessment, two areas were selected for case study analysis to 

11 which a common methodology could be applied—Chesapeake Bay and the Pamlico Sound. We 

12 considered the following factors in choosing these case study areas:

13  Availability of atmospheric deposition data 

14  Availability of existing water quality modeling that accounted for the role of atmospheric 

15 deposition

16  A large, mainstem river that feeds the system with adequate hydrologic unit code (HUC) 

17 delineation and point- and nonpoint-source input data 

18  Scientific stature of the case study area 

19  Scalability and generalization opportunities for risk analysis results from the case studies. 

20 These estuarine ecosystems have been the subjects of extensive research, which provides 

21 the data needed for a first phase of quantitative analysis of the role of nitrogen deposition in 

22 eutrophication. Other candidate estuarine systems will also be evaluated for potential future 

23 analyses, and freshwater ecosystems in the western United States will be the subject of case 

24 study analyses in a follow-on phase of this risk and exposure assessment. 

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

26 scaled the case study to one main stem river for each system: the Potomac River Basin for the 

27 Chesapeake Bay and the Neuse River Basin for the Pamlico Sound. 
Aquatic Nutrient Enrichment Case Study 

DRAFT Attachment 5, pg 11 August 2008 

Table 1.2-2. Science Advisory Board/EES Listing of Potential Assessment Areas for Evaluation 

of Benefits of Reductions in Atmospheric Deposition with Respect to Aquatic Nutrient 

Enrichment 

Ecosystem/

Region

Main CAA 

Pollutant(s) 

Percentage(s)

Attributable to 

Atmospheric

Deposition

Quantitative 

Ecological and 

Economic

Information EES Comments 

Coastal 

Waquoit

Bay

Nitrogen 30% Yes High priority. Higher 

loading from nondepositional sources may 

confound analysis. 

Chesapeake

Bay

Nitrogen 20%–30% Yes High priority. Loading from 

diverse sources, particularly 

agricultural, may confound 

analysis. 

Long Island 

Sound

Nitrogen;

mercury 

Nitrogen = 

23%–35%;

Mercury = ? 

Yes High priority. High 

nitrogen loading from 

wastewater treatment plants 

may confound analysis. 

Barnegat

Bay

Nitrogen 50% total; 

Direct

deposition

30–39%

Yes High priority. Direct 

linkage of ecological 

effects with atmospheric 

deposition; quantitative 

economic data exist. 

Tampa Bay Nitrogen; 

mercury 

Nitrogen = 

25%–30%

Yes Medium priority. Examined 

in previous EPA efforts. 

Variability in loading data 

may confound analysis. 

Gulf of 

Maine

Nitrogen Low ? Low priority. Linkage of 

nitrogen loadings and 

ecological impacts is not 

well established. Major 

source of nitrogen is openocean influx. 

Casco Bay Nitrogen; 

mercury 

Nitrogen = 

30%–40%

Mercury = 

84%–92%

Yes Medium priority. Good data 

on ecological and economic 

impacts are available. 
Aquatic Nutrient Enrichment Case Study 

DRAFT Attachment 5, pg 12 August 2008 

Ecosystem/

Region

Main CAA 

Pollutant(s) 

Percentage(s)

Attributable to 

Atmospheric

Deposition

Quantitative 

Ecological and 

Economic

Information EES Comments 

Rocky

Mountains

Nitrogen Nearly 100% Yes Medium priority. Levels of 

nitrogen loading much 

lower than for northeastern 

locations. Economic data 

may be lacking. 

1
Aquatic Nutrient E

DRAFT Attachment 5, pg 13 Table 1.2-3. Potential Assessment Areas for Aquatic Nutrient Enrichment Identified in the Draft ISA (U.S. EPA, 

Area Indicator 

DetailedIndicator Area Studies Models References in U.S. EPA, 2007 

Adirondacks Aquatic 

nutrientenrichment; terrestrial nutrientenrichment; mercury methylation 

 PIRLA I and II; 

AdirondackLakes Survey; EpisodicResponseProject; EMAP 

MAGIC;PnET-BGC

Baker and Laflen, 1983; Baker 1990b; Baker et al., 1990c; Ba1996; Benoit et al., 2003; Chen Driscoll, 2004; Confer et al., 1Cumming et al., 1992; Driscol1987a; Driscoll et al., 1991; Dal., 1998; Driscoll et al., 2001aet al., 2001b; Driscoll et al., 20Driscoll et al., 2003c; Driscoll 2007a; Driscoll et al., 2007b; E2007; GAO, 2000; Havens et aIto et al., 2002; Johnson et al., Landers et al., 1988; Lawrence 2007; NAPAP, 1998; Siegfried 1989; U.S. EPA, 2003; Sulliva1990; Sullivan et al., 2006a; Sual., 2006b; U.S. EPA, 1995b; Vet al., 1996; Whittier et al., 200Wigington et al., 1996; Zhai et 

ChesapeakeBay

Aquaticnutrientenrichment; aquatic nitrogen limited eutrophication

 Bricker et al., 1999; Bricker et 

Boesch et al., 2001; Boyer et aBoyer and Howarth, 2002; CoBrush, 1991; Fisher and Oppen1991; Harding and Perry, 19972007; Kemp et al., 1983; Malo1992; Officer et al., 1984; Orth Moore, 1984; Twilley et al., 19
Aquatic Nutrient E

DRAFT Attachment 5, pg 14 

Area Indicator 

DetailedIndicator Area Studies Models References in U.S. EPA, 2007 

Alpine and sub-alpinecommunities of the eastern slope of the RockyMountains,CO

Aquaticnutrientenrichment; terrestrial nutrientenrichment 

Biomass production;NO3 leaching; 

speciesrichness

 Baron et al., 1994; Baron et al

Baron, 2006; Bowman, 2000; and Steltzer, 1998; Bowman et Bowman et al., 1995; Bowman 2006; Burns, 2004; Fenn et al.Fisk et al., 1998; Korb and RanRueth et al., 2003; Seastedt an2001; Sherrod and Seastedt, 20Steltzer and Bowman, 1998; S2006; Williams and TonnessenWilliams et al.,1996a; Wolfe e

BeartoothMountain,WY

Aquaticnutrientenrichment 

Algaecomposition switch

 Saros et al., 2003 

Pamlico Estuary, NC 

Aquaticnitrogen limited eutrophication

Hypoxia;phytoplanktonbloom 

 Paerl et al., 1998 

RockyMountainNationalPark, CO 

Aquaticnutrientenrichment 

Diatom shifts Interlandi and Kilham, 1998 

Lake Tahoe, CA

Aquaticnutrientenrichment 

Primary productivity;chlorophyll a

 Goldman, 1988; Jassby et al., 
Aquatic Nutrient Enrichment Case Study 

DRAFT Attachment 5, pg 15 August 2008 

1 1.2.2 Potomac River and Estuary 

2 The Chesapeake Bay is the largest of 130 estuaries in the United States. It is a 

3 commercial and recreational resource for more than 15 million people who live in and near its 

4 watershed (i.e., drainage basin). The bay produces approximately 500 million pounds of oysters, 

5 crabs, and other seafood per year. The richness of its species can be seen in the value of the bay’s 

6 annual fish harvest, which is estimated at more than $100 million. The Chesapeake Bay estuary 

7 receives approximately 50% of its water from the Atlantic Ocean in the form of salt water. The 

8 other half of the water (i.e., fresh water) drains into the bay from a large 165,800-squarekilometer (km2

) (64,000-square-mile [mi29 ]) drainage watershed. Among the 150 major rivers and 

10 streams in the Chesapeake Bay drainage basin are the James, Potomac, York, Rappahannock, 

11 Patuxent, and Susquehanna. The Potomac watershed comprises about 22% of the land area and 

12 30% of the population of the total Chesapeake Bay watershed. As a result, pollution loads from 

13 the Potomac River have a significant impact on the health of the bay. The Chesapeake Bay 

14 contains on average more than 68 trillion liters (18 trillion gallons) of water 

15 (http://www.waterencyclopedia.com/Ce-Cr/Chesapeake-Bay.html).

16 The Potomac River is approximately 413 miles (665 km) long, with a drainage area of 

approximately 14,670 mi2

 (38,000 km217 ) and a population of approximately 5,350,000 people. It 

18 begins at Fairfax Stone, WV, and runs to Point Lookout, MD. In terms of area, this makes the 

19 Potomac River the fourth largest river along the Atlantic Coast of the United States and the 

20 twenty-first largest in the United States as a whole (http://www.fact-index.com/p/po/ 

21 potomac_river.html). As shown in Figure 1.2-1, as well as in Table 1.2-4 and Table 1.2-5, the 

22 Potomac River contains diverse watersheds in terms of topography, elevation (e.g., extending 

23 into the Shenandoah Mountains), and nutrient point and nonpoint sources (e.g., forestland, 

24 farmland, and the Washington, DC, metropolitan area). The basin lies in five geological 

25 provinces: the Appalachian Plateau, Ridge and Valley, Blue Ridge, Piedmont Plateau, and 

26 Coastal Plain. The watershed is approximately 12% urbanized, 36% agricultural use, and 52% 

27 forested. Atmospheric deposition has also been reported in the draft ISA to contribute from 5% 

28 to 15%–20% of the basin’s total nitrogen load (U.S. EPA, 2000; Boyer et al., 2000 respectively).
Aquatic Nutrient Enrichment Case Study 

DRAFT Attachment 5, pg 16 August 2008 

1

2 Figure 1.2-1. The Potomac River Watershed and Estuary. 

Table 1.2-4. Physical Characteristics of the Potomac River Estuary (NEEA Estuaries Database) 

Parameter Value Metadata 

Estuary area (km2) 1260 Estuary area, calculated from NOAA shapefiles 

Tidal fresh zone area (km2) 183 Tidal fresh area, calculated from NOAA 

shapefiles

Mixing zone area (km2) 1077 Mixing zone area, calculated from NOAA 

shapefiles

Saltwater zone area (km2) 0 Salt water area, calculated from NOAA shapefiles 

Estuary volume (m3) 6.4638E+9 Best estimate of volume from digital bathymetric 

chart if available; otherwise, NOAA planimetry 

Estuary depth (m) 5.13 From digital bathymetric chart if available; 

otherwise, NOAA planimetry 

Estuary perimeter (km) 1350 Perimeter of estuary, based on shapefile; can be 

used to calculate various aspect ratios 

Percent estuary open (%) 1.33 Percent of the perimeter that is the “open” (or 

oceanic) boundary; somewhat subjective 

Catchment area (km2) 36804 
Aquatic Nutrient Enrichment Case Study 

DRAFT Attachment 5, pg 17 August 2008 

Parameter Value Metadata 

Catchment mean elevation 

(m) 

330 Calculated from catchment shapefiles + Hydro1K 

(a global 1-km grid of elevation) 

Catchment maximum 

elevation (m) 

1433 Calculated from catchment shapefiles + Hydro1K 

(a global 1-km grid of elevation) 

Catchment/estuary area ratio 29.2 Area ratio, based on catchment and area data 

given above 

Table 1.2-5. Hydrological Characteristics of the Potomac River Estuary (NEEA Estuaries 

Database)

Parameter Value Metadata 

Tide height (m) 0.55 NOAA estimate of tide height, back-calculated 

from tide volume; in some cases, guessed from 

nearby systems 

Tide volume (m3) 6.93E+8 Tide height (m) x estuary area (km2) x 106

Tides/day (#) 2 NOAA designation 

Tide volume/day (m3.d-1) 1339130435 Calculated from tide volume and tides per day

Tide ratio 0.11 Tide height divided by estuary depth; a 

cleanup of a NOAA variable 

Stratification ratio 0.02649 Total freshwater flux per day divided by tide 

volume per day 

Percent freshwater (%) 14.5 Based on NOAA shapefiles of the three zones 

according to their designation 

Percent mixed water (%) 85.5 Based on NOAA shapefiles of the three zones 

according to their designation 

Percent seawater (%) 0 Based on NOAA shapefiles of the three zones 

according to their designation 

Average salinity (psu) 11 Based on NOAA estimate of freshwater 

volume, but scaled to “local coastal salinity,” 

below

Tidal exchange (days) 121 Exchange time as (Est_V/net fw_V per d) * 

(coastal_sal - avg_sal)/coastal_sal); a salinitybased estimate of exchange 

Tidal freshwater flush (d) 36 NOAA-based calculation, using (daily tide + 

freshwater volume)/system volume 

Daily freshwater/estuary 

area (m.d-1)

27.063 NOAA estimate of daily flow/estuary area 

Daily freshwater (m3.d-1)

(best)

34100000 NOAA estimate above or (if not available) 

NCPDI estimate 
Aquatic Nutrient Enrichment Case Study 

DRAFT Attachment 5, pg 18 August 2008 

Parameter Value Metadata 

Flow/estuary area (m.d-1)

(best)

27.063 Best estimate/estuary area 

Total freshwater Volume 

(1.d-1)

0.00549 Best estimate/estuary volume (= hydraulic 

exchange rate) 

Daily precipitation 

(m3

.d-1)

3.64e+06 Direct precipitation on system, derived from 

PRISM Shapefile 

Daily evaporation (m3.d-1) 2.26e+06 Direct evaporation from system, derived from 

LOICZ 0.5 degree database, originally from 

Wilmott 

Daily precipitation/estuary 

area (mm.d-1)

2.889 Daily precipitation/estuary area 

Daily evaporation/estuary 

area (mm.d-1)

1.794 Daily evaporation/estuary area 

Flow (m3.d-1) 2.33e+07 NCPDI_1982–1991 

1 1.2.3 Neuse River and Estuary 

2 The Neuse River is the longest river in North Carolina, and the Neuse River Basin is the 

3 third largest river basin in the state (Figure 1.2-2). The Neuse River is a mainstem river to the 

4 Pamlico Sound—one of the two largest estuaries on the Atlantic Coast. The river originates in 

5 north-central North Carolina and flows southeasterly until it reaches tidal waters upstream of 

6 New Bern. At New Bern, the river broadens dramatically and changes from a free-flowing river 

7 to a sound. While the Neuse River itself is 248 miles long, there are 3,497 freshwater stream 

8 miles, 16,414 acres of freshwater reservoirs and lakes, 369,977 estuarine acres, and 21 miles of 

9 Atlantic coastline within the entire Neuse River Basin. The drainage area for the basin is 

approximately 14,210 mi2

 (36,804 km210 ). There are 19 major reservoirs in the Neuse River Basin; 

11 most of these are located in the upper portion of the basin. The basin starts in the eastern 

12 Piedmont physiographic region, with approximately two-thirds of the basin located in the 

13 Coastal Plain (NCDENR, 2002). 

14 The Neuse River Basin encompasses all or portions of 18 counties and 74 municipalities. 

15 The basin has a population of approximately 1,320,379 according to the 2000 census. Fifty-six 

16 percent of the land in the basin is forested, and approximately 23% is in cultivated cropland. 

17 Only 8% of the land falls into the urban/built-up category. Despite the large amount of cultivated 

18 cropland and the relatively small amount of urban area, the basin has seen a significant decrease 

19 (-180,000 acres) in cultivated cropland and forest and an increase (+227,000 acres) in developed 
Aquatic Nutrient Enrichment Case Study 

DRAFT Attachment 5, pg 19 August 2008 

1 areas over the past 15 years (NRCS, 2001). The Neuse River Basin is divided into 14 sub-basins 

2 (6-digit NC Division of Water Quality sub-basins) (NCDENR, 2002). Tables 1.2-6 through 1.2-

3 8 provide physical, land use and population, and hydrological characteristics of the Neuse River 

4 Basin, respectively. 

5 There are 332,457 estuarine acres classified for shellfish harvesting (Class SA

6 [shellfishing]) in the Neuse River Basin. The Neuse River is important to the commercial blue 

7 crab (Callinectes sapidus) fishery in the eastern United States and accounted for approximately 

8 one-quarter of the blue crab harvest from 1994 to 2002 (Smith and Crowder, 2005). 

9 Eutrophication became a water quality concern in the lower Neuse River Basin in the late 1970s 

10 and early 1980s. Nuisance algal blooms prevalent in the upper estuary prompted investigations 

11 by the State. These investigations, as well as other studies, indicated that algal growth was being 

12 stimulated by excess nutrients entering the estuarine waters of the Neuse River. In 1988, a 

13 phosphate detergent ban was put in place, and the lower Neuse River Basin received the 

14 supplemental classification of nutrient-sensitive waters. Phosphorus loading was greatly reduced, 

15 and algal blooms in the river and freshwater portions of the estuary were reduced as a result of 

16 this action. However, the 1993 Neuse River Basin-wide Water Quality Plan (NC DENR, 1993) 

17 recognized that eutrophication continued to be a water quality problem in the estuary below New 

18 Bern. Extensive fish kills in 1995 prompted further study of the problem. Low dissolved oxygen 

19 levels associated with algal blooms were determined to be a probable cause of many of the fish 

20 kills. The algal blooms and correspondingly high levels of chlorophyll a prompted the State to 

21 place the Neuse River Estuary on the 1994, 1996, 1998, and 2000 303(d) List of Impaired 

22 Waters. It was determined that control of nitrogen was needed to reduce the extent and duration 

23 of algal blooms. 

24 Atmospheric deposition is believed to play a role in nutrient loading to the Neuse River 

25 and Pamlico Sound. As excerpted from Whitall and Paerl, the following discusses the role of 

26 atmospheric deposition to nutrient loading for sensitive waterbodies: 

27 Excessive nitrogen loading to nitrogen-sensitive waters, such as the Neuse 

28 River Estuary (North Carolina) has been shown to promote changes in microbial 

29 and algal community composition and function (harmful algal blooms), hypoxia 

30 and anoxia, and fish kills. Previous studies have estimated that wet atmospheric 

31 deposition of nitrogen (WAD-N), as deposition of dissolved inorganic nitrogen
Aquatic Nutrient Enrichment Case Study 

DRAFT Attachment 5, pg 20 August 2008 

(DIN: NO3, NH3/NH+1 4) and dissolved organic nitrogen, may contribute at least

2 15% of the total externally supplied or “new” nitrogen flux to the coastal waters 

3 of North Carolina. In a 3-year study from June 1996 to June 1999, Whitall and 

4 Paerl calculated the weekly wet deposition of inorganic and organic nitrogen at 11 

5 sites on a northwest–southeast transect in the watershed. The annual mean total 

6 (wet DIN + wet organics) WAD-N flux for the Neuse River watershed was 

calculated to be 956 mg N/m2

7 /yr (15,026 Mg N/yr). Seasonally, the spring

8 (March–May) and summer (June–August) months contain the highest total weekly 

9 nitrogen deposition; this pattern appears to be driven by nitrogen concentration in 

10 precipitation. There is also spatial variability in WAD-N deposition; in general, 

11 the upper portion of the watershed receives the lowest annual deposition and the 

12 middle portion of the watershed receives the highest deposition. Based on a range 

13 of watershed nitrogen retention and in-stream riverine processing values, we

14 estimate that this flux contributes approximately 24% of the total “new” nitrogen 

15 flux to the estuary (Whitall and Paerl, 2001). 
Aquatic Nutrient Enrichment Case Study 

DRAFT Attachment 5, pg 21 August 2008 

1

2 Figure 1.2-2. The Neuse River Watershed and Estuary. 
Aquatic Nutrient Enrichment Case Study 

DRAFT Attachment 5, pg 22 August 2008 

Table 1.2-6. Neuse River and Estuary Physical Characteristics (NEEA Estuaries Database) 

Parameter Value Metadata 

Estuary area (km2) 456 Estuary area, calculated from NOAA shapefiles

Tidal fresh zone area 

(km2)

5 Tidal fresh area, calculated from NOAA shapefiles 

Mixing zone area (km2) 451 Mixing zone area, calculated from NOAA 

shapefiles

Saltwater zone area 

(km2)

0 Saltwater area, calculated from NOAA shapefiles 

Estuary volume (m3) 1.304 x 109Best estimate of volume from digital 

bathymetric chart if available; otherwise, NOAA 

planimetry 

Estuary depth (m) 2.86 From digital bathymetric chart if available; 

otherwise, NOAA planimetry 

Estuary perimeter (km) 523 Perimeter of estuary, based on shapefile; can be 

used to calculate various aspect ratios 

Percentage estuary 

open (%) 

2.1 Percentage of the perimeter that is the “open” (or 

oceanic) boundary; somewhat subjective 

Catchment area (km2) 14,066 

Catchment mean 

elevation (m) 

56 Calculated from catchment shapefiles + Hydro1K 

(a global 1-km grid of elevation) 

Catchment maximum 

elevation (m) 

245 Calculated from catchment shapefiles + Hydro1K 

(a global 1-km grid of elevation) 

Catchment/estuary area 

ratio

30.8 Area ratio, based on catchment and area data given 

above

Table 1.2-7. Neuse River Basin Land Use and Population (NEEA Estuaries Database) 

Parameter Value Metadata 

Urban (km2) 1,328.66 

(9.5%)

USGS Land Use and Land Cover (LUDA) for 

entire watershed 1972 with census 1990 

information, base year early 1990s 

Agriculture (km2) 4,983.14 

(35.6%)

USGS LUDA for entire watershed 1972 with 

census 1990 information, base year early 1990s 

Forest (km2) 6,648.5 

(47.5%)

USGS LUDA for entire watershed 1972 with 

census 1990 information, base year early 1990s 

Wetland (km2) 1,020.46 

(7.3%)

USGS LUDA for entire watershed 1972 with 

census 1990 information, base year early 1990s 

Range (km2) 5.17998 (0%) USGS LUDA for entire watershed 1972 with

census 1990 information, base year early 1990s 
Aquatic Nutrient Enrichment Case Study 

DRAFT Attachment 5, pg 23 August 2008 

Parameter Value Metadata 

Total (km2) 13,985.93998 USGS LUDA for entire watershed 1972 with 

census 1990 information, base year early 1990s 

Population (#) 1,015,059 Based on gridded (1-km) U.S. 1990 census data, 

corrected for catchments extending outside the 

United States (with LANDSCAN) 

Population/estuary area 

(#.km-2)

2,226 Population based on gridded (1-km) U.S. 1990 

census data, corrected for catchments extending 

outside the United States (with LANDSCAN). 

Estuary area, calculated from NOAA shapefiles. 

Table 1.2-8. Neuse River and Estuary Hydrology (NEEA Estuaries Database) 

Parameter Value Metadata 

Tide height (m) 0.15 NOAA estimate of tide height, back-calculated from 

tide volume; in some cases, guessed from nearby 

systems 

Tide volume (m3) 6.84E+7 Tide height (m) x estuary area (km2) x 106

Tides/day (#) 2 NOAA designation 

Tide volume/day 

(m3

.d-1)

132,173,913 Calculated from tide volume and tides per day 

Tide ratio 0.05 Tide height divided by estuary depth; a cleanup of a 

NOAA variable 

Stratification ratio 0.08318 Total freshwater flux per day divided by tide volume 

per day 

Percent freshwater (%) 1.1 Based on NOAA shape files of the three zones 

according to their designation 

Percent mixed water (%) 98.9 Based on NOAA shape files of the three zones 

according to their designation 

Percent seawater (%) 0 Based on NOAA shape files of the three zones 

according to their designation 

Average salinity (psu) 13 Based on NOAA estimate of freshwater volume, but 

scaled to “local coastal salinity,” below 

Tidal exchange (days) 74 Exchange time as (Est_V/net fw_V per 

d)*(coastal_sal - avg_sal)/coastal_sal); a salinitybased estimate of exchange 

Tidal freshwater flush 

(d)

73 NOAA-based calculation, using (daily tide + 

freshwater volume)/system volume 

Daily freshwater/estuary 

area (m.d-1)

22.368 NOAA estimate of daily flow/estuary area 
Aquatic Nutrient Enrichment Case Study 

DRAFT Attachment 5, pg 24 August 2008 

Parameter Value Metadata 

Daily freshwater 

(m3

.d-1) (best) 

10,200,000 NOAA estimate above or (if not available) NCPDI

estimate 

Flow/estuary area 

(m.d-1) (best) 

22.368 Best estimate/estuary area 

Total freshwater volume 

(1.d-1)

0.00843 Best estimate/estuary volume (= hydraulic exchange 

rate) 

Daily precipitation 

(m3

.d-1)

1.72e+06 Direct precipitation on system, derived from PRISM 

shapefile

Daily evaporation 

(m3.d-1)

926,000 Direct evaporation from system, derived from 

LOICZ 0.5 degree database, originally from Wilmott

Daily precipitation/ 

estuary area (mm.d-1)

3.772 Daily precipitation/estuary area 

Daily evaporation/ 

estuary area (mm.d-1)

2.031 Daily evaporation/estuary area 

Flow (m3.d-1) 7.95e+06 NCPDI_1982–1991 

1 Ammonia emissions from fast-growing, intensive livestock feeding operations in the 

2 1980s and 1990s are believed to contribute to nitrogen deposition in eastern North Carolina 

3 watersheds. In 1997, the North Carolina General Assembly established moratoria on the 

4 construction or expansion of certain swine farms and on lagoons and animal waste management 

5 systems for certain swine farms. One of the original purposes of these moratoria was to allow 

6 completion of certain studies related to swine farms and animal waste management systems. The 

7 1998–2006 General Assemblies extended these moratoria because research on environmentally 

8 superior technologies was conducted. In 2007, Senate Bill 1465 was passed to establish swine 

9 waste management performance standards (North Carolina General Assembly, 2007). During 

10 that 10-year period, although the swine population was restricted from growth, there were no 

11 legislative constraints on the growth of poultry or other livestock. For example, poultry 

12 populations increased in two Neuse River Basin counties, according to the U.S. Department of 

13 Agriculture’s 2002 Ag Census. Statewide, the census reported an increase in poultry farms from 

14 5,094 in 1997 to 6,251 in 2002 statewide (USDA, 2002). (The 2007 Ag Census is not complete.) 

15 In Lenoir County in the Neuse River Basin, broilers increased from 297,000 in 1997 to 929,000 

16 in 2002, but the total number of all-poultry farms only increased by 3, from 47 to 50. The 

17 county’s turkey population decreased from 878,000 to 720,000 (USDA, 2002). In Wayne 

18 County, populations increased from 2.7 million to 3.8 million broilers and from 1.9 million to 2.0 
Aquatic Nutrient Enrichment Case Study 

DRAFT Attachment 5, pg 25 August 2008 

1 million turkeys, and the overall number of poultry farms in Wayne County decreased from 147 

2 in 1997 to 126 in 2002 (USDA, 2002). The continued contribution of poultry operations’ growth 

3 to nitrogen deposition during the moratoria has not been assessed, particularly in terms of its 

4 deposition in the Neuse River Basin. 

5 2. APPROACH AND METHODS 

6 Due to the requirement that this case study span both terrestrial and aquatic systems to 

7 accommodate indirect (i.e., to the watershed) and direct (i.e., to the water surface) deposition 

8 effects, as well as the requirement that it span a variety of indicators, we determined that a 

9 modeling approach was necessary to examine the impacts due to aquatic nutrient enrichment 

10 from nitrogen and sulfur deposition. 

11 There are several complicating factors to carrying out an analysis of eutrophication in 

12 waterbodies when one of the requirements is to include modeled output of atmospheric 

13 deposition from a high-level, detailed atmospheric model. This analysis is considered a 

14 multimedia analysis where the air, land, and water are involved. Typically, models or analysis 

15 methods existing in the literature focus on only one of those components. Links between the 

16 components with the desired output of eutrophication indicators are rare in the current literature 

17 or modeling environments. Additionally, the few instances that are available in the literature tend 

18 to focus on specific case study areas or on being highly empirical and difficult to scale or extend 

19 to alternate locations. All these facts must be considered when developing a method to examine 

the effects of Nr 20 , including NOx, deposition on aquatic nutrient enrichment. 

21 2.1 MODELING 

22 There are four basic steps necessary to undertake a modeling effort to examine the effects 

23 of nitrogen and sulfur deposition (RTI, 2007): 

24 1. Choose the specific question/problem to address. 

25 2. Choose the best models based on model formulation (e.g., are biological processes 

26 considered?), desired output, study area, data availability, and necessary 

27 uncertainty/sensitivity analyses for the models. 
Aquatic Nutrient Enrichment Case Study 

DRAFT Attachment 5, pg 26 August 2008 

1 3. Determine and set up any processes/algorithms necessary to match atmospheric modeling 

2 output (assumed to be from Community Multiscale Air Quality [CMAQ]) to the chosen 

3 receiving water or terrestrial/watershed model. 

4 4. Obtain the data needed for model parameterization. 

5 The problem to be addressed in this analysis is assessment of the effects of deposition of 

Nr 6 , including NOx, on aquatic nutrient enrichment. We need to identify the impacts of both direct 

7 (i.e., deposition on the waterbody surface) and indirect (i.e., deposition within the watershed and 

8 transport to the waterbody) deposition. We need a method that will provide measures of the 

9 indicators of eutrophication that were previously described in Section 1.1. 

10 A previous RTI International (RTI)* report (RTI, 2007) detailed the difficulty, along with 

11 the desire, to utilize atmospheric modeling in combination with the receiving-water and 

12 terrestrial/watershed models for analyzing the effects of reactive nitrogen, including NOx,

13 deposition. The multimedia approach to modeling is still in development; therefore, at this time, 

14 not many models are set up to immediately accept the output from an atmospheric model such as 

15 CMAQ. In the previous model investigation, RTI examined 35 receiving-water and 

16 terrestrial/watershed models, which represent a wide diversity of types of ecosystems; history, 

17 location, and spatial/temporal scale of application; scientific acceptance and organizational and 

18 agency support; complexity and requirements; state variables and processes; and management 

19 uses.

20 Several existing models accept atmospheric concentration or flux data, but the time-step, 

21 spatial resolution, and exact species required might all differ from the atmospheric model output. 

22 The RTI report (2007) provided a list of models that could fulfill the multimedia approach while 

23 utilizing CMAQ output as input for the atmospheric component to the model. These models 

24 include the Hydrologic Simulation Program-FORTRAN (HSPF), Regional Hydeo-Economic 

25 Simulation System (RHESSys), GT/MEL, Model of Acidification of Groundwater in 

26 Catchments (MAGIC), PnET-BGC, Integrated Nitrogen in Catchments (INCA), Spatially 

27 Referenced Regression on Watershed attributes (SPARROW), AQUATOX, Water Quality 

28 Analysis Simulation Program (WASP), Enhanced Stream Water Quality Model (QUAL2K), CE29 QUAL family of models, and Row Column AESOP/Estuary and Coastal Ocean Model with 

30 Sediment Transport (RCA/ECOMSED). These models are very different from one another in 
Aquatic Nutrient Enrichment Case Study 

DRAFT Attachment 5, pg 27 August 2008 

1 terms of the system components included, process representations, data requirements, and output 

2 parameters (for comprehensive details for each model refer to the RTI report [2007]). 

3 After determining which models could utilize CMAQ data, we then looked at the 

4 ecosystem component encompassed by the models. The choice of case study areas that include 

5 estuaries dictated that the model chosen must provide nutrient loads to an estuary waterbody and 

6 examine the impacts of those loads within the estuary itself. Although AQUATOX and 

7 QUAL2K are receiving-water models, they do not function for estuaries nor do they account for 

8 indirect deposition over the contributing watershed. The WASP, CE-QUAL family of models, 

9 and RCA/ECOMSED are receiving-water models, which can be parameterized for estuaries, but 

10 they do not simulate terrestrial processes. Several of the other models account for indirect 

11 deposition and are strictly terrestrial models. These models include RHESSys and GT/MEL. 

12 Other models include both the indirect deposition and direct deposition, but only over streams 

13 and lakes within the watershed. These models are HSPF, MAGIC, PnET-BGC, INCA, and 

14 SPARROW. 

15 From this analysis, it was apparent that a multiple step/model analysis would be required. 

16 We would need a step/model to examine the indirect deposition and a step/model to examine the 

17 estuarine effects. The challenge then became balancing analysis power against data, effort, and 

18 scalability requirements. Using the list of models above, we identified several that could be used 

19 to produce nutrient loads to the estuary, the obvious critical component of an eutrophication 

20 analysis. We determined that the best model for determining nitrogen loading to the estuary 

21 would track the atmospheric deposition of nitrogen through the watershed and to the estuary. 

22 This requirement eliminated models that did not provide stream networking (PnET-BGC, 

23 MAGIC) or that lumped land use categories together (INCA). The remaining models of HSPF 

24 and SPARROW are greatly different models. HSPF is a highly parameterized model that 

25 requires extensive data inputs and calibration. SPARROW is a hybrid statistical and process26 based model that requires much less data for parameterization but still includes spatial variation 

27 and source investigation. We therefore chose to use SPARROW to estimate nitrogen loadings to 

28 the estuary. 

29 We then sought to find the most applicable method for examining eutrophication effects 

30 in an estuary. The three identified models that could represent estuarine processes (i.e., WASP, 

31 CE-QUAL family of models, and RCA/ECOMSED) were systematically ruled out as 
Aquatic Nutrient Enrichment Case Study 

DRAFT Attachment 5, pg 28 August 2008 

1 possibilities. RCA/ECOMSED is a proprietary model with extensive data requirements and 

2 requires a high level of expertise. The CE-QUAL family of models has primarily been used by 

3 the U.S. Army Corps of Engineers. The various versions of CE-QUAL all have extensive data 

4 requirements, and no indications of model integration have been uncovered in the literature. 

5 WASP provides the output desired, but requires parameterization for each system of study. 

6 Considering that the SPARROW model will provide total nitrogen loads to the estuary and the 

7 fact that we seek to provide a method that is scalable and applicable to a variety of future study 

8 sites, we chose not to use the WASP model. 

9 With the elimination of the three identified dynamic modeling applications, a more 

10 descriptive method of evaluation was sought. We identified the NEEA method developed by 

11 NOAA as a likely candidate for eutrophication assessment. 

12 2.2 CHOSEN METHOD 

13 After examining several estuarine assessment options, the most comprehensive 

14 evaluation technique that could be applied on a wide scale was revealed to be an assessment of 

15 eutrophication as conducted in NOAA’s NEEA. This assessment has been titled Assessment of 

16 Estuarine Trophic Status (ASSETS; Bricker et al., 2007a). This eutrophication index (EI) results 

17 in an estimation of the likelihood that the estuary is experiencing eutrophication or will 

18 experience eutrophication in the future.

19 The ASSETS EI incorporates indirect deposition over the watershed through evaluating 

20 the nitrogen loading to the estuary. Thus, a decision was required on how to derive the nitrogen 

21 load to the estuary based on the CMAQ modeled data. Because the ASSETS EI is a more 

22 screening-level approach, the nitrogen load to the estuary is only required to be an annual 

23 estimate of total nitrogen loading. For these reasons, we have chosen to use the SPARROW 

24 model to provide the estimates of nitrogen loading to the estuary. 

25 The combination of SPARROW modeling and the ASSETS EI (Figure 2.2-1) provides a 

26 sound basis for conducting a eutrophication assessment. Both SPARROW and the ASSETS EI 

27 are supported by federal agencies and have been through several improvement iterations. As we 

28 will show in the following sections, the method provides a screening-level approach that includes 

29 an appropriate level of detail for determining the impacts on eutrophication in an estuary based 

30 on changes in atmospheric deposition loadings. 
Aquatic Nutrient Enrichment Case Study 

DRAFT Attachment 5, pg 29 August 2008 

SPARROW

Cumulative 

Nitrogen Load 

to Estuary

Nitrogen Load 

by Reach

Atmos 

Depo 

Load

Other 

Nonpoint 

Sources

Point 

Sources

Streams 

& Catchments

Land 

Use

Proportion of 

Load Due to 

Atmospheric 

Deposition by 

Reach

Proportion of 

Cumulative 

Load Due to 

Atmos. Depo. 

to Estuary

Influencing 

Factors

Overall 

Eutrophic 

Conditions

 Future 

Outlook

Susceptibility

Nutrient 

Inputs

Susceptibility

Primary 

Symptoms 

(Chlorophyll 

a, 

Macroalgae 

abundance)

ASSETS

Score/

Eutrophication 

Index

Heuristic 

Estimate 

of

Change

Secondary 

Symptoms

(Low DO, 

Loss of SAV, 

NTB/HAB)

1

2 Figure 2.2-1. Modeling methodology for case study. 

3 Both the Potomac and Neuse River Estuaries had ASSETS EI scores available, and both 

4 were the subject of past and ongoing SPARROW modeling of point and nonpoint sources, 

5 including atmospheric deposition. 

6 2.2.1 SPARROW 

7 2.2.1.1 Background and Description 

8 SPARROW is a watershed modeling technique designed and supported by the U.S. 

9 Geological Survey (USGS). The model relies on a nonlinear regression formulation to relate 

10 water quality measurements throughout the watershed of interest to attributes of the watershed. 

11 Both point and diffuse sources within the watershed are considered along with nonconservative 

12 transport processes (i.e., loss and storage of contaminants within the watershed). SPARROW 

13 follows the rules of mass balance while utilizing a hybrid statistical and process-based approach 
Aquatic Nutrient Enrichment Case Study 

DRAFT Attachment 5, pg 30 August 2008 

1 (Figure 2.2-2). “Because the dependent variable in SPARROW models (i.e., the mass of 

2 contaminant that passes a specific stream location per unit time) is, in mathematical terms, 

3 linearly related to all sources of contaminant mass in the model, all accounting rules relating to 

4 the conservation of mass will apply” (Schwartz et al., 2006). Additionally, since SPARROW is a 

5 statistical model at its core, it provides measures of uncertainty in model coefficient and water 

6 quality predictions. Utilization of the SPARROW model results in estimates of long-term, 

7 steady-state water quality in a stream. In most applications, SPARROW estimates represent 

8 mean annual stream loadings of a contaminant. 

9

10 Figure 2.2-2. Mass balance description applied to the SPARROW model formulation. 

11 A key component of SPARROW is its reliance on the spatial distribution of watershed 

12 characteristics and sources. The stream reach network is spatially referenced against all 

13 monitoring stations, geographic information systems (GIS) data for watershed properties, and 

14 source information. This structure allows for the simulation of fate and transport of contaminants 

15 from sources to streams and downstream endpoints. “Spatial referencing and the mechanistic 

16 structure in SPARROW have been shown to improve the accuracy and interpretability of model 

17 parameters and the predictions of pollutant loadings as compared to those estimated in 

18 conventional linear regression approaches (e.g., Smith et al., 1997; Alexander et al., 2000)” 

19 (Schwartz et al., 2006). This spatially distributed model structure based on a defined stream 

20 network allows separate statistical estimation of land and water parameters that quantify the rates 

21 of pollutant delivery from sources to streams and the transport of pollutants to downstream 

22 locations within the stream network (i.e., reaches, reservoirs, and estuaries) (Schwartz et al., 

23 2006). Figure 2.2-3 shows how each watershed and stream reach within the stream network 

24 defined for the SPARROW application (represented by different colors in the figure) is 

25 processed separately and linked to derive a final loading at a downstream location (the star 

26 labeled X). The SPARROW model is calibrated at each monitoring station (represented by stars 

27 in Figure 2.2-3) by comparing the modeled loads (a total of loads from each watershed segment 

28 and any upstream loads from previous calibrations) against monitored data at the station. In this 
Aquatic Nutrient Enrichment Case Study 

DRAFT Attachment 5, pg 31 August 2008 

1 case, the modeled load at downstream monitoring station X would include loads from upstream 

2 monitoring station Y and the five watershed segments between the two monitoring stations. 

3

4 Figure 2.2-3. Conceptual illustration of a reach network. 

5 Within this case study, we show the mathematical formulation of the basic version of 

6 SPARROW presented by McMahon and colleagues (2003) for consideration in Equations 1 to 3. 

7 “The additive contaminant source components and multiplicative land and water transport terms 

8 are conceptually consistent with the physical mechanisms that explain the supply and movement 

9 of contaminants in watersheds” (Schwartz et al., 2006). Preservation of mass, accounting for 

10 transport and decomposition at individual sources, is accomplished within SPARROW through 

11 the spatial referencing of all processes with respect to the stream network and the specific reach 

12 in which the process is carried out. Decomposition processes are represented through losses in 

13 delivery to the stream and within the stream reach itself (Equation 2) or within a reservoir 

14 (Equation 3). 

¦ ¦= ∈

−

=

N

n i j J

i

R

i j

S

i j

Z

Loadi n Sn je H H

j

1 ) (

, ,

( )

, β ε

α

15 (1) 

16 where

17 Load = Nitrogen load or flux in reach i, measured in metric tons 

18 n, N = Source index where N is the total number of individual n sources 

19 J(i) = Set of all reaches upstream, including reach i

20 βn = Estimated source coefficient for source n

 Sn.j 21 = Nitrogen mass from source n drainage to reach j
Aquatic Nutrient Enrichment Case Study 

DRAFT Attachment 5, pg 32 August 2008 

1 α = Estimated vector of land to water delivery coefficients

2 Zj = Land-surface characteristics associated with drainage to reach j

 HS

3 i., j = Fraction of nutrient mass present in water body j transported to water body i

4 as a function of first-order loss process associated with stream channels 

 HR

5 i ,j = Fraction of nutrient mass present in water body j transported to water body i

6 as a function of first-order loss process associated with lakes and reservoirs 

7 İi = Multiplicative error term assumed to be independent and identically 

8 distributed across separate sub-basins defined by intervening drainage areas 

9 between monitoring stations. 

= ∏ −

m

m i j m

S Hi j

exp( k L )

, , , 10 (2) 

11 where

 km = First-order loss coefficient (km-1 12 ) (A k value of 0.08, for example, indicates 

13 that nitrogen is removed at a rate of approximately 8% per km of channel 

14 length.)

15 m = Number of discrete flow classes 

16 Li.j.m = Length of the stream channel between water bodies j and i in flow class m.

∏

−

= −

l

l

R

i j H exp( kq )

1

,

17 (3) 

18 where

19 k = Estimated first-order loss rate (or settling velocity; units = m/yr)

 ql

-1 20 = Reciprocal areal hydraulic load of lake or reservoir (ratio of water-surface 

21 area to outflow discharge; units = yr/m) for each of the lakes and reservoirs 

22 (l) located between water bodies j and i.

23 SPARROW has been designed to identify and quantify pollution sources that contribute 

24 to the water quality conditions predicted by the model. Several different types of sources may be 

25 examined, and sources may be for an individual stream location or summarized for a grouping of 

26 stream locations. Examples of sources modeled within SPARROW include atmospheric 

27 deposition, point sources, animal agriculture, or land use–based supply of contamination. “The 

28 ability to develop quantitative information on pollution sources in SPARROW models stems 

29 from the ability to trace, for each contaminant category, the predicted in-stream flux through a 

30 given stream reach to the individual sources in each of the upstream reach watersheds 

31 contributing contamination to that reach” (Schwartz et al., 2006). Figure 2.2-4 highlights some 

32 of these sources in a conceptualization of the SPARROW model process. 
Aquatic Nutrient Enrichment Case Study 

DRAFT Attachment 5, pg 33 August 2008 

1

2 Figure 2.2-4. SPARROW model components (Schwartz et al., 2006). 

3 Complete procedures, such as calculation of monitoring station flux estimation (Figure 

4 2.2-4) and details on data formatting, will not be discussed in this paper. The reader is pointed to 

5 the documentation for the recently released SAS version of the SPARROW model available 

6 from the USGS SPARROW Web site (http://water.usgs.gov/nawqa/sparrow/sparrow-mod.html) 

7 for full details on the model. The reader may also review some of the previous SPARROW 

8 applications presented in Table 2.1-1. In the following sections describing SPARROW, we 

9 provide basic definitions of terms that aid in understanding SPARROW inputs and outputs and 

10 discuss some details that pertain to an application focused on atmospheric deposition inputs. 

11 Finally, we describe an alternate formulation of SPARROW that highlights contributions of 

12 ammonia to the total Nr load for use in the Neuse River Basin. 

Table 2.1-1. Examples of SPARROW Applications 

Location Citation 

National Smith and Alexander, 2000 

Major estuaries of the United States Alexander et al., 2001

Chesapeake Bay Preston and Brakebill, 1999; Brakebill and 

Preston, 2004 
Aquatic Nutrient Enrichment Case Study 

DRAFT Attachment 5, pg 34 August 2008 

Location Citation 

State of Kansas waters Kansas Department of Health and Environment, 

2004

Connecticut River Basin NEIWPCC, 2004 

State of New Jersey waters Smith et al., 1994 

New England waters Moore et al., 2004 

New Zealand river basins Alexander et al., 2002; Elliot et al., 2005 

North Carolina coastal watersheds McMahon et al., 2003 

Tennessee and Kentucky watersheds Hoos, 2005 

1 2.2.1.2 Key Definitions for Understanding SPARROW Modeling 

2 The following definitions have been summarized from the documentation accompanying 

3 the SAS application of the SPARROW model available from the USGS (Schwartz et al., 2006). 

4 Additional references are noted when used. 

5  Bootstrapping. This is the practice of estimating model coefficients by estimating those 

6 properties when sampling from an approximating distribution using replacement.

7  Delivered Yield (load per area). This is the amount of nutrient that is generated locally 

8 for each stream reach and weighted by the amount of in-stream loss that would occur 

9 with transport from the reach to the receiving water. The cumulative loss of nutrients 

10 from generation to delivery to the receiving water is dependent on the travel time and in11 stream loss rate of each individual reach (Preston and Brakebill, 1999). 

12  Incremental Yield (load per area). This yield represents the local generation of 

13 nutrients. It is the amount of nutrient that is generated locally (independent of upstream 

14 load) and contributed to the downstream end of each stream reach. Each stream reach and 

15 associated watershed is treated as an independent unit, quantifying the amount of nutrient 

16 generated (Preston and Brakebill, 1999). 

17  In-Stream Loss. This refers to stream attenuation processes that act on contaminant flux 

18 as it travels along stream reaches. A first-order decay process implies that the rate of 

19 removal of the contaminant from the water column per unit of time is proportional to the 

20 concentration or mass that is present in a given volume of water. According to a first21 order decay process, the fraction of contaminant removed over a given stream distance is 

22 estimated as an exponential function of a first-order reaction rate coefficient (expressed in 
Aquatic Nutrient Enrichment Case Study 

DRAFT Attachment 5, pg 35 August 2008 

1 reciprocal time units) and the cumulative water time of travel over this distance. Within 

2 SPARROW, the in-stream loss rate is assumed to vary as a function of stream channel 

3 length and various flow classes. 

4  Landscape Variables. These variables describe properties of the landscape that relate to 

5 climatic, or natural- or human-related terrestrial processes affecting contaminant 

6 transport. These typically include properties for which there is (1) some conceptual or 

7 empirical basis for their importance in controlling the rates of contaminant processing 

8 and transport, and (2) broad-scale availability of continuous measurements of the 

9 properties for use in model estimation and prediction. Examples include precipitation, 

10 evapotranspiration, soil properties like organic content or permeability, topographic 

11 index, or slope. Particular types of land-use classes, such as wetlands or impervious 

12 cover, may also be potentially used to describe transport properties of the landscape. 

13  Land-to-Water Delivery Factor. This factor describes the influence of landscape 

14 characteristics in the delivery of diffuse sources of contamination to the stream. The 

15 interaction of particular land-to-water delivery factors with individual sources may also 

16 be important to consider in SPARROW models. 

17  Monitoring Station Flux Estimation. This refers to the estimates of long-term flux used

18 as the response variable in the model. Flux estimates at monitoring stations are derived 

19 from station-specific models that relate contaminant concentrations from individual water 

20 quality samples to continuous records of streamflow and time. These estimates are what 

21 are used to calibrate the model in each application. 

22  Non-linear Regression. The SPARROW model equation is a nonlinear function of its 

23 parameters. As such, the model must be estimated using nonlinear techniques. The errors 

24 of the model are assumed to be independent across observations and have zero mean; the 

25 variance of each observation may be observation-specific. A general method commonly 

26 used for these types of problems, one in which it is not necessary to assume the precise 

27 distribution of the residuals, is nonlinear weighted least squares. This is the estimation 

28 method used by SPARROW. 

29  Segmented Watershed Network. This network relates to the system of joined stream 

30 reaches that define the watershed of interest. Previous SPARROW applications have 
Aquatic Nutrient Enrichment Case Study 

DRAFT Attachment 5, pg 36 August 2008 

1 relied on the River Reach File 1 (RF1) hydrography developed by U.S. EPA (1996) and 

2 the 1:100,000 scale National Hydrologic Dataset (NHD; USGS, 1999). These datasets 

3 may be used in their original form or modified as needed depending on application 

4 requirements 

5  Source. SPARROW distinguishes between source categories (e.g., point sources, 

6 atmospheric sources, and animal agriculture) and individual sources (i.e., the rate of 

7 supply of contaminant of a particular category originating in the watershed and draining 

8 to a specific stream reach). A variety of sources based on knowledge of the watershed 

9 and inferences from literature may be examined with SPARROW. 

10  Stream Reach. The most elemental spatial unit of the infrastructure used to estimate and 

11 apply the basic SPARROW models. Stream reaches define the length of stream channel 

12 that extends from one stream tributary junction to another. Each reach has an associated 

13 contributing drainage catchment. 

14  Total Yield (load per area). The amount of nutrient, including upstream load 

15 contributed to each stream reach. These estimates are calculated by stream reach and 

16 account for all potential sources cumulatively and individually (Preston and Brakebill, 

17 1999).

18 2.2.1.3 Concepts of Importance to Case Study SPARROW Application 

19 Previous SPARROW applications have typically relied on atmospheric deposition 

20 measurements from NADP and have used wet nitrate deposition as a surrogate for nitrogen 

21 deposition over the watershed of interest. Within the case studies that we will conduct, we will 

22 use estimates of atmospheric deposition from CMAQ. Several differences in the final 

23 parameterization of the SPARROW model will most likely result from this variation in input 

24 data.

25 We must first describe the expected rules of model coefficient estimation based on source 

26 type. When using direct measures of contaminant mass as a source estimate, “the source-specific 

27 parameter (αn) is expressed as a dimensionless coefficient that, together with standardized 

28 expressions of the land-to-water delivery factor, describes the proportion or fraction of the source 

29 input that is delivered to streams (note that source and land-to-water delivery coefficients that are 

30 standardized in relation to the mean values of the land-to-water delivery variables are necessary 
Aquatic Nutrient Enrichment Case Study 

DRAFT Attachment 5, pg 37 August 2008 

1 to compare and interpret the physical meaning of source coefficients). This fraction would be 

2 expected to be < 1.0 but > 0, reflecting the removal of contaminants in soils and ground water” 

3 (Schwartz et al., 2006).

4 An example of a source of this type would include atmospheric deposition where the 

5 model input would be the mass of nitrogen deposited over the watershed. When using only wet 

6 nitrate deposition as an estimate of nitrogen deposition, the model would be expected to account 

7 for the additional nitrogen species (e.g., organic nitrogen, dry deposition of nitrate) to the extent 

8 that they are correlated with the measured inputs of nitrate (Alexander et al., 2001). This

9 accounting is revealed by estimation within the model application of a land-to-water delivery 

10 fraction for wet nitrate deposition (i.e., product of the deposition coefficient and the exponential 

11 land-to-water delivery function) that exceeds 1.0. 

12 Although available estimates for the estuarine watersheds indicate that wet nitrate 

13 deposition is highly correlated with dry plus ammonium and organic wet deposition, and 

14 estimates of the ratio of total (dry plus wet) deposition to nitrate wet deposition for the estuarine 

15 watersheds range from 3.2 to 4.0 with an average of 3.6 (Alexander et al., 2001), the use of 

16 NADP wet nitrate measurements requires the assumption that the spatial distribution of the 

17 various nitrogen species across a watershed does not vary. With the inclusion of explicit nitrogen 

18 species in atmospheric deposition measures, this assumption will not be required, and we expect 

19 to find the land-to-water delivery fraction for the atmospheric deposition source term estimation 

20 to be below 1.0. This variation will be explored within the case studies as will be the general 

21 model fit with the improved atmospheric deposition inputs. 

22 2.2.1.4 Consideration of Ammonia in Total Reactive Nitrogen Load 

23 As highlighted in Section 2.2.1.1, SPARROW can examine a wide range of sources. In 

24 work conducted by RTI under the Smithfield Agreement for North Carolina, a modified 

25 formulation of SPARROW was developed to specifically examine reactive nitrogen loadings of 

26 ammonia in North Carolina. The methodology compiled North Carolina-specific inputs for a 

27 land parcel-based method of examining land use contributions to the SPARROW model. These 

28 inputs include instream loss rates based on North Carolina flow data, methods for looking at 

29 edge-of-field delivery for agricultural land parcels, and specific contributions of ammonia to 

30 deposition totals based on the location of localized emissions and land use. For a complete 
Aquatic Nutrient Enrichment Case Study 

DRAFT Attachment 5, pg 38 August 2008 

1 description of the previous Smithfield modeling effort, please refer to the methodology 

2 document (RTI, 2003). 

3 We have considered using the data developed during this study to examine the local 

4 contributions of ammonia to atmospheric deposition of reactive nitrogen that may not be 

5 considered within the CMAQ-modeled data. We will be able to use previously compiled data of 

6 gaseous ammonia emissions (because of the moratorium on swine operations since the late 

7 1990s, this previous data should still be valid for the 2002 timeframe) as an additional source 

8 term within a second SPARROW formulation that can be compared to the SPARROW 

9 formulation, relying only on the CMAQ and NADP atmospheric data. This additional analysis to 

10 separate atmospheric deposition of ammonia from local sources from other atmospheric sources 

11 of nitrogen to watersheds, including NOx provided by the CMAQ/NADP data, will provide a 

12 weight of evidence analysis of the atmospheric modeling methods for watersheds with extensive 

13 animal operations. 

14 2.2.2 ASSETS Eutrophication Index 

15 2.2.2.1 Background and Description 

16 The EI was defined by the NEEA Program and developed into a Pressure-State-Response 

17 framework termed ASSETS. It is categorical, where each of three indices results in a score that, 

18 when combined, result in a final overall score, also known as the ASSETS score or rating, which 

19 is representative of the health of the estuary. The indices are as follows: 

20  Influencing factors. Physical, hydrologic, and anthropogenic factors that characterize the 

21 susceptibility of the estuary to the influences of nutrient inputs (also quantified as part of 

22 the index) and eutrophication 

23  Overall eutrophic condition. An estimate of current eutrophic conditions derived from 

24 data for five symptoms known to be linked to eutrophication 

25  Future outlook. A qualitative measure of expected changes in the system.

26 The following excerpt from Whitall and colleagues describes the objectives in applying 

27 the ASSETS method: 

28 The ASSETS assessment method should be applied on a periodic basis to 

29 track trends in nutrient-related water quality over time in order to test 
Aquatic Nutrient Enrichment Case Study 

DRAFT Attachment 5, pg 39 August 2008 

1 management related hypotheses and provide a basis for more successful 

2 management. The null hypothesis being tested in this approach is: The change in 

3 anthropogenic pressure as a result of management response does not result in a 

4 change of state. The hypothesis is tested, e.g., to verify whether decreased 

5 pressure improves State, or whether increased pressure deteriorates State. In many 

6 cases, a reduction in pressure will result in an improvement of State, but in some 

7 cases, such as naturally occurring harmful algal bloom (HAB) advected from 

8 offshore, it will not (Whitehall et al., 2007). 

9 Influencing Factors 

10 Influencing factors help to establish a link between a system’s natural sensitivity to 

11 eutrophication and the nutrient loading and eutrophic symptoms actually observed. This 

12 understanding also helps to illustrate the relationship between eutrophic conditions and use 

13 impairments (Bricker et al., 2007a). Influencing factors are determined by calculating two factors 

14 of susceptibility and nitrogen load, where “susceptibility” provides a measure of a system’s 

15 nutrient retention based upon flushing and dilution, and “nitrogen loads” are a ratio between the 

16 nitrogen input to the system from the oceans versus from the land (Figure 2.2-5).

17

18 Figure 2.2-5. Influencing factors description and decision matrix (Bricker et al., 2007a). 
Aquatic Nutrient Enrichment Case Study 

DRAFT Attachment 5, pg 40 August 2008 

1 The following factors take into account both the natural characteristics of and human 

2 impacts to systems. 

3  Susceptibility. For a coastal system, susceptibility depends on the flow of water into and 

4 out of the system. This flushing capability is determined by the physical properties (e.g. 

5 size, mouth) of the system as well as the influence of tidal waters and inflow of 

6 freshwater from tributaries. When water flushes into and out of the system easily and 

7 quickly (i.e. there is a short residence time) nutrients flush out of the system rapidly and 

8 there is not enough time for eutrophic symptoms to develop. Systems with short 

9 residence times have low susceptibility. The opposite also holds true. When water, and 

10 therefore nutrients, does not flush quickly from the estuary or coastal system there is time 

11 for eutrophication effects to develop.

12  Nitrogen Load. For this assessment, the loading component is estimated as the ratio of 

13 nitrogen coming from the land (i.e., human-related) to that coming from the ocean and is 

14 given a rating of low, moderate, or high (Bricker et al., 2003; Ferreira et al., 2007). For 

15 example, a high rating means that > 80% of the nutrient load comes from land, whereas a 

16 low rating signifies a land-percentage of < 20%. This rating also provides insight into 

17 loading management because loads to systems with primarily ocean-derived nitrogen are 

18 not easily controlled. Understanding the sizes of current and expected future loads 

19 provides further insight into the application and success of management measures. 

20 Overall Eutrophic Condition

21 To assess the eutrophic conditions of a system, the NEEA relies on five symptoms. Each 

22 of the five symptoms, divided into primary and secondary categories, is assessed based on a 

23 combination of the following factors: concentration or occurrence, duration, spatial coverage, 

24 frequency of occurrence, and confidence in the data (Figure 2.2-6). The two primary symptoms, 

25 chlorophyll a and macroalgal abundance (Figure 2.2-7), were chosen as indicators of the first 

26 possible stage in the process of water quality degradation leading to eutrophication. The 

27 secondary symptoms, which in most coastal systems will develop from the primary symptoms, 

28 include low dissolved oxygen levels, loss of SAV, and occurrences of nuisance/toxic algal 

29 blooms (Figure 2.2-7). At times the secondary symptoms may also be present or develop 

30 without expression of primary symptoms. Nutrient concentrations are not employed as a 
Aquatic Nutrient Enrichment Case Study 

DRAFT Attachment 5, pg 41 August 2008 

1 symptom indicator because concentrations may vary between low and high values based on a 

2 number of factors, such as estuary susceptibility, which invalidates the use of nutrient 

3 concentrations alone as an indicator. As stated by Bricker and colleagues “Through the use of a 

4 simple model, the current framework was established to help understand the sequence, processes, 

5 and symptoms associated with nutrient enrichment. Despite its limitations, it represents an 

6 attempt to synthesize enormous volumes of data and derive a single value for eutrophication in 

7 each estuary, essentially representing a complex process in a simple way” (Bricker et al., 2007a). 
Aquatic Nutrient Enrichment Case Study 

DRAFT Attachment 5, pg 42 August 2008 

1

2 Figure 2.2-6. Overall eutrophic condition description and decision matrix (Bricker et al., 

3 2007a).
Aquatic Nutrient E

DRAFT Attachment 5, pg 43 

1

2 Figure 2.2-7. Detailed descriptions of primary and secondary indicators of eutrophication (Bricker et al., 
Aquatic Nutrient Enrichment Case Study 

DRAFT Attachment 5, pg 44 August 2008 

1 Future Outlook

2 The future outlook relies on a similar combination of factors as the influence factors. 

3 That is, a rating of the system susceptibility and nutrient loading in the future. The aim of this 

4 index is to estimate future changes in the system through a combination of any physical, 

5 hydrologic, or pollutant loadings to the system itself or to its contributing watershed through 

6 such actions as watershed management plans, development restrictions, policy changes resulting 

7 in nutrient reductions, etc. The matrix in Figure 2.2-8 is used to determine the future outlook 

8 rating.

9

10 Figure 2.2-8. Future outlook description and decision matrix (Bricker et al., 2007a). 

11 The last step is to combine the influencing factors, overall eutrophic conditions, and 

12 future outlook scores into a single overall score. The scores fall into one of five categories: High, 

13 Good, Moderate, Poor, or Bad.

14 2.2.2.2 Applications and Updates 

15 The ASSETS method developed out of the NEEA was first reported in 1999. Since that 

16 time, it has been used in several assessments across the country and internationally and has 

17 undergone revision and validation (Bricker et al., 1999, 2003, 2007b; Ferreira et al., 2007; 

18 Whitall et al., 2007). The original NEEA ASSETS assessment relied on questionnaires to experts 
Aquatic Nutrient Enrichment Case Study 

DRAFT Attachment 5, pg 45 August 2008 

1 for each estuary considered (Bricker et al., 1999). Later assessments determined that reliance on 

2 monitored data and less on reports from experts provided a more valid assessment tool (Bricker 

3 et al., 2006, 2007b). With the NEEA Update in 2007, an online database was completed in which 

4 data users and data holders could access and input data (Bricker et al., 2007b). Additional 

5 datasets have also been collected for smaller study areas (Bricker et al., 2006). These data 

6 systems provide a wealth of information from which analyses may be conducted. 

7 The original formulation of the ASSETS EI within the NEEA used watershed nutrient 

8 model estimates from SPARROW (Bricker et al., 1999). Although the updated ASSETS 

9 methodology has further apportioned nitrogen sources using the WATERSN model (Whitall et 

10 al., 2007), SPARROW is still appropriate for this study because we can define atmospheric 

11 deposition inputs relative to other nitrogen sources. 

12 2.2.3 Assessments Using Linked SPARROW and EI 

13 The link between the SPARROW model and the ASSETS EI occurs when the 

14 SPARROW output is used as the nitrogen load in the influencing factors calculation of the 

15 ASSETS EI. For each case study and future policy scenario, a new influencing factors score will 

16 be calculated based on the SPARROW-predicted nitrogen load, which will vary due to the 

17 different simulated atmospheric nitrogen contributions. As the influencing factors score is only 

18 one component of the ASSETS EI, we will discuss risk assessment/management approaches that 

19 can be gained from this approach to vary the nitrogen load to the estuary. 

20 Ferreira and colleagues (2007) provide an example of the eutrophication management 

21 approaches that can be undertaken using the ASSETS EI assessment method. Using this 

22 approach (Figure 2.2-9), trends in the changes in state of the system (i.e., overall eutrophic 

23 condition) are investigated based on changes in the pressures to the system (i.e., influencing 

24 factors). Typically, this analysis would be conducted by comparing ASSETS EI scores for the 

25 same system over different years. In the risk assessment analysis, we will examine how changes 

26 in pressure on the system (through changes in nitrogen loading to the estuary) affect the 

27 influencing factors score and overall ASSETS EI score for the estuary. Ideally, we would be able 

28 to predict a response curve such as the one shown in Figure 2.2-10 for each estuary, using the 

29 various atmospheric deposition scenarios that will be carried out in the risk assessment. 
Aquatic Nutrient Enrichment Case Study 

DRAFT Attachment 5, pg 46 August 2008 

1 However, apart from the baseline scenario for 2002, where we will use modeled atmospheric 

2 deposition of actual conditions, the analysis using the ASSETS EI will be theoretical. 

3

4 Figure 2.2-9. Conceptual approach to eutrophication management using the ASSETS EI 

5 assessment (Ferreira et al., 2007).

6

7 Figure 2.2-10. Example of response for case study analysis (Bricker et al., 2007b). 
Aquatic Nutrient Enrichment Case Study 

DRAFT Attachment 5, pg 47 August 2008 

1 For the 2002 baseline scenario, we will calibrate the modeled watershed nitrogen load 

2 using the SPARROW model to monitoring data from the streams within the watershed. We will 

3 also gather monitoring data from the estuaries for the values of the primary and secondary 

4 symptoms used in the Overall Eutrophic Condition (OEC) during 2002. We will use this 

5 compilation of data to evaluate the ASSETS EI for 2002. 

6 For the future policy scenarios, because we will be estimating nitrogen loadings to the 

7 estuary based on simulated atmospheric deposition scenarios instead of monitored conditions, we 

8 will not have direct measures of state on which to create the response curves (i.e., we will not 

9 have concurrent measurements of primary and secondary symptoms in the estuary because these 

10 are theoretical deposition scenarios). The influencing factors score will be calculated directly 

11 from the modeled watershed nitrogen loads and the susceptibility for the estuary, which will 

12 remain unchanged. However, consideration must also be given to whether the state (i.e., overall 

13 eutrophic condition) will change based on this change in nitrogen loading (i.e., 

14 pressure/influencing factor). Without monitoring data for each of the primary and secondary 

15 symptoms related to a specific nitrogen load calculated from the theoretical deposition scenarios, 

16 we cannot calculate a direct change in overall eutrophic condition score. Even with a 

17 comprehensive water quality model, any change in output would be subject to uncertainty when 

18 lacking monitoring data to calibrate the model, especially in such a complex process as 

19 eutrophication.

20 We propose to rely on expert judgment and expected thresholds within the ASSETS EI to 

21 determine if the overall eutrophication condition score, based the individual symptom measures, 

22 should change when the influencing factor score is changed. This proposed analysis method will 

23 be aided by the fact that calculation of the overall eutrophic condition score relies on a numerical 

24 ranking of primary and secondary indicators (i.e., 0 = low expression of indicator class; 1 = high 

25 expression of indicator class). By using the thresholds within the ASSETS scoring matrices, we 

26 can determine whether the previous symptom value was on the border between two categories, 

27 and therefore, have the possibility to change state categories as the influencing factors change. 

28 For example, a dissolved oxygen value of 1.9 mg/L would be on the verge between “hypoxia” 

29 and “biological stress” (Figure 2.2-7). With a large decrease in nitrogen loading, it is possible 

30 that this value would improve and cause the state rating to change to “biological stress” rather 

31 than to “hypoxia.” Changes in these values will then carry into the indicator scores. For each 
Aquatic Nutrient Enrichment Case Study 

DRAFT Attachment 5, pg 48 August 2008 

1 system, we will determine whether the score for each indicator class lies on the boundary 

2 between two score classifications (e.g., a score of 0.6 for secondary symptoms lies on the 

3 boundary between Moderate High and High rankings when there is a High ranking of primary 

4 symptoms). If a score lies close to the boundary and the nitrogen load to the estuary is 

5 substantially increased or decreased due to atmospheric changes, we will know whether to 

6 increase the score to the next higher category or leave the score as is, respectively, for each 

7 situation. If changes in scores cannot be supported by weight of evidence, we will seek 

8 consultation with experts on each case study system through written inquiry. 

9 Additionally, we will investigate whether historical trends tracked within the estuaries 

10 relate to any of the case study scenarios we will be evaluating. For instance, was there a previous 

11 year in which a nitrogen load to estuary was similar to a load predicted by the SPARROW 

12 model? If so, is there monitoring data from within the estuary on which we may assess the 

13 overall eutrophic condition related to that nitrogen load?

14 Overall results from this analysis will provide an estimated pressure-state curve as 

15 presented in Figure 2.2-10. These estimated curves can provide the opportunity to perform 

16 additional analyses where changes in ecosystem services related to changes in pressure and state 

17 may be examined. 

18 3. RESULTS 

19 3.1 CURRENT STATE OF SYSTEMS 

20 At this time, the current state of systems refers to previous work that has been conducted 

21 using the modeling and assessment methods we will utilize to conduct the 2002 base-case 

22 scenario and future policy scenarios. Therefore, in this draft, Sections 3.1.1 and 3.1.2 summarize 

23 previous work and provide details on changes that will be incorporated in the future base-case 

24 scenario.

25 3.1.1 Potomac River and Estuary Summary 

26 The previous work conducted on the Potomac River watershed and estuary was 

27 completed as part of a Chesapeake Bay SPARROW application that has been updated twice 

28 since its original formulation (Brakebill et al., 2001; Brakebill and Preston, 2004; Preston and 

29 Brakebill, 1999). The ASSETS EI was created for 2002 data in an eutrophication assessment 
Aquatic Nutrient Enrichment Case Study 

DRAFT Attachment 5, pg 49 August 2008 

1 involving the Gulf of Maine (Bricker et al., 2006). We summarize the results from both of these 

2 studies in the following sections and address how the updated 2002 base-case scenario will vary 

3 in setup. Table 3.1-1 summarizes both the previous work and the modifications that will be 

4 made for the future case study analyses. 

5 3.1.1.1 SPARROW Assessment 

6 The Version 3 Chesapeake Bay SPARROW application modeled the watershed for the 

7 time period of the late 1990s. Stream nitrogen load estimates from 87 sites were used to calibrate 

8 the model. The stream reach network used in this analysis relied on a modified version of the 

9 RF1 used in previous Chesapeake Bay SPARROW applications, but included 68 reservoirs that 

10 were not previously included. This analysis examined the sources of atmospheric deposition, 

11 fertilizer and manure application, point sources, septic systems, and land use. Details on the 

12 compilation of each of these GIS-based datasets can be found in the work by Brakebill and 

13 Preston (2004). Watershed characteristics that were in the model include precipitation, 

14 temperature, slope, soil permeability, and hydrogeomorphic regions. 

15 The future SPARROW assessment for the 2002 base-case scenario will use the same 

16 source inputs and watershed characteristics, except in the case of atmospheric deposition. The 

17 Version 3 Chesapeake Bay SPARROW application relied on 1997 mean deposition values of 

18 wet-deposition atmospheric nitrate using the 191-point measurements in the NADP program 

19 across the country. To determine atmospheric deposition of wet nitrate for each watershed 

20 segment (Figure 3.1-1), interpolation was used in conjunction with a Triangulated Irregular 

21 Network (Brakebill and Preston, 2004). As described in Section 2.2.1.3, relying on wet nitrate 

22 deposition as a surrogate for total nitrogen deposition requires an assumption of spatial 

23 homogeneity between the nitrogen species. In the 2002 base-case scenario, we will utilize the 

24 CMAQ output as atmospheric deposition inputs to the model. 

25 Figure 3.1-2 presents the results of the 1997 Chesapeake Bay SPARROW application by 

26 watershed segment. Results are presented for total and delivered yields (defined in 

27 Section 2.2.1.2) for the entire nitrogen load predicted by the model and for the nitrogen load 

28 derived from atmospheric sources. As shown by these results, the western watersheds in the 

29 mountains produce the greatest nitrogen load per area from atmospheric deposition, but it is the 

30 watersheds along the mainstem of the Potomac River that actual contribute the greatest amounts 
Aquatic Nutrient Enrichment Case Study 

DRAFT Attachment 5, pg 50 August 2008 

1 of nitrogen from the atmospheric deposition to the estuary. Analyzing the delivered yields 

produces a total nitrogen loading to the Potomac River Estuary of 34 x 103

2 metric tons 

3 nitrogen/yr. The nitrogen load to the estuary due to atmospheric deposition is estimated at 

approximately 2 x 103

4 metric tons nitrogen/yr or 6% of the total loading. These results will be 

5 compared and contrasted to the 2002 base-case scenario that will be completed using CMAQ 

6 results. We will determine whether spatial patterns in these total and delivered yields vary when 

7 additional nitrogen species are included in the analysis as compared to this current analysis 

8 where only wet nitrate was considered. 

9 The actual data inputs (except for atmospheric deposition) to the model will likely rely on 

10 a combination of data compiled for the Version 3 SPARROW application and for the recently 

11 released Phase 5 Chesapeake Bay Watershed Model (http://ches.communitymodeling.org/ 

12 models/CBPhase5/index.php#container). The Phase 5 model relies on a slightly different 

13 watershed and stream reach segmentation network, but provides data in annual increments,

14 including 2002. Upcoming work will combine the applicable data from these models to arrive at 

15 the best input dataset for the desired base-case scenario.
Aquatic Nutrient E

DRAFT Attachment 5, pg 51 Table 3.1-1. Key Facts/Aspects of the Potomac Case Study 

Category

Description of Current/Demonstration Research Source 

Description for FinaEffects

Spatial domain SPARROW: Entire Chesapeake Bay 

watershed, including the Potomac River watershedASSETS EI: Potomac estuary 

Brakebill and Preston, 2004; Bricker, et al.,2006

Potomac River watersThe spatial domain is 8-digit hydrologic uni02070001, 02070002, 02070004, 02070007, 02070010. The catchmreaches defined during Bay application (VersSPARROW model wibasis for analysis. For reaches, interpolation results of CMAQ will atmospheric inputs to 

Temporal domain SPARROW (Version 3): Late 1990s with 

water quality calibration of surface water nitrogen calculated for 1997, point discharges for 1995–1997, land use data created based on 1992 NLCD with updates using Landsat from 1992 and 1997, agricultural data from the late 1990s and 1997 Agricultural Censuses, and atmospheric data as described below ASSETS EI: 2002 basis 

Brakebill and Preston, 2004; Bricker, et al.,2006

2002 or surrounding ysurface water calibratifrom National Water QDatabase & STORET provided from the USBasin Assessment cur

Atmospheric nitrogen species

Wet-deposition atmospheric nitrate Brakebill and 

Preston, 2004 

Summation of availabCMAQ to total nitrog
Aquatic Nutrient E

DRAFT Attachment 5, pg 52 

Category

Description of Current/Demonstration Research Source 

Description for FinaEffects

Data origin of emission sources (modeling or monitoring)

1997 mean deposition values of wetdeposition atmospheric nitrate using the 191 point measurements in the NADP program; to get to atmospheric deposition for each watershed segment, interpolation was used in conjunction with a Triangulated Irregular Network. 

Brakebill and Preston, 2004 

CMAQ; If calibration water inputs is availab2002, atmospheric inpmay be mined from ththe demonstration rese

Analytic tool Separate analyses using SPARROW and 

the ASSETS EI 

RTI, 2003; Bricker et al., 2007a;

Combined analysis usthe ASSETS EI 

Tool output indicators SPARROW: Mean Annual TN Loading 

ASSETS EI: ASSETS overall score relating to likelihood of eutrophication 

 Change in ASSETS o

changing input nitrogeSPARROW modeling atmospheric depositio

Endpoint of indicators No service-related endpoint completed Habitat and water qua

(expressed in terms of effects (i.e., anoxia, lo

Linkage to endpoints (science that connects indicator value to endpoint)

N/A Inherent in the numer

using expression of sydissolved oxygen, har

Ecosystem services Fisheries, Recreation, Tourism Fisheries, recreation, t
Aquatic Nutrient E

DRAFT Attachment 5, pg 53 

Category

Description of Current/Demonstration Research Source 

Description for FinaEffects

Linkage method from endpoint to services 

N/A Quantitative: fisheries 

decreased species richeutrophication symptomonitoring data Qualitative: recreationto eutrophication symsurveysGaps: wealth of monitapplied to answer appwide variety of surveyconduct

1
Aquatic Nutrient Enrichment Case Study 

DRAFT Attachment 5, pg 54 August 2008 

1

2 Figure 3.1-1. Atmospheric deposition inputs based on interpolation of wet nitrate measures 

3 from NADP for the late 1990s’ Chesapeake Bay SPARROW application. 
Aquatic Nutrient Enrichment Case Study 

DRAFT Attachment 5, pg 55 August 2008 

1

2 Figure 3.1-2. Total nitrogen yields from all sources and from atmospheric deposition only as 

3 predicted by the late 1990s’ Chesapeake Bay SPARROW application. 

4 3.1.1.2 ASSETS EI Assessment 

5 An ASSETS EI was completed for the Potomac River Estuary (Figure 3.1-3) in a 2006 

6 NOAA project on the Gulf of Maine (Bricker et al., 2006). The data used to complete the scoring 

7 was from 2002. As such, this ranking provides the necessary data for the basis of the future 2002 

8 base-case scenario. The total nitrogen loading to the estuary relied on estimates from the 

9 Chesapeake Bay Watershed Model (Version 4.3), a model based on HSPF. The total nitrogen 

loading of 1.4 x 103

10 metric tons nitrogen/yr presented in the report is actually the nitrogen 

11 loading from only the Lower Potomac River watershed, as defined by Maryland’s Department of 

12 Natural Resources (MD DNR; MD DNR, 2004). The report also presented results from a 

13 previous SPARROW modeling effort in the Potomac River watershed from 1987, where the total 

nitrogen load to the estuary was predicted to be 20.6 x 103

14 metric tons nitrogen/yr. As there is a 

15 considerable difference between the two loadings presented in the report and the loading 

16 predicted by the most recent SPARROW application in the Chesapeake Bay, the influencing 
Aquatic Nutrient Enrichment Case Study 

DRAFT Attachment 5, pg 56 August 2008 

1 factors score will be reexamined upon verification of the total nitrogen load used to perform the 

2 ASSETS assessment. 

3 The current assessment using the total nitrogen load in question shows that the system 

4 has a high susceptibility to pressures and a high score for nutrient inputs, resulting in a score of 

5 High for influencing factors. Individual scores for the primary and secondary indicators vary, but 

6 result in an overall score of High for the overall eutrophic condition. It is these two scores that 

7 will be assessed in the future risk assessment analyses with emphasis on verifying this current 

8 ranking for the influencing factors score. As shown in the future outlooks assessments, the score 

9 of Improve Low is based on the expectations that future nutrient pressures will decrease and there 

10 will be significant population and development increases. These assumptions will be verified 

11 before continuing the case study analysis. 

12

13 Figure 3.1-3. The ASSETS EI scores for the Potomac River Estuary (Bricker et al., 2006). 

14 Below we provide a summary of the raw data that was used by Bricker and colleagues 

15 (2006) to create the final scores of the ASSETS EI presented in Figure 3.1-3. These data will be 

16 reviewed before use in the future policy scenarios. Several gaps in the raw data provided to this 

17 point exist, including measures of frequency of expression of the symptoms and macroalgae raw 

18 data.

19 The Overall Human Influence (OHI) score was developed based on the following data: 

20  The Potomac River has a high dilution potential, but a low flushing potential.

21  Combined with a low export potential, this gives the system an overall susceptibility 

22 rating of High.
Aquatic Nutrient Enrichment Case Study 

DRAFT Attachment 5, pg 57 August 2008 

1  Nitrogen loading for the system calculated the human influence to be 94.8% for 2002, 

2 which corresponds to a value of High.

3  With high inputs and high susceptibility, the OHI value is high for 2002. 

4 For the Overall Eutrophic Condition (OEC), water quality monitoring data used to 

5 determine primary and secondary symptoms comes from the Chesapeake Bay Program’s online 

6 database (www.chesapeakebay.net), the Virginia Institute of Marine Science, and the “Eyes on 

7 the Bay” Web site maintained by the MD DNR (http://mddnr.chesapeakebay.net/hab/). 

8 Summaries of the raw data used to determine the primary and secondary symptoms by Bricker 

9 and colleagues (2006) are as follows: 

10 Chlorophyll a (12 stations, 645 individual samples) 

11  Overall 90th percentile value for all 2002 data and all stations was 16.42 ug/L. 

 Spatial coverage of chlorophyll a 90th 12 percentile 

13  Low = 1% coverage

14  Medium = 59% 

15  High = 9%.

16  The highest spatial coverage above (which is for Medium) is adopted for the overall 

17 chlorophyll a value, and as such, the system gets an expression of High.

18 Dissolved Oxygen (11 stations and represents 1329 individual samples) 

19  Overall combined 10th percentile for all stations in 2002 was 4.2 mg/L, which also 

20 corresponds to that of biological stress.

21  Dissolved oxygen levels approximate spatial percentages:

22  No Problem = 23% 

23  Biological Stress = 28% 

24  Anoxia = 19%.

25 Salinity

26 A median salinity was calculated for the estuary using the Chesapeake Bay Program’s 

27 data for the years 1997–2002.
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DRAFT Attachment 5, pg 58 August 2008 

1 Submerged Aquatic Vegetation 

2  Used the 2001 and 2002 coverage dataset produced at the Virginia Institute of Marine 

3 Science from aerial photography flown in 2001 and 2002 

4  Calculated the change in SAV coverage by subtracting the areal coverage of 2001 from 

5 the areal coverage for 2002 

6  In 2001, SAV in the Potomac River had a spatial coverage of approximately 529,557.04 

square meters (m2

), whereas in 2002, there was an approximate 34-million m27 increase up 

to 34,479,090.57 m28 .

9 Harmful Algal Bloom (HAB) 

10  Data collected from the “Eyes on the Bay” Web site 2002 HAB report search) 

11  HABs were a large problem during 2002. There were multiple different blooms 

12 throughout the year,

13  Largest and longest bloom was that of Dinophysis accuminata, from February 2002 until 

14 about April 2002. (During the three months of the bloom, shellfish beds were closed, and 

15 no harvesting was allowed.) 

16  HABs carried the largest NEEA/ASSETS secondary symptoms value (High) and were 

17 combined with the overall primary symptom value to calculate the OEC. 

18 The OEC for the Potomac River in 2002 was High and was calculated from a primary 

19 symptoms value of High (from chlorophyll a 90th percentile) and a secondary symptoms value 

20 of High (from HAB). 

21 Bricker and colleagues (2006) provided a justification for a score of Improve Low for the 

22 Determining Future Outlook index by the following: 

23 For the Potomac River basin, nitrogen loading, phosphorus loading, and 

24 sediments all decreased between 1985 and 2002 (MD DNR, 2004). In contrast 

25 however, population growth in Maryland alone is projected to increase at an 

26 approximate 1% every year while the Potomac River basin itself includes many 

27 new suburban communities that are expected to continue to experience rapid 

28 suburban growth. 
Aquatic Nutrient Enrichment Case Study 

DRAFT Attachment 5, pg 59 August 2008 

1 So even though nitrogen, phosphorus, and sediment loading are 

2 decreasing, significant population increases and development may mask the 

3 decreases in loading and cause there to be only small positive changes in future 

4 nutrient pressures. Thus, with high susceptibility and only small improvements in 

5 future nutrient pressures, the overall calculation for DFO in the Potomac River is 

6 Improve Low for 2002. 

7 3.1.2 Neuse River and Estuary Summary 

8 3.1.2.1 SPARROW Assessment 

9 As described in Section 2.2.1.4, a modified SPARROW application was completed to 

10 account for Concentrated Animal Feeding Operations (CAFOs) and edge-of-field delivery rates 

11 to the Neuse River and estuary for a timeframe of the late 1990s–2002. Atmospheric nitrogen 

12 inputs were modeled from swine operations within North Carolina and were summarized from 

13 ambient monitoring stations for background atmospheric nitrogen inputs (Figure 3.1-4). The 

14 results of this modeling effort provide a preliminary assessment of the current state of the 

15 system, while parsing out swine inputs from other atmospheric nitrogen inputs that may be 

16 regulated by the National Ambient Air Quality Standards (NAAQS) for NOx. Below we describe 

17 how we expect these results to vary when using the 2002 base-case scenario. Table 3.1-2

18 provides a summary of the inputs and analysis methods that were used in the previous work and 

19 that will be used in the 2002 base-case scenario analysis. 
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DRAFT Attachment 5, pg 60 August 2008 

1

2 Figure 3.1-4. Background atmospheric total nitrogen deposition from NADP, CASTNET, and 

3 modeled data over the time period of 1996–2000 (RTI, 2003). 
Aquatic Nutrient E

DRAFT Attachment 5, pg 61 Table 3.1-2. Key Facts/Aspects of the Neuse Case Study 

Category

Description of Current/Demonstration Research Source Description for Final Cas

Spatial domain SPARROW: Neuse River watershed 

defined on the 14-digit hydrologic unit (HUC14) scale; coastal HUCs with nondentritic drainage patterns could not be built; stream reaches based on EPA Reach File Version 3 Eutrophication Index: Neuse Estuary 

RTI, 2003; Bricker et al., 2007a 

SPARROW: HUC14 repreNeuse River watershed Eutrophication Index: SamEstuary as used in NEEA U

Temporal domain Source data from 1995–2003; atmospheric 

data for 1996–2000 

RTI, 2003 Updated input data for 200

except swine emissions datthe same 

Atmospheric nitrogen species 

Wet and dry deposition data; reduced (NH4+), oxidized (NO3-+HNO3), and 

organic forms of nitrogen; used ambient monitoring sites with spatial interpolation 

RTI, 2003 Summation of available spe

total nitrogen (TN) 

Data origin of emission sources (modeling or monitoring)

15 wet deposition sites; 3 dry deposition sites; sources of data: CASTNET, NADP (Whitall and Paerl, 2001) 

RTI, 2003 CMAQ; If calibration data 

inputs is available for years atmospheric inputs for thesmined from the sources usedemonstration research. 

Analytic tool Separate analyses using SPARROW and the 

ASSETS EI 

RTI, 2003; Bricker et al., 2007a 

Combined analysis using SASSETS EI 

Tool output indicators

SPARROW: Mean annual TN loading ASSETS EI: ASSETS overall score relating to likelihood of eutrophication 

 Change in ASSETS overall 

changing input nitrogen loamodeling using various atmscenarios
Aquatic Nutrient E

DRAFT Attachment 5, pg 62 

Category

Description of Current/Demonstration Research Source Description for Final Cas

Endpoint of indicators

Habitat and water quality degradation (expressed in terms of eutrophication effects (i.e., anoxia, loss of SAV) 

 Habitat and water quality d

(expressed in terms of eutro(i.e., anoxia, loss of SAV) 

Linkage to endpoints (science that connects indicator value to endpoint)

Previous work not applicable to ecosystem services due to atmospheric deposition effects because it was focused on swine operations. Similar endpoints and linkages can be modified for SPARROW output. 

RTI, 2003 Inherent in the numerical sc

EI using expression of symdissolved oxygen, harmful 

Ecosystem services Fisheries, recreation, tourism Fisheries, recreation, tourisLinkage method from endpoint to services

N/A Quantitative: fisheries (e.g.

species richness) related to symptoms through monitorQualitative: recreational aceutrophication symptoms thGaps: wealth of monitoring answer applicable questionsurveys available to conduc
Aquatic Nutrient Enrichment Case Study 

DRAFT Attachment 5, pg 63 August 2008 

1 Results indicate that 90% of the predicted in-stream concentrations for watershed outlet 

2 reaches fall within the range of 1.5 mg/L and 5.1 mg/L (total nitrogen), with the distribution 

3 especially for total nitrogen more skewed toward the lower end of the range. This distribution is 

4 generally indicative of eutrophic conditions, based on stream classification work by Dodds and 

5 colleagues (1998). We compiled model outputs and aggregated them to assess both spatial 

6 patterns and relative contributions from the different source categories considered. With default 

7 input and delivery assumptions, we estimate swine waste accounts for 30% of the nitrogen 

8 loading to coastal waters from inland, free-flowing streams and rivers in the study area. 

9 For the entire study area (which included not only the Neuse River but also the Tar10 Pamlico, Cape Fear, White Oak, and New river basins), swine facilities are predicted to 

11 contribute 28% of the atmospheric nitrogen inputs. Ammonia transported from swine facilities 

12 that deposits directly onto estuarine waters is estimated to deposit at rates of 0.01 to 0.04 kg/ha/yr

13 for the different estuaries considered (i.e., Pamlico, Neuse, White Oak, and New), accounting for 

14 between 0.01% to 0.1% of the total estuarine loading. The rate of ammonia direct deposition to 

15 estuaries is estimated to be <1% of the estimated total nitrogen deposition rate accounting for 

16 non-swine sources, suggesting that local (i.e., indirect) ammonia gas transport and deposition is a 

17 more serious concern than ammonia transport directly to estuary waters. However, we cannot 

18 draw inferences about ammonium transport from swine facilities to estuaries because we did not 

19 attempt to model transport and deposition into the water system of swine waste as ammonium 

20 particles. 

21 We completed a model run in which all swine input source terms for both runoff and 

22 deposition were set to zero, providing a hypothetical “zero swine discharge” scenario. This 

23 scenario reduced the area-wide total nitrogen delivery to estuaries by 125 million kg/yr, with 

24 variable reductions in more local stream concentrations and loadings, depending on the relative 

25 influence from swine facilities. The largest change at a 14-digit watershed outlet level was an in26 stream improvement of 6.5 mg/L of total nitrogen, with the median improvement across all 

27 watershed outlet reaches being 0.14 mg/L. Our results demonstrate that ammonia deposition is a 

28 potentially significant component of surface water contributions in these North Carolina river 

29 basins.
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DRAFT Attachment 5, pg 64 August 2008 

1 Total nitrogen loads to the estuary calculated based on both the normal simulation and the 

2 “zero swine discharge” scenario will be compared against the updated modeling estimates 

3 completed for the 2002 base-case scenario. 

4 3.1.2.2 ASSETS EI Assessment 

5 The previous work completed using the ASSETS EI on the Neuse River Estuary was 

6 done as part of the NEEA. The latest available data was provided in the NEEA Update (Bricker 

7 et al., 2007a). The input load of nitrogen used as input to the ASSETS EI for that assessment was 

8 9,600,000 kg/yr. The exact source of this load estimate and the exact timeframe of the data used 

9 to calculate the ASSETS EI are still unknown at this time, although the data should fall within 

10 the period of 2000–2002 (S. Bricker, personal communication, 2008). As shown in Figure 3.1-5,

11 the current assessment of the Neuse Estuary reveals a Highly/Moderately Influenced or High

12 score for influencing factors where the nitrogen load is ranked as Moderate to High and a Bad

13 overall ASSETS score for the estuary. In the 2002 base-case scenario, we will examine whether 

14 or not these scores vary from this previous analysis. 
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DRAFT Attachment 5, pg 65 August 2008 

1

2 Figure 3.1-5. The ASSETS assessment summary for the Neuse River Estuary from the 2007 

3 NEEA Update (Bricker et al., 2007a). 

4 3.2 FUTURE CASE STUDY ASSESSMENTS 

5 The case studies presented in the Results section of this report summarized work that had 

6 already been completed using the methods we seek to use for analyzing future reduction 

7 scenarios in NOx and SOx deposition. The next draft of this report will present base-case 

8 scenarios for the case study assessments using the 2002 CMAQ atmospheric deposition data 
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DRAFT Attachment 5, pg 66 August 2008 

1 provided by EPA Clean Air Markets Division. The base-case scenario will provide a comparison 

2 point for the future reduction scenarios. Below we detail some of the differences we expect to 

3 see in the results and the additional data required to complete the 2002 base-case scenario as 

4 compared to the current summarized work. For any future comparisons between previous 

5 modeling efforts and the modeling efforts conducted under this risk and exposure assessment, we 

6 will examine the differences in atmospheric deposition inputs and the effects these differences 

7 may have on the modeling outcomes. 

8 3.2.1 Potomac River Watershed 

9 Within the Potomac River watershed, the atmospheric deposition of total nitrogen 

10 modeled using CMAQ for 2002 (Figure 3.2-1) can be compared to the atmospheric deposition 

11 inputs from NADP used in the Version 3 Chesapeake Bay SPARROW application, where 

12 interpolated values of wet deposition of nitrate were used (Figure 3.2-1). Comparisons between 

13 these two figures reveal that not only do the magnitudes of deposition values vary greatly, but 

14 the spatial aspects of deposition are almost completely reversed. Modeled total nitrogen 

15 deposition loads (normalized by area) are lowest in the more mountainous western side of the 

16 watershed and higher in the lowlands near the mouth of the Potomac River. Wet nitrate loads 

17 (normalized by area) were highest in the western, mountainous region of the watershed and loads 

18 in the southeastern portion of the watershed around the mouth of the Potomac River and the 

19 estuary.
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DRAFT Attachment 5, pg 67 August 2008 

1

2 Figure 3.2-1. Atmospheric deposition inputs based on CMAQ simulations for 2002 for the 

3 Potomac River watershed and estuary. These inputs can be compared to Figure 3.1-1. 

4 Greater spatial variability can also be seen when allocating the atmospheric deposition 

5 input loads to the segmented watershed units used within the Version 3 Chesapeake Bay 

6 SPARROW application. We suggest that the modeled CMAQ data be allocated to a smaller unit, 

7 such as the HUC14 level or the segmented watershed units from the Version 3 Chesapeake Bay 

8 SPARROW application or the updated NHD system. This will also allow for direct comparisons 

9 between the nitrate deposition from NADP used within the Version 3 Chesapeake Bay 

10 SPARROW application and the modeled nitrate deposition from CMAQ. 

11 The components of the ASSETS EI were completed for 2002 during an analysis by 

12 NOAA (Bricker et al., 2006). The nitrogen load used to calculate the influencing factor portion 

13 of the ASSETS EI relied on an estimate of nitrogen inputs from the lower Potomac watershed to 

14 the estuary from Version 4.3 of the Chesapeake Bay Watershed Model (Bricker et al., 2006; MD 

15 DNR, 2005) for 2002. Therefore, when updating the base-case scenario in the next draft of this 

16 report, we will incorporate the CMAQ deposition data into a new SPARROW application, and 
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DRAFT Attachment 5, pg 68 August 2008 

1 therefore provide an updated nitrogen load for use in calculating the influencing factors portion 

2 of the ASSETS EI. The 2002 score for the influencing factors portion of the ASSETS EI was 

3 evaluated as Highly Influenced (Figure 2.2-5). We expect the updated 2002 ASSETS EI score to 

4 remain the same. The remaining indices were evaluated at scores of High for the overall 

5 eutrophic condition (Figure 2.2-6), based on chlorophyll a and HAB symptoms, and Improve 

6 Low for the future outlook (Figure 2.2-8). The future outlook score will not be adjusted in the 

7 future policy scenarios because it relies on project population increases and slight reductions in 

8 nutrient loads. The overall eutrophic condition score will be assessed as discussed in Section 

9 2.2.3. The chlorophyll a measures, which lead to a primary symptom score of High, could be 

10 considered in the borderline range (a Medium concentration at 16.4 ug/L at a High spatial 

11 coverage of 59% where the ranges for a Medium concentration is 5 ug/L–20 ug/L and a High

12 spatial coverage is >50%). With improvements in nutrient loadings, it may be possible to predict 

13 a lower spatial coverage, which would improve the overall score. This score is an example of 

14 what will be examined through data mining, trend analysis, and expert judgment for the future 

15 policy scenarios.

16 3.2.2 Neuse River Watershed 

17 The Neuse River watershed was the subject of a detailed modeling effort by RTI in a 

18 previous project, where the influences of the swine industry in North Carolina on the surface and 

19 groundwater were analyzed. This effort resulted in modeled deposition of ammonia due to swine 

20 operations in the state. Background deposition rates of reactive, oxidized, and organic nitrogen 

21 were also calculated based interpolated data from NADP, CASTNET, and some modeled 

22 estimates. These previous deposition estimates are compared to the 2002 CMAQ-modeled 

23 atmospheric data for the Neuse River watershed (Figure 3.2-2), which will be used in the future 

24 base-case scenario. 
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DRAFT Attachment 5, pg 69 August 2008 

1

2 Figure 3.2-2. Atmospheric deposition inputs based on CMAQ simulations for 2002 for the 

3 Neuse River watershed and estuary. These inputs can be compared to Figure 3.1-4. 

4 The spatial variance and magnitude of atmospheric deposition of total nitrogen (i.e., 

5 speciated nitrogen will be compared in future drafts) differ greatly between the interpolated 

6 NADP/CASTNET/modeled data from 1996–2000 used in the current work and the CMAQ 

7 modeled results. The CMAQ results appear to follow the modeled ammonia deposition from 

8 swine operations with the Neuse watershed more than the background interpolated 

9 concentrations. Although the current work took both the ammonia deposition from local, land 

10 use-based sources and the total nitrogen background deposition into account as separate 

11 atmospheric sources to the model, we expect that the future base-case nitrogen loading to the 

12 estuary from an updated SPARROW application using CMAQ data will vary, at least on the 

13 HUC14 level, from the current work. It is possible that the variations of surface water total 

14 nitrogen loads on a HUC14 level will average out to produce a similar total nitrogen load to the 

15 estuary, but this will be unknown until completion of an updated SPARROW application using 

16 the CMAQ data. We suggest that the CMAQ data be partitioned on the HUC14 or even 12-km 
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DRAFT Attachment 5, pg 70 August 2008 

1 grid level rather than the HUC8 level. This will allow a more complete comparison between the 

2 modeled ammonia deposition because of local sources, and thus, a more complete analysis of 

3 deposition of Nr, including NOx, and its effects on nitrogen loadings to the estuary. 

4 The ASSETS EI reported for the Neuse River and estuary in the 2007 NEEA Update 

5 (Bricker et al., 2007a) represented conditions in the estuary for the 2000–2002 timeframe 

6 (Bricker, personal communication, 2008). We will seek to confirm the input data used in that 

7 assessment, but at this time, we expect to work under the same conditions as in the Potomac 

8 assessment, where the influencing factor portion of the score will change, but the overall 

9 eutrophic condition (i.e., rating of High) and future outlook (i.e., rating of Worsen High) portions 

10 will remain the same. 

11 3.2.3 Discussion on Analysis of Changing Nitrogen Loads 

12 As discussed in the previous section, the updated base-case scenario, as well as the future 

13 policy scenarios that examine reductions in NOx and SOx deposition, will rely on changes in the 

14 influencing factor score of the ASSETS EI because of changes in the predicted nitrogen load to 

15 the estuary. We must also consider that if there are considerable changes in the nitrogen loadings 

16 to the estuary, there may be changes in the indicators of the overall eutrophication condition as 

17 well. Without a comprehensive model that can calculate all five indicators as a function of 

18 nitrogen loads, we do not have a conclusive method to predict changes in these indicators for 

19 each policy scenario.

20 As we move forward in the policy scenarios, we will seek to use historical data to relate 

21 past nitrogen loads to each estuary to temporally corresponding indicators within the estuaries as 

22 an effort to create a response curve that can be used to predict whether or not the overall 

23 eutrophic condition score should be changed with each new calculated nitrogen load. This 

24 method will rely greatly on best professional judgment because it is not expected that large 

25 amounts of data will be available to create this curve (especially for the less studied systems 

26 discussed in the following section). Although this method will be highly speculative, it does 

27 make an attempt to account for changes in eutrophication, which would be otherwise overlooked 

28 in the analysis. 
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DRAFT Attachment 5, pg 71 August 2008 

1 4. IMPLICATIONS FOR OTHER SYSTEMS 

2 Selection of the analysis method for aquatic nutrient enrichment considered applications 

3 beyond a small number of case studies. The chosen method, consisting of a combination of 

4 SPARROW modeling for nitrogen loads and assessment of estuary conditions under the NOAA 

5 ASSETS EI, provides a highly scalable and widely applicable analysis method. Both components 

6 have been applied on a national scale—the national nutrient assessment using SPARROW 

7 (Smith and Alexander, 2000) and the NEEA using the ASSETS EI (Bricker et al., 1999, 2007a). 

8 Additionally, both have been used on a smaller scale. These previous analyses supply a large 

9 body of work—data, methods, and supporting experts—to draw from when conducting 

10 additional analyses or updating past applications. 

11 Requirements for applying this method to other systems include mandatory data inputs, 

12 the ability to formulate a SPARROW application on a reliable stream network, and an estuary 

13 under suspicion of eutrophication. Data requirements and model formulations have been 

14 described and detailed throughout this report.

15 The method breaks down when attempting to assess eutrophication impacts on inland 

16 waters. SPARROW modeling can still be applied to determine nitrogen loadings to an inland 

17 waterway, but the ASSETS EI would not apply, and as such, the indicators and overall likelihood 

18 of eutrophication could not be assessed. For these inland waters, an alternate methodology would 

19 be necessary to examine the effects of changing nitrogen loads within the waterbody. A variety 

20 of methods could possibly be applied, including empirical relationships or dynamic modeling. It 

21 is beyond the scope of this case study to further assess these inland waters. An additional case 

22 study in this project examines the effects of aquatic acidification on inland waters using dynamic 

23 modeling.

24 5. UNCERTAINTY 

25 There are several areas of uncertainty with this method of assessment for aquatic nutrient 

26 enrichment, which are summarized below. 

27  Data Inputs to SPARROW. Two prominent choices of stream networks currently exist

28 on which to build the updated SPARROW model for the Potomac watershed. The 

29 network used as part of the previous Chesapeake Bay SPARROW applications is 
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DRAFT Attachment 5, pg 72 August 2008 

1 available with data inputs from the 1997 Version 3 application. The stream network set 

2 up for the Phase 5 version of the Chesapeake Bay Model, which utilizes 2002 data, is 

3 very similar to the aforementioned 1997 version in streamlines, but differs in watershed 

4 segments. These watershed segments have been divided not only based on streamlines, 

5 but also on geographic considerations. This allows for better defined data inputs, but 

6 provides a source of uncertainty in modeling with SPARROW’s design of linked 

7 watershed segments based on streamlines. Upcoming work will combine the applicable 

8 data from these models to arrive at the best input dataset for the desired base-case 

9 scenario with the least amount of uncertainty possible. 

10  Modeling Uncertainty in SPARROW Estimates. With any measured or modeled 

11 results, there is a certain amount of uncertainty that should be quantified. Because 

12 SPARROW relies on a nonlinear regression basis, a number of parameters can be used to 

13 assess the uncertainty within the model and provide confidence intervals around the 

14 estimates. 

15  Sensitivity of SPARROW Formulation Due to Atmospheric Inputs. Differences in 

16 the final parameterization of the SPARROW model resulting from the use of the 

17 combination of CMAQ and NADP data as input to the model, in place of only NADP 

18 data as in previous applications, will allow for examination of the sensitivity of model 

19 parameters, evaluation statistics, and output to this more detailed data source. 

20  Calibration Data for SPARROW Estimates. Monitoring data will be used to calibrate 

21 the SPARROW model. By relying on data from federally recognized data systems, we 

22 aim to use data that has undergone quality assurance/quality control (QA/QC) 

23 procedures. Additionally, we will collaborate with the researchers who have conducted 

24 the previous SPARROW applications in each case study region to provide a rigorous 

25 check on the data used.

26  Data Inputs to the ASSETS EI. As with the monitoring data used in calibrating 

27 SPARROW, the data inputs used to calculate the ASSETS EI must be confirmed from 

28 the previous analyses that we will be relying on as having undergone QA/QC procedures. 

29  Heuristic Estimates of Future Outlook: The estimation of the future outlook score in 

30 the ASSETS EI currently relies on heuristic estimates from systems experts. Future 
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DRAFT Attachment 5, pg 73 August 2008 

1 NOAA efforts will seek to provide more scientifically structured estimates for this 

2 parameter, but at this time, we must rely on expert judgment on whether there will be 

3 increased or decreased pressures because of nutrient loads, population growth, and land 

4 use change.

5  Steady State Estimates/Mean Annual Estimates. Both SPARROW and the ASSETS 

6 EI currently provide only longer-term estimates of the system conditions. There is the 

7 possibility of conducting the analyses on a seasonal basis, which may be appropriate 

8 because the trends in eutrophication indicators are likely to vary seasonally. For the 

9 current risk assessment, the analyses will be carried out for the entire 2002 base case.

10  Future Dynamic Model Applications, Including Eutrophication Indicators. As

11 highlighted in the introductory sections, higher level modeling approaches could 

12 potentially be used to evaluate the eutrophication effects of interest if significant data 

13 resources, time, and expertise were available for a specific site. An approach of this kind 

14 would not be scalable or applicable to wider regions, but would provide estimates with 

15 less uncertainty for a studied system.

16  Use of a Screening Method. The methods used in this study are only of the screening

17 level. As identified in the previous bullet, the screening level was more appropriate for a 

18 scalable, widely applicable set of case studies than for a highly detailed modeling effort. 

19 Undoubtedly, details, such as the degree to which the soil-groundwater system affects 

20 atmospherically deposited nitrogen, will be less quantified than detailed processes using 

21 this method. However, for an initial approach to determining the aquatic nutrient 

22 enrichment effects on a system, the screening method provides a response curve that can 

23 be used in the evaluation of ecosystem services. 

24 6. CONCLUSIONS 

25 The following are to be developed more fully in later drafts after comment period: 

26  Screening level method appropriate due to lack of link development 

27  Summary of current states of development 

28  Response curve developments and discussion provide implications for standards 

29  Summary of policy scenarios for case studies when available
Aquatic Nutrient Enrichment Case Study 

DRAFT Attachment 5, pg 74 August 2008 

1  Future developments could supply higher level modeling efforts. 

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22 watershed organic matter loading: Consequences for hypoxia in the eutrophying Neuse 

23 River Estuary, NC, USA. Marine Ecology Progress Series 166:17–25.

24 Paerl, H.W. 1995. Coastal eutrophication in relation to atmospheric nitrogen deposition: current 

25 perspectives. Ophelia 41:237–259.
Aquatic Nutrient Enrichment Case Study 

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1 Paerl, H.W. 1997. Coastal eutrophication and harmful algal blooms: Importance of atmospheric 

2 deposition and groundwater as “new” nitrogen and other nutrient sources. Limnology and 

3 Oceanography 42:1154–1165.

4 Paerl, H.W., R.S. Fulton, P.H. Moisander, and J. Dyble. 2001. Harmful freshwater algal blooms, 

5 with an emphasis on cyanobacteria. The Scientific World 1:76–113.

6 Preston, S.D., and J.W. Brakebill. 1999. Applications of spatially referenced regression 

7 modeling for the evaluation of total nitrogen loading in the Chesapeake Bay watershed.

8 U.S. Geological Survey Water-Resources Investigations Report 99-4054. U.S. 

9 Department of the Interior, U.S. Geological Survey, MD-DE-DC Water Science Center, 

10 Baltimore, MD. Available at http://md.usgs.gov/publications/wrir-99-4054.

11 RTI International. 2003. Benefits of Adopting Environmentally Superior Swine Waste

12 Management Technologies in North Carolina: An Environmental and Economic 

13 Assessment. Final Report. Prepared for C.M. Williams, Animal and Poultry Waste 

14 Management Center at North Carolina State University. RTI International, Research 

15 Triangle Park, NC. 

16 RTI International. 2007. Review of Candidate Fate and Transport and Ecological Models.

17 Report prepared for the U.S. Environmental Protection Agency under Contract No. 

18 EP-D-06-003. RTI International, Research Triangle Park, NC. 

19 Schwarz, G.E., A.B. Hoos, R.B. Alexander, and R.A. Smith. 2006. The SPARROW Surface 

20 Water-Quality Model—Theory, Applications and User Documentation. USGS 

21 Techniques and Methods 6–B3, 248 p. and CD –ROM. U.S. Department of the Interior, 

22 U.S. Geological Survey, Reston, VA. 

23 Smith, M.D., and L.B. Crowder. 2005. Valuing ecosystem services with fishery rents: a lumped 

24 parameter approach to hypoxia in the Neuse River Estuary. Milan, Italy: Fondazione Eni 

25 Enrico Mattei (FEEM), NRM Nota di Lavoro (Natural Resources Management Working 

26 Papers), 115.05. Available at SSRN: http://ssrn.com/abstract=825587 (accessed 

27 November 5, 2007). 
Aquatic Nutrient Enrichment Case Study 

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1 Smith, R.A., and R.B. Alexander. 2000. Sources of nutrients in the nation’s watersheds. In

2 Managing Nutrients and Pathogens from Animal Agriculture. Proceedings from the 

3 Natural Resource, Agriculture, and Engineering Service Conference for Nutrient 

4 Management Consultants, Extension Educators, and Producer Advisors, Camp Hill, PA, 

5 March 28–30. Natural Resource, Agriculture, and Engineering Service, Cooperative 

6 Extension, Ithaca, NY. 

7 Smith, R.A., G.E. Schwarz, and R.B. Alexander. 1997. Regional interpretation of water-quality 

8 monitoring data. Water Resources Research 33(12):2781–2798.

9 Smith, R.A., R.B. Alexander, G.D. Tasker, C.V. Price, K.W. Robinson, and D.A. White. 1994. 

10 Statistical modeling of water quality in regional watersheds. Pp. 751–754 in Proceedings

11 of Watershed ‘93—A National Conference on Watershed Management, Alexandria, VA, 

12 March 21–24, 1993. EPA 840-R-94-002. U.S. Environmental Protection Agency, 

13 Washington, DC.

14 U.S. EPA (Environmental Protection Agency). 1996. Air quality criteria for ozone and related 

15 photochemical oxidants. EPA/600/AP-93/004aF-cF. 3v. U.S. Environmental Protection 

16 Agency, Office of Research and Development, Research Triangle Park, NC. Available at 

17 http://cfpub2.epa.gov/ncea/.

18 U.S. EPA (Environmental Protection Agency). 2000. Deposition of air pollutants to the great 

19 waters. Third report to Congress. EPA-453/R-00-005. U.S. Environmental Protection 

20 Agency, Office of Air Quality Planning and Standards, Research Triangle Park, NC. 

21 Available at http://www.epa.gov/air/oaqps/gr8water/3rdrpt/ (accessed January 16, 2008). 

22 U.S. EPA (Environmental Protection Agency). 2007. Integrated Science Assessment for Oxides 

23 of Nitrogen and Sulfur—Environmental Criteria. EPA/600/R-07/145A. U.S. 

24 Environmental Protection Agency, National Center for Environmental Assessment-RTP 

25 Division, Office of Research and Development, Research Triangle Park, NC. Available at 

26 http://www.epa.gov/ttn/naaqs/standards/no2so2sec/data/200712_external_peer_review.pd

27 f.
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1 U.S. EPA (Environmental Protection Agency). 2008. Draft Scope and Methods Plan for 

2 Risk/Exposure Assessment: Secondary NAAQS Review for Oxides of Nitrogen and Oxides 

3 of Sulfur. U.S. Environmental Protection Agency, Office of Air Quality Planning and 

4 Standards, Research Triangle Park, NC Available at 

5 http://www.epa.gov/ttn/naaqs/standards/no2so2sec/cr_pd.html. 

6 USDA (U.S. Department of Agriculture). 2002. 2002 Census of Agriculture – State Data: Table 

7 13 “Poultry – Inventory and Sales: 2002 and 1997.” U.S. Department of Agriculture, 

8 National Agricultural Statistics Service, Washington, DC. Available at 

9 http://www.nass.usda.gov/census/census02/volume1/us/st99_2_013_013.pdf. 

10 USGS (U.S. Geological Survey). 1999. Digital representation of “Atlas of United States Trees” 

11 by Elbert L. Little, Jr. Digital Version 1.0. U.S. Department of the Interior, U.S. 

12 Geological Survey, Denver, CO. Available at http://esp.cr.usgs.gov/data/atlas/little/ 

13 (accessed July 25, 2008). 

14 Valiela, I., and J. Costa. 1988. Eutrophication of Buttermilk Bay, a Cape Cod coastal 

15 embayment: Concentrations of nutrients and watershed nutrient budgets. Environmental

16 Management 12:539–551. 

17 Valiela, I., J. Costa, K. Foreman, J.M. Teal, B. Howes, and D. Aubrey. 1990. Transport of 

18 groundwater-borne nutrients from watersheds and their effects on coastal waters. 

19 Biogeochemistry 10:177–197. 

20 Whitall, D., and H.W. Paerl. 2001. Spatiotemporal Variability of Wet Atmospheric Nitrogen 

21 Deposition to the Neuse River Estuary, North Carolina. Journal of Environmental 

22 Quality 30:1508–1515. 

23 Whitall, D., S. Bricker, J. Ferreira, A.M. Nobre, T. Simas, and M. Silva. 2007. Assessment of 

24 Eutrophication in Estuaries: Pressure-State-Response and Nitrogen Source 

25 Apportionment. Environmental Management 40:678–690. 
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10 TERRESTRIAL NUTRIENT ENRICHMENT 

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12 Work Assignment 2-44 

13 Project Number 0209897.002.044 

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34 3040 Cornwallis Road

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1 CONTENTS 

2 Introduction..................................................................................................................................... v 

3 1. Background............................................................................................................................... 2 

4 1.1 Indicators, Endpoints, and Ecosystem Services............................................................... 2 

5 1.2 Case Study Site Selection ................................................................................................ 4 

6 1.3 Ecosystem Overview ....................................................................................................... 9 

7 1.3.1 Coastal Sage Scrub .............................................................................................. 9 

8 1.3.2 Mixed Conifer Forest......................................................................................... 13 

9 1.4 Historical Trends............................................................................................................ 19 

10 1.4.1 Coastal Sage Scrub ............................................................................................ 19 

11 1.4.2 Mixed Conifer Forest......................................................................................... 21 

12 2. Approach and Methodology ................................................................................................... 22

13 2.1 Understanding the Trends through Literature Review................................................... 22

14 2.1.1 Coastal Sage Scrub ............................................................................................ 22 

15 2.1.2 Mixed Conifer Forest......................................................................................... 22 

16 2.2 GIS Methodology........................................................................................................... 23

17 2.2.1 Overview............................................................................................................ 23 

18 2.2.2 Available Data Inputs ........................................................................................ 23 

19 2.2.3 Approach to Mapping ........................................................................................ 25 

20 3. Results..................................................................................................................................... 25 

21 3.1 Literature Review Findings............................................................................................ 26 

22 3.1.1 Coastal Sage Scrub ............................................................................................ 26 

23 3.1.2 Mixed Conifer Forest......................................................................................... 33 

24 3.1.2.1 Nitrogen and Ozone Effects on Conifers............................................ 33 

25 3.1.2.2 Nitrogen Effects on Lichens ............................................................... 36 

26 3.1.2.3 Nitrogen Saturation and Critical Loading Benchmarks...................... 39 

27 4. Implications for Other Systems .............................................................................................. 40

28 5. Uncertainty.............................................................................................................................. 41 

29 5.1 Coastal Sage Scrub ........................................................................................................ 41

30 5.2 Mixed Conifer Forest..................................................................................................... 44

31 6. Conclusions............................................................................................................................. 46 

32 6.1 Coastal Sage Scrub ........................................................................................................ 46

33 6.2 Mixed Conifer Forest..................................................................................................... 46

34 7. References............................................................................................................................... 47 

35
Terrestrial Nutrient Enrichment Case Study 

DRAFT Attachment 6, pg iv August 2008 

1 FIGURES

2 Figure 1.3-1 Range of coastal sage scrub communities. ............................................................. 9 

3 Figure 1.3-2 Presence of three threatened and endangered species in California’s 

4 coastal sage scrub ecosystem................................................................................ 11 

5 Figure 1.3-3 Range of California’s mixed conifer forest ecosystem......................................... 14 

6 Figure 1.3-4 Presence of two threatened and endangered species............................................. 15 

7 Figure 1.4-1 Decline of coastal sage scrub from 1977–2002. ................................................... 20 

8 Figure 3.1-1 Coastal sage scrub range and total nitrogen deposition using CMAQ 2002 

9 modeling results.................................................................................................... 29 

10 Figure 3.1-2 Current fire threats to coastal sage scrub communities. ....................................... 32 

11 Figure 3.1-3 Conceptual model of coastal sage scrub community in relation to fire and 

12 nitrogen deposition................................................................................................ 33 

13 Figure 3.1-4 Mixed conifer forest range and total nitrogen deposition using CMAQ 

14 2002 modeling results........................................................................................... 34 

15 Figure 3.1-5 Conceptual model for increased susceptibility to wildfire in mixed conifer 

16 forests.................................................................................................................... 36 

17 Figure 3.1-6 Importance of lichens as an indicator of ecosystem health................................... 37 

18 Figure 3.1-7 Presence of acidophyte lichens and the total nitrogen deposition in the 

19 California ranges................................................................................................... 38 

20 Figure 5.1-1 Relief map showing the loss of CSS over the past 30 years and fire threat. ........ 43 

21 Figure 5.1-2 Relief map showing the loss of CSS and CMAQ nitrogen deposition................. 43 

22

23 TABLES

24 Table 1.2-1 Summary of Indicators and Mapping Layers for Targeted Ecosystems................. 4 

25 Table 1.2-2 Potential Assessment Areas for Terrestrial Nutrient Enrichment Identified 

26 in the Draft ISA....................................................................................................... 6 

27 Table 1.3-1 Selected Flora and Fauna Associated with the Coastal Sage Scrub 

28 Community ........................................................................................................... 12 

29 Table 1.3-2 Selected Flora and Fauna Associated with the Mixed Conifer Ecosystems......... 16 

30 Table 1.3-3 List of Lichen Species Present in the Sierra Nevada and San Bernardino 

31 Mountain Ranges .................................................................................................. 18 

32 Table 3.1-1 Summary of Selected Experimental Variables across Multiple CSS Study 

33 Locations............................................................................................................... 27 

34 Table 3.1-2 Research Evidence of Ecosystem Responses to Nitrogen Relevant to 

35 Coastal Sage Scrub Communities......................................................................... 31

36
Terrestrial Nutrient Enrichment Case Study 

DRAFT Attachment 6, pg v August 2008 

1 ACRONYMS AND ABBREVIATIONS 

2 AM arbuscular mycorrhizae

3 CASTNet Clean Air Status and Trends Network 

4 cm centimeter 

5 CMAQ Community Multiscale Air Quality model 

6 CO2 carbon dioxide 

7 CSS coastal scrub sage 

8 EMAP Environmental Monitoring and Assessment Program 

9 FIA Forest Inventory and Analysis National Program 

10 FRAP Fire and Resource Assessment Program 

11 FWS U.S. Fish and Wildlife 

12 GIS geographic information systems 

13 ISA Integrated Science Assessment 

14 kg kilogram 

15 km kilometer 

16 LTER Long-Term Ecological Research 

17 m meter 

m

2

18 square meter 

19 MEA Millennium Ecosystem Assessment 

20 mm millimeter 

21 mo month 

22 NADP National Atmospheric Deposition Program 

NO3

-

23 nitrate

24 NOx nitrogen oxides 

25 NTN National Trends Network 

26 PNV Potential Natural Vegetation 

27 SMB Simple Mass Balance 

28 UNECE United Nations Economic Commission for Europe 

29 USDA U.S. Department of Agriculture 

30 USFS U.S. Forest Service 

31
Terrestrial Nutrient Enrichment Case Study 

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1 INTRODUCTION

2 This case study will demonstrate the influence of elevated levels of atmospheric nitrogen 

3 on selected ecosystems in California. The case study provides examples of habitats that may 

4 benefit from the management of atmospheric deposition, loading benchmarks, and ecosystem 

5 effects of nitrogen saturation. The investigations and approaches provided in this case study may 

6 be used to foster the research and management of elevated ambient air nitrogen and subsequent 

7 deposition on other terrestrial habitats across the United States. 

8 In this case study, we identified sites in California that are of particular public interest 

9 and where nitrogen appears to be modifying habitats. Many acres of the coastal sage scrub (CSS) 

10 community have been set aside in reserves to protect the habitats and the species of concern that 

11 reside within the habitats. The mixed conifer forests on the slopes of the San Bernardino and 

12 Sierra Nevada Mountains have important recreational value, protect water resources, and provide 

13 habitats for many other species. In the mixed conifer forest, we investigated lichen communities 

14 associated with the forest stands and nitrogen saturation to identify the effects of nitrogen 

15 loadings. These habitats provide a number of ecological services, including regulation (e.g., 

16 water), cultural and aesthetic value (e.g., recreation, natural landscape, and sense of place), and 

17 provisioning (e.g., timber) (MEA, 2005). In addition, these locations have the following 

18 characteristics that make them good candidates for case studies: 

19  There is public interest 

20  An adequate amount of data (especially geographic information systems [GIS]) is 

21 available

22  There are implications for other systems and ecosystem services 

23  Critical loading benchmarks may be developed 

24  The observed effects within the communities can be linked to atmospheric deposition of 

25 nitrogen.

26 A literature review was conducted to describe the habitats and species of concern, 

27 identify trends in habitats and their effects, and discuss research efforts that have investigated the 

28 variables and driving forces that may affect the communities. GIS data were obtained to explore 
Terrestrial Nutrient Enrichment Case Study 

DRAFT Attachment 6, pg 2 August 2008 

1 the spatial extent of the habitat and changes in that extent, the location of fire threat (an 

2 important variable in both CSS and mixed conifer forest habitats), and the location of species of 

3 concern. Additionally, the Community Multiscale Air Quality (CMAQ) 2002 modeling results 

4 were used to gain an understanding of how atmospheric deposition of nitrogen is spatially 

5 distributed. Spatial information and experimental results were acquired to help identify the 

6 driving forces in these ecosystems, describe the past and current extent of the habitats, and 

7 investigate the possibility of establishing benchmark loads. 

8 1. BACKGROUND 

9 The case study considered two ecosystems in California. For CSS, a conceptual model 

10 was developed that illustrates how nitrogen oxides (NOx) is intertwined as a driving variable in 

11 changing CSS communities and how CSS can be used as a model for the other sensitive and 

12 critical habitats and/or species of concern. For mixed conifer forests, we examined the effects of 

13 atmospheric nitrogen in the context of other variables (i.e., ozone and fire), the effects of 

14 nitrogen saturation on forest communities, and loading benchmarks. 

15 1.1 INDICATORS, ENDPOINTS, AND ECOSYSTEM SERVICES 

16 Major indicators for nutrient enrichment to terrestrial systems from air deposition of 

17 reactive nitrogen involve measurements based on available monitoring stations for wet 

18 deposition (National Atmospheric Deposition Program [NADP]/National Trends Network 

19 [NTN]) and limited networks for dry deposition (Clean Air Status and Trends Network 

20 [CASTNet]). Wet deposition monitoring stations can provide more information on an extensive

21 range of nitrogen species than can dry deposition monitoring stations. This creates complications 

22 when developing estimates for total nitrogen deposition levels because dry deposition data 

23 sources will likely be underestimated. In the Mediterranean systems of Southern California, dry 

24 deposition is particularly important. Individual studies measuring nitrogen deposition to 

25 terrestrial ecosystems that involve throughfall estimates for forested ecosystems can provide 

26 better approximations for total nitrogen deposition levels; however, such estimates and related 

27 bioassessment data are not available for the entire country. For terrestrial ecosystems, low 

28 calcium:nitrogen ratios in soils are commonly related to increased nitrification, potential 
Terrestrial Nutrient Enrichment Case Study 

DRAFT Attachment 6, pg 3 August 2008 

1 increases in soil acidity, and releases in nitrate to receiving waters; however, these measurements 

2 are not always widely available. 

3 The indicators for nutrient enrichment effects on ecosystems reflect a combination of 

4 inputs from various media (e.g., air, discharges, diffuse runoff, groundwater inputs). Given the 

5 nature of major indicators for atmospheric deposition on ecological systems, a data-fusion 

6 approach that combines monitoring indicators with modeling inputs and outputs is often used 

7 (Howarth, 2007), and such an approach was used in this case study. 

8 Ecosystem services are generally defined as the benefits individuals and organizations 

9 obtain from ecosystems. In the 2005 Millennium Ecosystem Assessment (MEA), ecosystem 

10 services are classified into four main categories: 

11  Provisioning. Includes products obtained from ecosystems. 

12  Regulating. Includes benefits obtained from the regulation of ecosystem processes. 

13  Cultural. Includes the nonmaterial benefits people obtain from ecosystems through 

14 spiritual enrichment, cognitive development, reflection, recreation, and aesthetic 

15 experiences. 

16  Supporting. Includes those services necessary for the production of all other ecosystem 

17 services (MEA, 2005).

18 A number of impacts on the endpoints of terrestrial ecosystems exist, including the 

19 following:

20  CSS

21 − Decline in CSS habitat, shrub abundance, species of concern – cultural and regulating 

22 − Increase abundance of non-natives – cultural and regulating 

23 − Increase in wildfires – cultural and regulating 

24  Mixed Conifer Forest 

25 − Change in habitat suitability and increased tree mortality – cultural and regulating 

26 − Decline in mixed conifer forest aesthetics – cultural 

27 − Increase in fire intensity, change in forest’s nutrient cycling, other nutrients become 

28 limiting – regulating 
Terrestrial Nutrient Enrichment Case Study 

DRAFT Attachment 6, pg 4 August 2008 

1 − Decline in surface water quality – regulating 

2 The terrestrial nutrient enrichment case study approach for CSS will focus on ecosystem 

3 services such as biodiversity; threatened and endangered species and rare species (both national 

4 and state); landscape view; water quality; and fire hazard mitigation. Linkage methods from 

5 endpoint to services could include measurement of changes in biodiversity and abundance and 

6 distribution of threatened and endangered species, comparison of past and present photography, 

7 and measurement of the distribution of soil moisture with depth and possible nitrate leakage. 

8 The case study approach for mixed conifer forests will focus on ecosystem services, such 

9 as visual and recreational aesthetics provided by the community and water quality. Linkage 

10 methods from endpoint to services could include measurement of the densification of stands, 

11 shifts in tree dominance, shifts in lichen communities, foliar nitrogen increases, and increased 

NO3

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12 in streams. 

13 1.2 CASE STUDY SITE SELECTION 

14 As described in the introduction of this report, the selection of case study areas specific to 

15 terrestrial nutrient enrichment began with GIS mapping. We used GIS datasets of physical, 

16 chemical, and biological properties that were indicative of potential terrestrial nutrient 

17 enrichment to identify sensitive areas in the United States (Table 1.2-1).

Table 1.2-1. Summary of Indicators and Mapping Layers for Targeted Ecosystems 

Targeted

Ecosystem

Effect Indicator(s) Mapping Layers 

Terrestrial 

Nitrogen

Enrichment 

 CEC

 C:N ratios 

 Ca:Al ratios 

 Air wet/dry deposition (corrected for 

throughfall using available data) 

 Forest soils from USFS 

 Forest type from USFS 

 STATSGO soils 

 NLCD

 CMAQ (N) by HUC 

Note: CEC = cation exchange capacity, C:N = carbon:nitrogen, Ca:Al = calcium:aluminum, 

HUC = hydrologic unit code, N = nitrogen, NLCD = National Land Cover Data, STATSGO = 

State Soil Geographic database, USFS = U.S. Forest Service 
Terrestrial Nutrient Enrichment Case Study 

DRAFT Attachment 6, pg 5 August 2008 

1 We also considered the potential case study areas identified in the Integrated Science 

2 Assessment (ISA) (U.S. EPA, 2007). Table 1.2-2 contains the relevant nutrient enrichment 

3 areas.

4 For purposes of the risk assessment, California’s CSS and mixed conifer forest 

5 communities were selected for an initial case study analysis. We considered the following factors 

6 in choosing these case study areas:

7  Availability of atmospheric ambient and deposition data (monitored or modeled) 

8  Availability of digitized datasets of biotic communities; fire-prone areas; and sensitive, 

9 rare species 

10  Scientific results of research on nitrogen effects for from the case study area 

11  Representation of western United States ecosystems potentially impacted by nitrogen 

12 deposition

13  Scalability and generalization opportunities for risk analysis results from the case studies. 

14 CSS has been the subject of intensive research in the past 10 years, which has provided 

15 the data needed for a first phase of GIS analysis of the role of nitrogen deposition in terrestrial 

16 ecosystems. California mixed conifer forests have an even longer record of study that includes 

17 investigations into the effects of atmospheric pollution, changes to forest structure, changes to 

18 the lichen communities, and measurements of nitrogen saturation. 
Terrestrial Nutrient E

DRAFT Attachment 6, pg 6 Table 1.2-2. Potential Assessment Areas for Terrestrial Nutrient Enrichment Identified in the Draft ISA (U.S. EP

Area Indicator 

DetailedIndicator Area Studies Models References in EPA, 2007 

Adirondacks Terrestrial 

acidification

 Paleoecological 

Investigation of Recent Lake Acidification (PIRLA) I and II; Episodic ResponseProject; Environmental Monitoring and Assessment Program (EMAP)

MAGIC;PnETBGC

Baker and Laflen, 1983; Baker et 1990b; Baker et al., 1990c; Baker 1996; Benoit et al., 2003; Chen aDriscoll, 2004; Confer et al., 198Cumming et al., 1992; Driscoll et 1987a; Driscoll et al., 1991; Drisc1998; Driscoll et al., 2001a; Drisc2001b; Driscoll et al., 2003b; Drial., 2003c; Driscoll et al., 2007a; et al., 2007b; Evers et al., 2007; G2000; Havens et al., 1993; Ito et aJohnson et al., 1994b; Landers et Lawrence et al., 2007; NAPAP, 1Siegfried et al., 1989; U.S. EPA, Sullivan et al., 1990; Sullivan et aSullivan et al., 2006b; U.S. EPA, Van Sickle et al., 1996; Whittier 2002; Wigington et al., 1996; Zha2007
Terrestrial Nutrient E

DRAFT Attachment 6, pg 7 

Area Indicator 

DetailedIndicator Area Studies Models References in EPA, 2007 

ShenandoahNationalPark

Terrestrial acidification

 Shenandoah 

WatershedStudy

MAGIC Baker and Christensen, 1991; Ba

1990b; Bulger et al., 1999; Bulge2000; Cosby et al., 2006; Dennis Bulger, 1995; Dennis et al., 1995et al., 2006; Eshleman and Hyer, Eshleman et al., 1995; Eshleman 1998; Galloway et al., 1983; Hye1995; MacAvoy and Bulger, 199et al., 1989; Schofield and DriscoSullivan et al., 2003; Sullivan et aWebb et al., 1995 

Alpine and sub-alpinecommunities of the eastern slope of the Rocky Mountains in Colorado

Terrestrial nutrientenrichment 

Biomass production;NO3leaching; speciesrichness

 Baron et al., 1994; Baron et al., 2

Baron, 2006; Bowman, 2000; Boand Steltzer, 1998; Bowman et alBowman et al., 1995; Bowman et 2006; Burns, 2004; Fenn et al., 20et al., 1998; Korb and Ranker, 20et al., 2003; Seastedt and VaccaroSherrod and Seastedt, 2001; SteltBowman, 1998; Suding et al., 20Williams and Tonnessen, 2000; Wet al.,1996a; Wolfe et al., 2001 

FernowExperimental Forest near Parsons, WV

Terrestrial nutrientenrichment 

Forestgrowth

 Adams et al., 1997, 2000; DeWal

2006; Edwards and Helvey, 1991et al., 2006; Peterjohn, 1996 

Bear Brook, ME

Terrestrial acidification

Sugar maple; red spruce 

 Elvir et al., 2003 
Terrestrial Nutrient E

DRAFT Attachment 6, pg 8 

Area Indicator 

DetailedIndicator Area Studies Models References in EPA, 2007 

HarvardForest

Terrestrial nutrientenrichment 

Forestgrowth—species

 Magill et al., 2004; Magill, 2004 

SouthernCalifornia

Terrestrial nutrientenrichment 

Forestgrowth—species; coastal sage scrub

 Bytnerowicz and Fenn, 1996, 200

Takemoto et al., 2001 

Jasper Ridge BiologicalPreserve in California

Terrestrial nutrientenrichment 

Grasslands Zavaleta et al., 2003 

Loch Vale, CO

Terrestrial nutrientenrichment 

Old-sprucegrowth

 Rueth et al., 2003 

RockyMountainNationalPark, CO 

Terrestrial nutrientenrichment 

Tundracomposition switch

 Interlandi and Kilham, 1998 

Source: U.S. EPA, 2007a. 

1
Terrestrial Nutrient Enrichment Case Study 

DRAFT Attachment 6, pg 9 August 2008 

1 1.3 ECOSYSTEM OVERVIEW 

2 1.3.1 Coastal Sage Scrub 

3 CSS consists of more than 50 aromatic shrub and sub-shrub species, which range from 

4 approximately 0.5 meters (m) to 2 m in height (Burger et al., 2003; Westman, 1981a). The range 

5 of CSS extends from north of San Francisco down to Baja California in the lower elevation 

6 coastal range of California (see Figure 1.3-1); however, the species composition may vary with 

7 location (Westman, 1981b). According to the California Natural Diversity Database, there are 22 

8 floristic alliances of CSS (i.e., Riversidian Sage Scrub, Venturan Sage Scrub, and Diegan Sage 

9 Scrub). These alliances consist of similar species that help determine the significance, rarity, and 

10 growth patterns of California vegetation types. 

11

12 Figure 1.3-1. Range of coastal sage scrub communities. 
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DRAFT Attachment 6, pg 10 August 2008 

1 CSS grows in a warm Mediterranean climate and is characterized by approximately 300 

2 millimeters (mm) of annual rainfall falling from December through March and little or no 

3 rainfall from April through November (Egerton-Warburton and Allen, 2000; Westman, 1981b). 

4 Underlying substrate types of CSS communities vary greatly across the CSS stands, although 

5 many CSS floristic alliances are found on unconsolidated sand, sandstone, conglomerate, and 

6 shale (Westman, 1981b). 

7 CSS is also known as “soft chaparral” because of its semi-deciduousness, drought8 tolerant nature and its less-rigid leaves, respective to chaparral species (Westman, 1981b). CSS is 

9 considered a fire-adapted community, meaning that although vegetation layers may be destroyed 

10 in fires, CSS soil seed banks can withstand fire, and in some species, require fire to open the seed 

11 cases. However, many CSS species can flourish and propagate in the absence of any fire (Keeler12 Wolf, 1995). CSS has been observed to maintain a permanent cover without fire or other 

13 disturbance regimes (e.g., land conversion, grazing) for at least a century (Westman, 1981a). 

14 The resprouting and competition of species post-fire is generally dependent upon fire 

15 intensity, fire frequency, and seasonal timing (Keeler-Wolf, 1995). CSS species are generally 

16 poor colonizers after a fire (Minnich and Dezzani, 1998). Annual forbs and any grass seedlings 

17 present in the post-fire soils are usually dominant in the first few growth cycles. Significant 

18 shrub growth is most likely to occur in later cycles, further disturbance not withstanding (Keeler19 Wolf, 1995). 

20 The CSS community also supports the growth of more than 550 herbaceous annual and 

21 perennial species between and beneath the shrub canopy. Of these herbs, nearly half are 

22 endangered, sensitive, or of special status (Burger et al., 2003). Additionally, several avian, 

23 arthropod, herpetofauna, and mammalian species depend on the CSS community for foraging, 

24 breeding, and/or residence. These include several threatened and endangered species, such as the 

25 coastal California gnatcatcher (Polioptila californica californica), the Stephens’ kangaroo rat 

26 (Dipodomys stephensi), and the Quino checkerspot butterfly (Euphydryas editha quino). Figure

27 1.3-2 presents the range of these three species. Table 1.3-1 presents a selected list of flora and 

28 fauna species that are associated with the CSS habitat. 
Terrestrial Nutrient Enrichment Case Study 

DRAFT Attachment 6, pg 11 August 2008 

1

2 Figure 1.3-2. Presence of three threatened and endangered 

3 species in California’s coastal sage scrub ecosystem.
Terrestrial Nutrient E

DRAFT Attachment 6, pg 12 Table 1.3-1. Selected Flora and Fauna Associated with the Coastal Sage Scrub Community 

Scientific Name Common Name Life Form Federal Listing* 

Buteo swainsoni Swainson’s Hawk Bird Polioptila californica californica Coastal California Gnatcatcher Bird Threatened Dipodomys merriami parvus San Bernardino Kangaroo Rat Mammal Endangered Dipodomys stephensi Stephens’ Kangaroo Rat Mammal Endangered Bufo microscaphus californicus Arroyo Toad Amphibian Endangered Euphydryas editha quino Quino Checkerspot Butterfly Insect Endangered Rhaphiomidas terminatus abdominalis

Delhi Sands Flower-Loving Fly

Insect Endangered 

Allium munzii Munz’s Onion Perennial forb Endangered Rosa minutifolia Small-Leaved Rose Shrub Deinandra conjugens Otay Tarplant Annual forb Threatened Cordylanthus orcuttianus Orcutt’s Bird’s Beak Annual forb Ambrosia pumila San Diego Ambrosia Perennial forb Proposed Endangered Acanthomintha ilicifolia San Diego Thorn-Mint Annual forb Threatened Campylorhynchusbrunneicapillus couesi 

Coastal Cactus Wren Bird 

Athene cunicularia Burrowing Owl Bird Cnemidophorus hyperythrus Orange-Throated Whiptail Reptile Phrynosoma coronatum blainvillei

San Diego Horned Lizard Reptile 

Masticophis lateralis euryxanthus Alameda Whipsnake Reptile Threatened * Status listed for threatened and endangered species only. Others may be species of concern, on federal watch 

status.
Terrestrial Nutrient Enrichment Case Study 

DRAFT Attachment 6, pg 13 August 2008 

1 The principal source of nitrogen to the CSS community is atmospheric nitrogen species 

2 (e.g., NOx, NHx), primarily originating from urban area automobile emissions and other 

3 emissions related to fossil fuels. These nitrogen species are transported and deposited onto the 

4 historically nitrogen-limited CSS soil in the form of nitrates and nitric acid. In the soil, these 

5 nitrogen species are potentially available for plant uptake and nutrient cycles. The effects of 

6 increased availability of nitrogen species in the CSS ecosystem are the focus of this case study. 

7 1.3.2 Mixed Conifer Forest 

8 The mixed conifer forest ecosystems stand approximately 30–50 m tall and consist of 

9 conifer species that dominate mid-range elevations (1300–2800 m) of the California San 

10 Bernardino and Sierra Nevada mountain ranges. The San Bernardino Mountains lie east of the 

11 Los Angeles Air Basin, and the Sierra Nevada Mountains span the majority of the state 

12 longitudinally. Figure 1.3-3 illustrates the range of mixed conifer forest in California. Mixed 

13 conifer forests have historically adapted to withstand fire at low, medium, and even high 

14 intensities. As in CSS communities, the climate is Mediterranean, where 80% of rainfall occurs 

15 from October through March (Takemoto et al., 2001). 
Terrestrial Nutrient Enrichment Case Study 

DRAFT Attachment 6, pg 14 August 2008 

1

2 Figure 1.3-3. Range of California’s mixed conifer forest ecosystem. 

3 Dominant tree species shift along a precipitation gradient. Ponderosa pine (Pinus

4 ponderosa), white fir (Abies concolor), sugar pine (P. lambertiana), and incense cedar 

5 (Calocedrus decurrens) are the predominant species on moist windward slopes, whereas Jeffrey 

6 pine (P. jeffreyi) and white fir are commonly found on leeward slopes, as well as at higher 

7 elevations in the mixed conifer elevation range. Important deciduous components of the mixed 

8 conifer forests are canyon live oak ((Quercus chrysolepis), black oak (Quercus kelloggi), and 

9 quaking aspen (Popus tremuloides). These stands support a number of shrubs, sub-shrubs, and 

10 annual and perennial forbs, as well as mountain meadows Minnich, 2007). A federal-listed 

11 species, the Mountain yellow-legged frog (Rana sierrae and Rana muscosa), and a number of 

12 state-listed species, such as the California Spotted Owl (S. occidentalis occidentalis), are 
Terrestrial Nutrient Enrichment Case Study 

DRAFT Attachment 6, pg 15 August 2008 

1 dependant on a mixed conifer ecosystem. The range of two of these selected species is illustrated 

2 in Figure 1.3-4. Table 1.3-2 shows selected flora and fauna associated with mixed conifer 

3 ecosystems. 

4

5 Figure 1.3-4. Presence of two threatened and endangered species. 
Terrestrial Nutrient E

DRAFT Attachment 6, pg 16 Table 1.3-2. Selected Flora and Fauna Associated with the Mixed Conifer Ecosystems 

Scientific Name Common Name Life Form Federal Listing* 

Abies concolor White fir Tree Pinus ponderosa Ponderosa pine Tree Pinus lambertiana Sugar pine Tree Calocedrus decurrens Incense cedar Tree Rana sierrae Sierra Madre yellow-legged frog Amphibian EndangeredSpea hammondii Western spadefoot Amphibian Rana muscosa Sierra Madre yellow-legged frog Amphibian EndangeredGlaucomys sabrinus Northern flying squirrel Mammal Glaucomys sabrinus californicus San Bernardino flying squirrel Mammal Ovis canadensis nelsoni Peninsular bighorn sheep Mammal Endangered Odocoileus hemionus Black-tailed deer Mammal Charina umbratica Southern rubber boa Reptile Packera bernardina San Bernardino ragwort Perennial forb Sidalcea pedata Bird-foot checkerbloom Perennial forb Endangered Perideridia parishii ssp. parishii Parish’s yampah Perennial forb Taraxacum californicum California dandelion Perennial forb Endangered Gilia leptantha ssp. leptantha San Bernardino gilia Shrub Piranga rubra Summer tanager Bird Haliaeetus leucocephalus Bald eagle Bird Delisted Strix occidentalis occidentalis California spotted owl Bird Strix nebulosa Great gray owl Bird * Status listed for Threatened and Endangered species only. Others may be species of concern, on federal watch 

status
Terrestrial Nutrient Enrichment Case Study 

DRAFT Attachment 6, pg 17 August 2008 

1 Additionally, several lichen species are associated with the mixed conifer stands. Lichens 

2 are formed by a symbiotic relationship between fungus and algae or cyanobacterium. In the 

3 mixed conifer ecosystem, lichens are generally epiphytic, living on conifers and obtaining 

4 nutrients from the atmosphere. Epiphytic lichen serve as food, habitat, and nesting material for 

5 various species in the pine stands (Fenn et al., 2008). The presence of individual species is 

6 determined by the amount of nitrogen present and the pH of the vegetation on which it grows; 

7 however, general categories for lichens have been developed according to species’ sensitivity to 

8 nitrogen. The categories include nitrophytes, neutrophytes, and acidophytes. (Jovan, 2008). 

9 Nitrophytes are generally associated with ammonia and high pH environments. Neutrophytes 

10 tolerate increased pH and ammonia, but exhibit slower growth patterns than nitrophytes. 

11 Acidophytes are sensitive to nitrogen species and deteriorate or die after relatively small 

12 increments of exposure to nitrogen species (Fenn et al., 2008). Table 1.3-3 presents a list of 

13 lichen species, classified by nitrogen sensitivity, that have been observed in the San Bernardino 

14 and Sierra Nevada mountain ranges 
Terrestrial Nutrient E

DRAFT Attachment 6, pg 18 Table 1.3-3. List of Lichen Species Present in the Sierra Nevada and San Bernardino Mountain Ranges (Jovan, 21983)

Nitrophytes Potential acidophytes Potential neutrophytes Unknown 

Candelaria concolor Bryoria fremontii Melanelia elegantula Ahtiana spFlavopunctelia flaventiorb Cetraria canadensis Melanelia exasperatula Alectoria sPhaeophyscia orbicularis Cetraria chlorophylla Melanelia glabra Collema fuPhyscia adscendens Cetraria merrillii Melanelia subargentifera EsslingeriaPhyscia aipolia Cetraria orbata Melanelia subelegantula Leptogium Physcia dimidiate Cetraria pallidula Melanelia subolivacea Letharia coPhyscia stellaris Cetraria platyphylla Parmelia hygrophilab Letharia vuPhyscia tenella Evernia prunastri Parmelia sulcata NodobryorPhysconia enteroxantha Hypogymnia enteromorpha Ramalina subleptocarphab NodobryorPhysconia perisidiosa Hypogymnia imshaugii Parmelina Xanthomendoza fallax Hypogymnia occidentalis Parmelina Xanthomendoza fulva Parmeliopsis ambigua Physcia bizXanthomendoza hasseana Platismatia glauca Physconia Xanthomendoza oregano Usnea filipendula Physconia Xanthoria candelaria Xanthoria polycarpa 
Terrestrial Nutrient Enrichment Case Study 

DRAFT Attachment 6, pg 19 August 2008 

1 1.4 HISTORICAL TRENDS 

2 1.4.1 Coastal Sage Scrub 

3 The CSS habitat is a unique system that has experienced a significant decline in coverage 

4 since vegetation types in Southern California were inventoried in 1929. Subsequently, this 

5 community has been designated for special status in California (CA DFG, 1993). This decline is 

6 due to urban encroachment and sprawl, increased fire frequencies, and pollution (Minnich and 

7 Dezzani, 1998). CSS is decreasing at a higher rate than habitat destruction alone would indicate 

8 (Allen et al., 1998; Fenn et al., 2003; Minnich and Dezzani, 1998) 

9 Non-native grasses were introduced to California by explorer expeditions and Franciscan 

10 missionaries arriving in the region prior to documentation of indigenous vegetation. However, 

11 accounts of herbaceous vegetation in the coastal range exist from the late 1700s and throughout 

12 the 1800s (Minnich and Dezzani, 1998). CSS was first scientifically inventoried during the 

13 California Forest and Range Experiment Station Vegetation Type Map (VTM) Survey, 

14 beginning in 1929. Through subsequent mapping surveys, fieldwork, and aerial photography 

15 comparisons, significant reductions in CSS were observed. Figure 1.4-1 illustrates the decline in 

16 CSS from 1977–2002. 
Terrestrial Nutrient Enrichment Case Study 

DRAFT Attachment 6, pg 20 August 2008 

1

2 Figure 1.4-1. Decline of coastal sage scrub from 1977–2002. 

3 Based on changes in CSS cover in VTM maps from the early 1930s–1990, it is estimated 

4 that approximately 18% of Riverside County CSS had been completely converted to non-native 

5 grasses, and an additional 42% of the cover had non-native grasses intermixed with CSS. 

6 Therefore, only 40% of the original CSS community in Riverside County remained intact and 

7 contiguous. Across the entire CSS range, Westman (1981a) estimated that only 10%–15% of the 

8 historical CSS extent remained in the late 1970s. This estimate is based upon the fraction of 

9 potential CSS land cover (in the absence of pressures) in which CSS vegetation was actually 

10 observed at the time of the study. The potential CSS land cover estimates may also be supported 

11 by the broad range in which specimens of the Quino checkerspot butterfly have historically been 

12 observed and collected (Mattoni et al., 1997). Therefore, the remaining extent of CSS is most 

13 likely between 10%–82% of the historical CSS coverage, depending on the development 

14 pressures and the spread of non-native grasses in each stand. Additionally, these non-native 
Terrestrial Nutrient Enrichment Case Study 

DRAFT Attachment 6, pg 21 August 2008 

1 grasses are less diverse and are not likely to support the majority of the sensitive, threatened, and 

2 endangered species that currently rely on CSS (Allen et al., 2005).

3 1.4.2 Mixed Conifer Forest 

4 The major trends observed in mixed conifer forests are “densification” and increased 

5 litter accumulation. Densification occurs when aboveground biomass is stimulated, resulting in 

6 increased numbers of needles, decreased average tree age, decreased overall trunk size, and 

7 increased branches (Grulke et al 2008; Minnich et al., 1995; Takemoto et al., 2001). In a 

8 retrospective comparison of conifer stands in the San Bernardino Mountains from 1932–1992, 

9 Minnich and colleagues (1995) noted significant shifts in age distribution, stand density, and 

10 branch density. Tree density increased approximately 77% according to the VTM surveys, and 

11 there were 3–10 times the number of trees in the younger age brackets when compared to 60 

12 years earlier. Additionally, a 79% increase in the average number of tree branches was reported 

13 in the San Bernardino conifer forests. Studies have indicated that increasing stand densities are 

14 also occurring within the Sierra Nevada Mountains (Minnich et al., 1995).

15 Increased litter on the forest floor has also been observed across the ecosystems, 

16 particularly in the mixed conifer forest stands in the San Bernardino mountain range. These 

17 forest stands have been observed to shed needles approximately six times faster than more 

18 remote northern Sierra Nevada conifer stands (Takemoto et al., 2001). Additionally, litterfall 

19 depths up to 15 centimeters (cm) have been noted in mixed conifer stands near Camp Paivika in 

20 the eastern San Bernadino range (Grulke et al., 2008).

21 Across the San Bernardino mountain range, a community composition shift was also 

22 noted. In mixed conifer stands where ponderosa pine has been historically dominant, trees in the 

23 youngest age bracket are now predominantly white fir and incense cedar. Additional research is 

24 needed to determine if a shift in community composition is also occurring in the Sierra Nevada 

25 mountain range (Minnich et al., 1995). 

26 Lichen communities associated with the mixed conifer ecosystems have also been 

27 dramatically altered (Fenn et al., 2003: Sigal and Nash, 1983). Of 16 lichen species reported to 

28 be associated with the San Bernardino mixed conifer forests in the early part of the 20th century, 

29 only 8 species were found 60 years later. Additionally, deterioration was observed on some of 
Terrestrial Nutrient Enrichment Case Study 

DRAFT Attachment 6, pg 22 August 2008 

1 the lichen, particularly in the areas with the highest levels of air pollution (Sigal and Nash, 

2 1983).

3 2. APPROACH AND METHODOLOGY 

4 2.1 UNDERSTANDING THE TRENDS THROUGH LITERATURE 

5 REVIEW 

6 2.1.1 Coastal Sage Scrub 

7 A literature search was conducted during this case study to obtain all relevant peer8 reviewed literature on the correlation between nitrogen enrichment and the decline in CSS 

9 communities in the northern and southern regions of California. Three major publication 

10 collection databases (i.e., ScienceDirect, Elsevier, and JSTOR) were searched for peer-reviewed 

11 journal articles that contained a combination of “nitrogen,” “nitrate,” or “nutrient,” and “coastal 

12 sage scrub” in the title, keywords, or abstract. When the literature research was summarized, it 

13 was observed that increasing nitrogen and decreasing CSS stands may be linked through iterative 

14 changes in soil nitrogen stores, increased fire frequency, and changes in water infiltration and 

15 retention zones in soil. Additional research was conducted to determine the plausibility of the 

16 iterative and magnifying effects of increased nitrogen on declining CSS stands. 

17 2.1.2 Mixed Conifer Forest 

18 Research involving nitrogen enrichment and the lichen communities was obtained using 

19 an approach similar to that conducted for the CSS literature search. A combination of “nitrogen,” 

20 “nitrate” or “nutrient,” and “mixed conifer,” “Ponderosa pine,” “Jeffrey pine,” or “lichen” were 

21 queried to obtain all relevant peer-reviewed articles in the San Bernardino and Sierra Nevada 

22 mountain ranges of California. The literature suggested that changes in the forest are complex 

23 and driven by atmospheric ozone, nitrogen, and fire. Lichen sensitivity to nitrogen was cited as a 

24 potential method to determine critical loading benchmarks for the habitat. Furthermore, it 

25 appeared that many parts of the mixed conifer forest ecosystem were experiencing signs of 

26 nitrogen saturation, providing an opportunity to consider community effects. 
Terrestrial Nutrient Enrichment Case Study 

DRAFT Attachment 6, pg 23 August 2008 

1 2.2 GIS METHODOLOGY 

2 2.2.1 Overview 

3 It is possible to delineate the areas in the Southern California case study area at risk for 

4 extirpation. Some of the factors that have been cited in the literature are available as either state5 level or national-level datasets. It is important to use spatial data that are temporally and spatially 

6 compatible, as well as to have well-documented metadata and the ability for data to be scaled-up 

7 for a national characterization. 

8 2.2.2 Available Data Inputs 

 Nitrogen deposition. Wet nitrogen deposition in the forms of NO3

–

 and NH4

+

9 are 

10 available nationally from the NADP. This national network of 321 sampling stations is 

11 not very dense and is more concentrated in the east and upper midwest areas of the 

12 United States. There are only four stations in the Southern California case study area, 

13 making interpolation between stations tenuous; therefore, NADP was not used in this 

14 assessment. Dry nitrogen deposition can be estimated using the output from the CMAQ 

15 2002 modeling system. This model produced estimates of many nitrogen species 

16 aggregated to 12-kilometer (km) squares. Although these data are fairly sparse, they are 

17 the best that are currently available. 

18  Range of CSS communities. There are two sources for the range of CSS communities. 

19 One is the Kuchler Potential Natural Vegetation (PNV) Groups data layer that was 

20 created to show “climax” vegetation that will occupy a site without disturbance or 

21 climate change. PNV is an expression of environmental factors, such as topography, 

22 soils, and climate across an area. Although this reveals where CSS communities might 

23 exist, a better source for the community range was the Fire and Resource Assessment 

24 Program (FRAP) data housed by the California Department of Forestry and Fire 

25 Protection. This agency classified California’s vegetation into 59 different categories, 

26 including coastal scrub, at a spatial resolution of 100 m. 

27  Fire threat. The California Department of Forestry and Fire Protection’s FRAP also 

28 compiles data about fire threat. These data consider fire rotation (i.e., how frequently fire 

29 occurs) and potential fire behavior, which take into account topography and potential 
Terrestrial Nutrient Enrichment Case Study 

DRAFT Attachment 6, pg 24 August 2008 

1 vegetative fuels. Fire threat is classified into four unique categories that range from 

2 moderate to extreme. 

3  Changes to CSS communities. In addition to the range of CSS communities in 2002, the 

4 California Department of Forestry and Fire Protection’s FRAP also publishes data 

5 compiled in 1977. Both datasets contained CSS community boundaries and were 

6 compiled using similar data sources and techniques. A GIS was used to overlay the two 

7 datasets, creating three types of change. CSS loss was ascribed to areas where CSS 

8 existed in 1977, but did not exist in 2002. No change was ascribed to areas where CSS 

9 existed in both 1977 and 2002. CSS growth was ascribed to areas where CSS did not 

10 exist in 1977, but did exist in 2002. 

11  Distribution of invasive species. Two data sources for invasive species were found for 

12 California. The first is the PLANTS program, which is part of the U.S. Department of 

13 Agriculture (USDA) (http://plants.usda.gov/index.html). This resource posts maps that 

14 indicate whether a species is present or not in a given county, but not the distribution of 

15 that species within the county. The second is the California Invasive Plant Council 

16 (http://www.cal-ipc.org/ip/mapping/statewide_maps/index.php), which lists the relative 

17 abundance by county of a select number of species. 

18  Threatened and endangered species habitat. The U.S. Fish and Wildlife Service 

19 (FWS) publishes critical habitat information for threatened and endangered species by 

20 state, county, and species through the Critical Habitat Portal (http://crithab.fws.gov/). For 

21 example, the Critical Habitat Portal locates 16 species for Riverside County, 5 of which 

22 are associated with the CSS community. 

23  Range of mixed conifer forest. The most recent (2002) land cover dataset from the

24 California Department of Forestry and Fire Protection’s FRAP site was also used to 

25 extract the range of mixed conifer forest. 

26  Distribution of acid sensitive lichens. The U.S Forest Service’s (USFS’s) Forest 

27 Inventory and Analysis National Program (FIA) datasets were the source of lichen 

28 distributions.

29  Fenn field sites. The locations of the field sites used by Fenn and colleagues (2008) for 

30 measuring nitrogen deposition were published in Empirical and Simulated Critical Loads 
Terrestrial Nutrient Enrichment Case Study 

DRAFT Attachment 6, pg 25 August 2008 

1 for Nitrogen Deposition in California Mixed Conifer Forests. These locations were listed 

2 as latitude and longitude coordinates, which were converted into a GIS layer with 

3 nitrogen deposition as an attribute. 

4 2.2.3 Approach to Mapping 

5 To scale the mapping approach used in this case study to a national level, datasets must 

6 be available nationally or be sufficient to provide a national picture. The terrestrial enrichment 

7 case study looked at the effects of atmospheric nitrogen on two ecosystems in California. The 

8 goal of mapping was to help illustrate CSS declines, areas of mixed conifer forest, species 

9 distributions, the distribution of atmospheric nitrogen deposition, and fire threat. 

10 In our mapping approach, we identified the following inputs:

11  The current range of CSS and mixed conifer forest communities

12  The areas with a high threat of fire

13  The areas with the highest nitrogen deposition. 

14 3. RESULTS 

15 Effects of elevated nitrogen deposition on the CSS and mixed conifer ecosystem are the 

16 result of long-term elevations in nitrogen rather than pulses. Additionally, it is difficult to 

17 quantify effects in both ecosystems because of confounding stressors, such as fire and ozone. 

18 Therefore, the literature available on long-term research and application of robust models on 

19 these ecosystems is extremely limited. 

20 The CSS case study relies upon peer-reviewed literature and spatial analyses to derive 

21 major conclusions regarding the effects of nitrogen. Spatial analyses was used to determine the 

22 changes in the extent of CSS community and associated habitat, as well as to investigate the 

23 effects of nitrogen and fire, another driving component in alteration of the CSS ecosystem. The 

24 reviewed literature includes greenhouse experiments, field observations, and field manipulation 

25 experiments that document the observed and measured effects of nitrogen. 

26 The mixed conifer case study also contains a peer-review literature summary; however, 

27 this case study focuses on the empirical loading benchmarks derived from an analysis by Fenn 

28 and colleagues (Fenn et al., 2008). The authors employ the Simple Mass Balance (SMB) model 
Terrestrial Nutrient Enrichment Case Study 

DRAFT Attachment 6, pg 26 August 2008 

1 and the DayCent simulation model to estimate critical loads. This case study focused on the 

2 results of the SMB model because of the simplicity and the incorporation of long-term values for 

3 soil nitrogen. 

4 3.1 LITERATURE REVIEW FINDINGS 

5 3.1.1 Coastal Sage Scrub 

6 CSS is subject to several pressures, such as land conversion, grazing, fire, and pollution, 

7 all of which have been observed to induce declines in other ecosystems (Allen et al., 1998). At 

8 one extreme, development pressure (i.e., the conversion of CSS to residential and commercial 

9 uses) will simply eliminate acres of habitat. Other pressures will come into play in modifying the 

10 remaining habitat. Research suggests that both fire and increased nitrogen can enhance the 

11 growth of non-native grasses in established CSS communities. Additionally, CSS declines have 

12 been observed when fire frequency is held constant and/or nitrogen is held constant, suggesting 

13 that both fire and nitrogen play a role in CSS decline when direct destructive factors are not an 

14 imminent threat. Table 3.1-1 contains a summary of selected experimental variables across 

15 multiple CSS study locations. 
Terrestrial Nutrient E

DRAFT Attachment 6, pg 27 Table 3.1-1. Summary of Selected Experimental Variables across Multiple CSS Study Locations 

Study Locations 

SoilNitrogen

AtmosphericNitrogen

VegetationChange

Mycorrhizae Change

FireCycle Author 

Riverside-Perris Plain* x x x Allen et alSanta Margarita Ecological Reserve

 x Burger et a

Santa Monica Mountains x x Carrington Orange County* x Rancho Jamul Ecological Reserve

 x 

Diffendorf

Voorhis Ecological Reserve

 x Drus, 2004 

Riverside-Perris Plain* x x Egerton-W

2000

Sedgwick Ranch Natural Reserve

x x Fierer and 

Southern California fuel breaks*

 x x Merriam e

Critical review* x x Keeley, 20Southern California burn sites*

 x x Keeley et 

Riverside-Perris Plain* x x Minnich aGreenhouse experiment x Padgett et Riverside-Perris Plain* x x Padgett an
Terrestrial Nutrient E

DRAFT Attachment 6, pg 28 

Study Locations 

SoilNitrogen

AtmosphericNitrogen

VegetationChange

Mycorrhizae Change

FireCycle Author 

University of California–Riverside Agricultural Research Station

 x Padgett et 

Riverside-Perris Plain* x x Siguenza eRiverside-Perris Plain* x x Sirulnik et Lake Skinner x Sirulnik et Riverside-Perris Plain* x Vourlitis e67 sites across CSS range* x Westman, Riverside-Perris Plain* x Wood et aLake Skinner Western Riverside County MultiSpecies Reserve 

 x x 

Greenhouse experiment x x 

Yoshida an

*Multiple data sites within the study location. 
Terrestrial Nutrient Enrichment Case Study 

DRAFT Attachment 6, pg 29 August 2008 

1 Increased nitrogen deposition has been observed to alter vegetation type in alpine plant 

2 communities in the Colorado Front Range, as well as in lichen communities in the western Sierra 

3 Nevada region (Fenn et al., 2003, 2008). In the case of CSS, it is hypothesized that many stands 

4 are no longer limited by nitrogen and have instead become nitrogen saturated due to atmospheric 

5 nitrogen deposition (Allen et al., 1998; Westman, 1981a). This is supported by the positive 

6 correlation between atmospheric nitrogen and soil nitrogen, increased long-term mortality of 

7 CSS shrubs, and increased nitrogen-cycling rates in soil and litter and soil fertility (Allen et al., 

8 1998; Padgett et al., 1999; Sirulnik et al., 2007a; Vourlitis et al., 2007). Figure 3.1-1 illustrates 

9 the levels of atmospheric nitrogen deposition on CSS communities using CMAQ 2002 modeling 

10 results.

11

12 Figure 3.1-1. Coastal sage scrub range and total nitrogen 

13 deposition using CMAQ 2002 modeling results. 
Terrestrial Nutrient Enrichment Case Study 

DRAFT Attachment 6, pg 30 August 2008 

1 The ecological effects of increased nitrogen are most easily explained chronologically 

2 through the seasonal stages of a semi-arid Mediterranean ecosystem. In the rainy, winter season, 

3 deposited surface nitrogen is transported deeper into the soil and is rapidly mineralized by 

4 microbes, thus making it available for plants. Faster nitrogen availability may favor the 

5 germination and growth of nitrophylous colonizers, more specifically non-native grasses (e.g., 

6 Bromus madritensis, Avena fatua, and Hirschfeldia incana). This earlier flourishing of grasses 

7 can create a dense network of shallow roots, which slows the diffusion of water through soil, 

8 decreases the percolation depth of precipitation, and decreases the water storage capability of the 

9 soil and underlying bedrock (Wood et al., 2006). Establishment of CSS species, such as 

10 Artemisia californica, Eriogonum fasciculatum, and Encelia farinose, may be reduced because of 

11 decreased water and nitrogen availability at the deeper depths where more woody CSS tap roots 

12 are found (Keeler-Wolf, 1995; Wood et al., 2006). These findings are supported by the increased 

13 percentage of shrub species established during wet years (Keeley et al., 2005). 

14 Elevated nitrogen may also play a role in altering the nutrient-uptake capabilities of CSS 

15 species by decreasing the species’ richness and abundance of mutualistic fungal communities, 

16 such as arbuscular mycorrhizae (AM) (Egerton-Warburton and Allen, 2000; Siguenza et al., 

17 2006). Although both CSS and non-native grass species have AM and other non-mycorrhizal 

18 fungal associations, which increase the surface area and capacity for nutrient uptake, CSS is 

19 predominantly colonized by a coarse AM species, and non-native grasses are more likely 

20 mutualistic with finer AM species. In the presence of elevated nitrogen, coarse AM colonizations 

21 were depressed in number and volume. At sites with the highest levels of soil nitrogen tested 

22 (e.g., 57 ȝg/g average annual soil nitrogen present in Jurupa Hills, Riverside County), a shift in 

23 the timing of AM growth was also observed. Therefore, it is suggested that these reduced 

24 mutualistic associations may contribute to a decline in the overall health of CSS via a loss in 

25 nutrient uptake capacity.

26 In a greenhouse fertilization experiment, soil nitrogen levels of 50 ȝg/g ammonium 

27 nitrate had a 100% mortality rate after 9 months of continuous growth. The plants began to 

28 senesce at approximately 6 months, whereas all lower exposure individuals were still healthy and 

29 remained healthy for more than 1 year (Allen et al., 1998). In the field, seasonal changes do not 

30 allow for 12 months of uninterrupted growth; therefore, the increased mortality shown in this 

31 study may be realized over much longer periods of time in situ. Additionally, studies have 
Terrestrial Nutrient Enrichment Case Study 

DRAFT Attachment 6, pg 31 August 2008 

1 suggested that soil nitrogen may now be increasing because of soil fertility in conjunction with 

2 atmospheric deposition so that the soil itself becomes an intrinsic source (Padgett et al., 1999). In 

3 combination with decreased establishment and the capacity for nutrient uptake, these responses 

4 to elevated nitrogen levels may represent a significant, detrimental, and long-term pressure on 

5 CSS at varying levels of nitrogen additions. Table 3.1-2 summarizes the various ecosystem 

6 responses to nitrogen levels that affect CSS communities.

Table 3.1-2. Research Evidence of Ecosystem Responses to Nitrogen Relevant 

to Coastal Sage Scrub Communities 

Environmental Impact Location Reference 

Enhanced growth of 

non-native species 

Southern California Minnich and Dezanni, 1998; Allen et 

al., 1998; Weiss, 2006; Westman, 

1981a,b

Nitrogen enrichment of soil 

and plants 

Riverside-Perris Plain, 

San Diego County 

Sirulnik et al., 2007a; Allen et al., 

1998; Padgett et al., 1999; Vourlitis et 

al., 2007 

Decreased growth 

regulation of shrubs 

Greenhouse experiment Padgett and Allen, 1999 

Decreased diversity of 

mycorrhizal communities 

Riverside-Perris Plain Egerton-Warburton and Allen, 2000; 

Siguenza et al., 2006 

Increased runoff and 

nutrient loss 

Santa Barbara Fierer and Gabet, 2002 

Altered fire cycle Riverside-Perris Plain Wood et al., 2006 

Increased dependent species 

vulnerability

All CSS; San Diego 

County

Weiss, 2006; Weaver, 1998 

Increased erosion Keeler-Wolf, 1995 

7 Fire is also an inextricable and significant component in CSS losses. Although CSS 

8 communities are fire resilient, non-native grass seeds are quick to establish in burned lands, 

9 reducing the water and nutrient amounts available to CSS for reestablishment (Keeler-Wolf, 

10 1995). Additionally, when annual grasses have established dominance, these species alter and 

11 increase the fire frequency due by senescing earlier in the annual season and increasing the dry, 

12 ignitable fuel availability (Keeley et al., 2005). With increased fire frequencies and faster non13 native colonizations, CSS seed banks are eventually eradicated from the soil, and the probability 
Terrestrial Nutrient Enrichment Case Study 

DRAFT Attachment 6, pg 32 August 2008 

1 of re-establishment decreases significantly (Keeley et al., 2005). Figure 3.1-2 represents the fire 

2 threats to CSS communities. 

3

4 Figure 3.1-2. Current fire threats to coastal sage scrub communities. 

5 It appears that both atmospheric nitrogen deposition and fire are critical factors involved 

6 in the decline of CSS. Figure 3.1-3 presents a preliminary conceptual model that provides an 

7 overview of system response to nitrogen and fire. Note that the model does not indicate that 

8 either fire or nitrogen deposition is playing a larger role than the other. Rather, current research 

9 indicates that both are playing critical roles. The model indicates some positive feedback loops 

10 and possible synergies between fire and nitrogen loadings and research questions that might be 

11 pursued.
Terrestrial Nutrient Enrichment Case Study 

DRAFT Attachment 6, pg 33 August 2008 

1

2 Figure 3.1-3. Conceptual model of coastal sage scrub community in relation 

3 to fire and nitrogen deposition. 

4 3.1.2 Mixed Conifer Forest 

5 The mixed conifer forest has been a subject of study for many years. There are a number 

6 of important stressors on the community, including atmospheric fire, ozone, and nitrogen. 

7 Although fire suppression in the 20th century is probably the most significant change that has led 

8 to alterations in the morphology and perhaps to shifts in forest composition (Minnich et al., 

9 1995), stress from elevated levels of ambient nitrogen concentrations is the subject of increasing 

10 research.

11 3.1.2.1 Nitrogen and Ozone Effects on Conifers 

12 Measurements documenting increases in nitrogen deposition have been recorded with 

13 some regularity since the 1980s (Bytnerowicz and Fenn, 1996); however, the Los Angeles area 

14 has seen elevated atmospheric nitrogen for the last 50 years (Bytnerowicz and Fenn, 1996). The 

15 pressures exerted on mixed conifer ecosystems in California form a gradient across the Sierra 

16 Nevada and San Bernardino mountain ranges. Nitrogen throughfall levels in the northern Sierra 

17 Nevada Mountains are as low as 1.4 kg nitrogen per hectare annually, whereas forests in the 
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DRAFT Attachment 6, pg 34 August 2008 

1 western San Bernardino Mountains experience throughfall nitrogen levels up to 33–71 kg 

2 nitrogen per hectare per year. The primary source of nitrogen in the western San Bernardino 

3 Mountains stems from fossil fuels combustion, such as vehicle exhaust. Other sources, such as 

4 agricultural processes, also play a prominent role in the western portions of the San Bernardino 

5 and Sierra Nevada mountains (Grulke et al., 2008). Figure 3.1-4 illustrates the current total 

6 nitrogen deposition on mixed conifer forests in California.

7

8 Figure 3.1-4. Mixed conifer forest range and total nitrogen deposition using

9 CMAQ 2002 modeling results. 
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DRAFT Attachment 6, pg 35 August 2008 

1 At the individual tree level, elevated atmospheric nitrogen can shift the ratio of 

2 aboveground to belowground biomass. Elevated pollution levels allow increased uptake of 

3 nutrients via the canopy, reduced nitrogen intake requirements on root structures, and increased 

4 demand for carbon dioxide (CO2) uptake and photosynthetic structures to maintain the carbon 

5 balances. Therefore, the increased nutrient availability stimulates aboveground growth and 

6 increases foliar production while reducing the demand for belowground nutrient uptake (Fenn et 

7 al., 2000). Carbon allocation gradually shifts from root to shoot, and fine root biomass is reduced 

8 (Fenn and Bytnerowicz, 1997; U.S. EPA, 2007a). Grulke and colleagues (1998) observed a 6- to 

9 14-fold increase in fine root mass in areas of low nitrogen deposition as compared to areas of 

10 high deposition. Medium roots also declined at high levels (Fenn et al., 2008). 

11 At the stand level, elevated atmospheric nitrogen has been associated with increased 

12 stand density. Although other factors, such as fire suppression and ozone, contribute elevated 

13 nitrogen and can increase mortality rates (U.S. EPA, 2007a). As older trees die, they are replaced 

14 with younger, smaller trees. Smaller trees allow more sunlight through the canopy and, combined 

15 with an increased availability of nitrogen, may allow for more trees to be established. Increased 

16 stand densities with younger-age classes are observed in the San Bernardino mountain range, 

17 where air pollution levels are among the highest found in the California conifer ranges studied 

18 (Minnich et al., 1995; Fenn et al., 2008).

19 It should be noted that the effects of ozone and atmospheric nitrogen are difficult to 

20 separate. The atmospheric transformation of nitrogen oxides can yield moderate concentrations 

21 of ozone as a byproduct (Grulke et al., 2008). Therefore, since elevated nitrogen levels are 

22 generally correlated with ozone concentrations, researchers often report changes in tree health 

23 and physiology as being the result of both (i.e., Grulke and Balduman, 1999). 

24 High concentrations of ozone and atmospheric nitrogen can generate increased needle 

25 and branch turnover. In areas subjected to low pollution, conifers may retain needles across 4 or 

26 5 years; however, in areas of high pollution, such as Camp Paivika in the San Bernardino 

27 Mountains, needle retention was generally less than 1 year (Grulke and Balduman, 1999; Grulke 

28 et al, 2008). Needle turnover significantly increases litterfall. Litter biomass has been observed to 

29 increase in areas with elevated nitrogen deposition up to 15 times more than in areas with low 

30 deposition (Fenn et al., 2000; Grulke et al., 2008). The increased litter deposition may facilitate 

31 faster rates of microbial decomposition initially, but may decompose over the long term because 
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DRAFT Attachment 6, pg 36 August 2008 

1 of changes in the carbon:nitrogen ratio and increasing lignin content over time (Grulke et al., 

2 2008; U.S. EPA, 2007a). The increased litter depth may then affect subcanopy growth and stand 

3 regeneration over long periods of time.

4 In addition to these effects, as well as the changes in decreased fine root mass, increased 

5 needle turnover, and the associated chemostructural alterations that occur as a result, mixed 

6 conifer forests with elevated pollutant levels have an increasing susceptibility to drought and 

7 beetle attack (Grulke et al., 1998, 2001; Takemoto et al., 2001). These stressors often result in 

8 the death of trees, producing an increased risk of wildfires. This complex model is displayed in 

9 Figure 3.1-5 as a graphic developed by Grulke and colleagues (2008). 

10

11 Figure 3.1-5. Conceptual model for increased susceptibility to wildfire in mixed conifer 

12 forests (Grulke et al., 2008). 

13 3.1.2.2 Nitrogen Effects on Lichens 

14 Lichens emerged as an indicator of nitrogen enrichment from the research on the effects 

15 of acid rain. Lichen species can be sensitive to air pollution; in particular, nitrogen deposition. 
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DRAFT Attachment 6, pg 37 August 2008 

1 Since the 1980s, information about lichen communities has been gathered, and lichens have been 

2 used as indicators to detect changes in forest communities. Jovan (2008) depicts how lichens 

3 might be considered as sentinels in the mixed conifer forest community (Figure 3.1-6).

4

5 Figure 3.1-6. Importance of lichens as an indicator of ecosystem health (Jovan, 2008). 

6 As nitrogen deposition increases, the relative abundance of acidophytic lichens decreases 

7 and the concentration of nitrogen in one of those species, Letharia vulpine, increases (Fenn et al., 

8 2008). Fenn and colleagues (2008) were able to quantify the change in the lichen community, 

9 noting that for every 1 kg nitrogen per hectare per year increase, the abundance of acidophytic 

10 lichens declined by 5.6%. Figure 3.1-7 illustrates the presence of acidophyte lichens and the 

11 total nitrogen deposition in the California ranges.
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DRAFT Attachment 6, pg 38 August 2008 

1

2 Figure 3.1-7. Presence of acidophyte lichens and the total nitrogen

3 deposition in the California ranges. 

4 In addition to abundance changes, species richness, cover, and health are affected in areas 

5 of high ozone and nitrogen concentrations. Fifty percent fewer lichen species were observed after 

6 60 years of elevated air pollution in San Bernardino mixed conifer forests, with the areas of 
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DRAFT Attachment 6, pg 39 August 2008 

1 highest pollution levels exhibiting low species richness, decreased abundance and cover, and 

2 morphological deterioration of existing lichen (Sigal and Nash, 1983). 

3 3.1.2.3 Nitrogen Saturation and Critical Loading Benchmarks 

4 The established signs of nitrogen saturation have been shown within the mixed conifer 

5 ecosystem. These symptoms include the following: 

6  Increased carbon and nitrogen cycling. The foliar turnover rates and changes in 

7 microbial decomposition both suggest that carbon and nitrogen cycles have been altered 

8 as a result of elevated nitrogen. Additionally, nitrogen fluxes in San Bernardino soils are 

9 elevated when compared to conifer forests in the northern Sierra Nevada Mountains 

10 (Bytnerowicz and Fenn, 1996). 

11  Decreased nitrogen uptake efficiency of plants. Changes in root:shoot ratio 

12 demonstrate structural alterations in response to increasing available nitrogen.

13  Increased loss of forest nitrates to streamwater (NO3 leachate). Elevated NO3 leachate

14 levels are estimated to have begun in the late 1950s and have been observed from the 

15 western conifer forests in the San Bernardino mountain range since 1979 (Fenn et al., 

16 2008). These losses are a result of high soil nitrogen driven by the combined litter, needle 

17 turnover, and throughfall nitrogen exerted in these areas (Bytnerowicz and Fenn, 1996).

18 Changes in root biomass and stream leachate, in addition to lichen species compositional 

19 shifts, have been used to develop benchmarks for nitrogen thresholds in the mixed conifer 

20 ecosystem. These critical loading benchmarks, or empirical loads, are designed to estimate the 

21 levels at which atmospheric nitrogen concentrations and subsequent deposition begin to affect 

22 selected components of the ecosystem, such as forest growth, health, and composition. Some 

23 benchmarks aim to estimate individual changes to an ecosystem, whereas others assess the levels 

24 at which the entire ecosystem will not be altered because of nitrogen deposition. The following 

25 sections discuss the possibility of using the mixed conifer forest as a model for benchmarking. 

26 Fenn and colleagues (2008) established a critical loading benchmark of 17 kilograms (kg) 

27 throughfall nitrogen per hectare annually in the San Bernardino and Sierra Nevada mixed conifer 

28 ecosystems. This benchmark represents the level of nitrogen deposition at which elevated 

29 concentrations of streamwater NO3 leachate or potential nitrogen saturation may occur. At this
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DRAFT Attachment 6, pg 40 August 2008 

1 deposition level, a 26% reduction in fine root biomass is anticipated (Fenn et al., 2008). 

2 Root:shoot ratios are therefore altered, and changes in nitrogen uptake efficiencies, litterfall 

3 biomass, and microbial decomposition are anticipated to be present at this nitrogen deposition 

4 level. This benchmark is based on 30–60 years of exposure to elevated atmospheric 

5 concentrations. At longer exposure levels, the benchmark is lower because of decreased nitrogen 

6 efficiencies of the ecosystem. This benchmark is exceeded in areas of the western San 

7 Bernardino Mountains, such as Camp Paivika. 

8 For the lichen community, Fenn and colleagues (2008) established a critical loading 

9 benchmark of 3.2 kg nitrogen per hectare per year and suggested that this level of atmospheric 

10 nitrogen deposition would result in little or no nitrogen-induced changes to the mixed conifer 

11 ecosystem. It should be noted that this level of nitrogen deposition is currently exceeded in the 

12 majority of the San Bernardino Mountains, as well as areas with urban influence in the 

13 southwestern Sierra Nevada Mountains mixed conifer stands (Fenn et al., 2008).

14 4. IMPLICATIONS FOR OTHER SYSTEMS 

15 The terrestrial enrichment case study looked at the effects of atmospheric nitrogen on two 

16 ecosystem types in California. We attempted to identify places where data were available that 

17 might have implications for other systems and ecosystem services and where we might find a 

18 compelling case that shows that the effects were due to atmospheric deposition of nitrogen. 

19 Other systems that are also sensitive might include the following: 

20  Ecosystems with nitrogen-sensitive epiphytes, such as lichen or mycorrhizae. Such 

21 systems may demonstrate shifts in community structure through changes in nutrient 

22 availability or modified provisioning services. 

23  Ecosystems that may have been exposed to long periods of elevated nitrogen 

24 deposition. The established signs of nitrogen saturation are increased leaching of nitrates 

25 into streamwater, decreased nitrogen uptake efficiency of plants, and increased carbon 

26 and nitrogen cycling. At prolonged elevated nitrogen levels, ecosystems are generally 

27 less likely to efficiently use, retain, or recycle nitrogen species at both the species and 

28 community levels. 
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DRAFT Attachment 6, pg 41 August 2008 

1  Critical habitats. Ecosystems that are necessary for endemic species or special 

2 ecosystem services should be monitored for possible changes due to nitrogen. 

3  Locations where there are seasonal releases of nitrogen. In both of the California 

4 habitats discussed in this case study, a large portion of nitrogen is deposited in the dry 

5 form and remains on the foliage and soil surface until the beginning of the rainy season in 

6 the winter when nitrogen will be flushed into the soil. 

7 In addition to the classic signs of nitrogen saturation, it is interesting to note that both 

8 CSS and the mixed conifer ecosystems had responses in epiphytic associations, as well as 

9 increased susceptibility to wildfire and invasion. Water use was also modified in these systems. 

10 The implication and inferential magnitude of these results may warrant future investigations. 

11 5. UNCERTAINTY 

12 5.1 COASTAL SAGE SCRUB 

13 There are several areas of uncertainty associated with this case study on CSS. 

14  Although current research indicates that both nitrogen deposition and fire have 

15 contributed to the decline of CSS, the interaction between the variables and the extent of 

16 their contributions requires further research. CSS declines have been observed in the 

17 absence of fire when elevated nitrogen levels are present, and they have also been 

18 observed in the absence of elevated nitrogen due to fire. Therefore, there is still a need for 

19 quantifiable and predictive results to indicate the pressure of each variable, as well as the 

20 pressure of the combined variables (if synergism is present). Additional studies are also 

21 required to test the proposed nitrogen-fire feedback loop and the associated 

22 biogeochemical elements (e.g., changes in water availability and mycorrhizal 

23 associations) that contribute to CSS decline.

24  Many studies allude to a degradation of CSS by assessing species’ richness and 

25 abundance, but it is not clear that indicators of CSS ecosystem health have been 

26 adequately explored. Assessing the health of CSS stands may help to identify a response 

27 curve to the factors associated with CSS decline. 
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DRAFT Attachment 6, pg 42 August 2008 

1  Ongoing CSS experiments are beginning to show changes in CSS in response to elevated 

2 nitrogen over relatively long periods of time (Allen, personal communication, 2008). The 

3 incremental process may be occurring slower than previous field research experiments 

4 have lasted, making the reasons for the decline appear variable or imperceptible over the 

5 duration of a typical study.

6  At this point, CSS is fragmented into many relatively small parcels. The CMAQ 2002 

7 data is being modeled at 4-km resolution. When these 4-km data become available, we 

8 will have a better sense of the relationship between the current distribution of CSS and 

9 atmospheric nitrogen loads. If we attempt to assess the relationship between atmospheric 

10 nitrogen loads and CSS in areas where fire threat is low, will we be able to acquire 

11 information on the condition of the habitat and species of concern. 

12  The last area of uncertainty is the relationship between current CSS distribution and the 

13 changing climate. 

14  Very little research exists regarding the effects of ozone on CSS. Although there is some 

15 support that ozone is negatively correlated with CSS, the role has yet to be quantified or 

16 consistently studied (Westman, 1981a). 

17 Figures 5.1-1 and 5.1-2, respectively, show how the primary drivers overlap with the

18 loss of CSS over the past 30 years and fire threat and the loss of CSS and CMAQ nitrogen 

19 deposition. The extent of this loss may be useful for calculating a loss in recreational value or 

20 sense of place for ecosystem valuation. Of interest are those places where fire threat is low and 

21 CSS still exists. These are the locations where it may be possible to get a first-order look at the 

22 differences found in the condition of the CSS habitat correlated to a gradient in atmospheric 

23 nitrogen deposition.
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DRAFT Attachment 6, pg 43 August 2008 

1

2 Figure 5.1-1. Relief map showing the loss of CSS over the past 30 years and fire threat.

3

4 Figure 5.1-2. Relief map showing the loss of CSS and CMAQ nitrogen deposition. 
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DRAFT Attachment 6, pg 44 August 2008 

1 An estimate of an appropriate secondary nitrogen standard or an empirical critical loading 

2 benchmark may be possible by investigating questions that may stem from the interpretation of 

3 CMAQ modeling data and the spatial extent of the CSS. Potential investigations include the 

4 following questions: 

5  Can we establish a longer trend using the VTM maps from the 1920s and 1930s that have 

6 recently been digitized? Can we look at such a trend on a map of development and 

7 atmospheric nitrogen deposition at certain points in time?

8  When will the 4-km CMAQ data become available? With a habitat that is as fragmented 

9 as the CSS, the finer CMAQ resolution data will be more useful for spatial 

10 understanding.

11  Is there information on CSS condition that can be obtained from managers of habitat 

12 reserves, especially on threatened and endangered species? 

13  Can we use the increased biomass production of non-native annual grasses, under certain 

14 levels of nitrogen addition, to estimate a standard or critical load (i.e., an amount of 

15 deposition that results in no decline or harm to a community) or an endpoint? 

16  Can we use the response of mycorrhizal species to nitrogen as a way to estimate a 

17 standard or an endpoint? 

18  Can we use the conceptual model to frame research questions that would lend themselves 

19 to revisiting existing research and analyzing it in a different way? 

20 5.2 MIXED CONIFER FOREST 

21 The currently known areas of uncertainty for mixed conifer forests are as follows: 

22  The long-term consequences of increased nitrogen on conifers are unclear. Although the 

23 results indicate an increased susceptibility to wildfire and disease, the long-term health of 

24 the stands and risk of cascading effects into the ecosystem require further investigation.

25  The effects of ozone for both mixed conifer and lichen confound the effects of nitrogen. 

26  The intermingling of fire and nitrogen cycling require additional research. 

27  Research suggests that critical loading thresholds can decrease over time if the nitrogen 

28 threshold is exceeded for long periods of time because of decreasing nitrogen efficiencies 

29 within nitrogen-saturated ecosystems (Fenn et al., 2008). This may indicate that a sliding-
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DRAFT Attachment 6, pg 45 August 2008 

1 scale approach will be required when evaluating ecosystems of varying nitrogen 

2 responses.

3  There remains considerable uncertainty in the potential response of soil carbon to 

4 increases in reactive nitrogen additions. 

5 The mixed conifer forest community presents an opportunity to examine the influence of 

6 varying nitrogen deposition loads on habitat condition. Applying a common gradient of nitrogen 

7 deposition loads will allow us to investigate how lichen communities and nitrogen saturation 

8 symptoms change across the gradient. Among the questions we might ask are the following: 

9  Are trends data available for lichen communities over the nitrogen deposition gradient to 

10 investigate critical load benchmarks? 

11  Are trends data available for nitrogen saturation indicators over the nitrogen deposition 

12 gradient to establish where critical load benchmarks were exceeded? 

13  What is the benchmark in nitrogen deposition to preserve and maintain the health of 

14 lichen communities? 

15  What is the benchmark in nitrogen deposition to minimize foliar impacts on conifer 

16 stands? 

17  What is the benchmark in nitrogen deposition to avoid nitrate leaching out of forest floors 

18 and impacting water quality? 

19  Can we use the conceptual model to frame research questions that would lend themselves 

20 to revisiting existing research and analyzing it in a different way? 

21  What role does ozone play in conjunction with nitrogen deposition in fire hazard? 

22  What role does elevation play in conifer health across the nitrogen deposition gradient? 

23  What is the relationship of nitrogen deposition to conifer root production? 

24  Decomposition of litterfall is often facilitated by heterotrophic bacteria and mycorrhizae. 

25 At what rate does elevated nitrogen deposition result in increased microbial metabolism 

26 in soil? 
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DRAFT Attachment 6, pg 46 August 2008 

1 6. CONCLUSIONS 

2 California’s CSS and mixed conifer forests on the slopes of the San Bernardino and 

3 Sierra Nevada mountains have important recreational value, protect water resources, and provide 

4 habitats for many other species. In the CSS community, there is compelling evidence that 

5 elevated nitrogen deposition is a driving force in the habitat degradation of CSS. A conceptual 

6 model was developed to help identify and parse the pressures and changes occurring within the 

7 ecosystem. In the mixed conifer forest, lichen communities and nitrogen saturation can provide a

8 means to monitor and quantify the effects of nitrogen loadings. Both habitats provide a number 

9 of ecological services, including regulation (water), cultural and aesthetic values (recreation, 

10 natural landscape, and sense of place), and provisioning services (timber) (MEA, 2005). 

11 6.1 COASTAL SAGE SCRUB 

12 The CSS community represents a unique and threatened habitat that includes many 

13 threatened and endangered plants and animals; however, this community has experienced 

14 significant declines in extent and quality over the past several decades. The process by which 

15 nitrogen is driving CSS decline is still being researched, but the indication is that increased 

16 atmospheric nitrogen is an important contributor. Nitrogen deposition has been observed to 

17 affect the ecosystem through altered mycorrhizae associations, nitrogen cycling, and rates of 

18 senescence. These effects, in conjunction with other ecosystem processes and identified 

19 stressors, are the basis of the conceptual model presented in this case study. In subsequent 

20 analyses, the conceptual model will allow us to formulate some questions about the effects of 

21 nitrogen on the system and help isolate the potential mechanisms that could be used to 

22 investigate empirical critical loading in the CSS community. 

23 6.2 MIXED CONIFER FOREST 

24 Unlike CSS, the mixed conifer ecosystem has no critical habitat, and threatened and 

25 endangered species may currently be less common. However, the forest communities exhibit 

elevated NO3

–

26 leaching in streamwater, reduction in nitrogen-sensitive lichen species, and 

27 reduced fine-root biomass in ponderosa pine (Pinus ponderosa). Each of these conditions is a 

28 valuable indicator of nitrogen loading. More research, however, is required to understand the 

29 interplay of fire suppression, ozone, and nitrogen deposition, along with other variables that 
Terrestrial Nutrient Enrichment Case Study 

DRAFT Attachment 6, pg 47 August 2008 

1 result in changes to the community. There appears to be enough data from lichen study and from 

2 locations where nitrogen saturation has occurred to develop empirical critical loads for 

3 benchmarking. 

4 The research reported in this case study shows that nitrogen deposition has altering 

5 effects that are modifying different types of valued ecosystems. With mixed conifer forests, the 

6 study shows that densification and changes in plant structure can help increase the forests’ 

7 susceptibility to wildfires. Also, species such as nitrogen-sensitive lichen, species that depend on 

8 these lichen, and species that depend on older mixed conifers may be disrupted. With mixed 

9 conifer forests, the study shows that although subtle changes in complex systems are occurring 

10 due to nitrogen deposition, these changes can be measured using indicators such as nitrogen 

11 saturation and lichen to gauge the load beyond which nitrogen is no longer beneficial to a forest 

12 system and its surface waters. 

13 7. REFERENCES 

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5 shrubs and exotic annuals common to Mediterranean coastal sage scrub of California. 

6 Plant Ecology 144:93–101.

7 Padgett, P.E., E.B. Allen, A. Bytnerowicz, and R.A. Minnich. 1999. Changes in soil inorganic 

8 nitrogen as related to atmospheric nitrogenous pollutants in southern California. 

9 Atmospheric Environment 33:769–781.

10 Sigal, L.L., and T.H Nash, III. 1983. Lichen communities on conifers in southern California: an 

11 ecological survey relative to oxidant air pollution. Ecology 64:1343–1354.

12 Siguenza, C., D.E. Crowley, and E.B. Allen. 2006. Soil microorganisms of a native shrub and 

13 exotic grasses along a nitrogen deposition gradient in southern California. Applied Soil 

14 Ecology 32:13–26.

15 Sirulnik, A.G., E.B. Allen, T. Meixner, and M.F. Allen. 2007a. Impacts of anthropogenic N 

16 additions on nitrogen mineralization from plant litter in exotic annual grasslands. Soil

17 Biology and Biochemistry 39:24–32.

18 Sirulnik, A.G., E.B. Allen, T. Meixner, M.F. Fenn, and M.F. Allen. 2007b. Changes in N cycling 

19 and microbial N with elevated N in exotic annual grasslands of southern California. 

20 Applied Soil Ecology 36:1–9.

21 Takemoto, B.K., A. Bytnerowicz, and M.E. Fenn. 2001. Current and future effects of ozone and 

22 atmospheric nitrogen deposition on California’s mixed conifer forests. Forest Ecology 

23 and Management 144:159–173.

24 U.S. EPA (Environmental Protection Agency). 2007a. Integrated Science Assessment (ISA) for 

25 Oxides of Nitrogen and Sulfur–Environmental Criteria. First External Review Draft. 

26 EPA/600/R-07/145A. U.S. Environmental Protection Agency, Washington, DC. 
Terrestrial Nutrient Enrichment Case Study 

DRAFT Attachment 6, pg 52 August 2008 

1 U.S. EPA (Environmental Protection Agency). 2007b. Preliminary Draft Excerpts of the 

2 Integrated Science Assessment for the NOx and SOx Secondary Draft Document.

3 Presented at the PM Secondary NAAQS Kickoff Meeting and NOx/SOx Secondary 

4 NAAQS Workshop, Chapel Hill, NC, sponsored by the U.S. Environmental Protection 

5 Agency, July 16–19. 

6 U.S. EPA (Environmental Protection Agency). 2008. Integrated Science Assessment for Oxides 

7 of Nitrogen and Sulfur–Environmental Criteria. Second External Review Draft. 

8 EPA/600/R-08/082. U.S. Environmental Protection Agency, Office of Research and 

9 Development, National Center for Environmental Assessment – RTP Division, Research 

10 Triangle Park, NC.

11 Vourlitis, G.L., G. Zorba, S.C. Pasquini, and R. Mustard. 2007. Carbon and nitrogen torage in 

12 soil and litter of southern Californian semi-arid shrublands. Journal of Arid Environments 

13 70:164–173.

14 Weaver, K.L. 1998. Coastal sage scrub variations in San Diego county and their influence on the 

15 distribution of the California gnatcatcher. Western Birds 29:392–405.

16 Weiss, S.B. 2006. Impacts of Nitrogen Deposition on California Ecosystems and Biodiversity. 

17 CEC-500-2005-165. California Energy Commission, PIER Energy-Related 

18 Environmental Research, Sacramento, CA. 

19 Westman, W.E. 1981a. Diversity relations and succession in Californian coastal sage scrub. 

20 Ecology 62:170–184.

21 Westman, W.E., 1981b. Factors influencing the distribution of species of Californian coastal 

22 sage scrub. Ecology 62:439–455.

23 Westman, W.1979 . Oxidant effects on California coastal sage scrub. Science 205:1001–1003.

24 Wood, Y., T. Meixner, P.J. Shouse, and E.B. Allen. 2006. Altered Ecohydrologic response 

25 drives native shrub loss under conditions of elevated N-deposition. Journal of 

26 Environmental Quality 35:76–92.
Terrestrial Nutrient Enrichment Case Study 

DRAFT Attachment 6, pg 53 August 2008 

1 Yoshida, L.C., and E.B. Allen. 2001. Response to ammonium and nitrate by a mycorrhizal 

2 annual invasive grass and a native shrub in southern California. American Journal of 

3 Botany 88:1430–1436.
United States 

Environmental Protection Agency 

Office of Air Quality Planning and Standards 

Health and Environmental Impacts Division 

Research Triangle Park, NC 

Publication N