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7 Policy Assessment for the Review of the 

8 Secondary National Ambient Air Quality 

9 Standards for NOx and SOx

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12 First External Review Draft 

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1 EPA-452/P-10-006 

2 March 2010 

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8 Policy Assessment for the Review of the 

9 Secondary National Ambient Air Quality Standards 

10 for NOx and SOx: 

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13 First External Review Draft

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37 U.S. Environmental Protection Agency 

38 Office of Air and Radiation 

39 Office of Air Quality Planning and Standards 

40 Health and Environmental Impacts Division 

41 Research Triangle Park, North Carolina 27711 

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1 DISCLAIMER

2 

3 

4 This document has been reviewed by the Office of Air Quality Planning and Standards, 

5 U.S. Environmental Protection Agency (EPA), and approved for publication. This draft 

6 document has been prepared by staff from the Office of Air Quality Planning and Standards, 

7 U.S. Environmental Protection Agency. Any opinions, findings, conclusions, or 

8 recommendations are those of the authors and do not necessarily reflect the views of the EPA. 

9 Mention of trade names or commercial products is not intended to constitute endorsement or 

10 recommendation for use. This document is being provided to the Clean Air Scientific Advisory 

11 Committee for their review, and made available to the public for comment. Any questions or 

12 comments concerning this document should be addressed to Dr. Bryan Hubbell, U.S. 

13 Environmental Protection Agency, Office of Air Quality Planning and Standards, C504-02, 

14 Research Triangle Park, North Carolina 27711 (email: hubbell.bryan@epa.gov ). 

15 
March 2010 i Draft – Do Not Quote or Cite 

1 TABLE OF CONTENTS 

2 List of Figures ................................................................................................................................ iv

3 List of Tables ................................................................................................................................ vii

4 List of Acronyms and Abbreviations ............................................................................................. ix

5 List of Key Terms ......................................................................................................................... xii

6 1. Introduction ............................................................................................................................... 1

7 1.1 Definitions of NOx and SOx for this Assessment ............................................................. 3

8 1.2 Policy Objectives ............................................................................................................. 4

9 1.3 Critical Policy Elements .................................................................................................. 6

10 1.4 Historical Context ............................................................................................................ 8

11 1.4.1 History of NOx and SOx NAAQS Review ........................................................... 8

12 1.4.2 History of Related Assessments and Agency Actions ....................................... 10

13 1.5 Proposed Conceptual Framework for Combined NOx SOx Standards .......................... 13

14 1.6 Policy Relevant Questions ............................................................................................. 16

15 2. Known or anticipated ecological effects ................................................................................. 22

16 2.1 Acidification: Evidence of effects on structure and function of terrestrial and 

17 freshwater ecosystems ................................................................................................... 23

18 2.1.1 What is the nature of acidification related ecosystem responses to 

19 reactive nitrogen and/ sulfur deposition? ........................................................... 24

20 2.1.2 What types of ecosystems are sensitive to such effects? In which ways 

21 are these responses affected by atmospheric, ecological, and landscape 

22 factors? ............................................................................................................... 26

23 2.1.3 What is the magnitude of ecosystem responses to acidifying deposition? ........ 26

24 2.1.4 What are the key uncertainties associated with acidification? ........................... 35

25 2.2 Nitrogen enrichment: Evidence of effects on structure and function of terrestrial 

26 and freshwater ecosystems ............................................................................................. 37

27 2.2.1 What is the nature of terrestrial and freshwater ecosystem responses to 

28 reactive nitrogen and/ sulfur deposition? ........................................................... 37

29 2.2.2 What types of ecosystems are sensitive to such effects? How are these 

30 responses affected by atmospheric, ecological, and landscape factors .............. 39

31 2.2.3 What is the magnitude of ecosystem responses to nitrogen deposition? ........... 40

32 2.2.4 What are the key uncertainties associated with nutrient enrichment? ............... 48

33 2.3 What Ecological effects are associated with gas-phase NOx and SOx? ......................... 49

34 2.3.1 What is the nature of ecosystem responses to gas-phase nitrogen and 

35 sulfur? ................................................................................................................ 50

36 2.3.2 What types of ecosystems are sensitive to such effects? How are these 

37 responses affected by atmospheric, ecological, and landscape factors? ............ 50

38 2.3.3 What is the magnitude of ecosystem responses to gas phase effects of 

39 NOx and SOx? .................................................................................................... 51

40 2.4 Summary ........................................................................................................................ 51

41 2.5 References ...................................................................................................................... 52
March 2010 ii Draft – Do No Quote or Cite 

1 3. Considerations of Adversity to Public Welfare ...................................................................... 62

2 3.1 How do we characterize adversity to public welfare? What are the relevant 

3 factors and how are they addressed in this document? .................................................. 62

4 3.1.1 What are the benchmarks for adversity from other sources? ............................. 62

5 3.1.2 Other EPA Programs and Federal Agencies ...................................................... 65

6 3.2 What are ecosystem services and how does this concept relate to public 

7 welfare? .......................................................................................................................... 69

8 3.3 What is the role of economics? ...................................................................................... 75

9 3.4 What is the evidence for effects on ecosystem services? How do we link 

10 ecological indicators to services? .................................................................................. 78

11 3.5 References ...................................................................................................................... 89

12 4 Addressing the Adequacy of the Current Standards ............................................................. 101

13 4.1 Are the structures of the current NOx and SOx secondary standards based on 

14 relevant ecological indicators such that they are adequate to determine and 

15 protect public welfare against adverse effects on ecosystems? ................................... 101

16 4.2 To what extent are the structures of the current NOx and SOx secondary 

17 standards meaningfully related to relevant ecological indicators of public 

18 welfare effects? ............................................................................................................ 103

19 4.3 To what extent do current monitoring networks provide a sufficient basis for 

20 determining the adequacy of current secondary NOx and SOx standards? .................. 106

21 4.3.1 What does the NADP monitoring network provide and what are the 

22 major limitations? ............................................................................................ 111

23 4.3.2 How do we characterize deposition through Monitoring and Models? ........... 112

24 4.4 What is our best characterization of atmospheric concentrations of NOy and 

25 SOx, and deposition of N and S?.................................................................................. 114

26 4.4.1 What are the current atmospheric concentrations of reactive nitrogen, 

27 NOy, reduced nitrogen, NHx, sulfur dioxide, SO2, and sulfate, SO4? .............. 115

28 4.5 Are adverse effects on the public welfare occurring under current air quality 

29 conditions for NO2 and So2 and would they occur if the nation met the current 

30 secondary standards? ................................................................................................... 130

31 4.5.1 To what extent do the current NOx and SOx secondary standards provide 

32 protection from adverse effects associated with deposition of 

33 atmospheric NOx, and SOx which results in acidification in sensitive 

34 aquatic and terrestrial ecosystems? .................................................................. 133

35 4.5.2 To what extent does the current NOx secondary standard provide 

36 protection from adverse effects associated with deposition of 

37 atmospheric NOx, which results in nutrient enrichment effects in 

38 sensitive aquatic and terrestrial ecosystems? ................................................... 138

39 4.5.3 Aquatic Nutrient Enrichment ........................................................................... 139

40 4.5.4 Terrestrial Nutrient Enrichment ....................................................................... 141

41 4.6 To what extent do the current NOx and/or SOx secondary standards provide 

42 protection from other ecological effects (e.g., mercury methylation) associated 

43 with the deposition of atmospheric NOx, and/or SOx? ................................................ 142

44 4.7 References .................................................................................................................... 143

45 5. Conceptual Design of an Ecologically Relevant Multi-pollutant Standard .......................... 145

46 5.1 Components of the design ............................................................................................ 145
March 2010 iii Draft – Do No Quote or Cite 

1 5.1.1 For which effects is there sufficient information to support setting 

2 standards?......................................................................................................... 146

3 5.2 Ecological Components of the Standard: Aquatic Acidification ................................. 147

4 5.2.1 Conceptual design considerations from the ISA and REA .............................. 149

5 5.2.2 Design options for aquatic acidification .......................................................... 157

6 5.3 Ecological Components of the Standard: Terrestrial Acidification, Terrestrial 

7 Nutrient Enrichment and Surface water Nutrient Enrichment ..................................... 167

8 5.3.1 Terrestrial Acidification ................................................................................... 167

9 5.3.2 Terrestrial and surface water nutrient enrichment ........................................... 168

10 5.3.3 Summary .......................................................................................................... 169

11 5.4 Linking Deposition to Atmospheric Concentration ..................................................... 169

12 5.4.1 Background ...................................................................................................... 169

13 5.4.2 Aggregation Issues ........................................................................................... 170

14 5.4.3 Air Quality Simulation Models ........................................................................ 171

15 5.4.4 Oxidized Sulfur and Nitrogen Pollutant Species ............................................. 172

16 5.4.5 Example Calculations ...................................................................................... 173

17 5.5 Example calculation for the conceptual design and derivation of AAPI ..................... 177

18 5.5.1 Example calculation for the conceptual design ............................................... 177

19 5.5.2 Derivation of the Atmospheric Acidification Potential Index (AAPI): ........... 185

20 5.6 References .................................................................................................................... 188

21 6. Options for Elements of the Standard ................................................................................... 190

22 6.1 What atmospheric indicators of oxidized nitrogen and sulfur are appropriate for 

23 use in a secondary NAAQS that provides protection for public welfare from 

24 exposure related to deposition of N and S? What averaging times and statistics 

25 for such indicators are appropriate to consider? .......................................................... 191

26 6.2 What is the appropriate averaging time for the air quality indicators NOy and 

27 SOx to provide protection of public welfare from adverse effects from 

28 acidification? ................................................................................................................ 193

29 6.3 What form(s) of the standard are most appropriate to provide protection of 

30 sensitive ecosystems from the effects of acidifying deposition related to ambient 

31 NOx and SOx concentrations? ...................................................................................... 194

32 6.4 What are the appropriate spatial extents of the boundaries for evaluating AAPI? 

33 Within those boundaries, what are the appropriate statistics to use in calculating 

34 the parameters of the AAPI, e.g. G, VNOy, VS, and NHx? Within those 

35 boundaries, what s the appropriate spatial averaging for the air quality indicators 

36 NOy and SOx to provide protection of public welfare from adverse effects from 

37 acidification? ................................................................................................................ 203

38 6.5 What are the options for specifying the targets for the ecological indicator for 

39 aquatic acidification? ................................................................................................... 203

40 6.5.1 What levels of impairment are related to alternative levels of ANC? ............. 204

41 6.6 What are the appropriate ambient air monitoring methods to consider in 

42 developing the standards? ............................................................................................ 208

43 6.6.1 What measurements would be used to characterize NOy and SOx

44 ambient air concentrations for the purposes of the AAPI based standard? ..... 208

45 6.6.2 What sampling frequency would be required? ................................................ 208
March 2010 iv Draft – Do No Quote or Cite 

1 6.6.3 What are the spatial scale issues associated with monitoring for 

2 compliance, and how should these be addressed? ........................................... 209

3 6.7 Taking into consideration information about ecosystem services and other 

4 factors related to characterizing adversity for the ecological effects being 

5 assessed in this review, what is an appropriate range of alternative standards for 

6 the Agency to consider? ............................................................................................... 210

7 7. Co-protection for Other Effects Using Standards to Protect Against Acidification ............. 213

8 7.1 To what extent would a standard specifically defined to protect against aquatic 

9 acidification likely provide protection from terrestrial acidification? ......................... 213

10 7.2 To what extent would a standard specifically defined to protect against aquatic 

11 acidification likely provide protection from terrestrial nutrient enrichment? .............. 214

12 7.3 To what extent would a standard specifically defined to protect against aquatic 

13 acidification likely provide protection from aquatic nutrient enrichment? ................. 215

14 8. Consideration of Issues Regarding Reduced and Oxidized Forms of Nitrogen ................... 216

15 9.1 Conclusions .................................................................................................................. 219

16 9.2 Summary of key uncertainties and research recommendations related to setting 

17 a secondary standard for NOx and SOx ........................................................................ 223

18 9.2.1 Research Needs to Reduce Uncertainty in the Next Review (focused on 

19 aquatic acidification) ........................................................................................ 223

20 9.2.2 Data Needs to Reduce Uncertainty in the Next Review (focused on 

21 aquatic acidification) ........................................................................................ 223

22 

23 LIST OF FIGURES 

24 Figure 1-1. Framework of an alternative secondary standard. ...................................................... 16

25 Figure 2-1. Ecological Effects Associated with Alternative Levels of Acid Neutralizing 

26 Capacity (ANC) .................................................................................................. 28

Figure 2-2. Average NO3

- concentrations (orange), SO42- 27 concentrations (red), and ANC 

28 (blue) across the 44 lakes in the Adirondack Case Study Area modeled 

29 using MAGIC for the period 1850 to 2050. ....................................................... 29

30 Figure 2-3. ANC concentrations of preacidification (1860) and current (2006) conditions 

31 based on hindcasts of 44 lakes in the Adirondack Case Study Area 

32 modeled using MAGIC. [Note: in this map, the symbol for red is 

33 reversed and should be < 0. The figure will be revised in the next draft.] ......... 30

34 Figure 2-4. Critical loads of acidifying deposition that each surface water location can 

35 receive in the Adirondack Case Study Area while maintaining or 

36 exceeding an ANC concentration of 50 μeq/L based on 2002 data. 

Watersheds with critical load values <100 meq/m2 37 /yr (red and orange 

38 circles) are most sensitive to surface water acidification, whereas 

watersheds with values >100 meq/m2 39 /yr (yellow and green circles) are 

40 the least sensitive sites. ....................................................................................... 31

Figure 2-5. Average NO3

- concentrations orange), SO42- 41 concentrations (red), and ANC 

42 (blue) levels for the 60 streams in the Shenandoah Case Study Area 

43 modeled using MAGIC for the period 1850 to 2050. ........................................ 32
March 2010 v Draft – Do No Quote or Cite 

1 Figure 2-6. ANC levels of 1860 (preacidification) and 2006 (current) conditions based on 

2 hindcasts of 60 streams in the Shenandoah Case Study Area modeled 

3 using MAGIC. .................................................................................................... 33

4 Figure 2-7. Critical loads of surface water acidity for an ANC of 50 μeq/L for 

5 Shenandoah Case Study Area streams. Each dot represents an estimated 

6 amount of acidifying deposition (i.e., critical load) that each stream’s 

7 watershed can receive and still maintain a surface water ANC >50 μeq/L. 

Watersheds with critical load values <100 meq/m2 8 /yr (red and orange 

9 circles) are most sensitive to surface water acidification, whereas 

watersheds with values >100 meq/m2 10 /yr (yellow and green circles) are 

11 the least sensitive sites. ....................................................................................... 34

12 Figure 2-8. Benchmarks of atmospheric nitrogen deposition for several ecosystem 

13 indicators with the inclusion of the diatom changes in the Rocky 

14 Mountain lakes (REA 5.3.1.2) ............................................................................ 42

15 Figure 2-9 (from REA figure 5.3-9). Observed effects from ambient and experimental 

16 atmospheric nitrogen deposition loads in relation to using CMAQ 2002 

17 modeling results and NADP monitoring data. Citations for effect results 

18 are from the ISA, Table 4.4 (U.S. EPA, 2008). .................................................. 43

19 Figure 3-1. Common anthropogenic stressors and the essential ecological attributes they 

20 affect. Modified from Young and Sanzone (2002) ............................................ 64

21 Figure 3-2. Representation of the benefits assessment process indicating where some 

22 ecological benefits may remain unrecognized, unquantified, or 

23 unmonetized. (Source: EBASP USEPA 2006). ................................................. 71

24 Figure 3-3. Conceptual model showing the relationships among ambient air quality 

25 indicators and exposure pathways and the resulting impacts on 

26 ecosystems, ecological responses, effects and benefits to characterize 

27 known or anticipated adverse effects to public welfare. [This figure to be 

28 revised for Second Draft Policy Assessment Document] ................................... 73

29 Figure 3-4. Locations of Eastern U.S. National Parks (Class I areas) relative to deposition 

30 of Nitrogen and Sulfur in sensitive aquatic areas ............................................... 74

31 Figure 3-5. Location of Western U.S. National Parks (Class I areas) relative to deposition 

32 of Nitrogen and Sulfur ........................................................................................ 75

33 Figure 3-6. Conceptual model linking ecological indicator (ANC) to affected ecosystem 

34 services. .............................................................................................................. 79

35 Figure 4-1. Routinely operating surface monitoring stations measuring forms of 

36 atmospheric nitrogen. ....................................................................................... 107

37 Figure 4-2. Routinely operating surface monitoring stations measuring forms of 

38 atmospheric sulfur. ........................................................................................... 108

39 Figure 4-3. Anticipated network of surface based NOy stations based on 2009 network 

40 design plans. The NCore stations are scheduled to be operating by 

41 January, 2011. ................................................................................................... 110

42 Figure 4-4. Location of approximately 250 National Atmospheric Deposition Monitoring 

43 (NADP) National Trends Network (NTN) sites illustrating annual 

44 ammonium deposition for 2005. Weekly values of precipitation based 

45 nitrate, sulfate and ammonium are provided by NADP. .................................. 112
March 2010 vi Draft – Do No Quote or Cite 

1 Figure 4-5. 2005 CMAQ modeled annual average NOy (ppb). These maps will be 

2 replaced with full CONUS maps in the next draft. .......................................... 117

Figure 4-6. 2005 CMAQ modeled annual average total reduced nitrogen (NHx) (as μg/m3 3 

4 nitrogen) ........................................................................................................... 118

Figure 4-7. 2005 CMAQ modeled annual average ammonia, NH3, (as μg/m3 5 N) ..................... 119

Figure 4-8. 2005 CMAQ modeled annual average ammonia, NH4, (as μg/m3 6 N) .................... 120

Figure 4-9. 2005 CMAQ modeled annual average SOx, (as μg/m3 7 S from SO2 and SO4). ........ 121

Figure 4-10. 2005 CMAQ modeled annual average SO2 (as μg/m3 8 S) ...................................... 122

Figure 4-11. 2005 CMAQ modeled annual average SO4 (as μg/m3 9 S). ..................................... 123

10 Figure 4-12. 2005 annual average sulfur dioxide concentrations based on CASTNET 

11 generated by the Visibility Information Exchange Web Sysytem 

12 (VIEWS). .......................................................................................................... 124

13 Figure 4-13. 2005 annual average sulfate concentrations based on CASTNET generated 

14 by the Visibility Information Exchange Web Sysytem (VIEWS). ................... 124

15 Figure 4-14. Annual average 2005 NOy concentrations from reporting stations in AQS. ......... 125

16 Figure 4-15. 2005 CMAQ modeled Oxidized Nitrogen Deposition (kgN/Ha/Yr) ..................... 126

17 Figure 4-16. 2005 CMAQ modeled Oxidized Sulfur Deposition (kgS/Ha/Yr) .......................... 127

18 Figure 4-17. Three hour average maximum 2005 SO2 concentrations based on the 

19 SLAMS reporting to EPA’s Air Quality System (AQS) data base. The 

20 current SO2 secondary standard based on a the maximum 3 hour average 

21 value is 500 ppb, a value not exceeded. While there are obvious spatial 

22 gaps, the majority of these stations are located to capture maximum 

23 values generally in proximity to major sources and high populations. 

24 Lower relative values are expected in more remote acid sensitive areas ......... 128

25 Figure 4-18. Annual average 2005 NO2 concentrations based on the SLAMS reporting to 

26 EPA’s Air Quality System (AQS) data base. The current NO2 secondary 

27 standard is 53 ppb, a value well above those observed. While there are 

28 obvious spatial gaps, the stations are located in areas of relatively high 

29 concentrations in highly populated areas. Lower relative values are 

30 expected in more remote acid sensitive areas. .................................................. 129

31 Figure 4-19. 2005 CMAQ derived annual average ratio of (NOy – NO2)/NOy. The 

32 fraction of NO2 contributing to total NOy generally is less than 50% in 

33 the Adirondack and Shenandoah case study areas. The ratio reflects the 

34 relative air mass aging associated with transformation of oxidized 

35 nitrogen beyond NO and NO2 as one moves from urban to rural 

36 locations. ........................................................................................................... 130

37 Figure 4-20. National map highlighting the 9 case study areas evaluated in the REA. .............. 133

38 Fig 5-1. Schematic diagram of the conceptual design of the standard ....................................... 146

39 Fig 5-2. Schematic diagram of the conceptual design of the standard based on aquatic 

40 acidification. From left to right, if a desired level of ANC is known, then 

41 the concentration of the atmospheric indicators that will cause that level 

42 may be calculated. From right to left, if the if the concentration of the air 

43 quality indicators are known than the ANC that will be caused may be 

44 calculated. ......................................................................................................... 148
March 2010 vii Draft – Do No Quote or Cite 

1 Figure 5-3. The depositional load function. ................................................................................ 158

2 Fig 5-4. A map of acid sensitive areas of the Eastern U.S. developed from a lithology3 based five-unit geologic classification system after methods in Sullivan 

4 et al. (2007). ...................................................................................................... 163

5 Figure 5-5. VS/N values for each grid cell in the eastern (right) and western (left) U.S. 

6 domains. The top maps are for sulfur and the bottom are for nitrogen. ........... 174

7 Figure 5-6. Schematic Diagram illustrating the procedure for converting deposition 

8 tradeoff curves of sulfur and nitrogen to atmospheric concentrations of 

9 SOx and NOx. .................................................................................................... 175

10 Figure 5-7. Inter-annual coefficients of variation (CV) of a) nitrogen and b) sulfur VS/N

11 values, based on a series of 2002-2005 CMAQ v4.7 simulation. .................... 176

12 Figure 5-8. Tradeoff curve for S and N deposition to protect from aquatic acidification in 

13 the Adirondacks using Neco equation 2 ............................................................. 181

14 Figure 5-9. Tradeoff curve for S and N deposition to protect from aquatic acidification in 

15 the Adirondacks using Neco equation 3 ............................................................. 181

16 Figure 5-10. Tradeoff curve for S and NOy deposition to protect from aquatic 

17 acidification in the Adirondacks using Neco equation 2 .................................... 183

18 Figure 5-11. Tradeoff curve for S and NOy deposition to protect from aquatic 

19 acidification in the Adirondacks using Neco equation 3 .................................... 183

20 Figure 5-12. Tradeoff curve for atmospheric concentration of SOx and NOy to protect 

21 from aquatic acidification in the Adirondacks using Neco equation 2 .............. 184

22 Figure 5-13. Tradeoff curve for atmospheric concentration of SOx and NOy to protect 

23 from aquatic acidification in the Adirondacks using Neco equation 3 .............. 185

24 Figure 6-1. Ecosystems sensitive to acidifying deposition in the Eastern U.S. (Note that 

25 Florida represents a special case where high levels of natural 

26 acidification exist unrelated to deposition) This map does not include all 

27 sensitive areas in the U.S. Certain mountainous areas of the Western 

28 U.S. are also sensitive to acidifying deposition. ............................................... 202

29 Figure 6-2. Number of fish species per lake or stream versus ANC level and aquatic 

30 status category (colored regions) for lakes in the Adirondack Case Study 

31 Area (Sullivan et al., 2006). .............................................................................. 206

32 

33 LIST OF TABLES 

34 Table 3-1. Crosswalk between Ecosystem Services and Public Welfare Effects ......................... 70

35 Table 5-1. Illustration of how selected models and water chemistry data were used to 

36 calculate critical loads in the REA. .................................................................. 151

37 Table 5-2. Summary of the ecological components of design option 1. ..................................... 166

38 Table 5-3. Oxidized sulfur and nitrogen species currently available in CMAQ 

39 simulations. Note that PNA concentrations are not available in current 

40 CMAQ extractions. ........................................................................................... 174

41 Table 5-4. Example Calculations for Determining the Percent of Water Bodies Achieving 

42 Target ANC Levels ........................................................................................... 180
March 2010 viii Draft – Do No Quote or Cite 

1 Table 5-5. Values for N and S deposition tradeoff curves for ANC = 50, protecting 32 

2 and 50% of the population, in Adirondacks case study area as illustrated 

3 on Fig 5-8 and Fig 5-9. Units are in meq/m2/yr unless noted otherwise. ........ 180

4 Table 5-6. Values for NOy and S deposition tradeoff curves for ANC = 50, protecting 32 

5 and 50% of the population in Adirondacks case study area as illustrated 

6 on Fig 5.10 and Fig 5.11. Units are in meq/m2/yr unless noted otherwise. ..... 182

7 Table 7-1. Results of comparing aquatic ANC50 critical loads to average terrestrial 

8 watershed area Bc:Al ratios. Left numbers in each column are the 

9 number of lakes or streams that had a lower critical load than the 

10 terrestrial calculated critical load. Right numbers in each column are the 

11 number of lakes that had a higher critical load than the watershed 

12 calculated terrestrial critical loads. ................................................................... 214

13 

14 

15 
March 2010 ix Draft – Do No Quote or Cite 

1 LIST OF ACRONYMS AND ABBREVIATIONS

2 AAPI Atmospheric Acidification Potential Index 

3 ADR Adirondack Mountains of New York 

Al3+ 4 aluminum 

5 ANC acid neutralizing capacity 

6 AQCD Air Quality Criteria Document 

7 AQRV air quality related values 

8 ASSETS EI Assessment of Estuarine Trophic Status eutrophication index 

9 Bc/Al Base cation to aluminum ratio, also Bc:Al 

10 C carbon 

11 Ca/Al calcium to aluminum ratio 

Ca2+ 12 calcium 

13 CAA Clean Air Act 

14 CASAC Clean Air Scientific Advisory Committee 

15 CASTNet Clean Air Status and Trends Network 

16 CCS coastal sage scrub 

17 Chl a chlorophyll a 

18 CLE critical load exceedance 

19 CMAQ Community Multiscale Air Quality model 

20 CSS coastal sage scrub 

21 CWA Clean Water Act 

22 DIN dissolved inorganic nitrogen 

23 DO dissolved oxygen 

24 DOI U.S. Department of Interior 

25 EMAP Environmental Monitoring and Assessment Program 

26 EPA U.S. Environmental Protection Agency 

27 FHWAR fishing, hunting and wildlife associated recreation survey 

28 FIA Forest Inventory and Analysis National Program 

29 FWS Fish and Wildlife Service 

30 GIS geographic information systems 

31 GPP gross primary productivity 

H+ 32 hydrogen ion 

33 H2O water vapor 

34 H2SO4 sulfuric acid 

35 ha hectare 

36 HAB harmful algal bloom 

37 HFC hydrofluorocarbon 

Hg+2 38 reactive mercury 

Hg0 39 elemental mercury 

40 HNO3 nitric acid

41 HONO nitrous acid 

42 HUC hydrologic unit code 

43 IMPROVE Interagency Monitoring of Protected Visual Environments 

44 ISA Integrated Science Assessment 

K+ 45 potassium 
March 2010 x Draft – Do No Quote or Cite 

1 kg/ha/yr kilograms per hectare per year 

2 km kilometer 

3 LRMP Land and Resource Management Plan 

4 LTER Long Term Ecological Monitoring and Research 

5 LTM Long-Term Monitoring 

6 MAGIC Model of Acidification of Groundwater in Catchments 

7 MCF Mixed Conifer Forest 

8 MEA Millennium Ecosystem Assessment 

Mg2+ 9 magnesium 

10 N nitrogen 

11 N2 gaseous nitrogen 

12 N2O nitrous oxide 

13 N2O3 nitrogen trioxide 

14 N2O4 nitrogen tetroxide 

15 N2O5 dinitrogen pentoxide 

Na+ 16 sodium 

17 NAAQS National Ambient Air Quality Standards 

18 NADP National Atmospheric Deposition Program 

19 NAPAP National Acid Precipitation Assessment Program 

20 NAWQA National Water Quality Assessment 

21 NEEA National Estuarine Eutrophication Assessment 

22 NEP net ecosystem productivity 

23 NH3 ammonia gas 

NH4

+ 24 ammonium ion 

25 (NH4)2SO4 ammonium sulfate 

NHx category label for NH3 plus NH4

+ 26 

27 NO nitric oxide 

28 NO2 nitrogen dioxide 

NO2

- 29 reduced nitrite 

NO3

- 30 reduced nitrate 

31 NOAA National Oceanic and Atmospheric Administration 

32 NOx nitrogen oxides 

33 NOy total oxidized nitrogen 

34 NPP net primary productivity 

35 NPS National Park Service 

36 NRC National Research Council 

37 NSWS National Surface Water Survey 

38 NTN National Trends Network 

39 NTR organic nitrate 

40 O3 ozone 

41 OAQPS Office of Air Quality Planning and Standards 

42 OW Office of Water 

43 PAN peroxyacyl nitrates 

44 PFC perfluorocarbons 

45 pH relative acidity 

46 ppb parts per billion 

47 ppm parts per million 
March 2010 xi Draft – Do No Quote or Cite 

1 ppt parts per trillion 

2 PSD prevention of significant deterioration 

3 REA Risk and Exposure Assessment 

4 REMAP Regional Environmental Monitoring and Assessment Program 

5 S sulfur 

6 S2O3 thiosulfate 

7 S2O7 heptoxide 

8 SAV submerged aquatic vegetation 

9 SF6 sulfur hexafluoride 

10 SMP Simple Mass Balance 

11 SO sulfur monoxide 

12 SO2 sulfur dioxide 

13 SO3 sulfur trioxide 

SO3

2- 14 sulfite 

15 SO4 wet sulfate 

SO4

2- 16 sulfate ion 

17 SOM soil organic matter 

18 SOx sulfur oxides 

19 SPARROW SPAtially Referenced Regressions on Watershed Attributes 

20 SRB sulfate-reducing bacteria 

21 STORET STORage and RETrieval 

22 TIME Temporally Integrated Monitoring of Ecosystems 

23 TMDL total maximum daily load 

24 TP total phosphorus 

25 USFS U.S. Forest Service 

26 USGS U.S. Geological Survey 

27 μeq/L microequivalents per liter 

μg/m3 28 micrograms per cubic meter 

29 

30 
March 2010 xii Draft – Do No Quote or Cite 

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

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

7 and monitoring occurs. 

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

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

10 plants. 

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

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

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

14 rates. 

15 Arid Region: A land region of low rainfall, where “low” is widely accepted to be less than 250 

16 mm precipitation per year. 

17 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+ 18 . Base cation saturation is a 

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

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

21 extremely sensitive to change. 

22 Ecologically Relevant Indicator: A physical, chemical, or biological entity/feature that 

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

24 that is easily measured/quantified to make it a useful predictor of ecological risk. 

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

26 significant (as defined by the analyst or decision maker) harmful effects on specified 

27 sensitive elements of the environment do not occur, according to present knowledge. 

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

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

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

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

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

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

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

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

36 EPA, 1992). 

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

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

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

40 well-circumscribed systems such as a small pond. 

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

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

43 or indirectly in improved human health and/or welfare. Examples of ecosystem benefits 

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

45 species, the quality of drinking water and recreational areas, and visibility. 
March 2010 xiii Draft – Do No Quote or Cite 

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

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

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

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

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

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

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

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

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

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

11 resulting in an increase in productivity. 

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

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

14 enrichment. 

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

16 or mist. 

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

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

19 and 250-mm precipitation per year. 

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

21 and/or SOx pollution (e.g., acidification, nutrient enrichment). The effect may be direct 

22 (e.g., a change in growth in response to a change in the mean, range, or variability of 

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

24 nitrogen consequently altering competitive dynamics between species and decreased 

25 biodiversity). 

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

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

+ 27 , NO, NO2, 

28 HNO3, N2O, NO3–, and organic compounds (e.g., urea, amines, nucleic acids). 

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

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

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

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

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

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

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

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

37 present in the atmosphere. 

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

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

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

41 weathering rates. 

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

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

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

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

46 property, hazards to transportation, and the effects on economic values and on personal 
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1 comfort and well-being, whether caused by transformation, conversion, or combination 

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

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

4 other precipitation. 

5 

6 

7 
Policy Assessment for the Review of the Secondary National Ambient Air Quality Standards for NOx and SOx

February, 2010 Draft – Do Not Quote or Cite 1 .

1 1. INTRODUCTION 

2 The U.S. Environmental Protection Agency (EPA) is presently conducting a review of 

3 the secondary National Ambient Air Quality Standards (NAAQS) for oxides of nitrogen (NOx) 

4 and oxides of sulfur (SOx). The EPA’s overall plan and schedule for this review were presented 

5 in the Integrated Review Plan for the Secondary National Ambient Air Quality Standards for 

6 Nitrogen Dioxide and Sulfur Dioxide (US EPA, 2007). The Integrated Review Plan (IRP) 

7 outlined the Clean Air Act (CAA or the Act) requirements related to the establishment and 

8 reviews of the NAAQS, the process and schedule for conducting the current review, and the key 

9 components in the NAAQS review process: an Integrated Science Assessment (ISA), Risk and 

10 Exposure Assessment (REA), and policy assessment/rulemaking. It presented key policy11 relevant issues to be addressed in this review as a series of questions that frames our 

12 consideration of whether the current secondary (welfare-based) NAAQS for NOx and SOx should 

13 be retained or revised. 

14 As part of this review, staff in the U.S. Environmental Protection Agency’s (EPA) Office 

of Air Quality Planning and Standards (OAQPS) prepared this first draft Policy Assessment.1 15 

16 The objective of this assessment is to evaluate the policy implications of the key scientific 

17 information contained in the document Integrated Science Assessment for Oxides of Nitrogen 

18 and Sulfur-Ecological Criteria (USEPA, 2008; henceforth referred to as the ISA), prepared by 

19 EPA’s National Center for Environmental Assessment (NCEA) and the results from the analyses 

20 contained in the Risk and Exposure Assessment for Review of the Secondary National Ambient 

21 Air Quality Standards for Oxides of Nitrogen and Oxides of Sulfur (U.S. EPA, 2009; henceforth 

22 referred to as the REA). This first draft also presents preliminary staff conclusions on a range of 

23 policy options that we believe are appropriate for the Administrator to consider concerning 

24 whether, and if so how, to revise the secondary (welfare-based) NOx and SOx NAAQS. 

 

1

 Preparation of a PA by OAQPS staff reflects Administrator Jackson’s decision to modify the NAAQS review 

process that was presented in the IRP. See http://www.epa.gov/ttn/naaqs/review.html for more information on the 

current NAAQS review process. 
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1 This policy assessment is intended to help “bridge the gap” between the scientific 

2 assessment contained in the ISA and the judgments required of the EPA Administrator in 

3 determining whether it is appropriate to retain or revise the secondary NAAQS for NOx and SOx. 

4 This policy assessment considers the available scientific evidence and quantitative risk-based 

5 analyses, together with related limitations and uncertainties, and focuses on the basic elements of 

air quality standards: indicators2

, averaging times, forms3 6 , and levels. These elements, which 

7 serve to define each standard, must be considered collectively in evaluating the welfare 

8 protection afforded by the secondary NOx and SOx NAAQS. Our development of this policy 

9 assessment is based on the assessment and integrative synthesis of information presented in the 

10 ISA and on staff analyses and evaluations presented in this document, and is further informed by 

11 comments and advice received from an independent scientific review committee, the Clean Air 

12 Scientific Advisory Committee (CASAC), in their review of the previous integrated science and 

13 risk and exposure assessments. The Policy Assessment is further informed by comments 

submitted by the public4 14 . To view related documents developed as part of the planning, science, 

15 and risk assessment phases of this review see 

16 http://www.epa.gov/ttn/naaqs/standards/no2so2sec/index.html. 

17 This document is organized around a conceptual framework for a combined NOx and SOx

18 secondary NAAQS and is focused on answering key policy questions related to the 

19 implementation of that conceptual framework. Chapter 2 provides a summary of ecological 

20 effects from the deposition of ambient NOx and SOx to sensitive ecosystems, drawing from the 

21 ISA and REA. Chapter 3 places those ecological effects within the context of “public welfare” 

22 by linking effects to ecosystem services or other benchmarks of public welfare. Chapter 4 

23 addresses the adequacy of the current NOx and SOx secondary NAAQS in addressing the impacts 

24 on public welfare from ecological effects. Chapter 5 develops the conceptual design for 

25 ecologically relevant multi-pollutant standards. Chapter 6 presents options for developing critical 

26 elements of a secondary NAAQS necessary to implement the conceptual design. Chapter 7 

27 describes how secondary NAAQS designed to protect a specific ecological endpoint may also 

28 provide protection for other ecological endpoints. Chapter 8 provides a consideration of issues 

 

2

 The “indicator” of a standard defines the chemical species or mixture that is to be measured in determining 

whether an area attains the standard. 

3

 The “form” of a standard defines the air quality statistic that is to be compared to the level of the standard in 

determining whether an area attains the standard.

4

 Summary information on public comments will be provided in a later draft of the policy assessment
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1 regarding reduced and oxidized forms of nitrogen. Chapter 9 concludes with preliminary staff 

2 conclusions regarding ranges of options for pollutant indicators, averaging times, forms, and 

3 levels for the secondary NOx and SOx NAAQS, including a discussion of staff initial conclusions 

4 on what levels of the secondary NAAQS might be requisite to protect public welfare. 

5 In this document we consider how the available scientific evidence and quantitative risk6 based analyses, together with related limitations and uncertainties, inform the review of each 

7 element of the NAAQS: indicator, averaging times, forms, and levels. These elements must be 

8 considered collectively in evaluating the welfare protection afforded by the secondary NAAQS

9 standards. This draft document does not contain final staff conclusions as to all the necessary 

10 components of an alternative secondary standard for NOx and/or SOx but rather describes the 

11 current state of thinking with regard to potential policy options and provides an appropriate 

12 context of information for the Administrator to consider in making decisions regarding the 

13 standards. 

14 While this policy assessment should be of use to all parties interested in the secondary 

15 NOx and SOx NAAQS review, it is written with an expectation that the reader has some 

16 familiarity with the technical discussions contained in the ISA and REA. 

17 EPA will be preparing a second draft Policy Assessment subsequent to receiving advice 

18 from the CASAC. The second draft will incorporate responses to comments received from 

19 CASAC, as well as comments submitted by the public. The second draft will also provide a more 

20 complete development of the conceptual model, and will provide a more complete set of staff 

21 conclusions on critical elements of the standards. EPA’s final Policy Assessment will address 

22 additional CASAC comments on the second draft, and will include sufficient information to 

23 inform the Administrator on critical elements of the standards, and staff conclusions regarding 

24 alternative levels of the standards. 

25 1.1 DEFINITIONS OF NOX AND SOX FOR THIS ASSESSMENT 

26 As discussed in detail in the REA (REA 1.3.1), in the atmospheric science community 

27 NOx is typically referred to as the sum of nitrogen dioxide (NO2), and nitric oxide (NO). From a 

28 Clean Air Act perspective, the family of NOx includes any gaseous combination of nitrogen and 

29 oxygen (e.g., NO2, NO, nitrous oxide [N2O], nitrogen trioxide [N2O3], nitrogen tetroxide [N2O4], 

30 and dinitrogen pentoxide [N2O5]). The term used by the scientific community to represent the 
Policy Assessment for the Review of the Secondary National Ambient Air Quality Standards for NOx and SOx

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

2 total oxidized nitrogen (NOy). NOy includes all nitrogen oxides, including e.g. total reactive 

3 oxidized atmospheric nitrogen, defined as NOx (NO and NO2) and all oxidized NOx products: 

4 NOy = NO2 + NO + HNO3 + PAN +2N2O5 + HONO+ NO3 + organic nitrates + particulate NO3 

5 (Finlayson-Pitts and Pitts, 2000). In this document, unless otherwise indicated, we use the term 

6 NOx interchangeably with NOy to refer to the complete set of oxidized nitrogen compounds. 

7 For this assessment, SOx is defined to include all oxides of sulfur, including multiple 

8 gaseous substances (e.g., SO2, sulfur monoxide [SO], sulfur trioxide [SO3], thiosulfate [S2O3], 

9 and heptoxide [S2O7], as well as particulate species, such as ammonium sulfate [(NH4)2SO4]). 

10 Throughout this text we refer to sulfate as SO4 and nitrate as NO3, recognizing that they have 

11 charges of -2 for sulfate and -1 for nitrate. 

12 1.2 POLICY OBJECTIVES 

13 In conducting this periodic review of the NOx and SOx secondary NAAQS, EPA has 

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

15 protecting the public welfare from adverse effects associated with oxides of nitrogen and sulfur. 

16 Although EPA has historically adopted separate secondary standards for oxides of nitrogen 

17 (NOx) and oxides of sulfur (SOx), EPA is conducting a joint secondary review of these standards 

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

19 chemistry perspective, as well as from an environmental effects perspective. The National 

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

21 appropriate, in forming the scientific basis for the NAAQS (NRC, 2004). There is a strong basis 

22 for considering these pollutants together, building upon EPA’s and CASAC’s past recognition of 

23 the interactions of these pollutants and on the growing body of scientific information that is now 

24 available related to these interactions and associated ecological effects. 

25 EPA sets secondary standards for two criteria pollutants related to NOx and SOx: ozone 

26 and particulate matter (PM). NOx is a precursor to the formation of ozone in the atmosphere, and 

27 under certain conditions, can combine with atmospheric ammonia to form ammonium nitrate, a 

28 component of fine PM. SOx is a precursor to the formation of particulate sulfate, which is a 

29 significant component of fine PM in many parts of the U.S. While there are a number of welfare 

30 effects associated with ozone and fine PM, including ozone damage to vegetation, and visibility 
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1 degradation related to PM, protection against those effects is provided by the ozone and fine PM 

2 standards. This review focuses on evaluation of the protection provided by NOx and SOx

3 secondary standards for effects associated with direct atmospheric concentrations of NOx and 

4 SOx, and effects associated with deposition of NOx and SOx to ecosystems, including deposition 

5 in the form of particulate nitrate and sulfate in their component forms. 

6 The ISA highlights the ecological effects associated with deposition of ambient NOx and 

7 SOx to ecosystems other than commercially managed forests and agricultural lands. This 

8 assessment evaluates information on gas-phase effects of NOx and SOx via stomatal exposure on 

9 vegetation, but primarily focuses on the effects of gas-phase NOx and SOx exposure via 

10 deposition on multiple ecological receptors. Highlighted effects include those associated with 

11 acidification and nitrogen nutrient enrichment. Based on these highlighted effects, EPA’s policy 

12 objective is to develop a framework for NOx and SOx standards that incorporate factors that will 

13 lead to standards that are ecologically relevant, and that recognizes the interactions between the 

14 two pollutants as they deposit to sensitive ecosystems, with an ultimate goal of setting standards 

15 that, based on the ecological criteria described in the ISA, and consistent with the requirements 

16 of the Clean Air Act, “are requisite to protect the public welfare from any known or anticipated 

17 adverse effects associated with the presence of such air pollutant in the ambient air.” 

18 In presenting policy options for the Administrator’s consideration, we note that the final 

19 decision on retaining or revising the current secondary standards for NOx and SOx is largely a 

20 public welfare policy judgment based on the Administrator’s informed assessment of what 

21 constitutes requisite protection against adverse effects to public welfare. A final decision should 

22 draw upon scientific information and analyses about welfare effects, exposure and risks, as well 

23 as judgments about the appropriate response to the range of uncertainties that are inherent in the 

24 scientific evidence and analyses. The ultimate determination as to what level of damage to 

25 ecosystems and the services provided by those ecosystems is adverse to public welfare is not 

26 wholly a scientific question, although it is informed by scientific studies linking ecosystem 

27 damage to losses in ecosystem services, and economic information on the value of those losses in 

28 ecosystem services. Our approach to informing these judgments, as discussed below, is 

29 consistent with the requirements of the NAAQS provisions of the Clean Air Act and with how 

30 EPA and the courts have historically interpreted the Act. These provisions require the 

31 Administrator to establish secondary NAAQS that, in the Administrator’s judgment, are requisite 
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1 to protect public welfare from any known or anticipated adverse effects associated with the 

2 presence of NOx and SOx in the ambient air. In so doing, the Administrator seeks to establish 

3 standards that are neither more nor less stringent than necessary for this purpose. 

4 For this first draft policy assessment, we have chosen to focus much of our discussion on 

5 the effects of ambient NOx and SOx on ecological impacts associated with acidifying deposition 

6 of nitrogen and sulfur, which is a transformation product of ambient NOx and SOx. We have the 

7 greatest confidence in the causal linkages between NOx and SOx and aquatic acidification effects, 

8 and we have the most complete information available with which to develop an ecologically 

9 meaningful structure for the standards. In future drafts, we expect to be able to explore whether 

10 and how the standards can be expanded to directly address effects of acidification on terrestrial 

11 ecosystems, and to address the effects of nutrient enrichment in terrestrial and aquatic 

12 ecosystems. 

13 1.3 CRITICAL POLICY ELEMENTS 

14 Our policy objective is guided by the information in the ISA and REA, framed within the 

15 legislative requirements of the CAA. This framing leads us to focus on critical policy elements 

16 (CPE) consistent with elements of Clean Air Act language. 

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

18 NAAQS and of the air quality criteria upon which the standards are based. The NAAQS are 

19 established for pollutants that are listed under section 108, based on three criteria, including 

20 whether emissions of the air pollutant cause or contribute to air pollution which may reasonably 

21 be anticipated to endanger public health or welfare and whose presence in the ambient air results 

22 from numerous or diverse mobile or stationary sources. The NAAQS are based on air quality 

23 criteria that reflect the latest scientific knowledge, useful in indicating the types and extent of 

24 identifiable effects on public health or welfare that may be expected from the presence of the 

25 pollutant in ambient air. The criteria refer to criteria issued pursuant to §108 of the Clean Air 

26 Act, which include “(A) those variable factors (including atmospheric conditions) which of 

27 themselves or in combination with other factors may alter the effects on public health or welfare 

28 of such air pollutant; (B) the types of air pollutants which, when present in the atmosphere, may 

29 interact with such pollutant to produce an adverse effect on public health of welfare; and (C) any 

30 known or anticipated adverse effects on welfare.” 
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1 The following critical policy elements for the design of ecologically relevant secondary 

2 standards for NOx and SOx are identified: 

3 (CPE 1) An evaluation of the effects of ambient NOx and SOx on ecosystems, and the 

4 relationship between those effects and the measure of dose in the ecosystem, 

5 indicated by the depositional loadings of N and S. 

6 (CPE 1.1) Evaluation of the relationship between response of ecological receptors, e.g. 

7 changes in diversity of fish species, and the response related to public welfare, 

8 e.g. loss in recreational fishing services. 

9 (CPE 1.2) Evaluation of the extent to which identified effects are occurring under recent 

10 conditions, and the extent to which meeting the current standards would 

11 provide protection against these effects. 

12 (CPE 2) An assessment of how best to characterize, in defining the standards, the 

13 variable ecosystem factors that affect the relationship between ecological 

14 effects and depositional loadings of N and S. 

15 (CPE 2.1) Specification of potential indicators of ecological effects, e.g. acid 

16 neutralizing capacity (ANC) that incorporates variability in ecosystem factors. 

17 (CPE 3) Characterization of the complex atmospheric transformations between 

18 ambient concentrations of NOx and SOx and deposition of N and S in the 

19 specification of a standard. 

20 (CPE 4) Specification of those factors, such as precipitation, which interact with 

21 ambient NOx and SOx to produce adverse effects on welfare, by affecting 

22 deposition of N and S. 

23 (CPE 5) Specification of the form for the standard(s), including ambient atmospheric 

24 indicators for NOx and SOx, with consideration of averaging times, and 

25 options for levels of the standard(s). 

26 The development of the conceptual framework for the NOx and SOx standards described 

27 in Section 1.4 will be motivated by these critical policy elements. However, in order to provide a 

28 historical context for this new framework, the next section provides a brief history of previous 

29 reviews of the NOx and SOx secondary NAAQS, as well as other relevant historical reviews of 

30 welfare effects associated with these pollutants. 
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1 1.4 HISTORICAL CONTEXT 

2 1.4.1 History of NOx and SOx NAAQS Review 

3 1.4.1.1 NOx NAAQS 

4 EPA began the most recent previous review of the NOx secondary standards in 1987 and 

5 in November 1991, EPA released an updated draft AQCD for CASAC and public review and 

6 comment (56 FR 59285). This draft document provided a comprehensive assessment of the 

7 available scientific and technical information on health and welfare effects associated with NO2

8 and other NOx. CASAC reviewed the draft document at a meeting held on July 1, 1993, and 

9 concluded in a closure letter to the Administrator that the document “provides a scientifically 

10 balanced and defensible summary of current knowledge of the effects of this pollutant and 

11 provides an adequate basis for EPA to make a decision as to the appropriate NAAQS for NO2” 

12 (Wolff, 1993). The AQCD Air Quality Criteria for Oxides of Nitrogen was then finalized (U.S. 

13 EPA, 1993). EPA also prepared a Staff Paper that summarized and integrated the key studies and 

14 scientific evidence contained in the revised NOx AQCD and identified the critical elements to be 

15 considered in the review of the NO2 NAAQS. CASAC reviewed two drafts of the Staff Paper and 

16 concluded in a closure letter to the Administrator that the document provided a “scientifically 

17 adequate basis for regulatory decisions on nitrogen dioxide” (Wolff, 1995). In October 1995, the 

18 Administrator announced her proposed decision not to revise either the primary or secondary 

19 NAAQS for NO2 (60 FR 52874; October 11, 1995). A year later, the Administrator made a final 

20 determination not to revise the NAAQS for NO2 after careful evaluation of the comments 

21 received on the proposal (61 FR 52852; October 8, 1996). The level for both the existing primary 

22 and secondary NAAQS for NO2 is 0.053 ppm (100 micrograms per cubic meter [μg/m3] of air), 

23 annual arithmetic average, calculated as the arithmetic mean of the 1-hour NO2 concentrations. 

24 1.4.1.2 SOx NAAQS 

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

26 secondary NAAQS for SO2 on April 30, 1971 (36 FR 8186). The secondary standards included a 

27 standard at 0.02 ppm in an annual arithmetic mean and a 3-hour average of 0.5 ppm, not to be 

28 exceeded more than once per year. These secondary standards were established solely on the 

29 basis of evidence of adverse effects on vegetation. In 1973, revisions made to Chapter 5 
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1 (“Effects of Sulfur Oxide in the Atmosphere on Vegetation”) of Air Quality Criteria for Sulfur 

2 Oxides (U.S. EPA, 1973) indicated that it could not properly be concluded that the vegetation 

3 injury reported resulted from the average SO2 exposure over the growing season, rather than 

4 from short-term peak concentrations. Therefore, EPA proposed (38 FR 11355) and then finalized 

5 (38 FR 25678) a revocation of the annual mean secondary standard. At that time, EPA was aware 

6 that SOx have other public welfare effects, including effects on materials, visibility, soils, and 

7 water. However, the available data were considered insufficient to establish a quantitative 

8 relationship between specific ambient SOx concentrations and effects (38 FR 25679). 

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

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

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

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

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

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

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

16 ecosystems. CASAC also noted that acid deposition involves, at a minimum, several different 

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

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

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

20 deposition problem. 

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

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

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

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

25 closure on the AQCD for SO2 in December 1981, EPA’s Office of Air Quality Planning and 

26 Standards published a Staff Paper in November 1982, but the paper did not directly assess the 

27 issue of acid deposition. Instead, EPA subsequently prepared the following documents: The 

28 Acidic Deposition Phenomenon and Its Effects: Critical Assessment Review Papers, Volumes I 

29 and II (U.S. EPA, 1984a, b), and The Acidic Deposition Phenomenon and Its Effects: Critical 

30 Assessment Document (U.S. EPA, 1985) (53 FR 14935 -14936). These documents, though they 
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1 were not considered criteria documents and did not undergo CASAC review, represented the 

2 most comprehensive summary of relevant scientific information completed by EPA at that point. 

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

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

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

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

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

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

9 measures. 

10 1.4.2 History of Related Assessments and Agency Actions 

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

12 (NAPAP) in response to growing concern about acidic deposition. The NAPAP was given a 

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

14 alternative control options to alleviate acidic deposition and its effects. During the course of the 

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

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

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

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

19 needed. The Clean Air Act Amendments of 1990 included numerous separate provisions related 

20 to the acid deposition problem. The primary and most important of the provisions, the 

21 amendments to Title IV of the Act, established the Acid Rain Program to reduce emissions of 

22 SO2 by 10 million tons and NOx emissions by 2 million tons from 1980 emission levels in order 

23 to achieve reductions over broad geographic regions. In this provision, Congress included a 

24 statement of findings that led them to take action, concluding that (1) the presence of acid 

25 compounds and their precursors in the atmosphere and in deposition from the atmosphere 

26 represents a threat to natural resources, ecosystems, materials, visibility, and public health; (2) 

27 the problem of acid deposition is of national and international significance; and (3) current and 

28 future generations of Americans will be adversely affected by delaying measures to remedy the 

29 problem. 
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1 Second, Congress authorized the continuation of the NAPAP in order to assure that the 

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

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

4 Third, Congress considered that further action might be necessary in the long term to 

5 address any problems remaining after implementation of the Title IV program and, reserving 

6 judgment on the form that action could take, included Section 404 of the 1990 Amendments 

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

8 on the feasibility and effectiveness of an acid deposition standard or standards to protect 

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

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

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

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

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

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

15 reasonable certainty at that time. 

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

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

18 welfare. Specifically, the definition of “effects on welfare” in Section 302(h) was expanded to 

19 state that the welfare effects include effects “...whether caused by transformation, conversion, or 

20 combination with other air pollutants.” 

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

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

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

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

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

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

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

28 109 of the Act.” The petition further stated that “recent federal studies, including the NAPAP 

29 Biennial Report to Congress: An Integrated Assessment, document the continued-and increasing30 damage being inflicted by acid deposition to the lakes and forests of New York, New England 

31 and other parts of our nation, demonstrating that the Title IV program had proven insufficient.” 
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1 The petition also listed other adverse welfare effects associated with the transformation of these 

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

3 warming, and tropospheric ozone and stratospheric ozone depletion. 

4 In a related matter, the Office of the Secretary of the U.S. Department of Interior 

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

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

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

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

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

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

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

12 comments and information submitted, along with the information provided by the petitioners and 

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

14 48699). 

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

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

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

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

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

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

(NAPAP, 2005).5 21 

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

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

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

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

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

27 these criteria pollutants 

 

5

 Note that a new NAPAP report is expected to be released later in 2010. The findings of that report will be 

considered in the final policy assessment. 
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1 1.5 PROPOSED CONCEPTUAL FRAMEWORK FOR COMBINED NOX

2 SOX STANDARDS 

3 There is a strong basis for considering NOx and SOx together at this time, building upon 

4 EPA’s and CASAC’s past recognition of the interactions of these pollutants and on the growing 

5 body of scientific information that is now available related to these interactions and associated 

6 ecological effects. The REA introduced a conceptual framework for ecologically meaningful 

7 secondary standards that recognized the complex processes by which ecosystems are exposed to 

8 ambient NOx and SOx. That framework provided a flow from ambient concentrations exposures 

9 via deposition to ecological indicators and effects (see Figure ES-2 in the REA Executive 

10 Summary). This sequence represents the process by which we can determine the risks associated 

11 with ambient concentrations of NOx and SOx. However, for the purposes of discussing a 

12 conceptual framework for design of standards to protect against those risks, a modified version 

13 of the risk framework is needed. 

14 Figure 1-1 depicts the framework by which we are considering the structure of an 

15 ecologically meaningful secondary standard. It is a conceptual diagram that illustrates how a 

16 level of protection related to an indicator of ecological effect(s) equates to atmospheric 

17 concentrations of NOx and SOx indicators. This conceptual diagram illustrates the linkages 

18 between ambient air concentrations and resulting deposition metrics, and between the deposition 

19 metric and the ecological indicator of concern. The Atmospheric Deposition Transformation 

20 Function translates ambient atmospheric concentrations of NOx and SOx to nitrogen and sulfur 

21 deposition metrics, while the Ecological Effect Function transforms the deposition metric into 

22 the ecological indicator. 

23 Development of a form for the standard that reflects this structure is a critical step in the 

24 overall standard setting process. The atmospheric levels of NOx and SOx that satisfy a particular 

25 level of ecosystem protection are those levels that result in an amount of deposition that is less 

26 than the amount of deposition that a given ecosystem can accept without excessive degradation 

27 of the ecological indicator for a targeted effect. 

28 The details of this conceptual framework are discussed in Chapter 5, including 

29 discussions of modifying factors that alter the relationship between ambient atmospheric 

30 concentrations of NOx and SOx and depositional loads of nitrogen and sulfur, and those that 

31 modify the relationship between deposition loads and the ecological indicator. 
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1 In setting NAAQS to protect public health and welfare, EPA has historically established 

2 standards which require the comparison of monitored concentrations of an air pollutant against a 

3 numerical metric of atmospheric concentration that does not vary geographically. This approach 

4 has appropriately protected public health as at-risk populations are widely distributed throughout 

5 the nation. As more is learned about the effects of pollutants such as NOx and SOx and the 

6 environment, however, such an approach may not be appropriate to provide the requisite level of 

7 protection to public welfare from effects on sensitive ecosystems. EPA is considering in this 

8 review of the secondary standard for NOx and SOx whether a standard that takes into account 

9 variable factors, such as atmospheric variables and ecosystem sensitivities, is the appropriate 

10 approach to protect the public welfare from the effects associated with the presence of these 

11 pollutants in the ambient air. 

12 EPA must undertake a thorough review of the air quality criteria for the pollutant at issue 

13 in reviewing a secondary NAAQS, and determine whether a current standard is requisite to 

14 protect the public welfare. Under section 108 of the CAA, air quality criteria are to “reflect the 

15 latest scientific knowledge useful in indicating the kind and extent of all identifiable effects” 

16 associated with the presence of the pollutant in the ambient air. It is clear from the language of 

17 the CAA that where the state of the science provides a basis for considering such effects, the 

18 review of the air quality criteria should encompass a broad analysis of “any” known or 

19 anticipated adverse effects, as well as the ways in which variable conditions such as atmospheric 

20 conditions may impact the effect of a pollutant and the ways in which other air pollutants may 

21 interact with the criteria pollutant to produce adverse effects. Specifically, section 108(a)(2) of 

22 the CAA provides that: 

23 Air quality criteria for an air pollutant shall accurately reflect the latest scientific 

24 knowledge useful in indicating the kind and extent of all identifiable effects on public health or 

25 welfare which may be expected from the presence of such pollutant in the ambient air, in varying 

26 quantities. The criteria for an air pollutant to the extent practicable, shall include information on: 

27  (A) those variable factors (including atmospheric conditions) which of themselves or 

28 in combination with other factors may alter the effects on public health or welfare of such 

29 air pollutants; 

30  (B) the types of air pollutants which, when present in the atmosphere, may interact 

31 with such pollutants to produce an adverse effect on public health or welfare; and 
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1  (C) any known or anticipated adverse effects on welfare. 

2 Based on this extensive review of the air quality criteria for an air pollutant, the 

3 Administrator is required to review and to revise, as appropriate, the secondary standard to 

4 ensure that the standard “is requisite to protect public welfare from any known or anticipated 

5 adverse effects associated with the presence of such air pollutant in the ambient air.” CAA § 

6 109(b) & (d). “Effects on welfare,” in turn, is defined to include a broad array of effects, 

7 including effects on soil, water, crops, vegetation, and manmade materials, “whether caused by 

8 transformation, conversion, or combination with other air pollutants.” CAA § 302(h). Thus, as 

9 with the sections of the CAA describing the issuance of air quality criteria, the CAA uses 

10 expansive language in describing the scope of EPA’s responsibility and the range of effects that 

11 EPA should take into account in setting a standard that is requisite to protect public welfare. The 

12 term “requisite,” however, indicates that section 109 is not open-ended. In considering the 

13 meaning of the term “requisite” in the context of the primary standards, the Supreme Court has 

14 agreed with EPA that such a standard is one that is “sufficient, but not more than necessary” to 

15 protect public health. Whitman v. American Trucking, 531 U.S. 457, 473 (2001). 

16 While EPA has most often considered the results of direct exposure to an air pollutant in 

17 the ambient air in assessing effects on public health and welfare, such as the health effects on 

18 humans when breathing in an air pollutant or the effects on vegetation through the uptake of air 

19 pollutants from the ambient air through leaves, EPA has also considered, where appropriate, the 

20 effects of exposure to air pollutants through more indirect mechanisms. For example, both in 

21 1978 and in 2008, EPA established a NAAQS for lead that addressed the health effects of 

22 ambient lead whether the lead particles were inhaled or were ingested after deposition on the 

23 ground or other surfaces. 73 FR 66964 (November 12, 2008), Lead Industries v. EPA, 647 F.2d 

24 1130 (DC Cir. 1980) (1978 NAAQS). The deposition of ambient NOx and SOx to terrestrial and 

25 aquatic environments can impact ecosystems through both direct and indirect mechanisms, as 

26 discussed in the REA and this document. Given Congress’ instruction to set a standard that “is 

27 requisite to protect the public welfare from “any known or anticipated adverse effects associated 

28 with the presence of such air pollutant in the ambient air,” 42 U.S.C. § 109 (b)(2) (emphasis 

29 added), this review appropriately attempts to take into consideration widely acknowledged 

30 effects, such as acidification and nutrient enrichment, which are associated with the presence of 

31 ambient SOx and NOx. 
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1 In this review, EPA is also attempting to develop a standard that takes into account the 

2 variability in effects from ambient levels of SOx and NOx. The CAA requires EPA to establish 

3 “national” standards, based on the air quality criteria, that provide the requisite degree of 

4 protection, but does not clearly address how to do so under the circumstances present here. One 

5 approach is to develop a secondary standard such as the one discussed in this Policy Assessment 

6 Document. Such a standard is designed to provide a generally uniform degree of protection 

7 throughout the country by allowing for varying concentrations of allowable ambient NOx and 

8 SOx, depending on atmospheric conditions and other variabilities, to achieve that degree of 

9 protection. Such a standard protects sensitive ecosystems wherever such ecosystems are found. 

10 This approach recognizes that setting a standard that is sufficient to protect the public welfare but 

11 not more than is necessary calls for consideration of a standard such as the one discussed in this 

12 document. 

Structure of an Ecologically-based Standard

Air Quality 

Indicator(s)

Measured over a 

specified averaging 

time; expressed in 

terms of a specified statistic (form)

Ecological 

Indicator

Ecological 

Effect

Function

Atmospheric 

Deposition

Transformation

Function

Deposition 

Metric

Variable/Fixed 

Factors:

Ecological

Variable/Fixed 

Factors:

Atmospheric

Landscape

Form of the Standard

Level of the Standard 13 

14 Figure 1-1. Framework of an alternative secondary standard. 

15 1.6 POLICY RELEVANT QUESTIONS 

16 In this policy assessment, a series of general questions frames our approach to identifying 

17 a range of policy options for consideration by the Administrator regarding secondary NAAQS 

18 for NOx and SOx. These questions are drawn from our Integrated Review Plan with 
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1 modifications based on further consideration by staff and comments from CASAC and the 

2 public. Our policy assessment begins by characterizing “known or anticipated adverse effects” 

3 on public welfare within our conceptual model (CPE 1). As noted earlier, this review is focusing 

on effects in unmanaged ecosystems (not commercial forests or agricultural lands6 4 ) resulting 

5 from ambient concentrations of NOx and SOx through deposition of N and S. In Chapter 2, we 

6 draw from the information and conclusions presented in the ISA and REA to address the 

7 following questions: 

8 1. What are the nature and magnitude of ecosystem responses to reactive nitrogen and 

9 sulfur deposition? 

10 a. How are these responses affected by landscape factors? 

11 b. What types of ecosystems are sensitive to such responses? 

12 2. To what extent can ecosystem responses to nitrogen deposition be separated into 

13 responses related to oxidized and reduced forms of reactive nitrogen compounds? 

14 In Chapter 3, we address the following questions related to linking effects to measures of 

15 adversity (CPE 1.1): 

16 1. How do we characterize adversity to public welfare? What are the sources of 

17 potentially relevant characterization for this policy assessment? 

18 2. What is the evidence of effects on ecosystem services, and how can those ecosystem 

19 services be linked to ecological indicators? 

20 3. To what extent are identified ecosystem effects important from a public welfare 

21 perspective, and what are the important uncertainties associated with estimating such 

22 effects? 

23 Once we have described ecological effects, we then provide an assessment of the 

24 adequacy of the existing NOx and SOx standards (CPE 1.2). We begin this assessment by 

25 drawing from the information and conclusions presented in the ISA and REA to address in 

26 Chapter 4 the following questions, which allow us to identify whether the structure of the current 

27 standards is appropriate relative to the key ecological effects assessed in the ISA and REA, 

 

6

 The decision to focus on unmanaged ecosystems is based on the weight of evidence of effects in those ecosystems. 

The majority of the scientific evidence regarding acidification and nutrient enrichment is based on studies in 

unmanaged ecosystems. Non-managed terrestrial ecosystems tend to have a higher fraction of N deposition 

resulting from atmospheric N (ISA 3.3.2.5). In addition, the ISA notes that agricultural and commercial forest lands 

are routinely fertilized with amounts of N (100 to 300 kg N/ha) that exceed air pollutant inputs even in the most 

polluted areas (ISA 3.3.9) 
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1 including acidification and excess nutrient enrichment and whether there is adequate information 

2 and analyses available at this time to assess the extent to which potentially adverse effects on 

3 aquatic and terrestrial ecosystems can be associated with current levels of atmospheric reactive 

4 nitrogen, accounting for the contributions of oxidized and reduced forms, and SOx and with 

5 levels that are at or below the current secondary standards: 

6 1. To what extent are effects that could reasonably be judged to be adverse to public 

7 welfare occurring under current conditions and would such effects occur if the nation 

8 met the current standards? To what extent do the current NOx and SOx secondary 

9 standards provide protection from effects associated with deposition of: 

10 a. Sulfur and oxidized nitrogen from atmospheric NOx, and SOx which results in 

11 acidification in sensitive aquatic and terrestrial ecosystems? 

12 b. Oxidized nitrogen from atmospheric NOx, which results in nutrient enrichment 

13 effects in sensitive aquatic and terrestrial ecosystems? 

14 c. Sulfur and oxidized nitrogen from atmospheric NOx and SOx which results in 

15 other ecological effects (e.g. mercury methylation)? 

16 2. In what way are the structures of the current NOx and SOx secondary standards 

17 inadequate to protect against public welfare effects? 

18 In Chapter 5, we follow our adequacy assessment by developing in greater detail the 

19 conceptual framework for the design of ecologically relevant multi-pollutant standards 

20 introduced in Section 1.4 above. To the extent that the available information calls into question 

21 the adequacy of protection afforded by the current standards and/or the appropriateness of the 

22 structure of the standards, we explore the extent to which available information supports 

23 consideration of alternative standards, in terms of atmospheric and ecological indicators and 

24 related averaging times, forms, and levels. This conceptual framework is designed to focus on 

25 resolving the following questions: 

26 1. (CPE 2.1) Does the available information provide support for the use of ecological 

27 indicators to characterize the responses of aquatic and terrestrial ecosystems to 

28 oxidized nitrogen and sulfur deposition? 

29 2. (CPE 1) Does the available information provide support for the development of 

30 appropriate ecological response to deposition relationship(s) that meaningfully relates 

31 oxidized nitrogen and sulfur deposition to relevant ecological indicators? Does a 
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1 quantified relationship exist between the level of a relevant ecological indicator and 

2 an amount of nitrogen and sulfur deposition? 

3 3. (CPE 2) What are the important variables in the ecological response to deposition 

4 relationship(s)? Are these relationships applicable nationally? What are the 

5 appropriate temporal scales for these relationships? 

6 a. How does ecological response to deposition relationship(s) depend upon spatially 

7 heterogeneous geologic factors (e.g. bedrock type, weathering rates) that govern 

8 sensitivity? 

9 b. How do we consider areas with high natural background acidification or nutrient 

10 loadings? 

11 4. (CPE 3) Does the available information provide support for the development of 

12 appropriate functions that characterize the relationships between atmospheric NOx

13 and SOx and the wet and dry deposition of total reactive nitrogen and sulfur? (CPE 4) 

14 How do these relationships depend upon relevant atmospheric factors (e.g., reduced 

15 forms of nitrogen, meteorological factors) and landscape factors? 

16 a. What deposition function is appropriate to use for the purpose of relating an 

17 amount of nitrogen and/or sulfur deposition in sensitive ecosystems to ambient 

18 concentrations of atmospheric reactive nitrogen, including oxides and reduced 

19 forms, and/or sulfur? What are the important variables in such a function? What 

20 are appropriate spatial and temporal scales to use in specifying such variables? 

21 Based on the conceptual framework for the structure of the ecologically relevant multi22 pollutant standards, we then address in Chapter 6 the elements of the standard needed to develop 

23 options for consideration by the Administrator. Development of these options will focus on 

24 addressing the following questions: 

25 1. (CPE 2.1) What ecological indicators are appropriate to use for the purpose of 

26 developing an alternative standard for the various ecological effects assessed in this 

27 review? 

28 2. (CPE 5) What indicators of oxides of nitrogen and sulfur are appropriate to use for 

29 the purpose of determining whether the resultant deposition is within the target values 

30 needed to achieve the desired degree of protection? What averaging times and forms 

31 are appropriate to consider? 
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1 3. (CPE 4) What approaches are available to specify non-atmospheric elements of the 

2 standard, e.g. weathering rates? Are there approaches that can simplify the structure 

3 of the standard by using discrete representations (bins) of continuous variables? 

4 4. What are the available approaches for accounting for reduced N in the structure of the 

5 standard? 

6 5. What is the most appropriate form for the standards to reflect the relationships 

7 between ambient NOx and SOx, acidifying deposition, and the ecological indicator for 

8 acidification? 

9 Several follow-up questions derive from our assessment of options for specifying the 

10 elements of a multipollutant standard. In Chapter 7, we address the questions: 

11 1. To what extent would a standard specifically defined to protect against one ecological 

12 effect (i.e., aquatic acidification) likely provide protection from other relevant 

13 ecological effects? 

14 2. What are the available approaches for combining multiple indicators into a single 

15 standard, e.g. using nitrogen effects to bound the tradeoff curve for NOx/SOx for 

16 aquatic acidification effects 

17 3. What are the available approaches to integrate potential standards for aquatic and 

18 terrestrial acidification and/or aquatic and terrestrial N enrichment? 

19 In Chapter 8, we plan to address in the second draft policy assessment issues regarding 

20 the adequacy of the current definitions of oxides of nitrogen and sulfur in specifying standards 

21 for protection against effects associated with deposition of nitrogen and sulfur. This discussion 

22 will be focused on the following questions: 

23 1. To what extent are effects associated with atmospheric nitrogen deposition reduced 

24 when NOx related deposition is reduced? 

25 2. To what extent can appropriate protection from relevant ecological effects be 

26 achieved by specifying indicators of atmospheric reactive nitrogen and sulfur 

27 compounds in terms of gas- and particle-phase nitrogen oxides and/or sulfur oxides? 

28 3. To what extent does the available information on welfare effects provide a basis for 

29 considering expanding the list of criteria pollutants to include all reactive nitrogen or 

30 gas-phase ammonia? What are the relative merits of listing total reactive nitrogen 

31 versus gas phase ammonia for protection of public welfare effects? 
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1 We conclude with a discussion of a range of options to consider in selecting pollutant 

2 indicators, averaging times, forms, and levels for the secondary NOx and SOx standards, 

3 including a discussion of staff initial conclusions on what levels of the standard for NOx and SOx

4 would be requisite to protect public welfare against adverse ecological effects. This discussion is 

5 informed by a consideration of the role of ecosystem services in helping to characterize what 

6 adversity to public welfare, focused on the following questions: 

7 1. (CPE 5) What are the risks of ecosystem service impairment under alternative levels 

8 of potential standards for NOx and SOx? 

9 2. (CPE 5) To what extent can information about ecosystem services be used to help 

10 characterize the extent to which differing levels of relevant ecological indicators 

11 reflect impacts that can reasonably be judged to be adverse from a public welfare 

12 perspective? 

13 3. (CPE 5) Are there relevant benchmarks for adversity to public welfare that can be 

14 derived from other sources? 

15 4. (CPE 5) Taking into consideration information about ecosystem services and other 

16 factors related to characterizing adversity to public welfare for the ecological effects 

17 being assessed in this review, what is an appropriate range of levels of protection to 

18 be achieved by alternative standards for the Agency to consider? 

19 
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1 2. KNOWN OR ANTICIPATED ECOLOGICAL EFFECTS 

2 This chapter addresses Critical Policy Element 1, evaluation of the effects of ambient 

3 NOx and SOx on ecosystems, and the relationship between those effects and the measure of dose 

4 in the ecosystem, indicated by the depositional loadings of N and S. In section 302(h) of the 

5 Clean Air Act, welfare effects addressed by a secondary NAAQS include, but are not limited to, 

6 “effects on soils, water, crops, vegetation, man-made materials, animals, wildlife, weather, 

7 visibility and climate, damage to and deterioration of property, and hazards to transportation, as 

8 well as effects on economic values and on personal comfort and well-being”. Of these welfare 

9 effects categories, the effects of NOx and SOx on aquatic and terrestrial ecosystems, which 

10 encompass soils, water, vegetation, wildlife, and contribute to economic value and well-being, 

11 are of most concern at concentrations typically occurring in the U.S. Direct effects of NOx and 

12 SOx on vegetation are also discussed in this chapter, and have been the focus of previous 

13 reviews. However, for this review, the focus of this chapter is on the known and anticipated 

14 effects to ecosystems caused by exposure to NOx and SOx through deposition. 

15 The information presented here is a concise summary of conclusions from the ISA and 

16 the REA. This chapter focuses on effects on specific ecosystems with a brief discussion on 

17 critical uncertainties associated with acidification and nutrient enrichment; Chapter 3 evaluates 

18 those effects within the context of alternative definitions of, including assessments of potential 

19 impacts on ecosystem services. Effects are broadly categorized into acidification and nutrient20 enrichment in the proceeding sections. This is background information intended to support new 

21 approaches for the design of ecologically relevant secondary NOx and SOx standards which are 

22 protective of U.S. ecosystems. More detailed information on the conceptual design and specific 

23 options for the proposed standards are presented in Chapters 5 and 6 of this policy assessment 

24 document. While we provide a summary of effects for all four of the primary effects categories, 

25 we reiterate that the focus of this first draft policy assessment is on effects related to aquatic 

26 acidification, without downplaying the potential significance of effects in other categories. 
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1 2.1 ACIDIFICATION: EVIDENCE OF EFFECTS ON STRUCTURE AND 

2 FUNCTION OF TERRESTRIAL AND FRESHWATER 

3 ECOSYSTEMS 

4 Sulfur oxides (SOx) and nitrogen oxides (NOx) compounds in the atmosphere undergo a 

5 complex mix of reactions and thermodynamic processes in gaseous, liquid, and solid phases to 

6 form various acidic compounds. These acidic compounds are removed from the atmosphere 

7 through deposition: either wet (e.g., rain, snow), fog or cloud, or dry (e.g., gases, particles). 

8 Deposition of these acidic compounds leads to ecosystem exposure and effects on ecosystem 

9 structure and function. Following deposition, these compounds can, in some instances, leach out 

of the soils in the form of sulfate (SO4

2-) and nitrate (NO3- 10 ), leading to the acidification of surface 

11 waters. The effects on ecosystems depend on the magnitude of deposition, as well as a host of 

12 biogeochemical processes occurring in the soils and waterbodies (REA 2.1). The chemical forms 

13 of nitrogen that may contribute to acidifying deposition include both oxidized and reduced 

14 species. 

When sulfur or nitrogen leaches from soils to surface waters in the form of SO4

2- or NO3- 15 , 

16 an equivalent amount of positive cations, or countercharge, is also transported. This maintains 

electroneutrality. If the countercharge is provided by base cations, such as calcium (Ca2+ 17 ), 

magnesium (Mg2+), sodium (Na+

), or potassium (K+), rather than hydrogen (H+ 18 ) and dissolved 

19 inorganic aluminum, the acidity of the soil water is neutralized, but the base saturation of the soil 

is reduced. Continued SO4

2- or NO3- 20 leaching can deplete the base cation supply of the soil. As 

the base cations are removed, continued deposition and leaching of SO4

2- and/or NO3- 21 (with 

H+

and Al3+ 22 ) leads to acidification of soil water, and by connection, surface water. A watershed’s 

23 ability to neutralize acidic deposition is determined by a host of biogeophysical factors, including 

24 base cation concentrations, weathering rates, uptake by vegetation, rate of surface water flow, 

25 soil depth, and bedrock. (REA 2.1) Some of these factors such as vegetation and soil depth are 

26 highly variable over small spatial scales, but others vary over larger spatial scales like geology. 

27 For the purpose of a national secondary standard, the most relevant characteristics are those that 

28 are less variable over small scales. 

29 Acidifying deposition of NOx and SOx and the chemical and biological responses 

30 associated with these inputs vary temporally. Chronic or long-term deposition processes result in 
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1 increases of N and S and the associated effects of acidifying deposition in the time scale of years 

2 to decades. Episodic or short term (i.e., hours or days) deposition refers to events in which the 

3 level of the acid neutralizing capacity (ANC) of a lake or stream is temporarily lowered. In 

4 aquatic ecosystems, short-term (i.e., hours or days) episodic changes in water chemistry can have 

5 significant biological effects. Episodic chemistry refers to conditions during precipitation or 

6 snowmelt events when proportionately more drainage water is routed through upper soil horizons 

7 that tend to provide less acid neutralizing than was passing through deeper soil horizons (REA 

8 4.2). Some streams and lakes may have chronic or base flow chemistry that is suitable for aquatic 

9 biota, but may be subject to occasional acidic episodes with lethal consequences. 

10 The following summary is a concise overview of the known or anticipated effects caused 

11 by acidification to ecosystems within the United States. Acidification affects both terrestrial and 

12 freshwater aquatic ecosystems. Terrestrial and aquatic processes are often linked; therefore 

13 responses to the following questions address both types of ecosystems unless otherwise noted. 

14 2.1.1 What is the nature of acidification related ecosystem responses to reactive 

15 nitrogen and/ sulfur deposition? 

16 The ISA concluded that deposition of SOx, NOx, and NHx leads to the acidification of 

17 ecosystems (EPA 2008). In the process of acidification, geochemical components of terrestrial 

18 and freshwater aquatic ecosystems are altered in a way that leads to effects on biological 

19 organisms. Deposition to terrestrial ecosystems often moves through the soil and eventually 

20 leaches into adjacent water bodies, moreover deposition to the land effects the water as well. 

21 The scientific evidence is sufficient to infer a causal relationship between acidifying 

22 deposition and effects on biogeochemistry and biota in aquatic ecosystems (ISA 4.2.2). The 

23 strongest evidence comes from studies of surface water chemistry in which acidic deposition is 

24 observed to alter sulfate and nitrate concentrations in surface waters, sum and surplus of base 

25 cations, acid, ANC, inorganic aluminum, calcium, and surface water pH (ISA 3.2.3.2). 

26 Consistent and coherent documentation from multiple studies on various species from all major 

27 trophic levels of aquatic systems shows that geochemical alteration caused by acidification can 

28 result in the loss of acid-sensitive biological species (ISA 3.2.3.3). For example, in the 

29 Adirondacks, of the 53 fish species recorded in Adirondack lakes about half (26 species) were 

30 absent from lakes with pH below 6.0 (Baker et al., 1990b). Biological effects are linked to 
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1 changes in water chemistry including ANC, inorganic Al, and pH. Decreases in ANC and pH 

2 and increases in inorganic Al concentration contribute to declines in taxonomic richness of 

3 zooplankton, macroinvertebrates, and fish, which often are sources of food for birds and other 

4 animal species in the ecosystem, as well as serving as a source of food and recreation for 

5 humans. Acidification of ecosystems has been shown to disrupt food web dynamics causing 

6 alteration to the diet, breeding distribution and reproduction of certain species of birds (ISA 

7 4.2.2.2. and Table 3-9). For example, breeding distribution of the common goldeneye 

8 (Bucephala clangula) an insectivorous duck, may be affected by changes in acidifying deposition 

9 (Longcore and Gill, 1993). Similarly, reduced prey diversity and quantity have been observed to 

10 create feeding problems for nesting pairs of loons on low-pH lakes in the Adirondacks (Parker 

11 1988). 

12 In terrestrial ecosystems, the evidence is sufficient to infer a causal relationship between 

13 acidifying deposition and changes in biogeochemistry (ISA 4.2.1.1). The strongest evidence 

14 comes from studies of forested ecosystems, with supportive information on other plant 

15 communities, including shrubs and lichens (ISA 3.2.2.1.). Three useful indicators of chemical 

16 changes and acidification effects on terrestrial ecosystems, showing consistency and coherence 

17 among multiple studies: soil base saturation, Al concentrations in soil water and soil C:N ratio 

18 (ISA 3.2.2.2). 

19 In soils with base saturation less than about 15 to 20% exchange ion chemistry is 

20 dominated by Al (Reuss, 1983). Under this condition, responses to inputs of sulfuric acid and 

21 nitric acid largely involve the release and mobilization of inorganic Al through cation exchange. 

22 The effect can be neutralized by weathering from geologic parent material or base cation 

exchange. The Ca2+ 23 and Al in soils are strongly influenced by soil acidification and both have 

been shown to have quantitative links to tree health, including Al interference with Ca2+ 24 uptake 

25 and Al toxicity to roots (Parker et al., 1989; U.S. EPA, 2009). Effects of nitrification and 

26 associated acidification and cation leaching have been consistently shown to occur only in soils 

27 with a C:N ratio below about 20 to 25 (Aber et al., 2003; Ross et al., 2004). 

28 Acidification has been shown to cause decreased growth and increased susceptibility to 

29 disease and injury in sensitive tree species. Red spruce (Picea rubens) dieback or decline has 

30 been observed across high elevation areas in the Adirondack, Green and White mountains 

31 (DeHayes et al., 1999). The frequency of freezing injury to red spruce needles has increased over 
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1 the past 40 years, a period that coincided with increased emissions of S and N oxides and 

2 increased acidifying deposition (DeHayes et al., 1999). Acidifying deposition may be 

3 contributing to episodic dieback in Sugar maple (Acer saccharum) through depletion of nutrient 

4 cations from marginal soils (Horsley et al., 2000; Bailey et al., 2004). Grasslands are likely less 

5 sensitive to acidification than forests (Blake et al., 1999; Kocky and Wilson 2001). 

6 2.1.2 What types of ecosystems are sensitive to such effects? In which ways are 

7 these responses affected by atmospheric, ecological, and landscape factors? 

8 The intersection between current deposition loading, historic loading, and sensitivity 

9 defines the ecological vulnerability to the effects of acidification. Freshwater aquatic and 

10 terrestrial ecosystems are the ecosystem types which are most sensitive to acidification. The ISA 

11 reports that the principal factor governing the sensitivity of terrestrial and aquatic ecosystems to 

12 acidification from sulfur and nitrogen deposition is geology (particularly surficial geology). 

13 Geologic formations having low base cation supply generally underlie the watersheds of acid14 sensitive lakes and streams. Other factors that contribute to the sensitivity of soils and surface 

15 waters to acidifying deposition include topography, soil chemistry, land use, and hydrologic 

16 flowpath. Episodic and chronic acidification tends to occur at relatively high elevation in areas 

17 that have base-poor bedrock, high relief, and shallow soils (ISA 3.2.4.1). 

18 2.1.3 What is the magnitude of ecosystem responses to acidifying deposition? 

19 Terrestrial and aquatic ecosystems differ in their response to acidifying deposition. 

20 Therefore the magnitude of ecosystem response is described separately for aquatic and terrestrial 

21 ecosystems in the following sections. The magnitude of response refers to both the severity of 

22 effects and the spatial extent of the U.S. which is affected. 

23 2.1.3.1 Aquatic 

24 Freshwater ecosystem surveys and monitoring in the eastern United States have been 

25 conducted by many programs since the mid-1980s, including EPA’s Environmental Monitoring 

26 and Assessment Program (EMAP), National Surface Water Survey (NSWS), Temporally 

27 Integrated Monitoring of Ecosystems (TIME) (Stoddard, 1990), and Long-term Monitoring 

28 (LTM) (Ford et al., 1993; Stoddard et al., 1996) programs. Based on analyses of surface water 

29 data from these programs, New England, the Adirondack Mountains, the Appalachian Mountains 
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1 (northern Appalachian Plateau and Ridge/Blue Ridge region), and the Upper Midwest contain 

2 the most sensitive lakes and streams (i.e., ANC less than about 50 μeq/L) since the 1980s. 

3 Portions of northern Florida also contain many acidic and low-ANC lakes and streams, although 

4 the role of acidifying deposition in these areas is less clear. The western U.S. contains many of 

5 the surface waters most sensitive to potential acidification effects, but with the exception of the 

6 Los Angeles Basin and surrounding areas, the levels of acidifying deposition are low in most 

7 areas. Therefore acidic surface waters are uncommon in the western U.S., and the extent of 

8 chronic surface water acidification that has occurred in that region to date has likely been very 

9 limited (ISA 3.2.4.2 and REA 4.2.2).

10 There are a number of species including fish, aquatic insects, other invertebrates and 

11 algae that are sensitive to acidification and cannot survive, compete, or reproduce in acidic 

12 waters (ISA 3.2.3.3). Decreases in ANC and pH have been shown to contribute to declines in 

13 species richness and abundance of zooplankton, macroinvertebrates, and fish (Keller and Gunn 

14 1995; Schindler et al., 1985). Reduced growth rates have been attributed to acid stress in a 

15 number of fish species including Atlantic salmon (Salmo salar), Chinook salmon (Oncorhynchus 

16 tshawytscha), lake trout (Salvelinus namaycush), rainbow trout (Oncorhynchis mykiss), brook 

17 trout (Salvelinus Fontinalis), and brown trout (Salmo trutta) (Baker et al., 1990). In response to 

18 small to moderate changes in acidity, acid-sensitive species are often replaced by other more 

19 acid-tolerant species, resulting in changes in community composition and richness. The effects of 

20 acidification are continuous, with more species being affected at higher degrees of acidification. 

21 At a point, typically a pH <4.5 and an ANC <0 μeq/L, complete to near-complete loss of many 

22 classes of organisms occur, including fish and aquatic insect populations, whereas others are 

23 reduced to only a few acidophilic forms. These changes in species integrity are because energy 

24 cost in maintaining physiological homeostasis, growth, and reproduction is high at low ANC 

25 levels (Schreck, 1981, 1982; Wedemeger et al., 1990; REA appendix 2.3). Decreases in species 

26 richness related to acidification have been observed in the Adirondack Mountains and Catskill 

27 Mountains of New York (Baker et al., 1996), New England and Pennsylvania (Haines and Baker, 

28 1986), and Virginia (Bulger et al., 2000). 

29 From the sensitive areas identified by the ISA, further “case study” analyses on aquatic 

30 ecosystems in the Adirondack Mountains and Shenandoah National Park were conducted to 

31 better characterize ecological risk associated with acidification (REA Chapter 4). 
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1 In the literature, ANC is the most widely used indicator of acid sensitivity and has been 

2 found in various studies to be the best single indicator of the biological response and health of 

3 aquatic communities in acid-sensitive systems (Lien et al., 1992; Sullivan et al., 2006; ISA). In 

the REA, surface water trends in SO4

2- and NO3- 4 concentrations and ANC levels were analyzed 

5 to affirm the understanding that reductions in deposition could influence the risk of acidification. 

6 ANC values were categorized according to their effects on biota, as shown in Figure 2-1. 

7 Monitoring data from the EPA-administered TIME/LTM and EMAP programs were assessed for 

8 the years 1990 to 2006, and past, present, and future water quality levels were estimated by both 

9 steady-state and dynamic biogeochemical models. 

10 

11 

12 Figure 2-1. Ecological Effects Associated with Alternative Levels of Acid 

13 Neutralizing Capacity (ANC) 

14 The analyses of the Adirondack Case Study Area indicated that although wet deposition 

15 rates for SO2 and NOx have been reduced since the mid-1990s, current concentrations are still 

well above pre-acidification (1860) conditions. Modeling predicts NO3

-

 and SO4

2- 16 are 17- and 5-
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1 fold higher today, respectively. The estimated average ANC across the 44 lakes in the 

2 Adirondack Case Study Area is 62.1 μeq/L (± 15.7 μeq/L); 78 % of all monitored lakes in the 

3 Adirondack Case Study Area have a current risk of Elevated, Severe, or Acute. Of the 78%, 31% 

4 experience episodic acidification, and 18% are chronically acidic today (REA 4.2.4.2). 

5 Based on a deposition scenario that maintains current emission levels to 2020 and 2050, 

6 the simulation forecast indicates no improvement in water quality in the Adirondack Case Study 

7 Area. The percentage of lakes within the Elevated to Acute Concern classes remains the same in 

8 2020 and 2050. 

0

20

40

60

80

100

120

140

1850 1900 1950 2000 2050

ANC Concentration

(μeq/L)

9 

Figure 2-2. Average NO3

-

concentrations (orange), SO4

2- 10 concentrations (red), 

11 and ANC (blue) across the 44 lakes in the Adirondack Case Study Area modeled 

12 using MAGIC for the period 1850 to 2050. 
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1 

2 Figure 2-3. ANC concentrations of preacidification (1860) and current (2006) 

3 conditions based on hindcasts of 44 lakes in the Adirondack Case Study Area 

4 modeled using MAGIC. [Note: in this map, the symbol for red is reversed and 

5 should be < 0. The figure will be revised in the next draft.] 

6 
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1 

2 Figure 2-4. Critical loads of acidifying deposition that each surface water location 

3 can receive in the Adirondack Case Study Area while maintaining or exceeding 

4 an ANC concentration of 50 μeq/L based on 2002 data. Watersheds with critical 

load values <100 meq/m2 5 /yr (red and orange circles) are most sensitive to surface 

water acidification, whereas watersheds with values >100 meq/m2 6 /yr (yellow and 

7 green circles) are the least sensitive sites. 

8 It is important to note that studies on fish species richness in the Adirondacks Case Study 

9 Area demonstrated the effect of acidification; of the 53 fish species recorded in Adirondack Case 

10 Study Area lakes, only 27 species were found in lakes with a pH <6.0. The 26 species missing 

11 from lakes with a pH <6.0 include important recreational species, such as Atlantic salmon, tiger 

12 trout (Salmo trutta X Salvelinus fontinalis), redbreast sunfish (Lepomis auritus), bluegill 

13 (Lepomis macrochirus), tiger musky (Esox masquinongy X lucius), walleye (Sander vitreus), 

14 alewife (Alosa pseudoharengus), and kokanee (Oncorhynchus nerka) (Kretser et al., 1989), as 

15 well as ecologically important minnows that are commonly eaten by sport fish. A survey of 

16 1,469 lakes in the late 1980s found 346 lakes to be devoid of fish. Among lakes with fish, there 

17 was a relationship between the number of fish species and lake pH, ranging from about one 

18 species per lake for lakes having a pH <4.5 to about six species per lake for lakes having a pH 
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1 >6.5 (Driscoll et al., 2001; Kretser et al., 1989). In the Adirondacks, a positive relationship exists 

2 between the pH and ANC in lakes and the number of fish species present in those lakes (ISA 

3 3.2.3.4). 

4 Since the mid-1990s, streams in the Shenandoah Case Study Area have shown slight 

declines in NO3

-

 and SO4

2- 5 concentrations in surface waters. Current concentrations are still 

6 above pre-acidification (1860) conditions. MAGIC modeling predicts surface water 

concentrations of NO3

-

 and SO4

2- 7 are10- and 32-fold higher today, respectively. The estimated 

8 average ANC across 60 streams in the Shenandoah Case Study Area is 57.9 μeq/L (± 4.5 μeq/L). 

9 55% of all monitored streams in the Shenandoah Case Study Area have a current risk of 

10 Elevated, Severe, or Acute. Of the 55%, 18% experience episodic acidification, and 18% are 

11 chronically acidic today (REA 4.2.4.3) 

12 Based on a deposition scenario that maintains current emission levels to 2020 and 2050, 

13 the simulation forecast indicates that a large number of streams still have Elevated to Acute 

14 problems with acidity. In fact, from 2006 to 2050, the percentage of streams with Acute Concern 

15 increases by 5%, while the percentage of streams in Moderate Concern decreases by 5%. 

16 Biological effects of increased acidification documented in the Shenandoah Case Study 

17 Area include a reduction in the condition factor in Blacknose Dace (Dennis and Bulgar 1995, 

18 Bulgar et al., 1999) and a decrease in fish biodiversity associated with decreasing stream ANC 

19 (Bulger et al., 1995; Dennis and Bulger, 1995; Dennis et al., 1995; MacAvoy and Bulger, 1995, 

20 Bulgar et al., 1999). On average, the fish species richness is lower by one fish species for every 

21 21 μeq/L decrease in ANC in Shenandoah National Park streams (ISA 3.2.3.4). 

0

20

40

60

80

100

120

1850 1900 1950 2000 2050

Years

ANC Concentration 

(μeq/L)

2050

22 

Figure 2-5. Average NO3

-

concentrations orange), SO4

2- 23 concentrations (red), and 

24 ANC (blue) levels for the 60 streams in the Shenandoah Case Study Area 

25 modeled using MAGIC for the period 1850 to 2050. 
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1 

2 

3 Figure 2-6. ANC levels of 1860 (preacidification) and 2006 (current) conditions 

4 based on hindcasts of 60 streams in the Shenandoah Case Study Area modeled 

5 using MAGIC. 
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1 

2 Figure 2-7. Critical loads of surface water acidity for an ANC of 50 μeq/L for 

3 Shenandoah Case Study Area streams. Each dot represents an estimated amount 

4 of acidifying deposition (i.e., critical load) that each stream’s watershed can 

5 receive and still maintain a surface water ANC >50 μeq/L. Watersheds with 

critical load values <100 meq/m2 6 /yr (red and orange circles) are most sensitive to 

surface water acidification, whereas watersheds with values >100 meq/m2 7 /yr 

8 (yellow and green circles) are the least sensitive sites. 

9 2.1.3.2 Terrestrial Acidification 

10 The ISA identified a variety of indicators that can be used to measure the effects of 

acidification in soils. Tree health has been linked to base cations (Bc) in soil (such as Ca2+, Mg2+ 11 

12 and potassium), as well as soil Al content. Tree species show similar sensitivities to Ca/Al and 

13 Bc/Al soil solution ratios, therefore these are good chemical indicators because they directly 

14 relate to the biological effects. Critical Bc/Al ratios for a large variety of tree species ranged 

15 from 0.2 to 0.8 (Sverdrup and Warfvinge, 1993, a meta-data analysis of laboratory and field 

16 studies). This range is similar to critical ratios of Ca/Al. Plant toxicity or nutrient antagonism 

17 was reported to occur at Ca/Al ratios ranging from 0.2 to 2.5 (Cronan and Grigal, 1995; meta18 data assessment) (REA pg 4-54, REA Appendix 5). 
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1 There has been no systematic national survey of terrestrial ecosystems to determine the 

2 extent and distribution of terrestrial ecosystem sensitivity to the effects of acidifying deposition. 

3 However, one preliminary national evaluation estimated that ~15% of forest ecosystems in the 

4 U.S. exceeds the estimated critical load based on soil chemistry for S and N deposition by >250 

eq ha-1

 yr

-1 5 (McNulty et al., 2007). Forests of the Adirondack Mountains of New York, Green 

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

7 Pennsylvania, and high-elevation forest ecosystems in the southern Appalachians are the regions 

8 most sensitive to terrestrial acidification effects from acidifying deposition (ISA 3.2.4.2). While 

9 studies show some recovery of surface waters, there are widespread measurements of ongoing 

10 depletion of exchangeable base cations in forest soils in the northeastern U.S. despite recent 

11 decreases in acidifying deposition, indicating a slow recovery time. 

12 In the REA, a critical load analysis was performed for sugar maple and red spruce forests 

13 in the eastern United States by using Bc/Al ratio in acidified forest soils as an indicator to assess 

14 the impact of nitrogen and sulfur deposition on tree health. These are the two most commonly 

15 studied species in North America for effects of acidification. At a Bc/Al ratio of 1.2, red spruce 

16 growth can be reduced by 20%. Sugar maple growth can be reduced by 20% at a Bc/Al ratio of 

17 0.6. The REA analysis determined the health of at least a portion of the sugar maple and red 

18 spruce growing in the United States may have been compromised with acidifying total nitrogen 

19 and sulfur deposition in 2002. Specifically, total nitrogen and sulfur deposition levels exceeded 

20 three selected critical loads for tree growth in 3% to 75% of all sugar maple plots across 24 

21 states. For Red Spruce, total nitrogen and sulfur deposition levels exceeded three selected critical 

22 loads in 3% to 36% of all red spruce plots across eight states. 

23 2.1.4 What are the key uncertainties associated with acidification? 

24 There are different levels of uncertainty associated with relationships between deposition, 

25 ecological effects and ecological indicators. In Chapter 7 of the REA, key uncertainties are 

26 characterized as follows to evaluate the strength of the scientific basis for setting a national 

27 standard to protect against a given effect (REA 7.0): 

28  Data Availability: high, medium or low quality. This criterion is based on the availability 

29 and robustness of data sets, monitoring networks, availability of data that allows for 

30 extrapolation to larger assessment areas, and input parameters for modeling and 
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1 developing the ecological effect function. The scientific basis for the ecological indicator 

2 selected is also incorporated into this criterion. 

3  Modeling Approach: high, fairly high, intermediate, or low confidence. This value is 

4 based on the strengths and limitations of the models used in the analysis and how accepted 

5 they are by the scientific community for their application in this analysis. 

6  Ecological Effect Function: high, fairly high, intermediate, or low confidence. This 

7 ranking is based on how well the ecological effect function describes the relationship 

8 between atmospheric deposition and the ecological indicator of an effect. 

9 2.1.4.1 Aquatic Acidification 

10 The REA concludes that the available data are robust and considered high quality. There 

11 is high confidence about the use of these data and their value for extrapolating to a larger 

12 regional population of lakes. The EPA TIME/LTM network represents a source of long-term, 

13 representative sampling. Data on sulfate concentrations, nitrate concentrations and ANC from 

14 1990 to 2006 used for this analysis as well as EPA EMAP and REMAP surveys, provide 

15 considerable data on surface water trends. 

16 There is fairly high confidence associated with modeling and input parameters. 

17 Uncertainties in water quality estimates (.i.e. ANC) from MAGIC was derived from multiple site 

18 calibrations. The 95% confidence interval for pre-acidification of lakes was an average of 15 

19 μeq/L difference in ANC concentrations or 10% and 8 μeq/L or 5% for streams (REA 7.1.2) The 

20 use of the critical load model used to estimate aquatic critical loads is limited by the uncertainties 

21 associated with runoff and surface water measurements and in estimating the catchment supply 

22 of base cations from the weathering of bedrock and soils (McNulty et al., 2007). To propagate 

23 uncertainty in the model parameters, Monte Carlo methods were employed to develop an inverse 

24 function of exceedences. There is high confidence associated with the ecological effect function 

25 developed for aquatic acidification. In calculating the ANC function, the depositional load for N 

26 or S is fixed by the deposition of the other, so deposition for either will never be zero (Figure 

27 7.1-6 REA). 

28 Terrestrial Acidification 

29 The available data used to quantify the targeted effect of terrestrial acidification are 

30 robust and considered high quality. The USFS-Kane experimental forest and significant amounts 
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1 of research work in the Allegheny Plateau have produced extensive, peer-reviewed datasets. A 

2 meta-analysis of laboratory studies showed that tree growth was reduced by 20% relative to 

3 controls for BC/Al ratios (ISA 7.2.1 and Figure 7.2-1). Sugar maple and red spruce were the 

focus of the REA since they are demonstrated to be negatively affected by Ca2+ 4 depletion and 

5 high concentrations of available Al, and occur in areas that receive high acidifying deposition, 

6 There is high confidence about the use of the REA terrestrial acidification data and their value 

7 for extrapolating to a larger regional population of forests. 

8 There is high confidence associated with the models, input parameters, and assessment of 

9 uncertainty used in the case study for terrestrial acidification. The Simple Mass Balance (SMB) 

10 model, a commonly used and widely applied approach for estimating critical loads, was used in 

11 the REA analysis (ISA 7.2.2). There is fairly high confidence associated with the ecological 

12 effect function developed for terrestrial acidification (REA 7.2.3). 

13 2.2 NITROGEN ENRICHMENT: EVIDENCE OF EFFECTS ON 

14 STRUCTURE AND FUNCTION OF TERRESTRIAL AND 

15 FRESHWATER ECOSYSTEMS 

16 The following summary is a concise overview of the known or anticipated effects caused 

17 by nitrogen nutrient enrichment to ecosystems within the United States. Nutrient-enrichment 

18 affects terrestrial, freshwater and estuarine ecosystems. Nitrogen deposition is often the main 

19 source of anthropogenic nitrogen in terrestrial and freshwater ecosystems. In contrast, nitrogen 

20 deposition often contributes to nitrogen-enrichment effects in estuaries, but does not drive the 

21 effects. Both oxides of nitrogen and reduced forms of nitrogen, e.g. NHx, contribute to nitrogen 

22 deposition. For the most part, nitrogen effects on ecosystems do not depend on whether the 

23 nitrogen is in oxidized or reduced form. Thus, this summary focuses on the effects of nitrogen 

24 deposition in total. We address the issue of incorporating the relative contributions of oxidized 

25 and reduced nitrogen into the standards in Chapters 5, 6, and 8. 

26 2.2.1 What is the nature of terrestrial and freshwater ecosystem responses to 

27 reactive nitrogen and/ sulfur deposition? 

28 The ISA found that deposition of nitrogen, including NOx and NHx leads to the nitrogen 

29 enrichment of ecosystems (EPA 2008). In the process of nitrogen enrichment, geochemical 
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1 components of terrestrial and freshwater aquatic ecosystems are altered in a way that leads to 

2 effects on biological organisms. 

3 The evidence is sufficient to infer a causal relationship between N deposition and the 

4 alteration of biogeochemical cycling in terrestrial ecosystems (ISA 4.3.1.1 and 3.3.2.1). This is 

5 supported by numerous observational, deposition gradient and field addition experiments. 

Stoddard (1994) identified the leaching of NO3

-

 in soil drainage waters and the export of NO3

- 6 in 

7 steam water as two of the primary indictors of N enrichment. Several N-addition studies indicate 

that NO3

- 8 leaching is induced by chronic additional of N (Edwards et al., 2002b; Kahl et al., 

9 1999; Peterjohn et al., 1996; Norton et al., 1999). Aber et al. (2003) found that surface water 

NO3

- 10 concentrations exceeded 1 μeq/L in watersheds receiving about 9 to 13 kg N/ha/yr of 

11 atmospheric N deposition. N deposition disrupts the nutrient balance of ecosystems with 

12 numerous biogeochemical effects. The chemical indicators that are typically measured include 

13 NO3− leaching, C:N ratio, N mineralization, nitrification, denitrification, foliar N concentration, 

and soil water NO3 − and NH4

+ 14 concentrations. Note that N saturation (N leaching from 

15 ecosystems) does not need to occur to cause effects. Substantial leaching of NO3− from forest 

16 soils to stream water can acidify downstream waters, leading to effects described in the previous 

17 section on aquatic acidification. Due to the complexity of interactions between the N and C 

18 cycling, the effects of N on C budgets (quantified input and output of C to the ecosystem) are 

19 variable. Regional trends in net ecosystem productivity (NEP) of forests (not managed for 

20 silviculture) have been estimated through models based on gradient studies and meta-analysis. 

21 Atmospheric N deposition has been shown to cause increased litter accumulation and carbon 

22 storage in above-ground woody biomass. In the West, this has lead to increased susceptibility to 

23 more severe fires. Less is known regarding the effects of N deposition on C budgets of non24 forest ecosystems. 

25 The evidence is sufficient to infer a causal relationship between N deposition on the 

26 alteration of species richness, species composition and biodiversity in terrestrial ecosystems (ISA 

27 4.3.1.2). The most sensitive terrestrial taxa are lichens. Empirical evidence indicates that lichens 

28 in the U.S. are affected by deposition levels as low as 3 kg N/ha/yr. Alpine ecosystems are also 

29 sensitive to N deposition, changes in an individual species (Carex rupestris) were estimated to 

30 occur at deposition levels near 4 kg /ha/yr and modeling indicates that deposition levels near 10 

31 kg N/ha/yr alter plant community assemblages. In several grassland ecosystems, reduced species 
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1 diversity and an increase in non-native, invasive species are associated with N deposition (Clark 

2 and Tillman, 2008; Schwinning et al., 2005). 

3 In freshwater ecosystems, the evidence is sufficient to infer a causal relationship between 

4 N deposition and the alteration of biogeochemical cycling in freshwater aquatic ecosystems (ISA 

5 3.3.2.3). N deposition is the main source of N enrichment to headwater streams, lower order 

6 streams and high elevation lakes. The most common chemical indicators that were studied 

7 included NO3− and dissolved inorganic nitrogen (DIN) concentration in surface waters as well as 

8 Chl a:total P ratio. Elevated surface water NO3− concentrations occur in both the eastern and 

9 western U.S. Bergstrom and Jansson (2006) report a significant correlation between N deposition 

10 and lake biogeochemistry by identifying a correlation between wet deposition and [DIN] and Chl 

11 a: Total P. Recent evidence provides examples of lakes and streams that are limited by N and 

12 show signs of eutrophication in response to N addition. 

13 The evidence is sufficient to infer a causal relationship between N deposition and the 

14 alteration of species richness, species composition and biodiversity in freshwater aquatic 

15 ecosystems (ISA 3.3.5.3). Increased N deposition can cause a shift in community composition 

16 and reduce algal biodiversity, especially in sensitive oligotrophic lakes. 

17 2.2.2 What types of ecosystems are sensitive to such effects? How are these 

18 responses affected by atmospheric, ecological, and landscape factors 

19 The numerous ecosystem types that occur across the U.S. have a broad range of 

20 sensitivity to N deposition. Organisms in their natural environment are commonly adapted to a 

21 specific regime of nutrient availability. Change in the availability of one important nutrient, such 

22 as N, may result in imbalance in ecological stoichiometry, with effects on ecosystem processes, 

23 structure and function (Sterner and Elser, 2002). In general, N deposition to terrestrial 

24 ecosystems causes accelerated growth rates in some species, which may lead to altered 

25 competitive interactions among species and nutrient imbalances, ultimately affecting 

26 biodiversity. The onset of these effects occurs with N deposition levels as low as 3 kg N/ha/yr in 

27 sensitive terrestrial ecosystems. In aquatic ecosystems, N that is both leached from the soil and 

28 directly deposited can pollute surface water. This causes alteration of the diatom community at 

29 levels as low as 1.5 kg N/ha/yr in sensitive freshwater ecosystems. 
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1 The degree of ecosystem effects lies at the intersection of N loading and N-sensitivity. N2 sensitivity is predominately driven by the degree to which growth is limited by nitrogen 

3 availability. Grasslands in the western United States are typically N-limited ecosystems 

4 dominated by a diverse mix of perennial forbs and grass species (Clark and Tilman, 2008; 

5 Suding et al., 2005). A meta-analysis by Lebauer and Treseder (2008) indicated that N 

6 fertilization increased aboveground growth in all non-forest ecosystems except for deserts. In 

7 other words, almost all terrestrial ecosystems are N-limited and will be altered by the addition of 

8 anthropogenic nitrogen. Likewise, a freshwater lake or stream must be N-limited to be sensitive 

9 to N-mediated eutrophication. There are many examples of fresh waters that are N-limited or N 

10 and P co-limited (ISA 3.3.3.2). In a meta-analysis that included 653 datasets, Elser et al. (2007) 

11 found that N-limitation occurred as frequently as P-limitation in freshwater ecosystems. 

12 Additional factors that govern the sensitivity of ecosystems to nutrient enrichment from N 

13 deposition include rates and form of N deposition, elevation, climate, species composition, 

14 length of growing season, and soil N retention capacity. (ISA 4.3). Less is known about the 

15 extent and distribution of the terrestrial ecosystems in the U.S. that are most sensitive to the 

16 effects of nutrient enrichment from atmospheric N deposition compared to acidification. 

17 2.2.3 What is the magnitude of ecosystem responses to nitrogen deposition? 

18 2.2.3.1 Terrestrial 

19 Little is known about the full extent and distribution of the terrestrial ecosystems in the 

20 U.S. that are most sensitive to impacts caused by nutrient enrichment from atmospheric N 

21 deposition. As previously stated, most terrestrial ecosystems are N-limited, therefore they are 

22 sensitive to perturbation caused by N additions (LeBauer and Treseder, 2008). Effects are most 

23 likely to occur where areas of relatively high atmospheric N deposition intersect with N-limited 

24 plant communities. The alpine ecosystems of the Colorado Front Range, chaparral watersheds of 

25 the Sierra Nevada, lichen and vascular plant communities in the San Bernardino Mountains and 

26 the Pacific Northwest, and the southern California coastal sage scrub (CSS) community are 

27 among the most sensitive terrestrial ecosystems. There is growing evidence that existing 

28 grassland ecosystems in the western United States are being altered by elevated levels of N 

29 inputs, including inputs from atmospheric deposition (Clark and Tilman, 2008; Suding et al., 

30 2005). 
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1 In the eastern U.S., the degree of N saturation of the terrestrial ecosystem is often 

2 assessed in terms of the degree of NO3− leaching from watershed soils into ground water or 

3 surface water. Stoddard (1994) estimated the number of surface waters at different stages of 

4 saturation across several regions in the eastern U.S. Of the 85 northeastern watersheds examined 

5 60% were in Stage 1 or Stage 2 of N saturation on a scale of 0 (background or pretreatment) to 3 

6 (visible decline). Of the northeastern sites for which adequate data were available for assessment, 

7 those in Stage 1 or 2 were most prevalent in the Adirondack and Catskill Mountains. Effects on 

8 individual plant species have not been well studied in the U.S. More is known about the 

9 sensitivity of particular plant communities. Based largely on results obtained in more extensive 

10 studies conducted in Europe, it is expected that the more sensitive terrestrial ecosystems include 

11 hardwood forests, alpine meadows, arid and semi-arid lands, and grassland ecosystems (ISA 

12 3.8.2). 

13 The REA used published research results (REA 5.3.1 and ISA Table 4.4) to identify 

14 meaningful ecological benchmarks associated with different levels of atmospheric nitrogen 

15 deposition. These are given by figure 2-8. The sensitive areas and ecological indicators identified 

16 by the ISA were analyzed further in the REA to create a national map that illustrates effects 

17 observed from ambient and experimental atmospheric nitrogen deposition loads in relation to 

18 CMAQ 2002 modeling results and NADP monitoring data. This map, reproduced in Figure 2-9, 

19 depicts the sites where empirical effects of terrestrial nutrient enrichment have been observed 

20 and site proximity to elevated atmospheric N deposition. 

21 
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March 2010 Draft – Do Not Quote or Cite 42

1 

2 Figure 2-8. Benchmarks of atmospheric nitrogen deposition for several 

3 ecosystem indicators with the inclusion of the diatom changes in the Rocky 

4 Mountain lakes (REA 5.3.1.2) 
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1. Nitrogen enrichment or eutrophication of lakes (Loch Vale, CO: 0.5 to1.5 kg/ha/yr; Niwot Ridge, CO: 4.71 

kg/ha/yr) 

2. Alpine lakes increase shift in diatom species (Rocky Mountains, CO: 2 kg/ha/yr) 

3. Alpine meadows’ elevated NO3

-

 levels in runoff (Colorado Front Range: 20, 40, 60 kg/ha/yr) 

4. Alpine meadows’ shift toward hairgrass (Niwot Ridge, CO: 25 kg/ha/yr) 

5. Nitrogen enrichment or nitrogen saturation (e.g., soil and foliar nitrogen concentration) (eastern slope of Rocky 

Mountains: 1.2, 3.6 kg/ha/yr; Fraser Forest, CO: 3.2 to 5.5 kg/ha/yr) 

6. Increased nitrogen mineralization rates and nitrification (Loch Vale, CO (spruce): 1.7 kg/ha/yr) 

7. Alpine tundra with increased plant foliage and decreased species richness (Niwot Ridge, CO: 50 kg/ha/yr) 

8. Nitrogen saturation, high NO3

-

 in streamwater, soil, leaves; high nitric oxide (NO) emissions (Los Angeles, CA, air 

basin: saturation at 24 to 25 kg/ha/yr (dry) and at 0.8 to 45 kg/ha/yr (wet); northeastern U.S.: 3.3 to 12.7 kg/ha/yr) 

9. Nitrogen saturation, high NO3

-

 in streamwater (San Bernardino Mountains, CA (coniferous): 2.9 and 18.8 kg/ha/yr) 

10. NO3

-

 leaching (New England; Adirondack lakes: 8 to10 kg/ha/yr) 

11. Nitrogen saturation, high dissolved inorganic nitrogen (San Bernardino Mountains, San Gabriel Mountains, CA, 

chaparral, hardwood, coniferous): 11 to 40 kg/ha/yr) 

12. Increased tree mortality and beetle activity (San Bernardino Mountains, CA (Ponderosa): 8 and 82 kg/ha/yr) 

13. Enhanced growth of black cherry and yellow poplar; possible decline in red maple vigor; increased foliar nitrogen 

(Fernow Forest, WV: 35.5 kg/ha/yr) 

14. Impacts on lichen communities (California MCF: 3.1 kg/ha/yr; Columbia R. Gorge, OR/WA: 11/5 to 25.4) 

15. Evidence that threatened and endangered species impacted San Francisco Bay, CA (checkerspot butterfly and 

serpentinitic grass invasion: 10 to15 kg/ha/yr; Jasper Ridge, CA: 70 kg/ha/yr) 

16. Decreased diversity of mycorrhizal communities (Southern California: ~10 kg/ha/yr) 

17. Decreased abundance of CSS (Southern California: 3.3 kg/ha/yr) 

18. Loss of grasslands (Cedar Creek, MN: 5.3 [1.3 to 9.8] kg/ha/yr) 

19. Decrease in abundance of desert creosote bush, increase in nonnative grasses (Mojave Desert and Chihuahuan 

Desert, CA: 1.7 kg/ha/yr and up) 

20. Decrease in pitcher plant population growth rate (Hawley Bog, MA and Molly Bog, VA: 10 to14 kg/ha/yr) 

1 Figure 2-9 (from REA figure 5.3-9). Observed effects from ambient and 

2 experimental atmospheric nitrogen deposition loads in relation to using CMAQ 

3 2002 modeling results and NADP monitoring data. Citations for effect results are 

4 from the ISA, Table 4.4 (U.S. EPA, 2008). 
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1 Based on information in the ISA and initial analysis in the REA, further case study 

2 analyses on terrestrial nutrient enrichment of ecosystems were developed for the CCS 

3 community and Mixed Conifer Forest (MCF) (EPA 2009). Geographic information systems 

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

5 benchmarks associated with CSS and mycorrhizal communities, as well as MCF’s nutrient6 sensitive acidophyte lichen communities, fine-root biomass in Ponderosa pine, and leached 

7 nitrate in receiving waters. 

8 The ecological benchmarks that were identified for the CSS and the MCF are included in 

9 the suite of benchmarks identified in the ISA (ISA 3.3). There are sufficient data to confidently 

10 relate the ecological effect to a loading of atmospheric nitrogen. For the CSS community, the 

11 following ecological benchmarks were identified: 

12  3.3 kg N/ha/yr – the amount of nitrogen uptake by a vigorous stand of CSS; above this 

13 level, nitrogen may no longer be limiting 

14  10 kg N/ha/yr – mycorrhizal community changes 

15 For the MCF community, the following ecological benchmarks were identified: 

16  3.1 kg N/ha/yr – shift from sensitive to tolerant lichen species 

17  5.2 kg N/ha/yr – dominance of the tolerant lichen species 

18  10.2 kg N/ha/yr – loss of sensitive lichen species 

19  17 kg N/ha/yr – leaching of nitrate into streams. 

20 These benchmarks, ranging from 3.1 to 17 kg N/ha/yr, were compared to 2002 

21 CMAQ/NADP data to discern any associations between atmospheric deposition and changing 

22 communities. Evidence supports the finding that nitrogen alters CSS and MCF. Key findings 

23 include the following: 2002 CMAQ/NADP nitrogen deposition data show that the 3.3 kg N/ha/yr 

24 benchmark has been exceeded in more than 93% of CSS areas (654,048 ha). These deposition 

25 levels are a driving force in the degradation of CSS communities. Although CSS decline has 

26 been observed in the absence of fire, the contributions of deposition and fire to the CSS decline 

27 require further research. CSS is fragmented into many small parcels, and the 2002 

28 CMAQ/NADP 12-km grid data are not fine enough to fully validate the relationship between 

29 CSS distribution, nitrogen deposition, and fire. 2002 CMAQ/NADP nitrogen deposition data 

30 exceeds the 3.1 kg N/ha/yr benchmark in more than 38% (1,099,133 ha) of MCF areas, and 
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1 nitrate leaching has been observed in surface waters. Ozone effects confound nitrogen effects on 

2 MCF acidophyte lichen, and the interrelationship between fire and nitrogen cycling requires 

3 additional research. 

4 2.2.3.2 Freshwater 

5 The magnitude of ecosystem response may be thought of on two time scales, current 

6 conditions and how ecosystems have been altered since the onset of anthropogenic N deposition. 

7 As noted previously, Elser et al. (2008) found that N-limitation occurs as frequently as P8 limitation in freshwater ecosystems (ISA 3.3.3.2). Recently, a comprehensive study of available 

9 data from the northern hemisphere surveys of lakes along gradients of N deposition show 

10 increased inorganic N concentration and productivity to be correlated with atmospheric N 

11 deposition (Bergström and Jansson 2006). The results are unequivocal evidence of N limitation 

12 in lakes with low ambient inputs of N, and increased N concentrations in lakes receiving N 

13 solely from atmospheric N deposition (Bergström and Jansson, 2006). These authors suggested 

14 that most lakes in the northern hemisphere may have originally been N-limited, and that 

15 atmospheric N deposition has changed the balance of N and P in lakes. 

16 Available data suggest that the increases in total N deposition do not have to be large to 

17 elicit an ecological effect. For example, a hindcasting exercise determined that the change in 

18 Rocky Mountain National Park lake algae that occurred between 1850 and 1964 was associated 

19 with an increase in wet N deposition that was only about 1.5 kg N/ha (Baron, 2006). Similar 

20 changes inferred from lake sediment cores of the Beartooth Mountains of Wyoming also 

21 occurred at about 1.5 kg N/ha deposition (Saros et al., 2003). Pre-industrial inorganic N 

22 deposition is estimated to have been only 0.1 to 0.7 kg N/ha based on measurements from remote 

23 parts of the world (Galloway et al., 1995; Holland et al., 1999). In the western U.S., pre24 industrial, or background, inorganic N deposition was estimated by (Holland et al., 1999) to 

25 range from 0.4 to 0.7 kg/ha/yr. 

26 Eutrophication effects from N deposition are most likely to be manifested in undisturbed, 

27 low nutrient surface waters such as those found in the higher elevation areas of the western U.S. 

28 The most severe eutrophication from N deposition effects is expected downwind of major urban 

29 and agricultural centers. High concentrations of lake or streamwater NO3−, indicative of 

30 ecosystem saturation, have been found at a variety of locations throughout the U.S., including the 
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1 San Bernardino and San Gabriel Mountains within the Los Angeles Air Basin (Fenn et al., 1996), 

2 the Front Range of Colorado (Baron et al., 1994; Williams et al., 1996), the Allegheny mountains 

3 of West Virginia (Gilliam et al., 1996), the Catskill Mountains of New York (Murdoch and 

4 Stoddard, 1992; Stoddard, 1994), the Adirondack Mountains of New York (Wigington et al., 

5 1996), and the Great Smoky Mountains in Tennessee (Cook et al., 1994) (ISA 3.3.8). 

6 2.2.3.3 Nitrogen Enrichment: Evidence of Effects on Estuaries 

7 In contrast to terrestrial and freshwater systems, atmospheric N load to estuaries 

8 contributes to the total load but does not necessarily drive the effects. In estuaries, N-loading 

9 from multiple anthropogenic and non-anthropogenic pathways leads to water quality 

10 deterioration, resulting in numerous effects including hypoxic zones, species mortality, changes 

11 in community composition and harmful algal blooms that are indicative of eutrophication. The 

12 following summary is a concise overview of the known or anticipated effects of nitrogen 

13 enrichment on estuaries within the United States. 

14 2.2.3.3.1 What is the nature of estuary responses to reactive nitrogen andsulfur 

15 deposition? 

16 In the ISA, the evidence is sufficient to infer a causal relationship between Nr deposition 

17 and the biogeochemical cycling of N and C in estuaries (ISA 4.3.4.1 and 3.3.2.3). In general, 

18 estuaries tend to be nitrogen-limited, and many currently receive high levels of nitrogen input 

19 from human activities (REA 5.1.1). It is unknown if atmospheric deposition alone is sufficient to 

20 cause eutrophication, however, the contribution of atmospheric nitrogen deposition to total 

21 nitrogen load is calculated for some estuaries and can be >40% (REA 5.1.1). 

22 The evidence is sufficient to infer a causal relationship between N deposition and the 

23 alteration of species richness, species composition and biodiversity in estuarine ecosystems (ISA 

24 4.3.4.2 and 3.3.5.4). Atmospheric and non-atmospheric sources of N contribute to increased 

25 phytoplankton and algal productivity, leading to eutrophication. Shifts in community 

26 composition, reduced hypolimnetic DO, reduced biodiversity, and mortality of submerged 

27 aquatic vegetation are associated with increased N deposition in estuarine systems. 
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1 2.2.3.3.2 What types of ecosystems are sensitive to such effects? How are these 

2 responses affected by atmospheric, ecological, and landscape factors? 

3 Because the productivity of estuarine and near shore marine ecosystems is generally 

4 limited by the availability of N, they are susceptible to the eutrophication effect of N deposition 

5 (ISA 4.3.4.1). A recent national assessment of eutrophic conditions in estuaries found the most 

6 eutrophic estuaries were generally those that had large watershed-to-estuarine surface area, high 

7 human population density, high rainfall and runoff, low dilution, and low flushing rates (Bricker 

8 et al., 2007). In the REA, the National Oceanic and Atmospheric Administration’s (NOAA) 

9 National Estuarine Eutrophication Assessment (NEEA) assessment tool, Assessment of 

10 Estuarine Tropic Status (ASSETS) categorical Eutrophication Index (EI) (Bricker et al., 2007) 

11 was used to evaluate eutrophication due to atmospheric loading of nitrogen. ASSETS EI is an 

12 estimation of the likelihood that an estuary is experiencing eutrophication or will experience 

13 eutrophication based on five ecological indicators: chlorophyll a, macroalgae, dissolved oxygen, 

14 nuisance/toxic algal blooms and submerged aquatic vegetation (SAV) (Bricker et al., 2007). 

15 In the REA, two regions were selected for case study analysis using ASSETS EI, the 

16 Chesapeake Bay and Pamlico Sound. Both regions received an ASSETS EI rating of Bad

17 indicating that the estuary had moderate to high pressure due to overall human influence and a 

18 moderate high to high eutrophic condition (REA 5.2.4.1 and 5.2.4.2). These results were then 

19 considered with SPAtially Referenced Regression (SPARROW) modeling to develop a response 

20 curve to examine the role of atmospheric nitrogen deposition in achieving desired reduction load. 

21 To change the Neuse River Estuary’s EI score from Bad to Poor not only must 100% of the total 

22 atmospheric nitrogen deposition be eliminated, but considerably more nitrogen from other 

23 sources as well must be reduced (REA section 5.2.7.2). In the Potomac River estuary, a 78% 

24 reduction of total nitrogen could move the EI score from Bad to Poor (REA 5.2.7.1). The results 

25 of this analysis indicated reductions in atmospheric deposition alone could not solve coastal 

26 eutrophication problems due to multiple non-atmospheric nitrogen inputs (REA 7.3.3). However, 

27 by reducing atmospheric contributions, it may help avoid the need for more costly controls on 

28 nitrogen from other sources. 

29 In general, estuaries tend to be N-limited (Elser et al., 2008), and many currently receive 

30 high levels of N input from human activities to cause eutrophication (Howarth et al., 1996; 

31 Vitousek and Howarth, 1991). Atmospheric N loads to estuaries in the U.S. are estimated to 
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1 range from 2-8% for Guadalupe Bay, TX on the lowest end to as high as 72% for St Catherines2 Sapelo estuary, GA (Castro et al., 2003). The Chesapeake Bay is an example of a large, well3 studied and severely eutrophic estuary that is calculated to receive as much as 30% of its total N 

4 load from the atmosphere. 

5 2.2.3.3.3 What is the magnitude of ecosystem responses to eutrophication? 

6 There is a scientific consensus that nitrogen-driven eutrophication in shallow estuaries 

7 has increased over the past several decades and that the environmental degradation of coastal 

8 ecosystems due to nitrogen, phosphorus, and other inputs is now a widespread occurrence (Paerl 

9 et al., 2001). For example, the frequency of phytoplankton blooms and the extent and severity of 

10 hypoxia have increased in the Chesapeake Bay (Officer et al., 1984) and Pamlico estuaries in 

11 North Carolina (Paerl et al., 1998) and along the continental shelf adjacent to the Mississippi and 

12 Atchafalaya rivers’ discharges to the Gulf of Mexico (Eadie et al., 1994). 

13 A recent national assessment of eutrophic conditions in estuaries found that 65% of the 

14 assessed systems had moderate to high overall eutrophic conditions and generally received the 

15 greatest N loads from all sources, including atmospheric and land-based sources (Bricker et al., 

16 2007). Most eutrophic estuaries occurred in the mid-Atlantic region and the estuaries with the 

17 lowest degree of eutrophication were in the North Atlantic (Bricker et al., 2007). Other regions 

18 had mixtures of low, moderate, and high degree of eutrophication (ISA 4.3.4.3). 

19 The mid-Atlantic region is the most heavily impacted area in terms of moderate or high 

20 loss of submerged aquatic vegetation due to eutrophication (ISA 4.3.4.2). Submerged aquatic 

21 vegetation is important to the quality of estuarine ecosystem habitats because it provides habitat 

22 for a variety of aquatic organisms, absorbs excess nutrients, and traps sediments (ISA 4.3.4.2). It 

23 is partly because many estuaries and near-coastal marine waters are degraded by nutrient 

24 enrichment that they are highly sensitive to potential negative impacts from nitrogen addition 

25 from atmospheric deposition. 

26 2.2.4 What are the key uncertainties associated with nutrient enrichment? 

27 There are different levels of uncertainty associated with relationships between deposition, 

28 ecological effects and ecological indicators. The criteria used in the REA to evaluate the degree 

29 of confidence in the data, modeling and ecological effect function are detailed in Chapter 7 of the 

30 REA and summarized in section 2.1.4 of this chapter (REA 7.0). 
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1 Aquatic 

2 The approach for assessing atmospheric contributions to total nitrogen loading in the 

3 REA, was to consider the main-stem river to an estuary (including the estuary) rather than an 

4 entire estuary system or bay. The biological indicators used in the NOAA ASSETS EI required 

5 the evaluation of many national databases including the USGS NAWQA files, EPA’s STORET 

6 database, NOAA’s Estuarine Drainage Areas data, and EPA’s water quality standards nutrient 

7 criteria for rivers and lakes (REA Appendix 6, Table 1.2.-1). Both the SPARROW modeling for 

8 nitrogen loads and assessment of estuary conditions under NOAA ASSETS EI, have been 

9 applied on a national scale. The REA concludes that the available data are medium quality with 

10 intermediate confidence about the use of these data and their values for extrapolating to a larger 

11 regional area (REA 7.3.1). Intermediate confidence is associated with the modeling approach 

12 using ASSETS EI and SPARROW. The REA states there is low confidence with the ecological 

13 effect function due to the results of the analysis which indicated that reductions in atmospheric 

14 deposition alone could not solve coastal eutrophication problems due to multiple non15 atmospheric nitrogen inputs (REA 7.3.3). 

16 Terrestrial 

17 Ecological thresholds are identified for CSS and MCF and these data are considered to be 

18 of high quality, however, the ability to extrapolate these data to larger regional areas is limited 

19 (REA 7.4.1). No quantitative modeling was conducted or ecological effect function developed 

20 for terrestrial nutrient enrichment reflecting the uncertainties associated with these depositional 

21 effects. 

22 2.3 WHAT ECOLOGICAL EFFECTS ARE ASSOCIATED WITH GAS23 PHASE NOX AND SOX? 

24 Acidifying deposition and nitrogen enrichment are the main focus of this policy 

25 assessment; however, there are other known ecological effects are attributed to gas-phase NOx

26 and SOx. Acute and chronic exposures to gaseous pollutants such as sulfur dioxide (SO2), 

27 nitrogen dioxide (NO2), nitric oxide (NO), nitric acid (HNO3) and peroxyacetyl nitrite (PAN) are 

28 associated with negative impacts to vegetation. The current secondary NAAQS were set to 

29 protect against direct damage to vegetation by exposure to gas-phase NOx or SOx, such as foliar 
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1 injury, decreased photosynthesis, and decreased growth. The following summary is a concise 

2 overview of the known or anticipated effects to vegetation caused by gas phase N and S. 

3 2.3.1 What is the nature of ecosystem responses to gas-phase nitrogen and sulfur? 

4 The 2008 ISA found that gas phase N and S are associated with direct phytotoxic effects 

5 (ISA 4.4). The evidence is sufficient to infer a causal relationship between exposure to SO2 and 

6 injury to vegetation (ISA 4.4.1 and 3.4.2.1). Acute foliar injury to vegetation from SO2 may 

7 occur at levels above the current secondary standard (3-h average of 0.50 ppm). Effects on 

8 growth, reduced photosynthesis and decreased yield of vegetation are also associated with 

9 increased SO2 exposure concentration and time of exposure.

10 The evidence is sufficient to infer a causal relationship between exposure to NO, NO2

11 and PAN and injury to vegetation (ISA 4.4.2 and 3.4.2.2). In sufficient concentrations, NO, NO2

12 and PAN can decrease photosynthesis and induce visible foliar injury to plants. Evidence is also 

13 sufficient to infer a causal relationship between exposure to HNO3 and changes to vegetation

14 (ISA 4.4.3 and 3.4.2.3). Phytotoxic effects of this pollutant include damage to the leaf cuticle in 

15 vascular plants and disappearance of some sensitive lichen species. 

16 2.3.2 What types of ecosystems are sensitive to such effects? How are these 

17 responses affected by atmospheric, ecological, and landscape factors? 

18 Vegetation in ecosystems near sources of gaseous NOx and SOx or where ambient 

19 concentrations of SO2, NO, NO2, PAN and HNO3 are higher are more likely to be impacted by 

20 these pollutants. Uptake of these pollutants in a plant canopy is a complex process involving 

21 adsorption to surfaces (leaves, stems and soil) and absorption into leaves (ISA 3.4.2). The 

22 functional relationship between ambient concentrations of gas phase NOx and SOx and specific 

23 plant response are impacted by internal factors such as rate of stomatal conductance and plant 

24 detoxtification mechanisms, and external factors including plant water status, light, temperature, 

25 humidity, and pollutant exposure regime (ISA 3.4.2). 

26 Entry of gases into a leaf is dependent upon physical and chemical processes of gas phase 

27 as well as to stomatal aperature. The aperature of the stomata is controlled largely by the 

28 prevailing environmental conditions, such as humidity, temperature, and light intensity. When 

29 the stomata are closed, resistance to gas uptake is high and the plant has a very low degree of 

30 susceptibility to injury. Mosses and lichens do not have a protective cuticle barrier to gaseous 
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1 pollutants or stomata and are generally more sensitive to gaseous sulfur and nitrogen than 

2 vascular plants (ISA 3.4.2). 

3 The appearance of foliar injury can vary significantly across species and growth 

4 conditions affecting stomatal conductance in vascular plants (REA 6.4.1). For example, damage 

5 to lichens from SO2 exposure includes reduced photosynthesis and respiration, damage to the 

algal component of the lichen, leakage of electrolytes, inhibition of nitrogen fixation, reduced K+ 6 

7 absorption, and structural changes (Belnap et al., 1993; Farmer et al., 1992, Hutchinson et al., 

8 1996). 

9 2.3.3 What is the magnitude of ecosystem responses to gas phase effects of NOx

10 and SOx? 

11 The phytotoxic effects of gas phase NOx and SOx are dependent on the exposure 

12 concentration and duration and species sensitivity to these pollutants. Effects to vegetation 

13 associated with NOx and SOx, are therefore, variable across the U.S. and tend to be higher near 

14 sources of photochemical smog. For example, SO2 is considered to be the primary factor 

15 contributing to the death of lichens in many urban and industrial areas, with fruticose lichens 

16 being more susceptible to SO2 than many foliose and crustose species (Hutchinson et al., 1996). 

17 The ISA states there is very limited new research on phytotoxic effects of NO, NO2, PAN 

18 and HNO3 at concentrations currently observed in the United States with the exception of some 

19 lichen species (ISA 4.4). Past and current HNO3 concentrations may be contributing to the 

20 decline in lichen species in the Los Angeles basin (Boonpragob and Nash 1991; Nash and Sigal, 

21 1999; Riddell et al., 2008). PAN is a very small component of nitrogen deposition in most areas 

22 of the United States (REA 6.4.2). Current deposition of HNO3 is contributing to N saturation of 

23 some ecosystems close to sources of photochemical smog (Fenn et al., 1998) such as the MCF’s 

24 of the Los Angeles basin mountain (Bytnerowicz et al., 1999). 

25 2.4 SUMMARY 

26 In summary, NOx and SOx in the atmosphere contribute to effects on individual species 

27 and ecosystems through direct contact with vegetation, and more significantly through deposition 

28 to sensitive ecosystems. The ISA concludes that the evidence is sufficient to conclude causal 

29 relationships between acidifying deposition of N and S and effects on freshwater aquatic 

30 ecosystems and terrestrial ecosystems, and between nitrogen nutrient enrichment and effects on 
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1 sensitive terrestrial and freshwater aquatic ecosystems. The ISA also concludes that a causal 

2 relationship is supported between nitrogen nutrient enrichment and effects on estuarine 

3 ecosystems; however, the contribution of atmospheric oxidized nitrogen relative to reduced 

4 nitrogen and non-atmospheric nitrogen is more difficult to determine. 

5 The REA provides additional support that under recent conditions, deposition levels have 

6 exceeded benchmarks for ecological indicators of acidification and nutrient enrichment that 

7 indicate that effects are likely to be occurring in significant numbers of lakes and streams within 

8 sensitive ecosystems. 

9 2.5 REFERENCES 

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12 Bioscience, 53, 375-389. 

13 Bailey SW; Horsley SB; Long RP; Hallett RA. (2004). Influence of edaphic factors on sugar 

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15 Baker JP; Bernard DP; Christensen SW; Sale MJ. (1990). Biological effects of changes in 

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1 Belnap J; Sigal L; Moir W; Eversman S. (1993). Identification of sensitive species, in lichens as 

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8 Blake L; Goulding KWT; Mott CJB; Johnston AE. (1999). Changes in soil chemistry 

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11 Boonpragob K; Nash THI. (1991). Physiological responses of the lichen Ramalina menziesii 

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28 chemistry, and models of susceptibility to acidification. (Project Completion Report to 
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1 the National Park Service). Charlottesville, VA, Department of Environmental Sciences; 

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3 Bulger AJ; Cosby BJ; Webb JR. (2000). Current, reconstructed past, and projected future status 

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5 1523. 

6 Bytnerowicz A; Padgett P; Percy K; Krywult M; Riechers G; Hom J. (1999). Direct effects of 

7 nitric acid on forest vegetation. In: Miller PR, McBride J (Eds.), Oxidant air pollution 

8 impacts in the Montane forests of Southern California: The San Bernardino Mountains 

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10 Castro MS; Driscoll CT; Jordan TE; Reay WG; Boynton WR. (2003). Sources of nitrogen to 

11 estuaries in the United States. Estuaries, 26, 803-814. 

12 Clark CM; Tilman D. (2008). Loss of plant species after chronic low-level nitrogen deposition to 

13 prairie grasslands. Nature, 451, 712-715. 

14 Cook RB; Elwood JW; Turner RR; Bogle MA; Mulholland PJ; Palumbo AV. (1994). Acid-base 

15 chemistry of high-elevation streams in the Great Smoky Mountains. Water Air Soil 

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20 nutrition and forest health. Bioscience, 49, 789-800. 

21 Dennis TD; MacAvoy SE; Steg MB; Bulger AJ. (1995). The association of water chemistry 

22 variables and fish condition in streams of Shenandoah National Park (USA). Water Air 

23 Soil Pollut, 85, 365-370. 

24 Dennis TE; Bulger AJ. (1999). The susceptibility of blacknose dace (Rhinichthys atratulus).to 

25 acidification in Shenandoah National Park. In: Bulger AJ; Cosby BJ; Dolloff CA; 

26 Eshleman KN; Galloway JN; Webb. JR (Eds.), Shenandoah National Park: Fish in 
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1 sensitive habitats. Project final report, Volume IV: Stream bioassays, aluminum toxicity, 

2 species richness and stream chemistry, and models of susceptibility to acidification. 

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11 Estuaries, 17, 754-765. 

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13 10 years of induced whole-watershed acidification. Water Air Soil Pollut, 140, 99-118. 

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22 Bernardino Mountains in southern California. For Ecol Manage, 82, 211-230. 

23 Ford J; Stoddard JL; Powers CF. (1993). Perspectives in environmental monitoring: an 

24 introduction to the U.S. EPA long-term monitonring (LTM) project. Water Air Soil 

25 Pollut, 67, 247-255. 
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1 Galloway JN; Schlesinger WH; Levy H; Michaels AF; Schnoor JL. (1995). Nitrogen-fixation: 

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5 inputs at Fernow Experimental Forest, West Virginia. Can J For Res, 26, 196-205. 

6 Haines TA; Baker JP. (1986). Evidence of fish population responses to acidification in the 

7 eastern United States. Water Air Soil Pollut, 31, 605-629. 

8 Holland EA; Dentener FJ; Braswell BH; Sulzman JM. (1999). Contemporary and pre-industrial 

9 global reactive nitrogen budgets. Biogeochemistry, 46, 7-43. 

10 Horsley SB; Long RP; Bailey SW; Hallett RA; Hall TJ. (2000). Factors associated with the 

11 decline disease of sugar maple on the Allegheny Plateau. Can J For Res, 30, 1365-1378. 

12 Howarth RW; Billen G; Swaney D; Townsend A; Jaworski N; Lajtha K; Downing JA; Elmgren 

13 R; Caraco N; Jordan T; Berendse F; Freney J; Kudeyarov V; Murdoch PS; Zhao-Liang Z. 

14 (1996). Regional nitrogen budgets and riverine N & P fluxes for the drainages to the 

15 North Atlantic Ocean: natural and human influences. Biogeochemistry, 35, 75-139. 

16 Hutchinson J; Maynard D; Geiser L. (1996). Air quality and lichens - a literature review 

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18 Management Program; U.S. Forest Service; U.S. Department of Agriculture (USDA). 

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20 budgets in the experimental and reference watersheds, Bear Brook Watershed in Maine 

21 (BBWM). Environ Monit Assess, 55, 113-131 

22 Keller W; Gunn JM. (1995). Lake water quality improvements and recovering aquatic 

23 communities. In: Gunn JM (Ed.), Restoration and recovery of an industrial region: 

24 progress in restoring the smelter-damaged landscape near Sudbury, Canada (pp. 67-80). 

25 New York, NY: Springer-Verlag. 
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1 Kochy M; Wilson SD. (2001). Nitrogen deposition and forest expansion in the northern Great 

2 Plains. J Ecol, 89, 807-817. 

3 Kretser W; Gallagher J; Nicolette J. (1989). Adirondack Lakes Study, 1984-1987: An Evaluation 

4 of Fish Communities and Water Chemistry: Ray Brook, NY; prepared for: Adirondack 

5 Lakes Survey (ALS) Corporation. 

6 LeBauer DS; Treseder KK. (2008). Nitrogen limitation of net primary productivity in terrestrial 

7 ecosystems is globally distributed. Ecology, 89,371-379. 

8 Lien L; Raddum GG; Fjellheim A. (1992). Critical loads for surface waters: invertebrates and 

9 fish. (Acid rain research report no 21). Oslo, Norway: Norwegian Institute for Water 

10 Research. 

11 Longcore JR; Gill JD (Eds.), (1993). Acidic depositions: effects on wildlife and habitats. 

12 (Wildlife Society technical review no 93-1). Bethesda, MD: The Wildlife Society. 

13 MacAvoy SW; Bulger AJ. (1995). Survival of brook trout (Salvelinus fontinalis) embryos and 

14 fry in streams of different acid sensitivity in Shenandoah National Park, USA. Water Air 

15 Soil Pollut, 85, 445-450. 

16 McNulty SG; Cohen EC; Myers JAM; Sullivan TJ; Li H. (2007). Estimates of critical acid loads 

17 and exceedances for forest soils across the conterminous United States. Environ Pollut, 

18 149, 281-292. 

19 Murdoch PS; Stoddard JL. (1992). The role of nitrate in the acidification of streams in the 

20 Catskill Mountains of New York. Water Resour Res, 28, 2707-2720 

21 Nash TH; Sigal LL. (1999). Epiphytic lichens in the San Bernardino mountains in relation to 

22 oxidant gradients. In: Miller PR, McBride JR (Eds.), Oxidant air pollution impacts on the 

23 montane forests of southern California: A case study of the San Bernardino mountains. 

24 Ecological Studies, 134, (pp. 223-234). New York, NY: Springer-Verlag. 
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1 Norton S; Kahl J; Fernandez I. (1999). Altered soil-soil water interactions inferred from stream 

2 water chemistry at an artificially acidified watershed at Bear Brook Watershed, Maine 

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5 anoxa. Origin, development and significance. Science, 223, 22-27. 

6 Paerl HW; Boynton WR; Dennis RL; Driscoll CT; Greening HS; Kremer JN; Rabalais NN; 

7 Seitzinger SP. (2001). Atmospheric deposition of nitrogen in coastal waters: 

8 Biogeochemical and ecological implications. In: Valigura RW; Alexander RB; Castro 

9 MS; Meyers TP; Paerl HW; Stacey PE; Turner RE (Eds.), Nitrogen loading in coastal 

10 water bodies: an atmospheric perspective. (Coastal and estuarine series; Volume 57, pp 

11 11-52). Washington, DC: American Geophysical Union. 

12 Paerl H; Pinckney J; Fear J; Peierls B. (1998). Ecosystem responses to internal and watershed 

13 organic matter loading: Consequences for hypoxia in the eutrophying Neusse River 

14 Estuary, NC, USA. Mar Ecol Prog Ser, 166, 17-25. 

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16 Adirondack Park, New York. Canadian Journal of Zoology, 66, 804-810. 

17 Parker DR; Zelazny LW; Kinraide TB. (1989). Chemical speciation and plant toxicity of 

18 aqueous aluminum. In: Lewis TE (Ed.), Environmental chemistry and toxicology of 

19 aluminum (pp. 117-145). American Chemical Society. 

20 Peterjohn WT; Adams MB; Gilliam FS. (1996). Symptoms of nitrogen saturation in two central 

21 Appalachian hardwood forest ecosystems.Biogeochemistry, 35, 507-522. 

22 Reuss JO. (1983). Implications of the calcium-aluminum exchange system for the effect of acid 

23 precipitation on soils. J Environ Qual, 12, 591-595. 

24 Riddell J; Nash Iii TH; Padgett P. (2008). The effect of HNO3 gas on the lichen Ramalina 

25 menziesii. Flora - Morphology, Distribution, Functional Ecology of Plants, 203, 47-54. 
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1 Ross DS; Lawrence GB; Fredriksen G. (2004). Mineralization and nitrification patterns at eight 

2 northeastern USA forested research sites. For Ecol Manage, 188, 317-335. 

3 Saros JE; Interlandi SJ; Wolfe AP; Engstrom DR. (2003). Recent changes in the diatom 

4 community structure of lakes in the Beartooth Mountain Range, USA. Arct Antarct Alp 

5 Res, 35, 18-23. 

6 Schindler DW; Mills KH; Malley DF; Findlay MS; Schearer JA; Davies IJ; Turner MA; Lindsey 

7 GA; Cruikshank DR. (1985). Long-term ecosystem stress: Effects of years of 

8 experimental acidification. Science, 228, 1395-1401. 

9 Schreck CB. (1981). Stress and rearing of salmonids. Aquaculture, 28, 241-249. 

10 Schreck CB. (1982). Stress and compensation in teleostean fishes: response to social and 

11 physical factors. In: Pickering AD (Ed.), Stress and fish (pp. 295-321). London: 

12 Academic Press. 

13 Schwinning S; Starr BI; Wojcik NJ; Miller ME; Ehleringer JE; Sanford RL Jr. (2005). Effects of 

14 nitrogen deposition on an arid grassland in theColorado Plateau cold desert. Journal of 

15 Rangeland Ecology and Management, 58, 565-574. 

16 Sterner RW; Elser JJ. (2002). Ecological stoichiometry: the biology of elements from molecules 

17 to the biosphere. Princeton, NJ: Princeton University Press. 

18 Stoddard JL. (1990). Plan for converting the NAPAP aquatic effects long-term monitoring 

19 (LTM) project to the temporally integrated monitoring of ecosystems (TIME) project. 

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21 Stoddard JL. (1994). Long-term changes in watershed retention of nitrogen: its causes and 

22 aquatic consequences. In Baker LA (Ed.), Environmental chemistry of lakes and 

23 reservoirs. (pp. 223-284). Washington, D.C.: American Chemical Society. 

24 Stoddard JL; Urquhart NS; Newell AD; Kugler D. (1996). The Temporally Interated Monitoring 

25 of Ecosystems (TIME) project design 2. Detection of regional acidification trends. Water 

26 Resour Res, 32, 2529-2538. 
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1 Suding KN; Collins SL; Gough L; Clark C; Cleland EE; Gross KL; Milchunas DG; Pennings S. 

2 (2005). Functional- and abundance-based mechanisms explain diversity loss due to N 

3 fertlization. Proc Natl Acad Sci USA, 102, 4387-4392. 

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6 (2006). Assessment of the extent to which intensively studied lakes are representative of 

7 the Adirondack Mountain region. (Final Report no 06-17).Corvallis, OR; prepared by 

8 Environmental Chemistry, Inc. for: Albany, NY; Environmental Monitoring Evaluation 

9 and Protection Program of the New York State Energy Research and Development 

10 Authority (NYSERDA). 

11 Sverdrup H; Warfvinge P. (1993). The effect of soil acidification on the growth of trees, grass 

12 and herbs as expressed by the (Ca+ Mg+ K)/Al ratio. Rep in Ecol & Eng, 2, 1993. 

13 US EPA (2008) U.S. EPA. Integrated Science Assessment (ISA) for Oxides of Nitrogen and 

14 Sulfur Ecological Criteria (Final Report). U.S. Environmental Protection Agency, 

15 Washington, D.C., EPA/600/R-08/082F, 2008. 

16 US EPA (2009) Risk and Exposure Assessment for Review of the Secondary National Ambient 

17 Air Quality Standards for Oxides of Nitrogen and Oxides of Sulfur-Main Content - Final 

18 Report. U.S. Environmental Protection Agency, Washington, D.C., EPA-452/R-09-008a 

19 Vitousek PM; Howarth RW. (1991). Nitrogen limitation on land and in the sea: how can it 

20 occur? Biogeochemistry, 13, 87-115. 

21 Williams MW; Baron JS; Caine N; Sommerfeld R; Sanford JR. (1996). Nitrogen saturation in 

22 the Rocky Mountains. Environ Sci Technol, 30, 640-646. 

23 Wigington PJ Jr; DeWalle DR; Murdoch PS; Kretser WA; Simonin HA; Van Sickle J; Baker JP. 

24 (1996b). Episodic acidification of small streams in the northeastern United States: Ionic 

25 controls of episodes. Ecol Appl, 6, 389-407. 
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1 Wedemeyer GA; Barton BA; MeLeay DJ. (1990). Stress and acclimation. In: Schreck CB, 

2 Moyle PB (Eds.), Methods for fish biology (pp. 178-196). Bethesda, MD: American 

3 Fisheries Society. 

4 
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1 3. CONSIDERATIONS OF ADVERSITY TO PUBLIC 

2 WELFARE 

3 3.1 HOW DO WE CHARACTERIZE ADVERSITY TO PUBLIC 

4 WELFARE? WHAT ARE THE RELEVANT FACTORS AND HOW 

5 ARE THEY ADDRESSED IN THIS DOCUMENT? 

6 The paradigm of looking at adversity to public welfare as deriving from disruptions in 

7 ecosystem structure and function has been used broadly by EPA to categorize effects from the 

8 cellular to the ecosystem level. An evaluation of adversity to public welfare might consider the 

9 type, intensity, and scale of the effect as well as the potential for recovery. 

10 Similar concepts were used in past reviews of secondary NAAQS for ozone, PM relating 

11 to visibility as well as initial reviews of effects from lead deposition. Because NOx and SOx are 

12 deposited from ambient sources into ecosystems where they affect changes to organisms, 

13 populations and ecosystems, the concept of adversity to public welfare as related to impacts on 

14 the public from alterations in structure and function of ecosystems is appropriate for this review. 

15 Other information that may be helpful to consider includes the role of critical loads and 

16 ecosystem service impacts as benchmarks or measures of impacts on ecosystems that may affect 

17 public welfare. Ecosystem services can be related directly to concepts of public welfare to 

18 inform discussions of societal adverse impacts. Subsequent sections will discuss each of these 

19 concepts as they relate to adversity. 

20 3.1.1 What are the benchmarks for adversity from other sources? 

21 3.1.1.1 Ozone and PM NAAQS Reviews 

22 The evaluation of adversity from a public welfare perspective in the context of ozone and 

23 particulate matter (PM) are relevant to this current review. Both ozone and PM have documented 

24 effects on ecological receptors. These criteria pollutants are being reviewed on a schedule as part 

25 of the NAAQS process. The ozone secondary standard is currently under reconsideration from 

26 the 2008 ruling with a proposal due on January 6, 2010. A draft Policy Assessment for PM is 

27 being developed for CASAC and public consultation. 
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1 3.1.1.1.1 Ozone 

2 Welfare effects of ozone are primarily limited to vegetation. These effects begin at the 

3 level of the individual cell and accumulate up to the level of whole leaves and plants. If effects 

4 occur on enough individual plants within the population, communities and ecosystems may be 

5 impacted. Prior to the 2008 ozone review, Ozone vegetation effects were classified as either 

6 “injury” or “damage” (FR 72 37889). “Injury” was defined as; encompassing all plant reactions, 

7 including reversible changes or changes in plant metabolism, quality or reduced growth that does 

8 not impair the intended use of the plant while “damage” includes those injury effects that reach 

9 sufficient magnitude as to reduce or impair the intended use of the plant (FR 72 37890). The 

10 “intended use” of the plant was imbedded with the concept of adversity to public welfare. 

11 Ozone-associated “damage” was considered adverse if the intended use of the plant was 

12 compromised (i.e. crops, ornamentals, plants located in Class I areas). Effects of ozone on single 

13 plants or species grown in monocultures such as agricultural crops and managed forests were 

14 evaluated without consideration of potential effects on natural forests or entire ecosystems. 

15 In the 2008 rulemaking, EPA expanded the characterization of adversity to go beyond the 

16 individual plant level and this language is continued in the 2010 ozone reconsideration. The 2008 

17 final rule and 2010 proposal conclude that a determination of what constitutes an “adverse” 

18 welfare effect in the context of secondary NAAQS review can appropriately occur by 

19 considering effects at higher ecological levels (populations, communities, ecosystems) as 

20 supported by recent literature. The ozone review uses the example of the construct presented in 

21 Hogsett et al. (1997) as a model for assessing risks to forests. This study suggests that adverse 

22 effects could be classified into one or more of the following categories: (1) economic production, 

23 (2) ecological structure, (3) genetic resources, and (4) cultural values”. Another recent 

24 publication, “A Framework for Assessing and Reporting on Ecological Condition: an SAB 

25 report” (Young and Sanzone, 2002) provides additional support for expanding the consideration 

26 of adversity beyond the species level and at higher levels by making explicit the linkages 

27 between stress-related effects at the species level and at higher levels within an ecosystem 

28 hierarchy (See Figure 3.1.1). 
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1 

2 Figure 3-1. Common anthropogenic stressors and the essential ecological 

3 attributes they affect. Modified from Young and Sanzone (2002) 

4 In the 2008 ozone NAAQS review and current ozone NAAQS proposal, the 

5 interpretation of what constitutes an adverse effect on public welfare can vary depending on the 

6 location and intended use of the plant. The degree to which O3-related effects are considered 

7 adverse to public welfare depends on the intended use of the vegetation and its significance to 

8 public welfare (73 FR 16496). Therefore, effects on vegetation (e.g., biomass loss, foliar injury, 

9 impairment of intended use) may be judged to have a different degree of impact on public 

10 welfare depending, for example, on whether that effect occurs in a Class I area, a city park, 

11 commercial cropland or private land. 

12 In the proposed ozone reconsideration in 2010 the Administrator has found that the types 

13 of information most useful in informing the selection of an appropriate range of protective levels 
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1 is appropriately focused on information regarding exposures and responses of sensitive trees and 

2 other native species known or anticipated to occur in protected areas such as Class I areas or on 

3 lands set aside by States, Tribes and public interest groups to provide similar benefits to the 

4 public welfare, for residents on those lands, as well as visitors to those areas. She further notes 

5 that while direct links between O3 induced visible foliar injury symptoms and other adverse 

6 effects (e.g., biomass loss) are not always found, visible foliar injury in itself is considered by the 

7 National Park Service (NPS) to affect adversely air quality related values (AQRV) in Class I 

8 areas. While the Administrator recognizes that uncertainty remains as to what level of annual tree 

9 seedling biomass loss when compounded over multiple years should be judged adverse to the 

10 public welfare, she believes that the potential for such anticipated effects should be considered in 

11 judging to what degree a standard should be precautionary (73 FR 16496). The range of 

12 proposed levels from 7 – 15 ppb includes at the maximum level of 15 ppb protection of 

13 approximately 75% of seedlings from more than 10% biomass loss. 

14 3.1.1.1.2 PM 

15 [To be added in the second draft policy assessment based on the draft PM policy 

16 assessment] 

17 3.1.2 Other EPA Programs and Federal Agencies 

18 Various federal laws and policies exist to protect ecosystem health. How other federal 

19 agencies and EPA offices consider ecosystem effects in carrying out their programs can help 

20 inform the Administrator when she evaluates the adversity of ecosystem impacts on public 

21 welfare. For example, an effect may be considered adverse to public welfare if it contributes to 

22 the inability of areas to meet water quality objectives as defined by the Clean Water Act. The 

23 following federal statutes and policies may prove helpful to consider. 

24 EPA Office of Water 

25 Section 101 of the Clean Water Act (CWA) (Declaration of Goals and Policy) states that 

26 the objective of the CWA is to restore and maintain the chemical, physical, and biological 

27 integrity of the Nation’s waters and to attain, where possible, water quality that protects fish, 

28 shellfish, wildlife and provides for water-based recreation. 
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1 The CWA also authorizes EPA to develop water quality criteria as a guide for the states 

2 to set water quality standards to protect aquatic life. In consideration of acidification effects, 

3 EPA’s Redbook, Quality Criteria for Water, published originally in 1976, recommends that 

4 alkalinity be 20 mg/l or more as CaCO3 for freshwater aquatic life except where natural 

5 concentrations are less. Alkalinity is the sum total of components in the water that tend to elevate 

6 the pH of the water above a value of about 4.5. 

7 As mentioned in the Redbook, alkalinity is expressed as CaCO3 in mg/l. Alkalinity 

8 differs slightly from ANC in that ANC includes other buffering compounds (Na, Mg, and K) as 

9 well and includes buffering capacity of particulates in the water sample. Since alkalinity is 

10 expressed as mg/l and ANC is expressed as μeq/l, alkalinity must be multiplied by 20 to be 

11 converted to μeq/l. Thus a recommended criterion of 20 mg/l alkalinity is roughly equivalent to 

12 an ANC of 400 μeq/l. 

13 The Clean Air Act’s Prevention of Significant Deterioration (PSD) program (42 

14 U.S.C. 7470) purposes include to “preserve, protect and enhance the air quality in national parks, 

15 wilderness areas and other areas of natural, recreational, scenic or historic value . . . .” Also, the 

16 PSD program charges the Federal Land Managers, including the NPS, with “. . . an affirmative 

17 responsibility to protect the air quality related values . . . “within federal Class I lands. (42 U.S.C. 

18 7475(d)(2)(B)). 

19 National Park Service 

20 The National Park Service (NPS) is responsible for the protection of all resources within 

21 the national park system. These resources include those that are related to and/or dependent upon 

22 good air quality, such as whole ecosystems and ecosystem components. The NPS, in its Organic 

23 Act (16 U.S.C. 1), is directed to conserve the scenery, natural and historic objects and wildlife 

24 and to provide for the enjoyment of these resources unimpaired for current and future 

25 generations. 

26 The Wilderness Act of 1964 asserts wilderness areas will be administered in such a 

27 manner as to leave them unimpaired and preserve them for the enjoyment of future generations. 

28 NPS Management Policies (2006) guide all NPS actions including natural resources 

29 management. In general, the NPS Management Policies reiterate the NPS Organic Act’s mandate 

30 to manage the resources “unimpaired.” 
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1 U.S. Fish and Wildlife Service 

2 On endangered species, Title 16 USC Chapter 35 Section 1531 states “The Congress 

3 finds and declares that— these species of fish, wildlife, and plants are of esthetic, ecological, 

4 educational, historical, recreational, and scientific value to the Nation and its people and that all 

5 Federal departments and agencies will use their authorities to conserve threatened and 

6 endangered species. 

7 The United States Fish and Wildlife Service (FWS) manages the National Wildlife 

8 Refuge System lands to “...ensure that the biological integrity, diversity, and environmental 

9 health of the Systems are maintained for the benefit of present and future generations of 

10 Americans.” 16 U.S.C. Section 668dd(a)(4)(B)(1997). 

11 U.S. Forest Service 

12 The National Forest units are managed consistent with Land and Resource Management 

13 Plans (LRMPs) under the provisions of the National Forest Management Act (NFMA). 16 

14 §U.S.C. 1604 (1997). LRMPs are, in part, specifically based on recognition that the National 

15 Forests are ecosystems and their management for goods and services requires an awareness and 

16 consideration of the interrelationships among plants, animals, soil, water, air, and other 

17 environmental factors within such ecosystems. 36 C.F.R. §219.1(b)(3) 

18 Any measures addressing Air Quality Related Values (AQRV) on National Forest 

19 System lands will be implemented through, and be consistent with, the provisions of an 

20 applicable LRMP or its revision (16 U.S.C. §1604(i)). Additionally, the Secretary of Agriculture 

21 must prepare a Renewable Resource Program that recognizes the need to protect and, if 

22 necessary, improve the quality of air resources. 16 U.S.C. §1602(5)(C). 

23 AQRVs in Wilderness areas may receive further protection by the previously mentioned 

24 1964 Wilderness Act. For Wilderness Areas in the National Forest System, the Act's 

25 implementing regulations are found at 36 C.F.R. §293 requiring these Wilderness Areas be 

26 administered to preserve and protect [their] wilderness character. 

27 Chesapeake Bay Total Maximum Daily Loads 

28 Under section 303(d) of the Clean Water Act, states, territories, and authorized tribes are 

29 required to develop lists of impaired waters. These are waters that are too polluted or otherwise 

30 degraded to meet the water quality standards set by states, territories, or authorized tribes. The 
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1 law requires that these jurisdictions establish priority rankings for waters on the lists and develop 

2 TMDLs for these waters. A Total Maximum Daily Load, or TMDL, is a calculation of the 

3 maximum amount of a pollutant that a waterbody can receive and still safely meet water quality 

4 standards. EPA is developing a TMDL for the Chesapeake Bay and its tributaries. The 

5 Chesapeake Bay Program has modeled the level of nitrogen that can reach the Bay and still meet 

6 the Bay’s water quality standards. The TMDL, with full public participation, will set waste load 

7 allocations for point source discharges and load allocations for nonpoint sources of nitrogen. Air 

8 deposition to the Bay and its watershed, as a source category, will have a specific allocation. The 

9 allocation can be used to calculate the level of ambient air concentrations of reactive nitrogen 

10 that are likely to meet the deposition allocation. To find the NOx portion of the allocation one 

11 would subtract the reduced forms from the total allocation. If the total load to the Bay of nitrogen 

12 from all the allocated source categories remains below the allocations, then the Bay is expected 

13 to meet the water quality standards, which are set to protect the designated uses of the Bay. Since 

14 the designated uses are set by the states with public input, not meeting the designated uses can be 

15 seen as having an adverse effect. 

16 United Nations Economic Commission for Europe (UNECE) 

17 [This information will be included in the second draft.] 

18 Critical Loads 

19 The term critical load is used to describe the threshold of air pollution deposition that 

20 causes a specified level of harm to sensitive resources in an ecosystem. A critical load is 

21 technically defined as “the quantitative estimate of an exposure to one or more pollutants below 

22 which significant harmful effects on specified sensitive elements of the environment are not 

23 expected to occur according to present knowledge” (Nilsson and Grennfelt, 1988). The 

24 determination of when a harmful effect becomes “significant” may be in the view of a researcher 

25 or through a policy development process. Researchers often use the term “critical loads” to 

26 describe when particular detrimental effects are realized, as is the case in Figure 2-1. In many 

27 European countries a critical loads framework is used to determine a level of damages to 

28 ecosystem services from pollution that are legally allowed. These critical loads are determined 

29 through a policy process. 
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1 Harmful effects due to acidification have been defined here as those that occur below a 

2 given ANC for aquatic systems and below a given Bc:Al ratio for terrestrial systems. However, 

3 the level at which an effect becomes harmful in that it causes adverse effects on public welfare is 

4 determined by the Administrator. 

5 3.2 WHAT ARE ECOSYSTEM SERVICES AND HOW DOES THIS 

6 CONCEPT RELATE TO PUBLIC WELFARE? 

7 An additional concept that may be useful in considering the issue of adversity to public 

8 welfare is ecosystem services. In the next section the concept of ecosystem services, its 

9 relationship to adversity and public welfare within the context of this review are explained. 

10 Characterizing a known or anticipated adverse effect to public welfare is an important 

11 component of developing any secondary NAAQS. According to the Clean Air Act, welfare 

12 effects include: 

13 effects on soils, water, crops, vegetation, manmade materials, animals, wildlife, 

14 weather, visibility, and climate, damage to and deterioration of property, and 

15 hazards to transportation, as well as effect on economic values and on personal 

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

17 combination with other air pollutants (CAA, Section 302(h)). 

18 While the text above lists a number of welfare effects, these effects are not an effect on 

19 public welfare in and of themselves. 

20 Ecosystem services can be generally defined as the benefits individuals and organizations 

21 obtain from ecosystems. Ecosystem services can be classified as provisioning (food and water), 

22 regulating (control of climate and disease), cultural (recreational), and supporting (nutrient 

23 cycling) (MEA 2005). Conceptually, changes in ecosystem services may be used to aid in 

24 characterizing a known or anticipated adverse effect to public welfare. In the context of this 

25 review, ecosystem services may also aid in assessing the magnitude and significance to the 

26 public of a resource and in assessing how NOx and SOx concentrations and deposition may 

27 impact that resource. The relationship between ecosystem services and public welfare effects is 

28 illustrated in Table 3.2.1. 
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Table 3-1. Crosswalk between Ecosystem Services and Public Welfare Effects 

Public Welfare Effect Ecosystem Service Service Category 

Soils Nutrient Cycling Supporting 

Water Drinking water, Recreation, 

Aesthetic 

Provisioning, Cultural 

Crops Food, Fuel Production Provisioning 

Vegetation Food, Recreation, Aesthetic, 

Nonuse 

Provisioning, Cultural 

Wildlife Recreation, Food, Nonuse Cultural, Provisioning 

Climate Climate Control Regulating 

*Personal Comfort and 

Wellbeing 

 

1 *All ecosystem services contribute to personal comfort and wellbeing. 

2 EPA has defined ecological goods and services for the purposes of a Regulatory Impact 

3 Analysis as the “outputs of ecological functions or processes that directly or indirectly contribute 

4 to social welfare or have the potential to do so in the future. Some outputs may be bought and 

5 sold, but most are not marketed” (US EPA 2006). Though this is not a definition specifically for 

6 use in the NAAQS process it may be a useful one in considering the scope of ecosystem services 

7 and the effects of air pollutants upon those services. Especially important is the 

8 acknowledgement that most of the goods and services supplied by ecosystems cannot be fully 

9 measured or monetized. Valuing ecological benefits, or the contributions to social welfare 

10 derived from ecosystems, can be challenging as noted in EPA’s Ecological Benefits Assessment 

11 Strategic Plan (US EPA 2006) and the Science Advisory Board report “Valuing the Protection of 

12 Ecological Systems and Services” (US EPA, 2009). It can be informative in characterizing 

13 adversity to public welfare to attempt to place an economic valuation on the set of goods and 

14 services that have been identified with respect to a change in policy however it must be noted 

15 that this valuation will be incomplete and illustrative only. The stepwise concept leading to the 

16 valuation of ecosystem services is graphically depicted in Figure 3-2. 
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1 

2 Figure 3-2. Representation of the benefits assessment process indicating where 

3 some ecological benefits may remain unrecognized, unquantified, or 

4 unmonetized. (Source: EBASP USEPA 2006). 

5 A conceptual model integrating the role of ecosystem services in characterizing known or 

6 anticipated adverse effects to public welfare is shown in Figure 3-3. Under Section 109 of the 

7 CAA, the secondary standard is to specify a level of air quality that is requisite to protect public 

8 welfare. For this review, the relevant air quality indicator is interpreted as ambient NOx and SOx

9 concentrations that can be linked to levels of deposition for which there are ecological effects 

10 that are adverse to public welfare. The case study analyses (described in Chapters 4 and 5 of the 

11 REA and summarized in Chapter 2 of this document) link deposition in sensitive ecosystems 

12 (e.g., the exposure pathway) to changes in a given ecological indicator (e.g., for aquatic 

13 acidification, changes in acid neutralizing capacity [ANC]) and then to changes in ecosystems 

14 and the services they provide (e.g., fish species richness and its influence on recreational 

15 fishing). To the extent possible for each targeted effect area, ambient concentrations of nitrogen 

16 and sulfur (i.e., ambient air quality indicators) were linked to deposition in sensitive ecosystems 

17 (i.e., exposure pathways), and then deposition was linked to system response as measured by a 

18 given ecological indicator (e.g., lake and stream acidification as measured by ANC). The 
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1 ecological effect (e.g., changes in fish species richness, etc.) was then, where possible, associated 

2 with changes in ecosystem services and their public welfare effects (e.g., recreational fishing). 

3 Knowledge about the relationships linking ambient concentrations and ecosystem 

4 services can be used to inform a policy judgment on a known or anticipated adverse public 

5 welfare effect. The conceptual model outlined for aquatic acidification in Figure 3-3 can be 

6 modified for any targeted effect area where sufficient data and models are available. For 

7 example, a change in an ecosystem structure and process, such as foliar injury, would be 

8 classified as an ecological effect, with the associated changes in ecosystem services, such as 

9 primary productivity, food availability, and aesthetics (e.g., scenic viewing), classified as public 

10 welfare effects. Additionally, changes in biodiversity would be classified as an ecological effect, 

11 and the associated changes in ecosystem services—productivity, recreational viewing and 

12 aesthetics—would be classified as public welfare effects. This information can then be used by 

13 the Administrator to determine whether or not the changes described are adverse to public 

14 welfare. In subsequent sections these concepts are applied to characterize the ecosystem services 

15 potentially affected by nitrogen and/or sulfur for each of the effect areas assessed in the REA. 
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1 

2 Figure 3-3. Conceptual model showing the relationships among ambient air 

3 quality indicators and exposure pathways and the resulting impacts on 

4 ecosystems, ecological responses, effects and benefits to characterize known or 

5 anticipated adverse effects to public welfare. [This figure to be revised for Second 

6 Draft Policy Assessment Document] 

7 These concepts can also be applied to the programs described in section 3.1. National 

8 parks represent areas of nationally recognized ecological and public welfare significance, which 

9 are afforded a higher level of protection. Therefore, staff has also focused on air quality and 

10 deposition in the subset of national park sites and important natural areas. Figures 3-4 and 3-5 

11 illustrate the spatial relationships between sensitive regions, Class 1 areas and nitrogen 

12 deposition levels. 
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1 

2 Figure 3-4. Locations of Eastern U.S. National Parks (Class I areas) relative to 

3 deposition of Nitrogen and Sulfur in sensitive aquatic areas 
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1 

2 Figure 3-5. Location of Western U.S. National Parks (Class I areas) relative to 

3 deposition of Nitrogen and Sulfur 

4 [Figures 3-4 and 3-5 will be revised for Second Draft policy Assessment Document] 

5 3.3 WHAT IS THE ROLE OF ECONOMICS? 

6 As discussed earlier in this document, a secondary NAAQS is required to be set at the 

7 “level(s) of air quality necessary to protect the public welfare from any known or anticipated 

8 adverse effects”. As part of the effort to determine the standard, EPA linked the changes in the 

9 ambient air concentrations of NOx and SOx to the changes in ecosystem services and ultimately 

10 to changes in public welfare (U.S. EPA, 2009). As previously mentioned most ecosystem 
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1 services are not amenable to monetization a small subset of changes in services can be described 

2 by economic valuation methods. And although economics on its own cannot determine which 

3 impact on public welfare is “adverse”, economics could be helpful in the context of a secondary 

4 NAAQS for determining the degree to which improvements are beneficial to public welfare and 

illustrating and aggregating those impacts.7 5 

6 The Role of Economics in Defining “Adversity” There is neither an economic definition 

7 of how much loss in public welfare is adverse nor an economic definition of adversity. While an 

8 economist might consider a particular scenario adverse because it might imply some harm or 

9 potential for improvement, there is no specific threshold level when a loss in welfare (e.g. loss in 

10 dollars) becomes adverse. An individual might be willing to give up some of their resources to 

11 avoid a threat or negative outcome (i.e., willing to pay to avoid a particular outcome). According 

12 to economic theory, if an individual is willing to give up something to avoid the outcome, then 

13 imposing the outcome on the individual must make them worse off, at which point an economist 

14 might colloquially describe the outcome as adverse. However, the amount they would have been 

15 willing to pay to avoid the outcome might be quite small, and might not rise to a level of harm 

16 that the Administrator interprets as “adverse” to public welfare. In summary, economics provides 

17 little guidance as to how the Administrator should interpret the word “adverse” in the context of 

18 public welfare. 

19 Ecosystem Services and Links to Public Welfare An ecosystem service framework 

20 provides a structure to measure changes in public welfare from changes in ecosystem functions 

21 affected by air pollution. EPA’s Risk Assessment for this rulemaking defines ecosystem services 

22 as “the ecological processes or functions having monetary or nonmonetary value to individuals 

23 or society at large” (EPA 2009.) The discipline of economics provides a useful approach for 

24 summarizing how the public values changes in the services provided by the environment. An 

25 ecosystem services framework (with or without valuation) can provide measures of changes in 

26 public welfare. 

 

7

 Section 109 of the Clean Air Act forbids consideration of the compliance costs of reducing pollution when setting 

a NAAQS. However, there is no prohibition regarding the consideration of the monetized impacts of welfare 

effects occurring due to levels of pollution above alternative standards in evaluating the adversity of the impacts to 

public welfare. Ecosystem services can be characterized as a method of monetizing the impacts of the air pollution. 

Although a separate regulatory document quantifying the costs and benefits of attaining a NAAQS is prepared 

simultaneously, this document is not considered when selecting a standard. 
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1 Economics as a Framework to Illustrate Changes in Public Welfare Economics can 

provide a framework to illustrate how public welfare8 2 changes in response to changes in 

3 environmental quality by quantitatively linking changes in ecosystem services to preferences. 

4 Economics assumes that the choices that individuals make reflect their preferences over certain 

5 outcomes and that, generally speaking, they will make choices that, in expectation, will make 

6 them as well off as possible given their resources. In economics revealed and stated preference 

7 methods are used to observe the choices individuals make to understand the outcomes 

8 individuals prefer. What individuals are willing to give up for an outcome is their willingness-to9 pay (WTP) for that outcome. An example of an outcome is an improvement in an ecosystem 

10 service. Often, to provide comparability to other goods and services, in economics these 

tradeoffs are framed relative to dollars for convenience.9 11 

12 Economics could inform the Administrator by valuing and characterizing the changes in 

13 public welfare from changes in the quantity and quality of ecosystem services. Overall, this 

14 assessment intends to characterize changes in ecosystem services from a scientific perspective 

15 using effects on ecosystem structures and functions or ecosystem integrity. Economics then 

16 estimates the effect on public welfare of these changes in the quantity and quality of ecosystem 

17 services. For example, a decrease in a particular bird species can be characterized by its effect on 

18 the ecosystem’s structure and function, while from an economic perspective, the effects would 

19 be based on the impact on public welfare or the value the public places on that species. A simple 

20 example is a comparison between a decrease in a bird species that is relatively unknown 

21 compared to a decrease in a very prominent species (e.g. Bald Eagle). The public is likely to 

22 have a higher WTP to avoid the latter, and thus the decrease would affect the public welfare 

23 more. 

24 There are important complications with using preferences to understand the effect of 

25 pollution on public welfare. For example, while the field of economics generally assumes that 

26 public preferences are the paramount consideration; these preferences may change when the 

27 public receives new information. Therefore, if individuals do not understand how pollution will 

 

8

 [A discussion of economic interpretation of “Public Welfare” will be included in the second draft] 9

 Often groups collectively make choices to engage in activities that improve the collective welfare of the group. For 

example, a community around an acidified lake might purchase lime and use it to reduce the acidity of the lake. The 

collective decisions can also be used to understand how people value improvements to ecosystem services. 

[Additional discussion will be included in the second draft related to collective actions that reveal preferences for 

improvements in relevant ecosystem services and how these collective actions, and the absence of these actions, can 

be interpreted.] 
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1 affect ecosystem services, or even how those ecosystem services affect their quality of life, then 

2 they will have a difficult time valuing changes in those services. Similarly, it may be very costly 

3 for individuals to learn and understand how changes in particular ecosystem services may affect 

4 them, in part because typically there are significant interdependencies within an ecosystem. 

5 Because of this complexity, individuals may implicitly value a species, or habitat, or ecosystem 

6 function because it supports an ecosystem service that they do clearly value. Furthermore, the 

7 public also has limited understanding regarding irreversibilities, tipping points, and other more 

complex aspects of ecosystems, which limits the ability to adequately value these ecosystems.10 8 

9 In addition, where and when a change in an ecosystem takes places is crucial for characterizing 

10 the associated change in an ecosystem service, and will also affect the value the public places on 

11 that change. 

12 3.4 WHAT IS THE EVIDENCE FOR EFFECTS ON ECOSYSTEM 

13 SERVICES? HOW DO WE LINK ECOLOGICAL INDICATORS TO 

14 SERVICES? 

15 The process used to link ecological indicators to ecosystem services is discussed 

16 extensively in Appendix 8 of the REA. In brief, for each effect area assessed the ecological 

17 indicators were linked to an ecological response that was subsequently linked, to the extent 

18 possible, to associated services. For example in the case study for aquatic acidification the 

19 chosen ecological indicator is ANC which can be linked to the ecosystem service of recreational 

20 fishing as illustrated in the conceptual model shown in Figure 3-6. Although recreational fishing 

21 losses are the only service effects that can be quantified or monetized at this time, there are, as 

22 can be seen in the Figure, numerous other ecosystem services that may be related to the 

23 ecological effects of acidification. 

 

10 While the public may not fully appreciate the interdependencies within ecosystems, they can learn them, but again 

it may be costly to do so. It is possible for individuals to value outcomes that are irreversible or result in discrete 

changes (i.e., tipping points) in the quality and quantity of ecosystem services. Avoiding irreversible outcomes 

should be and are more valued by individuals than outcomes that are not irreversible (Arrow and Fischer, 1974). 
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1 

2 Figure 3-6. Conceptual model linking ecological indicator (ANC) to affected 

3 ecosystem services. 

4 The next four sections summarize the current levels of certain ecosystem services for 

5 each of the effect areas analyzed in the REA and present results of analyses that have attempted 

6 to quantify and monetize the harms to public welfare, as represented by ecosystem services, due 

7 to nitrogen and sulfur deposition. 

8 Evidence for Adversity Related to Aquatic Acidification 

9 Acidification primarily affects the ecosystem services that are derived from the fish and 

10 other aquatic life found in these surface waters (REA, Section 5.2.1.3). Food is generally the 

11 most important provisioning services provided by inland surface waters (MEA, 2005). In the 

12 northeastern United States, the surface waters affected by acidification are not a major source of 

13 commercially raised or caught fish; however, they are a source of food for some recreational and 

14 subsistence fishers and for other consumers. Although data and models are available for 

15 examining the effects on recreational fishing, relatively little data are available for measuring the 

16 effects on subsistence and other consumers. For example, although there is evidence that certain 

17 population subgroups in the Northeastern United States, such as the Hmong and Chippewa ethnic 

18 groups, have particularly high rates of self-caught fish consumption (Hutchison and Kraft, 1994; 

19 Peterson et al., 1994), it is not known if and how their consumption patterns are affected by the 

20 reductions in available fish populations caused by surface water acidification.

21 Inland surface waters support several cultural services, such as aesthetic and educational 

22 services; however, the type of service that is likely to be most widely and significantly affected 
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by aquatic acidification is recreational fishing11 1 . Recreational fishing in lakes and streams is 

2 among the most popular outdoor recreational activities in the northeastern United States. Data 

3 from the 2006 National Survey of Fishing, Hunting, and Wildlife Associated Recreation 

4 (FHWAR) indicate that more than 9% of adults in this part of the country participate annually in 

5 freshwater fishing with 140 million freshwater fishing days. Based on studies conducted in the 

6 northeastern United States, Kaval and Loomis (2003) estimated average consumer surplus values 

7 per day of $35 for recreational fishing (in 2007 dollars). Therefore, the implied total annual value 

8 of freshwater fishing in the northeastern United States was $5 billion in 2006. 

9 In general, inland surface waters such as lakes, rivers, and streams provide a number of 

10 regulating services, such as hydrological regime regulation and climate regulation. There is little 

11 evidence that acidification of freshwaters in the northeastern United States has significantly 

12 degraded these specific services; however, freshwater ecosystems also provide biological control 

13 services by providing environments that sustain delicate aquatic food chains. 

14 The toxic effects of acidification on fish and other aquatic life impair these services by 

15 disrupting the trophic structure of surface waters (Driscoll et al., 2001). Although it is difficult to 

16 quantify these services and how they are affected by acidification, it is worth noting that some of 

17 these services may be captured through measures of provisioning and cultural services. For 

18 example, these biological control services may serve as “intermediate” inputs that support the 

19 production of “final” recreational fishing and other cultural services. 

20 What is the value of the impaired recreational fishing services? 

21 The previous section describes the ecosystem services that are most likely to be affected 

22 by N and S deposition, and it summarizes evidence regarding the current magnitude and values 

23 of recreational fishing services; however, it does not measure the degree to which these services 

24 are impaired by existing NOx/SOx levels. 

25 To address this limitation, the REA (Appendix 8) provides insights into the magnitude of 

26 ecosystem service impairments. 

27 Specifically, the REA focuses on measuring the benefits of ecosystem service 

28 enhancements resulting from the elimination of anthropogenic sources of NOx/SOx. Rather than 

29 asking how much public welfare is currently adversely affected relative to a scenario without 

 

11 Banzhaf et al (2006) has shown that non-use services are arguably a more significant source of benefits from 

reduced acidification than recreational fishing. 
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1 anthropogenic NOx/SOx, it asks a similar question of how much public welfare would improve if 

2 the emissions were eliminated. The REA provides quantitative estimates of selected ecosystem 

3 services impairments or enhancements for three main categories of ecosystem effects – aquatic 

acidification, terrestrial acidification, and aquatic nutrient enrichment12 4 . Within these three 

5 categories, the selection of specific ecosystem services for more in-depth analysis depended 

6 primarily on the expected magnitude of impairments and on the availability of appropriate data 

7 and modeling tools. 

8 The analysis of ecosystem service impairments due to aquatic acidification builds on the 

9 case study analysis of lakes in the New York Adirondacks. It estimates changes in recreational 

10 fishing services, as well as changes more broadly in “cultural” ecosystem services (including 

11 recreational, aesthetic, and nonuse services). First, the MAGIC model was applied to 44 lakes to 

12 predict what ANC levels would be under both “business as usual” conditions (i.e., allowing for 

13 some decline in deposition due to existing regulations) and pre-emission (i.e., background) 

14 conditions. When these model runs were initiated staff were interested in a prospective analysis 

15 of conditions assuming a 2010 implementation of “zero-out” emissions with a projected lag time 

16 to improvement of 10 years thus results were calculated for the year 2020. These predictions 

17 were then extrapolated to the full universe of Adirondack lakes. Second, to estimate the 

18 recreational fishing impacts of aquatic acidification in these lakes, an existing model of 

19 recreational fishing demand and site choice was applied. This model predicts how recreational 

20 fishing patterns in the Adirondacks would differ and how much higher the average annual value 

21 of recreational fishing services would be for New York residents if lake ANC levels 

22 corresponded to background (rather than business as usual) conditions. Aggregating these values 

23 across all NY residents implies that acidification of Adirondack lakes due to anthropogenic 

24 sources of NOx/SOx would impair annual recreational fishing services of NY residents by $6 

25 million to $11 million in 2020. Current annual impairments are most likely of a similar 

26 magnitude because, although current NOx/SOx levels are somewhat higher than those expected in 

27 2020 (under business as usual – given expected emissions controls associated with Title IV 

28 regulations but no additional nitrogen or sulfur controls), the affected NY population is also 

29 somewhat smaller (based on U.S. Census Bureau projections). 

 

12 Estimates for terrestrial nutrient enrichments were not generated due to the limited availability of necessary data 

and models for this effect category. 
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1 Third, to estimate impacts on a broader category of cultural ecosystem services, results 

2 from an existing valuation survey of NY residents were adapted and applied to this context. The 

3 survey used a contingent valuation approach to estimate the average annual household WTP for 

4 future reductions (20% and 45%) in the percent of Adirondack lakes impaired by acidification. 

5 These WTP estimates were then (1) rescaled to reflect predicted changes between business-as6 usual and background conditions in 2020 (MAGIC lake modeling results indicate that the 

7 percentage of impaired lakes would be 22 to 31 points lower under background conditions), and 

8 (2) aggregated across NY households. The aggregate annual value to NY residents in 2010 for a 

9 reduction in lake acidification to background levels by 2020 was estimated to range $4 million to 

10 $300 million in 2007 dollars. For comparison the previous section estimated the value of 

11 recreational fishing in the Northeastern states at approximately $5 billion in 2006. These results 

12 suggest that the value of avoiding current impairments to ecosystem services from Adirondack 

13 lakes are even higher than the estimate, because they occur today rather than in 2020 (i.e., no 

14 delayed effect) and because the percent of impaired lakes is slightly higher today than expected 

15 in 2020 under business-as-usual. These results imply significant value to the public derived from 

16 recreational fishing services. The analysis especially illustrates what may be the scale of all 

17 impacts to public welfare when viewed as a subset of all services impacted by acidification. 

18 Evidence for Terrestrial Acidification 

19 A similar model to Figure 3-6 can be drawn for terrestrial acidification that links Bc:Al 

20 ratio to reduced tree growth to decreases in timber harvest although we have less confidence in 

21 the significance of this linkage than we do for aquatic acidification. There are numerous services 

22 expected to be affected, but the means to adequately describe those losses does not as yet exist. 

23 These services include effects to forest health, water quality, and habitat, including decline in 

24 habitat for threatened and endangered species, decline in forest aesthetics, decline in forest 

25 productivity, increases in forest soil erosion and decreases in water retention (ISA, 2009; REA, 

26 2009; Krieger, 2001). 

27 Forests in the Northeastern United States provide several important and valuable 

28 provisioning services, which are reflected in the production and sales of tree products. 

29 Sugar maples are a particularly important commercial hardwood tree species in the 

30 United States, producing wood products like timber and maple syrup that provide hundreds of 
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1 millions of dollars in economic value annually (NASS, 2008). Red spruce is also used in a 

2 variety of wood products and provides up to $100 million in economic value annually. 

3 Forests in the Northeastern United States are also an important source of cultural 

4 ecosystem services, including nonuse (existence value for threatened and endangered species), 

5 recreational, and aesthetic services (ISA, 2009; REA, 2009). Red spruce forests are home to two 

6 federally listed species. 

7 Although we do not have the data to link acidification damages directly to economic 

8 values of lost recreational services in forests, these resources are valuable to the public. A recent 

9 study suggests that the total annual value of off-road driving recreation was more than $9 billion, 

10 total and value of hunting and wildlife viewing was more than $4 billion each in the Northeastern 

11 United States in 2006(Kaval and Loomis, 2003). In addition, fall color viewing is a recreational 

12 activity that is directly dependent on forest conditions. Sugar maple trees, in particular, are 

13 known for their bright colors and are, therefore, an essential aesthetic component of most fall 

14 color landscapes. Statistics on fall color viewing are much less available than for the other 

15 recreational and tourism activities; however, a few studies have documented the extent and 

16 significance of this activity. For example, Spencer and Holecek (2007) found that roughly 30% 

17 of residents reported at least one trip in the previous year involving fall color viewing. In a 

18 separate study conducted in Vermont, Brown (2002) reported that more than 22% of households 

19 visiting Vermont in 2001 made the trip primarily for the purpose of viewing fall colors. 

20 Two studies that have estimated values for protecting high-elevation spruce forests in the 

21 Southern Appalachians. Kramer et al. (2003) conducted a contingent valuation study estimating 

22 households’ WTP for programs to protect remaining high-elevation spruce forests from damages 

23 associated with air pollution and insect infestation (Haefele et al., 1991; Holmes and Kramer, 

24 1995). Median household WTP was estimated to be roughly $29 (in 2007 dollars) for the 

25 minimal program and $44 for the more extensive program. Another study by Jenkins, Sullivan, 

26 and Amacher (2002) estimated an aggregate annual value of $3.4 billion for avoiding a 

27 significant decline in the health of high-elevation spruce forests in the Southern Appalachian 

28 region. 

29 Forests in the Northeastern United States also support and provide a wide variety of 

30 valuable regulating services, including soil stabilization and erosion control, water regulation, 

31 and climate regulation (Krieger, 2001). Forest vegetation plays an important role in maintaining 
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1 soils in order to reduce erosion, runoff, and sedimentation that can adversely impact surface 

2 waters. In addition to protecting the quality of water in this way, forests also help store and 

3 regulate the quantity and flows of water in watersheds. Finally, forests help regulate climate 

4 locally by trapping moisture and globally by sequestering carbon. The total value of these 

5 ecosystem services is very difficult to quantify and the magnitude of these impacts is currently 

6 very uncertain. 

7 What is the value of current ecosystem service impairments? 

8 The analysis of ecosystem service impairments associated with terrestrial acidification 

9 specifically addresses impacts on the forest product provisioning services from two 

10 commercially important tree species – sugar maple and red spruce--that are particularly sensitive 

11 to the effects of acidification. Using data from the USFS Forest Inventory and Analysis (FIA) 

12 database, an exposure-response relationship was estimated for each species to measure the 

13 average negative effect of critical load exceedances (CLEs) of nitrogen and sulfur deposition on 

14 annual tree growth. These estimated relationships were then applied to sugar maple and red 

15 spruce stocks in the Northeast and North central regions to estimate the average percent increase 

16 in annual tree growth that would occur if all CLEs were eliminated. To estimate the aggregate17 level forest market impacts of eliminating CLEs starting in the year 2000, the tree-level growth 

18 adjustments were applied using the Forest and Agricultural Sector Optimization Model 

19 (FASOM), which is a dynamic optimization model of the U.S. forest and agricultural sectors. 

20 The public welfare gains linked to these markets from eliminating CLEs was estimated to be 

21 $0.69 million per year. These estimates can also be interpreted as the current value of 

22 impairments to forest provisioning services due to forest acidification effects from nitrogen and 

23 sulfur deposition. 

24 Nutrient Enrichment

25 For the purposes of the following sections nutrient enrichment refers only to that due to 

26 NOy deposition. Additionally these sections focus on the detrimental effects of that deposition. 

27 Staff acknowledges that a certain amount of NOx deposition in managed terrestrial ecosystems 

28 may have a beneficial effect. However no attempt has been made to quantify those beneficial 

29 effects. 
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1 Evidence for Aquatic Nutrient Enrichment 

2 Estuaries in the eastern United States are an important source of food production, in 

3 particular fish and shellfish production. The estuaries are capable of supporting large stocks of 

4 resident commercial species, and they serve as the breeding grounds and interim habitat for 

5 several migratory species (U.S. EPA, 2009). To provide an indication of the magnitude of 

6 provisioning services associated with coastal fisheries, from 2005 to 2007, the average value of 

7 total catch was $1.5 billion per year in 15 East Coast states. It is not known, however, what 

8 percentage of this value is directly attributable to or dependent upon the estuaries in these states. 

9 Based on commercial landings in Maryland and Virginia, the values for three key species—blue 

10 crab, striped bass, and menhaden- totaled nearly $69 million in 2007 in the Chesapeake Bay 

11 alone. 

12 Assessing how eutrophication in estuaries affects fishery resources requires bioeconomic 

13 models (i.e., models that combine biological models of fish population dynamics with economic 

14 models describing fish harvesting and consumption decisions), but relatively few exist (Knowler, 

15 2002). Kahn and Kemp (1985) estimated that a 50% reduction in SAV from levels would 

16 decrease the net social benefits from striped bass by $16 million (in 2007 dollars). In a separate 

17 analysis, Anderson (1989) modeled blue crab harvests under baseline conditions and under 

18 conditions with “full restoration” of SAV. In equilibrium, the increase in annual producer surplus 

19 and consumer surplus with full restoration of SAV was estimated to be $7.9 million (in 2007 

20 dollars). Mistiaen, Strand, and Lipton (2003) found that reductions in DO cause a statistically 

21 significant reduction in commercial harvest and revenues crab harvests. For the Patuxent River 

22 alone, a simulated reduction of DO from 5.6 to 4.0 mg/L was estimated to reduce crab harvests 

23 by 49% and reduce total annual earnings in the fishery by $275,000 (in 2007 dollars). 

24 In addition, eutrophication in estuaries may also affect the demand for seafood. For 

25 example, a well-publicized toxic pfiesteria bloom in the Maryland Eastern Shore in 1997 led to 

26 an estimated $56 million (in 2007 dollars) in lost seafood sales for 360 seafood firms in 

27 Maryland in the months following the outbreak (Lipton, 1999). Surveys by Whitehead, Haab, 

28 and Parsons (2003) and Parsons et al. (2006) indicated a reduction in consumer surplus due to 

eutrophication-related fish kills ranging from $2 to $5 per seafood meal.13 29 As a result, they 

 

13 Surprisingly, these estimates were not sensitive to whether the fish kill was described as major or minor or to the 

different types of information included in the survey. 
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1 estimated aggregate consumer surplus losses of $43 million to $84 million (in 2007 dollars) in 

2 the month after a fish kill. 

3 As mentioned in the REA (5.2.1.3), estuaries in the eastern United States also provide an 

4 important and substantial variety of cultural ecosystem services, including water-based 

5 recreational and aesthetic services. For example, FHWAR data indicate that 4.8% of the 

6 population in coastal states from North Carolina to Massachusetts participated in saltwater 

7 fishing, in 26 million saltwater fishing days in 2006 (U.S. DOI, 2007). Based on estimates in 

8 Section 5.2.1.3 of the REA, total recreational consumer surplus value from these saltwater 

9 fishing days was approximately $1.3 billion (in 2007 dollars). Recreational participation 

10 estimates for several other coastal recreational activities are also available for 1999–2000 from 

11 the NSRE. Almost 6 million individuals participated in motorboating in coastal states from North 

12 Carolina to Massachusetts. Again, based on analysis in the REA, the aggregate value of these 

13 coastal motorboating outings was $2billion per year. Almost 7 million participated in 

14 birdwatching, for a total of almost 175 million days per year, and more than 3 million 

15 participated in visits to nonbeach coastal waterside areas, for a total of more than 35 million days 

16 per year. 

17 Estuaries and marshes have the potential to support a wide range of regulating services, 

18 including climate, biological, and water regulation; pollution detoxification; erosion prevention; 

19 and protection against natural hazards (MEA, 2005c). The relative lack of empirical models and 

20 valuation studies imposes obstacles to the estimation of ecosystem services affected by nitrogen 

21 deposition. While atmospheric deposition contributes to eutrophication there is uncertainty in 

22 separating the effects of atmospheric nitrogen from nitrogen reaching the estuaries from many 

23 other sources. 

24 What is the value of current ecosystem service impairments? 

25 The aquatic nutrient enrichment case study relied on the NOAA Eutrophication Index as 

26 the indicator, which includes dissolved oxygen, HABs, loss of SAV and loss of water clarity. 

27 There are methods available to link some of the components to ecosystem services, most notably 

28 loss of SAV and reductions in DO. The REA analysis estimates the change in several ecosystem 

29 services including recreational fishing, boating, beach use, aesthetic services and nonuse 

30 services. The REA focuses on two major East Coast estuaries – the Chesapeake Bay and the 

31 Neuse River. Both estuaries receive between 20%-30% percent of their annual nitrogen loadings 
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1 through air deposition and both are showing symptoms of eutrophication. The analysis uses and 

2 adapts results from several existing studies to approximate effects on several ecosystem services, 

3 including commercial fishing, recreation, aesthetic enjoyment, and nonuse values. For example, 

4 it is estimated that atmospheric nitrogen reduces the annual benefits of recreational fishing, 

5 boating, and beach use in the Chesapeake Bay by $43-$217 million, $3-8 million, and $124 

6 million respectively, and reduces annual aesthetic benefits to nearshore residents by $39-102 

7 million. In the Neuse River, the value of annual commercial crab fishing services would be 

8 between $0.1-1 million higher without the contribution of atmospheric nitrogen, and recreation 

9 fishing services in the larger Albermarle Pamlico Sound estuary system (which includes the 

10 Neuse) would be $1-8 million greater per year. 

11 Evidence for Terrestrial Nutrient Enrichment 

12 The ecosystem service impacts of terrestrial nutrient enrichment include primarily 

13 cultural and regulating services. In CSS areas, concerns focus on a decline in CSS and an 

14 increase in nonnative grasses and other species, impacts on the viability of threatened and 

15 endangered species associated with CSS, and an increase in fire frequency. Changes in MCF 

16 include changes in habitat suitability and increased tree mortality, increased fire intensity, and a 

17 change in the forest’s nutrient cycling that may affect surface water quality through nitrate 

18 leaching (EPA, 2008). 

19 The value that California residents and the U.S. population as a whole place on CSS and 

20 MCF habitats is reflected in the various federal, state, and local government measures that have 

21 been put in place to protect these habitats. Threatened and endangered species are protected by 

22 the Endangered Species Act. The State of California passed the Natural Communities 

23 Conservation Planning Program (NCCP) in 1991, and CSS was the first habitat identified for 

24 protection under the program (see www.dfg.ca.gov/habcon/nccp). Private organizations such as 

25 The Nature Conservancy, the Audubon Society, and local land trusts also protect and restore 

26 CSS and MCF habitat. 

27 CSS and MCF are found in numerous recreation areas in California. Three national parks 

28 and monuments in California contain CSS, including Cabrillo National Monument, Channel 

29 Islands National Park, and Santa Monica National Recreation Area. All three parks showcase 

30 CSS habitat with educational programs and information provided to visitors, guided hikes, and 

31 research projects focused on understanding and preserving CSS. Over a million visitors traveled 
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1 through these three parks in 2008. MCF is highlighted in Sequoia and Kings Canyon National 

2 Park, Yosemite National Park, and Lassen Volcanic National Park, where more than 5 million 

3 people visited in 2008. 

4 The 2006 FHWAR for California (DOI, 2007) reports on the number of individuals 

5 involved in fishing, hunting, and wildlife viewing in California. Millions of people are involved 

6 in just these three activities each year. The quality of these trips depends in part on the health of 

7 the ecosystems and their ability to support the diversity of plants and animals found in important 

8 habitats found in CSS or MCF ecosystems and the parks associated with those ecosystems. 

9 Based on analyses in Section 5.3.1.3 of the REA (U.S.EPA, 2009), average values of the total 

10 benefits in 2006 from fishing, hunting, and wildlife viewing away from home in California were 

11 approximately $947 million, $169 million, and $3.59 billion, respectively. In addition, data from 

12 California State Parks (2003) indicate that in 2002, 68.7% of adult residents participated in trail 

13 hiking for an average of 24.1 days per year. The analyses in the REA (U.S.EPA, 2009) indicate 

14 that the aggregate annual benefit for California residents from trail hiking in 2007 was $11.59 

15 billion. 

16 CSS and MCF are home to a number of important and rare species and habitat types. CSS 

17 displays richness in biodiversity with more than 550 herbaceous annual and perennial species. Of 

18 these herbs, nearly half are endangered, sensitive, or of special status (Burger et al., 2003). 

19 Additionally, avian, arthropod, herpetofauna, and mammalian species live in CSS habitat or use 

20 the habitat for breeding or foraging. Communities of CSS are home to three important federally 

21 endangered species. MCF is home to one federally endangered species and a number of state22 level sensitive species. The Audubon Society lists 28 important bird areas in CSS habitat and at 

least 5 in MCF in California (http://ca.audubon.org/iba/index.shtml).14 23 

24 The terrestrial enrichment case study in Section 5.3.1.3 of the REA and Section 3.3.5 of 

25 the ISA identified fire regulation as a service that could be affected by nutrient enrichment of the 

26 CSS and MCF ecosystems by encouraging growth of more flammable grasses, increasing fuel 

27 loads, and altering the fire cycle. Over the 5-year period from 2004 to 2008, Southern California 

28 experienced, on average, over 4,000 fires per year burning, on average, over 400,000 acres per 

29 year (National Association of State Foresters [NASF], 2009). It is not possible at this time to 

30 quantify the contribution of nitrogen depositio, among many other factors, to increased fire risk. 

 

14 Important Bird Areas are sites that provide essential habitat for one or more species of bird. 
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1 The CSS and MCF were selected as case studies for terrestrial enrichment because of the 

2 potential that these areas could be adversely affected by excessive N deposition. To date, the 

3 detailed studies needed to identify the magnitude of the adverse impacts due to N deposition 

4 have not been completed. Based on available data, this report provides a qualitative discussion of 

5 the services offered by CSS and MCF and a sense of the scale of benefits associated with these 

6 services. California is famous for its recreational opportunities and beautiful landscapes. CSS 

7 and MCF are an integral part of the California landscape, and together the ranges of these 

8 habitats include the densely populated and valuable coastline and the mountain areas. Through 

9 recreation and scenic value, these habitats affect the lives of millions of California residents and 

10 tourists. Numerous threatened and endangered species at both the state and federal levels reside 

11 in CSS and MCF. Both habitats may play an important role in wildfire frequency and intensity, 

12 an extremely important problem for California. The potentially high value of the ecosystem 

13 services provided by CSS and MCF justify careful attention to the long-term viability of these 

14 habitats. 

15 The terrestrial nutrient enrichment case study relies on benchmark deposition levels for 

16 various species and ecosystems as indicators of ecosystem response. While it would be expected 

17 that deposition above those levels would have deleterious effects on the provision of ecosystem 

18 services in those areas, at this time it is possible only to describe the magnitude of the some of 

19 the services currently being provided. Methods are not yet available to allow estimation of 

20 changes in services due to nitrogen deposition. 

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4 U.S. Environmental Protection Agency (EPA). 2002. December. Environmental and Economic 

5 Benefit Analysis of Final Revisions to the National Pollutant Discharge Elimination 

6 System Regulation and the Effluent Guidelines for Concentrated Animal Feeding 

7 Operations (EPA-821-R-03-003). Washington, DC: U.S. Environmental Protection 

8 Agency, Office of Water, Office of Science and Technology. 

9 U.S. EPA (Environmental Protection Agency). 2006. Ecological Benefits Assessment Strategic 

10 Plan. EPA-240-R-06-001. Office of the Administrator. Washington, DC. Available at 

11 http://www.epa.gov/economics. 

12 U.S. Environmental Protection Agency (EPA). 2008. Integrated Science Assessment for Oxides 

13 of Nitrogen and Sulfur–Environmental Criteria.EPA/600/R-08/082. U.S. Environmental 

14 Protection Agency, Office of Research and Development, National Center for 

15 Environmental Assessment – RTP Division, Research Triangle Park, NC 

16 U S Environmental Protection Agency (EPA). 2009. Risk and Exposure Assessment for the 

17 Review of the Secondary national Ambient Air Quality Standards for Oxides of Nitrogen 

18 and Oxides of Sulfur. EPA/452/R/09/008a. U.S environmental Protection Agency, Office 

19 of Air Quality planning and Standards, Health and Environmental impacts Division, 

20 Research Triangle Park, NC. 

21 U.S. EPA (Environmental Protection Agency). 2009. Valuing the Protection of Ecological 

22 Systems and Services: A report of the EPA Science Advisory Board. EPA-SAB-09-012. 

23 Office of the Administrator. Washington, DC. Available at 

24 http://yosemite.epa.gov/sab/sabproduct.nsf/WebBOARD/ValProtEcolSys&Serv? 

25 Valigura, R.A., R.B. Alexander, M.S. Castro, T.P. Meyers, H.W. Paerl, P.E. Stacy, and 

26 R.E.Turner. 2001. Nitrogen Loading in Coastal Water Bodies: An Atmospheric 

27 Perspective.Washington, DC: American Geophysical Union. 
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1 Van Houtven, G. and A. Sommer. December 2002. Recreational Fishing Benefits: A Case Study 

2 of Reductions in Nutrient Loads to the Albemarle-Pamlico Sounds Estuary. Final Report. 

3 Prepared for the U.S. Environmental Protection Agency. Research Triangle Park, 

4 NC:RTI International. 

5 Van Houtven, G.L., J. Powers, and S.K. Pattanayak. 2007. “Valuing Water Quality 

6 Improvements Using Meta-Analysis: Is the Glass Half-Full or Half-Empty for National 

7 Policy Analysis?” Resource and Energy Economics 29:206-228. 

8 Vaughan, W.J. 1986. “The Water Quality Ladder.” Included as Appendix B in R.C. Mitchell, 

9 and R.T. Carson, eds. The Use of Contingent Valuation Data for Benefit/Cost Analysis in 

10 Water Pollution Control. CR-810224-02. Prepared for the U.S. Environmental Protection 

11 Agency, Office of Policy, Planning, and Evaluation. 

12 Virginia Department of Conservation and Recreation. 2007. “2006 Virginia Outdoors 

13 Survey.”Available at 

14 http://www.dcr.virginia.gov/recreational_planning/documents/vopsurvey06.pdf. 

15 Wallace, K.J. 2007. “Classification of Ecosystem Services: Problems and Solutions.” Ecological 

16 Conservation 139:235-246. 

17 Whitehead, J.C., T.C. Haab, and G.R. Parsons. 2003. “Economic Effects of Pfiesteria.” Ocean & 

18 Coastal Management 46(9-10):845-858. 

19 
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1 4 ADDRESSING THE ADEQUACY OF THE CURRENT STANDARDS 

2 Based on the information in Chapters 2 and 3, we conclude that there is support in the 

3 available effects-based evidence for consideration of secondary standards for NOx and SOx that 

4 are protective against adverse ecological effects associated with deposition of NOx and SOx to 

5 sensitive ecosystems. Having reached this general conclusion, we then to the extent possible 

6 evaluate the adequacy of the current NOx and SOx secondary standards by considering to what 

7 degree risks to sensitivity ecosystems would be expected to occur in areas that meet the current 

8 standards. Staff conclusions regarding the adequacy of the current standards are based on the 

9 available ecological effects, exposure and risk-based evidence. In evaluating the strength of this 

10 information, staff have taken into account the uncertainties and limitations in the scientific 

11 evidence. This chapter addresses key policy relevant questions that inform our determination 

12 regarding the adequacy of the structure and levels of the current secondary standards. The 

13 chapter begins with a discussion of the structure of the current standards, followed by a 

14 presentation of information on recent air quality relative to the existing standards, recent NOx

15 and SOx deposition levels, evaluation of recent deposition levels relative to levels where adverse 

16 ecological effects have been observed, and a set of conclusions regarding the adequacy of the 

17 current structure and levels of the standards. 

18 It is also appropriate in this review to consider whether the current standards are adequate 

19 to protect against the direct effects on vegetation resulting from ambient NO2 and SO2 which 

20 were the basis for the current secondary standards. We will include a discussion of this issue in 

21 the second draft policy assessment. 

22 4.1 ARE THE STRUCTURES OF THE CURRENT NOX AND SOX

23 SECONDARY STANDARDS BASED ON RELEVANT 

24 ECOLOGICAL INDICATORS SUCH THAT THEY ARE 

25 ADEQUATE TO DETERMINE AND PROTECT PUBLIC WELFARE 

26 AGAINST ADVERSE EFFECTS ON ECOSYSTEMS? 

27 The current secondary NOx and SOx standards are intended to protect against adverse 

28 effects to public welfare. For NOx, the current secondary standard was set identical to the 
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primary standard15 1 , e.g. an annual standard set for NO2 to protect against adverse effects on 

2 vegetation from direct exposure to ambient NOx. For SOx, the current secondary standard is a 3-

3 hour standard intended to provide protection for plants from the direct foliar damage associated 

4 with atmospheric concentrations of SO2. 

5 The ISA has established that the major effects of concern for this review of the NOx and 

6 SOx standards are associated with deposition of N and S associated with atmospheric 

7 concentrations of NOx and SOx (see Chapter 2). As such, the current secondary standards do not 

8 reflect the conclusions of the ISA in the major areas of indicator, form, or averaging times. By 

9 using atmospheric NO2 and SO2, concentrations as indicators the current standards address only 

10 a fraction of total atmospheric NOx and SOx, and do not take into account the effects from 

11 deposition of total atmospheric NOx and SOx. By addressing short-term concentrations of SO2, 

12 the current SO2 standard, while protective against direct foliar effects from gaseous SOx, does 

13 not take into account the findings of effects in the ISA, which notes the relationship between 

14 annual deposition of S and acidification effects which are likely to be more severe and 

15 widespread than phytotoxic effects under current ambient conditions. Acidification is a process 

16 which occurs over time, as the ability of an aquatic system to counteract acidic inputs is reduced 

17 as natural buffers are used more rapidly than they can be replaced through geologic weathering. 

18 The relevant period of exposure for ecosystems is therefore not the exposures captured in the 

19 short averaging time of the current SO2 standard. In addition, the ISA has concluded that NOx

20 and SOx and their deposition products jointly affect ecosystems, and as such the current separate 

21 structure of the NOx and SOx secondary standards does not take into account the joint ecological 

22 effects of the two pollutants. 

23 Current standards are specified as allowable single atmospheric concentration levels for 

24 NO2 or SO2. This type of structure does not take into account variability in the atmospheric and 

25 ecological factors that may alter the effects of NOx and SOx on public welfare. Consistent with 

26 section 108, the ISA includes in the air quality criteria consideration of how these variable 

27 factors impact the effects of ambient NOx and SOx on public welfare. Secondary standards are 

28 intended to address a wide variety of effects occurring in different types of environments and 

29 ecosystems. Ecosystems are not uniformly distributed either spatially or temporally in their 

 

15 The current primary NO2 standard has recently been changed to the 3 year average of the 98th percentile of the 

annual distribution of the 1 hour daily maximum of the concentration of NO2. The current secondary standard 

remains as it was set in 1971. 
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1 sensitivity to air pollution. Therefore, failure to account for the major determinants of variability, 

2 especially geologic conditions related to sensitivity to acidification and atmospheric conditions 

3 which govern rates of deposition, may lead to standards that do not provide appropriate levels of 

4 protection across ecosystems. We can state with confidence the current standards were not 

5 designed to be protective against those welfare effects tied to deposition of ambient NOx and SOx

6 and thus are not likely to be adequate to protect public welfare against known or anticipated 

7 adverse effects from deposition. 

8 Because most areas of the U.S. are in attainment with the current NO2 and SOx standards, 

9 it is possible to evaluate current conditions, and evaluate the impact on public welfare from the 

10 current effects on ecosystems from NOx and SOx deposition in areas that attain the current 

11 standards that use NO2 and SO2 as indicators. In addition, this chapter qualitatively addresses the 

12 adequacy of the structures of the existing standards relative to ecologically relevant standards for 

13 NOx and SOx, and sets up arguments for developing an ecologically relevant structure for the 

14 standards as described in Chapter 5. 

15 4.2 TO WHAT EXTENT ARE THE STRUCTURES OF THE CURRENT 

16 NOX AND SOX SECONDARY STANDARDS MEANINGFULLY 

17 RELATED TO RELEVANT ECOLOGICAL INDICATORS OF 

18 PUBLIC WELFARE EFFECTS? 

19 The current secondary standard for NOx, set in 1971, using NO2 as the atmospheric 

20 indicator, is 0.053 parts per million (ppm) (100 micrograms per cubic meter of air [μg/m3]), 

21 annual arithmetic average, calculated as the arithmetic mean of the 1-hour NO2 concentrations. 

22 This standard was selected to provide protection to the public welfare against acute injury to 

23 vegetation from direct exposure and resulting phytoxicity. During the last review of the NOx

24 standards, impacts associated with chronic acidification and eutrophication from NOx deposition 

25 were acknowledged, but the relationships between atmospheric concentrations of NOx and levels 

26 of acidification and eutrophication and associated welfare impacts were determined to be too 

27 uncertain to be useful as a basis for setting a national secondary standard (USEPA 1995). 

28 The current secondary standard for SOx, set in 1971, uses SO2 as the atmospheric 

29 indicator, is a 3-hour average of 0.5 ppm, not to be exceeded more than once per year. This 

30 standard was selected to provide protection to the public welfare against acute injury to 
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1 vegetation. In the last review of the SOx secondary standard, impacts associated with chronic 

2 acidification were acknowledged, but the relationships between atmospheric concentrations of 

3 SOx and levels of acidification, along with the complex interactions between SOx and NOx in 

4 acidification processes, were cited as critical uncertainties which made the setting of secondary 

5 NAAQS to protect against acidification inappropriate at that time (USEPA 1982). 

6 In the previous independent reviews of the NOx and SOx secondary standards, each 

7 review acknowledged the additional impacts of NOx and SOx on public welfare through the 

8 longer term impact of the pollutants once deposited to ecosystems. However, the previous 

9 reviews cited numerous uncertainties as the basis for not addressing those impacts in the setting 

10 of the standards. In addition, these previous reviews did not consider the common pathways of 

11 impact for the two pollutants acting on the same ecosystem endpoints. 

12 Three issues arise that call into question the ecological relevance of the current structure 

13 of the secondary standards for NOx and SOx. One issue is the exposure period that is relevant for 

14 ecosystem impacts. The majority of deposition related impacts are associated with depositional 

15 loads that occur over periods of months to years. This differs significantly from exposures 

16 associated with hourly concentrations of NO2 and SO2 as measured by the current standards. 

17 Even though the NO2 standard uses an annual average of NO2, it is focused on the annual 

18 average of 1-hour NO2 concentrations, rather than a cumulative metric or an averaging metric 

19 based on daily or monthly averages. A second issue is the choice of atmospheric indicators. NO2

20 and SO2 are used as the component of oxides of nitrogen and sulfur that are measured, but they 

21 do not provide a complete link to the direct effects on ecosystems from deposition of NOx and 

22 SOx as they do not capture all relevant species of oxidized nitrogen and oxidized sulfur that 

23 contribute to deposition. The ISA provides evidence that deposition related effects are linked 

24 with total nitrogen and total sulfur, and thus all forms of oxidized nitrogen and oxidized sulfur 

25 that are deposited will contribute to effects on ecosystems. This suggests that more 

26 comprehensive atmospheric indicators should be considered in designing ecologically relevant 

27 standards. Further discussions of the need for more ecologically relevant atmospheric indicators 

28 as well as the relative contributions to deposition from various species of NOx and SOx can be in 

29 found in Chapters 5 and 6. The third issue is that the current standards reflect separate 

30 assessments of the two individual pollutants, NO2 and SO2, rather than assessing the joint 

31 impacts of deposition of NOx and SOx to ecosystems, recognizing the role that each pollutant 
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1 plays in jointly affecting ecosystem indicators, functions, and services. The clearest example of 

2 this interaction is in assessment of the impacts of acidifying deposition on aquatic ecosystems. 

3 Acidification in an aquatic ecosystem depends on the total acidifying potential of the 

4 deposition of both N and S from both atmospheric deposition of NOx and SOx as well as the 

5 inputs from other sources of N and S such as reduced nitrogen and non-atmospheric sources. It is 

6 the joint impact of the two pollutants that determines the ultimate effect on organisms within the 

7 ecosystem, and critical ecosystem functions such as habitat provision and biodiversity. Standards 

8 that are set independently are less able to account for the contribution of the other pollutant. This 

9 suggests that interactions between NOx and SOx should be a critical element of the conceptual 

10 framework for ecologically relevant standards. There are also important interactions between 

11 NOx and SOx and reduced forms of nitrogen, which also contributes to acidification and nutrient 

12 enrichment. While the standards do not address reduced forms of nitrogen in the atmosphere, it is 

13 important that the structure of the standards address the role of reduced nitrogen in determining 

14 the ecological effects resulting from deposition of atmospheric NOx and SOx. Consideration will 

15 also have to be given to account for loadings coming from non-atmospheric sources as 

16 ecosystems will respond to these sources as well. 

17 In addition to the fundamental issues discussed above, the current structures of the 

18 standards do not address the complexities in the responses of ecosystems to deposition of NOx

19 and SOx. Ecosystems contain complex grouping of organisms that respond in various ways to the 

20 alterations of soil and water that result from deposition of nitrogen and sulfur compounds. 

21 Different ecosystems therefore respond in different ways depending on a multitude of factors 

22 that control how deposition is integrated into the system. For example, the same levels of 

23 deposition falling on limestone dominated soils have a very different effect than those falling on 

24 shallow glaciated soils underline with granite. One system may over time display no obvious 

25 detriment while the other may experience a catastrophic loss in fish communities. This degree of 

26 sensitivity is a function of many atmospheric factors which control rates of deposition as well as 

27 ecological factors which control how an ecosystem responds to that deposition. The current 

28 standards do not take into account spatial and seasonal variations not only in depositional 

29 loadings but also in sensitivity of ecosystems exposed to those loadings. 
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1 4.3 TO WHAT EXTENT DO CURRENT MONITORING NETWORKS 

2 PROVIDE A SUFFICIENT BASIS FOR DETERMINING THE 

3 ADEQUACY OF CURRENT SECONDARY NOX AND SOX

4 STANDARDS? 

5 There are over 1000 ground level monitoring platforms (Figures 4-1 and 4-2) that provide 

6 measurements of some form of atmospheric nitrogen or sulfur. The key pollutants for this 

7 assessment are total oxidized nitrogen (NOy), total reduced nitrogen (NHx), and total sulfur (ST). 

8 Total reactive oxidized atmospheric nitrogen, NOy, is defined as NOx (NO and NO2) and all 

9 oxidized NOx products: NOy = NO2 + NO + HNO3 + PAN +2N2O5 + HONO+ NO3 + organic 

10 nitrates + particulate NO3 (Finlayson-Pitts and Pitts, 2000). This definition of NOy reflects the 

11 operational principles of standard measurement techniques in which all oxidized nitrogen species 

12 are converted to nitrogen oxide (NO) through catalytic reduction and the resulting NO is detected 

13 through luminescence. Thus, NOy is truly defined as total oxidized nitrogen as converted to NO. 

14 NOy is not a strict representation of the all moles of oxidized nitrogen as the diatomic nitrogen 

15 species such as N2O5 yield 2 moles of NO. This definition is consistent with the relationship 

16 between atmospheric nitrogen and acidification processes as the reported NOy provides a direct 

17 estimate of the potential equivalents available for acidification. Total reduced nitrogen (NHx) 

18 includes ammonia, NH3, plus ammonium, NH4 (EPA, 2008). Reduced nitrogen plus oxidized 

19 nitrogen is referred to as total reactive nitrogen. Total sulfur (ST) includes SO2 gas and 

20 particulate sulfate, SO4. Ammonium and sulfate are components of atmospheric particulate 

21 matter as well as directly measured and modeled in precipitation as direct deposition 

22 components. As discussed in this section, there are only very limited routine measurements of 

23 total oxidized and reduced nitrogen. In addition, existing monitoring networks do not provide 

24 adequate geographic coverage to fully assess concentrations and deposition of reactive nitrogen 

25 and sulfur in and near sensitive ecosystems. 

26 
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1 

2 Figure 4-1. Routinely operating surface monitoring stations measuring forms of 

3 atmospheric nitrogen. 
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1 

2 Figure 4-2. Routinely operating surface monitoring stations measuring forms of 

3 atmospheric sulfur. 

4 The principal monitoring networks include the regulatory based State and Local Air 

5 Monitoring Stations (SLAMS) providing mostly urban-based SO2, NO and NOx, the PM2.5

6 chemical speciation networks Interagency Monitoring of Protected visual Environments 

7 (IMPROVE) and EPA’s Chemical Speciation Network (CSN) providing particle bound sulfate 

8 and nitrate, and the Clean Air Status and Trends Network (CASTNET) providing weekly 

9 averaged values of SO2, nitric acid, and particle bound sulfate, nitrate and ammonium. The 

10 private sector supported SouthEastern Aerosol Research and Characterization (SEARCH) Study 

11 network of 4-8 sites in the southeast provides the only routinely operating source of true 

12 continuous NO2, ammonia, and nitric acid measurements. SEARCH also provides PM2.5 size 

13 fractions of nitrate and sulfate. Collectively, the SLAMS, Photochemical Assessment 

14 Measurement Stations (PAMS), SEARCH and NCore networks will provide over 100 sites 

15 measuring NOy (Figure 4-3). The NCore network (Scheffe et al., 2009) is a multiple pollutant 
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1 network with co-located measurements of key trace gases (CO, SO2, O3, NO and NOy), PM2.5

2 and PM(10-2.5) mass and PM2.5 chemical speciation. Additional air pollutants, particularly 

3 volatile organic compounds (VOCs), will be measured at those sites that are part of the existing 

4 PAMS and National Air Toxics Trends (NATTS) platforms. The NATTS (EPA, 2008) include 

5 27 stations across the U.S. that monitor for a variety of hazardous air pollutants and are intended 

6 to remain in place to provide a longe term record. Additional measurements of ammonia and 

7 possibly true NO2 are under consideration. True NO2 is noted to differentiate from the NO2

8 determined through routine regulatory networks that have known variable positive bias for NO2. 

9 The network currently is being deployed and expected to be operational with nearly 75 sites by 

10 January, 2011. The sites are intended to serve as central site monitors capturing broadly 

11 representative (e.g., not strongly influenced by nearby sources) air quality in a suite of major and 

12 mid size cities, and approximately 20 sites are located in rural locations. 
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1 

2 Figure 4-3. Anticipated network of surface based NOy stations based on 2009 

3 network design plans. The NCore stations are scheduled to be operating by 

4 January, 2011. 

5 There are significant measurement gaps for characterizing NOy, NHx and SO2 in the 

6 nations ambient air observation networks (EPA, 2008) that lead to greater reliance on air quality 

7 modeling simulations to describe current conditions. National design of routinely operating 

8 ambient air monitoring networks is driven mostly by data uses associated with implementing 

9 primary NAAQS, with noted exceptions of the CASTNET and IMPROVE networks In addition 

10 to significant spatial gaps in sensitive ecosystem areas that arise from a population oriented 

11 network design, the current measurements for primary and secondary nitrogen are markedly 

12 different and in some instances of negligible value for secondary NOx and SOx standards. For 

13 example, a true NOx (NO plus NO2) measurement typically would capture less than 50% (see 

14 discussion below) of the total regional NOy mass in rural locations as the more aged air masses 

15 contain significant oxidized nitrogen products in addition to NOx. Note that the NOx monitors 
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1 used for NAAQS primary compliance purposes do capture varying amounts of transformed 

2 nitrogen species; however, the method provides biased low estimates with significant airshed 

3 induced variability relative to true NOy. With the exception of the SEARCH network in the 

4 southeast, there are virtually no routine networks that measure ammonia, although EPA is 

5 considering options for ammonia sampling in CASTNET and NCORE networks. Ammonium is 

6 reported in EPA chemical speciation networks, although the values are believed to be biased low 

7 due to ammonia volatization. 

8 CASTNET provides mostly rural measurements of SO2, total nitrate, and ammonium, and 

9 affords an existing infrastructure useful for future monitoring in support of a NOx and SOx

10 secondary standard. However, the lack of NOy, SOx and NHx measurements in sensitive 

11 ecosystems will require attention in the N/S secondary standard proposal. 

12 As a result of the limited monitoring networks for NOy and SOx in sensitive ecosystems, 

13 we are unable to use current monitoring data to fully assess whether the current standards have 

14 resulted in levels of NOy and SOx in sensitive ecosystems that would result in deposition levels 

15 that are or are not causing ecological effects adverse to public welfare. We supplement the 

16 available monitoring data with the use of sophisticated atmospheric modeling conducted using 

17 EPA’s CMAQ model. 

18 4.3.1 What does the NADP monitoring network provide and what are the major 

19 limitations? 

20 The National Atmospheric Deposition Program (NADP) includes approximately 250 

21 sites (Figure 4-4) across the U.S. providing annual total wet deposition based on weekly 

22 averaged measures of wet deposition of nitrate, ammonium and sulfate ions based on the 

23 concentrations of these ions in precipitation samples. Meteorological models have difficulty in 

24 capturing the correct spatial and temporal features of precipitation events, raising the importance 

25 of the NADP as a principal source of precipitation chemistry. The NADP has enabled several 

26 organizations to participate in a measurement program with a centralized laboratory affording 

27 measurement and analysis protocol consistency nationwide. Virtually every CASTNET site is 

28 located at an NADP site and the combined NADP/CASTNET infrastructure is a starting point for 

29 discussions addressing future N/S monitoring needs. The Organic bound nitrogen is not analyzed 

30 routinely in NADP samples. Consideration might be given to adding NADP sites in locations 
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1 where ambient air monitoring is conducted to assess compliance with a secondary NOx/SOx

2 standard. 

3 

4 Figure 4-4. Location of approximately 250 National Atmospheric Deposition 

5 Monitoring (NADP) National Trends Network (NTN) sites illustrating annual 

6 ammonium deposition for 2005. Weekly values of precipitation based nitrate, 

7 sulfate and ammonium are provided by NADP. 

8 4.3.2 How do we characterize deposition through Monitoring and Models? 

9 Routinely available directly measured precipitation to quantify wet deposition of sulfur 

10 and nitrogen species are provided through the NADP. Dry deposition is not a directly measured 

11 variable in routine monitoring efforts and, for all practical purposes, largely will remain a 

12 research endeavor that supports the parameterizations used for estimating dry deposition, as 

13 opposed to striving to develop operational methods. Estimates of dry deposition based on 

14 observations are provided through the CASTNET program. However, dry deposition is a 

15 calculated value represented as the product of ambient concentration (either observed or 

estimated through air quality modeling) and deposition velocity, Amb

i

Dry

i

Dry 16 Depi = v ⋅C . 
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1 Deposition velocity is modeled as a mass transfer process through resistance layers 

2 associated with the canopy, uptake by vegetation, water and soil which collectively are 

3 influenced by micrometeorology, land surface and vegetation types and species specific 

4 solubility and reactivity. Dry deposition is calculated through deposition velocity models 

5 capturing these features and using species specific ambient air concentrations. This approach 

6 conceptually is similar using either observed or modeled air concentrations. Dry deposition 

7 estimates from the Community Multi-scale Air Quality (CMAQ) model (EPA, 1999) have been 

8 used in this assessment to provide spatially more resolved and extensive estimates of dry 

9 deposition for sulfur and all reactive nitrogen (oxidized and reduced) species (CASTNET does 

10 not capture important gases such as nitrogen dioxide and peroxyacetyl nitrate). All of the 

11 relevant meteorological, land use, vegetation and elevation data required to estimate deposition 

12 velocities are generated or accessible in the CMAQ and/or meteorological pre-processors. 

13 4.3.2.1 Why are we using CMAQ to model deposition? How are we using it? Why is 

14 CMAQ the right model to use? What is the spatial and temporal resolution of 

15 CMAQ? What are the model years? What are the limitations to CMAQ? 

16 CMAQ provides a platform that allows for a consistent mass accounting approach across 

17 ambient concentrations and dry and wet deposition values. Recognizing the limitations of 

18 ambient air networks, CMAQ was used to estimate dry deposition to complement NADP wet 

19 deposition for MAGIC modeling and for the FAB critical load modeling. CMAQ promotes 

20 analytical consistency and efficiency across analyses of multiple pollutants. EPA’s Office of 

21 Research and Development continues to enhance the underlying deposition science in CMAQ. 

22 For the purposes of this policy assessment, CMAQ provides a consistent platform incorporating 

23 the atmospheric and deposition species of interest over the entire United States. The caveats and 

24 limitations of the use of model predictions are largely associated with the general reliance on 

25 calculated values, rather than measurements. Model evaluation addressing the comparison of 

26 predictions with observed values is addressed in the REA. Currently, there are efforts to improve 

27 a number of nitrogen related processes in CMAQ, recognizing comparatively less uncertainty 

28 with the treatment of sulfur. Active areas of model process improvement are in the treatment of 

29 lightning generated NOx and the transference of nitrogen between atmospheric and terrestrial and 

30 aquatic media, often referred to as bi-directional flux. Lightning NOx potentially provides a 
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1 significant contribution to wet deposition as the resulting NOx is rapidly entrained into aqueous 

2 cloud processes. Both the thermodynamics of soil processes and mass transfer of nitrogen 

3 species across the surface-atmosphere interface is governed by an assortment of temperature, 

4 moisture, advection and concentration patterns. These processes and mass transfer relationships 

5 are coupled within the emissions, meteorological, and chemical simulation processes and 

6 associated surface/vegetation and terrain information incorporated in or accessed by the CMAQ. 

7 In addition to research activities to improve the characterization of nitrogen-related processes in 

8 CMAQ, efforts are also underway to improve the general characterization of ammonia emissions 

9 which remains as an area of large uncertainty due to limited source data and the ubiquitous 

10 nature of these emissions. Another challenge for regional/national air quality modeling is 

11 properly representing the effects on pollutant concentrations, precipitation and therefore 

12 deposition of variable terrain features, particularly steep mountain-valley gradients and the 

13 interfaces to wide open basins encountered in the Western United States. 

14 The CMAQ was used in this assessment because it is the state of science model for 

15 treating simulating sources, formation, and fate of nitrogen and sulfur species. In addition to 

16 undergoing periodic independent scientific peer review, CMAQ bridges the scientific and 

17 regulatory communities as it is used extensively by EPA for regulatory air quality assessments 

18 and rules. CMAQ provides hourly estimates of the important precursor, intermediate and 

19 secondarily formed species associated with atmospheric chemistry and deposition processes 

20 influencing ozone, particulate matter concentrations and sulfur and nitrogen deposition. 

21 Simulations based on horizontal spatial scale resolutions of 12 km and 36 km were used in this 

22 PAD for 2002 – 2005. 

23 4.4 WHAT IS OUR BEST CHARACTERIZATION OF ATMOSPHERIC 

24 CONCENTRATIONS OF NOY AND SOX, AND DEPOSITION OF N 

25 AND S? 

26 Air quality models and blending of model results and observations are used to 

27 characterize current environmental state conditions due to the relative sparseness of monitoring 

28 coverage in sensitive ecosystems as well as gaps in coverage for specific atmospheric species of 

29 N and S most relevant to deposition, such as NOy, in available monitoring platforms. 
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1 4.4.1 What are the current atmospheric concentrations of reactive nitrogen, NOy, 

2 reduced nitrogen, NHx, sulfur dioxide, SO2, and sulfate, SO4? 

3 To provide information for use in characterizing the adequacy of the current standards, 

4 we assess the best available data for estimating the ambient concentrations of the major sources 

5 of atmospheric nitrogen and sulfur across the U.S. Acidification and nutrient enrichment 

6 processes are largely dependent on the cycling of total nitrogen and sulfur species. From an 

7 atmospheric perspective, it is convenient and consistent with current measurement and modeling 

8 frameworks to consider the reduced and oxidized forms of atmospheric nitrogen. Virtually all 

9 atmospheric sulfur is considered oxidized sulfur in the forms of particulate bound sulfate and 

10 gaseous sulfur dioxide. In order to assess current concentrations of reactive nitrogen and sulfur 

11 we evaluated data available from monitoring the existing networks as well as from the CMAQ 

12 model. Regarding the monitoring data, there are a number of important issues in understanding 

13 the measurements of NOy provided by different monitoring networks. In principle, measured 

14 NOy is based on catalytic conversion of all oxidized species to NO followed by 

15 chemiluminescence NO detection. We recognize the caveats associated with instrument 

16 conversion efficiency and possible inlet losses. The CMAQ treats the dominant NOy species as 

17 explicit species while the minor contributing non-PAN organic nitrogen compounds are 

18 aggregated. Atmospheric nitrogen and sulfur are largely viewed as regional air quality issues due 

19 to the importance of chemical conversion of primary emissions into secondarily formed species; 

20 a combination of ubiquitous sources, particularly mobile source emissions of NOx, and elevated 

21 emissions of NOx and SO2 that aid pollutant mass dispersal and broader physical transport over 

22 large distances. In effect, the regional nature is due to both transport processes as well as the 

23 relatively ubiquitous nature of sources combined with chemical processes that tend to form more 

24 stable species with extended atmospheric lifetimes. This regionalized effect, particularly 

25 throughout the Eastern United States, dominates the overall patterns discussed below of 

26 secondarily formed species such as sulfate or NOy, which is an aggregate of species where the 

27 more aged air masses consisting largely of chemically processed air is dominated by secondarily 

28 formed peroxyacetyl nitrate (PAN), particulate nitrate and nitric acid. 

29 Nationwide maps of CMAQ-predicted 2005 annual average NOy, NHx (NH3 and NH4), 

30 NH3, NH4, ST, SO4, and SO2 are provided in figures 4-5 through 4-11 respectively. Given the 

31 considerable gaps in air quality observation networks as discussed in the REA and ISA (2008), 
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1 modeled concentration patterns are used here to illustrate national representations of current air 

2 quality conditions for nitrogen and sulfur. The 2005 model year reflects the most recent available 

3 simulation for inclusion in this policy assessment. In addition, figures 4-12 and 4-13 provide 

4 maps of 2005 annual average SO2 and SO4, respectively based on CASTNET observations. Site 

5 specific annual average 2005 NOy measured concentrations at SLAMS (Figure 4-14) are 

6 typically are less than 40 ppb., The spatial patterns for the 2005 modeled and observed NOy and 

7 SO2 concentrations are similar to the 2002 CMAQ-based maps provided in the REA., largely 

8 capturing the influence of major source regions throughout the nation. A spreading of the 

9 oxidized sulfur fields (Figures 4-5 and 4-6), relative to SO2, is consistent with sulfate 

10 transformation and associated air mass aging and transport. Ammonia and ammonium 

11 concentration patterns (Figure 4-4) are influenced strongly by the ammonia emissions 

12 distribution, with marginal spreading associated with the addition of NH4. The NHx fields are 

13 more strongly influenced by source location, relative to sulfur, based on the fast removal of 

14 atmospheric ammonia through deposition. Total deposition for nitrogen and sulfur (Figures 4-15 

15 and 4-16) basically follow the patterns of ambient air concentrations. 

16 Current conditions indicate that the current SO2 and NO2 secondary standards are not 

17 exceeded (Figures 4-17 and 4-18) in locations where ecological effects have been observed, and 

18 where critical loads of nitrogen and sulfur are exceeded. This is consistent with the fact that NO2

19 accounts for only a fraction of NOy, and thus reductions in NO2 emissions would not be expected 

20 to fully address concentrations of NOy. The map in Figure 4-19 further illustrates this point by 

21 showing that the contribution of NO2 to NOy is often less than 50% in rural areas. 
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1 

2 Figure 4-5. 2005 CMAQ modeled annual average NOy (ppb). These maps will be 

3 replaced with full CONUS maps in the next draft. 

4 
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1 

2 Figure 4-6. 2005 CMAQ modeled annual average total reduced nitrogen (NHx) 

(as μg/m3 3 nitrogen) 

4 
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1 

Figure 4-7. 2005 CMAQ modeled annual average ammonia, NH3, (as μg/m3 2 N) 

3 
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1 

Figure 4-8. 2005 CMAQ modeled annual average ammonia, NH4, (as μg/m3 2 N) 

3 
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1 

Figure 4-9. 2005 CMAQ modeled annual average SOx, (as μg/m3 2 S from SO2 and 

3 SO4). 

4 

5 
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1 

Figure 4-10. 2005 CMAQ modeled annual average SO2 (as μg/m3 2 S) 

3 
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1 

Figure 4-11. 2005 CMAQ modeled annual average SO4 (as μg/m3 2 S). 

3 
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1 

2 Figure 4-12. 2005 annual average sulfur dioxide concentrations based on 

3 CASTNET generated by the Visibility Information Exchange Web Sysytem 

4 (VIEWS). 

5 

6 Figure 4-13. 2005 annual average sulfate concentrations based on CASTNET 

7 generated by the Visibility Information Exchange Web Sysytem (VIEWS). 

8 
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1 

2 Figure 4-14. Annual average 2005 NOy concentrations from reporting stations in 

3 AQS. 

4 
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1 

2 Figure 4-15. 2005 CMAQ modeled Oxidized Nitrogen Deposition (kgN/Ha/Yr). 

3 
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1 

2 Figure 4-16. 2005 CMAQ modeled Oxidized Sulfur Deposition (kgS/Ha/Yr). 

3 
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1 

2 Figure 4-17. Three hour average maximum 2005 SO2 concentrations based on the 

3 SLAMS reporting to EPA’s Air Quality System (AQS) data base. The current 

4 SO2 secondary standard based on the maximum 3 hour average value is 500 ppb, 

5 a value not exceeded. While there are obvious spatial gaps, the majority of these 

6 stations are located to capture maximum values generally in proximity to major 

7 sources and high populations. Lower relative values are expected in more remote 

8 acid sensitive areas. 

9 
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1 

2 Figure 4-18. Annual average 2005 NO2 concentrations based on the SLAMS 

3 reporting to EPA’s Air Quality System (AQS) data base. The current NO2

4 secondary standard is 53 ppb, a value well above those observed. While there are 

5 obvious spatial gaps, the stations are located in areas of relatively high 

6 concentrations in highly populated areas. Lower relative values are expected in 

7 more remote acid sensitive areas. 

8 
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1 

2 Figure 4-19. 2005 CMAQ derived annual average ratio of (NOy – NO2)/NOy. The 

3 fraction of NO2 contributing to total NOy generally is less than 50% in the 

4 Adirondack and Shenandoah case study areas. The ratio reflects the relative air 

5 mass aging associated with transformation of oxidized nitrogen beyond NO and 

6 NO2 as one moves from urban to rural locations. 

7 4.5 ARE ADVERSE EFFECTS ON THE PUBLIC WELFARE 

8 OCCURRING UNDER CURRENT AIR QUALITY CONDITIONS 

9 FOR NO2 AND SO2 AND WOULD THEY OCCUR IF THE NATION 

10 MET THE CURRENT SECONDARY STANDARDS? 

11 The previous sections have established that almost all areas of the U.S. were at 

12 concentrations of SO2 and NO2 below the levels of the current standards. In many locations, SO2

13 and NO2 concentrations are substantially below the levels of the standards. This suggests that 

14 levels of deposition and any effects on ecosystems due to deposition of NOx and SOx under 
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1 recent conditions are occurring even though areas meet or are below current standards. This 

2 section focuses on summarizing the evidence of effects occurring at deposition levels consistent 

3 with recent conditions. 

4 The ISA summarizes the available studies of relative nitrogen contribution and finds that 

5 in much of the U.S., NOx contributes from 50 to 75 percent of total atmospheric deposition [ISA 

6 Section 2.8.4]. While the proportion of total nitrogen loadings associated with atmospheric 

7 deposition of nitrogen varies across locations (N deposition in the Eastern U.S. includes 

8 locations with greater than 9 kg N/ha/year, and in the central U.S. high deposition locations with 

9 values on the order of 6 to 7 kg N/ha/year), the ISA indicates that atmospheric N deposition is 

10 the main source of new anthropogenic N to most headwater streams, high elevation lakes, and 

11 low-order streams. Atmospheric N deposition contributes to the total N load in terrestrial, 

12 wetland, freshwater, and estuarine ecosystems that receive N through multiple pathways. In 

13 several large estuarine systems, including the Chesapeake Bay, atmospheric deposition accounts 

14 for between 10 and 40 percent of total nitrogen loadings. 

15 Atmospheric concentrations of SOx account for nearly all S deposition in the US. For the 

16 period 2004–2006, mean S deposition in the U.S. was greatest east of the Mississippi River with 

17 the highest deposition amount, 21.3 kg S/ha/yr, in the Ohio River Valley where most recording 

18 stations reported 3 year averages >10 kg S/ha/yr. Numerous other stations in the East reported S 

19 deposition >5 kg S/ha/yr. Total S deposition in the U.S. west of the 100th meridian was 

20 relatively low, with all recording stations reporting <2 kg S/ha/yr and many reporting <1 kg 

S/ha/yr. S was primarily deposited in the form of wet SO4

2− 21 followed in decreasing order by a 

smaller proportion of dry SO2 and a much smaller proportion of deposition as dry SO4

2− 22 . 

23 New scientific evidence exists to address each of the areas of uncertainty raised in the 

24 previous reviews (summarized above). Based on the new evidence, the current ISA concludes 

25 that: 

26 (1) The evidence is sufficient to infer a causal relationship between acidifying deposition 

27 (to which both NOx and SOx contribute) and effects on biogeochemistry related to 

28 terrestrial and aquatic ecosystems; and biota in terrestrial and aquatic ecosystems. 

29 (2) The evidence is sufficient to infer a causal relationship between N deposition, to 

30 which NOx and NHx contribute, and the alteration of A) biogeochemical cycling of N 

31 and carbon in terrestrial, wetland, freshwater aquatic, and coastal marine ecosystems; 
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1 B) biogenic flux of methane (CH4), and N2O in terrestrial and wetland ecosystems; 

2 and C) species richness, species composition, and biodiversity in terrestrial, wetland, 

3 freshwater aquatic and coastal marine ecosystems. 

4 (3) The evidence is sufficient to infer a causal relationship between S deposition and 

5 increased Hg methylation in wetlands and aquatic environments. 

6 Subsequent to the previous review of the NOx secondary standard, a great deal of 

7 information on the contribution of atmospheric deposition associated with ambient NOx has 

8 become available. Chapter 3 of the REA provides a thorough assessment of the contribution of 

9 NOx to nitrogen deposition throughout the U.S., and the relative contributions of ambient NOx

10 and reduced forms of nitrogen. Staff concludes that based on that analysis, ambient NOx is a 

11 significant component of atmospheric nitrogen deposition, even in areas with relatively high 

12 rates of deposition of reduced nitrogen. In addition, staff initially concludes that atmospheric 

13 deposition of oxidized nitrogen contributes significantly to total nitrogen loadings in nitrogen 

14 sensitive ecosystems. 

15 As discussed throughout the risk and exposure assessment document, there are several 

16 key areas of risk that are associated with ambient concentrations of NOx and SOx. In previous 

17 reviews of the NOx and SOx secondary standards, the standards were designed to protect against 

18 direct exposure of plants to ambient concentrations of the pollutants. A significant shift in 

19 understanding of the effects of NOx and SOx has occurred since the last reviews, reflecting the 

20 large amount of research that has been conducted on the effects of deposition of nitrogen and 

21 sulfur to ecosystems. The most significant risks of adverse effects to public welfare are those 

22 related to deposition of NOx and SOx to both terrestrial and aquatic ecosystems. These risks fall 

23 into two categories: acidification and nutrient enrichment. These made up the emphasis of the 

24 REA, and are most relevant to evaluating the adequacy of the existing standards in protecting 

25 public welfare from adverse ecological effects. 
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1 4.5.1 To what extent do the current NOx and SOx secondary standards provide 

2 protection from adverse effects associated with deposition of atmospheric 

3 NOx, and SOx which results in acidification in sensitive aquatic and 

4 terrestrial ecosystems? 

5 The focus of the REA case studies was on determining whether deposition of sulfur and 

6 oxidized nitrogen in locations where ambient NOx and SOx was at or below the current standards 

7 was resulting in acidification and related effects. This review has focused on identifying 

8 ecological indicators that can link atmospheric deposition to ecological effects associated with 

9 acidification. NOx and SOx contribute to acidification in both aquatic and terrestrial ecosystems, 

10 although the indicators of effects differ. While there are some geographic areas with both 

11 terrestrial and aquatic ecosystems that are vulnerable to acidification, the case study areas do not 

12 fully overlap. Figure 4-20 shows the locations of the case studies evaluated in the REA. 

13 

14 Figure 4-20. National map highlighting the 9 case study areas evaluated in the 

15 REA. 
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1 4.5.1.1 Aquatic Acidification 

2 Based on the case studies conducted for lakes in the Adirondacks and streams in 

3 Shenandoah National Park, staff concludes that there is significant risk to acid sensitive aquatic 

4 ecosystems at atmospheric concentrations of NOx and SOx at or below the current standards. 

5 This conclusion is based on application of the MAGIC model to estimate the effects of 

6 deposition at levels consistent with atmospheric NOx and SOx concentrations that are at or below 

7 the current standards. An important ecological indicator for aquatic acidification effects is ANC, 

8 measuring the acid buffering capacity of a waterbody, and the case study focused on evaluating 

9 whether locations were likely to be below critical values of ANC given deposition levels 

10 associated with NOx and SOx concentrations that meet the current standards. In addition, the case 

11 studies assessed the ecological effects and some of the known ecosystem services that are 

12 associated with different levels of ANC in order to associate the ecological indicator with 

13 measures of public welfare that may be adversely affected by deposition levels consistent with 

14 concentrations of NOx and SOx that meet the current standards. 

15 Staff concludes that the evidence and risk assessment support strongly a relationship 

16 between atmospheric deposition of NOx and SOx and ANC, and that ANC is an excellent 

17 indicator of aquatic acidification. Staff also concludes that at levels of deposition associated with 

18 NOx and SOx concentrations at or below the current standards, ANC levels are expected to be 

19 below benchmark values that are associated with significant losses in fish species richness (REA 

20 Section 4). 

21 Many locations in sensitive areas of the U.S. have ANC levels below benchmark levels 

22 for ANC classified as severe, elevated, or moderate concern (see Figure 2-1). The average 

23 current ANC levels across 44 lakes in the Adirondack case study area is 62.1 (moderate 

24 concern), however, 44 percent of lakes had deposition levels exceeding the critical load for an 

25 ANC of 50, and 28 percent of lakes had deposition levels exceeding the critical load for an ANC 

26 of 20 μeq/L (REA Section 4.2.4.2). This indicates that almost half of the 44 lakes in the 

27 Adirondacks case study area are at an elevated concern levels, and almost a third are at a severe 

28 concern level. These levels are associated with greatly reduced fish species diversity, and losses 

29 in the health and reproductive capacity of remaining populations. Based on assessments of the 

30 relationship between number of fish species and ANC level in both the Adirondacks and 

31 Shenandoah areas, the number of fish species is decreased by over half at an ANC level of 20 
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1 μeq/L relative to an ANC level at 100 μeq/L (REA Figure 4.2-1). At levels below 20 μeq/L, 

2 populations of sensitive species, such as brook trout, may decline significantly during episodic 

3 acidification events. When extrapolated to the full population of lakes in the Adirondacks area 

4 using weights based on the EMAP probability survey (REA 4.2.6.1), 36 percent of lakes 

5 exceeded the critical load for an ANC of 50 μeq/L and 13 percent of lakes exceeded the critical 

6 load for an ANC of 20 μeq/L. 

7 Many streams in the Shenandoah case study area also have levels of deposition that are 

8 associated with ANC levels classified as severe, elevated, or moderate concern. The average 

9 ANC under recent conditions for the 60 streams evaluated in the Shenandoah case study area is 

10 57.9, indicating moderate concern. However, 85 percent of streams had recent deposition 

11 exceeding the critical load for an ANC of 50 μeq/L, and 72 percent exceeded the critical load for 

12 an ANC of 20 μeq/L. As with the Adirondacks area, this suggests that significant numbers of 

13 sensitive streams in the Shenandoah area are at risk of adverse impacts on fish populations under 

14 recent conditions. Many other streams in the Shenandoah area are likely to experience conditions 

15 of elevated to severe concern based on the prevalence in the area of bedrock geology associated 

16 with increased sensitivity to acidification suggesting that effects due to stream acidification could 

17 be widespread in the Shenandoah area (REA 4.2.6.2). 

18 The ISA notes that large portions of the Eastern U.S. are acid sensitive, and that current 

19 deposition levels exceed those that would allow recovery of the most acid sensitive lakes in the 

20 Adirondacks (ISA ES). In addition, because of past loadings, areas of the Shenandoah are 

21 sensitive to current deposition levels (ISA ES). Much of the West is naturally less sensitive to 

22 acidification, and as such, less focus is placed on the adequacy of the existing standards in these 

23 areas, with the exception of the mountainous areas of the West, which experience episodic 

24 acidification due to deposition. 

25 While most (99 percent) of stream kilometers in the U.S. are not chronically acidified 

26 under current conditions, a recent survey found sensitive streams in many locations in the U.S., 

27 including the Appalachian mountains, the Coastal Plain, and the Mountainous West (ISA 

28 Section 4.2.2.3). In these sensitive areas, between 1 and 6 percent of stream kilometers are 

29 chronically acidified. 

30 The ISA notes that “consideration of episodic acidification greatly increases the extent 

31 and degree of estimated effects for acidifying deposition on surface waters.” (ISA Section 
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1 3.2.1.6) Some studies show that the number of lakes that could be classified as acidified based on 

2 episodic acidification is 2 to 3 times the number of lakes classified as acidified based on chronic 

3 ANC. These episodic acidification events can have long term effects on fish populations (ISA 

4 Section 3.2.1.6). Under recent conditions, episodic acidification has been observed in locations 

5 in the Eastern U.S. and in the Mountainous Western U.S. (ISA Section 3.2.1.6). 

6 It can therefore be concluded that recent levels of NOx and SOx are associated with 

7 deposition that leads to ANC values below benchmark values known to cause ecological harm in 

8 sensitive aquatic systems, including lakes and streams in multiple areas of the U.S. These 

9 changes are known to have impacts on ecosystem services such as reductions in recreational 

10 fishing. While other ecosystem services (e.g. habitat provisioning, subsistence fishing, and 

11 biological control as well as many others) are potentially affected by reductions in ANC, 

12 confidence in the specific translation of ANC values to these additional ecosystem services is 

13 much lower. 

14 4.5.1.2 Terrestrial Acidification 

15 Based on the case studies on sugar maple and red spruce habitat, staff concludes that 

16 there is significant risk to terrestrial ecosystems from acidification at atmospheric concentrations 

17 of NOx and SOx at or below the current standards. This conclusion is based on application of the 

18 simple mass balance model to deposition levels associated with NOx and SOx concentrations at 

19 or below the current standards. The ecological indicator selected for terrestrial acidification is the 

20 base cation to aluminum ratio (BC:Al), which has been linked to tree health and growth. The 

21 results of the REA strongly support a relationship between atmospheric deposition of NOx and 

22 SOx and BC:Al, and that BC:Al is a good indicator of terrestrial acidification. At levels of 

23 deposition associated with NOx and SOx concentrations at or below the current standards, BC:Al 

24 levels are expected to be below benchmark values that are associated with significant losses in 

25 tree health and growth. Such degradation of terrestrial ecosystems could affect ecosystem 

26 services such as habitat provisioning, endangered species, goods production (timber, syrup, etc.) 

27 and many others. 

28 Many locations in sensitive areas of the U.S. have Bc/Al levels below benchmark levels 

29 classified as providing low to intermediate levels of protection to tree health. At a Bc/Al ratio of 

30 1.2 (intermediate level of protection), red spruce growth can be reduced by 20 percent. At a 
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1 Bc/Al ratio of 0.6 (low level of protection), sugar maple growth can be reduced by 20 percent. 

2 The REA did not evaluate broad sensitive regions. However, in the sugar maple case study area 

3 (Kane Experimental Forest), recent deposition levels are associated with a Bc/Al ratio below 1.2, 

4 indicating between intermediate and low level of protection, which would indicate the potential 

5 for a greater than 20 percent reduction in growth. In the red spruce case study area (Hubbard 

6 Brook Experimental Forest), recent deposition levels are associated with a Bc/Al ratio slightly 

7 above 1.2, indicating slightly better than an intermediate level of protection (REA Section 

8 4.3.5.1). 

9 Over the full range of sugar maple, 12 percent of evaluated forest plots exceeded the 

10 critical load for a Bc/AL ratio of 1.2, and 3 percent exceeded the critical load for a Bc/Al ratio of 

11 0.6. However, there was large variability across states. In New Jersey, 67 percent of plots 

12 exceeded the critical load for a Bc/Al ratio of 1.2, while in several states on the outskirts of the 

13 range for sugar maple, e.g. Arkansas, Illinois, no plots exceeded the critical load for a Bc/Al ratio 

14 of 1.2. For red spruce, overall 5 percent of plots exceeded the critical load for a Bc/Al ratio of 

15 1.2, and 3 percent exceeded the critical load for a Bc/Al ratio of 0.6. In the major red spruce 

16 producing states (Maine, New Hampshire, and Vermont), critical loads for a Bc/AL ratio of 1.2 

17 were exceeded in 0.5, 38, and 6 percent of plots. 

18 The ISA reported one study that estimated 15 percent of U.S. forest ecosystems exceeded 

19 the critical loads for acidity for N and S deposition by >250 eq/ha/year under current conditions 

20 (ISA Section 4.2.1.3). Staff believes that this represents a significant portion of sensitive 

21 terrestrial ecosystems. 

22 It can therefore be concluded that recent levels of NOx and SOx are associated with 

23 deposition that leads to BC:Al values below benchmark values that cause ecological harm in 

24 some sensitive terrestrial ecosystems. While effects are more widespread for sugar maple, there 

25 are locations with low to intermediate levels of protection from effects on both sugar maple and 

26 red spruce. While there are many other ecosystem services, including timber production, natural 

27 habitat provision, and regulation of water, climate, and erosion, potentially affected by 

28 reductions in BC:Al, linkages of BC:Al values to these additional ecosystem services is on the 

29 whole not well understood. 
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1 4.5.2 To what extent does the current NOx secondary standard provide protection 

2 from adverse effects associated with deposition of atmospheric NOx, which 

3 results in nutrient enrichment effects in sensitive aquatic and terrestrial 

4 ecosystems? 

5 Nutrient enrichment effects are due to nitrogen loadings from both atmospheric and non6 atmospheric sources. Evaluation of nutrient enrichment effects requires an understanding that 

7 nutrient inputs are essential to ecosystem health. The specific long term levels of nutrients in a 

8 system affect the types of species that occur over long periods of time. Short term additions of 

9 nutrients can affect species competition, and even small additions of nitrogen in areas that are 

10 traditionally nutrient poor can have significant impacts. In certain limited situations, additions of 

11 nitrogen can increase rates of growth, and these increases can have short term benefits in certain 

12 managed ecosystems. As noted earlier, this review of the standards is focused on unmanaged 

13 ecosystems. As a result, in assessing adequacy of the current standards, we are focusing on the 

14 adverse effects of nutrient enrichment in unmanaged ecosystems. However, the following 

15 discussion provides a brief assessment of effects in managed ecosystems. 

16 Impacts of nutrient enrichment in managed ecosystems may be positive or negative 

17 depending on the levels of nutrients from other sources in those areas. Positive effects can occur 

18 when crops or commercial forests are not receiving enough nitrogen nutrients. Nutrients 

19 deposited on crops from atmospheric sources are often referred to as passive fertilization. 

20 Nitrogen is a fundamental nutrient for primary production in both managed and unmanaged 

21 ecosystems. Most productive agricultural systems require external sources of nitrogen in order to 

22 satisfy nutrient requirements. Nitrogen uptake by crops varies, but typical requirements for wheat 

23 and corn are approximately 150 kg/ha/yr and 300 kg/ha/yr, respectively (NAPAP, 1990). These 

24 rates compare to estimated rates of passive nitrogen fertilization in the range of 0 to 5.5 kg/ha/yr 

25 (NAPAP, 1991). 

26 Information on the effects of changes in passive nitrogen deposition on forestlands and 

27 other terrestrial ecosystems is very limited. The multiplicity of factors affecting forests, including 

28 other potential stressors such as ozone, and limiting factors such as moisture and other nutrients, 

29 confound assessments of marginal changes in any one stressor or nutrient in forest ecosystems. 

30 The ISA notes that only a fraction of the deposited nitrogen is taken up by the forests, most of 

31 the nitrogen is retained in the soils (ISA 3.3.2.1). In addition, the ISA indicates that forest 
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1 management practices can significantly affect the nitrogen cycling within a forest ecosystem, and 

2 as such, the response of managed forests to NOx deposition will be variable depending on the 

3 forest management practices employed in a given forest ecosystem (ISA Annex C C.6.3) 

4 Increases in the availability of nitrogen in N-limited forests via atmospheric deposition could 

5 increase forest production over large non-managed areas, but the evidence is mixed, with some 

6 studies showing increased production and other showing little effect on wood production (ISA 

7 3.3.9). Because leaching of nitrate can promote cation losses, which in some cases create nutrient 

8 imbalances, slower growth and lessened disease and freezing tolerances for forest trees, the net 

9 effect of increased N on forests in the U.S. is uncertain (ISA 3.3.9).

10 In managed agricultural ecosystems, nitrogen inputs from atmospheric NOx comprise a 

11 small fraction (less than 3 percent) of total nitrogen inputs, which include commercially applied 

12 fertilizers as well as applications of composted manure. And because of the temporal and spatial 

13 variability in atmospheric deposition of NOx, it is unlikely that farmers would alter their 

14 fertilization decisions based on expected nitrogen inputs from NOx. And, in some locations, 

15 farmers need less nitrogen inputs due to production of excess nitrogen through livestock. In some 

16 locations, nitrogen production through livestock waste exceeds the absorptive capacity of the 

17 surrounding land, and as such, excess nitrogen from deposition of NOx in those locations reduces 

18 the capacity of the system to dispose of excess nitrogen, potentially increasing the costs of waste 

19 management from livestock operations (Letson and Gollehon, 1996). A USDA Economic 

20 Research Service report found that in 1997, 68 counties with high levels of confined livestock 

21 production had manure nitrogen levels that exceed the assimilative capacity of all the county’s 

22 crop and pasture land (Gollehon et al, 2001). In those locations, additional nitrogen inputs from 

23 NOx deposition will result in excess nitrogen, leading to nitrogen leaching and associated effects. 

24 4.5.3 Aquatic Nutrient Enrichment 

25 The REA case studies focused on coastal estuaries and revealed that while current 

26 ambient loadings of atmospheric NOx are contributing to the overall depositional loading of 

27 coastal estuaries, other non-atmospheric sources are contributing in far greater amounts in total, 

28 although atmospheric contributions are as large as some other individual source types. The 

29 ability of current data and models to characterize the incremental adverse impacts of nitrogen 

30 deposition is limited, both by the available ecological indicators, and by the inability to attribute 
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1 specific effects to atmospheric sources of nitrogen. The REA case studies used as the ecological 

2 indicator for aquatic nutrient enrichment, an index of eutrophication known as the Assessment of 

3 Estuarine Trophic Status Eutrophication Index (ASSETS EI). This index is a six level index 

4 characterizing overall eutrophication risk in a waterbody. This indictor is not sensitive to 

5 relatively large changes in nitrogen deposition. In addition, this type of indicator does not reflect 

6 the impact of nitrogen deposition in conjunction with other sources of nitrogen. 

7 For example, if NOx deposition is contributing nine tenths of the nitrogen loading 

8 required to move a waterbody from an ASSETS EI category of “moderate” to a category of 

9 “poor”, zeroing out NOx deposition will have no impact on the ASSETS EI value. However, if 

10 an area were to decide to put in place decreases in nitrogen loadings to move that waterbody 

11 from “poor” to “moderate,” the area would have to reduce the full amount of the loadings 

12 through other sources if atmospheric deposition were not considered. Thus, the adverse impact of 

13 atmospheric nitrogen is in its contribution to the overall loading, and reductions in NOx will 

14 decrease the amount of reductions from other sources of nitrogen loadings that would be required 

15 to move from a lower ASSETS EI category to a higher category. NOx deposition can also be 

16 characterized as reducing the risk of a waterbody moving from a higher ASSETS EI category to 

17 a lower category, by reducing the vulnerability of that waterbody to increased loadings from 

18 non-atmospheric sources. 

19 Based on the above considerations, staff preliminarily concludes that the ASSETS EI is 

20 not an appropriate ecological indicator for estuarine aquatic eutrophication. Staff further 

21 concludes that additional analysis is required to develop an appropriate indicator for determining 

22 the appropriate levels of protection from N nutrient enrichment effects in estuaries related to 

23 deposition of NOx. As a result, staff is unable to make a determination as to the adequacy of the 

24 existing secondary NOx standard in protecting public welfare from N nutrient enrichment effects 

25 in estuarine aquatic ecosystems. 

26 Additionally, nitrogen deposition can alter species composition and cause eutrophication 

27 in freshwater systems. In the Rocky Mountains, for example, deposition loads of 1.5 to 2 

28 kg/ha/yr which are well within current ambient levels are known to cause changes in species 

29 composition in diatom communities indicating impaired water quality (ISA Section 3.3.5.3). It 

30 then seems apparent then that the existing secondary standard for NOx does not protect such 

31 ecosystems and their resulting services from impairment. 
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1 4.5.4 Terrestrial Nutrient Enrichment 

2 The scientific literature has many examples of the deleterious effects caused by excessive 

3 nitrogen loadings to terrestrial systems. Several studies have set benchmark values for levels of 

4 N deposition at which scientifically adverse effects are known to occur. These benchmarks are 

5 discussed more thoroughly in Chapter 5 of the REA. Large areas of the country appear to be 

6 experiencing deposition above these benchmarks for example, Fenn et al. (2008) found that at 

7 3.1 kg N/ha/yr, the community of lichens begins to change from acidophytic to tolerant species; 

8 at 5.2 kg N/ha/yr, the typical dominance by acidophytic species no longer occurs; and at 10.2 kg 

9 N/ha/yr, acidophytic lichens are totally lost from the community. Additional studies in the 

10 Colorado Front Range of the Rocky Mountain National Park support these findings and are 

11 summarized in Chapter 6.0 of the Risk and Exposure Assessment. These three values (3.1, 5.2, 

12 and 10.2 kg/ha/yr) are one set of ecologically meaningful benchmarks for the mixed conifer 

13 forest (MCF) of the pacific coast regions. Nearly all of the known sensitive communities receive 

14 total nitrogen deposition levels above the 3.1 N kg/ha/yr ecological benchmark according to 

15 the12 km, 2002 CMAQ/NADP data, with the exception of the easternmost Sierra Nevadas. 

16 MCFs in the southern portion of the Sierra Nevada forests and nearly all MCF communities in 

17 the San Bernardino forests receive total nitrogen deposition levels above the 5.2 N kg/ha/yr 

18 ecological benchmark. 

19 Coastal Sage Scrub communities (CSS) are also known to be sensitive to community 

20 shifts caused by excess nitrogen loadings. Wood et al. (2006) investigated the amount of nitrogen 

21 utilized by healthy and degraded CSS systems. In healthy stands, the authors estimated that 3.3 

22 kg N/ha/yr was used for CSS plant growth (Wood et al., 2006). It is assumed that 3.3 kg N/ha/yr 

23 is near the point where nitrogen is no longer limiting in the CSS community. Therefore, this 

24 amount can be considered an ecological benchmark for the CSS community. The majority of the 

25 known CSS range is currently receiving deposition in excess of this benchmark. Thus, staff 

26 concludes that recent conditions where NOx ambient concentrations are at or below the current 

27 NOx secondary standards are not adequate to protect against anticipated adverse impacts from N 

28 nutrient enrichment in sensitive ecosystems (systems where N is not limiting). 
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1 4.6 TO WHAT EXTENT DO THE CURRENT NOX AND/OR SOX

2 SECONDARY STANDARDS PROVIDE PROTECTION FROM 

3 OTHER ECOLOGICAL EFFECTS (E.G., MERCURY 

4 METHYLATION) ASSOCIATED WITH THE DEPOSITION OF 

5 ATMOSPHERIC NOX, AND/OR SOX? 

6 It is stated in the ISA (ISA Sections 3.4.1 and 4.5) that mercury is a highly neurotoxic 

7 contaminant that enters the food web as a methylated compound, methylmercury. Mercury is 

8 principally methylated by sulfur-reducing bacteria and can be taken up by microorganisms, 

9 zooplankton and macroinvertebrates. The contaminant is concentrated in higher trophic levels, 

10 including fish eaten by humans. Experimental evidence has established that only inconsequential 

11 amounts of methylmercury can be produced in the absence of sulfate. Once methylmercury is 

12 present, other variables influence how much accumulates in fish, but elevated mercury levels in 

13 fish can only occur where substantial amounts of methylmercury are present. Current evidence 

14 indicates that in watersheds where mercury is present, increased SOx deposition very likely 

15 results in additional production of methylmercury which leads to greater accumulation of MeHg 

16 concentrations in fish (Munthe et al, 2007; Drevnick et al., 2007). 

17 The production of meaningful amounts of methylmercury (MeHg) requires the presence 

of SO4

2- and mercury, and where mercury is present, increased availability of SO42- 18 results in 

19 increased production of MeHg. There is increasing evidence on the relationship between sulfur 

20 deposition and increased methylation of mercury in aquatic environments; this effect occurs only 

21 where other factors are present at levels within a range to allow methylation. The production of 

22 methylmercury requires the presence of sulfate and mercury, but the amount of methylmercury 

23 produced varies with oxygen content, temperature, pH, and supply of labile organic carbon (ISA 

24 Section 3.4). In watersheds where changes in sulfate deposition did not produce an effect, one or 

25 several of those interacting factors were not in the range required for meaningful methylation to 

26 occur (ISA Section 3.4). Watersheds with conditions known to be conducive to mercury 

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

28 relationship between sulfur and methylmercury production is addressed qualitatively in Chapter 

29 6 of the Risk and Exposure Assessment. 
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1 With respect to sulfur deposition and mercury methylation, the final ISA determined: The 

2 evidence is sufficient to infer a causal relationship between sulfur deposition and increased 

3 mercury methylation in wetlands and aquatic environments. However, staff did not conduct a 

4 quantitative assessment of the risks associated with increased mercury methylation under current 

5 conditions. As such, staff are unable to make a determination as to the adequacy of the existing 

6 SO2 standards in protecting against welfare effects associated with increased mercury 

7 methylation. 

8 4.7 REFERENCES

9 B.J. Finlayson-Pitts and J.N. Pitts, 2000, Chemistry of the Upper and Lower Troposhere, 

10 Academic Press, San Diego, CA 

11 Drevnick, P.E., D.E. Canfield, P.R. Gorski, A.L.C. Shinneman, D.R. Engstrom, D.C.G. Muir, 

12 G.R. Smith, P.J. Garrison, L.B. Cleckner, J.P. Hurley, R.B. Noble, R.R. Otter, and J.T. 

13 Oris. 2007. Deposition and cycling of sulfur controls mercury accumulation in Isle 

14 Royale fish. Environmental Science and Technology 41(21):7266–7272. 

15 Fenn, M.E., S. Jovan, F. Yuan, L. Geiser, T. Meixner, and B.S. Gimeno. 2008. Empirical and 

16 simulated critical loads for nitrogen deposition in California mixed conifer forests. 

17 Environmental Pollution 155(3):492–511. 

18 Munthe, J., R.A. Bodaly, B.A. Branfireun, C.T. Driscoll, C.C. Gilmour, R. Harris, M. Horvat, M. 

19 Lucotte, and O. Malm. 2007. Recovery of mercury-contaminated fisheries. Ambio 36:33–

20 44. 

21 Scheffe, R.D., P. A. Solomon, R. Husar, T. Hanley, M. Schmidt, M. Koerber, M. Gilroy, J. 

22 Hemby, N. Watkins, M. Papp, J. Rice, J. Tikvart andR. Valentinetti, The National 

23 Ambient Air Monitoring Strategy: Rethinking the Role of National Networks, JAWMA, 

24 ISSN:1047-3289 J. Air & Waste Manage. Assoc. 2009, 59:579–590 DOI:10.3155/1047-

25 3289.59.5.579 

26 U.S. EPA (Environmental Protection Agency). 1982. Review of the National Ambient Air Quality 

27 Standards for Sulfur Oxides: Assessment of Scientific and Technical Information. 
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1 OAQPS Staff Paper. EPA-450/5-82-007. U.S. Environmental Protection Agency, Office 

2 of Air Quality Planning and Standards, Research Triangle Park, NC. 

3 U.S. EPA (Environmental Protection Agency). 1995. Review of the National Ambient Air Quality 

4 Standards for Nitrogen Dioxide: Assessment of Scientific and Technical Information. 

5 OAQPS Staff Paper. EPA-452/R-95-005. U.S. Environmental Protection Agency, Office 

6 of Air Quality Planning and Standards, Research Triangle Park, NC. September. 

7 U.S. EPA (Environmental Protection Agency). 2008. Integrated Science Assessment (ISA) for 

8 Oxides of Nitrogen and Sulfur–Ecological Criteria (Final Report). EPA/600/R9 08/082F. U.S. Environmental Protection Agency, National Center for Environmental 

10 Assessment–RTP Division, Office of Research and Development, Research Triangle 

11 Park, NC. Available at http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=201485. 

12 Wood, Y., T. Meixner, P.J. Shouse, and E.B. Allen. 2006. Altered Ecohydrologic response 

13 drives native shrub loss under conditions of elevated N-deposition. Journal of 

14 Environmental Quality 35:76–92. 

15 
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1 5. CONCEPTUAL DESIGN OF AN ECOLOGICALLY RELEVANT 

2 MULTI-POLLUTANT STANDARD 

3 The objective of this chapter is to describe the conceptual design for a national ambient 

4 air quality standard that links ecological indicators of concern to ambient air indicators of NOx

5 and SOx. In Chapter 4 of this policy assessment, the limitations of the design of the current 

6 secondary standards are described as they apply to protection of sensitive ecosystems. The 

7 conceptual design described in this chapter addresses those limitations. The overall concept for 

8 the standards starts by recognizing that the fundamental welfare effects associated with ambient 

9 NOx and SOx occur through the process of deposition to sensitive ecosystems. As detailed in 

10 Chapter 4, previous NOx and SOx NAAQS reviews only considered effects to vegetation via 

11 stomatal exposure. There is now sufficient data to link atmospheric concentrations to adverse 

12 effects in ecosystems that are caused by exposure via deposition to soils and surface waters. 

13 Deposition is a direct consequence of atmospheric concentration; however it is also modified by 

14 factors that vary across the landscape (e.g. elevation and groundcover). Likewise, ecological 

15 response to deposition can vary according to ecosystem sensitivity and the ecological indicator 

16 of concern. This is the first time a secondary standard for deposition effects related to NOx and 

17 SOx has been developed; therefore the conceptual design of a potential standard is described here 

18 prior to the specific details on the indicator, level, form and averaging time for such a potential 

19 standard that are presented in chapter 6. 

20 5.1 COMPONENTS OF THE DESIGN 

21 There are four main components to the conceptual design of the standard: atmospheric 

22 and ecological indicators, deposition metrics, functions that relate indicators to deposition 

23 metrics and factors that modify the functions. These components of the design are illustrated in 

24 Figure 5-1. The squares represent indicators. Ecological indicators are chemical or biological 

25 components of the ecosystem that can be linked to N and S deposition based on scientific 

26 evidence. Air quality indicators are the chemical species of the criteria air pollutants that best 

27 represent the atmospheric pollutants that cause ecological harm in the criteria pollutant 

28 categories of oxides of nitrogen and oxides of sulfur. Triangles indicate functions in which two 

29 variables are related. The ecological effect function is the relationship between the ecological 
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1 indicator and deposition over a range of values. The atmospheric deposition transformation 

2 function is the relationship between deposition and the atmospheric concentration of an air 

3 quality indicator. The circles represent factors which will modify the functions. Modifying 

4 factors can vary across the landscape. The spatial heterogeneity of modifying factors can be 

5 challenging to characterize, and therefore in some cases we present multiple options for how to 

6 incorporate them into the design. 

7 

8 Fig 5-1. Schematic diagram of the conceptual design of the standard. 

9 5.1.1 For which effects is there sufficient information to support setting standards? 

10 After review of the ISA and REA, CASAC concluded that aquatic acidification should be 

11 the focus for developing a multi-pollutant standard, based on the quantity and quality of data. 

12 CASAC also recommended that, in addition to aquatic acidification, the EPA should consider 

13 multiple ecological indicators and made the following statement in their letter to the EPA on 

14 August 28, 2009: 

15 “...the Panel finds the information in the current REA sufficient to inform setting 

16 separate standards for terrestrial acidification, eutrophication of western alpine 

17 lakes and terrestrial nutrient enrichment. However, the Panel believes that setting 

18 a standard for coastal nutrient enrichment would be difficult because of the 

19 substantial inputs of non-atmospheric sources of N to these systems.” 

20 The following sections describe the conceptual design for standards based on aquatic 

21 acidification, terrestrial acidification, eutrophication of high elevation western lakes and 

Air Quality 

Indicator(s) 

Ecological 

Indicator 

Ecological 

Response to 

Deposition 

 Function

Atmospheric 

Deposition 

Transformation 

Function 

Deposition 

Metric 

Variable/Fixed 

Modifying 

Factors

Variable/Fixed 

Modifying 

Factors
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1 terrestrial nutrient enrichment. The focus of the first draft will be on aquatic acidification, but 

2 this general conceptual framework will apply to a broader set of potential endpoints. 

3 5.2 ECOLOGICAL COMPONENTS OF THE STANDARD: AQUATIC 

4 ACIDIFICATION 

5 Details of the conceptual design of the NOx and SOx NAAQS based on aquatic 

6 acidification effects are presented in this section. A summary of our over all approach is given 

7 here to help provide context and support for the more detailed discussions that follow. 

8 At the catchment scale, ambient NOy and SOx add to the total deposition of N and S that 

9 lead to aquatic acidification. NHx is often another big component of the total N deposition. The 

10 load of deposition that causes a desired level of ANC will vary depending on the characteristics 

11 of the ecosystem. The level of ANC is tied to the degree of biological harm to the system from 

12 aquatic acidification. 

13 The components of the standard are modified for application to aquatic acidification and 

14 presented in Fig 5-2. The bidirectional arrows emphasize that the order in which one considers 

15 the links between ANC and atmospheric concentrations of NOx and SOx is conceptually 

16 important to the standard design. Moreover, different questions may be answered by working 

17 through Fig 5-2 from the left to the right versus the right to the left. For example, working from 

18 left to right, when a level of ANC is specified the deposition loadings of N and S that would 

19 cause the specified level of ANC can be calculated; in essence this would be a critical load for a 

20 specified ANC limit. A comparison between the total amount of deposited N and S to the critical 

21 load would determine whether the specified level of ANC is achieved for a catchment. Let’s now 

22 work through the equation from right to left. If the amount of N and S deposited to a given 

23 catchment is known, you could calculate the level of ANC that would result. The calculated 

24 ANC could then be compared to a benchmark value of ANC. In both of these approaches the 

25 amount of reduced N would be subtracted from the total N deposition to calculate deposition 

26 from NOy. The atmospheric concentrations of NOy and SOx would be calculated from the 

27 deposition of NOy and S according to the methods presented in section 5.4. To determine the 

28 appropriate conceptual design from the ecological components of the standard, the analysis from 

29 the REA is evaluated in which critical loads were calculated for a target value of ANC, thereby 

30 working from left to right on Fig 5-2. 
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1 

2 Fig 5-2. Schematic diagram of the conceptual design of the standard based on 

3 aquatic acidification. From left to right, if a desired level of ANC is known, then 

4 the concentration of the atmospheric indicators that will cause that level may be 

5 calculated. From right to left, if the if the concentration of the air quality 

6 indicators are known than the ANC that will be caused may be calculated. 

7 The secondary NAAQS would apply to all areas of the country. It is not practical to 

8 evaluate each catchment individually, and that is not the appropriate approach for a national 

9 standard. Here, EPA staff proposes to categorize landscape features nationally, such that within a 

10 category there are generally similar characteristics as far as the relationship of total deposited N 

11 and S to the ANC. Every part of the country would be assigned into one of these bins/ landscape 

12 categories. 

13 The secondary NAAQS would be based on a judgment as to a specified level of ANC. 

14 For each national acid-sensitivity bin/ landscape category there would be a range of critical loads 

15 for a specified ANC limit from the individual catchments within the total population aggregated 

16 to an acid-sensitivity category. Given that, the EPA would develop a deposition metric and 

17 associated tradeoff curve that represented the percentage of the catchments that would achieve 

18 the ANC (DL%ECO). Therefore a judgment would also need to be made to determine the 

Concentration 

of the 

Air Quality 

Indicator(s) 

Level of 

ANC related to 

biological effects

Ecosystem 

Acidification 

Model that 

relates ANC to 

deposition 

at catchmentscale

Atmospheric 

Deposition 

Transformation 

Function 

Deposition 

Loading of 

N +S 

which 

represent 

national 

scale 

landscape 

categories 

Modifying 

Factors 

Modifying 

Factors

Relationship between the amount of 

deposition and the effect on the selected 

ecological indicator, ANC (described in 5.2) 

Relationship between the amount of 

deposition and the concentration of NOx 

and SOx (described in 5.4) 
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1 percentage of ecosystems that would be targeted to achieve a specified ANC level that applies to 

2 a bin/category. 

3 The following discussions in this section focus on the ecological components of the 

4 standard (ecological indicator, the deposition metric, the ecological response function and its 

5 modifiers). Questions that are relevant to the design of the standard are used to organize these 

6 discussions. The first series of questions (section 5.2.1) considers information presented in the 

7 ISA and REA relevant to the conceptual design, while the second series of questions (section 

8 5.2.2) presents the proposed conceptual design in more detail with an example calculation based 

9 on the Adirondacks case study presented in section 5.5. 

10 5.2.1 Conceptual design considerations from the ISA and REA 

11 This section presents discussion of the ecological components of the design based on 

12 information in the ISA and REA. The information presented here is considered in the 

13 development of the design options that are proposed (section 5.2.2). 

14 5.2.1.1 Does the available information provide support for the use of ecological 

15 indicators to characterize the responses of aquatic ecosystems to nitrogen and 

16 sulfur deposition? 

17 Ecological indicators of acidification in aquatic ecosystems can be chemical or 

18 biological components of the ecosystem that are demonstrated to be altered by the acidifying 

19 effects of N and S deposition based on scientific evidence. A desirable ecological indicator for 

20 aquatic acidification will be one that is measurable or estimable, linked causally to deposition of 

21 N and S, and linked causally to ecological effects known or anticipated to adversely affect public 

22 welfare. 

23 As summarized in Chapter 2, aquatic acidification is indicated by changes in the surface 

24 water chemistry of ecosystems. In turn, the alteration of surface water chemistry has been linked 

25 to negative effects on the biotic integrity of freshwater ecosystems. There are a suite of chemical 

26 indicators that can be used to assess the effects of acidifying deposition on lake or stream acid27 base chemistry. These indicators include acid neutralizing capacity (ANC), surface water pH and 

concentrations of SO4

2-, NO3-

, Al, and Ca2+ 28 ; the sum of base cations; and the recently developed 

29 base cation surplus. ANC is the most widely used chemical indicator of acid sensitivity and has 

30 been found in various studies to be the best single indicator of the biological response and health 
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1 of aquatic communities in acid-sensitive systems (Lien et al., 1992; Sullivan et al., 2006). The 

2 utility of the ANC criterion lies in the association between ANC and the surface water 

constituents that directly contribute to or ameliorate acidity-related stress, in particular pH, Ca2+ 3 , 

4 and Al. ANC is also used because it integrates overall acid status (ISA 3.2.3 and REA 5.2.1) and 

5 the acid-related stress for biota that occupies the water that can be directly related to biological 

6 impairment, specifically the number of fish species (ISA 3.2.3). 

7 EPA staff thus concludes that the available information provides support for the use of 

8 ecological indicators to characterize the responses of aquatic ecosystems to nitrogen and sulfur 

9 deposition, and that ANC is the most supportable indicator. 

10 5.2.1.2 Does the available information provide support for the development of a 

11 function that relates total nitrogen and sulfur deposition to ecological 

12 indicators? 

13 There is evidence to support the link between deposition of N and S, water chemistry and 

14 biota. Atmospheric deposition of NOx and SOx causes aquatic acidification through the input of 

acid anions (e.g. NO3

-

 and SO4

2- 15 ). The anions are deposited either directly to the aquatic 

16 ecosystem, or indirectly via terrestrial ecosystems. In other words, when the anions are mobile in 

17 the terrestrial soil, they can leach into adjacent waterbodies. Acidification of ecosystems is 

18 reflected in a robust relationship between ANC of water and the deposition of NOx and SOx. 

19 In the REA, the relationship between deposition and ANC was investigated using models 

20 of ecosystem acidification (REA Chapter 4 and REA Appendix 4). These models characterize 

21 the relationship between deposition N and S and the ability of an ecosystem to counterbalance or 

22 buffer the deposition. The utility of the ecosystem acidification models is for simulating a variety 

23 of water and soil acidification responses at the laboratory, plot, hillslope, and catchment scales. 

24 For example, the ANC value caused by the current amount of deposition could be calculated, or, 

25 the level of deposition that causes a specified level of an ecosystem endpoint could be calculated 

26 (i.e. a critical load for ANC=50) (ISA appendix A). 

27 The models used in the REA were the Steady State Water Chemistry model (SSWC), the 

28 First-order Acid Balance model (FAB) and the Model of Acidification of Groundwater in 

29 Catchment (MAGIC). The SSWC and FAB models were used to calculate critical loads for 

30 specified ANC levels in the case study areas. MAGIC was used to develop weathering rates that 
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1 were needed for the Shenandoah critical loads calculation and the F-factor was used for 

2 weathering rates in the in the Adirondacks. MAGIC was also used to show long-term trends 

3 between anthropogenic N and S deposition on ANC dating back to pre-industrial times. It is 

4 important to note that acidification models are data intensive. Water chemistry data from the 

5 TIME and LTM programs, which are part of the Environmental Monitoring and Assessment 

6 Program (EMAP), were input to the acidifcation models. An abbreviated summary of 

7 acidification models and data inputs is given in Table 5.2-1, a complete list is in Appendix A. 

Table 5-1. Illustration of how selected models and water chemistry data were used to calculate 

critical loads in the REA. 

Weathering rate 

as input to CL 

model 

Water chemistry data 

input to CL model 

CL calculation: 

single value 

CL calculation: 

critical load 

function 

Adirondack F-factor EMAP SSWC FAB 

Shenandoah MAGIC EMAP SSWC FAB 

8 

9 In summary, the EPA staff concludes that the available information supports using the 

10 acidification models to characterize the relationship between total nitrogen and sulfur deposition 

11 and the ANC ecological indicator. 

12 5.2.1.3 Does a quantified relationship exist between the level of a relevant ecological 

13 indicator to an amount of nitrogen and sulfur deposition? 

14 A quantified relationship exists between the level of ANC and nitrogen and sulfur 

15 deposition. This relationship was analyzed to determine current risk for two case study areas, the 

16 Adirondacks and Shenandoahs, in the REA using a time series analysis and a critical load 

17 approach. The time series analysis was conducted using MAGIC and recent monitoring data. The 

18 critical loads analysis used water chemistry data from the Temporally Integrated Monitoring of 

19 Ecosystems (TIME) program and Long-term Monitoring (LTM) to calculate critical loads with the 

20 SSWC and FAB models. 

21 Long-term trends in surface water nitrate, sulfate and ANC were modeled using MAGIC 

22 for the two case study areas. This data was used to compare current surface water conditions 

23 (2006) with preindustrial conditions (i.e. preacidification or 1860). The results showed a 
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1 dramatic increase in the number of acidified lakes, characterized as a decrease in ANC levels, 

2 since the onset of anthropogenic N and S deposition (REA Appendix 4 Section 5) 

3 More recent trends in ANC, over the time period from 1990 to 2006, were assessed using 

4 monitoring data collected at the two case study areas. In both case study areas, nitrate and sulfate 

5 deposition decreased over this time period. In the Adirondacks, this corresponded to a decreased 

6 concentration of nitrate and sulfate in the surface waters and an increase in ANC (REA 4.2.4.2). 

7 In the Shenandoahs, there was a slight decrease in nitrate and sulfate concentration in surface 

8 waters corresponding to modest increase in ANC from 50 μeq/L in 1990 to 67 μeq/L in 2006 

9 (REA 4.2.4.3 and REA Appendix 4 Section 3.4). 

10 A critical load for ANC is the amount (or load per year) of N and S deposition above 

11 which a selected level of ANC will be exceeded for individual water bodies. In the REA case 

12 study analyses, critical loads and their exceedances were calculated for four values of ANC (i.e., 

13 ANC of 0, 20, 50, and 100 μeq/L) for 169 lakes in the Adirondacks and 60 streams in the 

14 Shenandoahs. Those four ANC values correspond to important points along the ANC response 

15 curve that are associated with levels of ecosystem impairment. The case studies used steady-state 

16 critical loads models and focus on the combined load of sulfur and nitrogen deposition, below 

17 which the ANC level would still support healthy aquatic ecosystems. For each waterbody, the 

18 total deposition in the year 2002 was compared with the estimated critical loads for the four 

19 critical limit thresholds to determine which sites exceed their critical limit of deposition and 

20 biological protection level. Estimates of deposition were based on the sum of measured wet 

21 deposition values from the year 2002 NADP network and modeled dry deposition values based 

22 on the year 2002 emissions and meteorology using the Community Multiscale Air Quality 

23 (CMAQ) model, respectively (REA 4.2). It is important to note that a single level of ANC may 

24 be caused by a range of deposition values due to heterogeneous sensitivity among watersheds. 

25 In summary, EPA staff concludes that a quantified relationship exists between the level 

26 of surface water ANC and an amount of nitrogen and sulfur deposition. This relationship is 

27 demonstrated by long-term trends going back to preindustrial conditions in the 1860s, recent 

28 trends since the 1990s and critical loads modeling based on 2002 deposition data. Models are the 

29 best way to evaluate how multiple environmental factors alter the relationship ANC and 

30 deposition. 
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1 5.2.1.4 What are the important variables in the ecological response to deposition 

2 relationship(s)? 

3 There are numerous variables that modify the ANC to deposition relationship. The effects 

4 of these modifiers are described by models that parameterize ecosystems to simulate the process 

5 of acidification. The steady-state models used for critical loads analysis in the REA required 

6 input data for between 17 and 20 environmental parameters. 

7 The basic principle of the steady-state approach is to determine the maximum acid input 

8 that will balance the system at a biogeochemical safe-limit. Safe-limit is a subjective term that 

9 relates to a particular benchmark (e.g. ANC = 20, 50, 100), representing protection against 

10 specific types and magnitudes of aquatic ecosystem response. The steady-state models that were 

11 used in the REA relate an aquatic ecosystem’s critical load to the weathering rate of its drainage 

12 basin expressed in terms of the base cation flux. Weathering rate of geologic parent material is 

13 the main source of base cations to an ecosystem. It is considered one of the governing factors to 

14 ecosystem critical loads, and therefore an important variable in the ecological response to 

15 deposition relationship. Landscape features that are correlated to ecosystem acid-sensitivity 

16 include lithology, elevation, percent forested watershed, and watershed area (Sullivan et al. 

17 2007). A more detailed summary of the models and the environmental variables incorporated 

18 into the models that were used in the REA is presented in Appendix A. 

19 Numerous environmental variables affect the acidification process. Therefore the input 

20 data required to run acidification models is rather extensive. For example, MAGIC, a dynamic 

21 process based model of acidification, requires atmospheric deposition fluxes for the base cations 

22 and strong acid anions as inputs to the model. The volume discharge for the catchment must also 

23 be provided to the model. Values for soil and surface water temperature, partial pressure of 

24 carbon dioxide and organic acid concentrations must also be provided at the appropriate 

25 temporal resolution. The aggregated nature of the model requires that it be calibrated to 

26 observational data from a system before it can be used to examine potential system response. The 

27 calibration procedure requires that stream water quality, soil chemical and physical 

28 characteristics, and atmospheric deposition data be available for each catchment. The water 

29 quality data needed for calibration are the concentrations of the individual base cations (Ca, Mg, 

Na, and K) and acid anions (Cl, SO4

2-, and NO3- 30 ) and the pH. The soil data used in the model 

31 include soil depth and bulk density, soil pH, soil cation-exchange capacity, and exchangeable 
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1 bases in the soil (Ca, Mg, Na, and K). The atmospheric deposition inputs to the model must be 

2 estimates of total deposition (wet and dry). 

3 In summary, the EPA staff concludes there are numerous variables which modify the 

4 ANC to deposition relationship. The relationships between environmental factors are described 

5 by models that parameterize ecosystems to simulate the process of acidification. Weathering rate 

6 of geologic parent material is the main source of base cations to an ecosystem, and it is therefore 

7 considered one of the governing factors of ecosystem critical loads. Landscape features that are 

8 correlated to ecosystem acid-sensitivity include lithology, elevation, percent forested watershed, 

9 and watershed area. Consideration of the effects of environmental variables on the relationship 

10 between environmental variables is extensive in ecosystem acidification models. The calibration 

11 procedure requires that stream water quality, soil chemical and physical characteristics, and 

12 atmospheric deposition data be available for each catchment. 

13 5.2.1.5 Are these relationships applicable regionally? 

14 The relationship between ANC and N + S deposition based on catchment- scale modeling 

15 is applicable regionally. Response to N and S deposition will vary catchment by catchment. 

16 However, modeling every catchment in a region (i.e. a spatial area that includes a large 

17 population of individual catchments) is implausible due to the extensive data requirements to 

18 inform the simulations. A method to extrapolate watershed-scale analysis to a region was 

19 developed in the REA. In that method, the critical loads (combined N+S load) developed for the 

20 case study sites were applied over a region using water quality data. Critical load exceedance 

21 (i.e., the amount of actual deposition above the critical load, if any) was calculated for each 

22 waterbody in the region to quantify the number of lakes or streams that receive harmful levels of 

23 deposition. Lakes and streams with positive exceedance values, where actual deposition was 

24 above its critical load, were not protected at that critical limit (e.g. ANC= 20, 50, 100; REA 

25 appendix 4). 

26 In the Adirondack case study conducted in the REA, critical load exceedances were 

27 extrapolated to lakes defined by the New England EMAP probability survey. The EMAP 

28 probability survey was designed to estimate, with known confidence, the status, extent, change, 

29 and trends in condition of the nation’s ecological resources, such as surface water quality. For 

30 the Adirondack Case Study Area, the regional EMAP probability survey of 117 lakes were used 
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1 to infer the number of lakes and percentage of lakes that receive acidifying deposition above 

2 their critical load of a population of 1,842 lakes. ANC limits of 20, 50, and 100 μeq/L were 

3 examined. 

4 In the Shenandoah case study, critical load exceedances were extrapolated using the 

5 SWAS-VTSSS LTM quarterly monitored sites to the population of brook trout streams that do 

6 not lie on limestone bedrock and/or are not significantly affected by human activity within the 

7 watershed. The total number of brook trout streams represented by the SWAS-VTSSS LTM 

8 quarterly monitored sites is approximately 310 streams out of 440 mountain headwater streams 

9 known to support reproducing brook trout. ANC limits of 20, 50, and 100 μeq/L were examined. 

10 (REA Appendix 4.3.1). 

11 In summary, approaches were developed in the REA to extrapolate the ANC-deposition 

12 relationship across a region. The data requirements for these approaches include (1) calculation 

13 of critical loads of ANC using a catchment-scale model (2) stream chemistry data across the 

14 region of concern, and (3) deposition loads across the region. With this information the 

15 deposition load that would cause the stream to exceed the critical limit of ANC was calculated as 

16 the critical load exceedance. 

17 5.2.1.6 Are these relationships applicable nationally? 

18 The relationship between ANC and N + S deposition is applicable nationally. Areas that 

19 have similar geologic underpinnings and weathering rates should show similar sensitivity to NOx

20 and SOx deposition. The critical load modeling that was used in the REA case studies requires 

21 parameterization to each catchment. The spatial scale is small (e.g. catchment level) and the data 

22 requirements are great (17+ environmental variables for each catchment) to use this method to 

23 determine critical loads across all sensitive regions of the U.S. at this time. It is important to note 

24 that acid-sensitivity often varies from catchment to catchment. Even if we did calculate critical 

25 loads data for each catchment, aggregation of the catchment-scale data is appropriate for a 

26 national standard. 

27 The technique developed in the REA for extrapolating catchment-specific results to a 

28 regional area determines the number of streams in a given area that show critical load (CL) 

29 exceedances based on a selected value of ANC and deposition values for 2002. The approach 

30 developed in the case study for extrapolating catchment-specific results to a regional area is not 
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1 immediately applicable across the U.S. because data for surface water chemistry and data for 

2 other input parameters is not available at a national scale. 

3 To summarize, the relationship between ANC and N + S deposition is applicable 

4 nationally. However the data required for critical loads analysis and extrapolation that is 

5 available on the regional scale is not available at the national scale. Considering this current data 

6 limitation the utility of the extrapolation approach developed in the REA to the national-scale is 

7 limited. Additional national-scale approaches are discussed in section 5.2.3. 

8 5.2.1.7 Summary 

9 In summary, EPA staff concludes that the available information from the ISA and REA 

10 supports the following characterization of aquatic acidification. First, there is sufficient support 

11 for the use of ecological indicators to characterize the responses of aquatic ecosystems to 

12 nitrogen and sulfur deposition, and that ANC is the most supportable indicator. The available 

13 information supports using the acidification models to characterize the ecological response, using 

14 ANC as the indicator, to nitrogen and sulfur deposition. Models are the best way to evaluate how 

15 multiple environmental factors alter the relationship ANC and deposition. 

16 Heterogeneous sensitivity among watersheds is due in part to landscape features. 

17 Weathering rate of geologic parent material is the main source of base cations to an ecosystem, 

18 and is therefore considered one of the governing factors of ecosystem critical loads. Landscape 

19 features that are correlated to ecosystem acid-sensitivity include lithology, elevation, percent 

20 forested watershed, and watershed area. 

21 Modeling every catchment in a region is implausible due to the extensive data 

22 requirements. The relationship between ANC and N + S deposition is applicable regionally. A 

23 method to extrapolate watershed-scale analysis to a region was developed in the REA. In that 

24 method, the critical loads (combined N+S load) developed for the case study sites were applied 

25 over a region using water quality data. The data requirements for the regional extrapolation 

26 include (1) calculation of critical loads for ANC using a catchment-scale model (2) stream 

27 chemistry data across the region of concern, and (3) deposition loads across the region. The 

28 approach developed in the case study areas is not immediately applicable across the U.S. because 

29 data for critical loads modeling and surface water chemistry is not available at a national scale. 

30 However, it is important to note that the relationship between ANC and N + S deposition is 
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1 applicable nationally. Areas that have similar geologic underpinnings should show similar 

2 sensitivity to NOx and SOx deposition. 

3 5.2.2 Design options for aquatic acidification 

4 The following design options describe the conceptual approach to integrating the 

5 ecological components of the standard outlined in section 5.1: ecological indicator, modifying 

6 factors, ecological response function and deposition metric. The goal is to illustrate how levels of 

7 NOx and SOx can be set to protect areas of the U.S. from acidic deposition. 

8 5.2.2.1 Is it appropriate to use ANC as the ecological indicator for the conceptual 

9 design of the NOx and SOx standard based on aquatic acidification? 

10 There is strong evidence supporting that ANC is an appropriate ecological indicator for 

11 aquatic acidification as discussed in Chapter 2 and Section 5.1.1 (as well as ISA 3.2.3 and REA 

12 5.2.1). Options for the level of the indicator are discussed in Chapter 6. The options for the levels 

13 are derived from experimental and observed evidence in the scientific literature showing the 

14 biological effects over a range of ANC values. 

15 5.2.2.2 What is the appropriate ecosystem acidification model(s) to represent the 

16 ecological response function? 

17 In the REA, critical loads were calculated for specified ANC levels using the SSWC and 

18 FAB models, these are referred to as acidification models, acid balance models or critical loads 

19 models. The different assumptions of each modeling approach have implications that should be 

20 considered in the conceptual design of a deposition-based NOx and SOx standard. Most notably, 

21 biogeochemical pathways of N and S deposition are considered differently in the two models. In the 

22 SSWC model, sulfate is assumed to be a mobile anion (i.e. S leaching = S deposition), while nitrogen is 

23 retained in the catchment by various processes. This assumption that all N is retained by the ecosystem 

24 and does not contribute to acidification is incorrect in many instances because nitrate leaching is 

25 observed. If nitrogen is leaching out of an ecosystem, obviously it has not been retained. Nitrate leaching 

26 is determined from the sum of the measured concentrations of nitrate and ammonia in the runoff. The 

27 critical load for sulfur that is calculated by SSWC can be corrected for the amount of nitrogen that 

28 contributes to acidification. When an exceedence value for the critical load is calculated, the critical load 

29 is subtracted from S deposition plus the amount of nitrate leaching, as it represents the difference between 
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1 N deposition and N retention by the ecosystem. N leaching data used in this calculation is considered 

2 robust. 

3 In contrast to the SSWC approach, the FAB model includes more explicit modeling of N 

4 processes including soil immobilization, denitrification, and wood removal, in-lake retention of N 

5 and S, as well as lake size. Although N cycling is more detailed in the FAB model, there is 

6 greater uncertainty in the input data needed to characterize the components of the N cycle. The 

7 FAB model yields a deposition load function for a specified level of an endpoint. This function is 

8 characterized by three nodes that are illustrated on Figure 5-3: 1) the maximum of amount of N 

9 deposition when S deposition equals zero (DLmax (N)), 2) the amount of N deposition that will 

10 be captured by the ecosystem before it leaches (DLmin(N)) and 3) the maximum amount of S 

11 sulfur deposition considering the N captured by the ecosystem (DLmax (S)). The function 

12 represents many unique pairs of N and S deposition that will equal the critical load for acidifying 

13 deposition. The slope portion of the function will vary according to attributes of the water body 

14 that is modeled, including lake size and in-lake retention. 

15 

16 Figure 5-3. The depositional load function. 

17 A third modeling approach, which synthesizes components of each model used in the 

18 REA, is suggested by staff for catchment scale modeling in developing the NAAQS. The 

19 foundation of the proposed approach is the SSWC model because there is high confidence in the 

20 input data required. The SSWC model for aquatic acidification is expressed as equation 1. 

DLANC (N S) ([BC]O [ANClim ])Q * 21 lim + = − (1) 

22 where, 
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1 DLANClim(N+S) = depositional load of S and N that does not cause the ecosystems to exceed a 

2 given ANClim

[BC]0

* 3 = the preindustrial concentration of base cations (equ/L) 

4 ANClimit = a “target” ANC level (equ/L) 

5 Q= surface water runoff (m/yr) (this is typically equal to precipitation –evapotranspiration 

6 

7 This model could be further constrained by a quantity of N which would which would be 

8 taken up, immobilized or denitrified by ecosystems and adjust the quantity of deposition required 

9 to meet a specified critical load. This term is represented as DLmin(N) in the FAB model and 

10 illustrated in Fig. 5-3. For application in the NAAQS and in the following discussion, the 

11 parameter is designated with the abbreviation NECO. The acid-base model constrained by NECO is 

12 expressed by equation 2. 

( ) [ ] [ ] DLANC N + S = BC O − ANC Q + Neco ( ) lim

*

lim 13 (2) 

14 

15 where, 

16 Neco= nitrogen retention and denitrification by terrestrial catchment and nitrogen retention in the 

17 lake 

18 

19 The term Neco could be derived multiple ways, each yielding different ultimate results. 

20 The first is by taking the mean value calculated to represent the long-term amount of N an 

21 ecosystem can immobilize and denitrify before leaching (i.e. N saturation) that is derived from 

22 the FAB model [denoted as DLmin(N) in the FAB model]. This approach requires the input of 

23 multiple ecosystem parameters. Its components are expressed by eq 3. 

( )( ) eco ret Nimm Nden 24 N = fNupt + N + 1 (3) − r +

25 where, 

26 Nupt= nitrogen uptake by the catchment 

27 Nimm= nitrogen immobilization by the catchment 

28 Nden=denitrification of nitrogen in the catchment, 
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1 Nret = in-lake retention of nitrogen 

2 f =forest cover in the catchment (dimensionless parameter) 

3 r = fraction lake/catchment ratio (dimensionless parameter) 

4 

5 The second approach for estimating Neco is to take the difference between N deposition 

6 and measured N leaching in a catchment as expressed by eq 4. 

Neco = DL N − Nleach 7 ( (4) )

8 It is unclear which approach for calculating NECO should be used in developing the 

9 NAAQS. The two equations can result in quite different values (See section 5.4 for an example 

10 calculation). 

11 To summarize, the SSWC model assumes N deposited to the ecosystem is retained by the 

12 ecosystem, while also assuming that all S deposition is leached and contributes to aquatic 

13 acidification. The critical load is calculated for S deposition, and the N that contributes to 

14 acidification is incorporated into the exceedance calculation. The FAB model considers a 

15 detailed accounting of the N cycle; however confidence in the input data to the model is more 

16 uncertain. The FAB approach yields a function which may be solved by many unique pairs of N 

17 and S deposition. A minimum amount of N deposition that will be captured by the ecosystem 

18 before it leaches is included in the calculation of the maximum amount of S deposition. A third 

19 approach is suggested by staff as the most appropriate approach for informing the structure of the 

20 NOx and SOx secondary standard. This approach constrains the critical load calculated from a 

21 SSWC method by a value of NECO [previously defined as DLmin(N)] which accounts for the 

22 amount of N deposition that would be taken up by the ecosystem and, therefore, would not 

23 contribute to acidification. 

24 5.2.2.3 How are results of acidification models aggregated to adequately represent a 

25 larger spatial area and inform a deposition metric? 

26 So far in this section, the ecological indicator would be established as ANC. Acidification 

27 models are considered the best way to describe the relationship between ANC and deposition and 

28 to describe how this relationship is altered by modifying factors. If deposition is known the 

29 model may be run to calculate the resultant ANC. If a target ANC level is desired the model may 
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1 be run to calculate the corresponding deposition load that should not be exceeded (i.e. the critical 

2 load). The following discussion will focus on the critical load application of the acidification 

3 model. It is important to emphasize that the acidification models are only applied at the spatial 

4 scale of the catchment. Spatial aggregation of critical loads are necessary to inform the 

5 discussion of appropriate design and levels of a national standard. 

6 Acidification models are parameterized for catchments. The critical loads that they 

7 calculate for N and S deposition based on a specified ANC limit vary at the small spatial scale of 

8 the catchment to the degree that acid-balancing properties of the catchments vary. Despite this 

9 variation, the goal of aggregating critical loads from multiple catchments is to develop an 

10 appropriately representative deposition value, which is adequately protective of ecosystems and 

11 could be applied over larger spatial areas. 

12 Staff proposes evaluating the critical loads for a specified ANC limit of a population of 

13 waterbodies to calculate a benchmark depositional load in which a specified percentage of the 

14 population does not exceed their critical load. This approach uses the distribution of critical loads 

15 from a population to derive a value that is intended to provide protection over a spatial area that 

16 is larger than the individual catchment for which a single critical load may be calculated. An 

17 example of this technique is calculated in section 5.5. The ecological indicator would be a single 

18 value of ANC, and the acidification models would calculate the critical loads for the specified 

19 ANC level for individual catchments across a spatial area. The deposition metric would be an 

20 amount of deposition such that a specified percentage of a population of water bodies does not 

21 exceed a critical load for the specified value of ANC. The deposition metrics could be calculated 

22 for populations of catchments that are categorized according to acid-sensitivity, as described in 

23 the next section. 

24 5.2.2.4 How are modifying factors of the ecological response to deposition function 

25 considered at the national-scale? 

26 As previously noted, critical loads for ANC vary at a small spatial scale, catchment by 

27 catchment. As it is implausible to model the acidification status of every catchment in the U.S, 

28 an alternative is to develop a deposition metric for a population of catchments, assuggested in the 

29 previous section. The following design options focus on relating acid-sensitivity, based on ANC, 

30 to a feature(s) of the landscape at a national-scale by creating acid-sensitivity categories. A 
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1 population of catchments could then be defined to represent these categories and a representative 

2 deposition metric chosen. 

3 Acid sensitivity classes based on bed rock geology 

4 Here an approach is presented in which ecosystem sensitivity to acidification is 

5 categorized into classes based on bedrock geology/ lithology. The approach is supported by 

6 conclusions from the ISA in which geologic bedrock is determined to be the governing factor 

7 that drives ecosystem sensitivity to acidification (ISA 3.2.4.1). Specifically, geologic bedrock 

8 with a low base cation supply leads to ecosystems that are sensitive to acidifying deposition. A 

9 method to develop a deposition metric, based on the distribution of critical loads of a 

10 representative population, for each category of acid-sensitivity is presented here. 

11 A map was developed to capture the heterogeneity of geologic bedrock that occurs across 

12 the eastern U.S. and link it to ecosystem acid-sensitivity (Fig5-4). The method is based on 

13 Sullivan et al.(2007) in which 70+ primary lithologies are grouped into 5 categories of acid14 sensitivity, using ANC as the ecosystem indicator upon which acid-sensitivity is based. Sullivan 

15 et al. (2007) evaluated multiple features of the landscape and found that geology is the landscape 

16 parameter that governs ecosystem sensitivity to acidic deposition. The analysis in Sullivan et al. 

17 2007 was conducted in the Southern Appalachian Mountains region, which included sites from 

18 the states of GA, TN, NC, KT, VA and WV. EPA is conducting additional analyses to further 

19 test the concept that lithology correlates to acid sensitivity in case study areas and in the western 

20 U.S. EPA staff intends that some of these additional analyses will be available at for review in 

21 the second draft of the policy assessment. 

22 As previously stated, acidification often varies catchment by catchment. Therefore there 

23 will be variation in terms of acid-sensitivity among catchments within each acid-sensitivity class 

24 designated by the map. Despite this variation, lithology is a nationally applicable landscape 

25 feature which is known to govern acid-sensitivity. Ultimate detail and rigor would be provided 

26 by modeling deposition and consequential acidification of each catchment in the U.S., an 

27 approach which would require knowledge of 17+ environmental parameters for each catchment. 

28 However classification of the landscape into categories based on geology provides a national29 scale landscape feature to extrapolate the results of catchment-scale modeling. 
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1 

2 Fig 5-4. A map of acid sensitive areas of the Eastern U.S. developed from a 

3 lithology-based five-unit geologic classification system after methods in Sullivan 

4 et al. (2007). 

5 Acid sensitivity based on multiple landscape features 

6 Although bedrock geology is a governing factor of acid sensitivity, multiple factors have 

7 been shown to contribute to sensitivity. Topography is a characteristic of the landscape that is 

8 often shown to correlate with acid-sensitivity, specifically low elevations, which generally 

9 receive some cations from higher elevation sites, are less sensitive that higher elevation sites 

10 (ISA 3.2.4.1). Could both topography and bedrock geology be included a national map of acid11 sensitivity? A map of high elevation could be layered over the map of bedrock categories. If all 

12 high elevation areas were within the sensitive geologic categories, then the additional parameter 

13 would further refine the spatial resolution of sensitivity within the bedrock categorization. 

14 Moreover, the approach will provide more spatial detail on the sensitivity within areas already 

15 considered sensitive based on bedrock geology. It’s unclear if elevation alone would help 
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1 identify new sensitive areas. It’s also unclear if greater spatial resolution of sensitivity within 

2 areas already identified as sensitive would be helpful in terms of relating the national-scale 

3 landscape features to critical loads. Should additional multiple features be considered when 

4 categorizing the landscape according to acid-sensitivity? We are providing this design option to 

5 elicit comment; it is presented as a conceptual idea. 

6 5.2.2.5 How is a deposition metric developed so that critical loads for catchments are 

7 aggregated to adequately represent classes of acid sensitivity based on 

8 geology? 

9 The values that represent a deposition metric for the acid-sensitivity categories could be 

10 derived from the critical loads calculated for the case study analysis in the REA. The case study 

11 sites (Adirondack and Shenandoah areas) occur in areas that are predominately composed of the 

12 two most acid-sensitive types of bedrock geology. Therefore the case study sites would represent 

13 those sensitivity categories. The deposition and atmospheric concentration tradeoff curves for a 

14 specified level of ANC for each bedrock geology site would be based on a deposition metric 

15 derived from the distribution of critical loads within the case study areas. It could be a central 

16 value such as the mean or median value or a value representing a percentile of the distribution, 

such as the 95th 17 percentile. Central estimates, such as the mean, would likely not be projected to 

18 achieve the target ANC of the majority of acid-sensitive ecosystems; therefore it may be 

19 preferable to calculate the spatially aggregated value for some percentage of catchments to 

20 project achieving the ANC for the more sensitive ecosystem types. For example, if projecting 

21 85%, 90% or 95% of the aquatic ecosystems achieving the ANC is selected, then the deposition 

metric that represents the critical load for the 85th, 90th or 95th 22 percentile of the population would 

23 be selected. An example calculation for the Adirondacks is presented in section 5.5. 

24 5.2.2.6 How is reduced nitrogen appropriately considered in the deposition metric? 

25 Reduced forms of nitrogen deposition are quickly converted to nitrate in the environment 

26 and use up the assimilative capacity of ANC at the same rate as oxidized forms of nitrogen 

27 deposition; therefore, reduced nitrogen deposition must be accounted for in the watershed. There 

28 are two basic approaches to accounting for the use of this assimilative capacity. 

29 The suggested approach is to subtract the loadings of reduced forms of nitrogen derived 

30 for a given spatial area from the deposition metric that represents selected percentage of critical 
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1 loads for a given population, such that the resultant deposition metric is for sulfur and oxidized 

2 nitrogen only. This approach assumes that the reduced forms of nitrogen deposition are relatively 

3 constant over time. This assumption could lead to over or under protection for an area depending 

4 on whether the actual concentrations of reduced forms of nitrogen increase or decrease over 

5 time. An example for how to subtract reduced nitrogen from the deposition metric based on 

6 nitrogen and sulfur is given in section 5.5. 

7 5.2.2.7 Summary 

8 In summary, the ecological components of the conceptual design for a standard base on 

9 aquatic acidification include the ecological indicator, ecological response function and its 

10 modifiers and the deposition metric. A summary how each component is considered in the 

11 conceptual design is given in Table 5-2. Using ANC as the ecological indicator, an approach is 

12 suggested for using an acidification model constrained by a parameter for ecosystem N retention 

13 to represent the ecological response function. The best way to calculate ecosystem N retention is 

14 as of yet unclear. It is proposed that the national landscape is categorized in terms of criteria that 

15 denote acid-sensitivity. It is well known that bedrock geology is a governing factor of acid16 sensitivity, in other words ecosystem response is modified across the landscape due in part to 

17 bedrock geology. It is unclear if landscape categorization based on geology is the best approach 

18 or other criteria/combination of criteria should be used. 

19 The distribution of critical loads for a specified target ANC from a population of 

20 catchments representing an acid-sensitivity category, based on geology or some combination of 

21 factors, can be calculated From this a deposition metric, an amount of deposition, could be 

22 calculated such that a specified target percentage of the population of water bodies in the acid23 sensitivity category does not exceed a critical load for the specified value of ANC. Moreover, the 

24 deposition metric would reflect both the selected level of ANC and the percentage of catchments 

25 in the representative population that do not exceed their critical load. Reduced nitrogen 

26 deposition, average over a determined spatial scale, would be subtracted from the deposition 

27 metric yielding a value for allowable deposition from NOy and SOx. The deposition from NOy

28 and SOx would be converted to atmospheric concentrations of NOy and SOx by the methods 

29 described in section 5.4. 
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Table 5-2. Summary of the ecological components of design option 1. 

Values given for illustrative purposes only. Levels are discussed in Chapter 6 and ultimately 

selected by the administrator. 

Ecological Indicator Ecological Response 

Function 

Modifying Factor Deposition Metric 

ANC; level reflects 

degree of 

Effects on aquatic 

biota in the ecosystem 

Acidification model 

constrained by a 

parameter for N 

retention 

Acid-sensitivity 

categories, based on 

geologic bed rock or a 

combination of factors, 

that may be applied at a 

national scale 

Determined from the 

distribution of critical 

loads from a 

population that can be 

related to an acidsensitivity category. 

Reduced nitrogen 

subtracted from the 

deposition metric to 

yield allowable 

deposition from NOx

and SOx. 

Concentration 

of 

Air Quality 

Indicator(s) 

Ecological Indicator 

ANC 

Proposed levels based 

on biological effects 

 Ecological 

Response to 

 Deposition Function 

 Acidifcation model 

Atmospheric 

Deposition 

Transformation 

Function 

See Section 5.4 

Deposition 

Metric 

Determined by 

the % of 

ecosystems 

represented

Modifying Factor 

Geology 

5 categories of sensitivity 

Variable/Fixed 

Modifying 

Factors
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1 5.3 ECOLOGICAL COMPONENTS OF THE STANDARD: 

2 TERRESTRIAL ACIDIFICATION, TERRESTRIAL NUTRIENT 

3 ENRICHMENT AND SURFACE WATER NUTRIENT 

4 ENRICHMENT 

5 These effects were not included in the conceptual design for the first draft of the PA, 

6 however a brief summary of our approach for developing standards that are protective of these 

7 ecological effects follows. 

8 5.3.1 Terrestrial Acidification 

9 The deleterious effects of terrestrial acidification on tree species is indicated by base 

10 cation to aluminum ratio (Bc:Al) of soils. Critical load functions were developed in the REA that 

11 relate Bc:Al threshold values (0.6, 1.2 and 10) to values of N+S deposition using the simple mass 

12 balance (SMB) model. The exceedance of these critical loads were calculated at the two study 

13 sites and then extrapolated over 24 states. Like aquatic acidification, sensitivity of terrestrial 

14 ecosystems to acidification is linked to the geologic bedrock. Moreover, areas that are sensitive 

15 to aquatic acidification should also be sensitive to terrestrial acidification. Therefore, an 

16 approach similar to that described for aquatic acidification could be developed. This would mean 

17 that a critical load based on Bc:Al at either 1.2 or 10 would be calculated to protect a percentage 

18 of the terrestrial landscape. This value would then be assigned to categories of acid sensitivity 

19 based on geology. 

20 This could result in two standards, one for aquatic ecosystems and one for terrestrial 

21 ecosystems. This leads to the question, are aquatic or terrestrial ecosystem more sensitive? To 

22 answer this question, an analysis was conducted in which critical loads for the Adirondacks and 

23 Shenandoah case study areas were calculated based on the terrestrial ecosystem indicator, Bc:Al, 

24 at the level of 1.2 and 10. The terrestrial critical loads were compared to the critical loads for 

25 aquatic ecosystems. A full description of this analysis and results is available in Chapter 7, the 

26 results are briefly summarized here. In the Adirondacks case study area, 7 of the 16 watersheds 

27 had terrestrial critical acid loads (based on a Bc:Al of 10.0) that were lower and therefore more 

28 sensitive to acidification than all the lakes in the watershed. However, when the terrestrial critical 

29 loads were calculated with a Bc:Al soil solution ratio of 1.2, only 5 of the 16 watersheds were 

30 protected by a terrestrial critical load that was lower than the aquatic critical loads of the lakes. In 
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1 the Shenandoah case study area terrestrial critical loads offered a higher level of protection than 

2 aquatic critical loads in only one watershed. If two standards were proposed, the one that allows 

3 lower ambient levels of NOx and SOx would be controlling in a given area. 

4 5.3.2 Terrestrial and surface water nutrient enrichment 

5 NOx and NHx are the main contributors to nitrogen deposition. The effects of nitrogen 

6 deposition on terrestrial ecosystems and surface waters are many. Most notable are the effects on 

7 ecosystem biodiversity found across the U.S and affecting multiple taxonomic groups including 

8 vascular plants, algae, mycorrhiza and lichens (ISA 3.3). Unlike terrestrial and aquatic 

9 acidification, there is no one, well-supported chemical or biological indicator of ecosystem 

10 effects that occurs across the nation. In order to develop a NAAQS based on nitrogen enrichment 

11 effects there needs to be one indicator that can be applied across the nation. It is possible that we 

12 could develop an index in which information on different ecological indicators could be input 

13 and the output would be an index score that could be consistently applied across the U.S. It is not 

14 clear how to develop such an index. 

15 Nitrogen critical loads are known for many ecosystem endpoints in the U.S. and are 

16 published in the scientific literature. Additionally, critical loads for ecosystems in Europe, many 

17 of which are similar to U.S. ecosystems, have been reported for over a decade, they are 

18 continually refined through periodic assessments of the scientific literature, and they are 

19 currently supported by a strong weight of peer-reviewed scientific information (ISA 3.3). 

20 Additional critical load modeling was not conducted in the REA because of two factors. There 

21 are numerous reports in the peer-reviewed scientific literature and there is no model available to 

22 conduct such analysis for multiple endpoints and ecosystems. However, based on nitrogen 

23 critical loads published in the literature, the REA evaluated the extent of the landscape 

24 represented by those critical loads and their exceedances (REA 5.0). 

25 A standard that integrates acidification and nutrient effects could conceptually be quite 

26 simple. The total nitrogen deposition allowed for a deposition metric based on acidification could 

27 be constrained so that it does not exceed a value based on a deposition metric for a nutrient 

28 related effect. 
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1 5.3.3 Summary 

2 Conceptual design of NOx and SOx NAAQS were not developed for terrestrial 

3 acidification and terrestrial/surface water nitrogen enrichment in the first draft PA, however a 

4 brief summary of a potential structure for these ecological effects is presented. The ecological 

5 indicator for terrestrial acidification would be Bc:Al because it relates to both atmospheric 

6 deposition of N+S and deleterious effects on tree growth. Critical loads would be related to acid7 sensitivity categories and calculated according to similar methods presented for aquatic 

8 acidification effects. This could result in two standards, one for aquatic ecosystems and one for 

9 terrestrial ecosystems. If two standards were proposed, the one that allows lower ambient levels 

10 of NOx and SOx would be controlling in a given area. Unlike terrestrial and aquatic acidification, 

11 there is no one, well-supported ecological indicator of nitrogen deposition effects that occurs 

12 across the nation. In order to develop a NAAQS based on nitrogen enrichment effects there 

13 needs to be one indicator that can be applied across the nation. Although, the specifics of an 

14 approach are unclear, it may be possible that we could develop an index in which information on 

15 different ecological indicators could be input and the output would be an index score that could 

16 be consistently applied across the U.S. A standard that integrates acidification and nutrient 

17 effects could conceptually be quite simple. The total nitrogen deposition allowed for a deposition 

18 metric based on acidification could be constrained so that it does not exceed a value based on a 

19 deposition metric for a nutrient related effect. 

20 5.4 LINKING DEPOSITION TO ATMOSPHERIC CONCENTRATION 

21 5.4.1 Background 

22 Atmospheric pollutants deposit onto land and water surfaces through at least two major 

23 mechanisms: direct contact with the surface (dry deposition), and transfer into liquid 

24 precipitation (wet deposition). The magnitude of each deposition process is related to the 

25 ambient concentration through the time-, location-, process- and species-specific deposition 

26 velocity (Seinfeld and Pandis, 1998) and can be conceptualized as: 

Amb

i

Dry

i

Dry 27 Depi = v ⋅C (1) 

Amb

i

Wet

i

Wet 28 Depi = v ⋅C (2) 
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March 2010 Draft – Do Not Quote or Cite 170

where v

Dry

i and v

Wet

i are the dry and wet deposition velocities, DepDryi and DepWeti 1 are the dry and 

wet deposition fluxes, CAmb

i 2 is the ambient concentration, and the i subscript indicates the 

3 pollutant species under study. The wet deposition velocity term is a conceptualized term and not 

4 a state variable that allows for the grouping of wet and dry deposition. The total deposition of 

5 each pollutant is 

Wet

i

Dry

i

Tot 6 Depi = Dep + Dep (3) 

7 Substituting Equations 1 and 2 into Equation 3 yields 

Amb

i

Wet

i

Amb

i

Dry

i

Tot 8 Depi = v ⋅C + v ⋅C (4) 

9 The total deposition of sulfur or nitrogen would therefore be: 

= ∑ + ⋅ ⋅ i

Amb

i i

Wet

i

Dry

i

Tot 10 DepS|N (v v ) m C (5) 

where mi 11 is the molar ratio of the atom (sulfur or nitrogen) of interest to the ith pollutant. 

Ambient sulfur- and nitrogen-containing pollutants include gases such as sulfur dioxide (SO2 12 ), 

ammonia (NH3

), various nitrogen oxides (NO, NO2, HONO, N2O5), nitric acid (HNO3 13 ), and 

14 organic nitrates such as peroxyacetyl nitrates (PAN); as well as particulate species such as sulfate 

(SO4

2-), nitrate (NO3-

), and ammonium (NH4

+ 15 ). As discussed in chapter 4, the definitions of NOy

16 and SOx species for the purposes of this review include the sulfur-containing species above and 

17 the above oxidized forms of nitrogen (NOy); ammonia and ammonium are not currently included 

18 as listed pollutants (see Chapter 8 for an expanded discussion of the role of NHx). 

19 5.4.2 Aggregation Issues 

20 Equation 5 provides a relationship for converting sulfur or nitrogen deposition to 

21 “equivalent” ambient concentrations,. A major issue to consider during such conversion is the 

22 treatment of spatial, temporal and chemical resolutions of the deposition data and the resulting 

23 standards. Since the objective is to set an ambient air quality standard for total oxidized sulfur 

24 and nitrogen, and this is also the chemical resolution provided by the ecosystem models, it is 

25 convenient to use a relationship with the following form: 
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March 2010 Draft – Do Not Quote or Cite 171

Amb

S N S N

Tot DepS|N V / C / 1 = ⋅ (6) 

2 where VS/N can be considered an aggregateddeposition to ambient air transformation ratio, 

3 referred to herein as the deposition transformation ratio, that relates total deposition of sulfur or 

nitrogen to the total ambient concentration, and represents an average of the species specific vi

Tot 4 

( = vi

Dry + viWet 5 ) values in Equation 5. The sulfur and nitrogen concentrations are the result of 

applying the mi values to the Ci

Amb 6 values in Equation 5. 

7 Since the deposition critical loads are expressed in terms of annual total deposition, the 

8 most relevant averaging time for equivalent ambient concentrations is the annual average. Data 

9 used to derive annual VS/N values will need to have the same spatial representativeness as the 

10 depositional loads. To be clear, the deposition transformation ratio is not a state variable, but 

11 simply is a calculated term that facilitates the linkage between deposition and concentrations 

12 which is a necessary step in developing ambient air indicators that are used to assess compliance 

13 with a NAAQS. There will be a tendency that is not scientifically defensible to compare 

14 deposition ratios with deposition velocities that are uniquely determined on a species by species 

15 basis influenced by numerous factors as discussed earlier. 

16 5.4.3 Air Quality Simulation Models 

17 Ideally, VS/N values would be derived for each area of interest from concurrently collected 

18 sulfur and nitrogen deposition and concentration measurements. However, no monitoring 

19 network currently exists that can provide such information. We therefore propose using output of 

20 the CMAQ model for initial calculation of VS/N values. 

21 CMAQ provides both concentrations and depositions for a large suite of pollutant species 

22 on an hourly basis for 12 km grids across the continental U.S. Its comprehensive structure is 

23 ideal for providing VS/N values that appropriately address the chemical and temporal aggregation 

24 issues discussed above, and weighted spatial averages of the gridded data can be used for areas 

25 that span multiple grid cells. Potential concerns with using CMAQ-predicted concentrations and 

26 depositions for this purpose stem from the various, but unquantifiable uncertainties in model 

27 formation and input data, which will be discussed in the next draft of this PAD. 

28 CMAQ does not directly calculate or use VS/N values; instead the following procedures 

29 are used in the code to model deposition: 
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1) vdry 1 values of gaseous pollutants are calculated in the CMAQ weather module called 

2 the Meteorology-Chemistry Interface Processor (MCIP) through a complex function of 

3 meteorological parameters (e.g. temperature, relative humidity) and properties of the geographic 

4 surface (e.g. leaf area index, surface wetness) 

2) vdry 5 values for particulate pollutants are calculated in the aerosol module of CMAQ, 

6 which, in addition to the parameters needed for the gaseous calculations, also accounts for 

7 properties of the aerosol size distribution 

3) vwet 8 values are not explicitly calculated. Wet deposition is derived from the cloud 

9 processing module of CMAQ, which performs simulations of mass transfer into cloud droplets 

10 and aqueous chemistry to incorporate pollutants into rainwater, all of which is conceptually 

contained in the vwet 11 parameter in Equation 2. 

12 Due to lack of direct measurements, no performance evaluations of CMAQ’s dry 

13 deposition calculations can be found; however, the current state of MCIP is the product of 

14 research that has been based on peer-reviewed literature from the past two decades (EPA, 1999) 

15 and is considered to be EPA’s best estimate of dry deposition velocities. Some bias has been 

16 found between CMAQ’s wet deposition predictions and measured values (Morris et al., 2005); 

17 recent analyses suggest that poor simulation of precipitation could be responsible for this (Davis 

18 and Swall, 2006), which can potentially be dealt with by recalculating wet deposition using 

19 precipitation measurements. Although the model is continually undergoing improvement, 

20 CMAQ is EPA’s state-of-the-science computational framework for calculating deposition 

21 velocities, and was therefore the logical first choice as a source for VS/N values. 

22 5.4.4 Oxidized Sulfur and Nitrogen Pollutant Species 

23 Ideally, all possible air pollutant species that contribute to ecological adversity would be 

24 considered for VS/N values. The pollutant list is constrained by the source of VS/N values, which is 

25 currently CMAQ output. Table 1 lists the oxidized sulfur and nitrogen species currently available 

26 in CMAQ whose data will be used for VS/N values. 

27 One issue that needs explicit consideration is the contributions of particles larger than 

28 PM2.5 to sulfur and nitrogen deposition. A recent review of particle deposition measurements 

29 (Grantz, Garner, and Johnson, 2003) showed that coarse particles generally deposit far more 

30 sulfate and nitrate in forest ecosystems than fine particles. However, CMAQ does not currently 
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March 2010 Draft – Do Not Quote or Cite 173

1 provide simulationsof coarse particulate sulfate and nitrate. This is an issue that needs to be 

2 addressed by developers of either the model or the future SOx/NOx measurement network to set 

3 scientifically sound standards. 

4 5.4.5 Example Calculations 

Figure 5-5 shows annual inverse VS/N values16 5 calculated for each 12 km grid in the 

6 eastern and western domains for a 2002 CMAQ v4.6 simulation, which is the quantity that would 

7 be used for conversion of deposition load tradeoff curves which illustrate (see Section 6) the 

8 combinations of NOy and SOx conventartions that would correspond to an established critical 

9 load. Figure 5-6 shows an example application of these ratios for a lake in the Adirondacks. 

10 Deposition load tradeoff curves for this lake (see Section 6for their calculation) are multiplied by 

11 the inverse VS/N value from the appropriate grid cell in Figure 1 to convert those depositions to 

12 ambient concentrations of sulfur and nitrogen. 

13 A CMAQ v4.7 simulation for multiple years (2002-2005) recently became available, 

14 which was used to examine the inter-annual variability of inverse VS/N values. The grid-specific 

15 coefficients of variation (CV) are shown in Figure 3. Figure 5-7 shows that CV values are 

16 relatively small (< 25%) in the Adirondacks and Shenandoah case study areas. This suggests that 

17 a 3-year average of the ratios may be a sufficiently stable representation of deposition velocities 

18 for converting the deposition load curves to ambient concentrations in future applications. 

 

16 Inverse VS/N values represent the multiplier needed to convert deposition levels into atmospheric concentrations of 

NOx and SOx. 
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Table 5-3. Oxidized sulfur and nitrogen species currently available in CMAQ simulations. Note 

that PNA concentrations are not available in current CMAQ extractions. 

1 

2 

3 

4 

5 Figure 5-5. VS/N values for each grid cell in the eastern (right) and western (left) 

6 U.S. domains. The top maps are for sulfur and the bottom are for nitrogen. 
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1 

2 Figure 5-6. Schematic Diagram illustrating the procedure for converting 

3 deposition tradeoff curves of sulfur and nitrogen to atmospheric concentrations of 

4 SOx and NOx. 
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1 a) 

2 b) 

3 Figure 5-7. Inter-annual coefficients of variation (CV) of a) nitrogen and b) sulfur 

4 VS/N values, based on a series of 2002-2005 CMAQ v4.7 simulation. 

5 
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1 5.5 EXAMPLE CALCULATION FOR THE CONCEPTUAL DESIGN 

2 AND DERIVATION OF AAPI 

3 Section 5.2 describes a proposed conceptual design for a NOx and SOx NAAQS based on 

4 aquatic acidification. To summarize the process of acidification, atmospheric deposition of NOx

5 and SOx contributes to acidification in aquatic ecosystems through the input of acid anions, such 

as NO3

- and SO42- 6 . The acid-base balance of headwater lakes and streams is controlled by the 

level of this acidifying deposition of NO3

- and SO42- 7 and a series of biogeochemical processes 

8 that produce and consume acidity in the watershed. The biotic integrity of freshwater ecosystems 

9 is then a function of the, acid-base balance and the resulting acidity-related stress on the biota 

10 that occupy the water. Given some “benchmark level” of ANC [ANClimit]) that appropriately 

11 protects biological integrity, the depositional load of acidity DL(N+S) is simply the input flux of 

12 acid anions from atmospheric deposition that result in a surface water ANC level equal to the 

13 [ANClimit] when balanced by the sustainable flux of base cations input and the sinks of nitrogen 

14 and sulfur in the watershed catchment. 

15 5.5.1 Example calculation for the conceptual design 

16 This section summarizes and provides an example calculation of the approach proposed 

17 by EPA staff to calculate (1) the acid-base balance of a catchment for a specified ANC level, (2) 

18 the N and S deposition tradeoff curves for a deposition metric, which represents a specified 

19 percentage of the total population of water bodies that do not exceed their critical load at a 

20 specified ANC level and (3) the conversion from tradeoff curves for N and S deposition to those 

21 for atmospheric concentrations of NOy and SOx. The equations representing deposition loads and 

22 associated tradeoff curves for a specified level of ANC are the basis for deriving the form of the 

23 standard discussed above in section (5.5.2). 

24 Equation (1) expresses the model that we suggest using to determine the amount of N and 

25 S that may be deposited onto a catchment to yield a specified level of ANC. 

( ) [ ] [ ] DLANC N + S = BC O − ANC Q + Neco ( ) lim

*

lim 26 (1) 

27 where, 
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1 DLANClim(N+S) = depositional load of S and N that does not cause the ecosystems to exceed a 

2 given ANClim

[BC]0

* 3 = the preindustrial concentration of base cations (equ/L) 

4 ANClimit = a “target” ANC level (equ/L) 

5 Q= surface water runoff (m/yr) (this is typically equal to precipitation –evapotranspiration 

6 Neco= nitrogen retention and denitrification by terrestrial catchment and nitrogen retention in the 

7 lake 

8 The term Neco could be derived multiple ways. The first is by taking the mean value 

9 calculated to represent the long-term amount of N an ecosystem can immobilize and denitrify 

10 before leaching (i.e. N saturation) that is derived from the FAB model. This approach requires 

11 the input of multiple ecosystem parameters. Its components are expressed by eq 2. 

( )( ) eco ret Nimm Nden 12 N = fNupt + N + 1 (2) − r +

13 where, 

14 Nupt= nitirogen uptake by the catchment 

15 Nimm= nitrogen immobilization by the catchment soil 

16 Nden=denitrification of nitrogen in the catchment, 

17 Nret = in-lake retention of nitrogen 

18 f =forest cover in the catchment (dimensionless parameter) 

19 r = fraction lake/catchment ratio (dimensionless parameter) 

20 

21 The second approach for estimating Neco is to take the difference between N deposition 

22 and measured N leaching in a catchment as expressed by eq 3. 

Neco = DL N − Nleach 23 ( (3) )

24 N deposition is composed of NHx deposition (NHxdep) and NOy deposition. It is known that 

25 NHXdep contributes to acidification, however the definition of NOx in the CAA does not include 

26 NHx, and as such is not defined to provide protection from the acidifying effects of NHx. 

27 Therefore, DLANClim(N) is separated into NHx and NOy. 

( ) [ ] ( ) ( ) ( ) DL lim Noy S DL lim N DL lim NHx DL lim SOx ANC + = ANC − ANC + ANC 28 (4) 
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1 Equation 1 and 4 will differ catchment by catchment because the acid-base balance of a 

2 catchment is a function of site-specific characteristics. However, for the standard it is desirable to 

3 calculate a deposition load for a specified ANC not for an individual catchment, but a larger 

4 population of catchments. The site specific values from equation 1 can be used to derive such a 

5 deposition loading, here called the deposition metric, which represents a group or percentage of 

6 water bodies that reach a specified ANC (or higher). For example, if it is desired that all water 

7 bodies reach a specified ANC, the allowable amount of deposition for all water bodies is equal to 

8 the lowest value calculated from equation 1 for the population of water bodies. Because the 

9 deposition metric represents a percentage of individual catchments from a population of water 

10 bodies, and not an individual catchment like DLANClim(S+N), the deposition metric is noted by 

11 the follow abbreviation DL%ECO. 

12 As an example of the above approach, we evaluate the population of 169 waterbodies in 

13 the Adirondacks used in the REA analysis. For each individual waterbody in the population 

14 DLANClim(S+N) at ANClim = 50 was calculated using the two equations for deriving the Neco 

15 term (eq 2 and 3). The distribution of deposition loads for the population was assessed and Table 

16 5-5 shows the a few selected values for DL%ECO. The mean value for DL%ECO for the 169 water 

17 bodies is presented, as well as the values for which 50, 75, 85, 95 and 100% of the water bodies 

18 in the population will not exceed their critical load at ANC=50. Note, only 32% of water bodies 

19 would not exceed their critical load at ANC=50 for the mean value DL%ECO because variability is 

20 high in the data set. The deposition and atmospheric concentration tradeoff curves for DL%ECO 

21 equal to 32% and 50% are plotted in the subsequent figures. The Administrator will choose 

22 which % of water bodies are projected to reach a targeted level of ANC as part of the overall 

23 decision on the elements of the standard; this selection may be higher or lower than the examples 

24 given here. 
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March 2010 180 Draft –Do Not Quote or Cite 

Table 5-4. Example Calculations for Determining the Percent of Water Bodies Achieving Target 

ANC Levels 

This example is based the population of DLANClim for and ANC=50 for 169 catchments in the Adirondacks. 

These catchments occur across on three categories of geologic sensitivity. We could separate the DLANClim 

values into sensitivity categories (if info is available) and do the analysis for each category or calculate one 

DLANClim for combined geologic categories. Units are in meq/m2/yr. 

NHx

dep 

Neco 

(eq2) 

DL%ECO (S+N) 

using Neco eq 2 

Neco 

(eq3) 

DL%ECO (S+N) 

using Neco eq 3 

% of lakes within 

the population that 

have ANC ≥ 50 

Mean 20.40 19.19 162.36 63.95 207.55 31.7% 

St dev 3.22 3.03 162.92 11.15 165.42 

St er 0.25 0.23 13.04 0.86 13.24 

Rank 

%tile 

 

50% 99.33 139.22 50% 

75% 65.62 110.37 75% 

85% 54.89 95.53 85% 

95% 45.12 83.99 95% 

100% 30.22 59.07 100% 

1 

2 The deposition tradeoff curves for N and S based on DL%ECO at ANC=50 using the two 

3 approaches for Neco and protective of 32 and 50% of the population of water bodies, are plotted 

4 on Fig 5-8 and 5-9. The values for the maximum deposition values for N and S are given in 

5 Table 5-5. 

6 

Table 5-5. Values for N and S deposition tradeoff curves for ANC = 50, protecting 32 and 50% 

of the population, in Adirondacks case study area as illustrated on Fig 5-8 and Fig 5-9. Units are 

in meq/m2/yr unless noted otherwise. 

% 

protection 

NHxdep Neco DL%ECO 

(max N) 

DL%ECO 

(max S) 

DL%ECO 

(max NOY) 

32 Eq 2 20.4 19.19 162.36 143.97 141.96 

50 Eq 2 20.4 19.19 99.33 80.14 78.9.3 

32 Eq 3 20.4 63.75 207.5 143.6 187.15 

50 Eq 3 20.4 63.75 139.22 75.27 118.82 

7 
Policy Assessment for the Review of the Secondary National Ambient Air Quality Standards for NOx and SOx

March 2010 181 Draft –Do Not Quote or Cite 

Fig 5.1 Tradeoff curve for S and N deposition to protect from aquatic 

acidification in the Adirondacks using Neco eq 2 

Max(N) =162.36

Max(S)=143.97

Max(N)= 99.33

N eco=19.19

NHx dep=20.40

Max(S)=80.14

0

50

100

150

200

0 50 100 150 200

N (meq/m2/yr)

S (meq/m2/yr)

ANC=50 & 32% lakes protected

ANC=50 & 50% lakes protected

Neco

NHx Deposition

1 

2 Figure 5-8. Tradeoff curve for S and N deposition to protect from aquatic 

3 acidification in the Adirondacks using Neco equation 2. 

Fig 5.2 Tradeoff curve for S and N deposition to protect from aquatic 

acidification in the Adirondacks using Neco eq 3 

Max(N) =207.5

Max(S) =143.6

Max(N)= 139.22

Max(S)=75.57

NHx dep

=20.40

Neco

=63.75

0

50

100

150

200

0 50 100 150 200

N (meq/m2/yr)

S (meq/m2/yr)

ANC=50 & 32% lakes protected

ANC=50 & 50% lakes protected

Neco

NHx Deposition

4 

5 Figure 5-9. Tradeoff curve for S and N deposition to protect from aquatic 

6 acidification in the Adirondacks using Neco equation 3. 
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March 2010 182 Draft –Do Not Quote or Cite 

1 As previously stated, it is known that NHx deposition (NHxdep) contributes to 

2 acidification. However, the criteria pollutant listed by EPA pursuant to section 108 (a) of the Act 

3 is oxides of nitrogen does not include NHx, and as such is not defined to provide protection from 

4 the acidifying effects of NHx. Therefore, in order to represent the role of NHxdep as a component 

5 of acidification it is subtracted from DL%ECO(S+N). The difference is the total allowable 

6 deposition from NOy and SOx to protect a selected % of catchments in the population at a 

7 selected level of ANC [DL%ECO (S + NOy)] as expressed in equation 5. 

( ) ( ) DL%ECO NOY + S = DL%ECO N + S − NHxDEP 8 (5) 

9 The NOy and S deposition tradeoff curves for ANC =50, protecting 32 and 50% of the 

10 water bodies, are presented in Table 5-6 and plotted on Fig 5-10 and 5-11. If NHx deposition is 

11 greater than Neco, then Neco disappears from the tradeoff curve (i.e. Fig 5-11). 

Table 5-6. Values for NOy and S deposition tradeoff curves for ANC = 50, protecting 32 and 

50% of the population in Adirondacks case study area as illustrated on Fig 5.10 and Fig 5.11. 

Units are in meq/m2/yr unless noted otherwise. 

% 

protection 

NHxdep Neco (Noy) DLmax(S) DLmax(Noy) 

32 Eq 2 20.4 Neco < NHxdep 141.96 141.96 

50 Eq 2 20.4 Neco < NHxdep 78.93 78.93 

32 Eq 3 20.4 43.35 143.6 187.15 

50 Eq 3 20.4 43.35 75.27 118.82 

12 
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March 2010 183 Draft –Do Not Quote or Cite 

acidification in the Adirondacks using Neco eq 2

Max(N) =141.96

Max(S)=141.96

Max(N)=77.72

Max(S)= 78.93

0

50

100

150

200

0 50 100 150 200

Noy (meq/m2/yr)

S (meq/m2/yr)

ANC=50 & 32% lakes protected

ANC=50 & 50% lakes protected

1 

2 Figure 5-10. Tradeoff curve for S and NOy deposition to protect from aquatic 

3 acidification in the Adirondacks using Neco equation 2. 

aquatic acidification in the Adirondacks using Neco eq 3

Max(S)=143.6

Max(Noy) =187.15

Max(S)=75.27

Max(Noy)=118.82 Neco(Noy)=43.35

0

50

100

150

200

0 50 100 150 200

NOy (meq/m2/yr)

S (meq/m2/yr)

ANC=50 & 32% lakes protected

ANC=50 & 50% lakes protected

Neco

4 

5 Figure 5-11. Tradeoff curve for S and NOy deposition to protect from aquatic 

6 acidification in the Adirondacks using Neco equation 3. 

7 The tradeoff curves for the atmospheric concentration of NOy and SOx are presented in 

8 Fig 5-12 and 5-13. Deposition values for NOy and S (from Table 5-6, Fig 5-10 and 5-11) were 

9 multiplied by the ratio of concentrations to depositions (previously referred to as aggregate 
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March 2010 184 Draft –Do Not Quote or Cite 

effective deposition velocities17) for NOx and SOx (VSOx = 0.03824755 μg/m3 1 /meq/m2 and 

VNOx = 0.04386373 μg/m3 2 /meq/m2). This is expressed in equation 5. These velocities were 

3 calculated by taking the median value of the concentration of oxidized N to deposition of 

4 oxidized N ratio in CMAQ for all grid cells over the Adirondack case study area. 

[ ( ) ] [ ( ) ] ( ) ECO OY ECO ECO NHxDEP DL% N ⋅Vnoy + DL% S ⋅Vsox = DL% N + S − 5 (6) 

Fig 5.5 Tradeoff curve for atmospheric concentration of Sox and Noy to 

protect from aquatic acidification in the Adirondacks using Neco eq 2 

0

5

10

0.00 10.00

Noy (ug/m3)

Sox (ug/m3)

ANC=50 & 32% lakes protected

ANC=50 & 50% lakes protected

6 

7 Figure 5-12. Tradeoff curve for atmospheric concentration of SOx and NOy to 

8 protect from aquatic acidification in the Adirondacks using Neco equation 2. 

 

17 Note to reviewers: in previous drafts we have referred to the ratios of deposition to concentration for NOy and 

SOx as “aggregate effective velocities.” We are revisiting this choice of terms, as it is not as accurate a reflection of 

the parameter as we might prefer. The concern with continuing to use the term “velocity” in this context is that it 

will be misinterpreted by the scientific community, and in order to avoid confusion, we will likely replace the term 

with “deposition ratio” or some other term that more accurately describes the parameter. 
Policy Assessment for the Review of the Secondary National Ambient Air Quality Standards for NOx and SOx

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Fig 5.6 Tradeoff curve for Sox and Noy atmospheric concentrations 

to protect from aquatic acidification in the Adirondacks using Neco 

eq 3 

0.00

10.00

0.00 10.00

Noy (ug/m3

)

Sox (ug/m3

)

ANC=50 & 32% lakes protected

ANC=50 & 50% lakes protected

1 

2 Figure 5-13. Tradeoff curve for atmospheric concentration of SOx and NOy to 

3 protect from aquatic acidification in the Adirondacks using Neco equation 3. 

4 5.5.2 Derivation of the Atmospheric Acidification Potential Index (AAPI): 

5 While the conceptual framework above provides a means for calculating tradeoff curves 

6 associated with a specific level of protection (indicated by a target ANC level) and a specific 

7 percentage of ecosystems protected within an overall sensitive area, it does not provide a clearly 

8 integrated statement that can be expressed as a level such as would be needed for the secondary 

9 standard. The goal of this development of the AAPI is to create an index which can be applied 

10 across the nation to convey the potential of an ecosystem to become acidified from atmospheric 

11 deposition. 

12 The definition of the AAPI form considered here is: 

13 Annual Average AAPI: Natural background ANC minus the contribution to 

14 acidifying deposition from NHx, minus the acidifying contribution of NOy and 

15 SOx. This term is essentially a calculated ANC value that represents a percentage 

16 of catchments in a population. 

17 In order to derive the AAPI, we start with the basic framework of critical loads discussed 

18 in the example above. 

19 The approach used to calculate N and S deposition values for a specified ANC at a 

20 catchment-scale is expressed in Equation 1. The deposition value for a specified ANC will vary 
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1 from catchment to catchment based on how the properties that counterbalance the acidifying 

2 deposition vary among catchments. Equation 5 expresses how to calculate a deposition metric for 

3 a specified ANC for a population of waterbodies that could represent a national acid-sensitivity 

4 category. Moreover, the quantity of deposition equal to a specified ANC limit (i.e. critical load) 

5 will vary in eq 1 and 5 depending on the characteristics of the catchment or population of 

6 catchments, respectively. The goal for a secondary NOx and SOx NAAQS is to develop a form 

7 for the standards that allows us to set a single value for the standard across the U.S. To 

8 accomplish this, we rearrange equation (1) to solve for ANC (place ANC on the left hand side of 

9 the equation): 

Q ANC N [BC] Q [DL(N) DL(S )] ⋅ = eco + O ⋅ − + *

lim 10 (7) 

[ ] [ ] DL( ) () N DL S

Q

N BC

Q

ANC = eco + O − +

1 * 1

lim 11 (8) 

12 In order to develop a form for the standard in which the level can be expressed as a single 

13 national value related to protection against effects that occur at specific values of ANC, a 

14 simplified version of equation (8) is: 

( ) 1 ( ) lim DL N S

Q

15 ANC = g ⋅ − + (9) 

16 where, g( )⋅ = sustainable flux of base cations from the ecosystem + ecological sinks of N. This 

17 term is equivalent to the pre-industrial ANC level, or the natural background ANC, expressed as: 

( ) [ ] 1 * Neco BC O Q

18 g ⋅ = ⋅ + (10) 

19 Building from equation 9, total nitrogen deposition is split into oxidized and reduced 

20 nitrogen because we need to be able to specify the standards in terms of oxides of nitrogen, and 

21 so the contribution of reduced nitrogen has to be separated. 

( ) [ ] ( ) () DL( ) NHx

Q

DL N DL S

Q

ANC g OY = ⋅ − + − ⋅ 1 1

lim 22 (11) 
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March 2010 187 Draft –Do Not Quote or Cite 

1 where, 

2 DL(NOX)= the depositional load of oxidized nitrogen 

3 DL(NHx)= the depositional load of reduced nitrogen, NHx. 

4 

5 In order to judge whether an ecosystem or group of ecosystems meets the ANClimit given 

6 observed NOy and SOx levels, the associated depositional loadings of NOy and S can be 

7 compared directly against calculated deposition tradeoff curves (eq 4), atmospheric 

8 concentrations of NOy and SOx can be compared against the atmospheric concentration tradeoff 

9 curves (eq 5) or, loadings of NOx and SOx can be input into the following equations to obtain the 

10 calculated value of ANC, equal to ANC*: 

11 ANC* = g(⋅) − [L(Noy) + L(SOx)]− L(NHx) (11) 

12 where, 

13 ANC*= the calculated value of ANC given loadings of N and S for comparison against an 

14 ANClimit. 

15 L(Nox+S)= the load of Nox+S anions based on observed atmospheric concentrations of NOy and 

16 SOx

17 L(NHx) = the load of reduced nitrogen deposition 

18 [Note that L(N) = L(Nox+NHx)] 

19 

20 In equation 11, the ANC* will vary based on the deposition load inputs of Nox, NHx and 

21 S at the site of interest. The deposition loads caused by NOy and S and NHx are inputs, leading to 

[ ] ( ) () DL( ) NHx

Q

L N L S

Q

ANC g OX = − + − ⋅ 1 1 22 * (12) 

23 If ANC* < ANClim, then the deposition of N and S exceeds the deposition load to maintain 

24 ANClimit. ANC* is still representative of the calculated ANC based on specific catchment level 

25 estimates of g, Q and NHx. 

26 AAPI is equivalent to the equation for calculating ANC* when the catchment specific 

27 values for g in equation (9) in Section 5.5.1. are replaced by representative values for acid 

28 sensitive areas (based on a percentile of water bodies targeted for an ANC level selected by the 
Policy Assessment for the Review of the Secondary National Ambient Air Quality Standards for NOx and SOx

March 2010 188 Draft –Do Not Quote or Cite 

1 Administrator), Q and NHx are replaced by average values for aggregate ecosystem areas, and 

2 L(NOX) and L(S) are replaced by terms translating atmospheric NOy and SOx into deposition: 

( ) [V NOy V SOx] Q

L NHx

Q

AAPI g NOy SOx = ⋅ − − ⋅ + ⋅ 1 ( ) 1 3 (13) 

4 where NOy and SOx are concentrations of NOy and SOx, respectively, VNOy and VSOx are the 

5 ratios of deposition to concentrations (deposition transformation ratios) for NOy and SOx, 

6 respectively. 

7 5.6 REFERENCES 

8 Davis, J. M., Swall, J. L., 2006. An examination of the CMAQ simulations of the wet deposition 

9 of ammonium from a Bayesian perspective. Atmospheric Environment 40, 4562–4573. 

10 EPA, 1999. Science Algorithms of the EPA Models-3 Community Multiscale Air Quality 

11 (CMAQ) Modeling System. Tech. Rep. EPA/600/R-99/030, U.S. Environmental 

12 Protection Agency, Washington DC. 

13 Grantz, D., Garner, J., Johnson, D., 2003. Ecological effects of particulate matter. Environment 

14 International 29, 213-239. 

15 Lien L; Raddum GG; Fjellheim A. (1992). Critical loads for surface waters: invertebrates and 

16 fish. (Acid rain research report no 21). Oslo, Norway: Norwegian Institute for Water 

17 Research 

18 Morris, R. E., McNally, D. E., Tesche, T. W., Tonnesen, G., Boylan, J. W., Brewer, P., 2005. 

19 Preliminary evaluation of the community multiscale air quality model for 2002 over the 

20 Southeastern United States. Journal of the Air and Waste Management Association 55, 

21 1694–1708. 

22 Seinfeld, J., Pandis, S., 1998. Atmospheric Chemistry and Physics. John Wiley and Sons, Inc., 

23 New York. 
Policy Assessment for the Review of the Secondary National Ambient Air Quality Standards for NOx and SOx

March 2010 189 Draft –Do Not Quote or Cite 

1 Sullivan TJ; Webb JR; Snyder KU; Herlihy AT; Cosby BJ. (2007). Spatial distribution of acid2 sensitive and acid-impacted streams in relation to watershed features in the southern 

3 Appalachian mountains. Water Air Soil Pollut, 182, 57-71. 

4 Sullivan TJ; Fernandez IJ; Herlihy AT; Driscoll CT; McDonnell TC; Nowicki NA; Snyder KU; 

5 Sutherland JW. (2006). Acid-base characteristics of soils in the Adirondack Mountains, 

6 New York. Soil Sci Soc Am J, 70, 141-152. 

7 
Policy Assessment for the Review of the Secondary National Ambient Air Quality Standards for NOx and SOx

March 2010 190 Draft –Do Not Quote or Cite 

1 6. OPTIONS FOR ELEMENTS OF THE STANDARD 

2 The elements of the standard include the ambient air indicator, the form, the level and the 

3 averaging time. The “indicator” of a standard defines the chemical species or mixture of the 

4 criteria air pollutant that is to be measured in determining whether an area attains the standard. 

5 The “form” of a standard defines the air quality statistic that is to be compared to the level of the 

6 standard in determining whether an area attains the standard. The “averaging time” defines the 

7 period of time over which the air quality indicator is averaged, e.g. annual average. The “level” 

8 is the specific quantity to which the air quality statistic will be compared. 

9 EPA has historically established NAAQS so that the locally-monitored ambient 

10 concentration of an air pollutant indicator is compared against a specified numerical level of 

11 atmospheric concentration, using a specified averaging time and statistical form. For example, 

12 the current secondary standard for oxides of nitrogen uses ambient concentrations of NO2 as the 

13 indicator. Attainment is determined by comparing the annual arithmetic mean of the measured 

14 maximum daily1-hour NO2 concentrations, for a calendar year, against the level of 0.053 ppm. 

15 As discussed in Chapters 4 and 5, a standard using this kind of approach for defining indicator, 

16 averaging time, form, and level is not the most appropriate way to protect sensitive ecosystems 

17 from effects associated with ambient concentrations of NOx and SOx. Moreover, the inherently 

18 complex and variable linkages between ambient concentrations of NOx and SOx, their deposited 

19 forms of nitrogen and sulfur, and the ecological responses that are associated with public welfare 

20 effects call for consideration of a more complex and ecologically relevant design of the standard 

21 that reflects these linkages. 

22 Chapter 5 provided a conceptual framework for a secondary standard that is designed to 

23 provide protection of ecosystems against the effects associated with deposition of ambient 

24 concentrations of NOx and SOx. This conceptual framework takes into account variable factors, 

25 such as atmospheric and ecosystem conditions that modify the amounts of deposited NOx and 

26 SOx, and the associated effects of deposited N and S on ecosystems. Based on the conceptual 

27 framework described in Chapter 5, this chapter provides a set of potential options for specifying 

28 the elements of the framework to define a secondary standard for NOx and SOx. Our 

29 development of options for the standards recognizes the need for a nationally applicable standard 

30 for protection against adverse effects to public welfare, while recognizing the complex and 

31 heterogeneous interactions between atmospheric concentrations of NOx and SOx, deposition, and 
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March 2010 191 Draft –Do Not Quote or Cite 

1 ecological response. These options will include elements of the framework related to the air 

2 quality indicator, the averaging time, the form, and the level, which are based on the ecological 

3 indicator, the ecological response to deposition function, the deposition metric, and the 

4 atmospheric deposition transformation function. 

5 To make the transition from the conceptual framework in Chapter 5, which is developed 

6 largely around the concept of critical loads, to elements of the standard, we propose to focus on 

7 developing a form of the standard that is based on the concepts of critical loads of NOx and SOx

8 deposition linked to target ANC values, recognizing the limitations in available data and related 

9 uncertainties. Our goal in developing the form of the standard is to create an index, directly 

10 expressed in terms of atmospheric concentrations of NOy and SOx, that can be applied across the 

11 nation to convey the potential of an ecosystem to become acidified from atmospheric deposition. 

12 This chapter is structured around questions related to the various elements of a standard. 

13 The chapter begins in section 6.1 with a discussion of atmospheric indicators. Section 6.2 then 

14 discusses averaging times for the atmospheric indicators. Section 6.3 suggests a possible 

15 ecologically relevant form of the standard. Section 6.4 provides a discussion of issues regarding 

16 the spatial area over which a standard might be evaluated, and related issues regarding spatial 

17 averaging within areas. Section 6.5 discusses options for specifying target levels for the 

18 ecological indicator for aquatic acidification. Section 6.6 addresses issues relating to monitoring 

19 of the atmospheric indicators. Section 6.7 concludes with a discussion of potential ranges of 

20 levels for the standard. 

21 6.1 WHAT ATMOSPHERIC INDICATORS OF OXIDIZED NITROGEN 

22 AND SULFUR ARE APPROPRIATE FOR USE IN A SECONDARY 

23 NAAQS THAT PROVIDES PROTECTION FOR PUBLIC WELFARE 

24 FROM EXPOSURE RELATED TO DEPOSITION OF N AND S? 

25 WHAT AVERAGING TIMES AND STATISTICS FOR SUCH 

26 INDICATORS ARE APPROPRIATE TO CONSIDER? 

27 Staff concludes that indicators other than NO2 and SO2 should be considered as the 

28 appropriate pollutant indicators for protection against the acidification effects associated with 

29 deposition of NOx and SOx. This conclusion is based on the recognition that all forms of 
Policy Assessment for the Review of the Secondary National Ambient Air Quality Standards for NOx and SOx

March 2010 192 Draft –Do Not Quote or Cite 

1 oxidized nitrogen and sulfur in the atmosphere contribute to deposition and resulting 

2 acidification, and as such NO2 and SO2 are incomplete indicators. Furthermore, staff concludes 

3 that NOy (total oxidized nitrogen) should be considered as an appropriate indicator for oxides of 

4 nitrogen. NOy is defined as NOx (NO and NO2) and all oxidized NOx products: including NO, 

5 NO2, and all other oxidized N-containing compounds transformed from NO and NO2 (Finlayson6 Pitts and Pitts, 2000). As described in Chapter 4, this set of compounds includes NO2 + NO + 

7 HNO3 + PAN +2N2O5 + HONO+ NO3 + organic nitrates + particulate NO3. Staff concludes that 

8 SOx should be considered as an appropriate indicator for oxides of sulfur. SOx includes sulfur 

9 monoxide (SO), sulfur dioxide, sulfur trioxide (SO3), and disulfur monoxide (S2O), and 

10 particulate-phase S compounds that result from gas-phase sulfur oxides interacting with particles. 

11 In principle, measured NOy based on catalytic conversion of all oxidized species to NO 

12 followed by chemiluminescence NO detection is consistent with this definition. We recognize 

13 the caveats associated with instrument conversion efficiency and possible inlet losses which are 

14 discussed in Section 5.6. The development of the function that converts atmospheric 

15 concentrations of NOy and SOx to N and S deposition which incorporates NOy estimates is based 

16 on the Community Multi-scale Air Quality (CMAQ) model (EPA, 1999). CMAQ treats the 

17 dominant NOy species as explicit species while the minor contributing non-PAN organic 

18 nitrogen compounds are aggregated. Total oxidized sulfur, SOx, requires independent 

19 measurements of particle bound sulfate and gaseous sulfur dioxide; methodology and network 

20 considerations are discussed in Section 5.6. The CMAQ treatment of SOx is the simple addition 

21 of both species which are treated explicitly in the model formulation. All particle size fractions 

22 are included in the CMAQ SOx estimates. At this time, we consider the contribution of coarse 

23 fraction (aerodynamic diameters between 2.5 and 10 microns) particle bound sulfate to be 

24 insignificant from a measurement perspective. Consequently, the routinely measured sulfate 

25 from IMPROVE and EPA speciation networks, as well as CASTNET, are viable candidates for 

26 measurement consideration. Consistent with units and the charge balance relationships applied in 

27 ecosystem acidification models, only mass as sulfur or nitrogen is considered requiring 

28 conversion of reported particle bound sulfate and nitrate. Precipitation mass is not included 

29 explicitly as part of an atmospheric NAAQS indicator. 
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1 6.2 WHAT IS THE APPROPRIATE AVERAGING TIME FOR THE AIR 

2 QUALITY INDICATORS NOY AND SOX TO PROVIDE 

3 PROTECTION OF PUBLIC WELFARE FROM ADVERSE EFFECTS 

4 FROM ACIDIFICATION? 

5 Based on the review of the scientific evidence, welfare effects associated with 

6 acidification result from annual cumulative deposition of nitrogen and sulfur, reflected in effects 

7 on the chronic ANC level (measured as annual ANC). It is important to note that chemical 

8 changes can occur over both long- and short-term timescales. Short-term (i.e., hours or days) 

9 episodic changes in water chemistry can also have significant biological effects. Episodic 

10 chemistry refers to conditions during precipitation or snowmelt events when proportionately 

11 more drainage water is routed through upper soil horizons that tend to provide less acid 

12 neutralizing than was passing through deeper soil horizons. Surface water chemistry has lower 

13 pH and acid neutralizing capacity (ANC) during events than during baseflow conditions. One of 

14 the most important effects of acidifying deposition on surface water chemistry is the short-term 

15 change in chemistry that is termed “episodic acidification.” Some streams may have chronic or 

16 base flow chemistry that is suitable for aquatic biota, but may be subject to occasional acidic 

17 episodes with lethal consequences. Episodic declines in pH and ANC are nearly ubiquitous in 

18 drainage waters throughout the eastern United States and are caused partly by acidifying 

19 deposition and partly by natural processes. As noted in Chapter 3 of the ISA, while ecosystems 

20 are also affected by episodic increases in acidity due to pulses of acidity during high rainfall 

21 periods and snowmelts, protection against these episodic acidity events can be achieved by 

22 establishing a higher chronic ANC level. Episodic acidification can result from either shorter 

23 term deposition episodes, or from longer term deposition on snowpack. Snowmelt can release 

24 stored N deposited throughout the winter, leading to episodic acidification in the absence of 

25 increased deposition during the actual episodic acidification event. Protection against a low 

26 chronic ANC level is provided by reducing overall annual average deposition levels for nitrogen 

27 and sulfur. This supports the conclusion that long term NOx and SOx concentrations are 

28 appropriate to provide protection against low chronic ANC levels, which protects against both 

29 long term acidification and acute acidic episodes. 
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March 2010 194 Draft –Do Not Quote or Cite 

1 Long term concentrations are often measured using annual averages. However, given the 

2 multi-year nature of responses to chronic acidification, multi-year averages of concentrations of 

3 NOy and SOx may also be appropriate. In the second draft policy assessment, we will provide an 

4 expanded discussion of the support for different options for the averaging time to best represent 

5 long-term concentrations of NOy and SOx related to chronic acidification. 

6 6.3 WHAT FORM(S) OF THE STANDARD ARE MOST APPROPRIATE 

7 TO PROVIDE PROTECTION OF SENSITIVE ECOSYSTEMS 

8 FROM THE EFFECTS OF ACIDIFYING DEPOSITION RELATED 

9 TO AMBIENT NOX AND SOX CONCENTRATIONS? 

10 Based on the evidence for joint effects of NOx and SOx through acidifying deposition, 

11 staff concludes that it is appropriate to consider changes to the form of the existing NOx and SOx

12 secondary standards to provide protection to ecosystems. Staff notes that in recent reviews of the 

13 secondary ozone standards, EPA has considered use of a form of the standard that reflects 

14 ecologically relevant exposures, by using a cumulative index which weights exposures at higher 

15 concentrations greater than those at lower concentrations based on scientific literature 

16 demonstrating the cumulative nature of O3-induced plant effects and the need to give greater 

17 weight to higher concentrations (EPA, 2007). See 75 FR 2938, 2999 (Janaury 19, 2010) In order 

18 to recognize the roles that NOx and SOx play in acidification based on their acidifying potentials, 

19 and to incorporate the important roles that reduced nitrogen and non-atmospheric variables play 

20 in determining the acidifying potentials of NOx and SOx, staff suggests using an Atmospheric 

21 Acidification Potential Index (AAPI) that is a more ecologically relevant form relative to the 

22 current ambient concentration based forms, based on the derivations in Section 5.5.1. The intent 

23 of the AAPI is in effect to weight atmospheric concentrations of NOx and SOx by their 

24 propensity to contribute to acidification through deposition, given the fundamental acidifying 

25 potential of each pollutant, and the ecological factors that govern acid sensitivity in different 

26 ecosystems. Thus the APPI is more relevant to protecting ecosystems from acidifying deposition 

27 compared to simple ambient concentration forms which do not reflect factors that affect 

28 acidifying potential. 

29 The AAPI is closely tied to the ecological indicator of acidification, ANC, so that the 

30 form of AAPI is intended to identify the atmospheric concentrations of NOx and SOx that will 
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March 2010 195 Draft –Do Not Quote or Cite 

1 result in an equivalent level of a target ANC for a percentage of aquatic ecosystems within a 

2 particular acid sensitive area. Thus, this form is ecologically relevant as it is tied directly to the 

3 ecological indicator that is most directly linked with known ecological effects. 

4 The AAPI incorporates the processes which modify both rates of deposition and 

5 ecological response to deposition caused by NOx and SOx. There is strong evidence in the 

6 scientific literature demonstrating that the amount of deposition caused by NOx and SOx is 

7 modified by atmospheric and landscape factors. Within the ecosystem there are factors, such as 

8 bedrock geology and topography, which modify the acidifying potential of the nitrogen and 

9 sulfur deposition resulting from ambient NOx and SOx concentrations. In addition reduced 

10 nitrogen contributes to total nitrogen loading. In this review, reduced nitrogen is treated as an 

11 additional modifying factor within the ecosystem, which reduces the buffering capacity of the 

12 ecosystem, and therefore it increases the impact or sensitivity to additional loading from oxidized 

13 forms of nitrogen. In effect this leaves less allowable deposition loading from NOx and SOx

14 before the ecosystem fails to achieve a target ANC level. Based on this evidence staff concludes 

15 that the form should include landscape and atmospheric factors, including reduced nitrogen, 

16 which modify the acidifying potential of ambient NOx and SOx concentrations. This form is 

17 consistent with the language of the CAA as discussed in Section 1.5. 

18 Selecting a more ecologically-relevant secondary standard form would also be directly 

19 responsive to the recommendation of the 2004 National Research Council’s report titled Air 

20 Quality Management in the United States (NRC, 2004) which encourages the Agency to evaluate 

21 its historic practice of setting the secondary NAAQS equal to the primary. 

22 In theory, the AAPI could address acidification potential related to both terrestrial 

23 acidification and aquatic acidification. For this first draft policy assessment, as discussed in 

24 Chapter 5, we define the AAPI for protection against aquatic acidification. In the second draft 

25 policy assessment, we will explore the potential to include protection against terrestrial 

26 acidification in the AAPI or a related index. 

27 The definition of the AAPI form considered here is: 

28 Annual Average AAPI: Natural background ANC minus the contribution to 

29 acidifying deposition from NHx, minus the acidifying contribution of deposition 

30 from NOy and SOx. 

31 Building from the derivation of ANC* provided in Section 5.5.2, the AAPI is equivalent 

32 to the equation for calculating ANC* when the catchment specific values for g in equation (9) in 
Policy Assessment for the Review of the Secondary National Ambient Air Quality Standards for NOx and SOx

March 2010 196 Draft –Do Not Quote or Cite 

1 Section 5.5.2. are replaced by representative values for acid sensitive areas (based on a percentile 

2 of water bodies targeted for an ANC level selected by the Administrator), Q and NHx are 

3 replaced by average values for aggregate ecosystem areas, and L(NOX) and L(S) are replaced by 

4 terms translating atmospheric NOy and SOx into deposition: 

( ) [V NOy V SOx] Q

L NHx

Q

AAPI g NOy SOx = ⋅ − − ⋅ + ⋅ 1 ( ) 1 5 (1) 

6 where NOy and SOx are concentrations of NOy and SOx, respectively, VNOy and VSOx are the 

7 ratios of deposition to concentrations (deposition transformation ratios) for NOy and SOx, 

8 respectively. Deposition transformation ratios are the estimated relationships between 

9 atmospheric concentrations of NOy and SOx and the collocated deposition of NOX and S. See 

10 Chapter 5.4.4 and 5.4.5 for further description of calculation of ratios of deposition to 

11 concentrations. 

12 Note that while equation (1) is used to calculate the value of AAPI for any observed 

13 values of NOy and SOx, the level of the standard for AAPI selected by the administrator should 

14 reflect a wide number of factors, including desired level of protection indicated by a target 

15 ANClimit, the specified percentile of waterbodies projected to achieve the target ANC, and the 

16 various factors and uncertainties involved in specifying all of the other aspects of the standard, 

17 such as the classification of landscape areas, the specification of reduced nitrogen deposition, the 

18 methodology to determine deposition of NOy and SOx, and the averaging time. As such the 

19 administrator may choose an AAPI level higher or lower than the target ANClimit to reflect the 

20 combined effect of the all of the components of the standard and their related uncertainty, such 

21 that the chosen AAPI, in the context of the overall standard, reflects her informed judgment as to 

22 a standard that is sufficient but not more than necessary to protect against adverse public welfare 

23 effects. 

24 How are AAPI parameters determined? 

25 Other than ambient levels of NOx and SOx, which would be measured values, EPA would 

26 determine and specify all of the values for the AAPI parameters, as discussed below. 

27 The natural background ANC, g, is a calculated value and is determined by two 

28 components: [BC]o* which is closely associated with underlying bedrock which strongly 

29 influences the contribution of base cations due to weathering, for which a representative value 
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March 2010 197 Draft –Do Not Quote or Cite 

1 could be determined for a limited set of geologic acid sensitivity classes, and Neco, which 

2 represents the amount of deposited nitrogen that is available for acidification due to uptake, 

3 denirification and immobilization. Neco is estimated using two different approaches: (1) the 

4 individual terms are estimated through available data and modeling or (2) Neco is calculated as 

5 nitrogen deposited minus nitrogen leached, using streamwater measurements of nitrate for 

6 leaching and estimates of nitrogen deposition based on model results and measurements. The 

7 details of these procedures are addressed in chapter 4 and Appendix 4 of the REA. 

8 The runoff parameter Q for each acid sensitive area is determined based on USGS 

9 mapping of runoff values (REFERENCE NEEDED). 

10 VNOy, VSOx are calculated from CMAQ by dividing the annual average NOy, SOx

11 concentration by the total NOy or SOx deposition, respectively, for each grid cell and then 

12 aggregating all grid cells in the acid sensitive area. 

13 L(NHx) is calculated using the same procedures applied to CMAQ results for deposited 

14 NHx. 

15 The VNOy and VSOx are spatially variable, and for the purposes of setting the standard, 

16 are determined based on the ratios of total sulfur and nitrogen depositions to concentrations from 

17 CMAQ model outputs (see Chapter 5 for details of calculation of deposition ratios). VNOy, VSOx

18 are calculated from CMAQ by dividing the annual average NOy or SOx concentration by the total 

19 NOy or SOx deposition, respectively, for each grid cell and then computing the mean or median 

20 of all grid cells in the acid sensitive area (the decision on whether the median or mean value 

21 should be used is an option for discussion; the mean will give more weight to outlier values 

22 relative to the median). 

23 NHx is spatially variabe and determined based on monitored and/or CMAQ modeled 

24 outputs. The average NHx deposition across grid cells within an acid sensitive region will be 

25 used to represent the depositional load of NHx. 

26 There will be multiple combinations of concentrations of NOx and SOx that result in a 

27 specific value of the AAPI. There will be no single combination of NOx and SOx that solves for a 

28 particular value of AAPI in all locations. easured concentrations of annual average NOx and SOx

29 necessary to meet the standards are thus expressed conditionally by the equality in (1), and not 

30 by fixed quantities. 
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March 2010 198 Draft –Do Not Quote or Cite 

1 In order to provide a set of values for elements of the form, e.g. to develop a specific set 

2 of parameter values for g, VNOy, VS, and Nx, we propose to classify locations in the U.S. into a 

3 set of areas based on sensitivity to acidification. Each area would be assigned a classification for 

4 the g parameter; for example, as described in Section 5.2.2.4, a set of classes of acidification 

5 sensitivity might be able to be developed based on underlying bedrock geology, or bedrock 

6 geology plus other ecosystem variables. The g parameter (natural background, or preindustrial, 

7 ANC) would then be estimated for each of those sensitivity classes, based on the critical load 

8 modeling available for each class. Each acid sensitive area would then be assigned a value of g 

9 based on the geology class in which it falls. In the case of VNOy, VS, and NHx, values for specific 

10 areas would be estimated based on the best available monitoring and/or modeling data. Given the 

11 limited availability of measured deposition velocities, staff concludes that the calculated 

12 deposition ratios based on the CMAQ modeling from 2005 provides the best available source of 

13 estimates of VNOy and VS. Evaluation of the stability of these estimates of deposition ratios over 

14 time (see Chapter 5) suggests that in most acid sensiive areas, deposition ratios are quite stable, 

15 with a coefficient of variation less than 25 percent across a four year period. While there are a 

16 limited number of sites that directly measure deposition of reduced nitrogen, staff concludes that 

17 the most widely available and defensible estimates of reduced nitrogen deposition (NHx) are the 

estimates obtained from the CMAQ modeling from 2005.18 18 

19 It is important to note for this form of the standard that the same AAPI can be obtained 

20 with differentcombinations of ambient NOx and SOx concentrations. The implication of the form 

21 of the standard expressed in equation (1) is that there will be a tradeoff curve that reflects the 

22 combinations of NOx and SOx that satisfy equation (1) for any specific value of the standard. The 

23 shape of the tradeoff curve will depend on the specific values of G, VNOy, VS, and NHx for a 

24 limited number of specific areas classified based on acid-sensitivity. As discussed in Chapter 5, 

25 all parts of the U.S. would be classified into areas based on acid-sensitivity. Within each such 

26 area, EPA would specify the parameter values of APPI, leading to a specific tradeoff curve for 

27 each area. The levels of NOy and SOx that meet an AAPI standard expressed for a given 

28 g( )⋅ [preindustrial ANC], Q, L(NHx) and VNOy and VSOx: 

 

18 Note to readers: Maps of CMAQ 2005 estimates of NHx deposition will be included in the second draft policy 

assessment, along with an evaluation of the representativeness of the 2005 NHx deposition for characterizing 

conditions over a multiyear period. 
Policy Assessment for the Review of the Secondary National Ambient Air Quality Standards for NOx and SOx

March 2010 199 Draft –Do Not Quote or Cite 

1 VNOy ⋅ NOy +VST ⋅ ST = g(⋅)⋅Q − L( (2) NHx) − AAPI ⋅Q

Note that [V NOy V ST ] NOy ST 2 ⋅ + ⋅ is essentially the critical load of NOy and S, expressed in terms 

3 of atmospheric concentrations. As such, equation (2) can also be expressed in a form similar to a 

4 typical critical load equation as discussed in Chapter 5, e.g. 

( ) ( ) * V NOy V ST BC0 APPI Q N L NHx 5 NOy ⋅ + ST ⋅ = − − eco − (3) 

6 This expression is based on 

g( )⋅ = (BC +)− L(NHx) − AAPI ⋅Q *

0 7 (4) 

8 The pairs of NOy and SOx that will meet a given AAPI limit are related through the following 

9 equations 

( ) NOy C

min

NOy

* 10 = (5) 

( ) ( ) max min

*

x NOy C NOy 11 SO = C SOx ∀ < (6) 

( ) ( ( ) ( ))

( ) ( ) min

*

min max

max

max

*

y y y

y y

x NO NO C NO

C NO C NO

C SOx SO C SOx ⋅ ∀ >

⎥

⎥

⎦

⎤

⎢

⎢

⎣

⎡

− 12 = + (7) 

13 where, 

* NOy 14 is the coordinate point for NOy

* SOx 15 is the coordinate point for SOx

( ) 16 Cmax SOx is the concentration of SOx in the atmosphere consistent with DLmax (S) 

( ) 17 Cmax NOy is the concentration of NOy in the atmosphere consistent with DL max (N) 

( ) Cmin NOy 18 is the concentration of NOy in the atmosphere consistent with DL min (N) 

( ) ( ) 1 max DLmax S

Vs

19 C SOx = (8) 
Policy Assessment for the Review of the Secondary National Ambient Air Quality Standards for NOx and SOx

March 2010 200 Draft –Do Not Quote or Cite 

( ) DL ( ) () N NHx NHx DL N

V

C NO

NO y

min Y min min

1 1 = − ∀ < (9) 

NHx DL (N) min 2 = 0∀ > 

( ) DL ( ) N

V

C NO

N

max Y max

1 3 = (10) 

4 where DLmax(S), DLmax (N), and DLmin(N).are based on the critical load within a sensitive 

areas that protects a specified percentile (e.g. 95% 5 ) of water bodies in the area. 

6 Note that Cmin(NOy) is a conditional function determined by the relationship between 

7 total nitrogen buffering capacity in an ecosystem and the amount of reduced nitrogen deposition. 

8 When reduced nitrogen deposition exceeds the buffering capacity of an ecosystem, then all 

9 atmospheric oxidized nitrogen contributes to acidification. When reduced nitrogen deposition is 

10 less than the buffering capacity of an ecosystem, then some amount of NOy is buffered (i.e. is 

11 reflected in Cmin(NOy) but that amount reflects the contribution of NHx to total nitrogen (the 

12 amount of buffering capacity used up by reduced nitrogen). In this case, some fraction of the 

13 atmospheric oxidized nitrogen may not contribute to acidification. 

14 Recall that these three variables are conditional on the chosen level of APPI, and reflect 

15 the depositional loadings that are associated with an equivalent level of ANC, e.g. for an APPI of 

16 50, the DLmax(S), DLmax(N), and DLmin(N) are associated with an ANC of 50. Also recall than 

17 DLmax(S) for a given ANC is a function of the “natural” flux of base cations to a watershed, 

18 runoff, and the amount of sulfur retention within a waterbody; DLmin(N) is the minimum amount 

19 of deposition of total nitrogen (NHx + NOx) that catchment processes can effectively remove 

20 without contributing to the acidic balance; and DLmax(N) for a given ANC is a function of 

21 DLmin(N) and the “natural” flux of base cations to a watershed, runoff, and the amount of 

22 nitrogen retention within a waterbody, assuming S is zero. In our framework, DLmin(N) is 

23 calculated from the FAB critical load modeling (equation 5 from Attachment A of the REA) or 

24 estimated through measured or modeled values of total nitrogen deposition and nitrate leaching. 

25 As discussed in Chapter 5, the specific estimation of G, VNOy, VS, and NHx in a specific 

26 sensitive area will depend on the spatial scale of the sensitive area. Sensitivity can be assessed at 

27 the level of individual catchments, however, this presents practical limitations for establishing 
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1 meaningful standards, as there are thousands of catchments within the U.S. Binning classes of 

2 sensitivity within larger spatial areas, e.g. the sensitive ecosystem areas displayed in Figure 6-1 

3 (reproducing Figure 4.2-2 in the REA), can provide a more manageable set of values of G, VNOy, 

4 VS, and NHx. These parameters can be estimated in several ways for the larger spatial areas. 

5 Mean or median values can be generated across catchments, however, this would lead to 

6 parameter estimates that do not reflect conditions in the more sensitive lakes in the region. 

7 Alternatively, in order to provide a desired level of protection in these larger defined spatial 

8 areas, estimates based on higher percentiles of the distributions of parameters across catchments 

can be generated, e.g. the 75th or 95th 9 percentile values of G, VNOy, VS, and NHxr could be used to 

10 provide protection for the more vulnerable aquatic ecosystems, however this would potentially 

11 lead to over-protection for less vulnerable ecosystems in the area. The Administrator may 

12 consider the balance between protection of particularly sensitive ecosystems and the overall 

13 protection for ecosystems in an area as an important element to consider in making decisions 

14 about the target level of ANC and the percent of aquatic ecosystems within an area targeted to 

15 achieve the specified ANC level. One potentially important modification to this process would 

16 be to first remove water bodies that are naturally acidic (e.g. that will not benefit from reductions 

17 in atmospheric NOx and SOx deposition) from the distribution of water bodies in the area prior to 

determining the mean or 95th 18 percentile. This will increase the likelihood that the estimated g 

19 parameter will be representative of ecosystems within an area that are sensitive to NOx and SOx

20 deposition. The second draft policy assessment will explore the implications of alternative 

21 combinations of target ANC and percent of aquatic ecosystems protected at the target ANC in 

22 areas of different sizes. The second draft policy assessment will also explore methods for 

23 determining values of g for areas that are clearly not sensitive to acidification from deposition of 

24 NOx and SOx. These areas may be areas that have very high levels of natural buffering, or may 

25 also be areas that are naturally acidified, such that the value of g is less than the target value of 

26 ANC. In these naturally acidified areas, reducing deposition from NOx and SOx will not be 

27 beneficial, because the areas are adapted to high levels of acidity. 
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1 

2 Figure 6-1. Ecosystems sensitive to acidifying deposition in the Eastern U.S. 

3 (Note that Florida represents a special case where high levels of natural 

4 acidification exist unrelated to deposition) This map does not include all sensitive 

5 areas in the U.S. Certain mountainous areas of the Western U.S. are also sensitive 

6 to acidifying deposition. 
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1 6.4 WHAT ARE THE APPROPRIATE SPATIAL EXTENTS OF THE 

2 BOUNDARIES FOR EVALUATING AAPI? WITHIN THOSE 

3 BOUNDARIES, WHAT ARE THE APPROPRIATE STATISTICS TO 

4 USE IN CALCULATING THE PARAMETERS OF THE AAPI, E.G. 

5 G, VNOY, VS, AND NHX? WITHIN THOSE BOUNDARIES, WHAT S 

6 THE APPROPRIATE SPATIAL AVERAGING FOR THE AIR 

7 QUALITY INDICATORS NOY AND SOX TO PROVIDE 

8 PROTECTION OF PUBLIC WELFARE FROM ADVERSE EFFECTS 

9 FROM ACIDIFICATION? 

10 [Note to reviewers: This section will be added in the second draft policy assessment. In 

11 the second draft we plan to provide initial sets of parameteir values for acid sensitive areas of 

12 the U.S., and include an exploration of how the standard might be specified for areas of the U.S. 

13 that are not sensitive to deposition of NOx and SOx. In addition, we plan to discuss the 

14 correlation between the extent of a spatial area and the importance of evaluating alternative 

15 percentiles of critical loads to protect a percentage of water bodies in an area, and to discuss 

16 how averaging of the VNOY, VSOx, and NHx should be conducted to best represent the 

17 parameters for an area.] 

18 6.5 WHAT ARE THE OPTIONS FOR SPECIFYING THE TARGETS 

19 FOR THE ECOLOGICAL INDICATOR FOR AQUATIC 

20 ACIDIFICATION? 

21 Chapter 5 discusses the rationale for use of ANC as the ecological indicator best suited to 

22 reflect the sensitivity of aquatic ecosystems to acidification. ANC as an indicator of acidification 

23 is causally linked to a number of measures of adversity to ecosystems, including declines in fish 

24 populations and diversity of aquatic species. ANC is also causally linked with deposition of 

25 nitrogen and sulfur. ANC is thus ideally suited to serve as the bridge between deposition and 

26 ecological effects. As such, staff concludes that ANC is the best available choice as the 

27 ecological indicator. CASAC has agreed that ANC represents a suitable ecological indicator for 

28 aquatic acidification (EPA-CASAC-09-013). Results from the REA confirm that ANC may be 
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1 used to establish impacts from current depositional loadings (REA 4.2.6). As explained above, 

2 ANC is an indicator of the effects expected to occur given the natural buffering capacity of an 

3 ecosystem and the loadings of nitrogen and sulfur resulting from atmospheric deposition. A 

4 target ANC limit based on a desired level of protection is an important input to the decisions of 

5 the level of AAPI and the percent of ecosystems to be protected. 

6 6.5.1 What levels of impairment are related to alternative levels of ANC?

7 As discussed in Chapters 2, 3, and 4, specific levels of ANC are associated with differing 

8 levels of ecosystem impairment, with higher levels of ANC resulting in fewer ecosystem 

9 impacts, and lower levels resulting in both higher intensity of impacts and a broader set of 

10 impacts. Logistic regression of species presence/absence data against ANC provides a 

11 quantitative dose-response function, which indicates the probability of occurrence of an 

12 organism for a given value of ANC. For example, the number of fish species present in a 

13 waterbody has been shown to be positively correlated with the ANC level in the water, with 

14 higher values supporting a greater richness and diversity of fish species (Figure 6-2). The 

15 diversity and distribution of phyto-zooplankton communities are also positively correlated with 

16 ANC. 

17 The relationship between ANC and ecosystem impacts is non-linear, with a sigmoidal 

18 shape. For freshwater systems, ANC levels can be grouped into five major classes: <0, 0–20, 20–

19 50, 50–100, and >100 microequivalents per liter (μeq/L), with each range representing a 

20 probability of ecological damage to the community. The five categories of ANC and expected 

21 ecological effects are described Table 2-1 in Chapter 2 and are supported by a large body of 

22 research completed throughout the eastern United States (Sullivan et al., 2006). 

23 Biota are generally not harmed when ANC values are >100 microequivalents per liter 

24 (μeq/L). The number of fish species also peaks at ANC values >100 μeq/L. This suggests that at 

25 ANC greater than 100, little risk from acidification exists in most aquatic ecosystems. At ANC 

26 levels below 100 μeq/L, overall health of an aquatic community can be maintained; however, 

27 fish fitness and community diversity begin to decline. At ANC levels between 100 and 50 μeq/L, 

28 the fitness of sensitive species (e.g., brook trout, zooplankton) also begins to decline. When ANC 

29 concentrations are <50 μeq/L, negative effects on aquatic biota are observed, including large 

30 reductions in diversity of fish species, and changes in health of fish populations, affecting 
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1 reproductive ability and fitness. ANC levels below 50 are generally associated with death or loss 

2 of fitness of biota that are sensitive to acidification. (ISA 5.2.2.1 and REA 5.2.1.2). 

3 Based on the field data from the Adirondacks and Shenendoah case study areas, ANC 

4 levels less than 50 are clearly adverse to ecosystem health, and are likely to lead to reductions in 

5 ecosystem services related to recreational fishing. ANC levels between 50 and 100 are 

6 potentially adverse to ecosystem health, and may result in losses in ecosystem services, but the 

7 effects are less severe and greater uncertainty exists as to the magnitude of ecosystem service 

8 impacts. A more comprehensive discussion of uncertainties related to ecological effects at 

9 different ANC levels and related ecosystem services will be included in the second draft policy 

10 assessment. 

11 The implications of the data from the Adirondacks and Shenendoah case study areas for 

12 relating ANC to adverse ecological impacts is transferable to other acid sensitive areas of the 

13 U.S. The relationship between species diversity and ANC is quite similar between the two case 

14 study areas (see REA Figure 4.2-1), which have different water body types and different 

15 geological and topographical features. While the species composition and thereby relative 

16 sensitivities of species are likely to vary across the landscape, the rate of impact is likely to be 

17 similar. The plot in Figure 6-2 shows a rapid decrease in fish species between an ANC of 100 

18 and an ANC of 0. This trend is what would be expected in many systems given similar changes 

19 in ANC. 

20 Consideration of the appropriate levels of ANC to target in the standard to reduce the 

21 likelihood of effects from aquatic acidification can be based upon the above presented categories 

22 of aquatic status in Table 2-1. Using this information as well as information provided by both the 

23 ISA and REA, the lowest two categories (0 and 0<20) would appear inadequate to protect against 

24 catastrophic loss of ecosystem function. While ecological effects occur at ANC levels below 50, 

25 the degree and nature of those effects is less significant than at levels below 20. Therefore, three 

26 levels of ANC – 20, 50, and 100 - would provide the Administrator with reasonable range of 

27 options in designing an AAPI for protecting public welfare. 

28 Given the level of ecosystem impairment occurring at ANC levels below 50, staff suggest 

29 that the greatest support is for the Administrator to consider a range for the target ANC between 

30 50 and 100 as a basis for the design of the standard. Selection of target ANC values closer to 50 

31 places less weight on the vulnerability of sensitive aquatic ecosystems, while selection of target 
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1 ANC values closer to 100 places more weight on sensitive species within acid sensitive 

2 ecosystems. Staff conclude that while target ANC values between 20 and 50 will not result in 

3 complete impairment of aquatic systems, the level of damages due to ANC as you get lower in 

4 this range are highly likely to result in adverse impacts to public welfare in many locations, due 

5 to the significant reductions in the number of fish species in affected waterbodies, and the 

6 reductions in health and reproductive fitness of fish populations and other aquatic organisms. 

7 

8 Figure 6-2. Number of fish species per lake or stream versus ANC level and 

aquatic status category19 9 (colored regions) for lakes in the Adirondack Case Study 

10 Area (Sullivan et al., 2006). 

11 The target ANC level specified in designing the standard is only one part in determining 

12 the overall protectiveness of the standard. The degree of protectiveness is based on all elements 

13 of the standard, including the target ANC, the size of the spatial areas over which the standard is 

14 applied, the percent of aquatic ecosystems targeted within a spatial area that is selected by the 

15 Administrator to achieve the selected ANC level, the atmospheric indicator, the method for 

16 calculating g, the calculated values for the deposition transformation ratios (VNOx and VSOx), 

 

19 The aquatic status categories are based on the literature and are discussed in detail in the REA (REA Appendix 4-

20) 
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1 and the calculated value for reduced nitrogen deposition (NHx). There are widely varying 

2 degrees of uncertainty associated with all of these elements, some being much more certain and 

3 others being much less certain. The specified target ANC level is a crucial part of developing a 

4 standard that is requisite to protect, but it is the overall design and content of the standard that 

5 must be considered in judging the adequacy of protection it provides. 

6 Consideration of the target ANC should also reflect that an adequate level of ANC should 

7 protect against episodic as well as long term effects. Selecting a higher chronic ANC level can 

8 provide greater protection against short term peaks in acidification. In addition, selection of ANC 

9 values in the range of 20 to 50 provides less protection against these short term episodic effects. 

10 Selection of target ANC values in the range from 50 to 100 provides additional protection 

11 against episodic peaks in acidification. 

12 When considering the appropriate level of a standard to protect against aquatic 

13 acidification, it is necessary to take into account both the time period desired for recovery as well 

14 as the potential of recovery. Ecosystems become adversely impacted by acidifying deposition 

15 over long periods of time and have variable time frames and abilities to recover from such 

16 perturbations. Modeling presented in the REA (REA Section 4.2.4) shows the estimated ANC 

17 values for Adirondack lakes and Shenandoah streams under pre-acidification conditions and 

18 indicates that for a small percentage of lakes and streams, natural ANC levels would have been 

19 below 50. Therefore, for these waterbodies, no reduction in input is likely to achieve an ANC of 

20 50 or greater. Conversely, for some lakes and streams the level of perturbation from long periods 

21 of acidifying deposition has resulted in very low ANC values compared to estimated natural 

22 conditions. For such waterbodies, the time to recovery would be largely dependent on future 

23 inputs of acidifying deposition. These concepts become important in the consideration of the 

24 desired level of protection of a standard and will be discussed further in the next draft of this 

25 document. 
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1 6.6 WHAT ARE THE APPROPRIATE AMBIENT AIR MONITORING 

2 METHODS TO CONSIDER IN DEVELOPING THE STANDARDS? 

3 6.6.1 What measurements would be used to characterize NOy and SOx ambient air 

4 concentrations for the purposes of the AAPI based standard? 

5 Ambient NOy, gaseous SO2 and particulate sulfate concentrations would be used in 

6 determining compliance with the AAPI. This would require measurements of NOy, sulfate and 

7 sulfur dioxide, all which are conducted as part of current routine monitoring networks (section 

8 3.2). There are issues requiring resolution associated with Federal Reference or Equivalency 

9 Measurement (FRM/FEM) status of measurement techniques, that to date have served as 

10 supplemental information, which will require resolution. A FRM for SO2 exists, but not for NOy

11 or sulfate. Only recently have NOy measurements, which historically were viewed as research 

12 venue measurements, been incorporated as “routine” observations, partly as a result of the NCore 

13 program. Acquiring FRM status may require better characterization of the conversion 

14 efficiencies, mass loss and clear guidance on operating and siting procedures. Particulate sulfate 

15 has been measured for several years in the IMPROVE, CASTNET and EPA CSN networks. The 

16 nation has over 500 24-hour average, every third day sulfate measurements produced by the 

17 PM2.5 speciation networks (IMPROVE and EPA CSN) and nearly 80 CASTNET sites that 

18 provide continuous weekly average samples of sulfate with an open inlet accommodating all 

19 particle sizes. However, with minor exceptions, the PM2.5 fraction accounts for nearly all sulfate 

20 mass. The sample collection period is not an issue for gaseous measurements of NOy and SO2

21 that operate continuously. Some concerns have been raised about the possibility of exclusion of 

22 coarse particles from NOy samplers operating at low flow conditions as well as potential 

23 difficulties of reducing organically bound and mineralized nitrate. These conversion efficiency 

24 and particle size fraction issues are viewed by EPA as relatively minor mass accounting issues 

25 that require more clarification but not necessarily technical resolution. 

26 6.6.2 What sampling frequency would be required? 

27 The averaging time for the standard is likely to be an annual average. Conceptually, 

28 extended sampling periods as long as one year would be adequate for the specific purposes of 

29 comparing to a standard. However, future assessments that characterize acidification and form 
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1 the scientific basis for subsequent standards reviews and allow for systematic checking of 

2 progress through accountability procedures benefit from more highly resolved data, especially 

3 the evaluation of air quality models that are key components of N/S deposition assessments. In 

4 addition, many of the monitoring approaches that are used throughout the nation sample (or at 

5 least report out) on daily (PM2.5 chemical speciation), weekly (CASTNET) and hourly (all 

6 inorganic gases) periods. There is a tradeoff to consider in sampling period design. For example, 

7 the weekly CASTNET collection scheme covers all time periods throughout a year, but only 

8 provides weekly resolution that misses key temporal and episodic features valuable for 

9 diagnosing model behavior. The every third day, 24-hour sampling scheme used in IMPROVE 

10 and EPA speciation monitoring does provide more information for a specific day of interest yet 

11 misses 2/3 of all sampling periods. The missing sampling period generally is not a concern when 

12 aggregating upward to a longer term average value as the sample number adequately represents 

13 an aggregated mean value. Additionally, there is a benefit to leveraging existing networks which 

14 should be considered in sampling frequency recommendations. A possible starting point would 

15 be to assume gaseous oxidized species, NOy and SO2, are run continually all year reporting 

16 values every hour, consistent with current routine network operations. Sulfate sampling periods 

17 should coincide with either the chemical speciation network schedules or CASTNET. There are 

18 advantages to coordinating with either network. Ammonia gas and ammonium ion present 

19 challenges in that they are not routinely sampled and analyzed for, and the combined quantity, 

20 NHx is of interest. Because NHx is of interest, some of the problems of volatile ammonia loss 

21 from filters may be mitigated. However, for model diagnostic purposes, delineation of both 

22 species at the highest temporal resolution is preferred. While levels of deposited reduced 

23 nitrogen would be specified by EPA for purposes of the APPI, monitoring of reduced nitrogen 

24 would be important but would not be used in the APPI itself. 

25 6.6.3 What are the spatial scale issues associated with monitoring for compliance, 

26 and how should these be addressed? 

27 The observation network for NOy, NHx and SOx is very modest and includes a 

28 monitoring network infrastructure that is largely population oriented. While there is platform and 

29 access infrastructure support provided by CASTNET, NADP and IMPROVE, those locations by 

30 themselves are not likely to provide the needed spatial coverage to address acid sensitive 
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1 watersheds across the United States. Ambient monitoring at every watershed may not be required 

2 due to the nature of the ambient air quality in acid sensitive areas. An understanding of the 

3 spatial variability of NOy, NHx, sulfate and SO2 will help inform monitoring. Critical load 

4 models are based on annual averages, which effectively serves to dampen much of the spatial 

5 variability. Furthermore, the development of an area-wide depositional load tradeoff curve 

6 implies focus on region wide characterization. Toward that end, CMAQ concentration fields will 

7 provide insight into the likely spatial representativeness of monitors leading to efficient 

8 application of monitoring resources. For example, the CMAQ based spatial coefficient of 

9 variation (standard deviation/mean) of oxidized nitrogen in the Adirondacks was 1.46%. 

10 Improved dry deposition estimates will result from enhancements of ambient monitoring 

11 addressing the N/S secondary standards as each additional location could serves a similar role 

12 that existing CASTNET sites provide in estimating dry deposition. 

13 6.7 TAKING INTO CONSIDERATION INFORMATION ABOUT 

14 ECOSYSTEM SERVICES AND OTHER FACTORS RELATED TO 

15 CHARACTERIZING ADVERSITY FOR THE ECOLOGICAL 

16 EFFECTS BEING ASSESSED IN THIS REVIEW, WHAT IS AN 

17 APPROPRIATE RANGE OF ALTERNATIVE STANDARDS FOR 

18 THE AGENCY TO CONSIDER? 

19 The secondary NAAQS will reflect the public welfare policy judgments of the 

20 Administrator, based on the science, as to the level of air quality which is requisite to protect the 

21 public welfare from any known or anticipated adverse effects associated with the pollutant in the 

22 ambient air. The exposure and risk assessment provide information regarding the effects 

23 associated with a number of different welfare endpoints at different levels of air quality, 

24 expressed in terms of the joint annual mean concentrations of NOx and SOx determined such that 

25 specific levels of ecosystem protection (for example, ANC greater than 50) are met. Staff also 

26 recognizes that in certain naturally acidic ecosystems, even though the ecological benchmarks 

27 are exceeded, e.g. ANC may be quite low; NOx and SOx are not contributing to effects because 

28 those systems have chronic natural acidity and will not benefit from reductions in atmospheric 

29 deposition. The secondary NAAQS are not intended to provide protection in these types of 
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1 naturally acidic systems. As noted earlier, we will be exploring methods to address the design of 

2 the standard relative to these naturally acidic systems in the second draft policy assessment. The 

3 secondary NAAQS are focused on providing protection in areas where ambient NOx and SOx are 

4 resulting in effects in ecosystems with low natural levels of acidification that are highly sensitive 

5 to additional inputs of acid deposition. 

6 Staff believes that ecosystem effects of NOx and SOx deposition in aquatic ecosystems 

7 are an important public welfare effect of concern. There are several sources of benchmark values 

8 for ANC that can help to inform a determination of adversity. [Additional information on 

9 benchmark values will be provided in the second draft policy assessment] Staff concludes that 

10 achieving ANC in the range of 50 to 100 would be likely to provide adequate protection against 

11 the effects of acidification on ecosystems. 

12 Based on our analyses of risks of impacts on aquatic species diversity and fitness and on 

13 the basis of the scientific effects literature, we anticipate that achieving the upper end of this 

14 ANC range would substantially decrease the effects of acidification due to NOx and SOx on 

15 aquatic ecosystems. Additionally, it is anticipated that achieving the upper end of this range 

16 would provide increased protection from NOx and SOx in areas with higher levels of variability 

17 in ecosystem sensitivity due to variability in meteorology, bedrock geology, topography, land 

18 use characteristics, or reduced nitrogen deposition. 

19 These ANC levels are estimated to protect sensitive aquatic ecosystems from significant 

20 negative effects of NOx and SOx deposition on aquatic biota, including large reductions in 

21 diversity of fish species, and changes in health of fish populations, affecting reproductive ability 

22 and fitness. It is recognized, however, that a standard set within this range would not protect the 

23 most sensitive aquatic ecosystems or species within those ecosystems from the effects of NOx

24 and SOx. At ANC levels below 100, while overall health of an aquatic community can be 

25 maintained, ANC levels are expected to be such that fish fitness and community diversity begin 

26 to decline. At ANC levels between 100 and 50, ANC levels are expected to be such that the 

27 fitness of sensitive species (e.g., brook trout, zooplankton) also begins to decline. Staff notes that 

28 at levels of ANC above 100, biota are generally not harmed. As such, achieving an ANC of 

29 greater than 100 would be expected to result in little damage from NOx and SOx deposition to 

30 aquatic ecosystems. 
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1 Specifying an appropriate range of levels for an AAPI standard that is designed and 

2 specified as discussed above involves consideration of the degree to which any specific AAPI 

3 would lead to achieving the desired ANC level, and a judgment as to the degree of protection of 

4 public welfare that is warranted. In general, staff initially conclude that it would be appropriate 

5 for the Administrator to consider an AAPI in the range of 50 to 100. Selection of a range of 

6 AAPI and selection of a specific level of AAPI within that range should incorporate a wide 

7 number of considerations, including the percent of water bodies within acid sensitive areas that 

8 the Administrator determines should be protected at the targeted ANC level. 

9 The Administrator should consider the uncertainties in the ecological effects observed in 

10 the literature and the adversity to public welfare associated with those effects. In determining the 

11 requisite level of protection for the public welfare from effects on aquatic ecosystems, the 

12 Administrator will need to weigh the importance of the predicted risks of these effects in the 

13 overall context of public welfare protection, along with a determination as to the appropriate 

14 weight to place on the associated uncertainties and limitations of this information. 

15 In addition, selection of a specific level of AAPI should consider uncertainties in the 

16 design and calculation of the parameters included in the AAPI, including uncertainties in the 

17 characterization of natural background ANC (indicated by g in the AAPI equation), spatial and 

18 temporal averaging of aggregate effective deposition velocities (indicated by VNOY and VSOx in 

19 the AAPI equation), and spatial and temporal averaging of NHx deposition (indicated by NHx in 

20 the AAPI equation). 

21 
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1 7. CO-PROTECTION FOR OTHER EFFECTS USING 

2 STANDARDS TO PROTECT AGAINST ACIDIFICATION 

3 To this point, the standard for NOx and SOx centers on ecosystem protection against 

4 aquatic acidification. This chapter focuses on the level of co-protection that this standard would 

5 provide for other ecological effects, including terrestrial acidification, terrestrial nutrient 

6 enrichment, and estuarine eutrophication. 

7 7.1 TO WHAT EXTENT WOULD A STANDARD SPECIFICALLY 

8 DEFINED TO PROTECT AGAINST AQUATIC ACIDIFICATION 

9 LIKELY PROVIDE PROTECTION FROM TERRESTRIAL 

10 ACIDIFICATION? 

11 In order to understand the level of protection provided by a NOx/SOx standard based on 

12 aquatic acidification to protect against terrestrial acidification effects, an analysis was conducted 

13 comparing the critical loads for lakes and streams that would be developed to protect for an 

14 aquatic ANC of 50 to the critical loads to protect for either a terrestrial Bc:Al ratio of 1.2 or 10 

15 averaged across a watershed area. See Appendix B for full analysis results. The analysis selected 

16 16 watersheds with 29 lakes in the Adirondacks case study area, 4 watersheds randomly selected 

17 from each of 4 categories of sensitivity reported in the REA: highly sensitive, moderately 

18 sensitive, low sensitivity, and not sensitive. In the Shenandoah case study area, there were a 

19 limited number of watersheds in the low sensitivity and not sensitive range, so 18 of the 20 

20 streams in 16 watersheds selected were located in highly and moderately sensitive categories. 

21 Results for the Adirondacks showed that critical loads for 29 lakes at an ANC of 50 were 

22 lower for 13 lakes than the critical load for the terrestrial watershed areas at a Bc:Al ratio of 10 

23 and for 21 lakes at a Bc:Al ratio of 1.2. Perhaps more significant was the result that 13 of the 16 

24 lakes in the highly and moderately sensitive areas had a lower critical load than the Bc:Al 10 

25 areas and 16 of 16 lakes in the highly and moderately sensitive areas had lower critical loads 

26 than the Bc:Al 1.2 areas. The Shenandoah region reflected similar results. See table 7.1 below 

27 for tabulated results. 

28 
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Table 7-1. Results of comparing aquatic ANC50 critical loads to average terrestrial watershed 

area Bc:Al ratios. Left numbers in each column are the number of lakes or streams that had a 

lower critical load than the terrestrial calculated critical load. Right numbers in each column are 

the number of lakes that had a higher critical load than the watershed calculated terrestrial 

critical loads. 

Highly Sensitive Moderately Sensitive Low Sensitivity Not Sensitive

Adirondack Bc:Al 10 7-0 6-3 0-7 0-6 

Adirondack Bc:Al 1.2 7-0 9-0 5-2 0-6 

Shenandoh Bc:Al 10 13-0 5-0 0-1 0-1 

Shenandoh Bc:Al 1.2 13-0 5-0 0-1 0-1 

1 

2 In summary, a comparison of the terrestrial and aquatic critical acid loads for watersheds 

3 in the Adirondacks and Shenandoah Case Study Areas indicated that, in general, the aquatic 

4 critical acid loads offered greater protection to the watersheds than did the terrestrial critical 

5 loads. Generally in situations where the terrestrial critical loads were more protective, the lakes 

6 or streams in the watershed were rated as having “Low Sensitivity” or “Not Sensitive” to 

7 acidifying nitrogen and sulfur deposition. Conversely, when the water bodies were more 

8 sensitive to deposition (“Highly Sensitive” or “Moderately Sensitive”), the aquatic critical acid 

9 loads generally provided a greater level of protection against acidifying nitrogen and sulfur 

10 deposition in the watershed. In the next draft of the Policy Assessment Document, we intend to 

11 expand this analysis by comparing more levels of ANC to other Bc:Al ratios. 

12 7.2 TO WHAT EXTENT WOULD A STANDARD SPECIFICALLY 

13 DEFINED TO PROTECT AGAINST AQUATIC ACIDIFICATION 

14 LIKELY PROVIDE PROTECTION FROM TERRESTRIAL 

15 NUTRIENT ENRICHMENT? 

16 This question will be answered in the next draft of the Policy Assessment Document. 

17 Once maximum depositional loads are calculated for broad areas, we can compare the derived 

18 maximum NOy limits to nutrient enrichment benchmarks found in the REA. Benchmarks for 

19 lichens, grasses, mychorrizae, and diatoms will be compared to the aquatic acidification limits 

20 for nitrogen. 
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1 7.3 TO WHAT EXTENT WOULD A STANDARD SPECIFICALLY 

2 DEFINED TO PROTECT AGAINST AQUATIC ACIDIFICATION 

3 LIKELY PROVIDE PROTECTION FROM AQUATIC NUTRIENT 

4 ENRICHMENT? 

5 The REA found that deposition of reactive nitrogen contributed to eutrophication of 

6 estuaries; however, it was also noted that atmospheric deposition of nitrogen is only part of the 

7 total nitrogen load to the estuaries. Due to the complications of separating out the effects of 

8 atmospheric deposition from the effects of other nitrogen loads, CASAC did not recommend that 

9 a secondary NAAQS be set to specifically protect against estuarine eutrophication. In the next 

10 draft of the Policy Assessment Document, we will attempt to analyze the benefit to the 

11 Chesapeake Bay that attaining an aquatic acidification standard would provide by decreasing 

12 nitrogen deposition to the watershed. 

13 

14 
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1 8. CONSIDERATION OF ISSUES REGARDING REDUCED 

2 AND OXIDIZED FORMS OF NITROGEN 

3 [To be added in the second draft Policy Assessment] 

4 

5 
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1 9. INITIAL CONCLUSIONS 

2 Staff initial conclusions on the elements of the secondary NOx and SOx standards for the 

3 Administrator’s consideration in making decisions on the secondary NOx and SOx standards are 

4 summarized below, together with supporting conclusions from previous chapters. We recognize 

5 that selecting from among alternative policy options will necessarily reflect consideration of 

6 qualitative and quantitative uncertainties inherent in the relevant evidence and in the assumptions 

7 of the quantitative exposure and risk assessments. Any such standard should protect public 

8 welfare from any known or anticipated adverse effects associated with the presence of the 

9 pollutant(s) in the ambient air. In providing these options for consideration, we are mindful that 

10 the Act requires standards that, in the judgment of the Administrator, are requisite to protect 

11 public welfare. The standards are to be neither more nor less stringent than necessary. 

12 To evaluate whether the current secondary NAAQS is adequate or whether consideration 

13 of revisions is appropriate, the conclusions and options for the Administrator to consider in this 

14 review are based on effects-, exposure- and risk-based considerations. The exposure and risk 

15 assessments reflect the availability of new tools, assessment methods, and a larger and more 

16 diverse body of evidence than was available in the last reviews. We have taken a weight of 

17 evidence approach that evaluates information across the variety of research areas described in the 

18 ISA and in addition includes assessments of air quality, exposures, and qualitative and 

19 quantitative risks associated with alternative air quality scenarios. 

20 Staff notes that since the last review, additional policy-relevant developments have 

21 occurred that may also warrant consideration by the Administrator when making decisions about 

22 what is requisite to protect public welfare. The NRC report (described in Chapter 6) states: 

23 “Whatever the reason that led EPA to use identical primary and secondary NAAQS in the past, it 

24 is becoming increasingly evident that a new approach will be needed in the future. There is 

25 growing evidence that the current forms of the NAAQS are not providing adequate protection to 

26 sensitive ecosystems and crops” (NRC, 2004). 

27 The last review raised the following key issues as a rationale for not setting a separate 

28 standard for NOx to protect against acidification and nutrient enrichment effects in sensitive 

29 ecosystems: 

30 1) Lack of enough consistent information to support a revision of the current secondary 

31 standard to protect these aquatic systems. 
Policy Assessment for the Review of the Secondary National Ambient Air Quality Standards for NOx and SOx

March 2010 218 Draft –Do Not Quote or Cite 

1 2) Lack of adequate quantitative evidence on the relationship between deposition rates 

2 and environmental impacts 

3 3) Significant uncertainties with regard to the long-term role of nitrogen deposition in 

4 surface water acidity and with regard to the quantification of the magnitude and 

5 timing of the relationship between atmospheric deposition and the appearance of 

6 nitrogen in surface water. 

7 In this current review, staff concludes that important new information has become 

8 available since the last review that supports revising the current NOx and SOx standards. 

9 Specifically, the ISA has concluded that there are causal relationships between NOx and SOx

10 acidifying deposition and effects on aquatic and terrestrial ecosystems, and the ISA and REA 

11 provide substantial quantitative evidence of effects occurring in locations that meet the current 

12 NO2 and SO2 standards. In addition, substantial new information, based on observational data 

13 and rigorous atmospheric modeling, has become available regarding the role of both nitrogen and 

14 sulfur deposition in acidification of sensitive water bodies. This information is sufficient to 

15 inform the development of revised secondary standards for NOx and SOx to protect against the 

effects of acidification20 16 . While there is also new information available on the role of nitrogen 

17 deposition on nutrient enrichment effects in terrestrial and aquatic ecosystems, and the ISA 

18 concludes there is a causal relationship between NOx and nutrient enrichment effects, for this 

19 first draft policy assessment, staff have focused on acidification effects due to the substantially 

20 greater amount of information available to inform the development of secondary standards. 

21 Staff highlights the progress made in considering the joint nature of ecosystem responses 

22 to acidifying deposition of NOx and SOx, and notes that the ability to consider revisions to the 

23 NOx and SOx secondary standards has been enhanced by our ability to consider a joint standard 

24 for NOx and SOx to protect against acidification effects. The development of an appropriate form 

25 of the standard linked to a common indicator of aquatic acidification, ANC, is also a significant 

26 step forward, as it allows for development of a standard for aquatic acidification designed to 

27 provide generally the sme degree of protection across the country, while still reflecting the 

28 underlying variability in ecosystem sensitivity to acidifying NOx and SOx deposition. 

29 

 

20 As we have note earlier in the document, in this draft we have focused on aquatic acidification. However, in the 

second draft policy assessment we plan to more fully explore the possibility of expanding the conceptual model to 

address terrestrial acidification. 
Policy Assessment for the Review of the Secondary National Ambient Air Quality Standards for NOx and SOx

March 2010 219 Draft –Do Not Quote or Cite 

1 9.1 CONCLUSIONS 

2 As noted throughout this document, because of the complex interactions between NOx

3 and SOx in the atmosphere and their impacts once deposited in ecosystems, the consideration of 

4 indicators, averaging times, forms, and levels for the two pollutants is being conducted jointly. In 

5 addition, as discussed in Chapters 5 and 6, we are considering structures for the standards that 

6 reflect a more scientifically derived understanding of the relationships between atmospheric 

7 concentrations of NOx and SOx and the primary indicators of ecosystem impacts. 

8 With respect to soil and water effects information, we have evaluated the conclusions 

9 drawn at the end of the last review in light of more recent evidence from studies for a variety of 

10 ecological effects endpoints. We place greater weight on U.S. studies due to the species-, site-, 

11 and climate-specific nature of ecological responses. With respect to quantitative exposure- and 

12 risk-based considerations, we have relied on both monitored and modeled NOx and SOx ambient 

13 concentrations and related deposition, as described in Chapter 3 of the REA. 

14 Uncertainties associated with the exposure and risk assessments are also discussed, 

15 including, where possible, some sense of the direction and/or magnitude of the uncertainties that 

16 should be taken into account as one considers these estimates. As with any analysis that relies on 

17 complex scientific models, there are a number of unknown and unquantifiable sources of 

18 uncertainty. However, each model that has been applied in the risk and exposure assessment 

19 represents the best available science and the models have all been subject to substantial levels of 

20 peer-review. 

21 The following secondary NAAQS conclusions encompass the breadth of policy-relevant 

22 considerations described in this policy assessment: 

23 (1) Based on the policy-relevant findings from the ISA described in Chapter 2, and while 

24 recognizing that important uncertainties and research questions remain, staff conclude 

25 that great progress has been made since the last reviews of the secondary standards 

26 for NOx and SOx. We generally find support in the available effects-based evidence 

27 for consideration of NOx and SOx standards that are at least as protective as the 

28 current standard and do not find support for consideration of NOx and SOx standards 

29 that are less protective than the current standard. The staff also concludes that 

30 consideration of joint standards for NOx and SOx is appropriate given the common 

31 atmospheric processes governing the deposition of NOx and SOx to sensitive 
Policy Assessment for the Review of the Secondary National Ambient Air Quality Standards for NOx and SOx

March 2010 220 Draft –Do Not Quote or Cite 

1 ecosystems, and given the combined effects of N and S deposition on acidification of 

2 soil and water. 

3 (2) Staff concludes that ambient NOx is a significant component of atmospheric nitrogen 

4 deposition, even in areas with relatively high rates of deposition of reduced nitrogen. 

5 Staff make this conclusion based on the analysis in Chapter 3 of the REA, which 

6 provides a thorough assessment of the contribution of NOx to nitrogen deposition 

7 throughout the U.S., and the relative contributions of ambient NOx and reduced forms 

8 of nitrogen. 

9 (3) Staff concludes based on the case study results provided in the REA, that current 

10 levels of NOx and SOx are associated with deposition that leads to ANC values below 

11 benchmark values that cause ecological harm and losses in ecosystem services. Staff 

12 concludes that the evidence and risk assessment support strongly a relationship 

13 between atmospheric deposition of NOx and SOx and ANC, and that ANC is an 

14 excellent indicator of aquatic acidification. Staff also concludes that at levels of 

15 deposition associated with NOx and SOx concentrations at or below the current 

16 standards, ANC levels are expected to be below benchmark values that are associated 

17 with significant losses in fish species richness, which is associated with reductions in 

18 recreational fishing services. While there are many other ecosystem services 

19 potentially affected by reductions in ANC, including subsistence fishing, natural 

20 habitat provision, and biological control, confidence in the specific translation of 

21 ANC values to these additional ecosystem services is much lower. 

22 (4) Losses in aquatic resources associated with ANC levels below 50 are clearly 

23 associated with significant losses in economic value. Based on the best available data, 

24 just in the northeastern U.S., current acidification levels are resulting in $4 million to 

25 $300 million in damages annually from lost recreational fishing. This estimate 

26 represents only a fraction of the total economic value of ecosystem damages as many 

27 impacted resources are not amenable to economic valuation methods. In addition, 

28 economic damages are also likely to occur in other areas affected by acidification, 

29 including New England, the Appalachian Mountains (northern Appalachian Plateau 

30 and Ridge/Blue Ridge region), and the Upper Midwest. Staff concludes that reducing 

31 acidifying deposition of NOx and SOx will result in improvements in public welfare 
Policy Assessment for the Review of the Secondary National Ambient Air Quality Standards for NOx and SOx

March 2010 221 Draft –Do Not Quote or Cite 

1 by increasing the quantity and quality of ecosystem services, including recreational 

2 fishing and other services associated with improved water quality. 

3 (5) Staff initially concludes based on the case study results that current levels of ambient 

4 NOx and SOx are associated with deposition that leads to BC:Al values below 

5 benchmark values that cause ecological harm and losses in ecosystem services. Staff 

6 concludes that the evidence and risk assessment support strongly a relationship 

7 between atmospheric deposition of NOx and SOx and BC:Al, and that BC:Al is a 

8 good indicator of terrestrial acidification. Staff also concludes that at levels of 

9 deposition associated with NOx and SOx concentrations at or below the current 

10 standards, BC:Al levels are expected to be below benchmark values that are 

11 associated with significant losses in tree health and growth, which are associated with 

12 reductions in timber production. While there are many other ecosystem services, 

13 including maple syrup production, natural habitat provision, and regulation of water, 

14 climate, and erosion, potentially affected by reductions in BC:Al, confidence in the 

15 specific translation of BC:Al values to these additional ecosystem services is much 

16 lower. 

17 (6) On the basis of the acidification and nutrient enrichment effects that have been 

18 observed to still occur under current ambient conditions and those predicted to occur 

19 under the scenario of just meeting the current secondary NAAQS, staff concludes that 

20 the current secondary NAAQS are inadequate to protect the public welfare from 

21 known and anticipated adverse welfare effects from aquatic and terrestrial 

22 acidification associated with deposition of NOx and SOx.. As discussed above, this 

23 conclusion derives from several lines of evidence.

24 (7) Staff has concluded, based on the completeness of the available evidence and 

25 quantitative risk information, that effects due to aquatic and terrestrial acidification 

26 are most suitable for defining secondary standards for NOx and SOx. Staff notes that 

27 in developing a standard designed to protect against the effects of acidification due to 

28 deposition of NOx and SOx, the resulting standards may not provide protection 

29 against known effects associated with nutrient enrichment in aquatic and terrestrial 

30 ecosystems. 
Policy Assessment for the Review of the Secondary National Ambient Air Quality Standards for NOx and SOx

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1 (8) It is appropriate to consider using indicators other than NO2 and SO2 as the indicators 

2 for a standard that is intended to address the ecological effects associated with 

3 deposition of NOx and SOx to sensitive ecosystems. Given the reasons discussed in 

4 Chapters 2, 4, and 5 of this policy assessment, staff concludes that NOx, as defined in 

5 the CAA, is best represented by the atmospheric indicator NOy, defined as NO2 + NO 

6 + HNO3 + PAN +2N2O5 + HONO+ NO3 + organic nitrates + particulate NO3 is the 

7 more appropriate indicator of oxides of nitrogen, and that SOx, defined to include 

8 sulfur monoxide (SO), sulfur dioxide, sulfur trioxide (SO3), and disulfur monoxide 

9 (S2O), and particulate-phase S compounds, is the more appropriate indicator of 

10 oxides of sulfur. 

11 (9) It is appropriate to use the annual average of concentrations of NOy and SOx as the 

12 averaging time for the secondary standards, based on the chronic nature of 

13 acidification, and the protection against episodic acidification provided by a standard 

14 based on annual average concentrations. 

15 (10) It is appropriate to consider changing the form of the secondary standards for NOx

16 and SOx as the current form does not take into account the linkages between NOx and 

17 SOx in the causation of effects associated with acidification of aquatic ecosystems. 

18 Based on the causal linkages between NOx and SOx, deposition of N and S, and the 

19 indicator of acidification, ANC, staff concludes that the current forms should be 

20 replaced with an atmospheric acidification potential index (AAPI), which reflects the 

21 important roles of underlying ecosystem characteristics, determinants of deposition, 

22 and reduced nitrogen deposition in determining the potential effects from deposition 

23 of NOx and SOx. 

24 (11) Staff initial conclusions regarding the elements of the standard, e.g. the target ANC, 

25 spatial extent of areas in which the standard will be evaluated, percentiles of aquatic 

26 ecosystems within sensitivity classes to be protected for alternative target ANC 

27 values, calculated values of deposition transformation ratios, natural buffering 

28 capacity, and reduced nitrogen deposition will be provided in the second draft of the 

29 policy assessment. In addition, staff initial conclusions regarding consideration of 

30 uncertainty and variability in elements of the standard will be developed in the second 

31 draft. 
Policy Assessment for the Review of the Secondary National Ambient Air Quality Standards for NOx and SOx

March 2010 223 Draft –Do Not Quote or Cite 

1 9.2 SUMMARY OF KEY UNCERTAINTIES AND RESEARCH 

2 RECOMMENDATIONS RELATED TO SETTING A SECONDARY 

3 STANDARD FOR NOX AND SOX

4 [This section is still under development. Summary of key uncertainties to be added in 

5 second draft policy assessment. Research and data needs are partial lists that will be more 

6 completely developed in subsequent versions.] 

7 9.2.1 Research Needs to Reduce Uncertainty in the Next Review (focused on 

8 aquatic acidification) 

9 Based on the information presented in this policy assessment, several information gaps 

10 arise that suggest further research is needed in the following areas: 

11  Developing relationships between aquatic acidity as measured by ANC, and effects on 

12 ecological effects and ecosystem services, especially due to incremental changes 

13  Developing nationwide weathering rates, or weathering rates for aquatic ecosystems 

14 sensitive to acidification 

15  Developing a better understanding of the uncertainty in critical loads for acidity 

16  Developing methods for calculating critical loads for surface water acidity when data are 

17 absent or of poor quality 

18  Evaluating ways to combine multiple critical load estimates for surface waters and soils on 

19 a national scale 

20  Estimating ways to determine critical load parameters across different media (e.g., surface 

21 waters, soils). 

22 9.2.2 Data Needs to Reduce Uncertainty in the Next Review (focused on aquatic 

23 acidification) 

24 Improved measurements of reduced nitrogen: Nitrification processes within watershed 

25 soil, sediment and vegetation systems effectively convert ammonia gas and ammonium ions to 

26 nitrates, which contribute to the overall acidifying loads in ecosystems; consequently, the 

27 atmospheric contributions of reduced nitrogen must be accounted for in acidification 

28 assessments. We would expect that all or a subset of ambient monitoring platforms supporting 
Policy Assessment for the Review of the Secondary National Ambient Air Quality Standards for NOx and SOx

March 2010 224 Draft –Do Not Quote or Cite 

1 the N/S secondary standard will measure both ammonia gas and ammonium ion along with 

2 oxidized sulfur and nitrogen species. 

3 Extended modeling of air quality and deposition to inform monitoring network design: In 

4 addition to providing deposition inputs for watershed models and critical loads analysis, the 

5 spatial and temporal flexibility afforded by air quality modeling can support monitoring network 

6 design and in inform the averaging time period (one or more years) to more appropriately 

7 account for inter-annual variability in NOx and SOx concentrations. 

8 Development of data fusion approaches to combine model results with observational 

9 data: Consideration also will be given to fusing model results with observation fields to improve 

10 spatial resolution by taking advantage of the landscape, emissions and meteorological 

11 information that affect spatial gradients while relying on observations to reduce the influence of 

12 model uncertainties. 
1 

2 

3 APPENDIX A 

4 CHAPTER 5: CONCEPTUAL DESIGN OF THE 

5 STANDARD 

6 

7 

8 First External Review Draft 

9 

10 

11 

12 Prepared by: 

13 

14 U.S. Environmental Protection Agency 

15 Office of Air Quality Planning and Standards 

16 Research Triangle Park, NC 27711 

17 

18 

19 
1 
Appendix A

March 2010 i Draft – Do Not Quote or Cite 

1 TABLE OF CONTENTS 

2 Appendix A Chapter 5: Conceptual Design of the Standard ........................................................ 1

3 A.1 Technical summary of methods used in the REA Aquatic Acidification analysis .......... 1

4 A.2 Technical summary of critical loads modeling in the REA ............................................. 2

5 1.2.1 Preindustrial Base Cation Concentration ............................................................. 5

6 1.2.2 F-factor ................................................................................................................. 6

7 

8 

9 LIST OF TABLES 

10 Table A.1. Brief summary of objects and methods used in the REA Aquatic Acidification 

11 analysis. ................................................................................................................ 1

12 Table A.2 Illustrates SSWC Approach – Environmental Variables ............................................... 7

13 Table A.3 FAB Approach – Environmental Variables ................................................................... 8

14 

15 
Appendix A

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1 This page intentionally left blank. 

2 
Appendix A 

March 2010 1 Draft – Do Not Quote or Cite 

1 APPENDIX A 

2 CHAPTER 5: CONCEPTUAL DESIGN OF THE STANDARD 

3 This is supplemental information to support the discussion of the conceptual design of the 

4 standard that is presented in Chapter 5 of the Policy Assessment Document. The aquatic 

5 acidification analyses developed in the REA used a number of different models and calculation 

6 techniques that are important for the development of the standard. The goal of this Appendix is 

7 to summarize information from the REA analysis that is most relevant to the Policy Assessment. 

8 A brief summary of the REA analyses are presented in section 1. In section 2 there is a general 

9 summary and technical discussion of the critical loads modeling approaches that were used in the 

10 REA, followed by a brief description of MAGIC model data requirements. 

11 A.1 TECHNICAL SUMMARY OF METHODS USED IN THE REA 

12 AQUATIC ACIDIFICATION ANALYSIS 

13 The aquatic acidification analysis is presented in Chapter 4 and Appendix 4 of the REA. 

14 The analysis uses multiple techniques to show the relationship between ANC and NOx and SOx

15 deposition, as well as determine the current level of risk to water bodies that occur in sensitive 

16 areas. A brief summary of the techniques and objectives of the REA analysis is given in Table 1. 

17 Table A.1. Brief summary of objects and methods used in the REA Aquatic Acidification 

18 analysis. 

Technique Objectives 

Time-series 

graphs of 

current 

conditions 

1 Data from monitoring networks collected from 1990 to 2006 were 

plotted to show trends in concentrations of pollutants, deposition and 

acidification for each case study site. The data included surface water 

concentration of nitrate, sulfate and ANC; deposition of sulfate and 

nitrate; as well as air concentration of SOx, NOx and NH4

MAGIC 1 Used to estimate the relationship between ANC values and 

anthropogenic NOx and SOx emission from the past (preacidification 

~1860), present (2002 and 2006) and projected into the future (2020 

and 2050). Analysis included 44 lakes from Adirondacks and 60 

streams from Shenandoah. 

 2 Used to develop input parameters for critical loads modeling (i.e. 

weathering rates) 

 3 Used for uncertainty analysis 
Appendix A 

March 2010 2 Draft – Do Not Quote or Cite 

Technique Objectives 

Critical Loads 

modeling 

1 SSWC and FAB models used to calculate critical loads for critical 

limits of ANC = 0, 20, 50, 100 

 2 Critical loads for ANC critical limits calculated for 169 lakes in the 

Adirondacks and 60 streams in the Shenandoah using water quality 

data from monitoring sites collected in 2006 

 3 Critical loads exceedences calculated by comparing the critical loads 

that were calculated by SSWC with deposition data from NADP for 

wet deposition and CMAQ for dry deposition, both for the year 2002 

Regional 

Extrapolation 

1 117 of the critical loads calculated for the Adirondacks were 

extrapolated to lakes defined by the New England EMAP probability 

survey, representing 1842 lakes, to infer the # of lakes that exceeded 

their critical load 

 2 69 of the critical loads calculated for the Shenandoah were 

extrapolated to 330 streams based on bed rock geology classification. 

1 

2 A.2 TECHNICAL SUMMARY OF CRITICAL LOADS MODELING IN 

3 THE REA 

4 The critical load of acidity for lakes or streams was derived from present-day water 

5 chemistry using a combination of steady-state models. Both the Steady-State Water Chemistry 

6 (SSWC) model and First-order Acidity Balance model (FAB) is based on the principle that 

7 excess base-cation production within a catchment area should be equal to or greater than the acid 

8 anion input, thereby maintaining the ANC above a preselected level (Reynolds and Norris, 2001; 

Posch et al. 1997). These models assume steady-state conditions and assume that all SO4

2– 9 in 

10 runoff originates from sea salt spray and anthropogenic deposition. Given a critical ANC 

11 protection level, the critical load of acidity is simply the input flux of acid anions from 

12 atmospheric deposition (i.e., natural and anthropogenic) subtracted from the natural (i.e., 

13 preindustrial) inputs of base cations in the surface water. Final Risk and Exposure Assessment 

14 September 2009 Appendix 4, Attachment A – 15 Aquatic Acidification Case Study Atmospheric 

15 deposition of NOx and SOx contributes to acidification in aquatic ecosystems through the input of 

acid anions, such as NO3- and SO4

2– 16 The acid balance of headwater lakes and streams is 

controlled by the level of this acidifying deposition of NO3- and SO4

2– 17 and a series of 

18 biogeochemical processes that produce and consume acidity in watersheds. The biotic integrity 

19 of freshwater ecosystems is then a function of the acid-base balance, and the resulting acidity-
Appendix A 

March 2010 3 Draft – Do Not Quote or Cite 

1 related stress on the biota that occupy the water. The calculated ANC of the surface waters is a 

2 measure of the acid-base balance: 

3 ANC = [BC]* - [AN]* (1) 

where [BC]* and [AN]* are the sum of base cations and acid anions (NO3- and SO4

2– 4 ), 

5 respectively. Equation (1) forms the basis of the linkage between deposition and surface water 

6 acidic condition and the modeling approach used. Given some “target” ANC concentration 

7 [ANClimit]) that protects biological integrity, the amount of deposition of acid anions (AN) or 

8 depositional load of acidity CL(A) is simply the input flux of acid anions from atmospheric 

9 deposition that result in a surface water ANC concentration equal to the [ANClimit] when 

10 balanced by the sustainable flux of base cations input and the sinks of nitrogen and sulfur in the 

11 lake and watershed catchment. 

12 Critical loads for nitrogen and sulfur (CL(N) + CL(S) ) or critical load of acidity CL(A) 

13 were calculated for each waterbody from the principle that the acid load should not exceed the 

14 nonmarine, nonanthropogenic base cation input and sources and sinks in the catchment minus a 

15 neutralizing to protect selected biota from being damaged: 

16 CL(N) + CL(S) or CL(A) = BC*dep + BCw – Bcu – AN - ANClimit (2) 

17 Where, 

18 BC*dep = (BC*=Ca*+Mg*+K*+Na*), nonanthropogenic deposition flux of base cations BCw = 

19 the average weathering flux, producing base cations 

20 Bcu (Bc=Ca*+Mg*+K*) = the net long-term average uptake flux of base cations in the biomass 

21 (i.e., the annual average removal of base cations due to harvesting) 

22 AN = the net long-term average uptake, denitrification, and immobilization of nitrogen anions 

(e.g. NO3

-) and uptake of SO4

2– 23 

24 ANClimit = the lowest ANC-flux that protects the biological communities. 

25 Since the average flux of base cations weathered in a catchment and reaching the lake or 

26 streams is difficult to measure or compute from available information, the average flux of base 

27 cations and the resulting critical load estimation were derived from water quality data (Henriksen 

28 and Posch, 2001; Henriksen et al., 1992; Sverdrup et al., 1990). Weighted annual mean water 

29 chemistry values were used to estimate average base cation fluxes, which were calculated from 

30 water chemistry data collected from the Temporally Integrated Monitoring of Ecosystems 
Appendix A 

March 2010 4 Draft – Do Not Quote or Cite 

1 (TIME)/Long-Term Monitoring (LTM) monitoring networks, that include Adirondack Longterm 

2 Monitoring (ALTM), Virginia Trout Stream Sensitivity Study (VTSSS), and the Shenandoah 

3 Watershed Study (SWAS), and Environmental Monitoring and Assessment Program (EMAP) 

4 (see Section 4.1.2.1 of Chapter 4). 

5 The preacidification nonmarine flux of base cations for each lake or stream, BC*0, is 

6 BC*0 = BC*dep + BCw - Bcu (3) 

7 Thus, critical load for acidity can be rewritten as 

8 CL(N) + CL(S) = BC*0 – AN - ANClimit = Q.([BC*]0 – [AN] - [ANC]limit), (4) 

9 where the second identity expresses the critical load for acidity in terms of catchment runoff (Q) 

10 m/yr and concentration ([x] = X/Q). The sink of nitrogen in the watershed is equal to the uptake 

11 (Nupt), immobilization (Nimm), and denitrification (Nden) of nitrogen in the catchment. Thus, 

12 critical load for acidity can be rewritten as 

13 CL(N) + CL(S) = {fNupt + (1 − r)(Nimm + Nden)} + ( [BC]0* − [ANClimit])Q (5) 

14 where f and r are dimensionless parameters that define the fraction of forest cover in the 

15 catchment and the lake/catchment ratio. The in-lake retention of nitrogen and sulfur was assumed 

16 to be negligible. Equation 5 described the FAB model that was applied when sufficient data was 

17 available to estimate the uptake, immobilization, and denitrification of nitrogen and the 

18 neutralization of acid anions (e.g. NO3-) in the catchment. In the case were data was not 

19 available, the contribution of nitrogen anions to acidification was assumed to be equal to the 

20 nitrogen leaching rate (Nleach) into the surface water. The flux of acid anions in the surface 

21 water is assumed to represent the amount of nitrogen that is not retained by the catchment, which 

22 is determined from the sum of measured concentration of NO3- and ammonia in the stream 

23 chemistry. This case describes the SSWC model and the critical load for acidity is

24 CL(A) = Q.([BC*]0 – [ANC]limit) (6) 

25 where the contribution of acid anions is considered as part of the exceedances calculation (see 

26 Section 1.2.5, below). For the assessment of current condition in both case study areas, the 

27 critical load calculation described in Equation 6 was used for most lakes and streams. The lack of 

28 sufficient data for quantifying nitrogen denitrification and immobilization prohibited the wide 

29 use of the FAB model. In addition, given the uncertainty in quantifying nitrogen denitrification 
Appendix A 

March 2010 5 Draft – Do Not Quote or Cite 

1 and immobilization, the flux of nitrogen anions in the surface water was assumed to more 

2 accurately reflect the contribution of NO3- to acidification. Several major assumptions are made: 

3 (1) steady-state conditions exist, (2) the effect of nutrient cycling between plants and soil is 

4 negligible, (3) there are no significant nitrogen inputs from sources other than atmospheric 

5 deposition, (4) ammonium leaching is negligible because any inputs are either taken up by biota 

6 or adsorbed onto soils or nitrate compounds, and (5) longterm sinks of sulfate in the catchment 

7 soils are negligible. 

8 1.2.1 Preindustrial Base Cation Concentration 

9 Present-day surface water concentrations of base cations are elevated above their 

10 steadystate preindustrial concentrations because of base cation leaching through ion exchange in 

the soil due to anthropogenic inputs of SO4

2– 11 to the watershed. For this reason, present-day 

12 surface water base cation concentrations are higher than natural or preindustrial levels, which, if 

13 not corrected for, would result in critical load values not in steady-state condition. To estimate 

14 the preacidification flux of base cations, the present flux of base cations was estimated, 

15 BC*t, given by BC*t = BC*dep + BCw – Bcu +BCexc, (7) 

16 Where BCexc = the release of base cations due to ion-exchange processes. Assuming that 

17 deposition, weathering rate, and net uptake have not changed over time, BCexc can be obtained 

18 by subtracting Equation 5 from Equation 7: 

19 BCexc = BC*t – BC*0 (8) 

20 This present-day excess production of base cations in the catchment was related to the long-term 

21 changes in inputs of nonmarine acid anions (ΔSO*2 + ΔNO3) by the F-factor (see below): 

22 BCexc = F (ΔSO*2 + ΔNO3) (9) 

23 For the preacidification base cation flux, solving Equation 5 for BC*0 and then substituting 

24 Equation 8 for BCexc and explicitly describing the long-term changes in nonmarine acid ion 

25 inputs: 

26 BC*0 = BC*t – F (SO*4,t - SO*4,0 + NO*3,t - NO*3,0) (10) 

27 The preacidification NO3- concentration, NO*3,0, was assumed to be zero. 
Appendix A 

March 2010 6 Draft – Do Not Quote or Cite 

1 1.2.2 F-factor 

2 An F-factor was used to correct the concentrations and estimate preindustrial base concentrations 

3 for lakes in the Adirondack Case Study Area. In the case of streams in the 

4 Shenandoah Case Study Area, the preindustrial base concentrations were derived from the 

5 MAGIC model as the base cation supply in 1860 (hindcast) because the F-factor approach is 

6 untested in this region. An F-factor is a ratio of the change in nonmarine base cation 

7 concentration due to changes in strong acid anion concentrations (Henriksen, 1984; Brakke et al., 

8 1990): 

9 F =([BC*]t - [BC*]0)/([SO4*]t - [SO4*]0 + [NO3*]t - [NO3*]0), (12) 

10 where the subscripts t and 0 refer to present and preacidification conditions, respectively. If F=1, 

11 all incoming protons are neutralized in the catchment (only soil acidification); at F=0, none of 

12 the incoming protons are neutralized in the catchment (only water acidification). The F-factor 

13 was estimated empirically to be in the range 0.2 to 0.4, based on the analysis of historical data 

14 from Norway, Sweden, the United States, and Canada (Henriksen, 1984). Brakke et al. (1990) 

15 later suggested that the F-factor should be a function of the base cation concentration: 

16 F = sin (π/2 Q[BC*]t/[S]) (13) 

17 where 

18 Q = the annual runoff (m/yr). [S] = the base cation concentration at which F=1; and for 

19 [BC*]t>[S] F is set to 1. For Norway [S] has been set to 400 milliequivalents per cubic meter 

20 (meq/m3)(circa.8 mg Ca/L) (Brakke et al., 1990). The preacidification SO42- concentration in 

21 lakes, [SO4*]0, is assumed to consist of a constant atmospheric contribution and a geologic 

22 contribution proportional to the concentration of base cations (Brakke et al., 1989). The 

23 preacidification SO42- concentration in lakes, [SO4*]0 was estimated from the relationship 

24 between [SO42-]o* and [BC]t* based on work completed by Henriksen et al., 2002 as described 

25 by the following equation: 

26 [SO42-]o* = 15 + 0.16 * [BC]t* (14) 
Appendix A 

March 2010 7 Draft – Do Not Quote or Cite 

1 Table A.2 Illustrates SSWC Approach – Environmental Variables

CL(A) = BC* 2 dep + BCw – Bcu – ANClimit 

CL(A) = Q.([BC* 3 ]0 – [ANC]limit) 

Variable 

Code 

Description Source 

1 BC*

dep Sum (Ca*+Mg*+K*+Na*), nonanthropogenic 

deposition flux of base cations 

Wet NADP and Dry 

CASTNET 

2 BCw Average weathering flux of base cations Calculated (5-17) 

3 Bcu Sum (Ca+Mg+K), the net long-term average 

uptake flux of base cations in the biomass 

USFS-FIA data 

4 ANClimit Lowest ANC-flux that protects the biological 

communities 

Set 

5 Ca* Sea Salt corrected Surface water concentration 

(μeq/L) growing season average. (Ca – (CL × 

0.0213)) 

Water quality data 

6 Mg* Sea Salt corrected Surface water concentration 

(μeq/L) growing season average. (Mg – (CL × 

0.0669)) 

Water quality data 

7 Na* Sea Salt corrected Surface water concentration 

(μeq/L) growing season average. (Na – (CL × 

0.557)) 

Water quality data 

8 K* Sea Salt corrected Surface water concentration 

(μeq/L) growing season average. (K – (CL × 

0.0.0206)) 

Water quality data 

9 SO4

*

 Sea Salt corrected Surface water concentration 

(μeq/L) growing season average. (SO4 – (CL × 

0.14)) 

Water quality data 

10 CL Surface water concentration (μeq/L) growing 

season average. 

Water quality data 

11 SO4

*

 Surface water concentration (μeq/L) growing 

season average. 

Water quality data 

12 NO3

*

 Surface water concentration (μeq/L) growing 

season average. 

Water quality data 

13 Q The annual runoff (m/yr) USGS 

14 [BC*]0 Preindustrial flux of base cations in surface water, 

corrected for sea salts 

Calculated from water 

quality data 

15 [SO4

*

]0 Preindustrial flux of sulfate in surface water, 

corrected for sea salts 

Estimated 

16 [NO3

*

]0 Preindustrial flux of nitrate, corrected for sea salts Equal to 0 

17 F Calculated factor Fix values 

4 

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Appendix A 

March 2010 8 Draft – Do Not Quote or Cite 

1 Table A.3 FAB Approach – Environmental Variables

DL(N) + DL(S) = {fNupt + (1 − r)(Nimm + Nden) + (Nret + Sret)} + ( [BC]0

* 2 − [ANClimit])Q 

Variable 

Code 

Description Source 

1 Ndepo Total N deposition NADP/CMAQ 

2 ANClimit Lowest ANC-flux that protects the biological 

communities 

Set 

3 [BC*]0 Preindustrial flux of base cations in surface water, 

corrected for sea salt 

Calculated from water 

quality data 

4 Ca* Sea Salt corrected Surface water concentration 

(μeq/L) growing season average. (Ca – (CL × 

0.0213)) 

Water quality data 

5 Mg* Sea Salt corrected Surface water concentration 

(μeq/L) growing season average. (Mg – (CL × 

0.0669)) 

Water quality data 

6 Na* Sea Salt corrected Surface water concentration 

(μeq/L) growing season average. (Na – (CL × 0.557)) 

Water quality data 

7 K* Sea Salt corrected Surface water concentration 

(μeq/L) growing season average. (K – (CL × 

0.0.0206)) 

Water quality data 

8 SO4

*

 Sea Salt corrected Surface water concentration 

(μeq/L) growing season average. (SO4 – (CL × 0.14)) 

Water quality data 

9 CL Surface water concentration (μeq/L) growing season 

average. 

Water quality data 

10 SO4

*

 Surface water concentration (μeq/L) growing season 

average. 

Water quality data 

11 NO3

*

 Surface water concentration (μeq/L) growing season 

average. 

Water quality data 

12 Q The annual runoff (m/yr) USGS 

13 f f is a dimensionless parameter that define the fraction 

of forest cover in the catchment 

14 r r is a dimensionless parameter that define the 

lake/catchment ratio 

14 Nret The in-lake retention of nitrogen Estimated 

15 Sret The in-lake retention of sulfur Estimated 

16 Nupt The net long-term average uptake flux of N in the 

biomass 

USFS-FIA data 

17 Nimm Immobilization of N in the soils Estimated fix value 

18 Nden Denitrification Estimated fix value 

19 Lake Size Lake size (ha) DLMs 

20 WSH Watershed area (ha) Calculated 

3 
Appendix A 

March 2010 9 Draft – Do Not Quote or Cite 

1 Data requirements for MAGIC 

2 The MAGIC model (Cosby et al., 1985a; 1985b; 1985c) is a mathematical model (a 

3 lumped-parameter model) of soil and surface water acidification in response to atmospheric 

4 deposition based on process-level information about acidification. A process model, such as 

5 MAGIC, characterizes acidification into (1)a section in which the concentrations of major ions 

are assumed to be governed by simultaneous reactions involving SO4

2- 6 adsorption, cation 

7 exchange, dissolution-precipitation- speciation of aluminum, and dissolution-speciation of 

8 inorganic carbon; and (2) a mass balance section in which the flux of major ions to and from the 

9 soil is assumed to be controlled by atmospheric inputs, chemical weathering, net uptake and loss 

10 in biomass and losses to runoff. At the heart of MAGIC is the size of the pool of exchangeable 

11 base cations in the soil. As the fluxes to and from this pool change over time owing to changes in 

12 atmospheric deposition, the chemical equilibria between soil and soil solution shift to give 

13 changes in surface water chemistry. The degree and rate of change of surface water acidity thus 

14 depend both on flux factors and the inherent characteristics of the affected soils. 

15 There are numerous input data required to run MAGIC making it rather data intensive. 

16 Atmospheric deposition fluxes for the base cations and strong acid anions are required as inputs 

17 to the model. These inputs are generally assumed to be uniform over the catchment. The volume 

18 discharge for the catchment must also be provided to the model. In general, the model is 

19 implemented using average hydrologic conditions and meteorological conditions in annual 

20 simulations, i.e., mean annual deposition, precipitation and lake discharge are used to drive the 

21 model. Values for soil and surface water temperature, partial pressure of carbon dioxide and 

22 organic acid concentrations must also be provided at the appropriate temporal resolution. 

23 The aggregated nature of the model requires that it be calibrated to observed data from a 

24 system before it can be used to examine potential system response. Calibrations are based on 

25 volume weighted mean annual or seasonal fluxes for a given period of observation. The length of 

26 the period of observation used for calibration is not arbitrary. Model output will be more reliable 

27 if the annual flux estimates used in calibration are based on a number of years rather than just 

28 one year. There is a lot of year-to-year variability in atmospheric deposition and catchment 

29 runoff. Averaging over a number of years reduces the likelihood that an “outlier” year (very dry, 

30 etc.) is used to specify the primary data on which model forecasts are based. On the other hand, 
Appendix A 

March 2010 10 Draft – Do Not Quote or Cite 

1 averaging over too long a period may remove important trends in the data that need to be 

2 simulated by the model. 

3 The calibration procedure requires that stream water quality, soil chemical and physical 

4 characteristics, and atmospheric deposition data be available for each catchment. The water 

5 quality data needed for calibration are the concentrations of the individual base cations (Ca, Mg, 

Na, and K) and acid anions (Cl, SO4

2-, and NO3- 6 ) and the pH. The soil data used in the model 

7 include soil depth and bulk density, soil pH, soil cation-exchange capacity, and exchangeable 

8 bases in the soil (Ca, Mg, Na, and K). The atmospheric deposition inputs to the model must be 

9 estimates of total deposition, not just wet deposition. In some instances, direct measurements of 

10 either atmospheric deposition or soil properties may not be available for a given site with stream 

11 water data. In these cases, the required data can often be estimated by: (a) assigning soil 

12 properties based on some landscape classification of the catchment; and (b) assigning deposition 

13 using model extrapolations from some national or regional atmospheric deposition monitoring 

14 network. Soil data for model calibration are usually derived as aerially averaged values of soil 

15 parameters within a catchment. If soils data for a given location are vertically stratified, the soils 

16 data for the individual soil horizons at that sampling site can be aggregated based on horizon, 

17 depth, and bulk density to obtain single vertically aggregated values for the site, or the stratified 

18 data can be used directly in the model. 

19 
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Methodologies for National Terrestrial and Aquatic 

Acidification Maximum Depositional Load 

Approaches: Determining Weathering Rates 

First External Review Draft 

Prepared for: 

U.S. Environmental Protection Agency 

Office of Air Quality Planning and Standards 

Research Triangle Park, NC 27711 

Prepared by: 

RTI International 

P.O. Box 12194 

Research Triangle Park, NC 27709 

EPA Contract Number EP-D-06-003 

RTI Project Number 0209897.004.080 

January 11, 2009 
1
Appendix B 

March 2010 i Draft – Do Not Quote or Cite 

1 Table of Contents 

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1. PURPOSE .............................................................................................................................................. 1

2. OVERVIEW OF ACIDIFICATION ...................................................................................................... 1

2.1 EPA’s Integrated Science Assessment and Risk and Exposure Assessment ............................ 1

2.2 Aquatic Acidification and Critical Acid Loads ......................................................................... 4

2.1.2 Terrestrial Acidification and Critical Acid Loads ................................................................. 9

3. AQUATIC BASE CATION WEATHERING METHODOLOGY ..................................................... 14

3.1 Aquatic Base Cation Weathering ............................................................................................ 14

3.2 Methodologies for Determining Base Cation Weathering Values in the United States .......... 16

3.2.1 Difficulties in estimating base cation weathering ............................................................... 16

3.2.2 Approaches to estimating BCw for Aquatic Acidification .................................................. 17

3.3 Proposed Methodology for Estimating and Mapping Base Cation Weathering for Aquatic 

Critical Acid Load Calculations .............................................................................................. 27

3.3.1 Potential limitations of proposed methodology .................................................................. 33

3.3.2 Uncertainty analyses ........................................................................................................... 34

4. TERRESTRIAL BASE CATION WEATHERING METHODOLOGY ............................................. 35

4.1 Introduction ............................................................................................................................. 35

4.2 Terrestrial Base Cation Weathering ........................................................................................ 36

4.3 Methodologies for Determining Base Cation Weathering Values in the United States .......... 39

4.3.1 Difficulties in estimating base cation weathering ............................................................... 39

4.3.2 Approaches to estimating BCw: ......................................................................................... 40

4.3.3 Proposed methodology for estimating and mapping base cation weathering for 

terrestrial critical acid load calculations ............................................................................. 50

4.3.5 Potential limitations of proposed methodology .................................................................. 78

4.3.6 “Field Tests” of model and uncertainty analyses ................................................................ 79

5. CONCLUSIONS AND RECOMMENDATIONS ............................................................................... 81

6. REFERENCES ..................................................................................................................................... 82

APPENDIX 1 Potentially Applicable National-Scale Geochemical Data .................................................. 97

APPENDIX 2 References for Table 3-2: Applications of the MAGIC Model ......................................... 102
Appendix B 

March 2010 ii Draft – Do Not Quote or Cite 

1 List of Figures 

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2-1. (a) Number of fish species per lake or stream versus acidity, expressed as acid neutralizing 

capacity for Adirondack Case Study Area lakes (Sullivan et al., 2006). (b) Number of fish 

species among 13 streams in Shenandoah National Park. Values of acid neutralizing 

capacity are means based on quarterly measurements from 1987 to 1994. The regression 

analysis shows a highly significant relationship (p < .0001) between mean stream acid 

neutralizing capacity and the number of fish species. ..................................................................... 5 

2-2. The relationship between the Bc/Al ratio in soil solution and the percentage of tree species 

(found growing in North America – native and introduced species) exhibiting a 20% 

reduction in growth relative to controls (after Sverdrup and Warfvinge, 1993). ........................... 10 

3-1. Process steps for estimating BCw using the MAGIC model with regional extrapolation .................... 28 

4-1. Areas of continental U.S. that were covered during the last glacial event (Reed and Bush, 

2005). ............................................................................................................................................. 39 

4-2. Process Steps for Estimating BCw Using the PROFILE Regional Model ........................................... 52 

4-3. Map Showing the Distribution and Status of SSURGO Data ............................................................. 64 

4-4. Soil Sampling Locations Included in the USGS Shacklette Dataset ................................................... 66 

4-5. Sample Density of USGS National Geochemical Survey ................................................................... 68 

4-6. NRCS Soil Pedon Sample Pit Locations (30,000 total) ....................................................................... 70

4-7. NRCS Soil Pedon Pit Sample Locations with Geochemical and Mineralogy Data ............................ 71 

List of Tables 

2-1. Aquatic Status Categories ...................................................................................................................... 6

2-2. Summary of Linkages between Acidifying Deposition, Biogeochemical Processes That 

Affect Ca2+, Physiological Processes That Are Influenced by Ca2+, and Effect on Forest 

Function ......................................................................................................................................... 11

2-3. The Three Indicator (Bc/Al)crit Soil Solution Ratios and Corresponding Levels of Protection 

to Tree Health and Critical Loads .................................................................................................. 14

3-1. Review of Modeling Approaches (and models) to Estimate Base Cation Weathering for 

Aquatic Critical Acid Load Determinations .................................................................................. 23

3-2. Locations of Previous MAGIC Applications within the U.S. and Canada1

 ........................................ 29

3-3. Input Data Requirements of MAGIC Model ....................................................................................... 31

4-1. Review of modeling approaches (and models) to estimate base cation weathering for 

terrestrial critical acid load determinations. ................................................................................... 46

4-2. The fourteen dominant minerals modeled within PROFILE. .............................................................. 50

4-3a. Data required to estimate BCw with the regional PROFILE model (version 5.0). The data in 

this table must be input by the user and are currently available as a continuous coverage 

layers for at least a portion of the conterminous United States. ..................................................... 53

4-3b. Data required to estimate BCw with the regional PROFILE model (version 5.0). The data in 

this table must be input by the user and are not currently available as a continuous 

coverage layers for at least a portion of the conterminous United States (will require 

development of national coverage layer). ...................................................................................... 53
Appendix B 

March 2010 iii Draft – Do Not Quote or Cite 

4-3c. Data required to estimate BCw with the regional PROFILE model (version 5.0). The data in 

this table are used to support calculations within the model and should be reviewed by the 

user. ................................................................................................................................................ 54

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4-4. Available datasets and databases for the conterminous United States that could be used to 

estimate BCw with the regional application of the PROFILE model (version 5.0). ....................... 55

4-5. Nitrogen and base cation uptake by forest type (from McNulty et al., 2007). ................................... 58

4-6 Datasets with Geochemical and Mineralogy Data for US Soils ........................................................... 61

4-7. Long-Term Ecological Research (LTER) sites that could potentially be suitable as “field 

test” sites to validate BCw estimates generated with the regional application of the 

PROFILE model (version 5.0). ...................................................................................................... 74
Appendix B 

March 2010 iv Draft – Do Not Quote or Cite 

1

2
Appendix B 

March 2010 1 Draft – Do Not Quote or Cite 

1 1. PURPOSE 

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The purpose of this Work Assignment Task is to develop methodologies for estimating 

national terrestrial and aquatic acidification maximum depositional loads. Separate approaches 

are developed for terrestrial and aquatic acidification because biogeochemical processes in 

aquatic and terrestrial ecosystems for nitrogen and sulfur are not identical. Information about the 

key physical, chemical, and biological parameters needed to predict acidification potential in 

ecosystems is not always available. For example, weathering rates are key to acidification but are 

not available in all parts of the U.S. Knowledge of an ecosystem’s weathering characteristics 

enables a more accurate assessment of whether acidifying deposition can be neutralized or 

exceeds an ecosystem’s critical load beyond which negative effects in aquatic and terrestrial 

health may occur. 

This report presents an introduction to aquatic and terrestrial acidification, followed by 

reviews of different approaches to estimating base cation weathering and detailed methodologies 

that could be used to estimate base cation weathering for aquatic and terrestrial critical load 

calculations. 

2. OVERVIEW OF ACIDIFICATION 

2.1 EPA’s Integrated Science Assessment and Risk and Exposure Assessment 

Deposition of SOx, NOx, and NHx can lead to ecosystem exposure to acidification. The 

Integrated Science Assessment (ISA) for Oxides of Nitrogen and Sulfur–Ecological Criteria 

(Final Report) (ISA) (U.S. EPA, 2008) reports that acidifying deposition has altered major 

biogeochemical processes in the United States by increasing the sulfur and nitrogen content of 

soils, accelerating sulfate (SO4

2−

) and nitrate (NO3

−

) leaching from soil to drainage water, 

depleting soil exchangeable base cations (especially calcium [Ca2+] and magnesium [Mg2+]) 

from soils, and increasing the mobility of aluminum (Al) within the soil (U.S. EPA, 2008, 

Section 3.2.1) 

The extent of soil acidification is a critical factor that regulates virtually all acidificationrelated ecosystem effects from sulfur and nitrogen deposition. Soil acidification occurs in 

response to both natural factors and acidifying deposition (U.S. EPA, 2008, Section 3.2.1). 

Under conditions of low atmospheric deposition of nitrogen and sulfur, the naturally produced 
Appendix B 

March 2010 2 Draft – Do Not Quote or Cite 

bicarbonate anion is often the dominant mobile anion, with SO4

2- and NO3-

 playing a limited role 

with respect to cation leaching. Increased atmospheric deposition of sulfur and nitrogen can 

result in marked increases in SO4

2- and NO3-

 soil fluxes resulting in the concomitant leaching of 

base cations (Ca2+, Mg2+) and toxic cations (Aln+ and H+

). 

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Acidification can impact the health of terrestrial and aquatic ecosystems. One of the 

effects of soil acidification is the increased mobility of dissolved inorganic Al, which is toxic to 

tree roots, fish, algae, and aquatic invertebrates (U.S. EPA, 2008, Sections 3.2.1.5, 3.2.2.1, and 

3.2.3). 

The changes in major biogeochemical processes and soil conditions caused by acidifying 

deposition have significant ramifications for the water chemistry and biological functioning of 

associated surface waters. Surface water chemistry indicates the negative effects of acidification 

on the biotic integrity of freshwater ecosystems. Surface water chemistry integrates the sum of 

terrestrial and aquatic processes that occur upstream within a watershed. Important terrestrial 

processes include nitrogen saturation, forest decline, and soil acidification (Stoddard et al., 

2003). Thus, water chemistry integrates and reflects changes in soil and vegetative properties and 

biogeochemical processes (U.S. EPA, 2008, Section 3.2.3.1). 

Ecological effects occur at four levels of biological organization: (1) the individual; (2) 

the population, which is composed of a single species of individuals; (3) the biological 

community, which is composed of many species; and (4) the ecosystem. Low ANC 

concentrations are linked with negative effects on aquatic systems at all four of these biological 

levels. For the individual level, impacts are assessed in terms of fitness (i.e., growth, 

development, and reproduction) or sublethal effects on condition. Surface water with low ANC 

concentrations can directly influence aquatic organism fitness or mortality by disrupting ion 

regulation and can mobilize dissolved inorganic aluminum, which is highly toxic to fish under 

acidic conditions (i.e., pH <6 and ANC <50 μeq/L). For example, research showed that as the pH 

of surface waters decreased to <6, many aquatic species, including fish, invertebrates, 

zooplankton, and diatoms, tended to decline sharply causing species richness to decline 

(Schindler, 1988). Van Sickle and colleagues (1996) also found that blacknose dace (Rhinichthy 

spp.) were highly sensitive to low pH and could not tolerate inorganic Al concentrations greater 

than about 3.7 micromolar (μM) for extended periods of time. For example, they found that after 
Appendix B 

March 2010 3 Draft – Do Not Quote or Cite 

6 days of exposure to high inorganic Al, blacknose dace mortality increased rapidly to nearly 

100%. 

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At the community level, species richness and community structure can be used to 

evaluate the effects of acidification. Species composition refers to the mix of species that are 

represented in a particular ecosystem, whereas species richness refers to the total number of 

species in a stream or lake. Acidification alters species composition and richness in aquatic 

ecosystems. There are a number of species common to many oligotrophic waterbodies that are 

sensitive to acidification and cannot survive, compete, or reproduce in acidic waters. In response 

to small to moderate changes in acidity, acid-sensitive species are often replaced by other more 

acid-tolerant species, resulting in changes in community composition and richness, but with little 

or no change in total community biomass. The effects of acidification are continuous, with more 

species being affected at higher degrees of acidification. At a point, typically a pH <4.5 and an 

ANC <0 μeq/L, complete to near-complete loss of many classes of organisms occur, including 

fish and aquatic insect populations, whereas others are reduced to only a few acidophilic forms. 

These changes in species integrity are because energy cost in maintaining physiological 

homeostasis, growth, and reproduction is high at low ANC levels (Schreck, 1981, 1982; 

Wedemeyer et al., 1990). 

In EPA’s Risk and Exposure Assessment for Review of the Secondary National Ambient 

Air Quality Standards for Oxides of Nitrogen and Sulfur (U.S. EPA, 2009), the negative impacts 

of acidifying deposition were assessed by conducting case studies of 1) aquatic acidification in 

Adirondack Mountains lakes and Shenandoah Mountains streams, and 2) terrestrial acidification 

in red spruce and sugar maple forests in the White Mountains of New Hampshire and in 

Pennsylvania, respectively. The results of these case studies revealed the significance of base 

cation weathering in predicting aquatic and terrestrial acidification impacts. The results further 

highlighted the need to select weathering methodologies that can be applied across geologically 

diverse ecosystems in the United States. This report uses the information from the Risk and 

Exposure Assessment as a starting point to identify and evaluate approaches to predicting 

weathering at other locations and larger scales in the United States. In this report, RTI 

recommends methodologies (including computer models) for application in the United States, 

assesses the availability of input data for those methodologies, identifies potential remedies to 
Appendix B 

March 2010 4 Draft – Do Not Quote or Cite 

limited data availability, and describes uncertainties with the methodologies in predicting 

acidification impacts. 

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2.2 Aquatic Acidification and Critical Acid Loads 

Surface water chemistry is a primary indicator of acidification and the resulting negative 

effects on the biotic integrity of freshwater ecosystems. Chemical parameters can be used to 

assess effects of acidifying deposition on lake or stream acid-base chemistry. These receptors 

include surface water pH and concentrations of SO4

2-, NO3-

, Al, and Ca2+; the sum of base 

cations; and the recently developed base cation surplus. Another widely used water chemistry 

indicator for both atmospheric deposition sensitivity and effects is acid neutralizing capacity 

(ANC). The utility of the ANC criterion lies in the association between ANC and the surface 

water constituents that directly contribute to or ameliorate acidity-related stress, in particular pH, 

Ca2+, and Al. ANC is also used because it integrates overall acid status and because surface 

water acidification models do a better job projecting ANC than they do for projecting pH and 

dissolved inorganic Al concentrations. 

For the purpose of this study, ANC of surface waters is simply measured as the total 

amount of strong base ions minus the total amount of strong acid anions: 

ANC = (Ca2+ + Mg2+ + K+ + Na+ + NH4) – (SO4

2- + NO3-

+ Cl-) (2-1) 

The unit of ANC is usually microequivalents per liter (μeq/L). If the sum of the 

equivalent concentrations of the base cations exceeds those of the strong acid anions, then the 

ANC of a waterbody will be positive. 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 high pH and 

Ca2+ concentrations; lower ANC is generally associated with low pH and Al3+ concentrations and 

a greater likelihood of toxicity to biota. 

Low ANC coincides with effects on aquatic systems (e.g., individual species fitness loss 

or death, reduced species richness, altered community structure). At the community level, 

species richness is positively correlated with pH and ANC (Kretser et al., 1989; Rago and 

Wiener, 1986) because energy cost in maintaining physiological homeostasis, growth, and 

reproduction is high at low ANC levels (Schreck, 1981, 1982; Wedemeyer et al., 1990). For 

example, Sullivan and colleagues (2006) found a logistic relationship between fish species 

richness and ANC class for Adirondack Case Study Area lakes (Figure 2-1a) that indicates the 
Appendix B 

March 2010 5 Draft – Do Not Quote or Cite 

probability of occurrence of an organism for a given value of ANC. In the Shenandoah Case 

Study Area, a statistically robust relationship between acid-base status of streams and fish 

species richness was also documented (Figure 2-1b). In fact, ANC has been found in various 

studies to be the best single indicator of the biological response and health of aquatic 

communities in acid-sensitive systems (Lien et al., 1992; Sullivan et al., 2006). 

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Biota are generally not harmed when ANC values are >100 microequivalents per liter 

(μeq/L). The number of fish species also peaks at ANC values >100 μeq/L (Bulger et al., 1999; 

Driscoll et al., 2001; Kretser et al., 1989; Sullivan et al., 2006). Below 100 μeq/L, ANC fish 

fitness and community diversity begin to decline (Figure 2-1). At ANC levels between 100 and 

50 μeq/L, the fitness of sensitive species (e.g., brook trout, zooplankton) also begins to decline. 

When ANC concentrations are <50 μeq/L, they are generally associated with death or loss of 

fitness of biota that are sensitive to acidification (Kretser et al., 1989; Dennis and Bulger, 1995). 

(a) (b) 

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Figure 2-1. (a) Number of fish species per lake or stream versus acidity, expressed as 

acid neutralizing capacity for Adirondack Case Study Area lakes (Sullivan et al., 2006). 

(b) Number of fish species among 13 streams in Shenandoah National Park. Values of 

acid neutralizing capacity are means based on quarterly measurements from 1987 to 

1994. The regression analysis shows a highly significant relationship (p < .0001) between 

mean stream acid neutralizing capacity and the number of fish species. 

When ANC levels drop to <20 μeq/L, all biota exhibit some level of negative effects. 

Fish and plankton diversity and the structure of the communities continue to decline sharply to 

levels where acid-tolerant species begin to outnumber all other species (Matuszek and Beggs, 

1988; Driscoll et al., 2001). Stoddard and colleagues (2003) showed that to protect biota from 

episodic acidification in the springtime, base flow ANC levels need to have an ANC of at least 
Appendix B 

March 2010 6 Draft – Do Not Quote or Cite 

30–40 μeq/L. Complete loss of fish populations and extremely low diversity of planktonic 

communities occur when ANC levels stay <0 μeq/L. Only acidophilic species are present, but 

their population numbers are sharply reduced (Sullivan et al., 2006). 

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The critical load approach can be used to connect current deposition of nitrogen and 

sulfur to the acid-base condition and biological risk to biota of lakes and streams in the study 

through the defined ANC thresholds. Calculating critical load exceedances (i.e., the amount of 

deposition above the critical load) allows the determination of whether current deposition poses a 

risk of acidification to a given group of waterbodies. Low critical load values (i.e., less than 50 

meq/m2.yr) mean that the watershed has a limited ability to neutralize the addition of acidic 

anions, and hence, it is susceptible to acidification. The greater the critical load value, the greater 

the ability of the watershed to neutralize the additional acidic anions and protect aquatic life. 

This approach also allows for the comparison of different levels of ANC thresholds (e.g., 0 

μeq/L (acidic), 20 μeq/L (minimal protection), 50 μeq/L (moderate protection), and 100 μeq/L 

(full protection)) and their associated risk to the biological community. Table 2-1 provides a 

summary of the biological effects experienced at each of these limits. 

Table 2-1. Aquatic Status Categories 

Category Label ANC Levels* Expected Ecological Effects 

Acute 

Concern 

<0 micro 

equivalent 

per Liter 

(μeq/L) 

Near complete loss of fish populations is expected. Planktonic 

communities have extremely low diversity and are dominated by 

acidophilic forms. The number of individuals in plankton species that 

are present is greatly reduced. 

Severe 

Concern 

0–20 μeq/L Highly sensitive to episodic acidification. During episodes of high 

acidifying deposition, brook trout populations may experience lethal 

effects. Diversity and distribution of zooplankton communities decline 

sharply. 

Elevated 

Concern 

20–50 μeq/L Fish species richness is greatly reduced (i.e., more than half of 

expected species can be missing). On average, brook trout populations 

experience sublethal effects, including loss of health, reproduction 

capacity, and fitness. Diversity and distribution of zooplankton 

communities decline. 

Moderate 

Concern 

50–100 

μeq/L 

Fish species richness begins to decline (i.e., sensitive species are lost 

from lakes). Brook trout populations are sensitive and variable, with 

possible sublethal effects. Diversity and distribution of zooplankton 

communities also begin to decline as species that are sensitive to 

acidifying deposition are affected. 
Appendix B 

March 2010 7 Draft – Do Not Quote or Cite 

Category Label ANC Levels* Expected Ecological Effects 

Low Concern >100 μeq/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. 

There are numerous methods and models that can be used to calculate critical loads for 

acidity. Drawing on the peer-reviewed scientific literature (Dupont et al., 2005), this study will 

use a steady-state critical load model that uses surface water chemistry as the base for calculating 

the critical load. A combination of the Steady-State Surface Water Chemistry (SSWC) and FirstOrder Acidity Balance (FAB) models were used to calculate the critical load. Both the SSWC 

model and FAB are based on the principle that excess base-cation production within a catchment 

area should be equal to or greater than the acid anion input, thereby maintaining the ANC above 

a preselected level (Reynolds and Norris, 2001; Posch et al., 1997). These models assume 

steady-state conditions and assume that all SO4

2– in runoff originates from sea salt spray and 

anthropogenic deposition. Given a critical ANC protection level, the critical load of acidity is 

simply the input flux of acid anions from atmospheric deposition (i.e., natural and 

anthropogenic) subtracted from the natural (i.e., preindustrial) inputs of base cations in the 

surface water. 

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Critical loads for nitrogen and sulfur (CL(N) + CL(S)) or critical load of acidity CL(A) 

are calculated for each waterbody from the principle that the acid load should not exceed the 

nonmarine, nonanthropogenic base cation input and sources and sinks in the catchment minus a 

neutralizing to protect selected biota from being damaged: 

CL(N) + CL(S) or CL(A) = BC*

dep + BCw – Bcu – AN - ANClimit (2-2)

where 

 BC*

dep = nonanthropogenic deposition flux of base cations 

(BC*=Ca*+Mg*+K*+Na*) 

 BCw = the average weathering flux, producing base cations 

 Bcu = the net long-term average uptake flux of base cations (Bc=Ca*+Mg*+K*) in 

the biomass (i.e., the annual average removal of base cations due to 

harvesting) 
Appendix B 

March 2010 8 Draft – Do Not Quote or Cite 

 AN = the net long-term average uptake, denitrification, and immobilization of 

nitrogen anions (e.g. NO3

-

) and uptake of SO4

-

 

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ANClimit = the lowest ANC-flux that protects the biological communities. 

In order to estimate a critical load from water quality data alone, a relation to the 

preacidification nonmarine flux of base cations for each lake or stream, BC*

0, is used. 

 BC*

0 = BC*dep + BCw - Bcu (2-3) 

Thus, the critical load for acidity can be rewritten as 

CL(N) + CL(S) = BC*

0 – AN - ANClimit = Q.([ BC*

]0 – [AN] - [ANC]limit) (2-4) 

where the second identity expresses the critical load for acidity in terms of catchment runoff (Q) 

m/yr and concentration ([x] = X/Q). In cases where data are available, the FAB model is applied 

to quantify the [AN] term of the critical load calculation (derivation provided in Appendix 4, 

Attachment A of U.S. EPA, 2009). Where data are not available the contribution of nitrogen 

anions to acidification was assumed to be equal to the nitrogen leaching rate into the surface 

water. The flux of acid anions in the surface water is assumed to represent the amount of 

nitrogen that is not retained by the catchment, which is determined from the sum of measured 

concentration of NO3

-

and ammonia in the stream chemistry. This case describes the SSWC 

model and the critical load for acidity is 

CL(A) = Q.([BC*]0 – [ANC]limit) (2-5) 

where the contribution of acid anions is considered as part of the exceedances calculation. With 

this approach several major assumptions are made: (1) steady-state conditions exist, (2) the effect 

of nutrient cycling between plants and soil is negligible, (3) there are no significant nitrogen 

inputs from sources other than atmospheric deposition, (4) ammonium leaching is negligible 

because any inputs are either taken up by biota or adsorbed onto soils or nitrate compounds, and 

(5) long-term sinks of sulfate in the catchment soils are negligible. 

To determine a value for BC*

0 with the SSWC method, estimates of BCdep are available 

from previous works including the recent REA (U.S. EPA, 2009). Assumptions or estimates for 

BCu and AN can be made based on attributes of the area of study, including vegetation 
Appendix B 

March 2010 9 Draft – Do Not Quote or Cite 

characteristics. But the average flux of base cations weathered in a catchment and reaching the 

lake or streams (BCw) is difficult to measure or compute from available information (Henriksen 

and Posch, 2001; Henriksen et al., 2002; Langan et al., 2001). In the previous work for the Risk 

and Exposure Assessment case studies (U.S. EPA, 2009) the average flux of base cations and the 

resulting critical load estimation were derived from water quality data (Henriksen and Posch, 

2001; Henriksen et al., 1992; Sverdrup et al., 1990). Weighted annual mean water chemistry 

values were used to estimate average base cation fluxes, which were calculated from water 

chemistry data collected from several national and regional monitoring programs. For a national 

assessment, however, new methods must be developed to estimate the BCw flux, which is critical 

to the critical load calculation, through consistent, nationally-applicable means. 

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2.1.2 Terrestrial Acidification and Critical Acid Loads 

Due to the impact of acidifying nitrogen and sulfur deposition on soil solution base cation 

(Bc) and aluminum concentrations, the Bc/Al ratio in the soil solution is often used as the 

chemical or critical indicator of terrestrial acidification. It was recently used as an indicator in the 

U.S. EPA’s Risk and Exposure Assessment for oxides of nitrogen and oxides of sulfur (U.S. 

EPA, 2009). This Bc/Al ratio links acidifying deposition to biological responses or end points, 

such as reduced plant or tree growth, within an ecosystem. In a meta-analysis of studies that 

explored the relationship between Bc/Al ratio in soil solution and tree growth, Sverdrup and 

Warfvinge (1993a) reported the Bc/Al ratios at which growth was reduced by 20% relative to 

control trees. This 20% reduction in tree growth was selected as the critical value because it was 

thought to represent a significant reduction in growth (H. Sverdrup personal communication, 

2009b) and approximates the Bc/Al value that would result in a 10% reduction in normal tree 

growth under field conditions (Sverdrup and Warfvinge, 1993a). Figure 2-2 presents the 

findings of Sverdrup and Warfvinge (1993a) based on 46 of the tree species (native and 

introduced) that grow in North America. This summary indicates that there is a 50% chance of 

negative tree response (i.e., >20% reduced growth) at a soil solution Bc/Al ratio of 1.2 and a 

75% chance at a Bc/Al ratio of 0.6. These findings clearly demonstrate a relationship between 

Bc/Al ratio and tree health; as the Bc/Al is reduced, there is a greater likelihood of a negative 

impact on tree health. 
Appendix B 

March 2010 10 Draft – Do Not Quote or Cite 

0.01

0.1

1

10

0 25 50 75

Bc/Al Ratio in Soil Solution 

100

(Bc/Al)crit = 1.2

(Bc/Al)crit = 0.6

0.01

0.1

1

10

0 25 50 75

Bc/Al Ratio in Soil Solution 

100

(Bc/Al)crit = 1.2

(Bc/Al)crit = 0.6

Cumulative Percentage of Species Exhibiting Reduced Growth Response 1

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Figure 2-2. The relationship between the Bc/Al ratio in soil solution and the percentage of 

tree species (found growing in North America – native and introduced species) exhibiting 

a 20% reduction in growth relative to controls (after Sverdrup and Warfvinge, 1993). 

The tree species most commonly studied in North America to assess the impacts of 

acidification due to total nitrogen and sulfur deposition are red spruce (i.e., Picea Rubens) and 

sugar maple (i.e., Acer saccharum). Both species are found in the eastern United States, and soil 

acidification is widespread throughout this area (Warby et al., 2009). Based on the results from a 

compilation of laboratory studies, red spruce growth can be reduced by 20% at a Bc/Al soil 

solution ratio of approximately 1.2, and a similar reduction in growth may be experienced by 

sugar maple at a Bc/Al ratio of 0.6 (Sverdrup and Warfvinge 1993a). 

Red spruce is found scattered throughout high-elevation sites in the Appalachian 

Mountains, including the southern peaks. Noticeable fractions of the canopy red spruce died 

within the Adirondack, Green, and White mountains in the 1970s and 1980s. Although a variety 

of conditions, such as changes in climate and exposure to ozone, may impact the growth of red 

spruce (Fincher et al., 1989; Johnson et al., 1988), acidifying deposition has been implicated as 

one of the main factors causing this decline. Based on the research conducted to date, acidifying 

deposition can cause a depletion of base cations in upper soil horizons, Al toxicity to tree roots, 

and accelerated leaching of base cations from foliage (U.S. EPA, 2008, Section 3.2.2.3). Such 

nutrient imbalances and deficiencies can reduce the ability of trees to respond to stresses, such as 

insect defoliation, drought, and cold weather damage (DeHayes et al., 1999; Driscoll et al., 
Appendix B 

March 2010 11 Draft – Do Not Quote or Cite 

2001), thereby decreasing tree health and increasing mortality. Additional linkages between 

acidifying deposition and red spruce physiological responses are indicated in Table 4.3-1. 

Within the southeastern United States, periods of red spruce decline slowed after the 1980s, 

when a corresponding decrease in SO2 emissions, and therefore acidic deposition, was recorded 

(Webster et al., 2004). 

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Sugar maple is found throughout the northeastern United States and the central 

Appalachian Mountain region. This species has been declining in the eastern United States since 

the 1950s. Studies on sugar maple have found that one source of this decline in growth is related 

to both acidifying deposition and base-poor soils on geologies dominated by sandstone or other 

base-poor substrates (Bailey et al., 2004; Horsley et al., 2000). These site conditions are 

representative of the conditions expected to be most susceptible to impacts of acidifying 

deposition because of probable low initial base cation pools and high base cation leaching losses 

(U.S. EPA, 2008, Section 3.2.2.3). The probability of a decrease in crown vigor or an increase in 

tree mortality has been noted to increase at sites with low Ca2+ and Mg2+ as a result of leaching 

caused by acidifying deposition (Drohan and Sharpe, 1997). Low levels of Ca2+ in leaves and 

soils have been shown to be related to lower rates of photosynthesis and higher antioxidant 

enzyme activity in sugar maple stands in Pennsylvania (St. Clair et al., 2005). In addition, plots 

of sugar maples in decline were found to have Ca2+/Al ratios less than 1, as well as lower base 

cation concentrations and pH values compared with plots of healthy sugar maples (Drohan et al., 

2002). Sugar maple regeneration has also been noted to be restricted under conditions of low soil 

Ca2+ levels (Juice et al., 2006). These indicators have all been shown to be related to the 

deposition of atmospheric nitrogen and sulfur. Additional linkages between acidifying deposition 

and sugar maple physiological responses are indicated in Table 2-2. 

Table 2-2. Summary of Linkages between Acidifying Deposition, Biogeochemical Processes That Affect 

Ca2+, Physiological Processes That Are Influenced by Ca2+, and Effect on Forest Function 

Biogeochemical Response to 

Acidifying deposition Physiological Response Effect on Forest Function 

Leach Ca2+ from leaf membrane Decrease the cold tolerance of 

needles in red spruce 

Loss of current-year needles in 

red spruce 

Reduce the ratio of Ca2+/Al in 

soil and soil solutions 

Dysfunction in fine roots of red 

spruce blocks uptake of Ca2+

Decreased growth and 

increased susceptibility to stress 

in red spruce 
Appendix B 

March 2010 12 Draft – Do Not Quote or Cite 

Biogeochemical Response to 

Acidifying deposition Physiological Response Effect on Forest Function 

Reduce the ratio of Ca2+/Al in 

soil and soil solutions 

More energy is used to acquire 

Ca2+ in soils with low Ca2+/Al 

ratios 

Decreased growth and 

increased photosynthetic 

allocation to red spruce roots 

Reduce the availability of 

nutrient cations in marginal soils 

Sugar maples on drought-prone 

or nutrient-poor soils are less 

able to withstand stresses 

Episodic dieback and growth 

impairment in sugar maple 

Source: Fenn and colleagues, 2006. 

Although the main focus of the Terrestrial Acidification Case Study outlined in the Risk 

and Exposure Assessment for Review of the Secondary National Ambient Air Quality Standards 

for Oxides of Nitrogen and Sulfur (U.S. EPA, 2009) was an evaluation of the negative impacts of 

nitrogen and sulfur deposition on soil acidification and tree health, it should be recognized that 

under certain conditions, nitrogen and sulfur deposition can have a positive impact on tree health. 

Nitrogen limits the growth of many forests (Chapin et al., 1993; Killam, 1994; Miller, 1988), and 

therefore, in such forests, nitrogen deposition may act as a fertilizer and stimulate growth. 

Forests where critical acid loads are not exceeded by nitrogen and sulfur deposition could 

potentially be included within this group of forests that respond positively to deposition. These 

potential positive growth impacts of nitrogen and sulfur deposition are discussed further, and the 

results of case study analyses are presented in Attachment A of Appendix 5 of the Risk and 

Exposure Assessment (U.S. EPA, 2009). 

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In summary, among potential influencing factors, including elevated ozone levels and 

changes in climate, the acidification of soils is one of the factors that can negatively impact the 

health of red spruce and sugar maple. Mortality and susceptibility to disease and injury can be 

increased and growth decreased with acidifying deposition. Therefore, the health of sugar maple 

and red spruce was used as the endpoints (ecological responses) to evaluate acidification in 

terrestrial systems. “Health” in the context of the Risk and Exposure Assessment terrestrial 

acidification case study was defined as the physiological condition of a tree that impacts growth 

and/or mortality. 

The Simple Mass Balance (SMB) model was used to estimate critical loads of acidity in 

the Risk and Exposure Assessment case study (Equation 2-1). The full derivation of this equation 

is detailed in the ICP Mapping and Modeling Manual (UNECE, 2004). 
Appendix B 

March 2010 13 Draft – Do Not Quote or Cite 

dep −=+ dep + − + + + deuiuw − ANC NNNBcBCClBCN)CL(S critle, 1 (2-1) 

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where 

 CL(S+N) = forest soil critical load for combined nitrogen and sulfur acidifying 

deposition ((N+S)comb) 

 BCdep = base cation (Ca2+ + K+

 + Mg2+ + Na+) deposition1

 Cldep = chloride deposition 

 BCw = base cation (Ca2+ + K+ + Mg2+ + Na+

) weathering 

 Bcu = uptake of base cations (Ca2+ + K+

 + Mg2+) by trees 

 Ni = nitrogen immobilization 

 Nu = uptake of nitrogen by trees 

 Nde = denitrification 

 ANCle,crit = forest soil acid neutralizing capacity of critical load leaching 

Some of these parameters had defined or selected input values (BCdep, Cldep, Ni, Nu and Nde), 

while four of these parameters, including BCw, Bcu, Nu and ANCle,crit, required calculation. 

For the Risk and Exposure Assessment’s terrestrial acidification case study, three values 

of the indicator of critical load, expressed as (Bc/Al)crit soil solution ratio, were selected to 

represent different levels of tree protection associated with total nitrogen and sulfur deposition: 

0.6, 1.2, and 10 (Table 2-3). The (Bc/Al)crit ratio of 0.6 represents the highest level of impact 

(lowest level of protection) to tree health and growth and was selected because 75% of species 

found growing in North America experience reduced growth at this Bc/Al ratio. In addition, a 

soil solution Bc/Al ratio of 0.6 has been linked to a 20% and 35% reduction in sugar maple and 

red spruce growth, respectively. The (Bc/Al)crit ratio of 1.2 is considered to represent a moderate 

level of impact, as the growth of 50% of tree species (found growing in North America) was 

negatively impacted at this soil solution ratio. The (Bc/Al)crit ratio of 10.0 represents the lowest 

level of impact (greatest level of protection) to tree growth; it is the most conservative value used 

in studies that have calculated critical loads in the United States and Canada (Canada (McNulty 

et al., 2007; NEG/ECP, 2001; Watmough et al., 2004). 

1 The ICP Mapping and Modeling Manual (UNECE, 2004) recommends that wet deposition be corrected for sea salt

on sites within 70 km of the coast. Both the HBEF and KEF case study areas are greater than 70 km from the coast,

so this correction was not used.
Appendix B 

March 2010 14 Draft – Do Not Quote or Cite 

Table 2-3. The Three Indicator (Bc/Al)crit Soil Solution Ratios and Corresponding Levels of Protection to 

Tree Health and Critical Loads 

Indicator (Bc/Al)crit Soil 

Solution Ratio 

Level of Protection to Tree 

Health Critical Load 

0.6 Low High 

1.2 Intermediate Intermediate 

10.0 High Low 

The prediction of tree protection achieved using each of these three indicator ratios of 

0.6, 1.2, and 10.0 includes an important estimation of base cation weathering as shown in 

Equation 2-1, above. The purpose of this report is to describe the methodologies, data 

requirements, data availability, and uncertainties associated with estimating base cation 

weathering. 

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3. AQUATIC BASE CATION WEATHERING METHODOLOGY 

The ISA (US EPA, 2008) reports that the principal factor governing the sensitivity of 

terrestrial and aquatic ecosystems to acidification from sulfur and nitrogen deposition is geology 

(particularly surficial geology). Geologic formations having low base cation supply generally 

underlie the watersheds of acid-sensitive lakes and streams. Other factors that contribute to the 

sensitivity of soils and surface waters to acidifying deposition include topography, soil 

chemistry, land use, and hydrologic flowpath. Surface waters in the same setting can have 

different sensitivities to acidification, depending on the relative contributions of near-surface 

drainage water and deeper groundwater (Chen et al., 1984; Driscoll et al., 1991; Eilers et al., 

1983). Lakes and streams in the United States that are sensitive to episodic and chronic 

acidification in response to SOx, and to a lesser extent NOX, deposition tend to occur at relatively 

high elevation in areas that have base-poor bedrock, high relief, and shallow soils (U.S. EPA, 

2008, Section 3.2.4.1). 

3.1 Aquatic Base Cation Weathering 

Base cation weathering for aquatic acidification critical loads must be representative of 

the catchment around the waterbody of interest. This aspect of quantification of the weathering 

rate provides the difference when calculating weathering rates for aquatic versus terrestrial 

analysis purposes. The process of weathering itself provides the only natural in-soil source of 

alkalinity that is available to neutralize acidity inputs to the system over the long term. Chemical 

weathering of the mineral matrix within soils supplies base cations that are removed from soil 
Appendix B 

March 2010 15 Draft – Do Not Quote or Cite 

due to acid inputs. Therefore, the rate of weathering of the soils within a catchment is dependent 

on the chemical and physical properties of the soil (Sverdrup et al., 1992; Whitfield et al., 2006). 

As indicated in Section 2.2, the average flux of base cations weathered in a catchment and 

reaching the lake or streams (BCw) is difficult to measure or compute from available information 

(Henriksen and Posch, 2001; Henriksen et al., 2002; Langan et al., 2001). Approaches also differ 

based on whether the weathering rate needs to account for only in-soil processes (profile 

measurements and models) or whether it needs to account for the flux of base cations to surface 

water (spatially integrated catchment data and models) (Langan et al., 2001). 

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In the Aquatic Acidification case study in the REA Report (U.S. EPA, 2009), BCw rates 

were not directly calculated. Instead, the F-factor approach was used to calculate the preacidification, non-marine flux of base cations (BC*

0) for each lake or stream. An F-factor 

(explained in Section 3.2.2) is a ratio of the change in non-marine base cation concentration due 

to changes in strong acid anion concentrations (Henriksen, 1984; Brakke et al., 1990), as shown 

in the following equations: 

 BC*

0 = BC*t – F (SO*4,t - SO*4,0 + NO*3,t - NO*3,0) (3-1) 

F = ([BC*

]t - [BC*]0)/([SO4*]t - [SO4

*

]0 + [NO3

*

]t - [NO3

*

]0) (3-2) 

where the subscripts t and 0 refer to present and pre-acidification conditions, respectively. The 

pre-acidification NO3

-

 concentration, NO*

3,0, was assumed to be zero. Several attempts have 

been made to create empirical relations for the F-factor and the pre-acidification SO4

*

concentration. Although the Aquatic Acidification case study relied on two of these relations, it 

must be noted that they were developed for areas outside of the U.S. and, therefore, cannot be 

applied to the conditions found within U.S. soils and climates without introducing a source of 

uncertainty (Henriksen and Posch, 2001; Henriksen et al., 2002; Brakke et al., 1989; Posch et al., 

1997). Notwithstanding the lack of U.S.-based empirical relations, the F-factor can be used to 

derive BCw estimates. Assuming that all atmospheric deposition of base cations that falls within 

a catchment passes through to the surface water and that one can accurately estimate the uptake 

of base cations within the catchment, the BCw could ultimately be backed out of these 

relationships. However, both of these assumptions are likely to introduce an additional amount of 

uncertainty into the BCw estimates. 
Appendix B 

March 2010 16 Draft – Do Not Quote or Cite 

For a national aquatic acidification assessment, different methods must be employed to 

estimate BCw rates. In some studies, simple assumptions for the BCw are utilized. For instance, in 

a study by Dupont and colleagues (2005) using the SSWC, the authors assumed that weathering 

rates were time-independent and did not affect critical load estimates. In more advanced process 

modeling applications, such as ones using the Model of Acidification of Groundwater in 

Catchments (MAGIC), weathering rates can be adjusted during calibration and allowed to vary 

over ranges like 0 and 5 times the observed watershed base cation export for base cation 

weathering (Sullivan et al., 2004). There are several different approaches to estimating the 

weathering rate of a soil or a catchment, ranging from empirical relations to mass balance 

methods to calibrated process models. According to Whitfield and colleagues (2006) “to date no 

method has proven to be superior in application to different soil types and differing levels of soil 

acidification.” The remainder of this section is intended to examine the BCw estimation methods 

that would be applicable to a national aquatic acidification critical loads analysis giving 

consideration to the limitations of the method and the possible data and processing requirements 

for the analysis. 

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3.2 Methodologies for Determining Base Cation Weathering Values in the United States 

3.2.1 Difficulties in estimating base cation weathering 

Consideration must be given to several factors in the estimation of base cation weathering 

fluxes for aquatic acidification (Sverdrup et al., 1992; Whitfield et al., 2006; Rapp and Bishop, 

2009; Henriksen and Posch, 2001; Henriksen et al., 2002): 

1. The weathering contribution of the entire catchment must be understood and not 

simply the weathering contribution of certain soil profiles within the catchment. 

Additionally, the various types of land use (e.g. agriculture or forest) within a 

catchment may all affect weathering rates differently. 

2. When utilizing soil profile weathering methods, the characteristics of the entire soil 

profile must be considered and weighted according to catchment composition as 

opposed to only the rooting zone in individual profiles as used in determining 

weathering for terrestrial acidification purposes. 

3. Based on the critical load method chosen, it is often necessary to assume that the BCw

remains constant over the length of the analysis. While this simplifies the estimation 

of BCw, it introduces uncertainty into any analysis. The length of the analysis 
Appendix B 

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scenario must be sufficiently long and have supporting data in order to provide a 

long-term average, which is not subjected to short-term variations. 

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4. The data requirements for a national assessment necessitate using similar data sources 

for all applications so that assumptions and methods can remain constant across the 

nation. 

5. The application of any empirical relations for calculation of BCw or intermediate 

component of BCw (e.g., the F-factor) must be validated against the geographic region 

in which they will be applied. Given that most empirical relations developed to date 

were based on data from European nations, these relations need to be recalibrated to 

data from the U.S. 

Given all of these factors, estimation of BCw for a national application poses a significant 

challenge. The methods detailed in the following section seek to balance the limitations and 

benefits of each approach to estimation of BCw. 

3.2.2 Approaches to estimating BCw for Aquatic Acidification 

Work presented in the scientific literature over the last two decades provides several 

different approaches researchers have taken to estimate the BCw rates for aquatic effects. These 

approaches do not always differentiate between the actual weathering processes in-soil and the 

other ion exchange processes taking place (Langan et al., 2001). Approaches to estimating BCw

also vary between terrestrial and aquatic studies. Aquatic studies of acidification must capture 

the weathering rates of all soil horizons which contribute base cations and not solely the rooting 

zone as specified in terrestrial acidification studies (Whitfield et al., 2006). 

Four general categories of approaches are outlined for determining BCw for aquatic 

acidification critical loads calculations using the SSWC. 

1. Budgets studies of catchments or watersheds; 

2. Historical weathering rate determinations; 

3. Empirical relations; and 

4. Process-based models. 

In the case of empirical data relations and process-based models, specific methods are 

provided. The strengths and weaknesses of either the general category or specific approach in 

terms of both utilization in aquatic acidification critical loads calculations and estimation of BCw

are examined in the following paragraphs and Table 3-1. 
Appendix B 

March 2010 18 Draft – Do Not Quote or Cite 

Budget Studies – Budget studies are simple means of determining fluxes within a system 

by balancing the masses coming into and going out of a system. In determining the BCw, a mass 

balance would be performed around the base cations fluxes within a watershed, where 

atmospheric deposition constitutes the main source input and streamflow the main output. Within 

the balance, base cation retention is also accounted for through uptake by biomass and 

immobilization in the soil. The BCw developed from budget studies represent integrated values 

for the whole watershed as desired for aquatic acidification estimates as opposed to only 

weathering from the rooting zone as desired for terrestrial acidification (Sverdrup and Warfvinge 

1988; Miller, 2001). Depending on how the balance is set up, the balance can be a single 

equation around the total base cation flux or a series of equations for each individual cation. The 

setup of the equations leads to the primary limitation of the method in that while it is a relatively 

simple concept, the individual fluxes within the balance are not easily measured or known 

(Bricker et al., 2003). 

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Most mass balance calculations require an assumption of steady-state behavior. This 

assumption is easily justifiable over long periods of record. Additional limitations of the method 

evolve from the number of unknown fluxes (e.g. weathering rate of individual minerals) within 

the equations defining the balance. Researchers have utilized a variety of techniques to overcome 

this limitation, including applying simplifying assumptions or adding additional equations. 

However, with each assumption or additional equation, a greater amount of uncertainty that must 

be quantified is added into the analysis. Data sources for a mass balance can also be variable 

depending on the complexity of the relationships defined within the balance. While databases 

and studies may exist for major elements at a variety of sites, comparable data for trace or more 

complex elements may be lacking (Velbel and Price, 2007). 

Historical Rate Determinations – This approach is detailed in Section 4 for terrestrial 

acidification approaches. Because the BCw flux required for aquatic acidification approaches 

requires characterization of the whole soil profile averaged across a catchment or watershed, this 

approach can become computationally intensive for aquatic purposes. While it is possible to 

conduct such an approach on a small scale for an aquatic acidification assessment, it is more 

likely suited to terrestrial applications and so explanation is provided in those sections of the 

document. 

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Appendix B 

March 2010 19 Draft – Do Not Quote or Cite 

Empirical Relations – A number of empirical relationships have been developed to 

calculate BCw, or related factors, from water quality data alone. Empirical relationships are only 

as strong as the data on which they are based and are only applicable to the geographic regions 

from which the calibration data is obtained. 

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F-Factor: The F-factor is defined as the ratio of change in non-marine base cation concentrations 

due to changes in strong acid anion concentrations (Henriksen, 1984; Brakke et al., 1990). (See 

Section 3.1.) A situation where F = 1 indicates that only soil acidification occurs within the 

catchment, i.e. all incoming protons are neutralized in the catchment. When F = 0, then only 

water acidification is occurring and none of the incoming protons are neutralized in the 

catchment. Using historical data from Norway, Sweden, U.S.A. and Canada, the F-factor was 

estimated empirically to be in the range 0.2–0.4 (Henriksen, 1984). Several empirical 

relationships have been developed in order to calculate the F-factor based on current base cation 

concentrations using data from Norway (Brakke et al., 1990) or on pre-acidification base cation 

concentration using data from Finland (Posch et al., 1993). 

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There are several limitations to using the F-factor. While it is simple to apply anywhere the data 

is available to satisfy the empirical relations, these relations are really only valid in Norway, 

Finland, or wherever the specific relation was derived. In several instances, researchers have 

applied the Norway- or Finland-based relations to Canadian (Watmough et al., 2005) and U.S. 

study locations (Henriksen et al., 2002; Dupont et al., 2005; U.S. EPA, 2009) with the assumption 

that the empirical equations provide adequate characterization of the relationship between base 

cation concentrations and the F-factor. 

A second major limitation in utilizing the F-factor is that this derived factor does not specifically 

quantify the BCw flux. Instead it provides calculation of the base cations leached from the soil, 

which includes BCw and base cations derived through deposition inputs to the system, or 

removed by harvesting (Henriksen et al., 2002; Rapp and Bishop, 2009). Although the SSWC is 

most often used with the F-factor, in a national application where we seek to specifically quantify 

the BCw, an alternative method should be used. 

Indicator element in conjunction with weathering ratios: Chen et al., 2004: “Weathering rates at 

Arbutus watershed could also be obtained using sodium as indicator element, as described by 

Gbondo-Tugbawa et al. (2001). The weathering inputs of the indicator element (sodium) could be 

derived using a mass balance approach, and the derived sodium weathering rate was used in 

conjunction with base cation weathering ratios reported by Johnson and Lindberg (1992) for the 

HF to derive weathering rates of other base cations. Using this method, the weathering rates of 

sodium and calcium derived for Arbutus watershed are very similar to values derived through 

calibration, whereas rates of magnesium and potassium derived using these two methods showed 

some discrepancies (Table II).” 

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Weathering rates vs. Stream chemistry or landscape variables: This approach begins with a set of 

BCw for a specific set of water bodies. The values of BCw are then regressed against the stream 

chemistry parameters, such as ANC, in order to find a correlation relationship. These regression 

relationships are then applied to stream chemistry of other water bodies within a defined region of 

interest to find the BCw for the water bodies. In areas where stream chemistry is not available, 

landscape variables can be used in place to find correlations with the BCw. 

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The limitation with this approach is that a statistically significant number of BCw values must be 

available from which to create a regression relationship. Also, the region in which the 

extrapolation is valid must be defined. In work by Sullivan and colleagues (2004), extrapolation 
Appendix B 

March 2010 20 Draft – Do Not Quote or Cite 

of modeled BCw values was completed using groupings of physiographic region and ANC class. 

The value of this approach is that it can specifically be applied to BCw and can be based on 

modeled, monitored, or estimated BCw values as long as there are a sufficient number of values 

for extrapolation. Other works have extrapolated ANC values based on chemistry and landscape 

variables in a similar manner with a high level of success (Sullivan et al., 2007b, Nanus et al., 

2009). 

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Process-based Models – Mineral weathering terms within modeling simulations can be a 

large source of uncertainty as the weathering term utilized in most process models, in attempts to 

represent reality, impacts the loss of base cations to surface waters. Therefore, when little is 

known about the true weathering rate or the constraints on its values, models must utilize 

calibration procedures against in-stream water chemistry data to arrive at a likely weathering rate 

(Chen et al., 2004; Sullivan et al., 2004). 

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Process-based models vary greatly in their range of processes represented, complexity of 

representations, time step, and required data inputs. Overall, there is no perfect model but the 

best candidate for a task can be chosen provided the available data, the area of concern, and the 

goals of the analysis. In this case, we would seek to use all available data resources in order to 

derive a range of spatially-explicit BCw values across the nation. 

Descriptions of the four candidate process models available for use across the country for 

determining BCw are provided below. In order to provide as concise a description as possible, 

these model summaries are taken directly from the scientific literature. Summation of the 

strengths and weaknesses of each model is provided after the model description. 

DayCent-Chem: “DayCent-Chem links together two widely accepted and tested models—(1) a 

daily time-step nutrient cycling and soil hydrology model, version 5 of the DayCent model 

[Parton et al., 1998], and (2) PHREEQC, an aqueous geochemical equilibrium model [Parkhurst 

and Appelo, 1999]—to form a model that simulates N, P, S, and carbon (C) ecosystem dynamics 

and soil and stream water acid-base chemistry (fig. 1.2). DayCent-Chem computes atmospheric 

deposition, soil water fluxes, snowpack and stream dynamics, plant production and uptake, soil 

organic matter decomposition, mineralization, nitrification, and denitrification (left side of fig. 

1.2) while utilizing PHREEQC’s low-temperature aqueous geochemical equilibrium calculations, 

including CO2 dissolution, mineral denudation, and cation exchange, to compute soil water and 

stream chemistry (right side of figure). DayCent-Chem’s daily soil solution and stream water 

chemistry calculations make it possible to use the model to investigate the potential for episodic 

acidification” (Hartman et al., 2009). 

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DayCent-Chem was recently applied to eight different mountain watersheds from the west to the 

east with success in certain capacities, therefore, making it a suitable candidate for a national 

analysis. These applications did highlight difficulties in determining realistic weathering rates in 

certain areas. With DayCent-Chem, a user must specify an initial value for weathering, which 

may be adjusted during calibration. In several instances, this value was first set to measured or 

estimated values for the area of interest and then modified largely during calibration (Hartman et 

al., 2009). While the daily time step and biotic processes represented by the model provide a 
Appendix B 

March 2010 21 Draft – Do Not Quote or Cite 

more complex view of the environment, they also add a complexity to the model that appears to 

greatly impact the estimation of the key parameter for this analysis. 

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MAGIC: “MAGIC is a lumped-parameter model of intermediate complexity, developed to 

predict the long-term effects of acidic deposition on surface water chemistry [Cosby et al., 1985a, 

1985b]. The model simulates soil solution chemistry and surface water chemistry to predict the 

monthly and annual average concentrations of the major ions in these waters. MAGIC consists of 

(1) a section in which the concentrations of major ions are assumed to be governed by 

simultaneous reactions involving SO4

2- adsorption, cation exchange, dissolution-precipitationspeciation of Al and dissolution-speciation of inorganic C and (2) a mass balance section in 

which the flux of major ions to and from the soil is assumed to be controlled by atmospheric 

inputs, chemical weathering, net uptake and loss in biomass and losses to runoff. At the heart of 

MAGIC is the size of the pool of exchangeable base cations in the soil. As the fluxes to and from 

this pool change over time owing to changes in atmospheric deposition, the chemical equilibria 

between soil and soil solution shift to give changes in surface water chemistry. The degree and 

rate of change of surface water acidity thus depend both on flux factors and the inherent 

characteristics of the affected soils” (Sullivan et al., 2004). 

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The strengths of MAGIC lie in its simplicity and ability to be applied for a large number of 

lakes/streams in batch processes. MAGIC has been in use since the 1980s, has been widely 

applied within the eastern portions of the country with more limited applications in the West. (See 

Section 3.3 for further discussion.) The simplicity of MAGIC’s mass balances approach also 

counts as one of its limitations because it may not account for all of the biotic processes that 

affect the weathering rate. MAGIC determines the BCw through calibration to water chemistry 

data. The “fuzzy optimization” procedures now built into MAGIC allow for an optimized value 

of the BCw to be determined from a series of calibrations at each modeling location (Sullivan et 

al., 2004). 

PnET-BGC: “PnET-BGC is an integrated forest-soil-water model that has been used to assess the 

effects of air pollution and land disturbances on forest and aquatic ecosystems [Gbondo-Tugbawa 

et al., 2001]. The model was developed by linking two submodels: PnET-CN (PnET-carbon and 

nitrogen) [Aber et al., 1997] and BGC [Gbondo-Tugbawa et al., 2001]. The main processes in the 

model include tree photosynthesis, growth and productivity, litter production and decay, 

mineralization of organic matter, immobilization of nitrogen, nitrification [Aber et al., 1997], 

vegetation and organic matter interactions of major elements, abiotic soil processes, solution 

speciation, and surface water processes [Gbondo-Tugbawa et al., 2001].... For lake simulations, 

it is assumed that the water column is completely mixed. The model predicts monthly 

concentrations and fluxes of major solutes in lake water, monthly concentrations and pools of 

exchangeable cations and adsorbed sulfate in soil, and monthly fluxes of major solutes from soil 

and forest vegetation” (Zhai et al., 2008). 

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Chen and others (2004) nicely summarize the tradeoffs associated with utilizing the PnET-BGC 

model: “A strength of PnET-BGC over other acidification models is its ability to simulate 

[vegetation and microbial processes]. However, this representation can also be a limitation. The 

model depicts large element pools in soil and large fluxes through biotic processes. Any change 

in these pools and fluxes will greatly influence the element budgets. If these simulated fluxes are 

not accurate, then model predictions will misrepresent element dynamics.” Additionally, almost 

all of the PnET-BGC applications to date have been completed within the eastern portions of the 

country mostly focusing in the Adirondacks and Hubbard Brook Experimental Forest (GbondoTugbawa et al., 2001; Chen et al., 2004; Zhai et al., 2008). Expanding this model to western or 

southern areas would necessitate large amounts of data gathering and processing as well as testing 

of the representation of the biotic processes found in these differing ecosystems. 
Appendix B 

March 2010 22 Draft – Do Not Quote or Cite 

PROFILE: “PROFILE [Warfvinge and Sverdrup, 1992] is a steady-state soil chemical model 

with a weathering rate sub-model that calculates weathering rates (for each base cation) explicitly 

using independent soil properties. Mineral dissolution reactions governing the rate of weathering 

involve many components in the liquid phase including H2O, H+

, OH−, CO2 and organic acids. 

These serve as the principle method for cataloguing the contribution of chemical reactions 

between soil solution and silicate minerals to base cation release. Inhibition of the reactions 

through increased concentrations of the products is accounted for by rate reduction factors. 

Precipitation of secondary minerals is subtracted from the total base cation release rate. Climate 

data, soil properties and detailed soil mineralogy are used as inputs to the model [Warfvinge and 

Sverdrup, 1992]” (Whitfield et al., 2006). 

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The PROFILE model is more fully explained in Section 4 for the terrestrial BCw approaches. 

While the PROFILE model provides a highly deterministic, process-based representation of 

mineral weathering, trying to utilize this model to determine the base cations weathering and 

reaching surface water bodies requires the representation of all soil horizons that may contribute 

to weathering and the summarization of BCw calculations by catchments surrounding each water 

body of interest. These two qualifications on top of the basic PROFILE application introduce a 

large amount of complexity into the modeling analysis. 
Table 3-1. Review of Modeling Approaches (and models) to Estimate Base Cation Weathering for Aquatic Critical Acid Loa

Model 

Approach 

Description of 

Method 

Data 

Requirements Model Complexity 

Suitability for 

Estimating BCw for 

Aquatic Critical Acid 

Load Determinations in 

The United States 

Suitability tor Mapping 

BCw Over Large 

Regions in The United 

States 

Budget Studies mass balance of 

inputs and outputs of base cations within catchment or watershed 

medium Medium low; BCw estimate is 

often an integrated value for whole catchment or watershed 

low - medium (based on data availability); may require Sr isotope ratio of stream chemistry to separate exchangeable versus weathered base cation sources 

Historical Rate Determinations 

loss of base cations in soil profile relative to stable element (Zr, Ti, quartz or rutile) 

low Low medium; restricted to 

sites with young soils of known age (eg., soils that have formed since the most recent glacial event, ~20,000 years ago) 

low; restricted to sites with young soils and sites where historical rate determinations have been conducted 

Empirical Data Relations 

modeled relationships between surface water characteristics and site conditions or atmospheric deposition measures 

low - high low – high low-medium low-medium 

F-Factor a factor that 

combines the effects of deposition and weathering 

low - medium 

Low low; most accurately 

applied to sites similar to those where the model was derived; if new derivations can be completed for the U.S. the suitability of this method would increase 

low; most accurately applied to sites similar to those where the model was derived; if new derivations can be completed for the U.S. the suitability of this method would increase 

March 2010 23 Draft 
March 2010 24 Draft 

Model 

Approach 

Description of 

Method 

Data 

Requirements Model Complexity 

Suitability for 

Estimating BCw for 

Aquatic Critical Acid 

Load Determinations in 

The United States 

Suitability tor Mapping 

BCw Over Large 

Regions in The United 

States 

Indicator 

element in 

conjunction with 

weathering 

ratios 

determine weathering rate through mass balance methods for element such as sodium (Na) then apply defined ratios to determine weathering rates of additional elements 

low-medium low low; most accurately 

applied to sites similar to those where the model was derived 

low; most accurately applied to sites similar to those where the model was derived 

Weathering 

rates vs. Stream 

chemistry or 

landscape 

variables 

utilize weathering rates determined by other methods and extrapolate to additional areas based on site characteristics 

low - medium 

low medium; suitability will 

depend on the ease at which derived weathering rates can be obtained and how strong the regressions between BCw and site 

characteristics are 

medium; relatively good success has been had at extrapolating BCw to 

additional sites based on stream chemistry; suitability will depend on data availability 

Process-Based Models 

Steady-state and dynamic models that rely on mathematical relationships representing soil and surface water processes 

medium - high 

medium - high 
March 2010 25 Draft 

Model 

Approach 

Description of 

Method 

Data 

Requirements Model Complexity 

Suitability for 

Estimating BCw for 

Aquatic Critical Acid 

Load Determinations in 

The United States 

Suitability tor Mapping 

BCw Over Large 

Regions in The United 

States 

DayCent-Chem Mineral weathering 

rates are set and then calibrated within the process model; Rates are specified by mineral and not necessarily base cations alone 

high high medium - high; provides 

daily time step results which can be used to estimate time to recovery or time to damage; uncertainty on how well model can simulate BCw

in some areas will impact confidence of results in these areas 

medium; DayCent-Chem has had trouble in estimating mineral weathering rates in some areas of the country 

MAGIC BCw determined 

through calibration to fulfill the requirements of a catchment mass balance by optimizing simulated soil and surface water chemistry to monitored values 

medium - high 

medium - high medium - high; 

numerous applications in the east with some, but fewer in number, applications in the west; little coverage in the Midwest but these areas are less of a concern for aquatic acidification effects 

medium - high; will be restricted in areas where soils data are lacking (some western areas); otherwise, highly applicable in any areas where MAGIC applications have been completed 

PnET-BGC BCw determined 

through calibration and held constant throughout dynamic modeling simulations 

high medium - high low-medium; model 

applications mostly completed only within northeastern U.S. vegetation and other biotic processes represented by the model would need validation to other regions of the country 

low-medium; because BCw is found through 

calibration alone for this model, the other model processes and input data must be validated for any application before calibration can be used for BCw
March 2010 26 Draft 

Model 

Approach 

Description of 

Method 

Data 

Requirements Model Complexity 

Suitability for 

Estimating BCw for 

Aquatic Critical Acid 

Load Determinations in 

The United States 

Suitability tor Mapping 

BCw Over Large 

Regions in The United 

States 

PROFILE BCw determined as 

a function of weathering of individual soil minerals and fieldbased soil and biotic conditions 

high high medium - high; may have 

restrictions in desert regions and areas that are lacking necessary data; also must be able to characterize catchment summary values and not solely individual profiles 

medium - high; may have restrictions in desert regions and areas that are lacking necessary data 
Appendix B 

March 2010 27 Draft – Do Not Quote or Cite 

3.3 Proposed Methodology for Estimating and Mapping Base Cation Weathering for 

Aquatic Critical Acid Load Calculations 

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In determining the proposed methodology for a national assessment, the identified 

strengths and weaknesses of each approach in the previous section had to be weighed against one 

another. Because every method required a large environmental data component, the largest 

deciding factor in the proposed approach became the number and spatial representation of 

previous applications of an approach within the United States. This decision factor immediately 

ruled out applying any of the empirical relationships (e.g. F-factor, relation of BCw to stream 

chemistry) derived primarily with data from other countries, although it did not rule out deriving 

new relationships using the same methods. Ultimately, the F-factor approach was not chosen 

because it did not directly provide a BCw rate. Additionally, application of an empirical relation 

alone provided little information on the long-term versus current state of the ecosystem. 

Therefore, a combination of a process-based model determination of BCw rates with regional 

expansion of these rates through empirical relations is proposed at the methodology for a 

national assessment. 

Utilizing a process-based model, which can calibrate BCw rates to stream or lake 

chemistry across any number of years, provides a credible long-term estimate of the BCw rate 

that can be input into the SSWC in order to obtain the system critical load. The process-based 

model most widely applied throughout the U.S. to date is the MAGIC model. The intermediate 

complexity of this model provides a balance between data inputs required to run the model and 

the processes involving base cations, nitrogen, and sulfur within a watershed, which is 

considered a requirement of providing a national assessment. Finally, because of its wide 

application, MAGIC has been extensively tested against independent databases providing the 

opportunity for iterative model testing and refinement (Sullivan, 2000). 

The following steps outline the main processes of the method: 

Step 1. Definition of MAGIC study sites and the regions to which each grouping of 

study sites may be extrapolated. 

Step 2. Data gathering and processing for population of the MAGIC model for 

each study site with additional regional data gathering of available 

stream/lake chemistry and landscape parameters. 

Step 3. MAGIC modeling application on selected stream/lake study sites where 

BCw is arrived at through calibration against water chemistry data. 
Appendix B 

March 2010 28 Draft – Do Not Quote or Cite 

Step 4. Extrapolation of BCw for modeled streams/lakes to other waterbodies 

within the region through correlation analysis using stream chemistry data, 

where available, and landscape parameters in its absence. 

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4 Figure 3-1 provides a flow chart of these steps and their components. 

I n p u t D a t a C l a s s e s

Identification of input data for the MAGIC model and of stream 

chemistry and landscape parameters 

Stream 

Chemistry

Soil 

Composition

Atmospheric 

Deposition 

(including annual 

precipitation)

Construct Model Input 

Tables

MAGIC Application at Study Sites with 

Calibration to Water Chemistry for BCw

Correlation Analysis between Site BCw Values 

and Stream Chemistry or Landscape Parameters

Mapping of BCw Values Across 

Regions of the Nation

2

3

Identification MAGIC study sites and applicable regions for 

extrapolation

1

Catchment/

Landscape 

Properties

4

Application of Regional Regression Relationships 

Developed by Site Grouping to Un-Modeled Sites

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Figure 3-1. Process steps for estimating BCw using the MAGIC model with regional 

extrapolation 
Appendix B 

March 2010 29 Draft – Do Not Quote or Cite 

Step 1. Definition of MAGIC study sites and the regions to which each grouping of 

study sites may be extrapolated. 

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MAGIC has been used to assess acidification impacts in a large number of areas across 

North America (Table 3-2). These previous applications should be utilized where possible to 

provide a starting point for the national analysis. Sites within the eastern United States likely 

provide a wide range of coverage from which initial extrapolations can begin. Within the midwest and western areas of the country, additional sites will need to be investigated. Authors of 

these studies should be contacted to obtain data sources and model results. Previous model 

applications should be compared for the years and objectives of the analysis and input data to 

determine if the results already created could be utilized in an extrapolation analysis without 

rerunning the model. 

Table 3-2. Locations of Previous MAGIC Applications within the U.S. and Canada1

Location(s) Reference 

25 lakes in south-central Ontario, Canada Aherne, J, P.J. Dillon, and B.J. Cosby. 2003. 

2 catchments located in Nova Scotia, Canada Dennis, I.F., T.A. Clair, and B.J. Cosby. 2005 

Maryland Ellis, H., and M. Bowman. 1994. 

36 lake catchments in the Adirondack 

Mountains of New York 

Church, M.R. and J. Van Sickle. 1999. 

40 to 50 sites within each of three 

physiographic provinces in the eight-state 

southern Appalachian Mountains region 

Sullivan, T.J., B.J. Cosby, A.T. Herlihy, J.R. Webb, 

A.J. Bulger, K.U. Snyder, P.F. Brewer, E.H. Gilbert, 

and D.L. Moore. 2004. 

33 representative watersheds in the 

Adirondacks 

Sinha, R., M.J. Small, P.F. Ryan, T.J. Sullivan, and 

B.J. Cosby. 1998. 

Shenandoah National Park Bulger, A. J; Dolloff, C. A.; Cosby, B. J.; Eshleman, 

K. N.; Webb, J. R., and Galloway, J. N. 1995 

60 Southern Appalachian streams Bulger AJ, Cosby BJ, Webb JR. 2000. 

Joyce Kilmer And Shining Rock Wilderness 

Areas (North Carolina/Tennessee) 

Sullivan, T.J. and B.J. Cosby. 2002 

Monongahela National Forest, West Virginia Sullivan, T.J. and B.J. Cosby. 2004 

Shasta Lake, Idaho Eilers J.M., B.J. Cosby, J.A. Bernet, T.A. Sullivan, 

1998. 

Libby Lake, Montana Bernett, J.A., Eilers J.M., B.J. Cosby. 1997. 

Popo Agie Wilderness, WY, and Weminuche 

Wilderness, CO 

Sullivan, T.J., Cosby, B.J., Bernert, J.A., and Eilers, 

J.M. 1998. 

Rocky Mountain, Grand Teton, Sequoia, and 

Mount Rainier National Parks 

Cosby and Sullivan. 2001 

The Loch, a subalpine lake in Rocky Mountain 

National Park in Colorado 

Sullivan, T.J., B.J. Cosby, K.A. Tonnessen, and D.W. 

Clow. 2005. 

2 locations in the Sierra Nevadas Sullivan and Eilers, 1996 

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References from this table are presented in Appendix 2. 
Appendix B 

March 2010 30 Draft – Do Not Quote or Cite 

When selecting sites for MAGIC analyses that will later be used in an extrapolation 

analysis, Sullivan and colleagues (2004) outlined two key considerations: 

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1. Do not select too many watersheds for modeling that occurred in the same general 

area in order to avoid skewing the results too heavily to one portion of the region for 

the following extrapolation step 

2. Screen sites to remove those in which the water chemistry data were not internally 

consistent or for which available data suggested the possibility of significant 

influence from road salt, geological sulfur, land use, or insect defoliation. 

Step 2. Data gathering and processing for population of the MAGIC model for 

each study site with additional regional data gathering of available 

stream/lake chemistry and landscape parameters. 

The data requirements of the MAGIC model are summarized in Table 3-3. The table 

includes both data inputs derived from monitoring data and constant parameters that the user 

must set based on available data and methods suggested by previous MAGIC applications. 

Additional information on data inputs can be found in: Cosby and colleagues, 1985a; Cosby et 

al., 1985b; Sullivan and Cosby, 2004; Sullivan et al., 2007c. 

Due to the wide range of water quality monitoring assessments conducted within the 

United States, a large amount of water quality data is typically available to work from. Similarly, 

in recent times advances and expansions of atmospheric modeling have been conducted 

providing a large amount of deposition estimates from which to pull model input data. The area 

of data most lacking, especially in the western United States, is the composition of soils. Sources 

of soil data are discussed in Section 4.3.3. Given that there may be areas in which soils data are 

not available, work by Sullivan and others used a tiered assessment of MAGIC applications to 

overcome this obstacle. The tiers consisted of: (1) chemistry data were available from within the 

watershed to be modeled with multiple soil sampling sites in an individual watershed aggregated 

on an area-weighted basis; (2) soils data within the catchment were missing but were available 

from a nearby watershed underlain by similar geology; and (3) soils data were neither available 

from within the watershed nor from nearby watersheds on similar geology. In order to populate 

soil characteristics for tier 2 and 3 watersheds, a surrogate approach was used meaning that these 

watersheds were paired with a watershed for which all input data were available. In order to be 

paired, watersheds had to have similar streamwater characteristics (ANC, sulfate, and base cation 
Appendix B 

March 2010 31 Draft – Do Not Quote or Cite 

concentrations), physical characterization (location, elevation), and bedrock geology data 

(Sullivan et al., 2004). 

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Table 3-3. Input Data Requirements of MAGIC Model 

Data Class Data Element Measure 

Catchment Area 

Relative area of lake/stream fraction 

Stream Chemistry 

pH unitless 

ANC eq/L 

Ca2+ eq/L 

Mg2+ eq/L 

K+ eq/L 

Na+ eq/L 

SO4

2- eq/L 

NO3

- eq/L 

Cl- eq/L 

Aluminum solubility constant log10

Slope of pH-pAl relationship unitless 

Aqueous Phase - Equilibrium 

Constants 

Organic acid log10

Organic aluminum 

Inorganic aluminum speciation 

Inorganic carbon speciation and 

dissociation of water 

Solid Phase – Weathering 

and Exchange Constants 

Cation exchange selectivity 

coefficients 

log10

Weathering rates (Ca2+, Mg2+, K+, 

NH4, SO4

2-, Cl-

, NO3

-

, F) 

eq/m2

/yr 

Soil Composition 

Thickness Depth (m) 

Total cation exchange capacity eq/kg 

Exchangeable bases (Ca2+, Mg2+, 

K+

, and Na+) 

mg/kg 

Bulk Density kg/m3

Porosity fraction 

pH unitless 

Sulfate adsorption half saturation eq/m3

Aluminum solubility constant log10

Slope of pH-pAl relationship unitless 

Atmospheric Deposition 

Annual precipitation Volume (m/yr) 

Ca2+ Total annual 

deposition 

(eq/ha/yr) 

Mg2+

K+

Na+

SO4

2-

NH4

NO3

-

Cl3
Appendix B 

March 2010 32 Draft – Do Not Quote or Cite 

Step 3. MAGIC modeling application on selected stream/lake study sites where 

BCw is arrived at through calibration against water chemistry data. 

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As was completed with the REA (U.S. EPA, 2009), batch processing of MAGIC models 

at a range of sites can be completed. Calibration of those sites with available data (streamwater 

chemistry, soil chemical and physical characteristics, and atmospheric deposition) is completed 

by setting values of the “fixed parameters” within the system and comparing the output of the 

model run to the observed values of such characteristics as stream ANC. There are eight 

parameters optimized through this method including the BCw rate. The eight observations used to 

drive the calibration procedure include the current soil exchangeable pool size and current output 

flux of each of the four base cations. The model is iteratively run adjusting the “fixed 

parameters” from a specified range of values (representing uncertainty in knowledge of these 

parameters), so that the outputs match the observed parameters within an acceptable margin of 

error. The set of “fixed parameters” that are obtained that allow the model to meet this 

acceptable of margin of error become the range of calibrated parameters from which the median 

is chosen to represent the parameter value for the watershed. “The use of median values assures 

that the simulated responses approximate the most likely behavior of each watershed, given the 

assumptions inherent in the model and the data used to constrain and calibrate the model” 

(Sullivan et al., 2004). This “fuzzy optimization” procedure has been developed for use with 

MAGIC modeling to help quantify the uncertainties within the modeled parameters (Sullivan et 

al., 2004). Using these calibration procedures of each site MAGIC run will provide not only an 

estimate of BCw but an expected range of values in which BCw falls, thereby providing bounds 

and certainty limits for the following extrapolation step. 

Step 4. Extrapolation of BCw for modeled streams/lakes to other waterbodies 

within the region through correlation analysis using stream chemistry data, 

where available, and landscape parameters in its absence. 

Regionalization of MAGIC modeling results can be completed through either “binning” 

sites based on characteristics like physiographic region and ANC concentration (Sullivan et al., 

2004) or creating regional regressions to relate site characteristics (chemistry or landscape) to a 

parameter of interest (e.g., ANC; Sullivan et al., 2007a). In order to provide some measure of 

“goodness of fit” to the extrapolations, we have chosen to proceed with creating regression 

relationships between the BCw determined through calibration of the MAGIC model and either 
Appendix B 

March 2010 33 Draft – Do Not Quote or Cite 

water chemistry or landscape parameters. In previous studies, the landscape variables considered 

for regression relationships with ANC have included elevation, watershed area, ecoregion, 

lithology, forest type and geological sensitivity class (Sullivan et al., 2007b). We expect to 

follow similar methods to create the relations with BCw (i.e., the response variable). Within each 

region of extrapolation landscape variables appropriate to the region will be selected. For 

example, the types of forest selected for inclusion may vary between an extrapolation in the 

Southern Appalachians as opposed to the Rocky Mountains. In all instances, there must be 

adequate representation of the variable within all modeled and non-modeled watersheds or it will 

be eliminated from the pool of candidate variables available for regression analysis. 

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Sullivan and others (2007b) relied on the corrected Aikake’s Information Criteria (AIC) 

to evaluate all possible correlation relations. The corrected version of the evaluation criteria was 

used because of the relatively small sample sizes available from which to build the regressions. 

Additional evaluation criteria can easily be applied for choosing the best-fitting and most 

meaningful regressions for extrapolation from a set of individual modeled sites to a larger set of 

regional sites. Potential criteria for evaluating individual variables within correlation models 

include partial F tests, t-values, and variance inflation factors. To evaluate the model as a whole 

statistics such as PRESS, coefficient of determination, adjusted coefficient of determination, 

Mallow’s Cp, and root mean square error of the model can all be utilized (Helsel and Hirsch, 

1992). If a commercial statistical package, such as SAS, is chosen to complete this portion of the 

analysis the predefined routines and groupings of evaluation statistics can be employed with 

relative ease. 

3.3.1 Potential limitations of proposed methodology 

The limitations with the proposed methodology can be divided into five distinct 

categories: 

1. MAGIC is an intermediate level model that does not take into account biotic 

processes which may affect the calculation of BCw rates within a watershed. 

2. While MAGIC is the most widely applied acidification model within the U.S. it still 

faces the challenge of having limited applications in the Midwest and western states. 

3. As an extension of the bias in eastern applications, processing and organization of the 

data required for input into the MAGIC model in the East far exceeds that of the 

West. Additionally, there is an indication that soils data are more incomplete or hard 
Appendix B 

March 2010 34 Draft – Do Not Quote or Cite 

to obtain in the West. Note that the terrestrial acidification national assessment faces 

even greater demands in terms of soil composition data needs. As such, there can be a 

combined effort in obtaining new data that will benefit both assessments. 

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4. The population and calibration of specific site applications of MAGIC across the 

country constitutes a major modeling effort. However, it may be possible to leverage 

previous applications. 

5. The proposed approach calls for the creation of several different regional 

extrapolations of BCw rates based on sets of individual MAGIC applications. The 

success of these extrapolations remains to be seen and will depend upon the 

limitations mentioned above in even applying the MAGIC model at a multitude of 

locations and upon the availability of a statistically significant number of model 

outcomes on which to base the regressions for each regional analysis. 

While these limitations may seem extensive, there are many possibilities for overcoming 

the limitations. For example, criteria on model application years can be relaxed to include more 

of the previously completed MAGIC applications in lieu of updating and rerunning models at the 

same sites. And, joint data collection between the aquatic and terrestrial acidification 

assessments can allow the most efficient use of resources and demands on other agencies. 

3.3.2 Uncertainty analyses 

As typical with any process based model, the major uncertainties in MAGIC include 

input data variability, model calibration uncertainty, and the ability of the mathematical model 

processes to represent reality. Within this national analysis, there will also be uncertainty 

associated with regional extrapolation of modeling results from individual watersheds to the 

region. However, Sullivan and colleagues (2004) state that these “errors and uncertainties are not 

additive, but rather would be expected to some extent to cancel each other out.” 

Several research projects have undertaken attempts to quantify the relative magnitude of 

the effects of sources of uncertainty for regional, long-term MAGIC simulations using Monte 

Carlo methods (Cosby et al., 1989b, 1989c, 1990; Hornberger et al., 1989, 1990). While the 

results of these studies indicated that the different sources of uncertainty can have varying levels 

of impacts on the outputs of the MAGIC model, the development of the “fuzzy optimization” 

technique for calibration was designed to reduce these impacts of uncertainty. With “fuzzy 

optimization” there is an explicit accounting within different uncertainty categories and a 
Appendix B 

March 2010 35 Draft – Do Not Quote or Cite 

resulting time-variable measure of overall simulation uncertainty for each state variable. One 

way the optimization procedure reduces uncertainty is in its selection of parameter and variable 

values for the “fixed parameters” from distributions of possible values rather than having a user 

select a single value during a single calibration (Sullivan et al., 2004). 

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Outside of the operation and calibration of the MAGIC model, the parameterization of 

the input data provides another source of uncertainty. As identified in previous sections, the soils 

composition data are expected to be the greatest source of uncertainty. If a tiered approach to 

populating soils data for watersheds lacking in data is used, uncertainty with the method can be 

examined by calibrating selected tier 1 watersheds twice, once using the appropriate site-specific 

soils data, and a second time using borrowed soils data from an alternate site, using either tier 2 

or tier 3 protocols. A comparison between the results from each of the scenarios can then be 

made to determine the magnitude of difference in output parameters. If this analysis can be done 

at multiple sites, than a sensitivity analysis can be performed over the results to determine if 

there is a consistent bias in results from modeling analyses utilizing tier 2 or 3 procedures 

(Sullivan et al., 2004). 

4. TERRESTRIAL BASE CATION WEATHERING METHODOLOGY 

4.1 Introduction 

Geology is one of the most important factors in determining the potential sensitivity of an 

area to terrestrial acidification (U.S. EPA, 2008, Section 3.2.4). In particular, the characteristics 

of the soils and the upper portion of the bedrock can impact the acid-neutralizing ability of the 

soils in a particular area. Acid-sensitive soils are those which contain low levels of exchangeable 

base cations and low base saturation (U.S. EPA, 2008, Section 3.2.4). Bedrock composition and 

soil pH are two characteristics that are directly related to the ability of a system to neutralize 

acid. Soils overlying bedrock, such as calcium carbonate (e.g., limestone), which is reactive with 

acid, are more likely to successfully neutralize acidifying deposition than soils overlying 

nonreactive bedrock. In addition, soils with higher pH (i.e., more alkaline) have a greater 

capacity to neutralize acidifying deposition. 

This section reviews the effect of acidification known as base cation weathering, 

describes its significance in estimating critical acid loads, and identifies methodologies for 

estimating base cation weathering. Further, this report recommends a methodology for potential 
Appendix B 

March 2010 36 Draft – Do Not Quote or Cite 

use in the review of the NOx and SOx secondary National Ambient Air Quality Standards and 

describes the steps and information resources needed to apply that methodology across the 

United States. 

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4.2 Terrestrial Base Cation Weathering 

In the calculation of terrestrial critical acid loads using the simple mass balance (SMB) 

methodology, base cation weathering (BCw)

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 is defined as “the release of base cations from 

minerals in the soil matrix due to chemical dissolution” (UNECE, 2004), and this weathering 

occurs in the rooting zone of the soil profile and consists of the release of calcium (Ca2+), 

magnesium (Mg2+), potassium (K+

) and sodium (Na+). It does not include the removal of base 

cations from soil ion exchange complexes (cation exchange sites) or the degradation of soil 

organic matter. Base cations from these sources have already been released through the 

weathering process. Base cation weathering is often a dominant source of base cations in soils, 

replacing Ca2+, Mg2+, K+

 and Na+ that are lost through leaching and uptake by plant (Langan et 

al., 1995; Langan et al., 1996; Ouimet, 2008). Therefore, BCw plays an important role in 

determining the sensitivity of a site to acidifying nitrogen and sulfur deposition (Hodson and 

Langan 1999a). The BCw term is also one of the most influential parameters in the SMB 

calculations of terrestrial critical acid loads. Li and McNulty (2007) determined that 49% of the 

variability in critical load estimates was due to this term. Sverdrup and colleagues (1995) 

(reference in Langan et al., 1996) determined that BCw can account for 90% of the variation in 

critical loads. 

For the Terrestrial Acidification case study in the Risk and Exposure Assessment (U.S. 

EPA, 2009), BCw rates were calculated using the clay-substrate method (McNulty et al., 2007). 

This method was selected for the Risk and Exposure Assessment because it is one of the most 

commonly used methods to estimate BCw for critical load analyses in North America (Ouimet et 

al., 2006; Watmough et al., 2006; McNulty et al., 2007; Pardo and Duarte, 2007), and has been 

used to map critical loads across the United States (McNulty et al., 2007). However, the 

applicability of the clay-substrate method is most likely limited because it is an empirical model 

that appears to be based on a modification of the soil type – texture approximation method that 

was developed on a restricted number sites in northern Europe that were glaciated during the last 

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 Within the SMB equation, Bcw refers to the weathering of Ca2+, Mg2+ and K+. 
Appendix B 

March 2010 37 Draft – Do Not Quote or Cite 

glacial advance (CLAD, 2009; H. Sverdrup personal communication, 2009a, UNECE, 2004). It 

relies on a classification of the acidity of soil parent material and soil clay content and consists of 

three equations (equations 4-1 – 4-34). 

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 Acid Substrate: ( ) ( ( ) ) 2

e 4 ×−×= clay%32.0clay%7.56BC (4-1) 

 Intermediate Substrate: ( ) ( ( ) ) 2 5 e ×−×+= clay%18.0clay%6.53500BC (4-2) 

 Basic Substrate: 500BC ( %clay2.59 ) 6 e = + × (4-3) 

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where 

 BCe = empirical soil base cation (Ca2+ + K+ + Mg2+ + Na+

) weathering rate 

(eq/ha/yr) 

 % clay = the percentage of clay (determined by particle size) within the rooting zone 

of soil profile. 

Critical load experts from both the United States and Canada have commented that the claysubstrate model, in general, appears to perform well in young soils that have formed since the 

last glaciations (approximately 20,000 years before present). However, the model may not be 

suitable or provide accurate estimates on older, more weathered soils that were not impacted by 

the last glaciation (P. Arp personal communication, 2009). These soils have undergone 

weathering for a longer period of time and the relationships between clay particle size and base 

cation release may not be as strong as in younger soils (H. Sverdrup personal communication, 

2009a). To our knowledge, however, there have been no published studies that have tested this 

hypothesis and compared BCw estimates generated with the clay-substrate model and other 

methods on sites underlain by old, more weathered and recently glaciated soils. At least one 

study has compared the clay-substrate BCw method with estimates from other models on 

glaciated soils in Canada and found that the rate estimates were similar within the area of 

assessment (Whitfield et al., 2006). 

Results from the Risk and Exposure Assessment (U.S. EPA, 2009) appear to support the 

distinction between the suitability of applying the clay-substrate model to glaciated versus older, 

non-glaciated soil environments. As outlined in Appendix 5 of the Risk and Exposure 
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March 2010 38 Draft – Do Not Quote or Cite 

Assessment, the regression analysis assessing the relationship between the growth of sugar 

maple (Acer saccharum) and critical acid load exceedance was not significant (p=0.38) when all 

plots were included in the analysis. However, when the analyses were restricted to sites located 

on younger, glaciated soils, which resulted in the removal of 25% of the data from the analyses, 

the linear regression relationship was significant at the p=0.10 level. Improvements in the 

significance of the relationship may, in part, have been due to the greater accuracy of the BCw

estimates in the critical load calculations for the plots north of the glaciations line. 

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The majority of the conterminous United States was not directly impacted by the most 

recent glacial advance (Figure 4-1) and some of the soils in these areas have not been influenced 

by glaciations in at least 700,000 years (Sverdrup et al., 1992). Only ten states had their full land 

area impacted by glaciers during the glacial advance 20,000 years ago. Therefore, if the concerns 

and supportive results regarding the suitability of the clay-substrate model for the estimation of 

BCw on older, non-recently-glaciated soils are correct, the model may not be an appropriate 

method to estimate BCw for a large portion of the United States. Given that the BCw parameter is 

one of the most influential variables within the SMB calculations to estimate critical acid loads, 

it is particularly important to use a method that provides accurate and defendable estimates of 

BCw. Therefore, any and all future work focused on estimating and mapping terrestrial critical 

acid loads in the United States, should acknowledge the potential limitations of the clay-substrate 

model and consider the adoption of a BCw modeling approach that is transferable and can be 

applied to multiple locations and different soil conditions and soil ages. 
Appendix B 

March 2010 39 Draft – Do Not Quote or Cite 

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Figure 4-1. Areas of continental U.S. that were covered during the last glacial event 

(Reed and Bush, 2005). 

4.3 Methodologies for Determining Base Cation Weathering Values in the United States 

4.3.1 Difficulties in estimating base cation weathering 

Base cation weathering is one of the most difficult parameters to estimate (Sverdrup et 

al., 1990; Ouimet and Duchesne, 2005; Langan et al., 1996), as it is a function of a time, soil 

mineralogy, and a variety of other environmental biotic and abiotic factors. Weathering occurs 

over centuries and millenia and results in the chemical and physical alterations of parent material 

and minerals. Minerals that are present in the soil may no longer resemble the original bedrock 

parent material (C. Smith personal communication, 2009). In addition, the soil may be derived 

from parent material that was transported to its current location and does not resemble the 

underlying bedrock. Abiotic factors including temperature and moisture and location on the 
Appendix B 

March 2010 40 Draft – Do Not Quote or Cite 

landscape and biotic factors including vegetation and soil microbes can also impact base cation 

weathering through removal of base cations and chemical weathering of minerals (Brady and 

Weil, 2002). Combined, these factors pose many challenges to determining BCw in the soil 

profile for terrestrial critical acid load estimations. As a result, a variety of BCw methods and 

approaches have been developed (Sverdrup et al., 1990). 

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4.3.2 Approaches to estimating BCw: 

Methods and models that have been developed to estimate BCw for critical acid load 

determinations differ significantly in the approaches used to generate weathering estimates 

(Langan et al., 1995; Sverdrup et al., 1990; UNECE, 2004). For the purposes of this work 

assignment, BCw methods and models are grouped into three main approaches: 

1. budget studies of catchments or watersheds; 

2. historical weathering rate determinations; and 

3. empirical and mathematical models. 

Each of these approaches vary in complexity, data intensity and scalability, thereby offering 

different strengths and weaknesses to estimating BCw. In addition, these approaches differ in 

their abilities to map BCw over regional and larger land areas. Table 4-1 provides a summary of 

the approaches to BCw, critical load models that use the approaches, the strengths and 

weaknesses of the different models, and the suitability of the approaches and models to map BCw

and therefore critical loads over large areas. For a model to be suitable for large-scale mapping, it 

must be quick to apply, supported by existing databases, not require extensive and costly 

analyses, and be transferable to sites with varying conditions and geological histories (Sverdrup 

et al., 1990). 

Budget Studies - The budget study approach, also referred to as input-output balances 

(Kolka et al., 1996; Langan et al., 1996; Starr et al., 1998), estimates BCw as a component of the 

mass balance input and output of cations within a catchment or watershed (Langan et al., 1996; 

Sverdrup et al., 1990; Sverdrup et al., 1998). In most catchments, the main source of input is 

atmospheric deposition and output is streamflow, and base cation retention is accounted for 

through uptake by biomass and immobilization in the soil. Base cation weathering is therefore 

determined through mass balance differences between these different input, output and storage 

pools. The main strengths of this method are that it only requires a moderate amount of input 

data and relies on data collected from the catchment or watershed. In addition, it offers the 

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Appendix B 

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potential for mapping multiple catchments, if the necessary input data is available. However, 

several of the drawbacks to this method include an assumption that the catchment is in a steadystate condition and the cation exchange capacity does not change over time (Langan et al., 1996; 

Miller, 2001; Sverdrup et al., 1998). In addition, it is often difficult to determine BCw within the 

rooting zone of individual soil profiles because the BCw estimates from budget studies represent 

integrated values for the whole watershed, the full soil profile, bedrock weathering and all soil 

processes (Sverdrup and Warfvinge 1988; Miller, 2001). It is also difficult to separate 

contributions of base cations from exchange sites versus mineral weathering and chemical 

dissolution (Sverdrup et al., 1990; Miller, 2001). Therefore, it is challenging, and potentially 

erroneous, to use budget studies of catchments to estimate BCw for terrestrial critical acid loads. 

It may be possible to modify the budget study approach and evaluate base cation input and 

output in the rooting zone of individual soil profiles (Kolka et al., 1996), and to separate base 

cations from exchanges soil pool versus weathering sources using techniques such as the analysis 

of strontium (Sr) isotope ratios (Miller et al., 1993). However, the soil profile approach would 

require lysimeter measurements of soil solution chemistry at each site and the soil solution would 

also need to be analyzed for Sr isotope ratios (87Sr/86Sr). Both of these analyses would be very 

time intensive and would not be practical over large areas. 

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Historical Rate Determinations – The historical weathering rate approach, also 

sometimes referred to as element depletion (Langan et al., 1996; Miller, 2001) or pedological 

mass balance (PMB) (Ouimet and Duchesne, 2005; Ouimet, 2008), estimates BCw by 

determining the relative depletion of base cations to the depletion of a stable element as a 

function of the age of the soil profile (Langan et al., 1996). Zirconium (Zr), titanium (Ti), rutile 

and sometimes quartz are typically selected as the stable soil elements for this method (Langan et 

al., 1996; Sverdrup et al., 1998) because they are very resistant to weathering (Starr et al., 1998). 

This technique is commonly applied to soils that were formed since the last glaciation, and 

characterizes the ratio of base cations to the stable element in the upper weathered soil horizons 

and the unweathered C horizon. It assumes that post glaciation, the mineral matrix of the soil 

consisted of freshly ground material that was not previously exposed to weathering (Sverdrup et 

al., 1998), the lowermost soil is representative of the parent material and the stable element is 

found in a constant proportion throughout the soil profile (Langan et al., 1996; Starr et al., 1998). 

Over time, base cations are weathered and lost from the profile through uptake and leaching, but 

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Appendix B 

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the concentration of the stable element remains constant due to resistance to weathering (Starr et 

al., 1998). Main strengths of this approach are that it is a good technique to estimate weathering 

in young soils, does not require a large amount of data and BCw is relatively easy to calculate. 

However, this approach also presents some major weaknesses. It estimates the historic BCw rate 

which may be differ from the current weathering rate. Historic weathering rates may 

underestimate current BCw because the historical weathering occurred under more neutral 

conditions with less acidifying deposition (Sverdrup et al., 1990). Conversely, historic 

weathering rates may be higher than current BCw if the original post glaciations soil contained a 

significant proportion of easily weathered material that have since been depleted (Miller, 2001). 

Studies have indicated that the initial phase of weathering lasts a few hundred to several 

thousand years and can deplete a maximum of 25% of the mass during this period (Sverdrup et 

al., 1998). In addition, the historic rate approach is not suitable for older, more weathered soils. 

In such soils, it is often difficult to determine the amount of time since the last glacial or mass 

disturbance event that caused the formation of newly ground material (H. Sverdrup personal 

communication, 2009b). Therefore, this method cannot be applied to all locations and it is often 

difficult or impossible to extrapolate results to larger geographical areas. Since a large proportion 

of the soils in the United States were not influenced by the most recent glaciation, the historic 

rate approach to estimate BCw for terrestrial critical acid load estimates could only be applied to 

a fraction of the land area. 

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Empirical and Mathematical Models – Empirical and mathematical models estimate BCw

based on laboratory- and field-based relationships between soil, abiotic and biotic factors. Over 

the past several decades, a large number of BCw models have been developed for terrestrial 

critical acid load determinations. Initial models were developed from a limited number of sites 

and data. More recent models incorporate a larger number of factors and are more complex and 

data intensive. One of the first BCw models was the Skokloster Assignment which is a semiempirical method that was devised during the Critical Load Workshop in Skokloster, Sweden in 

1988 (UNECE, 2004). It divides minerals into 5-6 mineral classes based on the dominant 

weatherable soil minerals and assigns a range of critical acid loads to each. This method was 

later expanded to include a larger range of minerals and to estimate BCw based on the relative 

abundance of fast versus slow weathering minerals. The Skokloster Assignment was originally 

based on soils with density, moisture content, clay content and pH conditions similar to the soils 

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in the three Gårdsjön catchments in Sweden, and was validated against a preliminary version of 

the PROFILE model, described further below (Hodson and Langan 1999b; H. Sverdrup personal 

communication, 2009b). 

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A second model, the Soil Type – Texture Approximation assigns weathering rate classes 

to soils based on soil texture and parent material acidity classes. It was developed for European 

forest soils (UNECE, 2004). As described earlier, it is believed that the clay-substrate model that 

is used extensively throughout North America was derived from the Soil Type – Texture 

Approximation. 

A third model, the Total Base Cation Content Correlation was developed using Zr(SiO4) 

and historical rate approach applied to eleven sites in Sweden (UNECE, 2004). Correlation 

between historical BCw rates and the total content of base cations in the undisturbed bottom soil, 

corrected for temperature, were used to develop equations to estimate the weathering of Ca2+, K+

and Mg+2 (Olsson et al., 1993). For a more complete review and description of these three 

empirical models see Hodson and Langan (1999) and UNECE (2004). In general, the main 

benefits of these models are the minimal data requirements, transferability, and the potential to 

be applied to multiple sites. Therefore, such models offer good options for mapping of BCw for 

critical acid load determinations. However, these models also have several key weaknesses 

which limit their utility for estimating BCw in many locations, including a large proportion of the 

United States. All of the models were determined using data from a limited number of sites 

within Sweden and other regions of Europe and are based on average or generalized 

relationships. Therefore, similar to the clay-substrate model, these models may do a reasonable 

job of estimating BCw on sites that were recently glaciated and/or have similar conditions to the 

Swedish sites. However, they should not be applied everywhere, as they may poorly estimate 

BCw on sites with older, more weathered soils. As stated for Total Base Cation Content 

Correlation, the method is only applicable to granitic soils (Hodson and Langan, 1999) and 

should be used with caution because the relationships are based on Nordic geological history 

(UNECE, 2004). 

A fourth model that supported the creation of some of the aforementioned empirical BCw

models, and is currently in its 5th version is PROFILE (Warfvinge and Sverdrup 1992 and 1995; 

Sverdrup, 1990). PROFILE (version 5.0) is a mechanistic, mathematical, steady-state, kinetics 

model that calculates the weathering of Ca2+, Mg2+, and K+

 in each horizon of a soil profile 
Appendix B 

March 2010 44 Draft – Do Not Quote or Cite 

(Akselsson et al., 2005). It is unique and differs from other empirical models in that it calculates 

BCw rates for soil from independently measured geochemistry and soil conditions (Jönsson et al., 

1995; Ouimet and Duschesne 2005). It combines laboratory-based evaluations of mineralspecific chemical dissolution with field-based conditions and other soil measurements to 

estimate individual weathering rates of Ca2+, Mg2+, and K+

 (Langan et al., 1996). The model 

includes 14 of the most common primary and secondary soil minerals (Table 4-2)

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3

, and their 

release of base cations in five separate reactions (Sverdrup et al., 1990; Hodson et al., 1997; 

Sverdrup et al., 1998): 

i) Reaction with hydrogen ion (H+

) and dissolved aluminum (Al) 

ii) Reaction with water and dissolved Al 

iii) Reaction with hydroxyl ion (OH-

) and dissolved Al 

iv) Reaction with carbon dioxide (CO2) 

v) Reaction with strongly complexing polydentate organic acids 

The reaction rates are calculated using constants contained within the model and data input by 

the user, and the total base cation release rate by chemical weathering is calculated as the sum of 

all parallel simultaneous process rates minus the rate of precipitation of secondary solid phases 

(Sverdrup et al., 1998). The rate equation (Equation 4-4) for the weathering of all minerals 

within the rooting zone of the soil profile is defined as:

 RW = Σ(horizons)Σ(minerals)i ri · Aexp · xi ·θ · z (4-4) 

where 

ri = dissolution rate of mineral i (kmolc/m2/s) – sum of the 5 separate reactions 

 Aexp = exposed surface of mineral matrix (m2/m3) 

 θ = soil moisture saturation (m3

/m3) 

3 Thirteen additional minerals can be added to PROFILE, as necessary (H. Sverdrup personal 

communication).
Appendix B 

March 2010 45 Draft – Do Not Quote or Cite 

1 xi = fraction of mineral i in the mineral matrix of the soil horizon 

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27

 z = soil layer thickness (m) 

The weathering rate is either increased or reduced by different soil and biotic and abiotic 

conditions, many of which are entered as input data by the user. Input data includes site climatic 

and deposition attributes, soil physical and chemical characteristics and biological components 

that influence the soil chemistry and BCw (input data required by PROFILE discussed further in 

Section 4.3.3). As summarized by Jönsson and colleagues (1995), “The weathering rate is 

increased by a high H+

 concentration, a high soil moisture (water) content, and a high CO2

pressure. Weathering reactions are product inhibited, i.e., decrease by high concentrations of 

reaction products in the soil solution such as inorganic aluminum and base cations. The surface 

activity is calculated as dependent on the mineral surface area, temperature and soil moisture 

saturation. The soil temperature impact on the weathering rate is expressed as an Arrhenius 

equation, as dependent on the activation energy. The soil moisture saturation is important for the 

reaction rate as the reactions will only take place on wetted surfaces. The degree of surface 

wetting, and thus surface activity, is considered to be a function of the soil moisture saturation. 

This is calculated from soil bulk density, the solid particle density and the volumetric water 

content.” For a more detailed description of the theory and calculations behind PROFILE, see 

Sverdrup (1990), Sverdrup and Warfvinge (1992, 1993a, 1995). 

A main weakness of the PROFILE model is that it is data intensive and complex, and can 

be difficult to parameterize. However, PROFILE does offer some significant benefits that set it 

apart from the other models. As described, it determines current BCw rates from laboratoryderived weathering rates of individual minerals and therefore is not bound to data from a specific 

location or region. Therefore, it can be used to determine and map BCw over large areas (Miller 

et al., 1993). Although it was developed in Sweden, it has been successfully applied to the 

mapping of BCw and critical loads in the Northeastern United States, Maryland, Minnesota, 

Pennsylvania, Thailand, China, Argentina, and Greece (Duan et al., 2002; Miller, 2001; Sverdrup 

et al., 1992; H. Sverdrup personal communication, 2009b). 
Table 4-1. Review of modeling approaches (and models) to estimate base cation weathering for terrestrial critical acid load 

Model Approach 

Description of 

Method Data Requirements Model Complexity 

Suitability for 

Estimating BCw for 

Terrestrial Critical Acid 

Load Determinations in 

The United States 

Suitab

Mappi

Over 

Region

United 

Budget Studies mass balance of inputs 

and outputs of base cations within catchment, watershed or soil profile 

medium medium low; BCw estimate is often 

an integrated value for whole catchment or watershed 

low - me(based oavailabirequire isotope stream cto separexchangversus weathercation s

Historical Rate Determinations 

loss of base cations in soil profile relative to stable element (Zr, Ti, quartz or rutile) 

low low medium; restricted to sites 

with young soils of known age (e.g., soils that have formed since the most recent glacial event, ~20,000 years ago) 

low; ressites witsoils anwhere hrate determihave beconduct

Empirical Models 

modeled relationships between soil attributes and abiotic and biotic site conditions 

low - high low - high low - high low - hig

March 2010 46 Draft 
March 2010 47 Draft 

Model Approach 

Description of 

Method Data Requirements Model Complexity 

Suitability for 

Estimating BCw for 

Terrestrial Critical Acid 

Load Determinations in 

The United States 

Suitab

Mappi

Over 

Region

United 

Skokloster 

Assignment 

BCw rate categorically 

determined by relative abundance of minerals grouped into 5-6 weathering rate classes; originally developed for soils similar to those found in the 3 Gårdsjön catchments in Sweden 

low low low; most accurately 

applied to sites similar to those where the model was derived 

low; moaccurateapplied similar twhere thwas der

Soil Type - 

Texture 

Approximation 

BCw categorically 

determined as a function of parent material acidity and soil texture, modified by temperature; developed from data from European forest soils 

low low low - medium; most 

accurately applied to sites similar to those where the model was derived 

low - memost acapplied similar twhere thwas der
March 2010 48 Draft 

Model Approach 

Description of 

Method Data Requirements Model Complexity 

Suitability for 

Estimating BCw for 

Terrestrial Critical Acid 

Load Determinations in 

The United States 

Suitab

Mappi

Over 

Region

United 

Total Base 

Cation Content 

Correlation 

BCw determined by 

correlations between historical rate determinations (Zr) and total content of base cations in the undisturbed bottom soil, corrected for temperature; based on data from eleven sites in Sweden 

low low low; restricted to sites with 

granitic soils and Nordic geological histories 

low; ressites witsoils angeologichistories 

Clay-Substrate 

Model 

BCw determined by 

one of three equations based on parent material acidity and % clay content; most likely a modification of the Soil Type - Texture Approximation 

low low low - medium; most 

accurately applied to sites similar to those where the model was derived (most likely young soils formed since the last glaciation) 

low - memost acapplied similar twhere thwas der(most likyoung sformed last glac
March 2010 49 Draft 

Model Approach 

Description of 

Method Data Requirements Model Complexity 

Suitability for 

Estimating BCw for 

Terrestrial Critical Acid 

Load Determinations in 

The United States 

Suitab

Mappi

Over 

Region

United 

PROFILE BCw determined as a 

function of weathering of individual soil minerals and fieldbased soil and biotic conditions 

high high medium - high; may have 

restrictions in desert regions and areas that are lacking necessary data 

medium may havrestrictiodesert rand areare lacknecessa
Appendix B 

March 2010 50 Draft – Do Not Quote or Cite 

Table 4-2. The fourteen dominant minerals modeled within PROFILE. 

Dominant Minerals 

K-Feldspar 

Plagioclase 

Albite 

Hornblende 

Pyroxene 

Epidote 

Garnet 

Biotite 

Muscovite 

Fe-Chlorite 

Mg-Vermiculite 

Apatite 

Kaolinite 

Calcite 

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4.3.3 Proposed methodology for estimating and mapping base cation weathering for 

terrestrial critical acid load calculations 

As has been outlined in the above review, there are multiple approaches to estimate BCw

for terrestrial critical acid loads. However, not all are suitable for both calculating and mapping 

terrestrial critical acid loads throughout the United States. Such an approach has to be quick and 

easy to apply, be supported by available data and be easily and accurately transferable to sites 

within the United States that differ in soil, biotic and abiotic properties and conditions. In 

addition, as stated by Miller (2001), “the most promising approach for a logically consistent 

estimation of the present-day weathering rate over broad regions is the application of model(s) 

that predict the weathering rate from first principles, given detailed measurements of the soil 

environment and laboratory-derived rate constants for specific mineral weathering reactions.” 

Therefore, an approach that is based on soil mineralogy and weathering of individual minerals is 

preferable. Of all the models that are currently available for determining BCw for terrestrial 

critical acid load determinations, PROFILE meets these requirements and appears to be the most 

suitable. Methodologically, it has few location restrictions and models BCw based on sitespecific mineralogy and soil and site conditions. In addition, it has already been successfully 

applied in both glaciated and non-glaciated regions of the United States to estimate and map BCw
Appendix B 

March 2010 51 Draft – Do Not Quote or Cite 

and critical acid loads (Miller et al., 1993; Sverdrup et al., 1992; H. Sverdrup personal 

communication, 2009b). Although, as with all models, PROFILE does have some weaknesses 

and limitations (discussed further in Section 4.3.5) that need to be acknowledged and addressed 

prior to application, critical load experts, in general, agree that PROFILE is the best model to 

date for estimating and mapping BCw rates for terrestrial critical acid load determinations in the 

United States (J. Aherne personal communication, 2009, J. Cosby personal communication, 

2009, J. Lynch personal communication, 2009, R. Ouimet personal communication, 2009, H. 

Sverdrup personal communication, 2009b). 

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There are two forms of PROFILE (version 5.0) that can be used for estimating BCw: the 

single site application which estimates BCw for a single location or soil profile, and the regional 

application which PROFILE can be run for a region or conterminous areas (C. Akselsson 

personal communication, 2009). For mapping BCw in the conterminous United States, the 

regional application of the model would be applied, and the estimation and mapping of BCw

would involve two main steps: 

Step 1. Identification of input data required by PROFILE and development of 

spatial data layers, national databases and default values for each data 

element within the model 

Step 2. Determination of polygon layer to spatially define the BCw rates and 

development of continuous coverage map of calculated BCw values. 

These process steps are further illustrated in the flowchart presented in Figure 4-2. 
Appendix B 

March 2010 52 Draft – Do Not Quote or Cite 

I n p u t D a t a C l a s s e s

Identification of input data required by PROFILE to estimate BCw

Existing

National-Level

GIS Coverages

(see table 3a)

Requires Development

and Delineation

of National-Level GIS 

Datalayers

(see table 3b)

PROFILE Default 

Values

Requires review by user

(see table 3c)

Construct National GIS 

Data Layers and convert to 

raster format

Determine and Delineate BCw polygons

Calculate mean values for National 

GIS Data Layers

for each

BCw Polygon

Organize and Format 

BCw Polygon data

as

PROFILE model

input file

Calculate BCw

For

BCw polygons

1

2

1

2 Figure 4-2. Process Steps for Estimating BCw Using the PROFILE Regional Model 
Appendix B 

March 2010 53 Draft – Do Not Quote or Cite 

Step 1. Identification of input data required by PROFILE and development of 

spatial data layers, national databases and default values for each data 

element within the model 

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PROFILE (version 5.0) is data intensive and requires the user to provide or review a total 

of 26 soil, climatic and biological input data (Table 4-3a-c). In addition, to run the regional 

application of PROFILE, it would be necessary to have each of these data parameters available 

as continuous coverages. A large proportion of these variables are already included in existing 

databases in the United States and could be easily converted into continuous coverage data layers 

for the conterminous United States, if not currently available in continuous coverage format. 

Others, such as soil mineralogy, would need be modeled or constructed from other data. Still 

others may need to be represented by default values from the literature, until more unique, 

spatially site-specific values are determined. 

Table 4-3a. Data required to estimate BCw with the regional PROFILE model (version 5.0). The data in 

this table must be input by the user and are currently available as a continuous coverage layers for at 

least a portion of the conterminous United States. 

PARAMETER UNITS DESCRIPTION 

precipitation m/yr 30-year long-term average 

cation deposition kEq/ha/yr ammonium (NH4

+

), Ca2+, Mg2+, K+, Na+, Al - wet and dry deposition 

anion deposition kEq/ha/yr sulphate (SO4

2-

), chloride (Cl-) and nitrate (NO3

-

) - wet and dry 

deposition 

number of soil layers # up to 5 layers (with the forest floor/organic layer being the first 

horizon) 

soil layer height m by layer 

temperature °C mean annual soil temperature by layer 

dry soil bulk density kg/m3 by layer 

run-off m/yr number between 0 and the precipitation rates. If there is no lateral 

flow, runoff rate should equal the precipitation rate times the % of 

precipitation leaving the last soil layer. 

13

Table 4-3b. Data required to estimate BCw with the regional PROFILE model (version 5.0). The data in 

this table must be input by the user and are not currently available as a continuous coverage layers for at 

least a portion of the conterminous United States (will require development of national coverage layer). 

PARAMETER UNITS DESCRIPTION 

net uptake kEq/ha/yr nitrogen (N), Ca2+, Mg2+, K+ - only applied if biomass removed 

through harvesting or fire 

cation uptake % % of total soil profile (all soil layers combined should sum to 100%). 

Can be estimated using root distribution 

nitrogen uptake % % of total soil profile (all soil layers combined should sum to 100%). 

Can be estimated using root distribution 

litterfall kEq/ha/yr N, Ca2+, Mg2+ and K+ - input to forest floor 

soil water content m3/m3 by layer 

surface area m2/m3 soil surface area by layer 
Appendix B 

March 2010 54 Draft – Do Not Quote or Cite 

PARAMETER UNITS DESCRIPTION 

logKgibbsite - by layer 

dissolved organic 

carbon (DOC) 

mg/L by layer 

mineralogy % % abundance of 14 dominant mineral groups (K-Feldspar, 

Plagioclase, Albite, Hornblende, Pyroxene, Epidote, Garnet, Biotite, 

Muscovite, Fe-Chlorite, Mg-Vermiculite, Apatite, Kaolinte, Calcite) 

1

Table 4-3c. Data required to estimate BCw with the regional PROFILE model (version 5.0). The data in 

this table are used to support calculations within the model and should be reviewed by the user. 

PARAMETER UNITS DESCRIPTION 

forest canopy kEq/ha/yr N, Ca2+, Mg2+, K+ - nutrients removed by or leached from 

canopy 

net mineralization kEq/ha/yr N, Ca2+, Mg2+, K+ - net accumulation of soil organic matter 

precipitation entering 

soil horizon 

% expressed as % of precipitation. If no lateral flow, % leaving 

top layer should be same as % entering underlying layer 

precipitation leaving 

soil horizon 

% expressed as % of precipitation. If no lateral flow, % leaving 

top layer should be same as % entering underlying layer 

CO2 pressure x atm entered as multiple of atmospheric pressure; typically ranges 

from 5 in the organic horizons to 40 in the mineral soil layers 

immobilization - nitrogen immobilization - constant 

nitrification - constant 

denitrification - constant 

nutrient uptake 

kinetics 

- coupled vs. uncoupled uptake of N and base cations / uptake 

mechanism (unspecific, vanselow and none) 

A total of eight parameters including climate, deposition, run-off and many of the soil 

variables have data available as continuous coverages for the conterminous the United States 

(Table 4-4), and for most of these variables, data exist for all 48 states. However, some of these 

databases are missing variables and/or data or may need to be modified. Currently, there is no 

data that describes wet and dry Al deposition. This data, however, is not available in most 

locations where PROFILE is applied, and this parameter is typically left blank within model (H. 

Sverdrup personal communication, 2009b). Therefore, the absence of this datalayer in the United 

States should not pose a problem for the BCw estimates. The soil temperature parameter within 

the SSURGO database is poorly populated and data only exists for seventeen states. However, 

mean annual air temperature is often used as a surrogate for soil temperature within PROFILE 

because the two temperature measures are similar in some of regions (Miller, 2001). In some 

cases, models describing the relationship between air and soil temperature are also available 

(e.g., Yin and Arp, 1993). Therefore, the use of air temperature instead of soil temperature or 

modeled soil temperature could be explored with the application of PROFILE in the United 

States, if necessary. 

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Table 4-4. Available datasets and databases for the conterminous United States that could be used to estimate BCw with th

of the PROFILE model (version 5.0). 

DATA SOURCE URL or REFERENCE 

DATE(S) OF 

AVAILABLE 

DATA UNITS RESOLUTION 

Total annual precipitation 

http://prism.oregonstate.edu/products/matrix.phtml?vartype=ppt&view=maps 

1971-2000 in/yr 0.64 km2 

Average maximum air temperature 

http://prism.oregonstate.edu/products/matrix.phtml?vartype=ppt&view=maps 

1971-2000 °F 0.64 km2 

Average minimum air Temperature 

http://prism.oregonstate.edu/products/matrix.phtml?vartype=ppt&view=maps 

1971-2000 °F 0.64 km2 

Run-off http://pubs.er.usgs.gov/djvu/HA/ha_710_plt.djvu 1951-1980 in/yr 1:2,000,000 Dry cation deposition (NH4+, Ca2+, Mg2+, 

K+, Na+) 

http://www.epa.gov/castnet/data.html 1987-2008 kg/ha 86 stations in the 

48 conterminous states 

Wet cation deposition (NH4+, Ca2+, Mg2+, 

K+, Na+) 

http://www.epa.gov/castnet/data.html, 

http://nadp.sws.uiuc.edu/maplib/grids/2008/ 

1987-2008, 

1994-2006 

kg/ha 86 stations in the 

48 conterminous states, 6.25 km2

Dry anion deposition (SO42-, Cl-, NO3-) 

http://www.epa.gov/castnet/data.html 1987-2008 kg/ha 86 stations in the 

48 conterminous states 

Wet anion deposition (SO42-, Cl-, NO3-) 

http://www.epa.gov/castnet/data.html, 

http://nadp.sws.uiuc.edu/maplib/grids/2008/ 

1987-2008, 

1994-2006 

kg/ha 86 stations in the 

48 conterminous states, 6.25 km2

NH4+ and NO3- wet and dry deposition 

Community Multiscale Air Quality (CMAQ) - http://www.epa.gov/AMD/CMAQ/ 

2002 kg/ha 12 km2 

Soil horizon depth HZDEPT_R field of chorizon table 

(http://soildatamart.nrcs.usda.gov/documents/SSURGOMetadataTableColumnDescriptions.pdf) 

1987-2008, 

1994-2006 

cm 1:12,000 - 

1:63,360 

Soil bulk density DB3BAR_R field of CHORIZON table 

(http://soildatamart.nrcs.usda.gov/documents/SSURGOMetadataTableColumnDescriptions.pdf) 

N/A g/cm3 1:12,000 - 

1:63,360 

March 2010 55 Draft 
March 2010 56 Draft 

DATA SOURCE URL or REFERENCE 

DATE(S) OF 

AVAILABLE 

DATA UNITS RESOLUTION 

Soil texture (% sand, silt, and clay) 

SANDTOT_R, SILTTOT_R, and CLAYTOT_R fields of CHORIZON table (http://soildatamart.nrcs.usda.gov/documents/SSURGOMetadataTableColumnDescriptions.pdf) 

N/A % 1:12,000 - 

1:63,360 

Soil stoniness (% of soil with particles >2mm) 

FRAGVOL_R in CHFRAGS table (http://soildatamart.nrcs.usda.gov/documents/SSURGOMetadataTableColumnDescriptions.pdf) 

N/A % 1:12,000 - 

1:63,360 

Soil temperature SOITEMPMM field of the COSOILTEMP table 

(http://soildatamart.nrcs.usda.gov/documents/SSURGOMetadataTableColumnDescriptions.pdf) 

N/A °C 

(average 

by month) 

1:12,000 - 

1:63,360 
Appendix B 

March 2010 57 Draft – Do Not Quote or Cite 

Although a portion of the input data to estimate BCw with PROFILE are already available 

as national data coverages, there are nine additional input parameters that are not currently 

described by nationwide datasets, and nine parameters that are built into the regional application 

of the model and may require review and adjustment prior to applying PROFILE in the United 

States. The nine input parameters that would require the development of national GIS coverages 

or datasets that could be applied throughout the Unites States include: net uptake, % base cation 

and nitrogen uptake, litterfall, soil water content, surface area, logKgibbsite, mineralogy, and 

dissolved organic carbon (DOC). 

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9 Net Uptake

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A national dataset of net uptake of nutrients by forest systems could be developed using 

the approach outlined by McNulty and colleagues (1997). Briefly, the United States Forest 

Service (USFS) and United States Geological Survey (USGS) dataset describing the 21 different 

forest types would be used to map forest cover in the 48 states, and nitrogen and base cation 

(Ca2+, Mg2+, K+

) uptake by each forest type would be determined using the average values 

presented in Table 4-5. These values were calculated by McNulty and colleagues (2007) and 

incorporate annual volume growth by region from the USFS Forest Inventory and Analysis 

(FIA) database and nitrogen and base cation contents by tree species and tree component from 

the Tree Chemistry Database (Pardo et al., 2004). Net uptake would only be necessary for sites 

that are actively managed and experience removal of biomass through logging and/or fire. 

Therefore, based on the assumption that only wilderness and conservation areas are not harvested 

or managed, these nitrogen and base cation uptake estimates would only be applied to forest 

areas that are not designated as wilderness by the National Wilderness Preservation System of 

the United States (McNulty et al., 2007). 
Appendix B 

March 2010 58 Draft – Do Not Quote or Cite 

Table 4-5. Nitrogen and base cation uptake by forest type 

(from McNulty et al., 2007). 

FOREST COVER TYPE 

NITROGEN 

UPTAKE 

(eq/ha/yr) 

BASE CATION 

UPTAKE (eq/ha/yr) 

white-red-jack pine 59.07 77.14

spruce fir 54.27 83.72

longleaf slash pine 154.74 227.22

loblolly shortleaf pine 140.41 208.58

oak pine 129.71 213.75

oak hickory 102.56 254.87

oak-gum-cypress 124.18 235.68

elm-ash-cottonwood 79.74 156.3

maple-beech-birch 101.76 190.51

aspen-birch 81.69 125.46

douglas-fir 109.89 179.03

hemlock-sitka-spruce 98.88 161.12

ponderosa pine 75.29 174.39

western white pine 40.69 37.11

lodgepole pine 40.19 61.25

Larch 65.1 77.14

fir-spruce 94.65 146

Redwood 100.92 156.62

Chaparral 106.6 201.61

pinyon-juniper 40.87 58.21

western hardwoods 135.21 263.33

1

2 Soil Surface Area

3

4

5

6

7

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9

10

11

Soil surface area is commonly determined in the laboratory using the Brunauer-EmmettTeller (BET) nitrogen absorption technique (Hodson et al., 1997). However, data from such 

analyses are not available for all soils in the United States. Therefore, it would be necessary to 

estimate surface area from other soil data. Within the PROFILE model, surface area is calculated 

with soil texture and particle size distribution data (Equation 4-5) (Alveteg et al., 2004), and 

Sverdrup and colleagues (1992), used this equation in their study of critical acid loads in 

Maryland. This same approach could be used for mapping soil surface areas in the United States. 

Soil texture is part of the U.S. Department of Agriculture- Natural Resources Conservation 

Service (USDA-NRCS) Soil Survey Geographic (SSURGO) database (Table 4.0). Therefore, it 
Appendix B 

March 2010 59 Draft – Do Not Quote or Cite 

would be possible to produce a continuous coverage map of soil surface areas in the United 

States. 

1

2

3

4

5

6

7

 Aw = (8.0xclay + 2.2xsilt + 0.3xsand) p / 1000 (4-5) 

where 

 Aw = total exposed surface area (m2

/m3) 

 x = weight fraction of clay, silt and sand when xclay + xsilt + xsand = 1; 

 p = soil density in kg/m3 

8 Soil Mineralogy

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25

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27

28

Soil mineralogy is one of the most important and influential variables within PROFILE. 

However, it is also a very time intensive and expensive measurement. Therefore, soil mineralogy 

data in the United States is sparse, and a continuous coverage layer of soil mineralogy does not 

exist. In most regional applications of PROFILE in Europe and other regions, the mineralogy 

input data are based on a combination of data from soil geochemical and mineralogy analyses 

and mineralogical composition based on output from a model such as the Analysis to Mineral 

(A2M) model (Posch and Kurz, 2007). The A2M model estimates all possible mineral 

compositions from total chemical analyses (Ca2+, Mg2+, K+

, Na+, Ti, Al, phosphorus (P), silicon 

(Si), iron (Fe)) of the soil and a pre-specified set of minerals that are likely to be present in the 

soil. The highest probability mineral composition is an output of the arithmetic mean of all 

extreme mineral modes. The resulting mineralogies are then mapped to “geological provinces” 

(Sverdrup et al., 1990) that have the same parent material bedrock but may differ in soil 

mineralogy in a consistent pattern (Sverdrup et al., 1990). Alternatively, the mineralogies can be 

mapped to “mineralogy polygons” that are delineated based on probable similarities in mineral 

compositions of the soils. Typically, the spatial borders of mineralogy polygons are determined 

by underlying parent material geology and/or soil type groupings that are likely to have the same 

mineralogies (H. Sverdrup personal communication, 2009b). In areas where the soils have 

formed from transported materials, such as glacial till, it is sometimes necessary to consider the 

surficial geology and model the origin and transport of materials to determine the parent material 

geology (McKenzie and Ryan, 1999). 
Appendix B 

March 2010 60 Draft – Do Not Quote or Cite 

Due to the diverse geological history of the United States, it may be necessary to include 

a variety of variables and databases in the characterization and mapping of mineralogy. Parent 

materials underlying soils in the conterminous United States vary extremely. These materials 

include not only bedrock beneath young soils, but also a variety of young and old regolith 

materials that include both residuum formed in place and all varieties of transported sediments. 

In addition, the soils are old and highly weathered in a large portion of the United States, and 

therefore, no longer resembles the mineral composition of the parent material. For example, the 

mineralogy of soils atop ancient residuum of the Appalachian region will vary significantly from 

the mineralogy of younger residuum of the Western mountain ranges. Also, the mineralogy of 

soil developed on the older loessal plain in the Mississippi basin will vary from the younger 

glacial deposits along the northern regions of the United States. Therefore, determination of soil 

mineralogy in the United States would require an approach that is able to recognize the varied 

geological histories, different parent material origins, and soil mineralogies that differ from the 

original parent material sources. Such an approach would involve the following steps be 

conducted simultaneously: 

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1. Delineation of mineralogy polygons based on soil classification at a level supported 

by available data 

2. Determination of mineralogy and geochemical data availability for each mineralogy 

polygon 

3. Comparison of mineralogy polygons with underlying bedrock and surficial geology 

4. Testing modeled mineralogy against actual mineralogy measurements 

22 Delineation of mineralogy polygons based on soil classification at a level supported by 

available data - Mapping and creation of a national GIS coverage of mineralogy in the 

conterminous United States would require the delineation of “mineralogy polygons”. 

“Mineralogy polygons” are spatially-defined polygons that are delineated based on probable 

similarities in mineral compositions of the soils. These polygons would need to be large enough 

in scale to be adequately covered by available mineralogy and soil analysis data, yet small 

enough to only represent single assemblages of soil minerals. Ideally, each mineralogy polygon 

should have at least one data point or soil profile analysis that describes the total analysis (Ca2+, 

Mg2+, K+

, Na+, Ti, Al, P, Si, and Fe) and/or mineralogy of the soil layers. Where data are 

missing, it would be necessary to interpolate data from other locations using correlations with 

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Appendix B 

March 2010 61 Draft – Do Not Quote or Cite 

surrounding and adjacent known data and other supporting criteria indicative of similar 

mineralogies and weathering patterns (e.g., geologic and physiographic regions, bedrock geology 

data, climatic regions, and others). 

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Within the United States, one of the most suitable coverages for the delineation of the soil 

mineralogy polygons is the SSURGO soils database (Table 4-6). The smallest unit within this 

database is the soil mapping unit which can consist of up to five individual soil series. A soil 

series is defined as “soils that are similar in all major profile characteristics (Brady and Weil, 

2002), and soils within the same series have been influenced by similar climate, topographic 

location, biota, parent material and pedological time frame. Therefore, the soil within a series, 

regardless of location would be expected to have identical or sufficiently similar mineralogies 

(C. Smith personal communication, 2009). The soil groupings within the higher levels of soil 

taxonomy may also be based on characteristics such as soil mineralogy. For example, soil orders 

are largely classified by the degree of weathering and soil development, with Entisols 

representing the youngest, least weathered soils, and Ultisols and Spodosols being more highly 

weathered. Therefore, it may be possible to group the soil mapping units at a higher level of 

taxonomy, such as the great group, family or order, as the “mineralogy polygons”. However, 

since soils are classified based on multiple formative factors, the “mineralogy polygons” could 

be a mixture of groupings based on different levels of soil taxonomy, with all groupings based on 

factors indicative of similar mineral assemblages in the soil. 

Detailed soil delineations have been completed for more than 80% of the conterminous 

United States and are used in the NRCS Soil Survey Geographic (SSURGO) dataset (Figure 4-

3). Data are missing for many public land areas (e.g., national forest lands), and there are 

approximately 21,000 soil series delineations within the conterminous United States. 

Table 4-6 Datasets with Geochemical and Mineralogy Data for U.S. Soils

DATA SOURCE RESOLUTION INCLUDED DATA 

Soil Survey 

Geographic 

Database 

(SSURGO) 

http://soils.usda.gov/survey/g

eography/ssurgo/ 

1:12,000 to 

1:63,360 

SSURGO is linked to a National Soil 

Information System (NASIS) attribute 

database. The attribute database gives 

the proportionate extent of the 

component soils (i.e., u soil series) and 

their properties for each map unit. The 

SSURGO map units consist of 1 to 3 

components each. There are 

approximately 15,000 and 20,000 soil 

series polygons delineated across the 

United States 
Appendix B 

March 2010 62 Draft – Do Not Quote or Cite 

DATA SOURCE RESOLUTION INCLUDED DATA 

U.S. General Soil 

Map (STATSGO 

II) 

http://soils.usda.gov/survey/g

eography/statsgo/ 

1:250,000 The tabular data contain estimated 

ranges (low, high, and representative 

values) of physical and chemical soil 

properties, soil interpretations depicting 

the range for the geographic extent of the 

map unit. Soil map units are linked to 

attributes in the tabular data, which give 

the proportionate extent of the 

component soils and their properties. 

Surficial Geology 

of the United 

States (1977) 

(also Map of 

Surficial Deposits 

and Materials in 

the Eastern and 

Central United 

States (East of 

102° West 

Longitude)) 

http://tin.er.usgs.gov/geology/

state/ or 

http://water.usgs.gov/GIS/met

adata/usgswrd/XML/ofr99-

77_geol75m.xml also 

http://pubs.usgs.gov/imap/i2789/ 

1:7,500,000 

(E of 102° W 

Longitude: 

1:2,000,000) 

Provides approximate areal extent of 

about 45 categories of regolith types 

across the conterminous United States. 

Compilation East of 102° West Longitude 

has further classified deposits generally 

within original polygons. 

Element 

Concentrations in 

Soils and Other 

Surficial Materials 

of the 

Conterminous 

United States 

(Shacklette Data, 

1977) 

USGS, Denver Federal 

Center Offices 

Sampling 

density: 1 

sample per 

6,000 km2

.; 

equivalent to 

the collection 

of samples on 

a 75-km grid. 

Ultra-low-density geochemical baseline 

data from 1,323 samples locations 

characterizing soils and other surficial 

materials in the conterminous United 

States. Elements analyzed included: Ag, 

Al, Ba, Be, B, Ca, Ce, Cr, Co, Cu, Ga, 

Ge, Hg, Fe, La, Li, Pb, Mg, Mn, Mo, Na, 

Nd, Ni, Nb, P, K, Rb, S, Sc, Se, Sr, Th, 

Ti, U, V, Yb, Y, Zn, Zr, and total carbon. 

The National 

Geochemical 

Survey – 

Database and 

Documentation 

(Version 5.0, ongoing) 

http://tin.er.usgs.gov/geoche

m/doc/home.htm and 

http://tin.er.usgs.gov/geoche

m/ 

Nominal grid 

spacing of 17 

by 17 

kilometers (i.e., 

minimum 

sample density 

of 1 sample 

per 289 km2

 in 

all land areas 

of the country 

Stream-sediment-based geochemical 

survey for the United States; Analytical 

methods include a 40-element ICP 

package plus single-element 

determinations of As, Se, and Hg by 

atomic absorption for every sample. 

about 60,000 stream-sediment samples 

that have been analyzed. Digital data 

files are presented in 6 categories. In 

total there are 43 individual data files for 

the Unites States. Some of the data has 

also been processed into vector data to 

produce maps showing the elemental 

concentration of As, Se, Hg, Pb, Zn, Cu, 

Al, Na, Mg, P, Ca, Ti, Mn, and Fe at the 

county level. Database contains 287 

attributes (77,212 records). 

Integrated 

Geologic Map 

Databases for the 

United States 

(1998-2007) 

http://gsa.confex.com/gsa/20

06AM/finalprogram/abstract_

110914.htm and 

http://tin.er.usgs.gov/geology/

state/ 

1:100,000 Seamless national-scale geologic spatial 

data-layer and database to support 

national and regional level projects, 

including mineral resource and 

geoenvironmental assessments. Data 

include general geologic unit age, 

dominant lithology (rocktype1 must be 

>50% of unit) and second most dominant 

lithology (rocktype2). 
Appendix B 

March 2010 63 Draft – Do Not Quote or Cite 

DATA SOURCE RESOLUTION INCLUDED DATA 

Soil Pedon Pit 

Data (on-going) 

USDA NRCS Area covered 

by a pedon 

varies from 10 

- 100 square 

feet; 

approximately 

30,000 soil 

pits/pedons in 

the NRCS 

database 

Geochemical elements: Al, Ca, Fe, K, 

Mg, Mn, Na, P, Si, Sr, Ti, and Zr. X-ray 

diffraction for clay mineralogy by horizon; 

optical mineralogy analysis is performed 

on the dominant sand fractions of the soil 

from the A-horizon, B-horizon, and Chorizon, or the most dominant horizon. 

More than 60 fields describing the 

minerals are listed in the database. The 

dataset is not uniform in that elemental 

analyses were routinely done through the 

1970’s but then these analyses were 

suspended through the 1980’s. 

Elemental analyses were resumed during 

the early 1990’s. It is estimated that as 

much as one third of the 30,000 soil 

pedons have geochemical data. 

Likewise, optical mineralogy is not 

performed for all pedons and the NRCS 

staff estimate that approximately as 

many as one third of the 30,000 soil pits 

have optical analysis results. Even 

though the number of pedons with data 

are similar for geochemical and optical 

analysis results, the data are not 

necessarily associated with the same set 

of pedons or even soil series. 

Physiographic 

Regions of the 

United States 

(Fenneman, 1946) 

http://water.usgs.gov/GIS/dsd

l/physio.e00.gz 

1:7,000,000 Geomorphic / physiographic broad-scale 

subdivisions based on terrain texture, rock 

type, and geologic structure and history. 

Nevin Fenneman's (1946) three-tiered 

classification of the United States - by 

division, province, and section. 

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Image source:

http://soils.usda.gov/survey/geography/ssurgo/

Figure 4-3. Map Showing the Distribution and Status of SSURGO Data 

March 2010 64 Draft 
Appendix B 

March 2010 65 Draft – Do Not Quote or Cite 

1 Determination of mineralogy and total analysis data availability for each mineralogy 

polygon - The soils for each spatially-defined “mineralogy polygon” would require % 

mineralogy to determine BCw with the PROFILE model. The relative abundances of 14 

dominant minerals are required as model input, and this % mineralogy can be based on direct 

measurements of soil mineralogy or can be determined with the A2M model. As outlined earlier, 

the A2M model is able to estimate the most probable % mineral composition, or proportion of 

mineral phases, of a soil based on total analysis data and the identification of the minerals that 

are likely to be present in the soil. Therefore, it would be necessary to determine the availability 

of such data for each of the “mineralogy polygons” in the conterminous United States.

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Currently, there are potentially three consistent national-scale datasets that contain 

various levels of mineralogy and total analysis data to serve as inputs for the A2M and PROFILE 

models. These include: 

• Chemical Analyses of Soils and other Surficial Materials of the Conterminous United 

States (Shacklette dataset) and accompanying Geochemical Landscapes Project data, 

• the more recent National Geochemical Survey data, and 

• the United States Department of Agriculture (USDA) NRCS pedon soil pit dataset. 

A summary of these datasets is outlined in Table 4-6. 

Chemical Analyses of Soils and other Surficial Materials of the Conterminous United 

States (Shacklette Data) and the Geochemical Landscapes Project datasets provide geochemical 

baseline data for soils and other surficial materials in the conterminous United States. The 

original Shacklette dataset contains geochemical data from soils and other regolith collected and 

analyzed by Hans Shacklette and colleagues beginning in 1958 and continuing until about 1976. 

This dataset has approximately 1,323 samples, at a sampling density of approximately 1 sample 

per 6,000 square kilometers (Figure 4-4). The soil samples within this dataset were analyzed for 

a large number of elements, including Ca, Fe, Mg, Na, P, K, and Ti (Gustavsson et al., 2001), 

that are required by the A2M model. However, assessments of mineralogy were not included in 

these original analyses. An additional drawback with the data set is its extremely low numbers of 

samples for the entire conterminous United States. However, more recent high-resolution studies 

(e.g., Smith et al., 2005) for select elements (e.g., Calcium) have illustrated that the regional 

patterns established by the Shacklette data are generally maintained except where areas have 

been affected by anthropogenic factors (Smith, 2006). 
Points on map show geochemical sample stations:•Black dots indicate samples from sample-collection phase 1,•white dots indicate samples from sample-collection phase 2,•gray dots indicate samples whose placement into phase 1 or phase 2 is uncertain.(All archived sample have been reanalyzed (personal communication with David Smith, 1-4-2009) 

Shacklette,Hansford T., and Josephine G

Figure 4-4. Soil Sampling Locations Included in the USGS Shacklette Dataset 

March 2010 66 Draft 
Appendix B 

The Geochemical Landscapes Project was begun in 1998 with most work occurring after 

2002. The purpose of the data collection is to increase the density of the Shacklette data locations 

to produce a high resolution geochemical dataset for North American soils of 6,000 data points 

(D. Smith personal communication, 2009). This is an on-going collaborative effort by the USGS, 

USDA Natural Resource Conservation Service, other federal agencies, and academia to build a 

national-scale soil geochemical survey. The project has just completed a third year of 

continental-sampling and completed sample collection for approximately 80% of the 

conterminous United States (D. Smith personal communication, 2009). The USGS anticipates 

that sampling may be completed for the conterminous United States in 2010; or 2011 at the 

latest. Both total and mineralogy analyses are being performed on these samples. Mineralogy 

analyses include x-ray diffraction on the clay fraction and optical analyses on the fine sands and 

silts. In addition, the original Shacklette data have been re-analyzed for mineralogy. 

The National Geochemical Survey (NGS) dataset is being built by on-going efforts by the 

USGS to produce a new stream-sediment-based geochemical survey for the United States at a 

spacing of 17 by 17 kilometers (i.e., minimum sample density of 1 sample per 289 km2

 in all 

land areas of the country) (Figure 4-5). The project has sought to capitalize on existing datasets 

and archived samples. For this reason the NGS is based primarily on analyses of stream 

sediments to build on the massive archives of data and samples from DOE’s National Uranium 

Evaluation (NURE) program. Much of the survey has entailed reanalysis of approximately 

35,000 archival samples from the NURE program. Where NURE samples do not exist, USGS 

has been working with cooperators to obtain new samples. In total, the project is expecting to 

have more than 60,000 samples. Most or all of the sampling has been completed for the 

conterminous United States and only few analyses are left to complete (D. Smith personal 

communication, 2009). The samples are being analyzed for 40 elements, including all of the 

elements which are necessary input for the A2M model. In addition, for a select number of 

samples mineralogy analyses (x-ray diffraction of clay fraction and optimal analyses of fine 

sands and silts) are also being conducted. 

March 2010 67 Draft – Do Not Quote or Cite 
Image source: http://tin.er.usgs.gov/geochem/doc/status.htm

Figure 4-5. Sample Density of USGS National Geochemical Survey 

March 2010 68 Draft 
Appendix B 

March 2010 69 Draft – Do Not Quote or Cite 

USDA NRCS Soil Pedon Pit Data were collected by the USDA NRCS for data required 

for delineation of soil series, map units, and associated attributes. The data are contained in the 

NRCS USSOILS database that provides data for the SSURGO database. There are currently 

approximately 30,000 soil pits/pedons in the NRCS database, and soil samples from these pits or 

pedons have been analyzed for a large variety of physical and chemical properties. These 

analyses include total chemical analysis, which includes elements required by A2M (e.g., Al, Ca, 

Fe, K, Mg, Na, P, Si, Ti). In addition, mineralogy has been characterized through two analyses: 

x-ray diffraction, which identifies clay mineralogy, and optical mineralogy which determines the 

mineral composition of the fine sand and silt fractions of the soil (C. Smith personal 

communication, 2009). However, these three analyses have not been conducted on all soils. Only 

11,747 of the 30,000 soil pits have been analyzed for at least one of the three parameters (Figure 

4-6), and only 4,710 soils have been analyzed for all three (Figure 4-7). 

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March 2010 70 Draft 

1234

Figure 4-6. NRCS Soil Pedon Sample Pit Locations (30,000 total) (Image created by RTI using data provided by NRCS on 12/2009)
March 2010 71 Draft 

Figure 4-7. NRCS Soil Pedon Pit Sample Locations with Geochemical and Mineralogy Data(Image created by RTI using data provided by NRCS on 12/2009) 

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Appendix B 

March 2010 72 Draft – Do Not Quote or Cite 

In summary, three main datasets have been identified that could provide the necessary 

total analyses and mineralogy data for each “mineralogy polygon.” These datasets would be 

combined into a single database and overlaid on top of the “mineralogy polygon” datalayer to 

determine the degree to which each polygon is covered by mineralogy and total analysis data. At 

the scale of a nationwide analysis, the data from each of these datasets is considered comparable 

given the sampling and analysis protocols that have been used (D. Smith personal 

communication, 2009). Although the combined database would be large and offer over 75,000 

data points, it is not likely that data would be available for all “mineralogy polygons.” In such 

cases, it would be necessary to determine the mineralogy through alternate methods. Potentially, 

interpolation between data points could be conducted using numerical probabilistic methods. In 

addition, it may be possible to determine probable mineralogy based on underlying bedrock or 

surficial geology (described further in next session). Methods involving professional judgment 

could also be used to interpret patterns and assign reasonable and appropriate values to express 

the apparent condition. If such an approach were taken, it would be necessary to work with soils 

experts who are familiar with the SSURGO database (e.g., NRCS Regional Staff) to make such 

judgments. 

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Comparison of mineral polygons with underlying bedrock and surficial geology - In 

many locations in the United States, soils have developed from the underlying bedrock or 

surficial materials. Therefore, it may be possible to validate, support or identify the mineralogies 

of each of the “mineralogy polygons” through a comparison with the physiographic regions of 

the United States and the underlying bedrock and surficial geology. The physiographic provinces 

are based on geology and topography. Therefore, these provinces relate geology and geological 

history with expected soil characteristics, and the locations of “mineralogy polygons” should 

broadly follow the patterns within these province boundaries. Similarly, the “mineralogy 

polygons” could be compared against the underlying geologies to determine the accuracy of the 

soil taxonomy groupings that were used to delineate the polygons. In addition, overlays of the 

“mineraology polygon” datalayer and bedrock or surficial geology could support the estimation 

of probable mineralogies and percent compositions for “mineralogy polygons” that are missing 

soil mineralogy and or total analysis data. 

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The USGS 1:100,000 scale bedrock geology GIS cover would be first used for the 

comparison between the “mineralogy polygons” and bedrock geology (Table 6.0). Most rock 
Appendix B 

March 2010 73 Draft – Do Not Quote or Cite 

types are typically characterized by less than 4 mineral types. Correlating the “mineralogy 

polygons” with the bedrock type can be used to obtain a gross approximation of mineral phases 

that would be expected in the residual parent materials and the corresponding soils. This would 

be a particularly useful protocol to apply to areas where the soils have formed in place from the 

weathering of the bedrock. For example, this approach could be used in unglaciated regions 

where Entisols or residuum predominant. 

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The 1:7,500,000 scale USGS surficial geology layer could be used as the source for 

comparison between “mineralogy polygon” and surficial geology (refer to Table 6.0). The 

surficial geology layer would identify the type of regolith on which the soil has developed. 

Regolith is defined here as any unconsolidated materials on top of bedrock, and consists of 

residuum which has formed in place and transported materials that have been deposited by 

gravity, wind, water or ice. Therefore, this layer will indicate the type of parent material that 

supported the development of the soil and will provide an indication of the potential mineral 

composition of the soil. Specific correlation of mineral types can be more difficult for 

transported deposits. However, an association is still possible as correlated with general up-grade 

areas that relate the likely origin, or areas of parent material, for the transported deposit. Even 

though more generalized approaches to determining the mineralogy are suggested by the 

available data, modeling the geologic source of parent materials by applying techniques similar 

to soil-landscape modeling or environmental correlation modeling could be conducted 

(McKenzie and Ryan, 1999). 

Test modeled mineralogy against actual mineralogy measurements - To validate and test 

the accuracy of the % mineral values assigned to the “mineralogy polygons”, comparisons 

should be made between the “mineralogy polygon” data layer and areas with detailed mineralogy 

soil analyses. Such sites may include the LTER sites outlined in Table 4-7 or those detailed 

within the scientific and geological literature. In addition, there may be locations where detailed 

mineralogy assessments have been conducted by mining companies or research groups that could 

be used to test the “mineralogy polygon” data layer. Comparisons would be particularly 

important for mineralogy polygons with % mineralogy determined by the A2M model. 

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Appendix B 

March 2010 74 Draft – Do Not Quote or Cite 

Table 4-7. Long-Term Ecological Research (LTER) sites that could potentially be suitable as “field test” 

sites to validate BCw estimates generated with the regional application of the PROFILE model (version 

5.0). 

LTER STUDY LOCATION ADDITIONAL INFORMATION 

H.J. Andrews 

Experimental Forest Cascade Mountains, Oregon http://andrewsforest.oregonstate.edu/ 

Coweeta LTER Southern Appalachian 

mountains, North Carolina http://www.lternet.edu/sites/cwt/ 

Harvard Forest Massachusetts http://www.lternet.edu/sites/hfr/ 

Hubbard Brook 

Experimental Forest 

White Mountain National 

Forest, New Hampshire http://www.lternet.edu/sites/hbr/ 

Kellogg Biological 

Station Southwest Michigan http://www.lternet.edu/sites/kbs/ 

Konza Prairie LTER Northeastern Kansas http://www.lternet.edu/sites/knz/ 

Niwot Ridge Colorado http://www.lternet.edu/sites/nwt/ 

Santa Barbara Coastal 

LTER California http://www.lternet.edu/sites/sbc/ 

Sevilleta LTER New Mexico http://www.lternet.edu/sites/sev/ 

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Development of national datasets or default values for the % base cation and nitrogen 

uptake (by layer), litterfall, soil water content, logKgibbsite, and DOC input parameters would 

most likely require the use of data from the literature and research conducted in the United 

States. The % base cation and nitrogen uptake by soil layer variables are a function of the 

distribution of fine roots, and rooting distributions are typically entered as one of four classes 

into PROFILE (H. Sverdrup personal communication, 2009b). These root distribution classes are 

based on data from an extensive literature search on the rooting habitats of common tree species 

in Europe (Sverdrup and Stjernquist, 2002). A similar literature search could be conducted for 

the main species within the 21 forest types in the United States, and the four root distribution 

classes could be adjusted accordingly. 

The litterfall parameter within PROFILE characterizes the amounts of N, Ca2+, Mg2+ and 

K+

 returned to the soil with the senescence of leaves, branches and stems. It is calculated as a 

function of the site-specific growth rates of individual tree species and the nutrient content of the 

different litter components. Since PROFILE is a steady-state model, the growth rates are 

averaged over the rotation of the stand. Litterfall values have been determined for European tree 

species (Sverdrup et al, 1990; Sverdrup and Stjernquist, 2002), and the same procedure could be 

used for estimating values for the main species in the 21 forest types in the United States. The 
Appendix B 

March 2010 75 Draft – Do Not Quote or Cite 

Tree Chemistry Database (Pardo et al., 2004) could be serve as the source of litter nutrient 

content and the USFS FIA database could potentially supply species-specific growth rates. 

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Soil water content is highly variable. However, since PROFILE is a steady-state model, it 

is necessary to use a single value representative of the water content throughout the year. In 

Sweden, a default value of 0.2 m2

/m3 is often used (Halveteg et al., 2004), and it would be 

necessary to establish a similar default value or set of default values for the United States. Such 

values could be obtained from the literature. In addition, it may be possible to estimate a set of 

soil water content estimates based on a simple water balance model that includes the influences 

of precipitation, run-off, soil texture and/or soil drainage classes (H. Sverdrup personal 

communication, 2009b). Data outlined in Table 4 and soil texture and the six drainage classes 

(Well Drained; Excessive; Moderately Well; Poorly; Somewhat Excessively; Somewhat Poorly) 

included within the SSURGO soils database could potentially be used in this simple water 

balance model. 

Soil dissolved organic carbon (DOC) and the logKgibbsite coefficient would also require 

the use of values from the literature. Currently, with the application of PROFILE within Europe, 

DOC is entered as 20 mg/L in the organic layers but drops rapidly with depth in the mineral soil 

horizons (Alveteg et al., 2004). These values are based on a compilation of data from European 

field sites (H. Sverdrup personal communication, 2009b) and are a function of the organic matter 

content of the soil (Sverdrup et al, 1990). Similar values and relationships would need to be 

established for forest systems in the United States based on available data and studies outlined in 

the literature. LogKgibbsite is a coefficient that describes the concentration of Al in the soil 

solution. It depends on the soil solution pH and differs by soil layer. Two sets of values have 

been developed for the application of PROFILE within Europe, with one set being used for clay 

soils and the other for non-clay soils (Sverdrup and Stjernquist, 2002). These values and 

grouping by soil clay content were based on data from the literature and the consistent trends in 

the gibbsite coefficients within and between soils (H. Sverdrup personal communication, 2009b). 

For the application of PROFILE within the United States, it would be necessary to review the 

logKgibbsite values, review the literature and potentially adjust the values as necessary to be 

representative of conditions found in the United States. 
Appendix B 

March 2010 76 Draft – Do Not Quote or Cite 

1 Regionally-based Built-in PROFILE Input Parameters

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There are nine variables that are currently built into the calculations of BCw for the 

regional application of PROFILE (version 5.0) and do not require input data from the user. The 

values of these variables were determined by field research in Europe, and are thought to vary 

minimally between sites or are calculated based on the input data. These variables include: forest 

canopy, net mineralization, % precipitation entering layer, % precipitation leaving layer, CO2

pressure, immobilization, nitrification, denitrification, nutrient uptake kinetic variables (C. 

Akselsson personal communication, 2009). Prior to applying PROFILE to map BCw throughout 

the United States, the values and equations used for each of these variables should be examined. 

It may be necessary to modify the model equations and/or replace the current values with 

those from the literature to ensure that the values within PROFILE are representative of 

conditions and processes in the United States (H. Sverdrup personal communication, 2009). 

Forest canopy, within PROFILE, accounts for the Ca2+, Mg2+, K+

 and N (as NH4

+

) that is 

absorbed from or leached into the precipitation that is in contact with the canopy. Potassium, 

Ca2+, Mg2+ are typically leached from the foliage and NH4

+ is absorbed. The default values 

within PROFILE are currently based on the results of field studies in Europe and are divided by 

forest type (deciduous versus non-deciduous). Net mineralization within the model is a function 

of soil organic matter content. Currently, net mineralization is set to “0” within PROFILE 

assuming that the forests are managed sustainably and net mineralization is at an equilibrium; 

Ca2+, Mg2+, K+

 and NH4

+

 released through mineralization of organic matter is taken up by the 

vegetation, returned as litter and remineralized. Therefore, there is no net loss or gain of nutrients 

through mineralization. However, the net mineralization default value of “0” can be changed if 

forest management is not sustainable and involves short rotations and/or practices such as whole 

tree harvesting which remove the foliage and a large pool of “mineralized” nutrients from the 

site. 

The % of precipitation entering and leaving the soil layers variables within PROFILE are 

determined based on the fine root distribution in the soil profile. Carbon dioxide pressure in the 

soil is estimated from a small dataset of measurements conducted in different regions of the 

world. The values that are used within PROFILE are a function of soil particle size. 

Immobilization of nitrogen within PROFILE is currently set to range between 0.5 – 1.0 kg 

N/ha/yr. This range of values was determined by the amount of the amount of nitrogen that has 
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accumulated in Northern European soils since the last glaciations. Denitrification and 

nitrification are currently determined by mathematical equations that include the influences of 

temperature, available soil nitrogen, soil moisture and soil pH. Nutrient uptake kinetics within 

PROFILE consists of coupled versus uncoupled uptake of nitrogen and base cations. Within 

PROFILE, uptake is set to “coupled” as a default because the uptake of Ca, Mg and Al and K 

and NH4+ are coupled (Sverdrup et al., 1990). Uptake kinetics within the model are also 

described as unspecified or vanselow depending on the uptake dynamics of base cations and Al 

absorbed to the root surface. Currently, within PROFILE, deciduous species and domestic crops 

are defaulted to vanselow kinetics and grasses, and conifers use unspecified kinetics (H. 

Sverdrup personal communication, 2009). Unspecified kinetics indicates that the ion exchange 

matrix on the root surface is indifferent to the valence of the absorbing ions (Sverdrup and 

Warfvinge, 1993). 

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Step 2. Determination of polygon layer to spatially define the BCw rates and 

development of continuous coverage map of calculated BCw values. 

Following the establishment of continuous coverage databases and national datasets and 

default values for the application of PROFILE (version 5.0) within the United States, it would be 

necessary to construct a spatially-explicit continuous datalayer for mapping BCw throughout the 

48 states. The resolution of the datalayer should be small scale and provide the highest level of 

detail permitted by the data. In addition, the location of individual BCw polygons should be tied 

to a variable or set of variables which strongly influence BCw. Since soil attributes including 

mineralogy, bulk density, volumetric water content and exposed surface area of minerals 

(discussed further in Section 4.3.6) are the largest sources of variability in the BCw calculations, 

it may be most appropriate to map BCw according to mineralogy, soil series or a higher level of 

soil taxonomy. Input data and default values for the 26 PROFILE variables would then be 

mapped to the delineated BCw polygon layer. When multiple or sections of multiple polygons of 

the same datalayer are present in a BCw polygon, a weighted average value for the data would be 

calculated. All the data for each BCw polygon would then be formatted according to the 

requirements of PROFILE and the PROFILE regional model would be run to produce maps of 

BCw. 
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1 4.3.5 Potential limitations of proposed methodology 

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Although PROFILE is arguably the most suitable model currently available for 

estimating and mapping BCw for terrestrial critical acid load determinations in the United States, 

the model does have some limitations that should be acknowledged and potentially remedied 

prior to application. The model and algorithms contained therein were developed in Sweden 

using Swedish soils as the basis for the soil chemical and physical relationships (Hodson et al., 

1997). The soils in Sweden are comparatively young, having formed since the last glaciations, 

approximately 10,000 years ago (Sverdrup and Warfvinge, 1988). Therefore, there is some 

concern that PROFILE may not accurately model base cation release in older soils (C. Smith 

personal communication, 2009). As discussed by Hodson and Langan (1999), PROFILE does 

not take into account the decreasing reactivity of minerals with duration of dissolution, and 

assumes that the reaction rates are constant regardless of time and duration of dissolution. In 

addition, the model assumes a constant versus decreasing reactive surface area as total surface 

area increases. According to the authors, these shortfallings were two of the main reasons that 

PROFILE did not show a decreased weathering rate with soil age relative to other models. 

However, at the same time PROFILE has been used to estimate BCw in multiple locations with 

older, more weathered soils, such as Maryland, China, Thailand, Argentina and Greece, and has 

performed with apparent success (Duan et al., 2002; Sverdrup et al., 1992; H. Sverdrup). 

PROFILE currently accounts for the weathering of 14 different minerals, with the 

potential to include 13 additional minerals, if necessary. Potentially, there may be minerals 

within the United States that are not represented within the 27 that are currently included within 

PROFILE. However, a total of 48 minerals have been investigated by the researchers that 

developed the model (H. Sverdrup personal communication, 2009b). Therefore, it may be 

possible to add additional minerals to PROFILE to ensure that it is able to address BCw in all 

regions of the United States. 

Additional limitations and concerns regarding the application of PROFILE to estimate 

BCw rates have been identified in a thorough review by Hodson and colleagues (1997). Some of 

the main issues brought up by the authors include: the need for a more consistent set of constants 

for the weathering rate equations; inaccuracies in the mineral compositions; errors in the 

calculation to determine surface area; and confounding influences of soil particles greater than 

2mm in size on soil bulk density. Hodson and colleagues (1997) point out the need to reexamine 
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the reaction rate coefficients associated with hornblende, tourmaline, staurolite, kaolinite, garnet, 

augite, biotite and chlorite, arguing that coefficients assigned to these minerals are not correct. 

Similarly, the authors claim that the compositions of the minerals used within PROFILE may be 

incorrect in some applications and may need to be modified by the user to more accurately 

reflect the soil being modeled. Hodson and colleagues (1997) also demonstrate the potential to 

over and underestimate BET surface area using the soil texture equation provided within 

PROFILE. They claim that the equation underestimated the surface area of a British soil by 65%. 

In part, the authors attributed these inaccuracies to the development of the soil texture – surface 

area relationship from only 92 mineral soil samples from Sweden. Lastly, Hodson and colleagues 

(1997) point out the need to recognize soil particles greater than 2mm in size in the soil bulk 

density estimates, as such particles can impact the density by as much as 50% for stony soils. 

The concerns raised by Hodson and colleagues (1997) appear to be valid and should be 

considered by users of the PROFILE model. However, the authors of the review critiqued an 

early version of PROFILE (version 3.01) and the most recent version of PROFILE may have 

already addressed some of these limitations. For example, the abundance of particle sizes greater 

than 2 mm is included in the current regional model of PROFILE (version 5.0). It should also be 

noted that Hodson and colleagues (1997) did acknowledge that despite the apparent weaknesses 

of PROFILE, BCw rates calculated with the model are comparable to those calculated using other 

methods. 

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In addition to the potential limitations of PROFILE as a model, application of PROFILE 

to map BCw rates throughout the United States may also present some drawbacks or restrictions. 

There may be areas of the United States where input data required by the model is not available. 

In such situations, it would be necessary to extrapolate data from areas with similar soil, biotic or 

abiotic conditions. Similarly, if data for specific variables are limited in many areas, it may be 

necessary to adopt best available default values over large areas, until more data and better 

coverage across the states is available. 

4.3.6 “Field Tests” of model and uncertainty analyses 

As outlined in the preceding sections, the proposed methodology to map BCw throughout 

the United States would involve the use of the regional application of PROFILE (version 5.0), 

continuous coverage data, and in some cases, input and default values from the literature. 

Therefore, at least a portion of the input data would not be site specific and would be entered as 
Appendix B 

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class values or generated by sub-models or mathematical relationships. Soil water content and 

soil mineralogy are examples of such data. It is largely unknown to what degree, if any, this 

proposed methodology designed for mapping large areas would influence and potentially distort 

the estimates of BCw. Therefore, to validate the weathering estimates from the proposed mapping 

methodology, it would be worthwhile to conduct “field tests” of the model output in different 

regions of the United States. Such “field tests” could consist of comparing the regional estimates 

of BCw with those determined with the single site version of PROFILE and site-specific data. 

(No actual on-the-ground field research required.) In addition, where available, the PROFILEgenerated BCw rate estimates could be compared with weathering rates determined by other 

methods. Both approaches would provide an indication of the quality and accuracy of estimates 

from the mapping methodology and regional application of PROFILE. Sites within the LongTerm Ecological Research (LTER) network would be good locations for the “field tests” due to 

the large amounts of data available at many of these sites. In addition, at some sites, such as 

Hubbard Brook, base cation weathering has been determined using methods other than the 

PROFILE model. A list of LTER sites within the conterminous 48 states that could potentially 

serve as “field test” sites is presented in Table 6.0. A sub-set of these sites representing different 

regions and conditions within the United States should be selected to validate the BCw estimates. 

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In addition to the validating the proposed methodology with “field test” site comparisons, 

uncertainty analyses should also be conducted on the BCw estimates that are generated with the 

methodology. There are a total of 26 parameters within the regional application of PROFILE 

(version 5.0) that require data entry by the user or review prior to applying the model, and each 

of these parameters could be expected to have a level of uncertainty. Therefore, cumulatively, 

the uncertainties associated with the BCw estimates could be quite large. In addition, because 

BCw is one of the most influential terms in the calculation of terrestrial critical acid loads, and 

critical loads can be used as a measure of the impact of acidifying nitrogen and sulfur deposition 

on terrestrial ecosystems, it is important to gain a good understanding of the uncertainty 

associated with the BCw estimates. Critical acid loads could potentially be used by decision 

makers to set policy and NOx and SOx emission standards within the United States. Furthermore, 

uncertainty analyses can reveal which parameters are the most influential in the BCw estimates, 

thereby guiding which parameters should receive the greatest attention in the development of the 

datasets and national coverages for the PROFILE model. 
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Earlier versions of the PROFILE model (version 3.01) have already been reviewed and 

analyzed by researchers based on the application of the model to sites in Norway, Sweden, 

Scotland and Wales (Jönsson et al., 1995; Hodson et al., 1996; Zak et al., 1996). Monte Carlo 

analyses testing the uncertainty associated with user defined input variables indicated that 

varying input parameter errors individually and simultaneously (within the range of values 

reported in the literature) resulted in a variation in model output of +/- 40% (Jönsson et al., 

1995). The authors also determined that bulk density, volumetric water content and exposed 

surface area of minerals were the largest source of variation in the output values. The least 

sensitive parameters were soil stratification, precipitation and percolation. Similar analyses were 

conducted by Hodson and colleagues (1996) who determined the influence of single input 

parameters, one at a time. Based on their analyses, BCw estimates could vary by over 100% using 

the ranges in parameters values measured in field studies. The authors also found that some 

minerals, such as K-feldspar, were particularly sensitive to variation in input values, and soil 

temperature, moisture content and exposed mineral surface area caused the largest amounts of 

variation in the BCw estimates. These results based on an earlier version of PROFILE suggest 

that ranges in input values can cause the BCw estimates from the model to vary by moderate to 

large amounts. However, the level of uncertainty associated with outputs from the most current, 

regional application of PROFILE (version 5.0) is still unknown. In addition, there has yet to be 

an assessment of the performance of the model in the United States and a determination of how 

ranges in data from different regions in the country would impact the variation in model output. 

Therefore, uncertainty analyses should be conducted as a component of the proposed 

methodology, to provide bounds to the range of output values associated with the BCw estimates 

for terrestrial critical acid load calculations in the United States. 

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5. CONCLUSIONS AND RECOMMENDATIONS 

The goal of this task was to inform EPA about the tools and data available to develop 

maximum deposition loads across the United States for aquatic and terrestrial acidification. In 

particular, this effort focused on methodologies to estimate Bcw, a parameter that plays a crucial 

role in predicting an ecosystem’s ability to neutralize acid deposition. Based on the findings of 

this literature review, discussions with experts, evaluation of tools, and assessment of data 
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availability, two process-based models are recommended: MAGIC for aquatic acidification (with 

extrapolation through regional regression modeling) and PROFILE for terrestrial acidification. 

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It is clear that addressing limitations on soil data availability in the United States will 

require considerable effort to populate both models; however, resources invested to satisfy this 

data need can be leveraged to the benefit of both terrestrial and aquatic modeling goals. It is also 

clear that the MAGIC and PROFILE models’ application in the United States has focused on 

select regions; however, model developers believe these models can be applied successfully in 

other regions, particularly regions with more sensitive ecosystems. Finally, neither MAGIC nor 

PROFILE models are readily accessible for public use. Therefore, it will not be practical to 

assume states and regions could operate the models. Rather, it would be more manageable for the 

models to be run at the Agency level with states and regional offices providing the needed input 

data. 

It is recommended that following EPA review of this report, candidate regions of the 

United States be identified for modeling and levels of effort be estimated to prepare the MAGIC 

and PROFILE models for operation and to collect and/or predict their input data. As part of the 

effort, it is recommended that RTI collaborate with recognized experts in the development and 

application of these two models. 

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Sullivan, T. J. Cosby, B.J. Herlihy, A.T. Webb, J.R. Bulger, A.J. Snyder, K.U. Brewer, P.F. 

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southern Appalachian Mountains, report, E&S Environ. Chem., Inc., Corvallis, OR. 

Sullivan, T.J. 2000. Aquatic Effects of Acidic Deposition. Lewis Publishers: Washington, D.C. 
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Lund, Sweden. 

Sverdrup, H., P. Warfvinge, M. Rabenhorst, A. Janicki, R. Morgan, and M. Bowman. 1992. 

Critical loads and steady-state chemistry for streams in the state of Maryland. 

Environmental Pollution. 77: 195-203. 

Sverdrup H.U. 1990. The Kinetics of Base Cation Release Due to Chemical Weathering. Lund 

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in field bioassays. Ecological Applications 6:408–421. 

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rates in small watersheds: Methodology, recent advances, and future directions. Applied 

Geochemistry 22: 1682–1700. 

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Critical Load with Biological Effects at Ontario Forests. Report 2. Environmental and Resource 

Studies, Trent University, ON, Canada. 

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on freshwater critical load calculations. Environmental Science & Technology. 39: 3255-

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development of critical loads for sulphur and nitrogen. Pp. 33–38 in Monitoring Science 

and Technology Symposium: Unifying Knowledge for Sustainability in the Western 

Hemisphere Proceedings RMRS-P-42CD. Edited by C. Aguirre-Bravo, P.J. Pellicane, 

D.P. Burns, and S. Draggan. U.S. Department of Agriculture, Forest Service, Rocky 

Mountain Research Station, Fort Collins, CO. 
Appendix B 

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Webb, J.R., Deviney, F. A., Galloway, J. N., Rinehart, C. A., Thompson, P. A., & Wilson, S. 

(1994). The acid-base status of native brook trout streams in the mountains of Virginia; a 

regional assessment based on the Virginia trout stream sensitivity study. Charlottesville, 

VA: University of Virginia. 

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pollutant emissions and climatic variability in growth of red spruce in the Great Smoky 

Mountains National Park. Water, Air, and Soil Pollution 159:225-248. 

Wedemeyer, G.A., B.A. Barton, and D.J. MeLeay. 1990. Stress and acclimation. pp. 178–198 in 

Methods for Fish Biology. Edited by C.B. Schreck and P.B. Moyle. Bethesda, MD: 

American Fisheries Society. 

Whitfield, C.J., S.A. Watmough, J. Aherne, and P.J. Dillon. 2006. A comparison of weathering 

rates for acid-sensitive catchments in Nova Scotia, Canada and their impact on critical 

load calculations. Geoderma. 136: 899-911. 

Yin, X. and P.A. Arp. 1993. Predicting forest soil temperatures from monthly mean air 

temperature and precipitation records. Canadian Journal of Forest Research, 23: 2521-

2536. 

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Research 44, W01421, doi:10.1029/2006WR005532. 
Appendix B 

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APPENDIX 1 

Potentially Applicable National-Scale Geochemical Data 

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Currently, there are potentially three consistent national-scale data sets that are most 

appropriate for use in this project: the Shacklette data, the more recent National Geochemical 

Survey data, and the NRCS pedon soil pit (i.e., LIMS database). 

Chemical Analyses of Soils and other Surficial Materials of the Conterminous United 

States (Shacklette Data) & the Geochemical Landscapes Project 

These data provide an ultra-low-density geochemical baseline for soils and other surficial 

materials in the conterminous United States. It is the most widely cited reference for 

geochemical background data and the data are most appropriately used to provide information on 

background concentrations of elements in soil for areas represented by small map scales. 

The data set contains geochemical data from soils and other regolith collected and 

analyzed by Hans Shacklette and colleagues beginning in 1958 and continuing until about 1976. 

Originally compiled as a paper record, the data was later included as part of the original USGS 

PLUTO database. Approximately 1,323 samples were collected through 1976. The 1,323 sample 

locations that comprise the Shacklette data represent a sampling density of approximately 1 

sample per 6,000 square kilometers (metadata); equivalent to the collection of samples on a 75-

km grid across the country. 

The sampling protocol called for removal of loose organic debris from the surface and 

then collection of soil from a depth of 0-20 cm (Smith et al., 2005). Where possible, sample 

locations were selected where surficial materials had been altered very little from their natural 

condition as evidenced by the presence of native plants. The sample material at most sites could 

be termed "soil" because it was a mixture of disintegrated rock and organic matter. Some of the 

sampled deposits, however, were not soils as defined above, but were other regolith types. These 

included desert sands, sand dunes, some loess deposits, and beach and alluvial deposits that 

contained little or no visible organic material. 

This national-level geochemical data set of 1,323 samples has been collected and 

analyzed according to standardized protocols. This is considered one of the principal strengths of 

the data set overall. The samples were chemically analyzed by various but compatible techniques 

in the U.S. Geological Survey laboratories in Denver, CO. Geochemical point-symbol maps were 

plotted for 40 elemental results and published as USGS. Professional Paper 1270 (Shacklette and 

Boerngen, 1984). The original elements analyzed included: Ag, Al, Ba, Be, B, Ca, Ce, Cr, Co, 

Cu, Ga, Ge, Hg, Fe, La, Li, Pb, Mg, Mn, Mo, Na, Nd, Ni, Nb, P, K, Rb, S, Sc, Se, Sr, Th, Ti, U, 

V, Yb, Y, Zn, Zr, and total carbon. A newer set of national-level interpolated maps displaying 

the geochemical distribution for 22 elements using the Shacklette data has since been published 

(Gustavsson, et al, 2001). Using weighted-median and Bootstrap procedures for interpolation and 
Appendix B 

March 2010 98 Draft – Do Not Quote or Cite 

smoothing, full-color maps were produced for seven major elements (Al, Ca, Fe, K, Mg, Na, and 

Ti) and 15 trace elements (As, Ba, Cr, Cu, Hg, Li, Mn, Ni, Pb, Se, Sr, V, Y, Zn, and Zr). 

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The major drawback with the data set is its extremely low numbers of samples for the 

entire conterminous United States. However, more recent high-resolution studies (e.g., Smith et 

al., 2005) have illustrated that the regional patterns established by the Shacklette data are 

generally maintained except where areas have been affected by anthropogenic factors (Smith, 

2006). 

Efforts are also on-going to build upon the Shacklette data by increasing the density of 

the sample locations and producing a high resolution geochemical data set for North America. 

Also referred to as the Geochemical Landscapes Project, this is a collaborative effort by the 

USGS, USDA Natural Resource Conservation Service, other federal agencies, and academia to 

build a national-scale soil geochemical survey that will eventually increase the sample density of 

the Shacklette data set. The Geochemical Landscapes project began in October 2002 in 

collaboration with partners in Canada (Geological Survey of Canada; Agriculture and Agri-Food 

Canada) and Mexico (Consejo de Recursos Minerales/Servicio Geológico de México; Instituto 

Nacional de Estadística Geografia e Informática) that has as its long-term goal a soil 

geochemical survey of North America (Smith et al., 2005). A 3-year pilot project was completed 

n 2004. During the pilot project soil samples were collected for major- and trace-elements from 

265 soil samples collected from two continental-scale transects in North America (Smith et al., 

2005). The project has just completed a third year of continental-sampling and completed sample 

collection for approximately 60% of the conterminous United States (D. Smith personal 

communication, 2009). The state areas that have been completed to date are: ME, NH, VT, CT, 

RI, MA, NY, MO, AR, MS, LA, NV, UT, CO, WY, KS, NJ, MD, WV, DE, NE, FL, SC, GA, 

AL, OK, NM, MT, ID, MN, and SD. The USGS anticipates that sampling may be completed for 

the conterminous US in 2010; or 2011 at the latest. However, funding doesn’t allow for analyses 

to be completed for a number of samples and several hundred grams of each sample is being 

archived for on-going and future analysis. 

National Geochemical Survey (NGS) 

Efforts are on-going by the USGS to produce a new stream-sediment-based geochemical 

survey for the United States at a nominal spacing of 17 by 17 kilometers (i.e., minimum sample 

density of 1 sample per 289 km2

 in all land areas of the country). Project mapping shows that the 

work is either complete or nearly completed. Unlike other national geochemical data collection 

efforts, the analytical routines and standards will be consistent throughout the survey. Analytical 

methods include a 40-element ICP package plus single-element determinations of As, Se, and Hg 

by atomic absorption for every sample. 

The project has sought to capitalize on existing datasets and also achieved samples. For 

this reason the NGS is based primarily on analyses of stream sediments to build on the massive 
Appendix B 

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achieves of data and samples from DOE’s National Uranium Evaluation (NURE) program. 

Much of the survey has entailed reanalysis of approximately 35,000 archival samples from the 

NURE program. Where NURE samples do not exist, USGS has been working with cooperators 

to obtain new samples. The project website reports a total of about 50,000 stream-sediment 

samples that have been analyzed for 42 elements, including arsenic, selenium, and mercury. Last 

reported during 2004, only about 10,000 more samples needed to be collected and analyzed to 

complete the national survey. Samples are generally categorized as follows: 

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8 3⁄4 Inherited Data: Much of the RASS and PLUTO data were inherited into the NGS; 

9 3⁄4 Independent Reanalyses of NURE samples: These sample were reanalyzed by USGS 

projects other than the NGS. Prior to the NGS, numerous USGS projects reanalyzed 

samples from the NURE archives. Other USGS projects have continued to reanalyze 

NURE samples in parallel with the NGS. In the majority of these cases, most or all of the 

NURE samples in an area were reanalyzed. 

3⁄4 NURE-Systematic. Systematic reanalyses of NURE samples done by the NGS. An 

archive of stream sediment and soil samples collected by the NURE program is stored at 

the USGS in Denver, Colo. Rules were established to select a subset of samples for 

reanalysis that maintains the NGS coverage. 

3⁄4 NURE-Targeted. Targeted reanalyses of NURE samples done by the NGS for various 

reasons. 

3⁄4 USGS-Resampling. Reanalyses of USGS archived project samples done by the NGS. The 

archive includes most of the samples for which there are analytical data in the National 

Geochemical Database, including those collected by USGS programs. 

3⁄4 Collaborative Sampling with State Programs. Collaborative sampling programs by the 

USGS and states. 

Digital data files are presented in 6 categories. In total there are 43 individual data files 

for the United States. Some of the data has also been processed into vector data to produce maps 

showing the elemental concentration of As, Se, Hg, Pb, Zn, Cu, Al, Na, Mg, P, Ca, Ti, Mn, and 

Fe at the county level. 

USDA NRCS Soil Pedon Pit Data 

The USDA NRCS measures soil geochemical characteristics along with performing 

quantitative and bulk mineralogy tests and other physiochemical measurements for soil series 

delineated across the United States. This data set and associated detail was discovered through 

communication with NRCS staff (C. Smith personal communication, 2009; T. Reinsch personal 

communication, 2009). Most of the geochemical and mineralogy data is associated with 

individual soil pedons. The NRCS defines a pedon as the smallest unit that can be called a soil. It 

is a three-dimensional sample that extends from the soil surface to the deepest roots or genetic 

soil horizons. The area covered by a pedon varies from 10 - 100 square feet, depending on 

changes in soil properties. Pits are dug to expose the pedons and the NRCS generally refers to 
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the data associated with the pedons as soil pit data. There are currently approximately 30,000 soil 

pits/pedons in the NRCS database. 

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Groups of pedons with very similar characteristics that are closely associated in the 

landscape are called polypedons. Polypedons that have a common set of characteristics that fall 

within a particular range are delineated as a basic soil unit referred to as a soil series which have 

been identified as the basic unit of the proposed data framework, as previously discussed. The 

same soil series delineations can occur in different and distant areas (i.e., across county areas, 

states, or regions). A variety of data are used to define a soil (e.g., geomorphic position in the 

landscape, relationship to the water-table, supported flora, geology, number and type of horizons, 

sediment texture, sediment color variations, etc.), and therefore geochemical and mineralogy 

data has not been collected from every soil pedon associated with an individual series of the 

same name since associations can be made based on a number of these other related 

characteristics. However, geochemical and quantitative mineralogy data has been measured for a 

significant number of pedons and soil series locations across the country. 

Since soils of the same series name possess enough similarities to be classified as similar 

soils it is thought that the geochemistry data can also be extrapolated to pedons of like soil series 

(C. Smith personal communication, 2009). Assigning mineral phases to the soil series that do not 

have either geochemical or mineralogy data associated with their pedons will require 

professional judgment by researchers familiar with the soil pit data and soil taxonomy to make 

geochemical data extrapolations with a degree of confidence. In these cases the characteristics of 

surrounding soils would be used to extrapolate geochemistry or mineralogy, or another data set 

could be used to aid in the characterization. GIS tools would be used to help automate these 

determinations where necessary. NRCS staff would aid RTI in determining rules and developing 

database relationship tables that could be used in automating any extrapolation of this data. The 

NRCS would also aid RTI in evaluating the reasonableness of the results. 

Since soil series are delineated across the conterminous United States the pit data could 

potentially provide a complete geochemical and mineralogy data layer for determining 

mineralogy. Since mineralogy is already associated with the geochemical data a more accurate 

assignment of mineral modes may be possible using this data set. Laboratory analysis includes 

the major geochemical elements: Al, Ca, Fe, K, Mg, Mn, Na, P, Si, Sr, Ti, and Zr. In addition, xray diffraction is used to indentify clay mineralogy generally for each horizon of a pedon, and 

optical mineralogy analysis is performed on the dominant sand fractions of the soil from the Ahorizon, B-horizon, and C-horizon, or the most dominant horizon. More than 60 fields describing 

the minerals are listed in the database. The dataset is not uniform in that elemental analyses were 

routinely done through the 1970’s but then these analyses were suspended through the 1980’s. 

Elemental analyses were resumed during the early 1990’s. It is estimated that as much as one 

third of the 30,000 soil pedons have geochemical data. Likewise, optical mineralogy is not 

performed for all pedons and the NRCS staff estimate that approximately as many as one third of 

the 30,000 soil pits have optical analysis results. Even though the number of pedons with data are 
Appendix B 

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similar for geochemical and optical analysis results the data is not necessarily associated with the 

same set of pedons or even soil series. 

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References 

Smith, Chris, 2009. Personal communication between Chris Smith, (affiliation) and Scott 

Guthrie, RTI International, 12/16/09. 

Reinsch, Thomas, 2009. Personal communication between Thomas Reinsch, (affiliation) and 

Scott Guthrie, RTI International, 12/18/09. 
Appendix B 

March 2010 102 Draft – Do Not Quote or Cite 

APPENDIX 2 

References for Table 3-2: Applications of the MAGIC Model 

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Aherne, J, P.J. Dillon, and B.J. Cosby. 2003. Acidification and recovery of aquatic ecosystems in 

south central Ontario, Canada: regional application of the MAGIC model. Hydrol. Earth 

Syst. Sci 7: 561-573. 

Bernett, J.A., J.M. Eilers, and B.J. Cosby. 1997. Overview, Libby Lake Modeling Workshop. 

E&S Environmental Chemistry, Inc., Corvallis, OR. 

Bulger A.J., B.J. Cosby, and J.R. Webb. 2000. Current, reconstructed past, and projected future 

status of brook trout (Salvelinus fontinalis) streams in Virginia. Canadian Journal of Fish 

and Aquatic Science 57:1515-1523. 

Bulger, A.J., C.A. Dolloff, B.J. Cosby, K.N. Eshleman, J.R. Webb, and J.N. Galloway. 1995. 

Sensitivity of Blacknose Dace (Rhinichthys Atratulus) to Moderate Acidification Events 

in Shenandoah National Park, U.S.A. Water, Air, & Soil Pollution 153(1-4): 125-134. 

Church, M. R. and J. Van Sickle. 1999. Potential relative future effects of sulfur and nitrogen 

deposition on lake chemistry in the Adirondack Mountains, United States. Water 

Resource. Res. 35: 2199-2211. 

Cosby, B.J. and T.J. Sullivan. 2001. Quantification of dose-response relationships and critical 

loads of sulfur and nitrogen for six headwater catchments in Rocky Mountain, Grand 

Teton, Sequoia, and Mount Rainer national parks. E&S Report 97-15-01. 

Dennis, I.F., T.A. Clair, and B.J. Cosby. 2005. Testing the MAGIC acid rain model in highly 

organic, low-conductivity waters using multiple calibrations. Environmental Modeling 

and Assessment 10(4): 303-314. 

Eilers J.M., B.J. Cosby, J.A. Bernet, and T.A. Sullivan. 1998. Analysis of the Response of Shasta 

Lake, Idaho to Increases in Atmospheric Sulfur and Nitrogen Using the MAGIC 

MODEL. E&S Environmental Chemistry, Inc., Corvallis, OR. 
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Ellis, H., and M. Bowman. 1994. Critical Loads and Development of Acid Rain Control Options. 

Journal of Environmental Engineering 120(2): 273-289 

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Sinha, R., M.J. Small, P.F. Ryan, T.J. Sullivan, and B.J. Cosby. 1998. Reduced-Form Modelling 

of Surface Water and Soil Chemistry for the Tracking and Analysis Framework. Water, 

Air, & Soil Pollution 105(3-4): 617-642. 

Sullivan, T.J. and B.J. Cosby. 2004. Aquatic Critical Load Development for the Monongahela 

National Forest, West Virginia. Report prepared for the USDA Forest Service 

Monongahela National Forest. 

Sullivan, T.J. and B.J. Cosby. 2002. Critical Loads of Sulfur Deposition to Protect Streams 

within Joyce Kilmer and Shining Rock Wilderness Areas from Future Acidification. 

Report for the USDA Forest Service. 

Sullivan, T.J., and J.M. Eilers. 1996. Assessment of Deposition Levels of sulfur and Nitrogen 

Required to Protect Aquatic Resources in Selected Sensitive Regions of North America. 

E and S Environmental Chemistry, EPA/600/R-96-123, Corvallis Environmental 

Research Lab, OR. 

Sullivan, T.J., B.J. Cosby, K.A. Tonnessen, and D.W. Clow. 2005. Surface water acidification 

responses and critical loads of sulfur and nitrogen deposition in Loch Vale watershed, 

Colorado. Water Resources Research 41: WO1021. 

Sullivan, T.J., B.J. Cosby, A.T. Herlihy, J.R. Webb, A.J. Bulger, K.U. Snyder, P.F. Brewer, E.H. 

Gilbert, and D.L. Moore. 2004. Regional model projections of future effects of sulfur and 

nitrogen deposition on streams in the southern Appalachian Mountains. Water Resour. 

Res. 40: W02101. 

Sullivan, T.J., B.J. Cosby, J.A. Bernert, and J.M. Eilers. 1998. Model Evaluation of 

dose/response relationships and critical loads for nitrogen and sulfur deposition to the 

watersheds of lower saddlebag and white dome lakes. 
United States 

Environmental Protection Agency 

Office of Air Quality Planning and Standards 

Health and Environmental Impacts Division 

Research Triangle Park, NC 

Publication N