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Policy Assessment for the Review of the
Secondary National Ambient Air Quality
Standards for Oxides of Nitrogen, Oxides of
Sulfur and Particulate Matter
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EPA-452/R-24-003
January 2024
Policy Assessment for the
Review of the Secondary National Ambient Air Quality
Standards for
Oxides of Nitrogen, Oxides of Sulfur and Particulate Matter
U.S. Environmental Protection Agency
Office of Air Quality Planning and Standards
Health and Environmental Impacts Division
Research Triangle Park, NC
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DISCLAIMER
This document has been prepared by staff in the U.S. Environmental Protection Agency's
Office of Air Quality Planning and Standards. Any findings and conclusions are those of the
authors and do not necessarily reflect the views of the Agency. This document does not represent
and should not be construed to represent any Agency determination or policy. Mention of trade
names or commercial products does not constitute endorsement or recommendation for use.
1
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TABLE OF CONTENTS
1 INTRODUCTION 1-1
1.1 Purpose 1-2
1.2 Legislative Requirements 1-3
1.3 Background on Criteria and Secondary Standards for Nitrogen Oxides and Sulfur
Oxides and Particulate Matter 1-5
1.3.1 Nitrogen Oxides 1-5
1.3.2 Sulfur Oxides 1-6
1.3.3 Related Actions Addressing Acid Deposition 1-8
1.3.4 Most Recent Review of the Secondary Standards for Oxides of Nitrogen
and Oxides of Sulfur 1-9
1.3.5 Particulate Matter 1-11
1.4 Current Review 1-14
1.5 Organization of This Document 1-16
References 1-18
2 AIR QUALITY AND DEPOSITION 2-1
2.1 Atmospheric Transformation of Nitrogen, Sulfur, and PM Species 2-1
2.1.1 Oxides of Sulfur 2-2
2.1.2 Oxidized Nitrogen 2-2
2.1.3 Reduced Nitrogen 2-3
2.1.4 Atmospheric Processing 2-4
2.2 Sources and Emissions of Nitrogen, Sulfur, and PM Species 2-4
2.2.1 NOx Emissions Estimates and Trends 2-5
2.2.2 SO2 Emissions Estimates and Trends 2-8
2.2.3 NH3 Emissions Estimates and Trends 2-10
2.3 Monitoring Ambient Air Concentrations and Deposition 2-13
2.3.1 NOx Monitoring Networks 2-14
2.3.2 SO2 Monitoring Networks 2-15
2.3.3 PM2.5 and PM10 Monitoring Networks 2-16
2.3.4 Routine Deposition Monitoring 2-18
2.3.5 Satellite Retrievals 2-22
2.4 Recent Ambient Air Concentrations and Trends 2-23
2.4.1 NO2 Concentrations and Trends 2-23
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2.4.2 S02 Concentrations and Trends 2-28
2.4.3 PM2.5 Concentrations and Trends 2-34
2.4.4 Ammonia Concentrations and Trends 2-40
2.5 Nitrogen and Sulfur Deposition 2-42
2.5.1 Estimating Atmospheric Deposition 2-42
2.5.2 Uncertainty in Estimates of Atmospheric Deposition 2-44
2.5.3 National Estimates of Deposition 2-47
2.5.3.1 Contribution from NH3 2-52
2.5.3.2 Contribution from International Transport 2-53
2.5.4 Trends in Deposition 2-54
References 2-62
3 CURRENT STANDARDS AND GENERAL APPROACH FOR THIS REVIEW 3-1
3.1 Basis for the Existing Secondary Standards 3-1
3.2 Prior Review of Deposition-Related Effects 3-3
3.3 General Approach for This Review 3-6
3.3.1 Approach for Direct Effects of the Pollutants in Ambient Air 3-10
3.3.2 Approach for Deposition-Related Ecological Effects 3-10
3.3.3 Identification of Policy Options 3-13
References 3-15
4 NATURE OF WELFARE EFFECTS 4-1
4.1 Direct Effects of Oxides of N and S in Ambient Air 4-2
4.2 Acid Deposition-Related Ecological Effects 4-5
4.2.1 Freshwater Ecosystems 4-5
4.2.1.1 Nature of Effects and New Evidence 4-6
4.2.1.2 Freshwater Ecosystem Sensitivity 4-8
4.2.1.3 Key Uncertainties 4-12
4.2.2 Terrestrial Ecosystems 4-12
4.2.2.1 Nature of Effects and New Evidence 4-12
4.2.2.2 Terrestrial Ecosystem Sensitivity 4-14
4.2.2.3 Key Uncertainties 4-15
4.3 Nitrogen Enrichment and Associated Effects 4-16
4.3.1 Aquatic and Wetland Ecosystems 4-17
4.3.1.1 Nature of Effects and New Evidence 4-18
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4.3.1.2 Aquatic Ecosystem Sensitivity 4-20
4.3.1.3 Key Uncertainties 4-24
4.3.2 Terrestrial Ecosystems 4-24
4.3.2.1 Nature of Effects and New Evidence 4-25
4.3.2.2 Terrestrial Ecosystem Sensitivity 4-27
4.3.2.3 Key Uncertainties 4-28
4.4 Other Deposition-Related Effects 4-29
4.4.1 Mercury Methylation 4-29
4.4.2 Sulfide Toxicity 4-30
4.4.3 Ecological Effects of PM Other Than N and S Deposition 4-30
4.5 Public Welfare Implications 4-31
References 4-39
5 EXPOSURE CONDITIONS ASSOCIATED WITH EFFECTS 5-1
5.1 Aquatic Ecosystem Acidification 5-3
5.1.1 Role of ANC as Acidification Indicator 5-4
5.1.2 Conceptual Model and Analysis Approach 5-10
5.1.2.1 Spatial Scale 5-12
5.1.2.2 Chemical Indicator 5-15
5.1.2.3 Critical Load Estimates Based on ANC 5-16
5.1.2.4 Critical Load-Based Analysis 5-18
5.1.2.5 Waterbody Deposition Estimates 5-19
5.1.3 Estimates for Achieving ANC Targets with Different Deposition Levels 5-19
5.1.3.1 National-scale Analysis 5-20
5.1.3.2 Ecoregion Analyses 5-25
5.1.3.3 Case Study Analyses 5-42
5.1.4 Characterization of Uncertainty 5-43
5.1.5 Summary of Key Findings 5-46
5.2 Nitrogen Enrichment in Aquatic Ecosystems 5-48
5.2.1 Freshwater Wetlands 5-49
5.2.2 Freshwater Lakes and Streams 5-49
5.2.3 Estuaries, Coastal Waters and Coastal Wetlands 5-50
5.2.4 Summary: Key Findings and Associated Uncertainties 5-52
5.3 Effects of S and N Deposition in Terrestrial Ecosystems 5-53
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5.3.1 Soil Chemistry Response 5-55
5.3.2 Effects on Trees 5-57
5.3.2.1 Steady-State Mass Balance Modeling of Terrestrial Acidification.... 5-57
5.3.2.2 Experimental Addition Studies 5-60
5.3.2.3 Observational or Gradient Studies 5-61
5.3.3 Other Effects 5-65
5.3.3.1 Effects on Herbs and Shrubs 5-65
5.3.3.2 Effects on Lichen 5-67
5.3.4 Summary: Key Findings and Associated Uncertainties 5-68
5.3.4.1 Deposition and Risks to Trees 5-69
5.3.4.2 Deposition Studies of Herbs, Shrubs and Lichens 5-72
5.4 Other Effects of Oxides of N and S and of PM in Ambient Air 5-73
5.4.1 Sulfur Oxides 5-74
5.4.2 Nitrogen Oxides 5-75
5.4.3 Particulate Matter 5-76
References 5-78
6 RELATIONSHIPS OF DEPOSITION TO AIR QUALITY METRICS 6-1
6.1 Overview 6-1
6.1.1 Review of the Processes Affecting Atmospheric Deposition 6-2
6.1.2 Scales of Influence for Depositional Pathways Amid a Changing Chemical
Environment 6-3
6.1.3 Analyses in the 2012 Review (Transference Ratio) 6-5
6.1.4 Organization of this Chapter 6-6
6.2 Relating Air Quality to Deposition 6-6
6.2.1 Historical Trend Analyses of Emissions, Concentrations, and Deposition 6-6
6.2.2 Class I Area Sites - Relationships Between Air Concentrations and
Deposition 6-28
6.2.2.1 Relationships in Chemical Transport Model Simulations 6-32
6.2.2.2 Relationships between Air Quality and Wet Deposition Observations.. 6-
40
6.2.2.3 Relationships between Observed Air Quality and TDep Estimates of
Deposition 6-45
6.2.2.4 Conclusions 6-51
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6.2.3 National SLAMS Network - Relationships Between Air Concentrations
and Deposition 6-51
6.2.4 National-scale Sites of Influence Analyses 6-63
6.2.4.1 Methodology 6-63
6.2.4.2 Results 6-65
6.2.4.3 Conclusions 6-81
6.3 Limitations and Uncertainties 6-82
6.3.1 Characterization of Uncertainty 6-84
6.3.2 Sensitivity Analyses Related to Aspects of Trajectory-Based Assessment... 6-89
6.4 Key Observations 6-91
6.4.1 SO: Metrics 6-93
6.4.2 NO2 and PM2.5 Metrics 6-96
References 6-100
7 REVIEW OF THE STANDARDS 7-1
7.1 Evidence and Exposure/Risk Based Considerations for Effects Other than Ecosystem
Deposition-related Effects of S and N 7-2
7.1.1 Sulfur Oxides 7-2
7.1.2 Nitrogen Oxides 7-3
7.1.3 Particulate Matter 7-5
7.2 Evidence and Exposure/Risk-Based Considerations for S and N Deposition-Related
Effects 7-6
7.2.1 Evidence of Ecosystem Effects of S and N deposition 7-7
7.2.2 S Deposition and S Oxides 7-9
7.2.2.1 Quantitative Information for Ecosystem Risks Associated with S
Deposition 7-10
7.2.2.2 General Approach for Considering Public Welfare Protection 7-15
7.2.2.3 Relating SOx Air Quality Metrics to Deposition of S Compounds.... 7-27
7.2.3 N Deposition and N Oxides and PM 7-37
7.2.3.1 Quantitative Information for Ecosystem Risks Associated with N
Deposition 7-37
7.2.3.2 General Approach for Considering Public Welfare Protection 7-40
7.2.3.3 Relating Air Quality Metrics to N Deposition-related Effects of N
Oxides and PM 7-46
7.3 CASAC Advice and Public Comments 7-55
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7.4 Summary of Staff Conclusions 7-61
7.5 Areas for Future Research Related to Key Uncertainties 7-83
References 7-85
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CHAPTER APPENDICES
APPENDIX 5A. RISK AND EXPOSURE ASSESSMENT FOR AQUATIC ACIDIFICATION
APPENDIX 5B. ADDITIONAL DETAIL RELATED TO KEY TERRESTRIAL
ECOSYSTEM STUDIES
APPENDIX 6A. DERIVATION OF THE ECOREGION AIR QUALITY METRICS
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TABLE OF TABLES
Table 2-1. Average annual mean NO2 concentration in 1967-1971 in select cities 2-26
Table 2-2. Regional changes in deposition between 2000-2002 and 2019-2021: (a) total S
deposition; (b) total, oxidized and reduced N deposition (U.S. EPA, 2022b) 2-55
Table 3-1. Existing secondary standards for S oxides, PM, and N oxides 3-3
Table 5-1. Percentage of waterbodies nationally for which annual average S deposition
during the five time periods assessed exceed the waterbody CL (for CLs>0) for
each of the ANC targets 5-20
Table 5-2. Ecoregion median S deposition estimates derived as medians of all ecoregion
grid cell estimates (TDep) 5-28
Table 5-3. Summary of ecoregion medians derived as median of TDep S deposition
estimates at CL sites within each ecoregion 5-28
Table 5-4. Number of ecoregion-time period combinations with more than 10, 15, 20, 25,
and 30% of waterbodies exceeding their CLs for three ANC targets as a
function of ecoregion-level estimates of annual average S deposition 5-31
Table 5-5. Percentage of ecoregion-time periods combinations with at least 90, 85, 80, 75
and 70% of waterbodies estimated to achieve an ANC at/above the ANC
targets of 20, 30 and 50 |ieq/L as a function of annual average S deposition for
18 eastern ecoregions (90 ecoregion-time period combinations) 5-34
Table 5-6. Annual average S deposition at/below which modeling indicates an ANC of 20,
30 or 50 |aeq/L can be achieved in the average, 70% and 90% of waterbodies in
each study area 5-43
Table 5-7. Acid deposition levels estimated for BC:A1 targets in 24-state range of red
spruce and sugar maple using steady-state simple mass balance model (2009
REA) 5-59
Table 5-8. Acidic deposition levels estimated for several BC:A1 ratio targets by steady-
state mass balance modeling for sites in northeastern U.S 5-60
Table 5-9. Tree effects and associated S/N deposition levels from observational studies
using USFS/FIA data 5-64
Table 6-1. Estimated atmospheric lifetimes of S- and N-containing species based on
literature review 6-4
Table 6-2. Regional changes in deposition between 2000-2002 and 2019-2021: (a) total S
deposition; (b) total, oxidized and reduced N deposition (U.S. EPA, 2022) 6-15
Table 6-3. Collocated CASTNET, NADP/NTN, and IMPROVE monitoring stations used
in this analysis of air concentration and deposition 6-30
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Table 6-4. Correlation coefficients for TDep-estimated S deposition and annual average
SO2 concentrations (averaged over three years) at SLAMS in the CONUS, by
time period and region 6-53
Table 6-5. Correlation coefficients for TDEP-estimated S deposition and annual second
highest 3-hr SO2 concentration (averaged over three years), at SLAMS in the
CONUS by region and time period 6-57
Table 6-6. Correlation coefficients for N deposition (TDep) and annual average NO2
concentrations (averaged over three years) at SLAMS in the CONUS by region
and time period 6-61
Table 6-7. Correlation coefficients for TDep-estimated N deposition and annual average
PM2.5 concentrations (averaged across three years) at SLAMS in the CONUS... 6-63
Table 6-8. Correlation coefficients of TDep-estimated S deposition and annual SO2
EAQMs by time period and region 6-69
Table 6-9. Correlation coefficients of TDep-estimated ecoregion median S deposition and
3-hr SO2 EAQM values at upwind site of influence by time period and region. . 6-72
Table 6-10. Correlation coefficients of ecoregion N deposition and upwind NO2 annual
EAQM values by time period and region 6-75
Table 6-11. Correlation coefficients of PM2.5 EAQM values with TDep-estimated median
N deposition in downwind ecoregions 6-78
Table 6-12. Correlation coefficients of TDep-estimated median S deposition and upwind
PM2.5 EAQM values 6-81
Table 6-13. Characterization of key uncertainties in analyses that relate air quality to
deposition 6-85
Table 7-1. Summary of the eastern ecoregion and time period combinations achieving
different ANC targets with estimated S deposition at or below different values. 7-18
Table 7-2. Ecoregions estimated to have different percentages of waterbodies achieving
different ANC targets for the five deposition periods analyzed 7-19
Table 7-3. Summary of current standards and range of potential policy options for
consideration 7-82
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TABLE OF FIGURES
Figure 2-1. Schematic of most relevant individual pollutants that comprise oxides of
nitrogen, oxides of sulfur, and particulate matter 2-1
Figure 2-2. 2020 NOx emissions estimates by source sector (U.S. EPA, 2023a). Note: The
NEI, and this figure, do not include emissions from lightning 2-6
Figure 2-3. 2020 NOx emissions density across the U.S. (U.S. EPA, 2023a) 2-6
Figure 2-4. Trends in NOx emissions by sector between 2002 and 2022 (U.S. EPA, 2023b).. 2-7
Figure 2-5. Estimates of 2020 SO2 emissions by source sector (U.S. EPA, 2023a) 2-8
Figure 2-6. Estimates of 2020 SO2 emissions density across the U.S. (U.S. EPA, 2023a) 2-9
Figure 2-7. Trends in SO2 emissions by sector between 2002 and 2022 (U.S. EPA, 2023b). 2-10
Figure 2-8. Estimates of 2020 NH3 emissions by source sector (U.S. EPA, 2023a) 2-11
Figure 2-9. Estimates of NH3 emissions density across the U.S. (U.S. EPA, 2023a) 2-12
Figure 2-10. Trends in NH3 emissions by sector between 2002-2022 (U.S. EPA, 2023b) 2-13
Figure 2-11. Locations of NO2 monitors operating during the 2019-2021 period 2-15
Figure 2-12. Locations of SO2 monitors operating during the 2019-2021 period 2-16
Figure 2-13. PM2.5 mass monitors operating during the 2019-2021 period 2-17
Figure 2-14. PM10 mass monitors operating during the 2019-2021 period 2-17
Figure 2-15. PM2.5 speciation monitors operating during the 2019-2021 period 2-18
Figure 2-16. Location of NTN monitoring sites with different symbols for how many years
the site has operated (through 2017) 2-19
Figure 2-17. Location of CASTNET monitoring sites and the organizations responsible for
collecting data. (NPS = National Park Service, BLM = Bureau of Land
Management) 2-20
Figure 2-18. Location of AMoN monitoring sites with sites active shown in dark blue and
inactive sites in light blue. (There is an additional site in AK not shown here.).. 2-22
Figure 2-19. Design values for the 1-hour primary NO2 NAAQS (98th percentile of daily
maximum 1-hour concentrations, averaged over 3 years; ppb) at monitoring
sites with valid design values for the 2019-2021 period 2-24
Figure 2-20. Primary and secondary NO2 annual design values for 2021 2-25
Figure 2-21. Distributions of annual 98th percentile, maximum 1-hour NO2 values at U.S.
sites 2-25
Figure 2-22. Distributions of annual mean NO2 values at U.S. sites 2-26
Figure 2-23. Annual average concentrations of HNO3 in: 1996 (top) and 2019 (bottom) 2-27
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Figure 2-24. Primary SO2 standard design values (99th percentile of 1-hour daily maximum
concentrations, averaged over 3 years) for the 2019-2021 period at monitoring
sites with valid design values 2-29
Figure 2-25. Secondary SO2 standard design values (2nd highest 3-hourly average) for the
year 2021 at monitoring sites with valid design values 2-29
Figure 2-26. Distributions of 99th percentile of maximum daily 1-hour SO2 design values at
U.S. sites (1980-2021) 2-30
Figure 2-27. Distributions of secondary SO2 standard design values at U.S. sites, excluding
sites in Hawaii (2000-2021) 2-31
Figure 2-28. Distribution of annual average SO2 concentrations (ppb) at SLAMS in the U.S.,
excluding Hawaii (2000-2021) 2-32
Figure 2-29. Relationship of annual SO2 concentrations, averaged across three years, to
design values for the current 3-hr secondary standard (upper) and the 1-hr
primary standard (lower) at SLAMS (2000-2021). Sites in Hawaii excluded 2-33
Figure 2-30. Map showing pie charts of PM2.5 component species at selected U.S.
monitoring sites based on 2019-2021 data 2-34
Figure 2-31. Primary and secondary annual PM2.5 standard design values (2019-2021) 2-36
Figure 2-32. Primary and secondary 24-hour PM2.5 design values (2019-2021 period) 2-36
Figure 2-33. Annual average NO3" concentrations (|ig/m3) as measured at selected NCore,
CSN, and IMPROVE sites for the 2019-2021 period 2-37
Figure 2-34. Annual average SO42" concentrations (|ig/m3) as measured at selected NCore,
CSN, and IMPROVE sites for the 2019-2021 period 2-37
Figure 2-35. Trends in annual average concentrations for nitrate (NO3") as measured at
selected NCore, CSN, and IMPROVE sites from 2006 through 2021 2-38
Figure 2-36. Trends in annual average concentrations for sulfate (SO42") as measured at
selected NCore, CSN, and IMPROVE sites from 2006 through 2021 2-38
Figure 2-37. Distributions of annual mean PM2.5 design values (|ig/m3) at U.S. sites across
the 2000-2021 period 2-39
Figure 2-38. Distributions of the annual 98th percentile 24-hour PM2.5 design values (|ig/m3)
at U.S. sites across the 2000-2021 period 2-40
Figure 2-39. Annual average ammonia concentrations as measured by the Ammonia
Monitoring Network in 2010 (top) and 2020 (bottom). Data source: NADP
(2012) and NADP (2021) 2-41
Figure 2-40. Data sources for calculating total deposition. Dark blue indicates observations,
white boxes indicate chemical transport modeling results, and light blue boxes
are the results of model-measurement fusion 2-44
Figure 2-41. Annual average total deposition of nitrogen (2019-2021) 2-48
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Figure 2-42. Annual average dry deposition of nitrogen (2019-2021) 2-48
Figure 2-43. Annual average wet deposition of nitrogen (2019-2021) 2-49
Figure 2-44. Annual average wet deposition of ammonium (2019-2021) 2-49
Figure 2-45. Annual average wet deposition of nitrate (2019-2021) 2-50
Figure 2-46. Annual average total deposition of sulfur (2019-2021) 2-51
Figure 2-47. Percentage of total deposition of sulfur that occurs as wet deposition across the
2019-2021 period 2-51
Figure 2-48. Annual average dry deposition of ammonia (2019-2021) 2-52
Figure 2-49. Average percent of total N deposition in 2019-2021 as reduced N (gas phase
NH3 and particle phase NH4+) 2-53
Figure 2-50. TDEP-estimated total S deposition: 2000-2002 (left) and 2019-2021 (right) 2-56
Figure 2-51. TDEP-estimated total N deposition: 2000-2002 (left) and 2019-2021 (right) 2-56
Figure 2-52. TDEP-estimated dry N deposition: 2000-2002 (left) and 2019-2021 (right) 2-57
Figure 2-53. TDEP-estimated wet N deposition: 2000-2002 (left) and 2019-2021 (right) 2-57
Figure 2-54. TDEP-estimated dry oxidized N deposition: 2000-2002 (left) and 2019-2021
(right) 2-58
Figure 2-55. TDEP-estimated dry reduced N deposition: 2000-2002 (left) and 2019-2021
(right) 2-58
Figure 2-56. TDEP-estimated NH3 deposition: 2000-2002 (left) and 2019-2021 (right) 2-58
Figure 2-57. Projected percent change in total N deposition in Class 1 areas from 2016,
based on a scenario for 2032 that includes implementation of existing national
rules on mobile and stationary sources (U.S. EPA, 2022a) 2-60
Figure 2-58. Projected percent change in total S deposition in Class 1 areas from 2016,
based on a scenario for 2032 that includes implementation of existing national
rules on mobile and stationary sources (U.S. EPA, 2022a) 2-61
Figure 3-1. Overview of general approach for review of the secondary N oxides, SOx, and
PM standards 3-8
Figure 3-2. General approach for assessing the currently available information with regard
to consideration of protection provided for deposition-related ecological effects
on the public welfare 3-11
Figure 4-1. Surface water ANC map, based on data compiled by Sullivan (2017) (ISA,
Appendix 8, Figure 8-11) 4-11
Figure 4-2. Conceptual model of the influence of atmospheric N deposition on freshwater
nutrient enrichment (ISA, Appendix 9, Figure 9-1) 4-19
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Figure 4-3. Potential effects on the public welfare of ecological effects of N Oxides, SOX,
and PM 4-37
Figure 4-4. Locations of areas designated Class I under section 162(a) of the Clean Air
Act 4-38
Figure 5-1. Total macroinvertebrate species richness as a function of pH in 36 streams in
western Adirondack Mountains of New York, 2003-2005. From Baldigo et al.
(2009); see ISA, Appendix 8, section 8.3.3 and p. 8-12 5-6
Figure 5-2. Critical aquatic pH range for fish species. Notes: Baker and Christensen (1991)
generally defined bioassay thresholds as statistically significant increases in
mortality or by survival rates less than 50% of survival rates in control waters.
For field surveys, values reported represent pH levels consistently associated
with population absence or loss. Source: Fenn et al. (2011) based on Baker and
Christensen (1991). (ISA, Appendix 8, Figure 8-3) 5-7
Figure 5-3. Number of fish species per lake versus acidity status, expressed as ANC, for
Adirondack lakes. Notes: The data are presented as the mean (filled circles) of
species richness within 10 [j,eq/L ANC categories, based on data collected by
the Adirondacks Lakes Survey Corporation. Source: Modified from Sullivan et
al. (2006) (ISA, Appendix 8, Figure 8-4) 5-9
Figure 5-4. Conceptual model for aquatic acidification analyses 5-11
Figure 5-5. Level II ecoregions of the contiguous U.S 5-13
Figure 5-6. Level III ecoregions grouped into acid sensitivity categories 5-14
Figure 5-7. Waterbodies for which annual average S only deposition for 2001-03 exceed
CLs for ANC thresholds: a. 20, b. 30, c. 50, d, 50/20 [j,eq/L 5-21
Figure 5-8. Waterbodies for which annual average S only deposition for 2006-08 exceed
CLs for ANC thresholds: a. 20, b. 30, c. 50, d, 50/20 [j,eq/L 5-22
Figure 5-9. Waterbodies for which annual average S only deposition for 2010-12 exceed
CLs for ANC thresholds: a. 20, b. 30, c. 50, d, 50/20 [j,eq/L 5-23
Figure 5-10. Waterbodies for which annual average S only deposition for 2014-16 exceed
CLs for ANC thresholds: a. 20, b. 30, c. 50, d, 50/20 [j,eq/L 5-24
Figure 5-11. Waterbodies for which annual average S only deposition for 2018-20 exceed
CLs for ANC thresholds: a. 20, b. 30, c. 50, d, 50/20 [j,eq/L 5-25
Figure 5-12. Locations of aquatic critical loads (x's) within level III ecoregion boundaries. .. 5-27
Figure 5-13. Percentage of waterbodies exceeding their CLs per ecoregion for ANC of 20
|ieq/L, with shading indicating the maximum ecoregion percentage exceeding
CLs for ANC of 50 |ieq/L (upper panel). Symbols on the upper line of the grey
shaded area indicate the ecoregion with this maximum. Ecoregion locations are
shown on map (lower panel), with bold indicating those designated as "West"
(N=7) and regular font indicating eastern ecoregions (N=18) 5-29
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Figure 5-14. Percentage of ecoregion-time period combinations with less than or equal to
10, 15, 20, 25 and 30% of waterbodies exceeding their CLs for ANC of 20
(top), 30 (middle) and 50 |ieq/L (bottom) for 18 eastern ecoregions 5-33
Figure 5-15. Percentage of waterbodies in each of the 25 ecoregions estimated to achieve
ANC values of 20 (E&W), 30 (E only) and 50 (E only) |ieq/L as a function of
ecoregion annual average S deposition for 2014-2016 (median across CL sites).5-36
Figure 5-16. Percent of waterbodies in each of the 25 ecoregions estimated to achieve ANC
values of 20 (E&W), 30 (E only) and 50 (E only) |ieq/L as a function of
ecoregion annual average S deposition for 2018-2020 (median across CL sites).5-37
Figure 5-17. Map of critical load exceedances for S only deposition from 2018-20 (top) and
2014-16 (bottom) for ANC threshold of 20 [j,eq/L 5-38
Figure 5-18. Map of critical load exceedances for S only deposition from 2018-20 (top) and
2014-16 (bottom) for an ANC threshold of 30 [j,eq/L 5-39
Figure 5-19. Map of critical load exceedances for S only deposition from 2018-20 (top) and
2014-16 (bottom) for an ANC threshold of 50 [j,eq/L 5-40
Figure 5-20. Map of critical load exceedances for S only deposition from 2018-20 (top) and
2014-16 (bottom) for an ANC threshold of 50 [j,eq/L for East and 20 [j,eq/L for
the West 5-41
Figure 5-21. Location of the five case study areas 5-42
Figure 6-1. General approach for assessing the currently available information with regard
to consideration of protection provided for deposition-related ecological effects
on the public welfare 6-1
Figure 6-2. Primary pathways by which emitted pollutants are transformed and deposited.
Blue arrows indicate that chemical transformation can occur during transport.
Bold arrow indicates primary loss mechanism pathway. Bolded pollutants are
NAAQS indicators; grey font is for non-criteria pollutant (ammonia) 6-4
Figure 6-3. Trends in NO + NO2 emissions by sector from 2002 to 2022 (U.S. EPA,
2023b) 6-7
Figure 6-4. Trends in SO2 emissions by sector from 2002 to 2022 (U.S. EPA, 2023b) 6-7
Figure 6-5. Trends in NH3 emissions by sector from 2002 to 2022 6-8
Figure 6-6. Trends in design values for the annual and hourly NO2 standards (2000 and
2022). (blue = primary and secondary annual standard; brown = primary 1-hour
standard) 6-9
Figure 6-7. Trends in design values for the primary SO2 standard (99th percentile of 1-hour
daily maximum concentrations, averaged over three years) (upper panel) and in
annual average SO2 concentrations at SLAMS in the U.S., excluding Hawaii
(lower panel) 6-10
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Figure 6-8. Trends in design values for PM2.5 standards. The lower black dashed line marks
the level of the primary annual standard (12 |ig/m3). The secondary annual
PM2.5 standard level is 15 |ig/m3. The upper black dashed line marks the level
of the primary and secondary 24-hour standards (35 |ig/m3) 6-11
Figure 6-9. Trends in annual average concentrations of NO3", as measured at select NCore,
CSN, and IMPROVE sites from 2006 through 2021 6-12
Figure 6-10. Trends in annual average concentrations of SO42" as measured at select NCore,
CSN, and IMPROVE sites from 2006 through 2021 6-13
Figure 6-11. On a national scale, TDep-estimated total S deposition has decreased between
2000-2002 (top) and 2019-2021 (bottom) 6-16
Figure 6-12. Trend in TDep estimates of S deposition (2000-2021) at all 92 CASTNET sites
(upper) and the subset of 63 eastern sites (lower) 6-17
Figure 6-13. On a national scale, TDep-estimated total N deposition has decreased between
2000-2002 (top) and 2019-2021 (bottom) in some areas. This nationwide trend
is influenced by reductions in NOx emissions and by rising NH3 emissions 6-18
Figure 6-14. TDep-estimated dry N deposition: 2000-2002 (top) and 2019-2021 (bottom) 6-20
Figure 6-15. TDep-estimated wetN deposition: 2000-2002 (top) and 2019-2021 (bottom).... 6-21
Figure 6-16. Dry oxidized N deposition (TDep estimates): 2000-2002 (top) and 2019-2021
(bottom) 6-23
Figure 6-17. TDep-estimated dry reduced N deposition: 2000-2002 (top) and 2019-2021
(bottom) 6-24
Figure 6-18. TDep-estimated NH3 deposition: 2000-2002 (top) and 2019-2021 (bottom) 6-25
Figure 6-19. TDep estimated components of N deposition at all 92 CASTNET sites (2000-
2021): oxidized and reduced N (upper) and by oxidized and reduced N
component species (lower) 6-27
Figure 6-20. Locations of co-located CASTNET, NADP/NTN, and IMPROVE monitoring
sites, denoted by CASTNET site identifier. The NADP/NTN and IMPROVE
station identifiers are listed in Table 6-3 6-31
Figure 6-21. Annual average TDep-estimated dry and wet deposition of N and S (2017-
2019) at Class I areaNADP sites in Table 6-3. Boxes indicate interquartile
range 6-31
Figure 6-22. Annual average SOx concentration, ppb, (left), total S deposition, kg/ha-yr,
(middle), and associated deposition:concentration ratios (right), estimated from
a 21-year (1990-2010) CMAQ simulation 6-34
Figure 6-23. Annual average N oxides concentration, ppb (left), total N deposition, kg/ha-yr,
(middle), and associated deposition:concentration ratios (right), as estimated
from a 21-year (1990-2010) CMAQ simulation 6-34
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Figure 6-24. Scatter plot matrix of annual average CMAQ-simulated total S deposition
versus annual average CMAQ-simulated concentrations of SO2 and PM2.5 for
27 grid cells in Class 1 areas from a 21-year simulation (1990-2010) 6-36
Figure 6-25. Scatter plot matrix of annual average CMAQ-simulated total oxidized nitrogen
deposition versus annual average CMAQ-simulated concentrations of NO2 and
particulate nitrate for 27 Class 1 areas from 1990-2010. Colors in scatterplots
indicate NFb concentrations 6-38
Figure 6-26. Scatter plot matrix of annual average CMAQ-simulated total reduced nitrogen
deposition versus annual average CMAQ-simulated concentrations of PM2.5
(NO3") and NH3 for 27 Class 1 areas from 1990-2010. Colors in scatterplots
indicate NO2 concentrations 6-39
Figure 6-27. Scatter plot matrix of annual average wet S deposition (NADP) with annual
average concentrations of S042~(IMPR0VE) ancj total S (SO2 + SO42",
CASTNET) concentrations for 27 Class 1 areas (2000-2019) 6-41
Figure 6-28. Scatter plot matrix of annual average wet N deposition (NADP) with annual
average TNO3 (CASTNET) and NO3" (IMPROVE) concentrations for 27 Class
1 areas (2000-2019) 6-42
Figure 6-29. Scatter plot matrix of annual average wet N deposition (NADP) with annual
average wet deposition of NH4+ and NO3" (NADP) deposition for 27 Class I
areas (2000-2019) 6-43
Figure 6-30. Scatter plot matrix of annual average wet deposition of N and S (NADP) with
annual average PM2.5 (IMPROVE) for 27 Class 1 areas (2000-2019) 6-44
Figure 6-31. Total S deposition (TDep) versus annual average ambient air concentrations
(2000-2019) of PM2.5 (left; IMPROVE), S042" (center; IMPROVE) and total S
(right; CASTNET) at 27 Class I area sites. Linear regressions are shown as
black lines 6-46
Figure 6-32. Total N deposition (TDep) versus annual average ambient air concentrations
(2000-2019) of PM2.5 (left; IMPROVE), annual average N03~(center;
IMPROVE), and TNO3 (right; CASTNET) at 27 Class I area sites. Linear
regressions are shown as black lines 6-47
Figure 6-33. Total N deposition (TDep) versus annual average ambient air concentrations
(2000-2019) of total particulate N (left; IMPROVE), total particulate N (center;
CASTNET), and NH4+ (right; CASTNET) at 27 Class I area sites. Linear
regressions are shown as black lines 6-49
Figure 6-34. WetN deposition (NADP) versus annual average ambient air concentrations
(2000-2019) of total particulate N (right; IMPROVE), total particulate N
(center; CASTNET), and NH4+ (right; CASTNET) at 27 Class I area sites.
Linear regressions are shown as black lines 6-50
Figure 6-35. TDep estimated S deposition and annual average SO2 concentrations (3-year
average) at SLAMS across the CONUS (upper) and in the East (lower) 6-54
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Figure 6-36. TDep-estimated dry S deposition (upper) and wet S deposition (lower) versus
annual SO2 concentrations (3-year average) at SLAMS in the CONUS 6-56
Figure 6-37. TDep estimated total S deposition and design values for the SO2 secondary
standard (annual second maximum 3-hour concentration), averaged over three
years, at SLAMS across CONUS (upper) and in East (lower) 6-58
Figure 6-38. TDep-estimated N deposition and annual average NO2 concentrations (3-year
average) at SLAMS across the CONUS (upper), and in the East (lower) 6-60
Figure 6-39. N deposition (TDep) and annual average PM2.5 concentration (averaged over
three years) at SLAMS across the CONUS (upper), and in East (lower) 6-62
Figure 6-40. TDep-estimated median S deposition in all ecoregions (upper) and eastern
ecoregions (lower) versus upwind annual SO2 EAQM-weighted values 6-67
Figure 6-41. TDep-estimated median S deposition in all ecoregions (upper) and eastern
ecoregions (lower) versus upwind annual SO2 EAQM-max values 6-68
Figure 6-42. TDep-estimated median S deposition in all ecoregions (upper) and eastern
ecoregions (lower) versus upwind 3-hour SO2 EAQM-weighted values 6-70
Figure 6-43. TDep-estimated median S deposition in all ecoregions (upper) and eastern
ecoregions (lower) versus upwind 3-hour SO2 EAQM-max values 6-71
Figure 6-44. TDep-estimated median N deposition in all ecoregions (upper) and eastern
ecoregions (lower) versus upwind annual NO2 EAQM-weighted values 6-73
Figure 6-45. TDep-estimated median N deposition in all ecoregions (upper) and eastern
ecoregions (lower) versus upwind annual NO2 EAQM-max values 6-74
Figure 6-46. TDep-estimated median N deposition in all ecoregions (upper) and eastern
ecoregions (lower) versus upwind annual PM2.5 EAQM-weighted values 6-76
Figure 6-47. TDep-estimated median N deposition in all ecoregions (upper) and eastern
ecoregions (lower) versus upwind annual PM2.5 EAQM-max values 6-77
Figure 6-48. TDep-estimated median S deposition in all ecoregions (upper) and eastern
ecoregions (lower) versus upwind annual PM2.5 EAQM-weighted values 6-79
Figure 6-49. TDep-estimated median S deposition in all ecoregions (upper) and eastern
ecoregions (lower) versus upwind annual PM2.5 EAQM-max values 6-80
Figure 6-50. Plot of annual SO2 EAQM values against TDep total S deposition across 84
ecoregions. The individual pairs are color-coded by 3-year periods and the
symbols differentiate between sites in the eastern U.S. and western U.S. This
figure is based on EAQM data using 48-hour trajectories, the NARR-32
meteorological data, and a monitor inclusion criterion of 1% 6-90
Figure 6-51. Plot of annual SO2 EAQM values against TDep total S deposition across 84
ecoregions. The individual pairs are color-coded by 3-year periods and the
symbols differentiate between sites in the eastern U.S. and western U.S. This
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figure is based on EAQM data using 120-hour trajectories, the NAM-12
meteorological data, and a monitor inclusion criterion of 0.5% 6-91
Figure 6-52. Estimated annual SO2 emissions, nationally (NEI), averaged over three years,
from 2001-2020 6-95
Figure 6-53. TDep estimates of ecoregion median S deposition. Whiskers mark 5th and 95th
percentiles; estimates above 95th percentiles are black dots. [TDep v2018.02.] ..6-95
Figure 6-54. Box and whisker of annual NO2 concentrations from 2001-2020 at SLAMS
monitors. Whiskers show the 5th and 95th percentiles of NO2 concentrations,
with data outside the 5th and 95th percentiles shown as black dots 6-97
Figure 6-55. Box and whisker plot of TDep estimates of median total N deposition in all
CONUS ecoregions (2001-2020). Whiskers show the 5th and 95th percentiles,
with data points outside the 5th and 95th percentiles shown as black dots. TDep
version: v2022.02, downloaded on September 7th, 2022 6-98
Figure 6-56. Fraction of total PM2.5 at CSN sites that is either NO3" or NH4+ in 2020-2022
(upper) and across five time periods at consistently sampled sites (lower) 6-99
Figure 7-1. Percent of waterbodies per ecoregion estimated to achieve ANC at or above 50
|aeq/L (left panel) or 20 |ieq/L (right panel). Western ecoregions in bold font
and solid lines (versus regular font and dashed lines for Eastern ecoregions) 7-21
Figure 7-2. Ecoregion 90th, 75th and 50th percentile S deposition estimates at REA
waterbody sites summarized for all 25 ecoregions (left) and the 18 eastern
ecoregions (right) 7-22
Figure 7-3. Distributions of EAQM -Max annual SO2 concentrations (3-year average) at
ecoregion sites of influence identified in trajectory-based analyses for multiple
levels of ecoregion median S deposition (based on zonal statistic) in the five
time periods (2001-2003, 2006-2008, 2010-2012, 2014-2016, 2018-20) 7-31
Figure 7-4. Distributions of maximum annual average SO2 concentrations (3-year average)
at ecoregion sites of influence identified in trajectory-based analyses for
multiple levels of ecoregion median (left) and 90th percentile (right) S
deposition in the 25 REA ecoregions for the five time periods (2001-2003,
2006-2008, 2010-2012, 2014-2016, 2018-20). Ecoregion medians and 90th
percentiles derived from TDep estimates at sites with CLs in the ecoregion 7-33
Figure 7-5. Distributions of annual SO2 concentrations at SLAMS FRM/FEM monitors,
averaged across three consecutive years, for the five time periods of the REA
(left) and annual averages from 2000 to 2021 (right) 7-35
Figure 7-6. Temporal trend in ecoregion median estimates of total N deposition in
ecoregions for which 2018-2020 TDep estimated reduced N deposition is
>60% (left), 50-60% (middle) and <50%(right) 7-49
Figure 7-7. Ecoregion median total N deposition (upper panel) and percentage of total
comprised of reduced N (lower panel) based on TDep estimates (2018-2020). ..7-50
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Figure 7-8. Estimated total N deposition (upper panel) and percentage of total comprised
by NH3 deposition (lower panel) based on TDep grid cells (2018-2020) 7-51
Figure 7-9. Temporal trend in annual NO2 concentrations at SLAMS across U.S.: design
values for existing standard (left) and 3-year averages of design values (right).. 7-53
Figure 7-10. Annual NO2 design values (left) and annual NO2 concentrations, averaged over
three years (right) associated with 1-hour NO2 design values at SLAMS 7-53
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1 INTRODUCTION
This document, Policy Assessment for the Review of the Secondary National Ambient Air
Quality Standards for Oxides of Nitrogen, Oxides of Sulfur and Particulate Matter (hereafter
referred to as PA), presents the policy assessment for the U.S. Environmental Protection
Agency's (EPA's) current review of the secondary national ambient air quality standards
(NAAQS) for oxides of nitrogen (N oxides), oxides of sulfur (SOx), and particulate matter
(PM).1 This review differs from the review of the secondary standards for oxides of nitrogen and
sulfur completed in 2012 in that the current review includes consideration of the secondary PM
standards, in addition to the secondary standards for oxides of nitrogen and sulfur. Given the
contribution of nitrogen compounds to PM, including but not limited to those related to N
oxides, the current review provides for an expanded and more integrated consideration of N
deposition and the current related air quality information. Regarding PM, welfare effects
associated with visibility impairment, climate effects, and materials effects (i.e., damage and
soiling) are being addressed in the separate review of the NAAQS for PM. In the context of the
secondary standards for oxides of nitrogen,2 oxides of sulfur and PM, the scope pertains to the
protection of the public welfare from adverse effects related to ecological effects.3
This PA, prepared by staff of the EPA's Office of Air Quality Planning and Standards,4
considers key policy-relevant issues, drawing on those identified in the Integrated Review Plan
for the Secondary National Ambient Air Quality Standards for Ecological Effects of Oxides of
Nitrogen, Oxides of Sulfur and Particulate Matter (IRP; U.S. EPA, 2017) and the Integrated
Science Assessment for Oxides of Nitrogen, Oxides of Sulfur and Particulate Matter - Ecological
1 This review focuses on the presence in ambient air of oxides of nitrogen, oxides of sulfur, and particulate matter.
The standards that are the focus of this review are the secondary standards for NO2, set in 1971 (36 FR 8186,
April 30, 1971), for S02, set in 1971 (36 FR 8186, April 30, 1971), forPMio, set in 2012 (78 FR 3085, January
15, 2013), and for PM2 5, set in 2012 (78 FR 3085, January 15, 2013). These standards are referred to in this
document as the "current" or "existing" standards.
2 In this document, the term, oxides of nitrogen, refers to all forms of oxidized nitrogen (N) compounds, including
NO, NO2, and all other oxidized N-containing compounds formed from NO and NO2. This follows usages in the
Clean Air Act section 108(c): "Such criteria [for oxides of nitrogen] shall include a discussion of nitric and
nitrous acids, nitrites, nitrates, nitrosamines, and other carcinogenic and potentially carcinogenic derivatives of
oxides of nitrogen." By contrast, within much of the air pollution research and control communities, the terms
"oxides of nitrogen" and "nitrogen oxides" are restricted to refer only to the sum of NO and NO2, and this sum is
commonly abbreviated NOx. Where used in this document (e.g., Chapter 2), the definition used is provided.
3 Welfare effects of PM other than ecological effects, such as visibility effects and materials damage, were addressed
in the separate PM NAAQS review completed in 2020 and are part of the reconsideration of that 2020 decision, a
proposed decision for which was published early in 2023 (88 FR 5558, January 27, 2023).
4 The terms "staff," "we," and "our" throughout this document refer to the staff in the EPA's Office of Air Quality
Planning and Standards (OAQPS).
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Criteria (ISA or 2020 ISA; U.S. EPA, 2020).This document is organized into seven chapters,
encompassing information on air quality, the nature of effects and exposure conditions
associated with effects, relationships between deposition and air quality metrics, and a review of
the standards. A detailed description of chapters within this document (and associated
appendices) is provided in section 1.5 below. In this introductory chapter, we present information
on the purpose of the PA (section 1.1), legislative requirements for reviews of the NAAQS
(section 1.2), and an overview of the history of the N oxides, SOx, and PM NAAQS reviews
(section 1.3). Section 1.4 describes progress and next steps in the current review.
1.1 PURPOSE
The PA, when final, presents an evaluation, for consideration by the EPA Administrator,
of the policy implications of the currently available scientific information, assessed in the ISA,
any quantitative air quality, exposure or risk analyses based on the ISA findings, and related
limitations and uncertainties. Ultimately, final decisions on the secondary N oxides, SOx, and
PM NAAQS will reflect the judgments of the Administrator. The role of the PA is to help
"bridge the gap" between the Agency's scientific assessment and quantitative technical analyses,
and the judgments required of the Administrator in determining whether it is appropriate to retain
or revise the NAAQS.
In evaluating the question of adequacy of the current standards and whether it may be
appropriate to consider alternative standards, the PA focuses on information that is most
pertinent to evaluating the standards and their basic elements: indicator, averaging time, form,
and level.5 These elements, which together serve to define each standard, must be considered
collectively in evaluating the public health and public welfare protection the standards afford.
The development of the PA is also intended to facilitate advice to the Agency and
recommendations to the Administrator from an independent scientific review committee, the
Clean Air Scientific Advisory Committee (CASAC), as provided for in the Clean Air Act
(CAA). As discussed below in section 1.2, the CASAC is to advise on subjects including the
Agency's assessment of the relevant scientific information and on the adequacy of the current
standards, and to make recommendations as to any revisions of the standards that may be
5 The indicator defines the chemical species or mixture to be measured in the ambient air for the purpose of
determining whether an area attains the standard. The averaging time defines the period over which air quality
measurements are to be averaged or otherwise analyzed. 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. For
example, the form of the annual NAAQS for fine particulate matter (PM 2.5) is the average of annual mean
concentrations for three consecutive years, while the form of the 3-hour secondary NAAQS for SO2 is the second-
highest 3-hour average in a year. The level of the standard defines the air quality concentration used for that
purpose.
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appropriate. The EPA generally makes available to the CASAC and the public one or more drafts
of the PA for CASAC review and public comment.
In this PA, we consider the available scientific information, as assessed in the Integrated
Science Assessment for Oxides of Nitrogen, Oxides of Sulfur and Particulate Matter - Ecological
Criteria, (ISA [U.S. EPA, 2020]) which included literature through May 2017, and additional
policy-relevant quantitative air quality, exposure and risk analyses. Advice and comments from
the CASAC and the public on the PA has informed the evaluation and conclusions in this final
PA.
The PA is designed to assist the Administrator in considering the currently available
scientific evidence and quantitative air quality, exposure and risk information, and in formulating
judgments regarding the standards. The final PA will inform the Administrator's decision in this
review. Beyond informing the Administrator and facilitating the advice and recommendations of
the CASAC, the PA is also intended to be a useful reference to all interested parties. In these
roles, it is intended to serve as a source of policy-relevant information that supports the Agency's
review of the secondary NAAQS for N oxides, SOx, and PM, and it is written to be
understandable to a broad audience.
1.2 LEGISLATIVE REQUIREMENTS
Two sections of the CAA govern the establishment and revision of the NAAQS. Section
108 (42 U.S.C. 7408) directs the Administrator to identify and list certain air pollutants and then
to issue air quality criteria for those pollutants. The Administrator is to list those pollutants
"emissions of which, in his judgment, cause or contribute to air pollution which may reasonably
be anticipated to endanger public health or welfare"; "the presence of which in the ambient air
results from numerous or diverse mobile or stationary sources"; and for which he "plans to issue
air quality criteria...." (42 U.S.C. § 7408(a)(1)). Air quality criteria are intended to "accurately
reflect the latest scientific knowledge useful in indicating the kind and extent of all identifiable
effects on public health or welfare which may be expected from the presence of [a] pollutant in
the ambient air...." 42 U.S.C. § 7408(a)(2).
Section 109 (42 U.S.C. 7409) directs the Administrator to propose and promulgate
"primary" and "secondary" NAAQS for pollutants for which air quality criteria are issued (42
U.S.C. § 7409(a)). Under section 109(b)(2), a secondary standard must "specify a level of air
quality the attainment and maintenance of which, in the judgment of the Administrator, based on
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such criteria, is requisite to protect the public welfare from any known or anticipated adverse
effects associated with the presence of [the] pollutant in the ambient air."6
In setting primary and secondary standards that are "requisite" to protect public health
and welfare, respectively, as provided in section 109(b), the EPA's task is to establish standards
that are neither more nor less stringent than necessary. In so doing, the EPA may not consider the
costs of implementing the standards. See generally, Whitman v. American Trucking Ass 'ns, 531
U.S. 457, 465-472, 475-76 (2001). Likewise, "[ajttainability and technological feasibility are not
relevant considerations in the promulgation of national ambient air quality standards" (American
Petroleum Institute v. Costle, 665 F.2d 1176, 1185 (D.C. Cir. 1981)). However, courts have
clarified that in deciding how to revise the NAAQS in the context of considering standard levels
within the range of reasonable values supported by the air quality criteria and judgments of the
Administrator, EPA may consider "relative proximity to peak background ... concentrations" as
a factor (American Trucking Ass 'ns, v. EPA, 283 F.3d 355, 379 (D.C. Cir. 2002)).
Section 109(d)(1) of the Act requires periodic review and, if appropriate, revision of
existing air quality criteria to reflect advances in scientific knowledge on the effects of the
pollutant on public health and welfare. Under the same provision, the EPA is also to periodically
review and, if appropriate, revise the NAAQS, based on the revised air quality criteria.7
Section 109(d)(2) addresses the appointment and advisory functions of an independent
scientific review committee. Section 109(d)(2)(A) requires the Administrator to appoint this
committee, which is to be composed of "seven members including at least one member of the
National Academy of Sciences, one physician, and one person representing State air pollution
control agencies." Section 109(d)(2)(B) provides that the independent scientific review
committee "shall complete a review of the criteria.. .and the national primary and secondary
ambient air quality standards...and shall recommend to the Administrator any new... standards
and revisions of existing criteria and standards as may be appropriate...." Since the early 1980s,
this independent review function has been performed by the CAS AC of the EPA's Science
Advisory Board.
Section 109(b)(2) specifies that "[a]ny national secondary ambient air quality standard
prescribed under subsection (a) shall specify a level of air quality the attainment and
maintenance of which in the judgment of the Administrator, based on such criteria, is requisite to
6 Under CAA section 302(h) (42 U.S.C. § 7602(h)), effects on welfare include, but are not limited to, "effects on
soils, water, crops, vegetation, manmade materials, animals, wildlife, weather, visibility, and climate, damage to
and deterioration of property, and hazards to transportation, as well as effects on economic values and on personal
comfort and well-being."
7 This section of the Act requires the Administrator to complete these reviews and make any revisions that may be
appropriate "at five-year intervals."
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protect the public welfare from any known or anticipated adverse effects associated with the
presence of such air pollutant in the ambient air." Consistent with this statutory direction, EPA
has always understood the goal of the NAAQS is to identify a requisite level of air quality, and
the means of achieving a specific level of air quality is to set a standard expressed as a
concentration of a pollutant in the air, such as in terms of parts per million (ppm), parts per
billion (ppb), or micrograms per cubic meter ([j,g/m3). Thus, while deposition-related effects are
included within the "adverse effects associated with the presence of such air pollutant in the
ambient air," EPA has never found a standard that quantifies atmospheric deposition onto
surfaces to constitute a national secondary ambient air quality standard.
1.3 BACKGROUND ON CRITERIA AND SECONDARY STANDARDS
FOR NITROGEN OXIDES AND SULFUR OXIDES AND
PARTICULATE MATTER
Secondary NAAQS were first established for oxides of nitrogen, oxides of sulfur and
particulate matter in 1971 (36 FR 8186, April 30, 1971). Since that time, the EPA has
periodically reviewed the air quality criteria and secondary standards for these pollutants, with
the most recent reviews that considered the evidence for ecological effects of these pollutants
being completed in 2012 and 2013 (77 FR 20218, April 3, 2012; 78 FR 3086, January 15, 2013).
The subsections below summarize key proceedings from the initial standard setting in 1971 to
the last reviews in 2012-2013. Key aspects of the scientific evidence supporting the standards is
summarized in sections 3.1 and 3.2 below.
1.3.1 Nitrogen Oxides
The EPA first promulgated NAAQS for oxides of N in April 1971 after reviewing the
relevant science on the public health and welfare effects in the 1971 Air Quality Criteria for
Nitrogen Oxides (air quality criteria document or AQCD).8 With regard to welfare effects, the
1971 AQCD described effects of NO2 on vegetation and corrosion of electrical components
linked to particulate nitrate (U.S. EPA, 1971). The primary and secondary standards were both
set at 0.053 ppm NO2 as an annual average (36 FR 8186, April 30, 1971). In 1982, the EPA
published an updated AQCD (U.S. EPA, 1982a). Based on the 1982 AQCD, the EPA proposed
to retain the existing standards in February 1984 (49 FR 6866, February 23, 1984). After
considering public comments, the EPA published the final decision to retain these standards in
June 1985 (50 FR 25532, June 19, 1985).
8 In reviews initiated prior to 2007, the AQCD provided the scientific foundation (i.e., the air quality criteria) for the
NAAQS. Since that time, the Integrated Science Assessment (ISA) has replaced the AQCD.
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The EPA began a second review of the primary and secondary standards for oxides of
nitrogen in 1987 (52 FR 27580, July 22, 1987). In November 1991, the EPA released an updated
draft AQCD for CASAC and public review and comment (56 FR 59285, November 25, 1991).
The CASAC reviewed the draft document at a meeting held on July 1, 1993, and concluded in a
closure letter to the Administrator that the document provided "an adequate basis" for EPA's
decision-making in the review (Wolff, 1993). The final AQCD was released later in 1993 (U.S.
EPA, 1993). Based on the 1993 AQCD, the EPA's Office of Air Quality Planning and Standards
(OAQPS) prepared a Staff Paper,9 drafts of which were reviewed by the CASAC (Wolff, 1995;
U.S. EPA, 1995a). In October 1995, the EPA proposed not to revise the secondary NO2 NAAQS
(60 FR 52874; October 11, 1995). After consideration of the comments received on the proposal,
the Administrator decided not to revise the NO2NAAQS (61 FR 52852; October 8, 1996). The
subsequent (and most recent) review of the N oxides secondary standard was a joint review with
the secondary standard for SOx, which was completed in 2012 (see section 1.3.4 below).
1.3.2 Sulfur Oxides
The EPA first promulgated secondary NAAQS for sulfur oxides in April 1971 based on
the scientific evidence evaluated in the 1969 AQCD (U.S. DHEW, 1969a [1969 AQCD]; 36 FR
8186, April 30, 1971). These standards, which were established on the basis of evidence of
adverse effects on vegetation, included an annual arithmetic mean standard, set at 0.02 ppm
SO2,10 and a 3- hour average standard set at 0.5 ppm SO2, not to be exceeded more than once per
year. In 1973, based on information indicating there to be insufficient data to support the finding
of a study in the 1969 AQCD concerning vegetation injury associated from SO2 exposure over
the growing season, rather than from short-term peak concentrations, the EPA proposed to
revoke the annual mean secondary standard (38 FR 11355, May 7, 1973). Based on
consideration of public comments and external scientific review, the EPA released a revised
chapter of the AQCD and published its final decision to revoke the annual mean secondary
standard (U.S. EPA, 1973; 38 FR 25678, September 14, 1973). At that time, the EPA
additionally noted that injury to vegetation was the only type of SO2 welfare effect for which the
evidence base supported a quantitative relationship, stating that although data were not available
9 Prior to reviews initiated in 2007, the Staff Paper summarized and integrated key studies and the scientific
evidence, and from the 1990s onward also assessed potential exposures and associated risk. The Staff paper also
presented the EPA staffs considerations and conclusions regarding the adequacy of existing NAAQS and, when
appropriate, the potential alternative standards that could be supported by the evidence and information. More
recent reviews present this information in the Policy Assessment.
10 Established with the annual standard as a guide to be used in assessing implementation plans to achieve the annual
standard was a maximum 24-hour average concentration not to be exceeded more than once per year (36 FR
8187, April 30, 1971).
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at that time to establish a quantitative relationship between SO2 concentrations and other public
welfare effects, including effects on materials, visibility, soils, and water, the SO2 primary
standards and the 3-hour secondary standard may to some extent mitigate such effects. The EPA
also stated it was not clear that any such effects, if occurring below the current standards, are
adverse to the public welfare (38 FR 25679, September 14, 1973).
In 1979, the EPA announced initiation of a concurrent review of the air quality criteria
for oxides of sulfur and PM and plans for development of a combined AQCD for these pollutants
(44 FR 56730, October 2, 1979). The EPA subsequently released three drafts of a combined
AQCD for CASAC review and public comment. In these reviews, and guidance provided at the
CASAC August 20-22, 1980 public meeting on the first draft AQCD, the CASAC concluded that
acidic deposition was a topic of extreme scientific complexity because of the difficulty in
establishing firm quantitative relationships among emissions of relevant pollutants, formation of
acidic wet and dry deposition products, and effects on terrestrial and aquatic ecosystems (53 FR
14935, April 26, 1988). The CASAC also noted that a fundamental problem of addressing acid
deposition in a criteria document is that acid deposition is produced by several different criteria
pollutants: oxides of sulfur, oxides of nitrogen, and the fine particulate fraction of suspended
particles (U.S. EPA, 1982b, pp. 125-126). The CASAC also felt that any document on this
subject should address both wet and dry deposition, since dry deposition was believed to account
for a substantial portion of the total acid deposition problem (53 FR 14936, April 26, 1988;
Lippman, 1987). For these reasons, CASAC recommended that, in addition to including a
summary discussion of acid deposition in the final AQCD, a separate, comprehensive document
on acid deposition be prepared prior to any consideration of using the NAAQS as a regulatory
mechanism for the control of acid deposition.
Following CASAC closure on the AQCD for oxides of sulfur in December 1981, the
EPA released a final AQCD (U.S. EPA, 1982b), and the EPA's OAQPS prepared a Staff Paper
that was released in November 1982 (U.S. EPA, 1982c). The issue of acidic deposition was not,
however, assessed directly in the OAQPS staff paper because the EPA followed the guidance
given by the CASAC, subsequently preparing the following documents to address acid
deposition: The Acidic Deposition Phenomenon and Its Effects: Critical Assessment Review
Papers, Volumes I and II (U.S. EPA, 1984a, b) and The Acidic Deposition Phenomenon and Its
Effects: Critical Assessment Document (U.S. EPA, 1985) (53 FR 14935 -14936, April 26, 1988).
Although these documents were not considered criteria documents and had not undergone
CASAC review, they represented the most comprehensive summary of scientific information
relevant to acid deposition completed by the EPA at that point.
In April 1988, the EPA proposed not to revise the existing secondary standards for SO2
(53 FR 14926, April 26, 1988). This proposed decision with regard to the secondary SO2
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NAAQS was due to the Administrator's conclusions that (1) based upon the then-current
scientific understanding of the acid deposition problem, it would be premature and unwise to
prescribe any regulatory control program at that time and (2) when the fundamental scientific
uncertainties had been decreased through ongoing research efforts, the EPA would draft and
support an appropriate set of control measures (53 FR 14926, April 26, 1988). This review of the
secondary standard for SOx was concluded in 1993, subsequent to the Clean Air Act
Amendments of 1990 (see section 1.3.3 below). The EPA decided not to revise the secondary
standard, concluding that revisions to the standard to address acidic deposition and related SO2
welfare effects was not appropriate at that time (58 FR 21351, April 21, 1993). In describing the
decision, the EPA recognized the significant reductions in SO2 emissions, ambient air SO2
concentrations and ultimately deposition expected to result from implementation of the Title IV
program, which was expected to significantly decrease the acidification of water bodies and
damage to forest ecosystems and to permit much of the existing damage to be reversed with time
(58 FR 21357, April 21, 1993). While recognizing that further action might be needed to address
acidic deposition in the longer term, the EPA judged it prudent to await the results of the studies
and research programs then underway, including those assessing the comparative merits of
secondary standards, acidic deposition standards and other approaches to controlling acidic
deposition and related effects, and then to determine whether additional control measures should
be adopted or recommended to Congress (58 FR 21358, April 21, 1993).
1.3.3 Related Actions Addressing Acid Deposition
In 1980, Congress created the National Acid Precipitation Assessment Program
(NAPAP). During the 10-year course of this program, a series of reports were issued and a final
report was issued in 1990 (NAPAP, 1991). On November 15, 1990, Amendments to the CAA
were passed by Congress and signed into law by the President. In Title IV of these Amendments,
Congress included a statement of findings including the following: "1) the presence of acidic
compounds and their precursors in the atmosphere and in deposition from the atmosphere
represents a threat to natural resources, ecosystems, materials, visibility, and public health; ... 3)
the problem of acid deposition is of national and international significance; ... 5) current and
future generations of Americans will be adversely affected by delaying measures to remedy the
problem...". The goal of Title IV was to reduce emissions of SO2 by 10 million tons and N
oxides emissions by 2 million tons from 1980 emission levels in order to achieve reductions over
broad geographic regions/areas. In envisioning that further action might be necessary in the long
term, Congress included section 404 of the 1990 Amendments. This section requires the EPA to
conduct a study on the feasibility and effectiveness of an acid deposition standard or standards to
protect "sensitive and critically sensitive aquatic and terrestrial resources" and at the conclusion
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of the study, submit a report to Congress. Five years later the EPA submitted to Congress its
report titled Acid Deposition Standard Feasibility Study: Report to Congress (U.S. EPA, 1995b)
in fulfillment of this requirement. The Report to Congress concluded that establishing acid
deposition standards for sulfur and nitrogen deposition might at some point in the future be
technically feasible although appropriate deposition loads for these acidifying chemicals could
not be defined with reasonable certainty at that time.
The 1990 Amendments also added new language to sections of the CAA pertaining to
ecosystem effects of criteria pollutants, such as acid deposition. For example, a new section
108(g) was inserted, stating that "[t]he Administrator may assess the risks to ecosystems from
exposure to criteria air pollutants (as identified by the Administrator in the Administrator's sole
discretion)." The definition of welfare in section 302(h) was expanded to indicate that welfare
effects include those listed therein, "whether caused by transformation, conversion, or
combination with other air pollutants." Additionally, in response to legislative initiatives such as
the 1990 Amendments, the EPA and other Federal agencies continued research on the causes and
effects of acidic deposition and related welfare effects of SO2 and implemented an enhanced
monitoring program to track progress (58 FR 21357, April 21, 1993).
1.3.4 Most Recent Review of the Secondary Standards for Oxides of Nitrogen and Oxides
of Sulfur
In December 2005, the EPA initiated a joint review11 of the air quality criteria for oxides
of nitrogen and sulfur and the secondary NAAQS for NO2 and SO2 (70 FR 73236, December 9,
2005).12 The review focused on the evaluation of the protection provided by the secondary
standards for oxides of nitrogen and oxides of sulfur for two general types of effects: (1) direct
effects on vegetation of exposure to gaseous oxides of nitrogen and sulfur, which are the type of
effects that the existing NO2 and SO2 secondary standards were developed to protect against, and
(2) effects associated with the deposition of oxides of nitrogen and sulfur to sensitive aquatic and
terrestrial ecosystems (77 FR 20218, April 3, 2012).
11 Although the EPA has historically adopted separate secondary standards for oxides of nitrogen and oxides of
sulfur, the EPA conducted a joint review of these standards because oxides of nitrogen and sulfur and their
associated transformation products are linked from an atmospheric chemistry perspective, as well as from an
environmental effects perspective. The joint review was also responsive to the National Research Council (NRC)
recommendation for the EPA to consider multiple pollutants, as appropriate, in forming the scientific basis for the
NAAQS (NRC, 2004).
12 The review was conducted under a schedule specified by consent decree entered into by the EPA with the Center
for Biological Diversity and four other plaintiffs. The schedule, which was revised on October 22, 2009 provided
that the EPA sign notices of proposed and final rulemaking concerning its review of the oxides of nitrogen and
oxides of sulfur NAAQS no later than July 12, 2011 and March 20, 2012, respectively.
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The Integrated Review Plan (IRP) for the review was released in December 2007, after
review of a draft IRP by the public and CASAC (72 FR 57570, October 10, 2007; Russell, 2007;
U.S. EPA, 2007). The first and second drafts of the ISA were released in December 2007 and
August 2008, respectively, for the CASAC and public review (72 FR 72719, December 21,
2007; 73 FR 10243, February 26, 2008; Russell and Henderson, 2008; 73 FR 46908, August 12,
2008; 73 FR 53242, September 15, 2008; Russell and Samet, 2008a). The final ISA was released
in December 2008 (73 FR 75716, December 12, 2008; U.S. EPA, 2008a [2008 ISA]). Based on
the scientific information in the ISA, the EPA planned and developed a quantitative Risk and
Exposure Assessment (REA), two drafts of which were made available for public comment and
reviewed by the CASAC (73 FR 10243, February 26,2008; 73 FR 50965, August 29, 2008;
Russell and Samet, 2008b; 73 FR 53242, September 15, 2008; 74 FR 28698, June 17, 2009;
Russell and Samet, 2009). The final REA was released in September 2009 (U.S. EPA, 2009a; 74
FR 48543; September 23, 2009).
Drawing on the information in the final REA and ISA, the EPA OAQPS prepared a PA,
two drafts of which were made available for public comment and review by the CASAC (75 FR
10479, March 8, 2010; 75 FR 11877, March 12, 2010; Russell and Samet, 2010b; 75 FR 57463,
September 21, 2010; 75 FR 65480, October 25, 2010; Russell and Samet, 2010a). The final PA
was released in January 2011 (U.S. EPA, 2011). Based on additional discussion subsequent to
release of the final PA, the CASAC provided additional advice and recommendations on the
multipollutant, deposition-based standard described in the PA (76 FR 4109, January 24, 2011; 76
FR 16768, March 25, 2011; Russell and Samet, 2011).
For the purpose of protection against the direct effects on vegetation of exposure to
gaseous oxides of nitrogen and sulfur, the PA concluded that consideration should be given to
retaining the current standards. With respect to the effects associated with the deposition of
oxides of nitrogen and oxides of sulfur to sensitive aquatic and terrestrial ecosystems, the PA
focused on the acidifying effects of nitrogen and sulfur deposition on sensitive aquatic
ecosystems. Based on the information in the ISA, the assessments in the REA, and the CASAC
advice, the PA concluded that consideration be given to a new multipollutant standard intended
to address deposition-related effects, as described in section 3.2 below.
On August 1, 2011, the EPA published a proposed decision to retain the existing annual
average M^and 3-hour average SO2 secondary standards, recognizing the protection they
provided from direct effects on vegetation (76 FR 46084, August 1, 2011). Further, after
considering the multipollutant approach to a standard developed in the PA, the Administrator
proposed not to set such a new multipollutant secondary standard in light of a number of
uncertainties (summarized in section 3.2 below). Additionally, the Administrator proposed to
revise the secondary standards by adding secondary standards identical to the NO2 and SO2
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primary 1-hour standards set in 2010, noting that these new standards13 would result in
reductions in oxides of nitrogen and sulfur that would likely reduce nitrogen and sulfur
deposition to sensitive ecosystems (76 FR 46084, August 1, 2011). After consideration of public
comments, the final decision in the review was to retain the existing standards to address the
direct effects on vegetation of exposure to gaseous oxides of nitrogen and sulfur and also, to not
set additional standards particular to effects associated with deposition of oxides of nitrogen and
sulfur on sensitive aquatic and terrestrial ecosystems at that time (77 FR 20218, April 3, 2012).
Technical aspects of the approach described in the 2011 PA and the Administrator's decision-
making are summarized in section 3.2 below.
The EPA's 2012 decision was challenged by the Center for Biological Diversity and
other environmental groups. The petitioners argued that having decided that the existing
standards were not adequate to protect against adverse public welfare effects such as damage to
sensitive ecosystems, the Administrator was required to identify the requisite level of protection
for the public welfare and to issue a NAAQS to achieve and maintain that level of protection.
The D.C. Circuit disagreed, finding that the EPA acted appropriately in not setting a secondary
standard given the EPA's conclusions that "the available information was insufficient to permit a
reasoned judgment about whether any proposed standard would be 'requisite to protect the
public welfare . . . ' ,"14 In reaching this decision, the court noted that the EPA had "explained in
great detail" the profound uncertainties associated with setting a secondary NAAQS to protect
against aquatic acidification.15
1.3.5 Particulate Matter
The EPA first established a secondary standard for PM in 1971 (36 FR 8186, April 30,
1971), based on the original AQCD, which described the evidence as to effects of PM on
visibility, materials, light absorption and vegetation (U.S. DHEW, 1969b). To provide protection
generally from visibility effects and materials damage, the secondary standard was set at 150
|ig/m3, as a 24-hour average, from total suspended particles (TSP), not to be exceeded more than
once per year (36 FR 8187; April 30, 1971).16
In October 1979, the EPA announced the first periodic review of the air quality criteria
and NAAQS for PM (44 FR 56730, October 2, 1979). As summarized in section 1.3.2 above, the
13 The 2010 primary 1-hour standards included the NO2 standard set at a level of 100 ppb and the SO2 standard set at
a level of 75 ppb.
14 Center for Biological Diversity, et al. v. EPA, 749 F.3d 1079, 1087 (2014).
15 Id. at 1088.
16 Additionally, a guide to be used in assessing implementation plans in assessing implementation plans to achieve
the 24-hour standard was set at 60 |ig/m3. as an annual geometric mean (36 FR 8187; April 30, 1971).
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EPA developed a new AQCD for PM and SOx, drafts of which were reviewed by the CASAC
(U.S. EPA, 1982b). Subsequently, the EPA OAQPS developed a Staff Paper (U.S. EPA, 1982d),
two drafts of which were reviewed by the CASAC (Friedlander, 1982). Further, the EPA
OAQPS prepared an Addendum to the 1982 staff paper, which also received CASAC (Lippman,
1986; U.S. EPA, 1986). After consideration of public comments on a proposed decision, the final
decision in this review revised the indicator for PM NAAQS from TSP to particulate matter with
mass median diameter of 10 microns (PMio) (49 FR 10408, March 20, 1984; 52 FR 24634, July
1, 1987). With an indicator of PMio, two secondary standards were established to be the same as
the primary standards. A 24-hour secondary standard was set at 150 |ig/m3, with the form was
one expected exceedance per year, on average over three years. Additionally, an annual
secondary standard was set at 50 |ig/m3, with a form of annual arithmetic mean, averaged over
three years (52 FR 24634, July 1, 1987).
In April 1994, the EPA initiated the second periodic review of the air quality criteria and
NAAQS for PM. In developing the AQCD, the Agency made available three external review
drafts to the public and for CASAC review; the final AQCD was released in 1996 (U.S. EPA,
1996). The EPA's OAQPS prepared a Staff Paper that was released in November 1997, after
CASAC and public review of two drafts (U.S. EPA, 1996; Wolff, 1996). Revisions to the PM
standards were proposed in 1996, and in 1997 the EPA promulgated revisions (61 FR 65738;
December 13, 1996; 62 FR 38652, July 18, 1997). With the 1997 decision, the EPA added new
standards, using PM2.5 as the indicator for fine particles (with PM2.5 referring to particles with a
nominal mean aerodynamic diameter less than or equal to 2.5 |im). The new secondary standards
were set equal to the primary standards, in all respects, as follows: (1) an annual standard with a
level of 15.0 |ig/m3, based on the 3-year average of annual arithmetic mean PM2.5 concentrations
from single or multiple community-oriented monitors;17 and (2) a 24-hour standard with a level
of 65 |ig/m3, based on the 3-year average of the 98th percentile of 24-hour PM2.5 concentrations
at each monitor within an area. Further, the EPA retained the annual PMio standard, without
revision, and revised the form of the 24-hour PMio standard to be based on the 99th percentile of
24-hour PMio concentrations at each monitor in an area.
Following promulgation of the 1997 PM NAAQS, petitions for review were filed by
several parties, raising a broad range of issues. In May 1999, the U.S. Court of Appeals for the
17 The 1997 annual PM2.5 standard was compared with measurements made at the community-oriented monitoring
site recording the highest concentration or, if specific constraints were met, measurements from multiple
community-oriented monitoring sites could be averaged (i.e., spatial averaging"). In the last review (completed in
2012) the EPA replaced the term "community-oriented" monitor with the term "area-wide" monitor. Area-wide
monitors are those sited at the neighborhood scale or larger, as well as those monitors sited at micro - or middle-
scales that are representative of many such locations in the same core-based statistical area (CBSA) (78 FR 3236,
January 15, 2013).
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District of Columbia Circuit (D.C. Circuit) upheld the EPA's decision to establish fine particle
standards, (American Trucking Ass 'ns, Inc. v. EPA, 175 F. 3d 1027, 1055-56 [D.C. Cir. 1999]).
The D.C. Circuit also found "ample support" for the EPA's decision to regulate coarse particle
pollution, but vacated the 1997 PMio standards, concluding that the EPA had not provided a
reasonable explanation justifying use of PMio as an indicator for coarse particles (American
Trucking Ass 'ns v. EPA, 175 F. 3d at 1054-55). Pursuant to the D.C. Circuit's decision, the EPA
removed the vacated 1997 PMio standards, and the pre-existing 1987 PMio standards remained in
place (65 FR 80776, December 22, 2000). The D.C. Circuit also upheld the EPA's determination
not to establish more stringent secondary standards for fine particles to address effects on
visibility (American Trucking Ass 'ns v. EPA, 175 F. 3d at 1027). The D.C. Circuit also addressed
more general issues related to the NAAQS, including issues related to the consideration of costs
in setting NAAQS and the EPA's approach to establishing the levels of NAAQS (as summarized
in section 1.2 above).
In October 1997, the EPA initiated the third periodic review of the air quality criteria and
NAAQS for PM (62 FR 55201, October 23, 1997). After the CASAC and public review of
several drafts of the AQCD, the EPA released the final AQCD in October 2004 (U.S. EPA,
2004a and 2004b). The EPA's OAQPS finalized the Staff Paper in December 2005 (U.S. EPA,
2005). On December 20, 2005, the EPA announced its proposed decision to revise the NAAQS
for PM and solicited public comment on a broad range of options (71 FR 2620, January 17,
2006). On September 21, 2006, the EPA announced its final decisions to revise the PM NAAQS
to provide increased protection of public health and welfare, respectively (71 FR 61144, October
17, 2006). Revisions to the secondary standards were identical to those for the primary standards,
with the decision describing the protection provided specifically for visibility and non-visibility
related welfare effects (71 FR 61203-61210, October 17, 2006). With regard to the standards for
fine particles, the EPA revised the level of the 24-hour PM2.5 standards to 35 |ig/m3, retained the
level of the annual PM2.5 standards at 15.0 |ig/m3, and revised the form of the annual PM2.5
standards by narrowing the constraints on the optional use of spatial averaging. With regard to
the standards for PMio, the EPA retained the 24-hour standards, with levels at 150 |ig/m3, and
revoked the annual standards.
Several parties filed petitions for review of the 2006 PM NAAQS decision. One of these
petitions raised the issue of setting the secondary PM2.5 standards identical to the primary
standards. On February 24, 2009, the D.C. Circuit issued its opinion in the case American Farm
Bureau Federation v. EPA, 559 F. 3d 512 (D.C. Cir. 2009) and remanded the standards to the
EPA because the Agency failed to adequately explain why setting the secondary PM standards
identical to the primary standards provided the required protection for public welfare, including
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protection from visibility impairment {Id. at 528-32). The EPA responded to the court's remands
as part of the subsequent review of the PM NAAQS, which was initiated in 2007.
In June 2007, the EPA initiated the fourth periodic review of the air quality criteria and
the PM NAAQS (72 FR 35462, June 28, 2007). Based on the NAAQS review process, as revised
in 2008 and again in 2009, the EPA held science/policy issue workshops on the primary and
secondary PM NAAQS (72 FR 34003, June 20, 2007; 72 FR 34005, June 20, 2007), and
prepared and released the planning and assessment documents that comprise the review process
(i.e., IRP [U.S. EPA, 2008b], ISA [U.S. EPA, 2009b], REA planning document for welfare [U.S.
EPA, 2009c], and an urban-focused visibility assessment [U.S. EPA, 2010], and PA [U.S. EPA,
2011]). In June 2012, the EPA announced its proposed decision to revise the NAAQS for PM (77
FR 38890, June 29, 2012). In December 2012, the EPA announced its final decisions to revise
the primary and secondary PM2.5 annual standards (78 FR 3086, January 15, 2013). With regard
to the secondary standards, the EPA retained the 24-hour PM2.5 and PM10 standards, with a
revision to the form of the 24-hour PM2.5, to eliminate the option for spatial averaging (78 FR
3086, January 15, 2013).
Petitioners challenged the EPA's final rule. Petitioners argued that the EPA acted
unreasonably in revising the level and form of the annual standard and in amending the
monitoring network provisions. On judicial review, the revised standards and monitoring
requirements were upheld in all respects (NAM v. EPA, 750 F.3d 921, D.C. Cir. 2014).
The subsequent review of the PM secondary standards, completed in 2020, focused on
consideration of protection provided from visibility effects, materials damage, climate effects (85
FR 82684, December 18, 2020). The evidence for ecological effects of PM is addressed in the
review of the air quality criteria and standards described in this PA.18
1.4 CURRENT REVIEW
In August 2013, the EPA issued a call for information in the Federal Register for
information related to the newly initiated review of the air quality criteria for oxides of sulfur and
oxides of nitrogen and announced a public workshop to discuss policy-relevant scientific
information to inform the review (78 FR 53452, August 29, 2013). Based in part on the
information received in response to the call for information, the EPA developed a draft IRP
which was made available for consultation with the CASAC and for public comment (80 FR
69220, November 9, 2015). Comments from the CASAC and the public on the draft IRP were
considered in preparing the final IRP (Diez Roux and Fernandez, 2016; U.S. EPA, 2017). In
18 Welfare effects of PM considered in the review of the PM secondary standards completed in 2020, and
reconsidered more recently, include effects on visibility and climate and materials damage (88 FR 5558, January
27, 2023).
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developing the final IRP, the EPA expanded the review to also include review of the criteria and
standards related to ecological effects of PM in recognition of linkages between these pollutants
(oxides of nitrogen, oxides of sulfur and PM) with respect to deposition and atmospheric
chemistry, as well as from an ecological effects perspective (U.S. EPA, 2017). Addressing the
pollutants together enables a comprehensive consideration of the nature and interactions of the
pollutants, which is important for ensuring thorough evaluation of the scientific information
relevant to ecological effects ofN and S deposition.
In March 2017, the EPA released the first external review draft of the Integrated Science
Assessment (ISA) for Oxides of Nitrogen, Oxides of Sulfur, and Particulate Matter Ecological
Criteria (82 FR 15702, March 30, 2017), which was then reviewed by the CASAC at a public
meeting on May 24-25, 2017 (82 FR 15701, March 30, 2017) and August 31,2017 (82 FR
35200, July 28, 2017; Diez Roux and Fernandez, 2017). With consideration of comments from
the CASAC and the public, the EPA released a second external review draft (83 FR 29786, June
26, 2018), which was reviewed by the CASAC at public meetings on September 5-6, 2018 (83
FR 2018; July 9, 2018) and April 27, 2020 (85 FR 16093, March 30, 2020; Cox, Kendall, and
Fernandez 2020a).19 The EPA released the final ISA in October 2020 (85 FR 66327, October 19,
2020; U.S. EPA, 2020). In planning for quantitative aquatic acidification exposure/risk analyses
for consideration in the PA, the EPA solicited public comment and consulted with the CASAC
(83 FR 31755, July 9, 2018; Cox, Kendall, and Fernandez, 2020b; U.S. EPA, 2018; 83 FR
42497, August 22, 2018).
The draft PA was completed in May 2023 and made available for review by the CASAC
and for public comment (88 FR 34852, May 31, 2023). The CASAC review was conducted at
public meetings held on June 28-29, 2023 (88 FR 17572, March 23, 2023), and September 5-6,
2023 (88 FR 45414, July 17, 2023). The CASAC conveyed advice on the standards and
comments on the draft PA in its September 27, 2023 letter to the Administrator (Sheppard,
2023). The CASAC advice on the standards is summarized in section 7.3 and considered in the
conclusions in section 7.4. The CASAC comments on the draft PA have informed completion of
this document. Additions and changes to the PA in consideration of those comments and public
comments include the following.
• Chapter 1: A new section has been added that describes the 1990 CAA Amendments
(section 1.3.3), and text has been revised or added to clarify a number of aspects
including the PM effects considered in this review.
• Chapter 2: A number of revisions have been made to Chapter 2 in consideration of
CASAC comments. These include an expanded overview of the acid deposition process
and chemical complexity of sulfur and nitrogen oxides; more specific source
19 A change in CASAC membership contributed to an extended time period between the two public meetings.
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categorization of NH3; and the relevance of the Clean Air Status and Trends Network
(CASTNET) for this review. Some information has been moved into or repeated in
Chapter 6 for improved cohesion in that chapter.
• Chapter 3: Clarification has been added regarding the effects considered in prior reviews
of the PM standards and regarding some aspects of the aquatic acidification index
developed in the 2012 review.
• Chapter 4: The discussion of N enrichment effects has been elevated, and the discussion
of the evidence for effects in estuarine and coastal waters, particularly, has been
appreciably expanded in light of CASAC comments.
• Chapter 5: The discussion of quantitative information pertaining to N enrichment effects
in aquatic systems has been appreciably expanded, particularly as related to the evidence
in estuarine and coastal areas, for which a new section has been added (section 5.2.3).
Many revisions have been made to the description of the aquatic acidification REA and
its results, both in this chapter and in the accompanying detailed appendix (5A) to
provide clarification on a number of aspects, including those raised by the CASAC.
Among these are the inclusion of a systematic uncertainty characterization of the aquatic
acidification REA in Appendix 5 A, section 5 A.3.
• Chapter 6: This chapter and the accompanying appendix (6A) have been substantially
expanded in light of CASAC advice and comments. For example, a new systematic
uncertainty characterization of the full array of air quality analyses has been included
(section 6.3), with additional sensitivity analyses to address several CASAC comments
on the trajectory-based analyses (e.g., stress test the selection of the sites of influence).
Further, the presentation of trajectory-based analyses has been augmented to more
completely describe the methodology and the basis for methodological choices in the
approach employed. The analysis itself has incorporated longer trajectories to better
account for long depositional lifetimes of some pollutants. A new discussion of co-
occurring trends in emissions, ambient air concentrations and estimated deposition, which
were noted in several aspects of CASAC comments, has been included in section 6.2.1.
• Chapter 7: In addition to appreciable revisions to accommodate consideration of the
expanded and improved aspects of Chapters 4, 5 and 6, a new section has been added that
summarizes the CASAC advice on the standards in this review. The conclusions section
has also been revised to take into account the changes across the PA and advice from the
CASAC.
The timeline for the remainder of this review is governed by a consent decree that requires the
EPA to sign a notice of proposed decision by April 9, 2024, and a final decision notice by
December 10, 2024 (Center for Biological Diversity v. Regan [N.D. Cal., No. 4:22-cv-02285-
HSG]).
1.5 ORGANIZATION OF THIS DOCUMENT
This PA includes staffs evaluation of the policy implications of the scientific assessment
of the evidence presented and assessed in the 2020 ISA and of results of quantitative assessments
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based on that information presented and assessed in this document. This evaluation informs
staffs conclusions and identification of policy options for consideration in this review of the
secondary standards addressing public welfare effects associated with the presence of oxides of
nitrogen, oxides of sulfur, and PM in the ambient air.
Following this introductory chapter, this document presents policy relevant information
drawn from the 2020 ISA as well as assessments that translate this information into a basis for
staff conclusions as to policy options that are appropriate to consider in this review. The
discussions are generally framed by addressing policy-relevant questions that have been adapted
from those initially presented in the 2017 IRP.
• Chapter 2 provides an overview of current information on N oxides, SOx, and PM-related
emissions, how these pollutants are transformed in the atmosphere and contribute to
deposition of S and N compounds. Chapter 2 also summarizes current air concentrations
and long-term trends of these pollutants and associated deposition, as well as key aspects
of the ambient air monitoring requirements.
• Chapter 3 summarizes the basis for the existing standards, describes key conclusions from
2012 review, recognizes key aspects of decision-making in NAAQS reviews and
provides an overview of approach taken in this PA to consider the secondary standards
with regard to protection for both direct and deposition-related effects.
• Chapter 4 provides an overview of the evidence as assessed in the 2020 ISA regarding
ecosystem effects of N oxides, S oxides and PM in ambient air, and potential implications
for effects of public welfare significance.
• Chapter 5 summarizes the information regarding exposure conditions associated with
effects. The quantitative REA for aquatic acidification performed in this review based on
the available evidence and quantitative tools is described, with associated details
presented in Appendix 5 A. For other categories of effects, the available quantitative
information regarding direct and deposition-related effects of N oxides, SOx, and PM to
deposition related effects is summarized, with associated details regarding terrestrial
effects information presented in Appendix 5B.
• Chapter 6 describes analyses and associated relationships between the deposition of S and
N compounds and air quality metrics related to SOx, N oxides, and PM in ambient air.
The analyses in this chapter (for which associated details are presented in Appendix 6A)
are intended to inform an understanding of the relationships between ambient air
concentrations and deposition, both in locations near sources and in rural areas, where
there may be sensitive ecosystems of concern for this review.
• Chapter 7 discusses evidence- and air quality/exposure/risk-based considerations and
summarizes conclusions regarding an array of options appropriate for consideration.
Consideration is given to the adequacy of protection afforded by the current standards for
both direct and deposition-related effects. This chapter also identifies key uncertainties
and associated needs for additional future research.
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REFERENCES
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Committee, to the Honorable Michael S. Regan, Administrator, Re: CASAC Review of
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U.S. EPA (1971). Air Quality Criteria for Nitrogen Oxides. Air Pollution Control Office.
Washington DC. EPA 450-R-71-001. January 1971. Available at:
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U.S. EPA (1973). "Effects of Sulfur Oxide in the Atmosphere on Vegetation". Revised Chapter 5
of Air Quality Criteria for Sulfur Oxides. Office of Research and Development. Research
Triangle Park, N.C. EPA-R3-73-030. September 1973. Available at:
https://nepis.epa.gov/Exe/ZyPDF.cgi?Dockey=2000X8F8.PDF.
U.S. EPA (1982a). Air Quality Criteria for Oxides of Nitrogen. Office of Research and
Development. Research Triangle Park, N.C. EPA/600/8-82/026F. December 1982.
Available at: https://nepis. epa.gov/Exe/ZyPDF. cgi?Dockey=500021LI.PDF
U.S. EPA (1982b). Air Quality Criteria for Particulate Matter and Sulfur Oxides. Volume I-III.
Office of Research and Development. Research Triangle Park, N.C. EPA/600/8-82/029.
December 1982. Available at:
https://nepis.epa.gov/Exe/ZyPDF.cgi?Dockey=3000188Z.PDF
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https://nepis. epa.gov/Exe/ZyPDF. cgi?Dockey=300053KV.PDF.
U.S. EPA (1982c). Review of the National Ambient Air Quality Standards for Sulfur Oxides:
Assessment of Scientific and Technical Information. OAQPS Staff Paper. Office of Air
Quality Planning and Standards. Research Triangle Park, NC. EPA-450/5-82-007.
November 1982. Available at:
https://nepis. epa.gov/Exe/ZyPDF. cgi?Dockey=300068A0.PDF.
U.S. EPA (1982d). Review of the National Ambient Air Quality Standards for Particulate
Matter: Assessment of Scientific and Technical Information. OAQPS Staff Paper. Office
of Air Quality Planning and Standards. Research Triangle Park, NC. EPA-450/5-82-001.
January 1982. Available at:
https://nepis. epa.gov/Exe/ZyPDF. cgi?Dockey=2000NH6N.PDF.
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U.S. EPA (1984a). The Acidic Deposition Phenomenon and Its Effects: Critical Assessment
Review Papers. Volume I Atmospheric Sciences. Office of Research and Development,
Washington DC. EPA600/8-83-016AF. July 1984. Available at:
https://nepis.epa.gov/Exe/ZyPDF. cgi?Dockey=2000G4AJ.PDF.
U.S. EPA (1984b). The Acidic Deposition Phenomenon and Its Effects: Critical Assessment
Review Papers. Volume II Effects Sciences. Office of Research and Development,
Washington DC. EPA-600/8- 83-016BF. July 1984. Available at:
https://nepis. epa.gov/Exe/ZyPDF. cgi?Dockey=2000G5FI.PDF.
U.S. EPA (1985). The Acidic Deposition Phenomenon and Its Effects: Critical Assessment
Document. Office of Research and Development, Washington, DC. EPA-600/8-85/001.
August 1985. Available at:
https://nepis. epa.gov/Exe/ZyPDF. cgi?Dockey=2000AD53.PDF.
U.S. EPA (1986). Review of the National Ambient Air Quality Standards for Particulate Matter:
Updated Assessment of Scientific and Technical Information. Addendum to the 1982
OAQPS Staff Paper. Office of Air Quality Planning and Standards, Research Triangle
Park, NC. EPA-450/05-86-012. December 1986. Available at:
https://nepis. epa.gov/Exe/ZyPDF. cgi?Dockey=910113 UH.PDF.
U.S. EPA (1993). Air Quality Criteria for Oxides of Nitrogen. Volume I-III. U.S. Office of
Research and Development, Research Triangle Park, NC. EPA/600/8-91/049aF-cF.
August 1993. Available at:
https://nepis. epa.gov/Exe/ZyPDF. cgi?Dockey=30001LZT.PDF
https://nepis. epa.gov/Exe/ZyPDF. cgi?Dockey=300056QV.PDF
https://nepis. epa.gov/Exe/ZyPDF. cgi?Dockey=30001NI2.PDF.
U.S. EPA (1995a). Review of the National Ambient Air Quality Standards for Nitrogen Dioxide:
Assessment of Scientific and Technical Information, OAQPS Staff Paper. Office of Air
Quality Planning and Standards, Research Triangle Park, NC. EPA-452/R-95-005.
September 1995. Available at:
https://nepis.epa.gov/Exe/ZyPDF.cgi?Dockey=00002UBE.PDF.
U.S. EPA (1995b). Acid Deposition Standard Feasibility Study: Report to Congress. Office of
Air and Radiation, Acid Rain Division, Washington, DC. EPA-430-R-95-001a. October
1995. Available at: https://nepis.epa.gov/Exe/ZyPDF.cgi?Dockey=2000WTGY.PDF.
U.S. EPA (1996). Review of the National Ambient Air Quality Standards for Particulate Matter:
Policy Assessment of Scientific and Technical Information (OAQPS Staff Paper). Office
of Air Quality Planning and Standards, Research Triangle Park, NC. EPA-452\R-96-013.
July 1996. Available at: https://nepis.epa.gov/Exe/ZyPDF.cgi?Dockey=2000DLIE.PDF.
U.S. EPA (2004a). Air Quality Criteria for Particulate Matter. (Vol I of II). Office of Research
and Development, Research Triangle Park, NC. EPA-600/P-99-002aF. October 2004.
Available at: https://nepis.epa.gov/Exe/ZyPDF.cgi?Dockey=P 100LFIQ.PDF.
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U.S. EPA (2004b). Air Quality Criteria for Particulate Matter. (Vol II of II). Office of Research
and Development, Research Triangle Park, NC. EPA-600/P-99-002bF. October 2004.
Available at: https://nepis.epa.gov/Exe/ZyPDF.cgi?Dockey=P 100LG7Q.PDF.
U.S. EPA (2007). Integrated Review Plan for the Secondary National Ambient Air Quality
Standards for Nitrogen Dioxide and Sulfur Dioxide. Office of Research and
Development, Research Triangle Park, NC, EPA-452/R-08-006. December 2007.
Available at: https://nepis.epa.gov/Exe/ZyPDF.cgi?Dockey=P 1001FDM.PDF.
U.S. EPA (2005). Review of the National Ambient Air Quality Standards for Particulate Matter:
Policy Assessment of Scientific and Technical Information, OAQPS Staff Paper. Office
of Air Quality Planning and Standards, Research Triangle Park, NC. EPA-452/R-05-
005a. December 2005. Available at:
https: //nepis. epa.gov/Exe/ZyPDF. cgi?Dockey=P1009MZM.PDF.
U.S. EPA (2008a). Integrated Science Assessment (ISA) for Oxides of Nitrogen and Sulfur
Ecological Criteria. Office of Research and Development, Research Triangle Park, NC.
EPA/600/R-08/082F. December 2008. Available at:
https: //nepis. epa.gov/Exe/ZyPDF.cgi?Dockey=P 100R7MG.PDF.
U.S. EPA (2008b). Integrated Review Plan for the National Ambient Air Quality Standards for
Particulate Matter. Office of Air Quality Planning and Standards, Research Triangle Park,
NC. EPA 452/R-08-004. March 2008. Available at:
https: //nepis. epa.gov/Exe/ZyPDF. cgi?Dockey=P 1001FB9.PDF.
U.S. EPA (2009a). Risk and Exposure Assessment for Review of the Secondary National
Ambient Air Quality Standards for Oxides of Nitrogen and Oxides of Sulfur (Main
Content). Office of Air Quality Planning and Standards, Research Triangle Park, NC.
EPA-452/R-09-008a. September 2009. Available at:
https://nepis. epa.gov/Exe/ZyPDF. cgi?Dockey=P 100FNQV.PDF.
U.S. EPA (2009b). Integrated Science Assessment for Particulate Matter. Office of Research and
Development, Research Triangle Park, NC. EPA/600/R-08/139F. December 2009.
Available at: https://nepis.epa.gov/Exe/ZyPDF.cgi?Dockey=P10060Z4.PDF.
U.S. EPA (2009c). Particulate Matter National Ambient Air Quality Standards (NAAQS): Scope
and Methods Plan for Urban Visibility Impact Assessment. Office of Air Quality
Planning and Standards, Research Triangle Park, NC. EPA-452/P-09-001. February
2009. Available at: https://nepis.epa.gov/Exe/ZyPDF.cgi?Dockey=P100FLUX.PDF.
U.S. EPA (2010). Particulate Matter Urban-Focused Visibility Assessment - Final Document.
Office of Air Quality Planning and Standards, Research Triangle Park, NC. EPA-452/R-
10-004. July 2010. Available at:
https://nepis. epa.gov/Exe/ZyPDF. cgi?Dockey=P 100FO5D.PDF.
U.S. EPA (2011). Policy Assessment for the Review of the Secondary National Ambient Air
Quality Standards for Oxides of Nitrogen and Oxides of Sulfur. Office of Air Quality
Planning and Standards, Research Triangle Park, NC. EPA-452/R-ll-005a, b. February
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2011. Available at: https://nepis.epa.gov/Exe/ZyPDF.cgi?Dockey=P1009R7U.PDF
https://nepis. epa.gov/Exe/ZyPDF. cgi?Dockey=P1009RHY.PDF.
U.S. EPA (2017). Integrated Review Plan for the Secondary NAAQS for Oxides of Nitrogen and
Oxides of Sulfur and Particulate Matter - Final. Office of Air Quality Planning and
Standards, Research Triangle Park, NC. EPA-452/R-17-002. January 2017. Available at:
https://nepis.epa.gov/Exe/ZyPDf.cgi?Dockey=P 100R607.PDF.
U.S. EPA (2018). Review of the Secondary Standards for Ecological Effects of Oxides of
Nitrogen, Oxides of Sulfur, and Particulate Matter: Risk and Exposure Assessment
Planning Document. Office of Air Quality Planning and Standards, Research Triangle
Park, NC. EPA-452/D-18-001. August 2018. Available at:
https://nepis.epa.gov/Exe/ZyPDF. cgi?Dockey=P 100V7JA.PDF.
U.S. EPA (2020) Integrated Science Assessment (ISA) for Oxides of Nitrogen, Oxides of Sulfur
and Particulate Matter Ecological Criteria (Final Report, 2020). Office of Air Quality
Planning and Standards, Research Triangle Park, NC. EPA/600/R-20/278. September
2020. Available at: https://nepis.epa.gov/Exe/ZyPDF.cgi?Dockey=P 1010WR3.PDF.
Wolff, GT (1993). Letter from George T. Wolff, Chair, Clean Air Scientific Advisory
Committee to the Honorable Carol M. Browner, Administrator, U.S. EPA. Re: Clean Air
Scientific Advisory Committee Closure on the Air Quality Criteria Document for Oxides
of Nitrogen. September 30, 1993. EPA-SAB-CASAC-LTR-93-015. Office of the
Administrator, Science Advisory Board Washing, DC Available at:
https://casac. epa. gov/ords/sab/f?p=113:12:1342972375271:::12.
Wolff, GT (1995). Letter from George T. Wolff, Chair, Clean Air Scientific Advisory
Committee to the Honorable Carol M. Browner, Administrator, Re: CASAC Review of
the Staff Paper for the Review of the National Ambient Air Quality Standards for
Nitrogen Dioxide: Assessment of Scientific and Technical Information. August 22, 1995.
EPA-SAB-CASAC-LTR-95-004. Office of the Administrator, Science Advisory Board
Washing, DC Available at:
https://nepis.epa.gov/Exe/ZyPDF.cgi?Dockey=P 100FL6Q.PDF.
Wolff, GT (1996). Letter from George T. Wolff, Chair, Clean Air Scientific Advisory
Committee to the Honorable Carol M. Browner, Administrator, Re: Closure by the Clean
Air Scientific Advisory Committee (CASAC) on the Staff Paper for Particulate Matter.
June 13, 1996. EPA-SAB-CASAC-LTR-96-008. Office of the Administrator, Science
Advisory Board Washing, DC Available at:
https://nepis.epa.gov/Exe/ZyPDF.cgi7Dockey=9100TTBM.PDF.
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2 AIR QUALITY AND DEPOSITION
This chapter begins with an overview of the atmospheric processes for N oxides and
oxides of sulfur (SOx), including those present as particulate matter (PM). This includes a
description of the most relevant pollutants and how they can be transformed in the atmosphere
and contribute to deposition of nitrogen (N) and sulfur (S) species (section 2.1). Subsequent
sections summarize the sources of N oxides, SOx, and PM emissions (section 2.2), describe
measurement of relevant species including national monitoring networks and methods (section
2.3), describe recent observed trends in N, S, and PM species concentrations (section 2.4), and
describe the way deposition estimates are developed (section 2.5).
2.1 ATMOSPHERIC TRANSFORMATION OF NITROGEN, SULFUR,
AND PM SPECIES
This section briefly describes the key processes associated with atmospheric deposition
of nitrogen and sulfur species, including both gaseous species and those that are present as PM.
The pathway from emission to eventual deposition is specific across pollutants and is influenced
by a series of atmospheric processes and often non-linear chemical transformations that occur at
multiple spatial and temporal scales. Figure 2-1 is a simple schematic that identifies some of the
individual pollutants that are part of oxides of nitrogen, oxides of sulfur, and PM, as well as how
they can be interconnected. Each of these three categories of species are discussed more fully
below.
NO, NO,, HN03,
n2o5, hno4,
HONO, PAN, other
\organic nitrates
"elemental and
organic carbon
metals, other
Oxides of
Sulfur
Oxides of
Nitrogen
Particulate
Matter
Figure 2-1. Schematic of most relevant individual pollutants that comprise oxides of
nitrogen, oxides of sulfur, and particulate matter.
2-1
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2.1.1 Oxides of Sulfur
Sulfur dioxide (SO2) is one of a group of highly reactive gases collectively known as
"oxides of sulfur" (SOx). Oxides of sulfur may include sulfur monoxide (SO), SO2, sulfur
trioxide (SO3), disulfur monoxide (S2O), and various aerosol forms including sulfuric acid
(H2SO4), bisulfite (HSO3"), sulfite (SO32"), hydrogen bisulfate and, principally, sulfate (S042")
As discussed in more detail in section 2.2, SOx is mostly emitted from combustion processes
(e.g., stationary fuel combustion sources) in the form of SO2. Aerosol SO42" may also be emitted
directly from combustion. Sulfur dioxide is generally present at higher concentrations in the
ambient air than the other gaseous SOx species (ISA, Appendix 2, section 2.1), and as a result,
the indicator for the NAAQS for SOx is SO2.
Once emitted to the atmosphere SO2 can react in both the gas phase and in aqueous
solutions such as clouds and particles to form SO42" (McMurry et al., 2004). There are multiple
pathways for this process to occur. SO2 is generally oxidized to sulfate following dissolution in
cloud droplets, which can yield fast rates of sulfate production (up to 100% per hour). In the
daytime, atmospheric oxidation may also convert gas phase SO2 to H2SO4, which quickly and
nearly completely condenses on existing particles or forms new sulfate particles (ISA, Appendix
2, section 2.3.2). The SO2 to sulfate conversion typically occurs at rates of 0.1 to 10% per hour
(Eatough et al., 1994), with higher rates associated with higher temperatures, sunlight, and the
presence of oxidants. The conversion rates are determined by the availability of oxidants. The
principal oxidizing agents for SO2 are hydrogen peroxide, ozone and oxygen. Their relative level
of influence depends on their concentration and the pH (Seinfeld and Pandis, 1998). Depending
on the availability of ammonia, sulfate may also be present as ammonium bisulfate (NH4HSO4)
or ammonium sulfate ((NH4)2S04).
Sulfate particles generally fall within the fine particle size range and contribute to PM2.5
concentrations. The atmospheric lifetime of sulfate particles is relatively long, ranging from 2 to
10 days (as compared to SO2, which is usually removed from the atmosphere within 2 days of its
emission). As a result, sulfate concentrations tend to be regionally homogeneous (see section
2.4.2). Dry deposition can be an influential removal process for SO2 on local scales, with a
lifetime of approximately one day to one week. Following oxidation of SO2 to particulate SO42",
wet deposition is generally the primary removal process. The wet deposition lifetime for
atmospheric S is about one week (2008 ISA section 2.6.3.1).
Although particulate sulfate can dry deposit, it is more efficiently removed by
precipitation (wet deposition) (e.g., Mulcahy et al., 2020).
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2.1.2 Oxidized Nitrogen
The oxidized nitrogen species, nitric oxide (NO) and nitrogen dioxide (NO2), are
collectively referred to as NOx. As discussed in more detail in section 2.2, the largest sources of
NOx emissions are related to fossil fuel combustion, which includes anthropogenic sources such
as power plants, industrial facilities, motor vehicles, and wood burning stoves. Non-
anthropogenic sources of NOx can include wildfires, biological soil processes, and lightning. In
the atmosphere, NO and NO2 can be converted to other forms of oxidized nitrogen, including
nitric acid (HNO3), peroxynitric acid (HNO4), nitrous acid (HNO2), and peroxyacetyl nitrate
(PAN) or other forms of organic nitrogen. The term "oxides of nitrogen" refers to all forms of
oxidized nitrogen compounds (NOy), including nitric oxide, nitrogen dioxide and all other
oxidized nitrogen-containing compounds formed from NO and NO2 (ISA Appendix 2, section
2.3.1). The indicator for the NAAQS for oxides of N is NO2.
Oxidation of NOx in the daytime or dinitrogen pentoxide (N2O5) hydrolysis in cold,
nighttime conditions produce HNO3. HNO3 either settles onto surfaces directly (via dry
deposition) or be scavenged by particles or cloud water to form nitrate (ISA Appendix 2, section
2.3.1). Facilitated by cold, humid conditions and the availability of excess NH3, some of these
compounds can partition from the gas phase into the solid or liquid phases as particulate nitrate
(generically referred to as NO3") and contribute to PM2.5 concentrations. While almost all sulfate
exists in the fine particle range, nitrate has a larger range in its size distribution and may either be
fine or coarse, such that not all nitrate contributes to PM2.5. Each form of oxidized nitrogen is
removed from the atmosphere at different rates. For example, nitric acid quickly settles onto
surfaces (via dry deposition) while particulate nitrate is more efficiently removed by
precipitation (wet deposition).
2.1.3 Reduced Nitrogen
Reduced nitrogen, distinct from oxidized nitrogen, can also contribute to PM2.5 formation
and lead to adverse deposition-related effects. Ammonia is the most common form of
atmospheric reduced nitrogen. Animal livestock operations and fertilized fields are the largest
emission sources of NH3, but there are combustion-related sources as well, such as vehicles and
fires. Ammonia plays an important role as a precursor for atmospheric particulate matter and can
be both deposited and emitted from plants and soils in a bidirectional exchange. NH3 may
contribute to inorganic PM2.5 formation (as ammonium, NH4+) based on the availability of acid
gases (HNO3, H2SO4) and favorable meteorological conditions (low temperatures and high
relative humidity). Ammonia reacts with gas phase HNO3 to form ammonium nitrate or can
partially or fully neutralize particle sulfate. The amount of ammonia present (along with organic
compounds) is one determinant of the balance of ammonium sulfate and ammonium nitrate and
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therefore influences the spatial extent of N and S deposition (ISA, Appendix 2, section 2.3.3).
Ammonia tends to dry deposit near sources, but in particle form, ammonium (NH4+) can be
transported farther distances and is most efficiently removed by precipitation. The sum of NH3
and NH4+ is referred to as NHx.
2.1.4 Atmospheric Processing
Once emitted to the atmosphere, SOx, NOy, and NHx are chemically transformed and
transported until they are eventually removed from the atmosphere by deposition. The transport
of emitted pollutants is a function of local and regional meteorological conditions such as wind
fields and atmospheric stability that collectively govern how the pollutant species are advected
and diffused. The formation of inorganic particulate matter following gas phase emission of SOx,
NOy and/or NH3 is also sensitive to meteorological conditions (e.g., temperature, relative
humidity), and the availability of basic (NH3) or acidic (H2SO4, HNO3) species. Along with the
meteorological conditions, landscape characteristics and the chemical lifetime of a pollutant are
also major factors in determining the distance at which pollutants contribute to deposition. Since
the chemical form is important to determining the rate of dry and wet deposition (i.e., whether or
not a pollutant deposits to the soil or vegetative surfaces), as well as the relationship between air
concentrations and deposition, we use process-based models and quality-assured ambient air
measurements to understand the transformation from emissions to concentrations to deposition
(see sections 2.2 and 2.5).
2.2 SOURCES AND EMISSIONS OF NITROGEN, SULFUR, AND PM
SPECIES
The sources and precursors to gaseous and particulate forms of SOx, NOy, and NHx vary
and can include a combination of anthropogenic and natural sources. Anthropogenic sources of
SO2, NOx, and NH3 include power plants, industrial sources, motor vehicles, and agriculture.
The National Emissions Inventory (NEI)1 is a comprehensive and detailed estimate of air
emissions of criteria pollutants, precursors to criteria pollutants, and certain hazardous air
pollutants from air emissions sources. The NEI is released every three years based primarily
upon data provided by State, Local, and Tribal air agencies for sources in their jurisdictions and
supplemented by data developed by the EPA. For some sources, such as power plants, direct
emission measurements enable the emissions estimates to be more certain than other sectors
without such direct measurements. It should be recognized that emission inventories are
inherently uncertain and contain assumptions that may influence the estimates of their magnitude
1 https://www.epa.gov/air-emissions-inventories/national-emissions-inventorv-nei
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and trends. The 2020 NEI was released to the public on March 31, 2023. These 2020 data will be
used for the summaries shown in the following sections describing emission estimates and
trends. The reader is referred to the 2020 NEI2 for further details (U.S. EPA, 2023a).
2.2.1 NOx Emissions Estimates and Trends
Figure 2-2 shows the relative contributions of various sources to total U.S. NOx
emissions3 in 2020, based on estimates contained in the NEI (U.S. EPA, 2023a). Anthropogenic
sources account for a majority of NOx emissions in the U.S., with highway vehicles (26%),
stationary fuel combustion (25%), and non-road mobile sources (19%) identified as the largest
contributors to total emissions. Highway vehicles include all on-road vehicles, including light
duty as well as heavy duty vehicles, both gasoline- and diesel-powered. The stationary fuel
combustion sector includes electricity generating units (EGUs), as well as commercial,
institutional, industrial, and residential combustion of biomass, coal, natural gas, oil, and other
fuels. Non-road mobile sources include aircraft, commercial marine vessels, locomotives, and
non-road equipment. Other anthropogenic NOx sources include agricultural field burning,
prescribed fires, and various industrial processes such as cement manufacturing and oil and gas
production. Natural sources of NOx include emissions from lightning as well as from plants and
soil (biogenic), which represent 12% of the total NOx emissions. In addition, fires (i.e., wild,
prescribed, and agricultural) are estimated to represent 5% of the overall emissions of NOx. Soil
emission estimates come from the Biogenic Emissions Inventory System, version 4 (BEIS)
model in the NEI. Biomass burning emissions (wild and prescribed fires) come from the Blue
Sky Pipeline framework (developed by the U.S. Forest Service,
https://github.com/pnwairfire/bluesky). More information on both these models can be found in
our 2020 NEI Technical Support Document (TSD) 4
Figure 2-3 shows the NOx emissions density in tons/year per square mile for each U.S.
County. The majority of NOx emissions tend to be located near urban areas, which tend to have
the most vehicle traffic and industrial sources. However, there are also some counties in rural
areas with higher NOx emissions due to the presence of large stationary sources such as EGUs or
oil and gas extraction and generation.
2 https://www.epa.gov/air-emissions-inventories/2020-national-emissions-inventorv-nei-data
3 For all source categories, NOx is compiled from emissions measurements that express NOx mass based on the
molecular weight of NO2, which is 46 g/mole (40 CFR 51.40). Even though emissions from most sources initially
consist mainly of NO, this expression of NOx by NO2 molecular weight is considered appropriate due to the fast
rate of transformation of NO to NO2 under ambient air conditions or when the emissions are exposed to any type
of oxidant. While NOx is made up of NO2, NO, and, for mobile sources, HONO, the combination of these by
mass is more simply done using a single molecular weight.
4 https://www.epa.gov/svstem/files/documents/2023-03/NEI202Q TSD Section8 Biogenics O.pdf.
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NOx Emissions {8,916 kTon/year)
Stationary Fuel Combustion 25%
All Fires 5%
Non-Road Mobile 19%
Highway Vehicles 26%
Biogenics 12%
Industrial
Processes 12%
Other 1 %
Figure 2-2. 2020 NOx emissions estimates by source sector (U.S. EPA, 2023a). Note: The
NEI, and this figure, do not include emissions from lightning.
Nitrogen Oxides Emissions Density in tons/year/miA2 (# Counties)
~ 0-1.9(1,356) ~ 2-4.9(1,130) a 5-9.9(412) ¦ 10-19.9(196) ¦ 20-648(127)
Figure 2-3. 2020 NOx emissions density across the U.S. (U.S. EPA, 2023a).
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Total anthropogenic NOx emissions have trended strongly downward across the U.S.
between 2002 and 2022 (U.S. EPA, 2023b). Nationwide estimates indicate a 70% decrease in
anthropogenic NOx emissions over this time period as a result of multiple regulatory programs
(e.g., including the NOx SIP Call, the Cross-State Air Pollution Rule (CSAPR), and the Tier 3
Light-duty Vehicle Emissions and Fuel Standards) implemented over the past two decades, as
well as changes in economic conditions. As seen in Figure 2-4, the overall decrease in NOx
emissions has been driven primarily by decreases from the three largest emissions sectors.
Specifically, compared to the 2002 start year, estimates for 2022 (from the 2020 NEI) indicate an
84% reduction in NOx emissions from highway vehicles, a 68% reduction in NOx emissions
from stationary fuel combustion, and a 54% reduction in NOx emissions from non-road mobile
sources.
inventory Year
Figure 2-4. Trends in NOx emissions by sector between 2002 and 2022 (U.S. EPA,
2023b).
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2.2.2 SOi Emissions Estimates and Trends
Fossil fuel combustion is the main anthropogenic source of SO2, primarily from coal-
fired EGUs (48%). Sulfur is present to some degree in all fossil fuels, especially coal, and occurs
as reduced organosulfur compounds. In the most common types of coal (anthracite, bituminous,
subbituminous, and lignite), sulfur content varies between 0.4 and 4% by mass. Sulfur in fossil
fuels is almost entirely converted to SO2 during combustion. Other major anthropogenic sources
of SO2 emissions include industrial processes (27%) and stationary source fuel combustion (9%).
Mobile sources, and agricultural and prescribed fires are smaller contributors. Figure 2-5 shows
the percentage contribution of specific source categories to the total anthropogenic (plus
wildfire) SO2. Across all source categories, directly emitted sulfates are about 5% of the total
emitted sulfur, although it can vary by source.
Figure 2-6 shows the SO2 emissions density in tons/year per square mile for each U.S.
county. The majority of SO2 emissions tend to be located near large point sources such as coal-
fired EGUs or large industrial facilities. Counties near urban areas also tend to have higher SO2
emissions due to the higher concentration of industrial facilities. In some cases, counties in rural
areas can also have higher emissions due to oil and gas extraction or fires.
Figure 2-5. Estimates of 2020 SO2 emissions by source sector (U.S. EPA, 2023a).
S02 Emissions (1,845 kTon/year)
- Other 2%
Mobile Sources 1 %
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~ 0-0.29(2,389) ~ 0.3-0.99 (429) ~ 1-2.99 (222) ~ 3-9.99(111) ¦ 10-329(70)
Figure 2-6. Estimates of 2020 SO2 emissions density across the U.S. (U.S. EPA, 2023a).
Similar to NOx, and for many of the same reasons, SO2 emissions have declined
significantly since 2002. Figure 2-7 illustrates the emissions changes over the 2002-2022 period.
The data shows an 87% decrease in total SO2 emissions over the period, including reductions of
91% in emissions from EGUs and 96% in emissions from mobile sources.
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Inventory Year
Figure 2-7. Trends in SO2 emissions by sector between 2002 and 2022 (U.S. EPA, 2023b).
2.2.3 NH3 Emissions Estimates and Trends
Ammonia is emitted directly into the atmosphere, unlike other atmospheric N species
(e.g., organic N) that are formed through photochemical reactions. Figure 2-8 shows the
percentage contribution of specific source categories to the total anthropogenic (plus wildfires)
NH3. In 2020, livestock waste (49%), fertilizer application (33%) and aggregate fires (11%)
contributed most significantly to total annual emissions (5.5 million tons NH3). Vehicles emit
NFb due to the unintended formation of NFb from catalytic converters reducing NOx under fuel
rich conditions for gasoline vehicles and from overdosing of urea in selective catalytic systems in
modern heavy-duty vehicles (Easter and Bohac, 2016; Jeon et al., 2016; Khalek et al., 2015).
While mobile source contributions to total NFb emissions are only about 2% at the national
level, there is a growing body of evidence suggesting that vehicular sources may be
underestimated in the NEI (Sun et al., 2017; Chen et al., 2022). Any underestimation in mobile
source NFb emissions would mostly impact urban areas, where there is a lot of on-road mobile
source traffic. The latest version of EPA's Motor Vehicle Emission simulator, MOVES4
(https://www.epa.gov/moves/latest-version-motor-vehicle-emission-simulator-moves). has been
updated to incorporate real-world measurements of NFb emissions from vehicles, and it suggests
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higher NH3 emissions from onroad vehicles than previous inventories. This simulator, MOVES4,
will be used in future versions of NEI. Figure 2-9 shows the NH3 emissions density in tons per
year per square mile for each U.S. county. Ammonia emissions are greatest in counties with
significant agricultural output (e.g., central U.S., parts of CA, and eastern NC).
NH3 Emissions (5,485 kTon/year)
Livestock Waste 49%
Figure 2-8. Estimates of 2020 NH3 emissions by source sector (U.S. EPA, 2023a).
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~ 0-1.99(1,905) ~ 2-4.99 (1,076) ~ 5-9.99(187) ¦ 10-19.99(41) ¦ 20-71(12)
Figure 2-9. Estimates of N'lh emissions density across the U.S. (U.S. EPA, 2023a).
Figure 2-10 shows NFb emission trends from 2002-2022 by sector. In comparison with
NOx and SOx emission trends, which demonstrated dramatic decreases over the past few
decades, the annual rate of Nib emissions has increased by over 20 percent since 2002. The two
largest contributors are livestock waste and fertilizer application which have increased by 11%
and 44%, respectively, from 2002 to 2022. However, there is greater uncertainty in NFb
emissions trends (ISA, Appendix 2, section 2.2.3) than with the other pollutants. This is partly
due to a lack of control programs nationally for agricultural sources of NFb. It is worth noting
that variabilities associated with local management practices related to animal husbandry makes
these emissions a bit more uncertain than emissions derived from, for example, direct
measurements from EGU sources. The EPA has improved its models for simulating both
livestock waste emissions and the fertilizer application process to inform development of the
2020 NEI which is expected to have reduced these uncertainties (U.S. EPA, 2023a).
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n
Livestock Waste
¦¦
Fertilizer Application
Wildfires
um
Agricultural & Prescribed Fires
Waste Disposal
Mobile Sources
\^u
Stationary Fuel Combustion
Other
Inventory Year
Figure 2-10. Trends in NH3 emissions by sector between 2002-2022 (U.S. EPA, 2023b).
2.3 MONITORING AMBIENT AIR CONCENTRATIONS AND
DEPOSITION
To promote uniform enforcement of the air quality standards set forth under the CAA, the
EPA has established federal reference methods (FRMs) and federal equivalent methods (FEMs)
for ambient air sample collection and analysis. Measurements for determinations of NAAQS
compliance must be made with FRMs or FEMs. Federal reference methods and national
monitoring networks have been established for NO2 as the indicator of oxides of nitrogen, SO2 as
the indicator of sulfur oxides, and PM2.5 and PM10 as indicators for PM.
As described briefly below, multiple monitoring networks measure the atmospheric
concentrations of nitrogen oxides, SOx, and PM, as well as wet deposition of N and S
compounds. The largest routinely operating network that measures ambient air concentrations is
the State and Local Air Monitoring Stations (SLAMS) network which includes measurement of
one or more NAAQS pollutants at each site. There are three multipollutant networks involving
NAAQS measurements which are largely sited at SLAMS.5 These networks include: the
National Core (NCore) multi-pollutant monitoring network, the Photochemical Assessment
Monitoring Stations (PAMS) network, and the near-road network. The NCore network is notable
in that it provides a core of sites, mostly located in urban areas, that provide collocated
measurements of SO2, NO, NOy, and PM components including ammonium, nitrate, and sulfate,
5 A small number of multipollutant sites may have a monitor type different than SLAMS such as Tribal or Non-EPA
Federal (e.g.. National Park Service [NPS]).
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although with sparser coverage than the FRM networks for SO2 or NO2. Collocated, ambient air
measurements of SO2, SO42" and NOy (NOy is measured rather than NOx) from NCore can be
used to help estimate total deposition of oxides of nitrogen and sulfur. The primary objective of
the PAMS network is to support the implementation of the ozone NAAQS; it also measures
NOy, as well as NO2. The near-road network is intended to capture short-term peak NO2
concentrations for comparison to the NO2 primary NAAQS. Many of the near-road sites are also
required to have collocation with PM2.5 and carbon monoxide (CO) monitors. One of the
challenges associated with interpreting monitoring data in the context of a deposition-related
secondary standard is that many, but not all, of the monitor sites are located in urban or suburban
areas (where air quality concentrations are highest and human populations are greatest), while
many of the areas where deposition effects are potentially of greatest concern tend to be in more
rural areas.
2.3.1 NOx Monitoring Networks
There were 491 monitoring sites, mostly in major metropolitan areas, reporting hourly
NO2 concentration data to the EPA during the 2019-2021 period; 80% of these NO2 monitoring
sites are part of the SLAMS network (U.S. EPA 2021a). This network relies on a
chemiluminescent FRM and on multiple FEMs that use either chemiluminescence or direct
measurement methods of NO2. Chemiluminescent-based FRMs only detect NO in the sample
stream. Therefore, a two-step process is employed to measure NO2, based on the subtraction of
NO from NOx. Data produced by chemiluminescent analyzers include NO, NO2, and NOx
measurements. As discussed in the ISA the traditional chemiluminescence FRM is subject to
potential measurement biases resulting from interference by N oxides other than NO or NO2
(ISA, Appendix 2, p. 2-34).6 These potential biases are measurement uncertainties that can
impact exposure analyses. However, within metropolitan areas, where a majority of the NO2
monitoring network is located and is influenced by strong NOx sources, the potential for bias
related to other N oxides is relatively small.
Another important subset of SLAMS sites is the near-road monitoring network, which
was required as part of the 2010 NO2 primary NAAQS review and began operating in 2014.
Near-road sites are required in each metropolitan statistical area (MSA) with a population of
1,000,000 or greater, and an additional near-road site is required in each MSA with a population
of 2,500,000 or greater. There were 73 near-road monitors in operation during the 2019-2021
period. Finally, there are also a number of Special Purpose Monitors (SPMs), which are not
required but are often operated by air agencies for short periods of time (i.e., less than 3 years) to
6 The N oxides other than NO and NO2 are often collectively abbreviated as NOz (i.e., NOy = NOx +NOz).
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collect data for human health and welfare studies, as well as other types of monitoring sites,
including monitors operated by tribes and industrial sources. The SPMs are typically not used to
assess compliance with the NAAQS. The locations of all NO2 monitoring sites operating during
the 2019-2021 period are shown in Figure 2-11.
« SLAMS (250) • NCORE/PAMS (74) O NEAR ROAD (73) • SPM/OTHER (94)
Figure 2-11. Locations of NO2 monitors operating during the 2019-2021 period.
2.3.2 SO2 Monitoring Networks
There were 505 monitoring sites reporting hourly SO2 concentration data to the EPA
during the 2019-2021 period (U.S. EPA 2021b). Over 75% of the SO2 sites are part of the
SLAMS network. Measurements are made using ultraviolet fluorescence instruments, which are
designated as FRMs or FEMs and the data are reported as hourly concentrations with either the
maximum 5-minute concentration for each hour or twelve 5-minute average concentrations for
each hour. Additionally, as of 2015, States are required to monitor or model ambient air SO2
levels in areas with stationary sources of SO2 emissions of over 2,000 tons per year. The EPA
identified over 300 sources meeting these criteria according to 2014 emissions data, and some
States chose to set up ambient air monitoring sites to assess compliance with the SO2 NAAQS.
Some of these monitors are operated by the States as SLAMS monitors, while others are
operated by the industrial sources. The locations of all SO2 monitoring sites (FRM or FEM)
operating during the 2019-2021 period are shown in Figure 2-12.
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O SLAMS (301) • NCORE (77) O INDUSTRIAL (57) • SPM/OTHER (61)
Figure 2-12. Locations of SO2 monitors operating during the 2019-2021 period.
2.3.3 PM2.5 and PM10 Monitoring Networks
As with NOx and SO2, the main network of monitors providing ambient air PM mass
data for use in NAAQS implementation activities is the SLAMS network (including NCore).
PM2.5 monitoring was required for near-road network sites as part of the 2012 PM NAAQS
review and these sites monitors were phased into the network between 2015 and 2017. Near-road
sites are also required in each MSA with a population of 1,000,000 or greater. The PM2.5
monitoring program remains one of the largest ambient air monitoring programs in the U.S.
There were 1,067 monitoring sites reporting PM2.5 data to the EPA during the 2019-2021 period
(U.S. EPA 2021c). Figure 2-13 shows the locations of these monitoring sites. Approximately
50% of these monitoring sites operate automated FEMs which report continuous (hourly) PM2.5
data while the remaining sites operate FRMs which collect 24-hour samples every day, every 3rd
day, or every 6th day. There were 724 monitoring sites reporting PM10 data to the EPA during the
2019-2021 period. Figure 2-14 shows the locations of these monitoring sites. Approximately
61% of these monitoring sites operate FEMs that report continuous PM10 data while the
remaining sites operate FRMs that typically collect samples every day, every 3rd day, or every
6th day.
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® SLAMS (801) • NCORE (70) O NEAR ROAD (59) • SPM/OTHER (137)
Figure 2-13. PM2.5 mass monitors operating during the 2019-2021 period.
• SLAMS (520) • NCORE (23) O INDUSTRIAL (91) • SPM/OTHER (90)
Figure 2-14. PMio mass monitors operating during the 2019-2021 period.
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Due to the complex nature of fine particles, the EPA and States implemented the
Chemical Speciation Network (CSN) to better understand the components of fine particle mass
at selected locations across the country. PM2.5 speciation measurements are also collected at
NCore stations. Additionally, specific components of fine particles are measured through the
Interagency Monitoring of Protected Visual Environments (IMPROVE) monitoring program,
which supports the regional haze program and tracks changes in visibility in Federal Class I
areas as well as many other rural and some urban areas. The IMPROVE network consists of
more than 100 monitoring sites in national parks and other remote locations and has also
provided a reliable, long-term record of particulate mass and species components. The locations
of the CSN (a mix of 3-day and 6-day sampling frequency) and IMPROVE (3-day sampling
frequency) sites reporting speciated PM2.5 data to the EPA during the 2019-2021 period are
shown in Figure 2-15.
• CSN (107) • IMPROVE (151) O NCORE (49) • OTHER (9)
Figure 2-15. PM2.5 speciation monitors operating during the 2019-2021 period.
2.3.4 Routine Deposition Monitoring
Wet deposition is measured as the product of pollutant concentration in precipitation and
precipitation amounts (e.g., in rain or snow). Concentration in precipitation is currently measured
as a weekly average by the National Atmospheric Deposition Program/National Trends Network
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(NADP/NTN) across a national network of approximately 250 sites using a standard
precipitation collector. The NADP precipitation network was initiated in 1978 to collect data on
amounts, trends, and distributions of acids, nutrients, and cations in precipitation. The NTN is
the only network (shown in Figure 2-16) that provides a long-term record of precipitation
chemistry across the U.S. Sites are mainly located away from urban areas and pollution sources.
An automated collector ensures that the sample is exposed only during precipitation (wet-only
sampling). Nitrate, sulfate, and ammonium are all measured. Relatively high confidence has been
assigned to wet deposition estimates because of established capabilities for measuring relevant
chemical components in precipitation samples.
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Figure 2-16. Location of NTN monitoring sites with different symbols for how many years
the site has operated (through 2017). Source: NADP/NTN site information
database (https://nadp.slh.wisc.edu/networks/national-trends-network/,
accessed August 2023)
In contrast, direct measurements of dry deposition flux are rare and difficult, and dry
deposition fluxes of gases and particles are estimated from concentration measurements by an
inferential technique described in the 2008 ISA (U.S. EPA, 2008). Ambient air concentrations
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are measured in the Clean Air Status and Trends Network (CASTNET), which was established
under the 1991 CAA Amendments to assess trends in acidic deposition. CASTNET is a long-
term environmental monitoring network with approximately 100 sites (see Figure 2-17 for a map
of U.S. sites) located throughout the U.S. and Canada, managed and operated by the U.S. EPA in
cooperation with other federal, state, and local partners (www.epa.gov/castnet).
Figure 2-17. Location of CASTNET monitoring sites and the organizations responsible
for collecting data. (NPS = National Park Service, BLM = Bureau of Land
Management).
The CASTNET is the only network in the U.S. that provides a consistent, long-term data
record of ambient air concentrations of S and N species that dry deposition fluxes can be
estimated from. It complements the NTN, and nearly all CASTNET sites are collocated with or
near an NTN site. Together, these two monitoring programs are designed to provide data
necessary to estimate long-term temporal and spatial trends in total deposition (dry and wet).
Species measured in CASTNET include: O3, SO2, HNO3, nitrate, sulfate, and ammonium among
others. Weekly ambient air concentrations of gases and particles are collected with an open-face
3-stage filter pack. Ozone measurements occur on an hourly basis. While CASTNET data are
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more useful for estimating dry deposition than data from FRM networks, monitors are generally
sparse and deposition is only determined for discrete locations. Also, not all of the species that
contribute to total sulfur and nitrogen deposition are measured in CASTNET (Schwede et al.,
2011). Despite these disadvantages, CASTNET data can still be very useful if used in
combination with modeled estimates (Schwede et al., 2011), as discussed further in section 2.5.
The CASTNET has recently been reviewed by the EPA's Scientific Advisory Board with
regard to its past functioning and current status, and to consider optimization of the network. A
change in the distribution or number of sites or a shift in the instrument payload could affect our
understanding of changes in deposition, potentially in response to new emission controls, as well
as efforts to improve understanding of the link between air concentration and deposition. The
Science Advisory Board released a draft letter of recommendations on October 11, 2023, which
is available on its website
(https://sab.epa.gov/ords/sab/f?p=100:18:130347838466:::18:P18 JD:2626).
There are differences in the measurement techniques that require careful consideration
when used for analysis. The IMPROVE and CSN techniques are most efficient at collecting
particles with a diameter smaller than 2.5 microns (PM2.5), while the CASTNET samplers, which
do not use size-selected inlets, also measure larger particles. This is relevant because larger
particles are often from natural sources such as wind-blown soil, dust, or sea salt. Gas-phase
nitric acid can condense onto these particles, forming particulate nitrate. Since these larger
particles deposit quickly, this can be a significant portion of the total N deposition. However, as
most CASTNET sites are located in rural areas, the expectation is that unless these sites are
disproportionately impacted by local coarse particle sources (e.g., by sea salt in coastal areas),
that most of the PM collected is PM2.5. Furthermore, the timing of the measurements is not the
same. CASTNET filter packs are deployed in the field for the entire 7-day measurement period,
while IMPROVE and CSN are 24-hour measurements. Since ammonium nitrate is semi-volatile,
and as temperature and humidity conditions change, these particles can evaporate off the filter as
gas-phase ammonia and nitric acid. Each network deploys a different approach to minimizing
these evaporative losses or capturing the volatilized nitrate and ammonia (Lavery et al., 2009).
When collocated and compared to reference techniques, the correlation between these
measurement techniques depends on meteorological conditions.
The NADP also maintains the Ammonia Monitoring Network (AMoN) which is
collocated with CASTNET designed to capture long-term trends in ambient air NH3
concentrations and deposition. There are currently 106 AMoN sites covering 34 states (see
Figure 2-18). In part because CASTNET was developed to investigate drivers of acid rain, most
AMoN sites are located in the Eastern USA. However, there are large NH3 emission sources in
the Midwest and Western USA that may not be sufficiently sampled with current AMoN
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coverage. It is possible that satellite products for NH3 concentration observations, such as the
Cross-Track Infrared Sounder (Shephard et al., 2020) or Infrared Atmospheric Sounding
Interferometers (Van Damme et al., 2021), may be used to infer NH3 variability over these
spatial gaps in the interim. The AMoN uses passive filter-based samplers which are deployed for
two-week periods. Both gaseous ammonia and particle ammonium concentrations are measured.
O
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AMoN Monitoring Sites
• Active
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Figure 2-18. Location of AMoN monitoring sites with sites active shown in dark blue and
inactive sites in light blue. (There is an additional site in AK not shown here.)
2.3.5 Satellite Retrievals
Satellite retrievals, field studies and aircraft campaigns (the latter two discussed in the
next section) complement the regulatory networks for investigation into the variability, trends
and drivers of N, S and PM. Satellite retrievals, in particular, provide a spatially expansive, long-
term record that can bridge gaps between ground monitors and offer insight into species' trends
over time.
Each of NO2, SO2, M h and PM2.5 are measured by existing satellites, such as MODIS
and OMI for N02, IASI and CRIS for Mb and MODIS, CALIPSO, GOES-R and GOES-S,
among others, for PM2.5 via aerosol optical depth. While deposition is not measured directly,
2-22
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satellite retrievals have been combined with model simulations to map deposition distributions
(e.g., Kharol et al. 2018, which illustrated a shift in the dominant form of nitrogen deposition,
from oxidized to reduced, over the continental U.S.). The spatial distributions of these species
generally reflect our understanding based on ground measurements (e.g., Nowlan et al., 2014),
lending confidence to the potential for satellite measurements to investigate variability in
atmospheric composition (ISA, Appendix 2, section 2.4.2.2 and section 2.4.4.2). There has been
substantial progress in improving retrieval algorithms to confidently infer a lower limit of
detection, and upcoming geostationary satellite missions such as MAIA, TEMPO and TropOMI
will increase the spatiotemporal resolution of concentration retrievals to improve capacity and
confidence in satellite inference of species variability.
2.4 RECENT AMBIENT AIR CONCENTRATIONS AND TRENDS
2.4.1 NO2 Concentrations and Trends
The secondary NO2 NAAQS is the annual mean concentration, with a level of 53 ppb.
There are two primary NO2 NAAQS. One is the 98th percentile of the 1-hour daily maximum
concentrations averaged over 3 years, with a level of 100 parts per billion (ppb). The other is the
annual mean concentration, with a level of 53 ppb. As shown in Figures 2-19 and 2-20, there are
no locations with NO2 design values7 in violation of these standards during the 2019-2021
period. In this period, the highest NO2 concentrations mostly occurred in urban areas across the
western U.S. (e.g., Los Angeles, Phoenix, Las Vegas, Denver). The maximum design value for
the 1-hour standard during the 2019-2021 period was 80 ppb, while the annual mean design
value for 2021 was 30 ppb. Both maximum design values occurred at near-road sites in the Los
Angeles metropolitan area; this area has historically had some of the highest NO2 concentrations
in the U.S. For the 2019-2021 period, the mean average hourly NO2 value, across valid
monitoring sites, was 16.3 ppb.
Nitrogen dioxide concentrations have been declining across the U.S. for decades, in
response to cleaner motor vehicles, emissions reductions at stationary fuel combustion sources,
and economic factors. For example, in Los Angeles metropolitan area annual NO2 design values
were almost twice as high in the early 1980's (U.S. EPA, 1985). Figures 2-21 and 2-22 show the
trends in the annual 98th percentile of the daily maximum 1-hour NO2 concentrations and in the
annual mean NO2 concentrations across the U.S. going back to 1980. The trends are sharply
downward for both NO2 metrics. At the beginning of the trends record, it was not uncommon for
7 A design value is a statistic that describes the air quality status of a given location relative to the level of the
NAAQS. Design values are typically used to designate and classify nonattainment areas, as well as to assess
progress towards meeting the NAAQS. Design values are computed and published annually by EPA
(https://www.epa.gov/air-trends/air-quality-design-values).
2-23
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locations to exceed the NO2 NAAQS, especially the standard with the shorter averaging time.
However, the last violations of the NO2 annual standard occurred in 1991. Over the past decade,
the downward trends in NO2 levels across the U.S. have continued, but at a slower rate than what
was experienced from 1980 to 2010. Given that deposition-related impacts can adversely affect
ecosystems (forests/trees, streams/fish) over the course of decades (as discussed in more detail in
Chapter 5 of this assessment), it is important to recognize that effects of the high NO2 levels
observed in 1980, and preceding decades when NO2 levels were even higher, may still be
impacting ecosystem health. Figure 2-23 indicates dramatic changes in HNO3 concentrations
between 1990s and 2019. Prior to 1980, the monitoring networks were somewhat sparser, but
NO2 data exist for certain cities. The EPA's very first Trends Report (U.S. EPA, 1973) reported
annual average NO2 values in five U.S. cities for the 1967-1971 period. At that time, annual
average NO2 concentrations averaged 75 ppb over the cities where data existed (i.e., off the chart
of the 1980-2021 trend shown in Figure 2-22). See Table 2-1 for a summary of these older NO2
annual means.
• 3 - 25 ppb (67 sites) O 26 - 50 ppb (222 sites) O 51 - 75 ppb (41 sites) O 76 - 100 ppb (1 sites)
Figure 2-19. Design values for the 1-hour primary NO2 NAAQS (98th percentile of daily
maximum 1-hour concentrations, averaged over 3 years; ppb) at monitoring
sites with valid design values for the 2019-2021 period.
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• 1 - 10 ppb (297 sites) ® 11 - 20 ppb (99 sites) O 21 - 30 ppb (8 sites)
Figure 2-20. Primary and secondary NO2 annual design values for 2021.
Figure 2-21. Distributions of annual 98th percentile, maximum 1-hour NO2 values at U.S.
sites. The red line shows number of sites in each boxplot per year.
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70
65
60
55
50
45
40
° 35
O
z
30
25
20
10
5
0
Number of N02 Sites
N02 NAAQS Level
Figure 2-22. Distributions of annual mean NO2 values at U.S. sites. The red line shows
number of sites in each boxplot per year.
Table 2-1. Average annual mean NO2 concentration in 1967-1971 in select cities.
Location
1967-1971 Annual Mean NO2 Concentration (ppb)
Chicago
120.5
Cincinnati
60.4
Denver
65.1
Philadelphia
76.1
St, Louis
54,1
5-city average
75.3
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Suuivc: CASTNET USEPA/CAMD 07/3
-------
2.4.2 SOi Concentrations and Trends
The secondary SO2 standard is the 3-hour average concentration, with a level of 0.5 ppm
(500 ppb), not to be exceeded more than once per year. The primary SO2 standard is the 99th
percentile of daily maximum 1-hour concentrations, averaged over 3 years, with a level of 75
ppb. As shown in Figure 2-24, for the 2019-2021 period, there were 15 locations with SO2
design values in violation of the primary SO2 standard. The maximum design value was 376 ppb
at a monitoring site near an industrial park in southeast Missouri. The sites with design values
exceeding the NAAQS in Hawaii are due to natural SO2 emissions from recurring volcanic
eruptions. Both peak and mean SO2 concentrations are higher at source-oriented monitoring sites
than non-source sites. Mean hourly SO2 concentrations during 2019-2021 are 3 ppb (5.1 ppb at
source-oriented sites, 1.6 ppb at urban non-source sites, and 0.9 ppb at rural non-source sites).
Figure 2-25 displays the annual second highest 3-hour average SO2 concentrations
(design values for the existing secondary standard) across the U.S. in 2021. The values at all sites
with valid secondary SO2 design values were less than the 500 ppb level and the vast majority of
sites had design values that were less than 20 ppb. Like concentrations of NO2, SO2
concentrations have been declining across the U.S. for decades, primarily in response to
emissions reductions at stationary fuel combustion sources.
Figure 2-26 shows the downward trend in design values for the primary SO2 NAAQS
over the past 40 years. The last year in which the 3-year average of the annual 99th percentile
daily maximum 1-hour concentrations was greater than 75 ppb is 1994. Since then, the entire
distribution of values has continued to decline such that the median value across the network of
sites is now less than 10 ppb. Additional sites were added to the network in 2017 near major
industrial sources of SO2 and this likely caused the slight increase in the median concentration
observed in 2017. Figure 2-27 shows the sharp downward trend in secondary SO2 concentrations
across the U.S. Again, the highest values in the distribution in recent years are from the sites near
industrial sources. Figure 2-28 shows trends in annual average SO2 concentrations, with an
overall decline from 2000-2021. Additionally, Figure 2-29 presents scatterplots of annual
average SO2 concentrations (averaged over three years) and primary and secondary standard
design values at SLAMS across the U.S. for the same time period.
2-28
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• 0 - 25 ppb (274 sites) O 51-75 ppb (19 sites) • 101 - 250 ppb (6 sites)
® 26-50 ppb (46 sites) © 76- 100 ppb (7 sites) • 251 - 376 ppb (2 sites)
Figure 2-24. Primary SO2 standard design values (99th percentile of 1-hour daily
maximum concentrations, averaged over 3 years) for the 2019-2021 period at
monitoring sites with valid design values.
Q 21-50 ppb (57 sites) © 101 - 200 ppb (3 sites)
Figure 2-25. Secondary SO2 standard design values (2nd highest 3-hourly average) for the
year 2021 at monitoring sites with valid design values.
2-29
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Figure 2-26. Distributions of 99th percentile of maximum daily 1-hour SO2 design values
at U.S. sites (1980-2021). The red line shows number of sites in each boxplot per
year. Orange dots represent design values in Hawaii determined to have been
influenced by volcanic emissions. Note: the y-axis is in logarithmic scale.
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Al! Monitors Except Hawaii
I 1 1 1 1-
I 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1-
^ c# (J? o? ^ <# qVN
Figure 2-27. Distributions of secondary SO2 standard design values at U.S. sites, excluding
sites in Hawaii (2000-2021).
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40-
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Figure 2-28. Distribution of annual average SO2 concentrations (ppb) at SLAMS in the
U.S., excluding Hawaii (2000-2021).
2-32
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35-
-O
Q.
Q.
•
2000
•
2008
• 2016
•
2001
•
2009
• 2017
30-
•
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•
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. ••*••• *. *
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Current 1 hr
Primary Standard
•
2000-2002
•
2007-2009
• 2014-2016
•
2001-2003
•
2008-2010
• 2015-2017
•
2002-2004
•
2009-2011
• 2016-2018
•
2003-2005
•
2010-2012
• 2017-2019
•
2004-2006
•
2011-2013
2018-2020
•
2005-2007
•
2012-2014
2019-2021
•
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•
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• •
• V • ** "
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"&1 fi.< ' 'Jiv •». .*••• "
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150 200 250
S02 1 hr DV (ppb)
300
350 400
Figure 2-29. Relationship of annual SO2 concentrations, averaged across three years, to
design values for the current 3-hr secondary standard (upper) and the 1-hr
primary standard (lower) at SLAMS (2000-2021). Sites in Hawaii excluded.
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2.4.3 PM2.5 Concentrations and Trends
There are two primary and two secondary standards for PM2.5. There are standards in
terms of annual means, averaged over 3 years, with levels at 12.0 jig/mJ (primary standard) and
15.0 ug/rn3 (secondary standard). There are also 24-hour standards in terms of the 98th percentile
of daily PM2.5 values, averaged over 3 years, with a level of 35 |ig/m3(for both the primary and
secondary standards). As discussed in section 2.1, PM2.5 is a mixture of substances suspended as
small liquid and/or solid particles. Figure 2-30 displays a map with pie charts showing the major
PM2.5 species as a fraction of total PM2.5 mass as measured at selected NCore, CSN, and
IMPROVE sites during the 2019 to 2021 period. The six species shown are SO42", NO3",
elemental carbon (EC), organic carbon (OC), crustal material, and sea salt. The mix of PM2.5
components can vary across the U.S. For example, in the Appalachian region, the predominant
contributor to total PM2.5 mass is sulfate. Conversely, in the upper Midwest, the largest
component term tends to be NO3". This regional variability in PM2.5 composition has
implications for the spatial nature of N and S deposition.
1 * bb
Sea Sail
Figure 2-30. Map showing pie charts of PM2.5 component species at selected U.S.
monitoring sites based on 2019-2021 data. Note: total PM2.5 mass may vary
from site to site.
2-34
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Figures 2-31 and 2-32 show maps of the annual and 24-hour PM2.5 design values,8
respectively, at U.S. ambient air monitoring sites based on monitoring data from the 2019-2021
period. All sites in the eastern U.S. were meeting the annual primary and secondary PM2.5
NAAQS of 12.0 |ig/m3 and 15.0 |ig/m3, and the 24-hour primary and secondary PM2.5 NAAQS
of 35 |ig/m3 during this period. Many sites in the western U.S. were still violating the 24-hour
PM2.5 NAAQS in 2019-2021, while a smaller number of sites, mostly in California, were also
violating the annual PM2.5 NAAQS (28 sites exceed the primary NAAQS level of 12.0 |ig/m3,
and 9 sites exceed the secondary annual PM2.5 NAAQS level of 15.0 |ig/m3). It should be noted
that large areas of the western U.S. were impacted by smoke from wildfires in 2020 and 2021
and these smoke-impacted concentrations are included in the 2019-2021 data shown here. The
highest annual PM2.5 design values are located in the San Joaquin Valley of California, while the
highest 24-hour PM2.5 design values are located in Mono County, California, which was heavily
impacted by wildfire smoke in 2020.
Figures 2-33 and 2-34 display the average NO3" and SO42" concentrations over the U.S.
during the period 2019-2021. As discussed above, S042~concentrations are highest in the Ohio
River valley and along the Gulf of Mexico, whereas NCb'concentrations are highest in the upper
Midwest, along the northeast urban corridor, and in parts of California. Figures 2-35 and 2-36
show trends in annual average concentrations for NO3" and S042~based on sites that collected
data for at least 12 out of 16 years from 2006 to 2021. Broad national reductions in NOx
emissions have resulted in substantial decreasing trends in NCb'concentrations in most of the
U.S., especially in areas where NCb'concentrations were historically highest. Similarly,
reductions in SO2 emissions have resulted in significant reductions in S042~concentrations
nationally and especially in the eastern U.S. National, annual average PM2.5 concentrations have
declined despite the relatively consistent trend in NH3 emissions. While not shown here, trends
in other PM2.5 components like EC and OC were more variable, with some sites showing
substantial decreases and the remaining sites having no clear trend. Ammonium sulfate and
ammonium nitrate make up less than one-third of the PM2.5 mass at the majority of sites and only
a few sites have more than half of the PM2.5 mass from these compounds.
There are also NAAQS for PM10 (24-hour primary and secondary standards, both with a
level of 150 |ig/m3 that is not to be exceeded more than once per year, averaged over three
years). While PM2.5 mass is composed mainly of sulfates, nitrates, and other organic matter that
can contribute to ecosystem impacts (ISA, Appendix 2, section 2.1), PM10-2.5 is mostly
composed of crustal material as well as sea salt in coastal areas. There is little discussion of
8 The annual design value for both primary and secondary standards is an annual mean, averaged over 3 years. The
24-hour design value for both standards is the annual 98th percentile 24-hour average concentration, averaged
over 3 years.
2-35
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PMio-2.5 effects in this document because these particles have faster settling velocities and the
composition of this mass is expected to have less impact on deposition-related welfare impacts.
• 1.8 - 6.0 ug/mA3 (102 sites) O 9.1-12.0 ug/mA3 (145 sites) • 15.1-17.8 ug/mA3 (9 sites)
O 6.1 - 9.0 ug/mA3 (472 sites) G 12.1 - 15.0 ug/mA3 (19 sites)
Figure 2-31. Primary and secondary annual PM2.5 standard design values (2019-2021).
• 5 - 15 ug/mA3 (96 sites) © 26 -35 ug/mA3 (87 sites) • 51-100 ug/mA3 (44 sites)
O 16-25 ug/mA3 (511 sites) © 36 - 50 ug/mA3 (33 sites) • 101 - 181 ug/mA3 (3 sites)
Figure 2-32. Primary and secondary 24-hour PM2.5 design values (2019-2021 period).
2-36
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• 0 - 0.49 ug/mA3 (158 sites) © 1-1.49 ug/mA3 (48 sites) • 2 - 3.42 ug/mA3 (8 sites)
© 0.5 - 0.99 ug/mA3 (65 sites) © 1.5-1.99 ug/mA3 (25 sites)
Figure 2-33. Annual average NOr concentrations (jxg/m3) as measured at selected NCore,
CSN, and IMPROVE sites for the 2019-2021 period.
© 0.5 - 0.99 ug/mA3 (127 sites) © 1.5-1.99 ug/mA3 (4 sites)
Figure 2-34. Annual average SO-i2" concentrations (jig/m3) as measured at selected NCore,
CSN, and IMPROVE sites for the 2019-2021 period.
2-37
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~ Decreasing > 0.1 ug/mA3/yr (11 sites) ° No Significant Trend (102 sites)
v Decreasing < 0.1 ug/mA3/yr (138 sites)
Figure 2-35. Trends in annual average concentrations for nitrate (NO3) as measured at
selected NCore, CSN, and IMPROVE sites from 2006 through 2021.
~ Decreasing > 0.1 ug/mA3/yr (108 sites) v Decreasing < 0.1 ug/mA3/yr (146 sites)
Figure 2-36. Trends in annual average concentrations for sulfate (SO42 ) as measured at
selected NCore, CSN, and IMPROVE sites from 2006 through 2021.
2-38
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The trends in total PM2.5 mass between 2000 and 2021 are shown in Figures 2-37 (annual
standard) and 2-38 (24-hour standard). These plots show the national distribution of PM2.5
concentrations, along with the number of PM2.5 monitoring sites reporting data in each year. The
median of the annual average PM2.5 concentrations decreased by 38 percent, from 12.8 |ig/m3 in
2000 to 8 |ig/m3 in 2021. Similarly, the median of the annual 98th percentile 24-hour PM2.5
concentrations decreased by 35 percent, from 32 |ig/m3 in 2000 to 21 |ig/m3 in 2021. Both the
annual average and 98th percentile 24-hour PM2.5 concentrations decreased steadily from the
early 2000s until 2016, and have fluctuated in recent years, especially in the upper tail of the
distribution. These fluctuations are largely due to large-scale wildfire events that have occurred
in recent years. The size of the PM2.5 monitoring network increased rapidly following the
establishment of a PM2.5 NAAQS in 1997, and the network has been relatively stable at around
1,200 sites since 2002.
E 35 -
O 25 -
Number of PM2.5 Sites
PM2.5 NAAQS Level
Figure 2-37. Distributions of annual mean PM2.5 design values (jig/m3) at U.S. sites across
the 2000-2021 period. The red line shows the number of sites included in each
boxplot per year.
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Figure 2-38. Distributions of the annual 98th percentile 24-hour PM2.5 design values
(jig/m3) at U.S. sites across the 2000-2021 period. The red line shows the
number of sites included in each boxplot per year.
2.4.4 Ammonia Concentrations and Trends
The AMoN network has collected measurements of ammonia gas since 2010 (NADP,
2012) and the number of sites within the network has increased over time. Figure 2-39 compares
observed NH3 concentrations between 2011 and 2020. The highest observed ammonia
concentrations across the U.S. tend to occur in the central U.S. where values can exceed 2.4
|ig/m3. Consistent with expectations from the slightly increasing trends in ammonia emissions,
we also see increases in NH3 concentrations over this 10-year period over many parts of the
country, although there can be some variability from site to site.
2-40
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2020
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•
Figure 2-39. Annual average ammonia concentrations as measured by the Ammonia
Monitoring Network in 2010 (top) and 2020 (bottom). Data source: NADP
(2012) and NADP (2021).
2-41
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2.5 NITROGEN AND SULFUR DEPOSITION
The impacts of nitrogen and sulfur emissions on public welfare endpoints via deposition
are broad, complex, and variable. Contributing to the challenge of determining the impacts of
these pollutants are past levels of deposition of N and S, as well as other non-air related sources
of these pollutants to the surface. The focus of this review is on deposition-related impacts to
ecological systems from air emissions of NO2, SO2, and PM. Therefore, it is important to be able
to characterize deposition levels across the U.S., in order to be able to understand the relationship
between pollutant concentrations, deposition, and subsequent adverse effects to public welfare.
Assessing the adequacy of any standard will require the ability to relate air quality concentrations
(past and present) to deposition levels (past and present). Since the previous review, the amount
of N and S deposition has changed, and it is important to develop the most up-to-date datasets for
the assessment of atmospheric deposition to capture these changes. This review assesses both
existing measurement data and modeling capabilities.
2.5.1 Estimating Atmospheric Deposition
As introduced in section 2.3.4, measurements of deposition are incomplete and limited.
While wet deposition has been routinely monitored at many locations across the U.S. for more
than 30 years (NADP, 2021), dry deposition is not routinely measured. As a result, most total
(wet + dry) deposition estimates are based on a combination of existing measurements and model
simulations. In 2011, the NADP established the Total Deposition (TDep) Science Committee
with the goal of providing estimates of total S and N deposition across the U.S. for use in
estimating critical loads and other assessments. A hybrid approach has been developed to
estimate total deposition based on a fusion of measured and modeled values, where measured
values are given more weight at the monitoring locations and modeled data are used to fill in
spatial gaps and provide information on chemical species that are not measured by routine
monitoring networks (Schwede and Lear, 2014). One of the outputs of this effort are annual
datasets of total deposition estimates in the U.S. which are referred to as the TDep datasets
(technical updates available from NADP, 2021; ISA, Appendix 2, section 2.6).
Figure 2-40 provides a simple flowchart of the TDep measurement-model fusion. For wet
deposition, the approach is to combine the concentrations of nitrate, ammonium and sulfate in
precipitation as measured at NADP/NTN sites with precipitation amounts as estimated in the
Parameter-elevation Relationships on Independent Slopes Model (PRISM) dataset.9 The result is
a spatially complete wet deposition dataset at 4 kilometer (km) horizontal resolution.
9 The PRISM (Parameter-elevation Regressions on Independent Slopes Model) database is maintained by the
PRISM Climate Group who compile data from multiple monitoring networks and develop spatial climate datasets
to investigate short- and long-term climate patterns, https://prism.oregonstate.edu.
2-42
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The dry deposition fusion is shown on the right side of Figure 2-40. The figure shows
that two intermediate datasets are created as part of the TDep process: an interpolated
measurement and a bias-corrected simulation. The interpolated measurement dataset relies on the
CASTNET monitoring network, which measures gas-phase SO2 and NOy and particulate-phase
SO42", NO3", and NH4+. Samples are collected for biweekly periods and chemically analyzed. The
inlet allows particles of all sizes to be collected and is designed to support estimates of total
oxidized nitrogen and sulfur dry deposition. The observed concentration of each chemical
species is used to bias correct concentration simulations from a 12-km Community Multiscale
Air Quality (CMAQ) model simulation. Because our analysis relies on the TDep representation
on bias-corrected NO2 and SO2 concentrations, rather than directly on CMAQ simulated
concentrations, we do not evaluate CMAQ concentrations in this document. The bias-corrected
concentrations are then multiplied by the effective dry deposition velocity. The effective dry
deposition velocity is the mean dry deposition velocity over the week-long measurement. This
assessment calculates the effective dry deposition velocity, weighting the average by the hourly
concentration. Meteorological processes influence both the dry deposition velocity and the
concentration. The result is a set of point estimates of dry deposition. The final step is to apply
inverse distance weighted interpolation based on the spatial covariance of each species (Schwede
& Lear, 2014) to estimate dry deposition for the same 4-km horizontal resolution grid as the wet
deposition dataset.
One shortcoming is that the measurement sites are often far apart, and the TDep
interpolation does not fully capture variability between the measurement locations. The TDep
method develops a bias-corrected dry deposition estimate using a CMAQ simulation. The bias
correction calculates the difference between the seasonal-average CMAQ concentrations and the
CASTNET concentration measurements. The bias correction at each CASTNET monitoring site
is spatially interpolated to create a 4-km horizontal resolution surface. The seasonally summed
CMAQ dry deposition dataset is interpolated from 12-km to the 4-km horizontal resolution then
adjusted by the bias correction estimated from the modeled and measured air concentrations.
This assumes that bias in concentrations can be applied to correct a bias in dry deposition, which
is reasonable if the bias is due to errors in emissions or chemical production but may not be
appropriate if the bias is due to inaccuracies in the dry deposition rate. The four seasonally
summed datasets are summed to create an annual total dry deposition for each species. The final
TDep product is a measurement-model fusion that, for dry deposition, more closely reflects
measured concentrations close to CASTNet monitors while relying more heavily on modeled
concentrations farther away. There is a dearth of dry deposition measurements that would be
necessary to evaluate the model's representation of deposition velocity, but CMAQ modeled wet
deposition and concentrations have been evaluated against ground monitors (e.g., Appel et al.,
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2021, Hogrefe et al., 2023), as well as satellite data (in the case of concentrations, e.g., Pleim et
al., 2019).
Wet de positioning changes)
Dry deposition (re-calculated)
NADP wet
deposition
measurements
precipitation
measurements
TDEPwet
deposition:
(SO/,NO,-,
NH/)
CASTNET
concentration
measurements
CMAQiir
concentration
modeling
Bias-Corrected
dry deposition
daraset
CMAQdry
deposition
velocity
Other species
CMAQ dry
deposition
Figure 2-40. Data sources for calculating total deposition. Dark blue indicates observations,
white boxes indicate chemical transport modeling results, and light blue boxes are
the results of model-measurement fusion.
2.5.2 Uncertainty in Estimates of Atmospheric Deposition
Uncertainty in the resulting model-measurement fusion can be attributed to sources of
deposition that are not characterized by the models or measurements, uncertainties in the CMAQ
model results, and uncertainty in the spatially averaged deposition due to variability that is not
accounted for in the models. While there are multiple approaches to estimating uncertainty, this
review relies on what has been reported in the literature. One approach is to compare the results
from multiple models with similar scientific credibility. To the extent that different models
employ different scientific assumptions or parameterizations, this approach can give insight into
the scientific uncertainty. Another approach is to compare the modeling results to measurements,
or to withhold a subset of the data to be used as validation. This approach can provide a more
quantitative assessment, but it is limited by the availability of measurements. This section
summarizes the relevant studies that were used to provide a general assessment of uncertainty in
TDep estimates of N and S deposition.
One source of uncertainty in the model-measurement fusion is the origin of the
deposition data. Some components of deposition are directly measured, some are the result of
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combining model results and measurements, and some are from modeling results only. The first
step in assessing uncertainty is to assess the uncertainty from each part of the TDep calculation.
Wet deposition is calculated using NADP NTN nitrogen and sulfur wet deposition
measurements, which are spatially interpolated and combined with the PRISM estimates of
precipitation. The PRISM dataset compares well with NADP NTN precipitation measurements
(Daly et al., 2017) and the meteorological simulations from this assessment.
Dry deposition relies on a combination of measurements and models and is more
challenging to assess. For oxidized nitrogen, air concentration of HNO3 and NO3" particulate
matter are measured at CASTNET monitoring sites. Several other compounds, such as NO2,
HONO, N2O5, and organic nitrogen compounds formed from photochemistry, are either not
routinely measured or not routinely measured in remote areas. The CMAQ model estimates that
the deposition of the latter compounds (NO2, HONO, N2O5) is on average 13% of the oxidized
nitrogen deposition and is largest near emission sources and urban areas (Walker et al., 2019).
For reduced nitrogen compounds, CASTNET includes measurements of NH4+, and
AMoN includes measurements of NH3 and often these monitors are collocated. However,
because of the relative paucity of ammonia measurements, they are not used for bias correction
as part of the TDep model-measurement fusion. Dry deposition of ammonia is from the CMAQ
simulation. Lastly, sulfur-based compounds, SO2 and particulate matter SO42" are measured at
CASTNET monitoring sites.
The CMAQ model is used to estimate the dry deposition velocity for all species. Like any
complex system, the effect of uncertainties in one model process can be reduced by
compensating processes. For example, consider uncertainties in the dry deposition velocity. If
the simulated rate of dry deposition is too high, then dry deposition would be higher in the
model. The enhanced dry deposition would also cause concentrations to be lower, which would
in turn cause wet deposition to be lower. In this case, the dry deposition would be too high, the
lower wet deposition would compensate for this, and the total deposition would be affected less.
Uncertainties that affect the rate of dry deposition relative to wet deposition will have less of an
effect on total deposition and can be minimized by averaging over time and space. On the other
hand, if the emission rates were too high, then concentrations would be higher, and both dry and
wet deposition would be higher. Uncertainties that affect air concentrations, such as emissions,
will affect both wet deposition and dry deposition, and consequently total deposition (Dennis et
al., 2013). Examining both air concentrations and deposition can yield insight into the nature and
magnitude of uncertainties in the model results.
Although it is challenging to constrain dry deposition velocities due to the dearth of
measurements, previous studies have assessed CMAQ concentration and wet deposition biases
relevant to the TDep application of CMAQ concentration fields and deposition velocities.
2-45
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Because nitrate and sulfate concentrations are bias adjusted in the TDep model-measurement
fusion, their concentration errors have less of an effect on the estimate of dry deposition in areas
near the measurement stations. Following Appel et al. (2011), CMAQ underestimates NH4+ wet
deposition over 2002-2006 in comparison with NTN data. Implementing a precipitation
correction exacerbates this bias, suggesting that precipitation errors at least partially compensate
for an even larger underestimate in NH3 concentration. On the other hand, incorporating a bi-
directional parameterization for NH3 reduced the bias in annual, precipitation-corrected NH4+
wet deposition from a normalized mean bias of-19% to -6% (Appel et al., 2011). More recent
CMAQ updates have included additional updates to the NH3 bi-directional parameterization
(Bash et al., 2013; Pleim et al., 2019), while noting that some extent of a model underestimate in
NH3 concentration persists in a more recent CMAQ evaluation (Appel et al., 2021). The model
underestimate in NH3 concentrations has also been supported by short-term field studies in
locations outside of NTN, in particular downwind of agricultural areas (e.g., Butler et al., 2015).
Because the ammonia concentration and the ammonia dry deposition are not constrained by
measurements in the TDep model-measurement fusion calculations, it is likely that the resulting
estimates for current conditions reported in this assessment underestimate ammonia dry
deposition due to the underestimate in ammonia concentrations.
In addition to assessing the uncertainty of the CMAQ model, it is also necessary to assess
the uncertainty in the NADP NTN and CASTNET measurements. The concentration and
deposition measurements have a specified level of precision defined in the data quality
objectives for each monitoring network. The NADP NTN monitors specify a less than 10%
uncertainty and for the CASTNET air concentration measurements the uncertainty is specified as
+/- 20%. This is achieved through quality assurance and data management protocols. However,
this may not be a complete assessment of the uncertainty. In the case of CASTNET, several
studies have collocated reference monitors and inter-compared the different measurement
techniques. Differences in sulfate tend to be small. But for nitrate and ammonium in particulate
matter, the different sampling methods can yield larger differences (ISA, Appendix 2, section
2.4.5). The differences are thought to be increased by high humidity or influence from coastal
airmasses that affect the PM composition, and accordingly may not be relevant everywhere in
the U.S. Fully characterizing the differences that arise from different monitoring techniques is
beyond the scope of this assessment. Instead, this assessment relies on the data quality objectives
as a proxy for uncertainty.
Lastly, the fusion of the model and measurements to a set spatial grid also contributes to
uncertainty. The grid representation of the model-measurement fusion may obscure fine
resolution variability leading to uncertainty in the deposition to a specific ecosystem. The dry
deposition velocity can differ considerably depending on the surface conditions, complex terrain,
2-46
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elevation, and land cover. For example, the dry deposition velocity of nitric acid (HNO3) is four
times faster over a forest than a lake. In regions with varied terrain, this can create substantial
variability in the dry deposition that is not captured at the 4-km horizontal spatial scale of the
TDep interpolation. This is also substantial in coastal areas or city-wildland interfaces. A study
by Paulot et al. (2018) estimated that grid-based results from models may underestimate
deposition to natural vegetation by 30%. Another issue is the spatial resolution may obscure
gradients in concentration. This is especially true of compounds such as NO2 that have high
concentrations near emission sources, but degrade quickly, leading to large spatial gradients.
Thus, this type of uncertainty is likely less than in other, more populated areas.
2.5.3 National Estimates of Deposition
Total sulfur and total nitrogen deposition estimates for the continental U.S. at 4-km
horizontal resolution have been developed for calendar years 2000 through 2021 (NADP, 2021).
These data are used in quantitative analyses of ecosystem exposure and risk in the later sections
of this document. Figure 2-41 illustrates that nitrogen deposition in 2019-2021 is estimated to be
highest in and around regions with large sources. This mostly includes regions of intensive crop
and animal livestock production, which are large sources of NH3 emissions. Total nitrogen
deposition results from both the dry and wet deposition pathways as shown in Figures 2-42 (dry)
and 2-43 (wet). Dry deposition tends to occur in source-oriented hot spots (e.g., parts of IA,
MN, NC, and TX) and is dominated by ammonia (discussed in more detail in 2.5.3.1), while wet
deposition tends to be more homogenous, but highest in the central U.S. The wet deposition of
N estimates for 2019-2021 have contributions from both ammonium (Figure 2-44) and nitrate
(Figure 2-45), with ammonium being larger.
2-47
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Total deposition of nitrogen 1921
Source: V2022.1. data: CASTNET/CMAO/NADP USEPA 11/21/22
Figure 2-41. Annual average total deposition of nitrogen (2019-2021).
Dry N deposition 1921
Source: V2022.2, data: CASTNET/CMAQ/NADP USEPA 06/15/23
Figure 2-42. Annual average dry deposition of nitrogen (2019-2021).
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Wet N deposition 1921
Source: v2022.2, data: CASTNET/CMAQ/NADP USEPA 06/15/23
Figure 2-43. Annual average wet deposition of nitrogen (2019-2021).
Wet deposition of ammonium 1921
Source: V2022.2, data: CASTNET/CMAQ/NADP USEPA 04/04/23
Figure 2-44. Annual average wet deposition of ammonium (2019-2021).
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The total sulfur deposition estimates for 2019-2021 are shown in Figure 2-46. For this
recent period, sulfur deposition is generally higher in the eastern U.S. (e.g., along the Gulf Coast
and in the Mississippi Valley). The large majority of sulfur deposition in the most recent time
period is caused by wet deposition, with the exception of a few areas in the western U.S., as
shown by Figure 2-47.
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Figure 2-46. Annual average total deposition of sulfur (2019-2021).
Figure 2-47. Percentage of total deposition of sulfur that occurs as wet deposition across
the 2019-2021 period.
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2.5.3.1 Contribution from NH3
Ammonia contributes to total nitrogen deposition, but it is not an oxidized form of
nitrogen, so it is not part of the definition of "oxides of nitrogen." In addition, although ammonia
is a precursor to PM formation, ammonia is a gas and not a component of particulate matter.
Accordingly, ammonia, itself, is not among the criteria pollutants that are part of this review, and
therefore we have quantified the contribution of ammonia to nitrogen deposition separately from
the other components of nitrogen deposition.
Figure 2-48 shows the dry deposition of ammonia over a recent period (2019-2021). It
can be observed, when comparing with Figure 2-42 (note: scales differ), that the majority of dry
N deposition is from ammonia (i.e., reduced nitrogen). Figure 2-49 displays the percentage of
total N deposition that results from reduced nitrogen. Total nitrogen deposition is the sum of the
deposition of ammonia, ammonium, and oxidized nitrogen compounds. The contribution of
reduced nitrogen to total N deposition exceeds 70% in areas with high ammonia concentrations,
including areas of intensive livestock and crop production in eastern North Carolina, parts of
Iowa, Minnesota, Texas, and the Central and Imperial valleys in California (Figure 2-48). In
other areas, this contribution more commonly ranges from 40-60% (Figure 2-49).
Dry deposition of ammonia 1921
Source: V2022.2, cata: CASTNET/CMAQ/NADF OSEPA C4/04/23
Figure 2-48. Annual average dry deposition of ammonia (2019-2021).
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Pet of total N as reduced N 1921
Source: V2022.1. data: CASTNET/CMAO/NADP USEPA 11/21/22
Figure 2-49. Average percent of total N deposition in 2019-2021 as reduced N (gas phase
NH3 and particle phase NH4+).
2.5.3.2 Contribution from International Transport
On a national average scale, only a small fraction of sulfur and nitrogen deposition can be
attributed to natural emissions or international transport (ISA, Appendix 2, section 2.6.8).
Chemical transport models have been used to quantify these contributions (Horowitz et al., 2003;
Zhang et al., 2012; Lee et al., 2016). The natural sources of oxidized nitrogen include microbial
activity in unfertilized soils and lightning. Natural sources of ammonia include microbial activity
in unfertilized soils and wild animals. Chemical transport model simulations have been used to
estimate that natural emission sources contribute 16% of the total N deposition in the U.S.
Because ammonia and most forms of oxidized N have relatively short atmospheric lifetimes, on
the order of hours for gas phase NOx and NH3 and days for ammonium and/or nitrate PM2.5,
international transport contributes just 6% of the N deposition, except within 100 km of the U.S.-
Canada or U.S.-Mexico borders, where the contribution is estimated to be at most 20%. U.S.
anthropogenic emissions account for 78% of reactive N deposition over the contiguous United
States (ISA, Appendix 2, section 2.6.8). Sulfur is naturally emitted from plankton in the ocean
and from geologic activity - volcanoes, fumaroles, etc. Like N, relatively little sulfur deposition
2-53
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can be attributed to international transport. Chemical transport model simulations have been used
to estimate that approximately 10% of S in the eastern U.S. can be attributed to natural and
international sources. In the western U.S., this increases to 20%, since there is lower S deposition
from anthropogenic sources, more geologic emission sources, and closer proximity to long range
transport from international sources. In areas with high S deposition, less than 1% can be
attributed to natural and international sources (ISA, Appendix 2, section 2.6.8).
2.5.4 Trends in Deposition
With the changes in emissions and air concentrations described above, total deposition of
oxidized nitrogen and sulfur have also decreased significantly since 2000 (Feng et al., 2020;
McHale et al., 2021). Between the three-year period 2000-2002 and 2018-2020, national average
S deposition over the contiguous U.S. has declined by 68% and total N deposition has declined
by 15%) (U.S. EPA, 2022b). Table 2-2 presents a regional breakout of trends in total S, total N,
oxidized N, and reduced N deposition represented as kilograms per hectare (kg/ha). The change
in total N deposition is a combination of declining oxidized N and increasing reduced N, which
is consistent with the trends in emissions and air concentrations described above. Emissions of
NOx and wet deposition of nitrate have a positive correlation, but because the formation of
ammonium is related to the availability of nitrate and sulfate, the correlation between NH3
emissions and NH4+ wet deposition is weaker and negative (Tan et al., 2020). While dry
deposition is more uncertain in magnitude, both surface-based and remote-sensing measurements
indicate increasing ammonia concentrations, which points to an increasing trend for ammonia
dry deposition, especially in areas with significant agricultural emissions in the Midwest and
Central Valley of California where ammonia dry deposition has become the largest contributor to
inorganic N deposition (Li et al., 2016). As expected, the data suggest that dry deposition of
nitric acid has decreased significantly over the past two decades and is likely a key contributor to
the decrease in total nitrate deposition and decreasing trends in oxidized nitrogen deposition
(ISA, Appendix 2, section 2.7).
Figures 2-50 through 2-56 display the spatial patterns of TDep-estimated deposition
across a range of pollutants for two periods (2000-2002 and 2019-2021) to further illustrate the
changes in deposition patterns across the U.S. over the past two decades. As shown in Figure 2-
50, S deposition has decreased sharply across the U.S. over this period due to the significant
decreases in sulfur emissions. Sulfur deposition in the Ohio River Valley region is particularly
notable. The trends in N deposition are more heterogeneous. Total N deposition has decreased
over parts of the Ohio River Valley and downwind regions in the northeastern U.S. (Figure 2-
51), but there are parts of the country where increases in N deposition are estimated to have
occurred over the past two decades (e.g., Texas).
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Table 2-2. Regional changes in deposition between 2000-2002 and 2019-2021: (a) total S
deposition; (b) total, oxidized and reduced N deposition (U.S. EPA, 2022b).
(a) Change in total S deposition
Form of S Deposition Region 2000-2002 2019-2021 % change
Mid-Atlantic
15.9
2.1
-87
Midwest
11.2
2.2
-80
North Central
3.5
1.5
-56
Total Deposition of Sulfur
Northeast
8.7
1.5
-83
(kg S ha-1)
Pacific
1.0
0.6
-38
Rocky Mountain
1.0
0.6
-46
South Central
5.4
2.8
-49
Southeast
10.3
2.6
-74
(b) Change in total, oxidized and reduced N deposition
2019-
Form of N Deposition
Region
2000-2002
2021
% change
Mid-Atlantic
13.4
8.5
-36
Midwest
12.2
9.8
-20
North Central
8.5
9.5
+11
Total Deposition of Nitrogen
Northeast
10.4
6.2
-40
(kg N ha-1)
Pacific
3.8
3.1
-18
Rocky Mountain
3.0
3.1
+3
South Central
7.8
9.0
+16
Southeast
10.8
8.4
-23
Mid-Atlantic
10.3
4.0
-62
Midwest
8.0
3.6
-54
North Central
4.1
2.6
-37
Total Deposition of Oxidized Nitrogen
Northeast
7.7
2.9
-62
(kg N ha-1)
Pacific
2.4
1.4
-42
Rocky Mountain
1.9
1.3
-35
South Central
5.0
3.1
-39
Southeast
7.7
3.4
-56
Mid-Atlantic
3.0
4.6
+51
Midwest
4.3
6.2
+45
North Central
4.4
6.9
+56
Total Deposition of Reduced Nitrogen
Northeast
2.7
3.3
+22
(kg N ha-1)
Pacific
1.4
1.7
+22
Rocky Mountain
1.1
1.8
+72
South Central
2.8
6.0
+111
Southeast
3.1
5.0
+63
The states included in each region are as follows: Mid-Atlantic: DE, MD, NJ, PA, VA, WV; Midwest: IL, IN, KY, Ml, OH, Wl;
North Central: IA, KS, MN, MO, ND, NE, SD; Northeast: CT, MA, ME, NH, NY, Rl, VT; Pacific: CA, NV, OR, WA; Rocky
Mountain: AZ, 00, ID, MT, NM, UT, WY; South Central: AR, LA, OK, TX; Southeast: AL, FL, GA, MS, NC, TN, SC.
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2000-2002
Total S
(kg-S/tia)
-o
2019-2021
Figure 2-50. TDep-estimated total S deposition: 2000-2002 (left) and 2019-2021 (right).
Figure 2-51. TDep-estimated total N deposition: 2000-2002 (left) and 2019-2021 (right).
Looking into the components of these trends in N deposition, it can be seen from Figure
2-52 that most of the widespread changes in N deposition across the U.S., both increases and
decreases, are due to changes in dry deposition of N. Figure 2-53 shows that while there have
been some changes in wet N deposition over the past 20 years (e.g., decreases near Lake Ontario;
increases in parts of southern MN), these levels and patterns have remained relatively unchanged
compared to dry N deposition.
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Figure 2-52. TDep-estimated dry N deposition: 2000-2002 (left) and 2019-2021 (right).
2000-2002
2019-2021
(kg-N/ha)
Figure 2-53. TDep-estimated wet N deposition: 2000-2002 (left) and 2019-2021 (right).
The aggregate trends in dry deposition of N are driven by two largely opposing trends in
the dry deposition of oxidized nitrogen and reduced nitrogen. Two decades ago, there were large
amounts of dry oxidized N deposition (5-10 kg N/ha) over much of the eastern U.S. which are
not seen in the more current period (< 5 kg N/ha), as shown in Figure 2-54. Conversely, while
there were isolated hotspots or dry reduced N deposition in the 2000-2002 timeframe, the
number and magnitude of these hotspots has increased significantly in the more recent 2019-
2021 period, as shown by Figure 2-55, especially in places like AR, IA, MN, MO and TX.
Figure 2-56 confirms that the increases in dry deposition of reduced N are closely linked to
increases in Nlb deposition.
2-57
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2019-2021
Dry oxN
(kg-N/ha)
2000-2002
Figure 2-54. TDep-estimated dry oxidized N deposition: 2000-2002 (left) and 2019-2021
(right).
Figure 2-55. TDep-estimated dry reduced N deposition: 2000-2002 (left) and 2019-2021
(right).
Figure 2-56. TDep-estimated Mb. deposition: 2000-2002 (left) and 2019-2021 (right).
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The trends in deposition of reduced nitrogen should be viewed with some caution, in part
because before 2011, ambient air NH3 monitoring was rare. For particulate matter, the trend in
ammonium (NH4+) has followed the downward trends in sulfate and nitrate, because in order for
NH3 to partition into the particle phase, an anion, such as sulfate or nitrate, is needed to
neutralize it. Satellite-based measurements and chemical transport models have been used to
augment the surface-based measurements of ammonia and ammonium to better understand
trends. These studies also show increasing ammonia concentrations, especially in parts of the
Midwest, South-east, and West near agricultural sources (Warner et al., 2016; Warner et al.,
2017; Yu et al., 2018; Nair et al., 2019; He et al., 2021). These trends are attributed to a
combination of warmer temperatures causing greater emissions, increasing agricultural activity,
and less available sulfate and nitrate, shifting particle ammonium to gas-phase ammonia.
While there is always uncertainty in projecting future trends, the EPA generally expects
reductions in total national N and S deposition over the next decade, although this will depend on
trends in reduced N deposition. In a recent regulatory impact assessment for the proposed
revisions to the PM NAAQS, the EPA used the CMAQ model to simulate an illustrative
implementation scenario that included additional emissions reductions of NOx and SO2 (U.S.
EPA, 2022a). The emission scenarios for these simulations included impacts projected for the
Inflation Reduction Act of 2022 Tax Incentive Provisions, the 2023 Good Neighbor Plan, and the
2022 Control of Air Pollution from New Motor Vehicles: Heavy-Duty Engine and Vehicle
Standards, among other finalized rules. Rules that were not yet finalized at the time of the
Inflation Reduction Act's release (e.g., 2023 11 lb and d and MATS proposals) were not
included. The percent change in total N and total S deposition projected to occur by the model in
2032 (from a baseline 2016 scenario) within Class I areas is shown in Figure 2-57 and Figure 2-
58, respectively. In this scenario, deposition in Class I Areas is expected to continue to decline as
existing regulations are implemented, due to reductions in NOx and SO2 emissions. While
national NH3 emissions were projected to increase between 2016 and 2032 based on anticipated
changes in activity (e.g., growth in livestock), these increases were insufficient to offset the
reductions in deposition associated with NOx and SO2 emission reductions (U.S. EPA, 2022a).
The projected average deposition reduction for N and S is about 10%, with largest reductions
occurring in the East. The projected reduction in S emissions in the Pacific Coast states is
relatively minor, but there is already very little S deposition and very few SO2 emission sources
in this region. It should be noted that there is considerable uncertainty in the change in future
deposition related to the potential for revision to the annual average PM2.5 primary standard (88
FR 5558, January 27, 2023). The emission sources that typically contribute most to the areas of
highest PM2.5 concentrations can be located relatively far from more remote Class I Areas and
can have a highly variable effect on deposition in those areas. Second, as part of implementation
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of PM2.5 standards, States can elect to reduce emission sources that contribute to organic carbon
PM2.5 which would be expected to have little impact on deposition.
N Change in Deposition
scenario minus base case
Figure 2-57. Projected percent change in total N deposition in Class 1 areas from 2016,
based on a scenario for 2032 that includes implementation of existing
national rules on mobile and stationary sources (U.S. EPA, 2022a).
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S Change in Deposition
scenario minus base case
Figure 2-58. Projected percent change in total S deposition in Class 1 areas from 2016,
based on a scenario for 2032 that includes implementation of existing
national rules on mobile and stationary sources (U.S. EPA, 2022a).
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Q (2014). Global dry deposition of nitrogen dioxide and sulfur dioxide inferred from
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Paulot, F, Malyshev, S, Nguyen, T, Crounse, JD, Shevliakova, E and Horowitz, LW (2018).
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ammonia source in the United States and China. Environ Sci Technol 51: 2472-2481.
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3 CURRENT STANDARDS AND GENERAL APPROACH
FOR THIS REVIEW
This review focuses on evaluation of the currently available evidence and quantitative
analyses related to the welfare effects of oxides of S and N (also referred to as SOx and N
oxides) and the ecological effects of PM1 in consideration of several overarching policy-relevant
questions. The first such question considers whether the currently available scientific evidence
and quantitative information support or call into question the adequacy of the public welfare
protection for these effects afforded by the current secondary standards for these pollutants. In
this context we consider two categories of effects: (1) effects associated with the airborne
pollutants (sometimes referred to as "direct effects" of the pollutants in ambient air), and (2)
effects associated with deposition of the pollutants or their transformation products into aquatic
and terrestrial ecosystems.
This chapter describes the basis for the existing secondary standards (section 3.1) and the
approach taken in the 2012 review of deposition-related effects (section 3.2) and also outlines the
approach being taken in this review of the current NO2, SO2 and PM secondary standards
(section 3.3).
3.1 BASIS FOR THE EXISTING SECONDARY STANDARDS
The existing secondary standards for SOx and N oxides were established in 1971 (36 FR
8186, April 30, 1971). The secondary standard for SO2 is 0.5 ppm, as a 3-hour average, not to be
exceeded more than once per year (40 CFR §50.5). The secondary standard for N oxides is 0.053
ppm NO2QOO micrograms per cubic meter [[j,g/m3] of air), as the arithmetic mean of the 1-hour
NO2 concentrations over the course of a year (40 CFR §50.11). Both standards were selected to
provide protection to the public welfare related to effects on vegetation (U.S. DHEW, 1969; U.S.
EPA, 1971).
The welfare effects evidence for SOx in previous reviews indicates a relationship
between short- and long-term SO2 exposures and foliar damage to cultivated plants, reductions in
productivity, species richness, and diversity (U.S. DHEW, 1969; U.S. EPA, 1982a; U.S. EPA,
2008). At the time the standard was set, concentrations of SO2 in the ambient air were also
associated with other welfare effects, including effects on materials and visibility (U.S. DHEW,
1 As noted in Chapter 1, other welfare effects of PM, such as visibility and materials damage were addressed in the
separate PM NAAQS review completed in 2020 and are part of the reconsideration of that 2020 decision, a
proposed decision for which was published earlier this year (88 FR 5558, January 27, 2023). Given the presence
of S and N compounds in PM, the ecological effects of PM are included in this review.
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1969). However, the available data were not sufficient to establish a quantitative relationship
between specific SO2 concentrations and such effects (38 FR 25679, September 14, 1973). These
two categories of effects have more recently been considered in the PM secondary NAAQS
reviews (e.g., 85 FR 82684, December 18, 2020). Accordingly, direct effects on vegetation of
SOx in ambient air is the basis for the existing secondary standard for SOx.
The welfare effects evidence for N oxides in previous reviews includes foliar injury, leaf
drop, and reduced yield of some crops (U.S. EPA, 1971; U.S. EPA, 1982a; U.S. EPA, 1993; U.S.
EPA, 2008). Since it was established in 1971, the secondary standard for N oxides has been
reviewed three times, in 1985, 1996, and 2012 (50 FR 25532, June 19, 1985; 61 FR 52852;
October 8, 1996; 77 FR 20218, April 3, 2012). Although those reviews identified additional
effects related to N deposition, they all have concluded that the existing standard provided
adequate protection related to the vegetation effects of airborne N oxides (i.e., the "direct"
effects of N oxides in ambient air).
The existing secondary standards for PM include two PM2.5 standards and one PM10
standard. The PM2.5 standards are 35 ug/m3 as the average of three consecutive annual 98th
percentile 24-hour averages and 15.0 ug/m3, as an annual mean concentration, averaged over
three years (40 CFR 50.13). The PM10 standard is 150 ug/m3 as a 24-hour average, not to be
exceeded more than once per year on average over three years (40 CFR §50.6). These PM mass-
based standards were most recently reviewed in the PM NAAQS review completed in 2013 with
regard to protection for an array of effects that include effects on visibility, materials damage,
and climate effects, as well as ecological effects. It is only the latter - ecological effects,
including those related to deposition - that fall into this current review that combines
consideration of these effects with the welfare effects of N oxides and SOx. In the 2013 review,
with the revision made to the form of the annual PM2.5 standard, it was concluded that those
standards provided protection for ecological effects (e.g., 78 FR 3225-3226, 3228, January 15,
2013). In reaching this conclusion, it was noted that the PA for the review explicitly excluded
discussion of the effects associated with deposited PM components of N oxides and SOx and
their transformation products which were being addressed in the joint review of the secondary
NO2 and SO2 NAAQS (78 FR 3202, January 15, 2013). The ecological effects of PM considered
include direct effects on plant foliage; effects of the ecosystem loading of PM constituents such
as metals or organic compounds (2009 ISA [U.S. EPA, 2009b], section 2.5.3). For all of these
effects, the 2013 decision recognized an absence of information that would support any different
standards and concluded the existing standards, with the revision to the form of the annual PM2.5
standard (to remove the option for spatial averaging consistent with this change to the primary
annual PM2.5 standard), provided the requisite protection (78 FR 3086, January 15, 2013).
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Table 3-1. Existing secondary standards for S oxides, PM, and N oxides.
Pollutant
Indicator
Averaging Time
Level
Form
S Oxides
S02
3 hours
0.5 ppm
Not to be exceeded more than once per year
PM
PM2.5
1 year
15 |jg nr3
Annual mean, averaged over 3 years
24 hours
35 |jg nr3
98th percentile, averaged over 3 years
PM10
24 hours
150 |jg
nr3
Not to be exceeded more than once per year on
average over 3 years
N Oxides
N02
1 year
53 ppb
Annual mean
3.2 PRIOR REVIEW OF DEPOSITION-RELATED EFFECTS
The most recent review of the NO2 and SO2 secondary standards was completed in 2012.
In that review, the EPA recognized that a significant increase in understanding of the effects of N
oxides and SOx had occurred since the prior secondary standards reviews for those pollutants,
reflecting the large amount of research that had been conducted on the effects of deposition of
nitrogen and sulfur to ecosystems (77 FR 20236, April 3, 2012). Considering the extensive
evidence available at that time, the Agency concluded that the most significant current risks of
adverse effects to public welfare associated with those pollutants are those related to deposition
of N and S compounds to both terrestrial and aquatic ecosystems (77 FR 20236, April 3, 2012).
Accordingly, in addition to evaluating the protection provided by the secondary standards for N
oxides and SOx from effects associated with the airborne pollutants, the 2012 review also
included extensive analyses of the welfare effects associated with nitrogen and sulfur deposition
to sensitive aquatic and terrestrial ecosystems (77 FR 20218, April 3, 2012).
Based on the available evidence, the risks of atmospheric deposition analyzed in the 2009
REA related to two categories of ecosystem effects, acidification and nutrient enrichment (U.S.
EPA, 2009a). The analyses included assessment of risks of both types of effects in both
terrestrial and aquatic ecosystems. While the available evidence supported conclusions regarding
the role of atmospheric deposition of S and N compounds in acidification and nutrient
enrichment of aquatic and terrestrial ecosystems, there was variation in the strength of the
evidence and of the information supporting the multiple quantitative linkages between the
pollutants in ambient air and responses of terrestrial and aquatic ecosystems, their associated
biota, and potential public welfare implications. As a result, the focus in the 2012 review with
regard to consideration of a secondary standard to provide protection from deposition-related
effects of was on the information related to aquatic acidification (U.S. EPA, 2011, Chapter 7).
With regard to acidification-related effects in terrestrial ecosystems, the 2009 REA had
analyzed risks to sensitive tree species in the northeastern U.S. using the ecological indicator,
soil BC:A1 (base cations to aluminum) ratio, which has links to tree health and growth (U.S.
EPA, 2009a). While the analyses indicated results of potential concern with regard to 2002 levels
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of acid deposition, several uncertainties affected the strength of associated conclusions. As noted
in the 2012 decision, an important drawback in understanding terrestrial acidification is related to
the sparseness of available data for identifying appropriate BC: A1 ratio target levels, and that the
then-available data were based on laboratory responses rather than on field measurements (77 FR
20229, April 3, 2012). The 2012 decision also recognized uncertainties with regard to empirical
case studies in the ISA noting that other stressors present in the field that are not present in the
laboratory may confound the relationship between N oxides and SOx deposition and terrestrial
acidification effects (2008 ISA, section 3.2.2.1; 77 FR 20229, April 3, 2012). The REA analyses
of aquatic acidification (which involved water quality modeling of acid deposition in case study
watersheds and prediction of waterbody acid neutralizing capacity [ANC] response), however,
provided strong support to the evidence for a relationship between atmospheric deposition of N
and S compounds and loss of acid neutralizing capacity in sensitive ecosystems, with associated
aquatic acidification effects.
Consideration of the nutrient enrichment-related effects of atmospheric N and S
deposition with regard to identification of options to provide protection for deposition-related
effects was limited by several factors. For example, while there is extensive evidence of
deleterious effects of excessive nitrogen loadings to terrestrial ecosystems, the co-stressors
affecting forests, including other air pollutants such as ozone, and limiting factors such as
moisture and other nutrients, confound the assessment of marginal changes in any one stressor or
nutrient in a forest ecosystem, leaving the information on the effects of changes in N deposition
on forestlands and other terrestrial ecosystems limited (U.S. EPA, 2011, section 6.3.2). Further,
the 2008 ISA noted that only a fraction of the deposited N is taken up by the forests, with most
of the N retained in the soils (2008 ISA, section 3.3.2.1), and that forest management practices
can significantly affect the nitrogen cycling within a forest ecosystem. Accordingly, the response
of managed forests to N oxides deposition will be variable depending on the forest management
practices employed in a given forest ecosystem (2008 ISA, Annex C, section C.6.3). Factors
affecting consideration of aquatic eutrophication effects included the appreciable contributions of
non-atmospheric sources to waterbody nutrient loading which affected our attribution of specific
effects to atmospheric sources of N, and limitations in the ability of the available data and
models to characterize incremental adverse impacts of N deposition (U.S. EPA, 2011, section
6.3.2).
Thus, in light of the evidence and findings of these analyses, and advice from the
CASAC, the PA concluded it appropriate to place greatest confidence in findings related to the
aquatic acidification-related effects of N oxides and SOx relative to other deposition-related
effects. Therefore, the PA focused on aquatic acidification effects from deposition of N and S
compounds in identifying policy options for providing public welfare protection from
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deposition-related effects of N oxides and SOx, concluding that the available information and
assessments were only sufficient at that time to support development of a standard to address
aquatic acidification. Consistent with this, the PA concluded it was appropriate to consider a
secondary standard in the form of an aquatic acidification index (AAI) and identified a range of
AAI values (which correspond to minimum ANC levels) for consideration (U.S. EPA, 2011,
section 7.6.2).
Conceptually, the AAI is an index that utilizes the results of ecosystem and air quality
modeling to estimate waterbody ANC. Thus, the standard level for an AAI-based standard is a
national minimum target ANC for waterbodies in the ecoregions of the U.S. for which the data
were considered adequate for these purposes. While the NAAQS have historically been set in
terms of an ambient air concentration, an AAI-based standard was envisioned to have a single
value established for the AAI, but the concentrations of SOx and N oxides would be specific to
each ecoregion, taking into account variation in several factors that influence waterbody ANC,
and consequently could vary across the U.S. The factors, specific to each ecoregion, which it was
envisioned would be established as part of the standard, include: surface water runoff rates and
so-called "transference ratios," which are factors applied to back-calculate or estimate the
concentrations of SOx and N oxides corresponding to target deposition values that would meet
the AAI-based standard level, which is also the target minimum ANC (U.S. EPA, 2011, Chapter
7).2 The ecoregion-specific values for these factors would be specified based on then available
data and simulations of the CMAQ model, and codified as part of such a standard. As part of the
standard, these factors would be reviewed in the context of each periodic review of the NAAQS.
After consideration of the PA conclusions, the Administrator concluded that while the
conceptual basis for the AAI was supported by the available scientific information, there were
limitations in the available relevant data, and uncertainties associated with specifying the
elements of the AAI, specifically those based on modeled factors, that posed obstacles to
establishing such a standard under the CAA. It was recognized that the general structure of an
AAI-based standard addressed the potential for contributions to acid deposition from both N
oxides and of SOx, and quantitatively described linkages between ambient air concentrations,
deposition, and aquatic acidification, considering variations in factors affecting these linkages
across the country. However, the Administrator judged that the limitations and uncertainties in
the available information were judged to be too great to support establishment of a new standard
2 These were among the ecoregion-specific factors that comprised the parameters F1 through F4 in the AAI equation
(2011 PA, p. 7-37). The parameter F2 represented the ecoregion-specific estimate of acidifying deposition
associated with reduced forms of nitrogen, NHX (2011 PA, p. 7-28 and ES-8 to ES-9). The 2011 PA suggested
that this factor could be specified based on a 2005 CMAQ model simulation over 12-km grid cells or monitoring
might involve the use of monitoring data for NHX applied in dry deposition modeling. It was recognized that
appreciable spatial variability, as well as overall uncertainty, were associated with this factor.
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that could be concluded to provide the requisite protection for such effects under the Act (77 FR
20218, April 3, 2012).
These uncertainties generally related to the quantification of the various elements of the
standard (the "F factors"), and their representativeness at an ecoregion scale. These uncertainties
and the complexities in this approach were recognized to be unique to the 2012 review of the
NAAQS for N and S oxides and were concluded to preclude the characterization and degree of
protectiveness that would be afforded by an AAI-based standard, within the ranges of levels and
forms identified in the PA, and the representativeness of F factors in the AAI equation described
in the 2011 PA (77 FR 20261, April 3, 2012).
"... the Administrator recognizes that characterization of the uncertainties in the
AAI equation as a whole represents a unique challenge in this review primarily as
a result of the complexity in the structure of an AAI based standard. In this case,
the very nature of some of the uncertainties is fundamentally different than
uncertainties that have been relevant in other NAAQS reviews. She notes, for
example, some of the uncertainties uniquely associated with the quantification of
various elements of the AAI result from limitations in the extent to which
ecological and atmospheric models, which have not been used to define other
NAAQS, have been evaluated. Another important type of uncertainty relates to
limitations in the extent to which the representativeness of various factors can be
determined at an ecoregion scale, which has not been a consideration in other
NAAQS." [77 FR 20261, April 3, 2012]
The Administrator concluded that while the existing secondary standards were not adequate to
provide protection against potentially adverse deposition-related effects associated with N oxides
and SOx, it was not appropriate under Section 109 to set any new or additional standards at that
time to address effects associated with deposition of N and S compounds on sensitive aquatic
and terrestrial ecosystems (77 FR 20262-20263, April 3, 2012).
3.3 GENERAL APPROACH FOR THIS REVIEW
As is the case for all NAAQS reviews, this secondary standards review is fundamentally
based on using the Agency's assessment of the current scientific evidence and associated
quantitative analyses to inform the Administrator's judgments regarding secondary standards that
are requisite to protect the public welfare from known or anticipated adverse effects. The
approach planned for this review of the secondary N oxides, SOx, and PM standards will build
on the last reviews, including the substantial assessments and evaluations performed over the
course of those reviews, and considering the more recent scientific information and air quality
data now available to inform understanding of the key policy-relevant issues in the current
review.
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The evaluations in the PA, including the scientific assessments in the ISA (building on
prior such assessments) augmented by quantitative air quality, exposure and risk analyses, are
intended to inform the Administrator's public welfare policy judgments and conclusions,
including his decisions as to whether to retain or revise the standards. The PA considers the
potential implications of various aspects of the scientific evidence, the air quality, exposure, or
risk-based information, and the associated uncertainties and limitations. In so doing, the
approach for this PA involves evaluating the available scientific and technical information to
address a series of key policy-relevant questions using both evidence- and exposure/risk-based
considerations.3 Together, consideration of the full set of evidence and information available in
this review will inform the answer to the following initial overarching question for the review:
• Do the currently available scientific evidence and exposure-/risk-based information
support or call into question the adequacy of the public welfare protection afforded by
the current secondary standards?
In reflecting on this question in Chapter 7 of this PA, we consider the available body of
scientific evidence, assessed in the ISA (summarized in Chapters 4 and 5), and considered as a
basis for developing or interpreting the quantitative information, including air quality and
exposure analyses (summarized in Chapters 5 and 6), including whether it supports or calls into
question the scientific conclusions reached in the last review regarding welfare effects related to
SOx, N oxides and PM in ambient air. Information available in this review that may be
informative to public policy judgments on the significance or adversity of key effects on the
public welfare is also considered. Additionally, the currently available exposure and risk
information, whether newly developed in this review or predominantly developed in the past and
interpreted in light of current information, is considered. Further, in considering this question
with regard to these secondary standards, we give particular attention to exposures and risks for
effects with the greatest potential for public welfare significance.
The approach to reaching conclusions on the current secondary standards and, as
appropriate, on potential alternative standards, including consideration of policy-relevant
questions that frame the current review, is illustrated in Figure 3-1.
3 Generally in NAAQS reviews, the term "evidence" refers to the scientific information evaluated and interpreted in
the ISA, and the term "exposure/risk" refers to quantitative analyses of air quality, exposure and risk which have
also been described as Risk and Exposure Assessments. The quantitative exposure/risk analyses are developed
based on the scientific information in the ISA. In this review, the exposure/risk assessment (aka REA) is focused
on aquatic acidification. It is summarized in Chapter 5 and described in detail in Appendix 5A. Other quantitative
information drawn from the ISA and studies assessed in the ISA is also presented in Chapter 5 and Appendix 5B.
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Adequacy of Current Standard(s)
Figure 3-1. Overview of general approach for review of the secondary N oxides, SOx, and
PM standards.
3-8
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The Agency's approach in its review of secondary standards is consistent with the
requirements of the provisions of the CAA related to the review of NAAQS and with how the
EPA and the courts have historically interpreted the CAA. As discussed in section 1.2 above,
these provisions require the Administrator to establish secondary standards that, in the
Administrator's judgment, are requisite (i.e., neither more nor less stringent than necessary) to
protect the public welfare from known or anticipated adverse effects associated with the presence
of the pollutant in the ambient air. In so doing, the Administrator considers advice from the
CASAC and public comment.
Consistent with the Agency's approach across all NAAQS reviews, the approach of this
PA informs the Administrator's judgments based on a recognition that the available welfare
effects evidence generally reflects a range of effects that include ambient air exposure
circumstances for which scientists generally agree that effects are likely to occur as well as lower
levels at which the likelihood and magnitude of response become increasingly uncertain. The
four basic elements of the NAAQS (i.e., indicator, averaging time, form, and level) are
considered collectively in evaluating the protection afforded by the current standard, or any
alternative standards considered. The CAA does not require that standards be set at a zero-risk
level, but rather at a level that reduces risk sufficiently so as to protect the public welfare from
known or anticipated adverse effects.
The Agency's decisions on the adequacy of the current secondary standards and, as
appropriate, on any potential alternative standards considered in a review, are largely public
welfare policy judgments made by the Administrator. In general, conclusions reached by the
Administrator in secondary NAAQS reviews on the amount of public welfare protection from
the presence of the pollutant(s) in ambient air that is appropriate to be afforded by a secondary
standard take into account a number of considerations, among which are the nature and degree of
effects of the pollutant, including his judgments as to what constitutes an adverse effect to the
public welfare, as well as, the strengths and limitations of the available and relevant information,
with its associated uncertainties. Across reviews, it is generally recognized that such judgments
should neither overstate nor understate the strengths and limitations of the evidence and
information nor the appropriate inferences to be drawn as to risks to public welfare, and that the
choice of the appropriate level of protection is a public welfare policy judgment entrusted to the
Administrator under the CAA taking into account both the available evidence and the
uncertainties (80 FR 65404-05, October 26, 2015). Thus, the Administrator's final decisions in
such reviews draw upon the scientific information and analyses about welfare effects,
environmental exposures and risks, and associated public welfare significance, as well as
judgments about how to consider the range and magnitude of uncertainties that are inherent in
the scientific evidence and quantitative analyses.
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3.3.1 Approach for Direct Effects of the Pollutants in Ambient Air
As in past reviews of secondary standards for SOx, N oxides and PM, this review will
continue to assess the protection provided by the standards from effects of the airborne
pollutants. Accordingly, this PA draws on the currently available evidence as assessed in the
ISA, including the determinations regarding the causal nature of relationships between the
airborne pollutants and ecological effects, which focus most prominently on vegetation, and
quantitative exposure and air quality information (summarized in Chapters 4 and 5). Based on
this information, we will consider the policy implications, most specifically in addressing the
overarching question articulated in section 3.3 above. Building from these considerations, the PA
concludes whether the evidence supports the retention or revision of the current NO2 and SO2
secondary standards. With regard to the effects of PM, we will take a similar approach, based on
the evidence presented in the current ISA and conclusions from the review of the PM NAAQS
concluded in 2013 (in which ecological effects were last considered) to assess the effectiveness
of the current PM standard to protect against these types of impacts.
3.3.2 Approach for Deposition-Related Ecological Effects
In addition to evaluating the standards as to protection for effects of the airborne
pollutants, we are also evaluating the standards as to protection from deposition-related effects.
In so doing, we have considered the quantitative analyses conducted in the last review of the
relationships between N oxides and SOx and deposition related effects and considerations for
secondary standards. The overall approach we are employing takes into account the nature of the
welfare effects and the exposure conditions associated with effects in order to identify
deposition-level benchmarks appropriate to consider in the context of public welfare protection.
To identify metrics relevant to air quality standards (and their elements), we apply relationships
developed from air quality measurements near pollutant sources and deposition estimates in
sensitive ecoregions. From these, we identify an array of policy options that might be expected to
provide protection from adverse effects to the public welfare. This approach is illustrated in
Figure 3-2 below.
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Figure 3-2. General approach for assessing the currently available information with
regard to consideration of protection provided for deposition-related
ecological effects on the public welfare.
Our consideration of the nature of the welfare effects draws on the overview provided in
Chapter 4, based on the evidence presented in the ISA, key limitations in this evidence, and the
associ ated uncertainties. These effects encompass both effects of airborne N oxides and SOx, as
well as deposition-related effects, including terrestrial and aquatic acidification effects, as well as
effects from N enrichment. In so doing, we take note of the public welfare implications of such
effects (as summarized in section 4.3).
Next, we consider the current information on exposure conditions associated with effects
(Chapter 5) in order to identify deposition levels appropriate to consider in the context of public
welfare protection. We investigate the extent to which the available evidence provides
quantitative information linking N oxides, SOx, and PM to deposition-related effects that can
inform judgements on the likelihood of occurrence of such effects under air quality that meets
the current standards. In critically assessing the available quantitative information, we recognize
that the impacts of N and S deposition, which include ecosystem acidification and nutrient
enrichment, are influenced by past deposition. The historical deposition associated with N
oxides, SOx, and PM in ambient air has modified soil and waterbody chemistry with associated
impacts on terrestrial and aquatic ecosystems and organisms (U.S. EPA, 2020; U.S. EPA, 2008;
U.S. EPA, 1982b).4
4 The role of historical deposition in current ecosystem circumstances (e.g., waterbody acidification and loss of
aquatic species, terrestrial acidification, and aquatic eutropliication) and the complications affecting recovery
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These impacts from the dramatically higher deposition of the past century can affect how
ecosystems and biota respond to more recent lower deposition rates, complicating interpretation
of impacts related to more recent, lower deposition levels. This complexity is illustrated by
findings of some studies that compared soil chemistry across 15-30-year intervals (1984-2001
and 1967-1997) and reported that although atmospheric deposition in the Northeast declined
across those intervals, soil acidity increased (ISA, Appendix 4, section 4.6.1). As noted in the
ISA, "[i]n areas where N and S deposition has decreased, chemical recovery must first create
physical and chemical conditions favorable for growth, survival, and reproduction" (ISA, p. IS-
102). Thus, the extent to which S and N compounds are retained in soil matrices, once deposited,
with potential effects on soil chemistry, as well as ambient air concentrations and associated
deposition, influence the dynamics of the response of the various environmental pathways to
changes in air quality.
Based on the information summarized in Chapter 5 for aquatic and terrestrial systems, we
seek to identify deposition levels associated with welfare effects of potential concern for
consideration with regard to secondary standard protection. In so doing, one objective is to
discern for what effects the evidence is most robust with regard to established quantitative
relationships between deposition and ecosystem effects. In this context, we present an analysis of
the findings in the currently available evidence, as well as additional quantitative analyses as
they relate to effects of airborne N oxides, SOx, and PM and deposition-related effects. The
information for terrestrial ecosystems is derived primarily from analysis of the evidence
presented in the ISA. For aquatic ecosystems, we give primary focus to aquatic acidification, for
which we have conducted quantitative risk and exposure analyses based on available modeling
applications (primarily based on steady-state, rather than dynamic, models) that relate acid
deposition and acid neutralizing capacity in U.S. waterbodies (see section 5.1 and Appendix 5A).
In parallel with the assessments described in Chapter 5, we have utilized air quality data
and trajectory-based air quality modeling to characterize atmospheric transport of the pollutants
from their occurrence at monitors near their point of release to distant ecoregions where they
might be expected to deposit (Chapter 6). Based on these analyses which inform an
understanding of the relative contributions of source locations to individual ecoregions in the
U.S., we evaluate quantitative relationships of air pollutant concentrations with atmospheric
deposition rates. This includes consideration of air quality measurements near pollutant sources
have been noted in scientific assessments for NAAQS reviews ranging from the 1982b AQCD for PM and SOx to
the current ISA (ISA, sections IS.2.3, IS.5.1.2, IS.6.1.1.1, and IS. 11, Appendix 4, section 4.8.5, Appendix 6,
section 6.6.3, Appendix 7, sections 7.1.5, 7.1.7, and 7.2.7, Appendix 8, sections 8.3.1.1, 8.4.1,8.4.4, 8.4.5 8.6.6,
and 8.6.8, Appendix 9, 9.3.2.1, Appendix 10, section 10.2.5, Appendix 12, section 12.3.3.4; 2008 ISA, sections
3.2.1.2, 3.2.3, 3.2.4.3 and 3.2.4.4; 1982b AQCD, section 1.7 and Chapter 7).
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and deposition estimates in sensitive ecoregions. We have considered existing standard metrics,
as well as other potential metrics that might effectively control deposition. In so doing, we also
recognize key uncertainties and limitations in relating deposition to measurements of air quality,
as well as uncertainties and limitations associated with various exposure metrics. Thus, in
combination with the identified deposition levels of interest, we consider the extent to which
existing standards provide protection from these levels and seek to identify potential alternative
standards that might afford such protection and identify an array of policy options for
consideration in this review (Chapter 7).
3.3.3 Identification of Policy Options
This PA provides a range of potential policy options, supported by the science, to inform
the Administrator's decisions regarding secondary standards that provide the "requisite" public
welfare protection from these pollutants in ambient air. In so doing, this PA considers the
evidence and quantitative analyses for direct effects of the pollutants in ambient air as well as the
effects of the pollutants deposited into aquatic and terrestrial ecosystems, as described in sections
3.3.1 and 3.3.2 above, with regard to the policy-relevant questions identified for the review.
Based on those considerations (discussed in Chapter 7), we consider the overarching questions
for the review with regard to the extent to which the current information calls into question any
of the existing standards, and the extent to which new or revised standards may be appropriate to
consider. Key aspects of the available information, its limitations and associated uncertainties are
discussed and conclusions reached with regard to protection from effects of the airborne
pollutants and deposition-related effects. We note that the recent lower air concentrations and
deposition estimates may lead to additional uncertainty in linking air quality to deposition than
was the case with the higher concentrations and deposition of the past.
In considering potential alternative standards, as appropriate, we evaluate what the
current information, including emissions and air quality analyses available in Chapters 2 and 6,
may indicate regarding the relationships between N oxides, SOx, and PM and N and S
deposition, the influence of different averaging times, and what the quantitative analyses indicate
regarding the extent to which one or more standards may have potential for controlling
deposition-related and other effects of concern (Chapter 7). In so doing, we consider potential
alternative standards of the same indicator and averaging time as existing standards, as well as
options involving different averaging times and/or indicators, in order to inform the
Administrator's judgements on the currently available information and what the available
information indicates regarding what control of air quality (and as appropriate, associated
deposition) may be exerted by alternative standards. Finally, the PA presents staff conclusions on
whether the current evidence and quantitative analyses call into question the adequacy of
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protection from ecological effects afforded by the SO2, NO2, and PM secondary standards, and
what alternative standards may be appropriate for the Administrator to consider.
In identifying policy options appropriate to consider for providing protection from
deposition-related effects, we are mindful of the long history of greater and more widespread
atmospheric emissions that occurred in previous years (both before and after establishment of the
existing NAAQS) and that has contributed to acidification and/or nutrient enrichment of aquatic
and terrestrial ecosystems, the impacts of which exist to some extent in some ecosystems today.
This historical backdrop additionally complicates policy considerations related to deposition-
related effects and the identification of appropriate targets for protection in ecosystems today that
might be expected to protect key ecosystem functions in the context of changing conditions over
time.
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REFERENCES
U.S. DHEW (U.S. Department of Health, Education and Welfare) (1969). Air quality criteria for
sulfur oxides. National Air Pollution Control Administration. Washing, D.C. Pub. No.
AP-50. January 1969. Available at:
https://nepis. epa.gov/Exe/ZyPDF. cgi?Dockey=20013JXZ.PDF.
U.S. EPA (1971). Air Quality Criteria for Nitrogen Oxides. Air Pollution Control Office.
Washington DC. EPA 450-R-71-001. January 1971. Available at:
https://nepis. epa.gov/Exe/ZyPDF. cgi?Dockey=20013K3B.PDF.
U.S. EPA (1982a). Review of the National Ambient Air Quality Standards for Sulfur Oxides:
Assessment of Scientific and Technical Information. OAQPS Staff Paper. Office of Air
Quality Planning and Standards. Research Triangle Park, NC. EPA-450/5-82-007.
November 1982. Available at:
https://nepis.epa.gov/Exe/ZyPDF.cgi?Dockey=300068A0.PDF.
U.S. EPA (1982b). Air Quality Criteria for Particulate Matter and Sulfur Oxides. Volume I-III.
Office of Research and Development. Research Triangle Park, N.C. EPA/600/8-82/029.
December 1982. Available at:
https://nepis. epa.gov/Exe/ZyPDF. cgi?Dockey=3000188Z.PDF
https://nepis. epa.gov/Exe/ZyPDF. cgi?Dockey=300018EV.PDF
https://nepis. epa.gov/Exe/ZyPDF. cgi?Dockey=300053KV.PDF.
U.S. EPA (1993). Air Quality Criteria for Oxides of Nitrogen. Volume I-III. U.S. Office of
Research and Development, Research Triangle Park, NC. EPA/600/8-91/049aF-cF.
August 1993. Available at:
https://nepis. epa.gov/Exe/ZyPDF. cgi?Dockey=30001LZT.PDF
https://nepis. epa.gov/Exe/ZyPDF. cgi?Dockey=300056QV.PDF
https://nepis. epa.gov/Exe/ZyPDF. cgi?Dockey=30001NI2.PDF.
U.S. EPA (2008). Integrated Science Assessment (ISA) for Oxides of Nitrogen and Sulfur
Ecological Criteria. Office of Research and Development, Research Triangle Park, NC.
EPA/600/R-08/082F. December 2008. Available at:
https://nepis. epa. gov/Exe/ZyPDF. cgi ?Doc key =P100R 7MG.PDF.
U.S. EPA (2009a). Risk and Exposure Assessment for Review of the Secondary National
Ambient Air Quality Standards for Oxides of Nitrogen and Oxides of Sulfur (Main
Content). Office of Air Quality Planning and Standards, Research Triangle Park, NC.
EPA-452/R-09-008a. September 2009. Available at:
https://nepis. epa.gov/Exe/ZyPDF. cgi?Dockey=P 100FNQV.PDF.
U.S. EPA (2009b). U.S. EPA. Integrated Science Assessment for Particulate Matter. Office of
Research and Development, Research Triangle Park, NC. EPA/600/R-08/139F.
December 2009. Available at:
https://nepis. epa.gov/Exe/ZyPDF. cgi?Dockey=P10060Z4.PDF.
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U.S. EPA (2011). Policy Assessment for the Review of the Secondary National Ambient Air
Quality Standards for Oxides of Nitrogen and Oxides of Sulfur. Office of Air Quality
Planning and Standards, Research Triangle Park, NC. EPA-452/R-ll-005a, b. February
2011. Available at: https://nepis.epa.gov/Exe/ZyPDF.cgi?Dockey=P1009R7U.PDF
https://nepis. epa.gov/Exe/ZyPDF. cgi?Dockey=P1009RHY.PDF.
U.S. EPA (2020) Integrated Science Assessment (ISA) for Oxides of Nitrogen, Oxides of Sulfur
and Particulate Matter Ecological Criteria (Final Report, 2020). Office of Air Quality
Planning and Standards, Research Triangle Park, NC. EPA/600/R-20/278. September
2020. Available at: https://nepis.epa.gov/Exe/ZyPDF.cgi?Dockey=P 1010WR3.PDF.
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4 NATURE OF WELFARE EFFECTS
In this chapter we summarize the current evidence on the ecosystem effects of oxides of
nitrogen, oxides of sulfur and particulate matter in ambient air. We consider both the evidence
for direct effects of the pollutants in ambient air and for the effects of the associated atmospheric
deposition into aquatic and terrestrial ecosystems. Of the welfare effects categories listed in
section 302(h) of the Clean Air Act, the effects of oxides of nitrogen, oxides of sulfur and
particulate matter on aquatic and terrestrial ecosystems, which encompass soils, water,
vegetation, and wildlife, are the focus of this review.
In addition to direct effects of the pollutants in ambient air, oxides of N and S, and PM in
ambient air contribute to deposition of N and S, as summarized in section 2.5 above, which can
affect ecosystem biogeochemistry, structure, and function in multiple ways. These effects
include nutrient enrichment, primarily associated with excess N, and acidification, due to N and
S deposition. Both N and S are essential nutrients. Nitrogen availability, however, is sometimes
the limiting factor for plant growth and productivity in aquatic and terrestrial ecosystems.1
Accordingly, increases in the inputs of N-containing compounds to an ecosystem can affect
vegetation growth and productivity, which in natural systems (both aquatic and terrestrial) can
affect the relative representation and abundance of different species as a result of differing N
requirements and growth characteristics among different species. Sulfur and N compounds can
contribute to the acidity of terrestrial and aquatic ecosystems. The extent to which S and N
deposition contribute to ecosystem acidification or to which N deposition contributes to nitrogen
enrichment, and associated ecological effects, depends on characteristics of the deposited
compounds and the receiving ecosystem.
Ecosystem effects considered in the currently available evidence include effects on the
presence and abundance of different species, with the associated potential for changes in
ecosystem function (ISA, section IS.2.2.4). The ecological metrics that have commonly been
assessed, and for which there are effects related to atmospheric deposition, include species
richness, community composition and biodiversity. Species richness is the number of species in a
particular community and community composition additionally accounts for the number of
individuals of each species. For example, two sites may both have 10 species of trees but differ
in tree community composition because one may have nearly all individuals from one species
and the second may have equal representation by all 10 species (ISA, section IS.2.2.4). The term
1 In addition to N, phosphorus is the other essential nutrient for which availability sometimes is the limiting factor in
plant growth and productivity, e.g., in many aquatic systems. Sulfur is rarely limiting in natural systems (ISA,
Appendix 7, section 7.1 and Appendix 4, section 4.3).
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biodiversity has a broader meaning intended to encompass ecosystem function and services that
relate to the species composition and population sizes of the community. As numerous studies
demonstrate, "the number and diversity of organisms in a system control the abundance of
habitat for other species, the biogeochemical cycling of nutrients and carbon, and the efficiency
at which biotic systems are able to transform limited resources into biomass" (ISA, p. IS-16).
This PA focuses on the evidence described in the 2020 ISA, and prior ISAs and AQCDs
for the three criteria pollutants and focuses on effects on specific ecosystems and biological
receptors from N and S deposition and both the confidence and key uncertainties associated with
those effects. The summaries of this evidence below are organized to address the following
questions.
• What is the nature of the welfare effects associated with N and S and PM? Is there
new evidence on welfare effects beyond those identified in the last reviews? Does the
newly available evidence alter prior conclusions?
• What does the available evidence indicate regarding ecosystems at particular risk
from deposition-related effects, and what are associated important, or key,
uncertainties?
• What are important uncertainties in the evidence? To what extent have such
uncertainties identified in the evidence in the past been reduced and/or have new
uncertainties been recognized?
The summaries in this chapter begin with the direct effects of oxides of N and S in
ambient air in section 4.1, followed by subsections regarding deposition-related effects. Section
4.2 focuses on effects of deposition-related aquatic acidification, while 4.3 focuses on effects
related to nitrogen enrichment. Other deposition-related effects, including those associated with
PM in ambient air, are summarized in section 4.4. Lastly, section 4.5 addresses considerations of
the public welfare effects given that the public welfare implications of the evidence regarding S
and N related welfare effects are dependent on the type and severity of the effects, as well as the
extent of the effect at a particular biological or ecological level of organization. In section 4.5,
we discuss such factors here in light of judgments and conclusions made in NAAQS reviews
regarding effects on the public welfare.
4.1 DIRECT EFFECTS OF OXIDES OF N AND S IN AMBIENT AIR
There is a well-established body of scientific evidence that has shown that acute and
chronic exposures to oxides of N and S, such as SO2, NO2, NO, HNO3 and PAN in the air, are
associated with negative effects on vegetation. Such scientific evidence, as was available in
1971, was the basis for the current secondary NAAQS for oxides of sulfur and oxides of
nitrogen, as summarized in section 3.1 above. The current scientific evidence continues to
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demonstrate such effects, with the ISA specifically concluding that the evidence is sufficient to
infer a causal relationship between gas-phase SO2 and injury to vegetation (ISA, Appendix 3,
section 3.6.12), and between gas-phase NO, NO2 and PAN and injury to vegetation (ISA,
Appendix 3, section 3.6.2). The ISA additionally concluded the evidence to be sufficient to infer
a causal relationship between exposure to HNO3 and changes to vegetation, noting that
experimental exposure can damage leaf cuticle of tree seedlings and HNO3 concentrations have
been reported to have contributed to declines in lichen species in the Los Angeles basis (ISA,
Appendix 3, section 3.6.3).
Uptake of gas phase N and S pollutants in a plant canopy is a complex process involving
adsorption to surfaces (leaves, stems and soil) and absorption into leaves (ISA, Appendix 3,
sections 3.1, 3.2 and 3.3). Several factors affect the extent to which ambient air concentrations of
gas-phase N and S pollutants elicit specific plant responses. These include rate of stomatal
conductance and plant detoxification mechanisms, and external factors such as plant water status,
light, temperature, humidity, and pollutant exposure regime (ISA Appendix 3, sections 3.2 and
3.3). The entry of gases into a leaf depends on atmospheric chemical processes and physical
characteristics of the surfaces, including the stomatal aperture. Stomatal opening is controlled
largely by environmental conditions, such as water availability, humidity, temperature, and light
intensity. When the stomata are closed, resistance to gas uptake is high and the plant has a very
low degree of susceptibility to injury (ISA, Appendix 3, section 3.1). However, "unlike vascular
plants, mosses and lichens do not have a protective cuticle barrier to gaseous pollutants, which is
a major reason for their sensitivity to gaseous S and N" (ISA, Appendix 3, p. 3-2).
Specifically for SOx, we note that high concentrations in the first half of the twentieth
century have been blamed for severe damage to plant foliage that occurred near large ore
smelters during that time (ISA, Appendix 3, section 3.2). In addition to foliar injury, which is
usually a rapid response, SO2 exposures have also been documented to reduce plant
photosynthesis and growth. The appearance of foliar injury can vary significantly among species
and growth conditions (which affect stomatal conductance). The research activity on SO2 effects
on vegetation has declined since the 1980s, especially in the U.S., due to the appreciable
reductions in ambient air concentrations of SO2 (ISA, Appendix 3, section 3.2). For lichens,
damage from SO2 exposure has been observed to include decreases in photosynthesis and
respiration, damage to the algal component of the lichen, leakage of electrolytes, inhibition of
nitrogen fixation, decreased potassium absorption, and structural changes (ISA, Appendix 3,
section 3.2; Belnap et al., 1993; Farmer et al., 1992, Hutchinson et al., 1996).
Although there is evidence of plant injury associated with SO2 exposures dating back
more than a century (ISA, Appendix 3, section 3.2), as exposures have declined in the U.S.,
some studies in the eastern U.S. have reported increased growth in some S02-sensitive tree
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species. For example, studies by Thomas et al. (2013) with eastern red cedar in West Virginia
have reported significant growth rate increases in more recent years. In this study, the authors
conducted a multivariate correlation analysis using historical climate variables, atmospheric CO2
concentrations, and estimated emissions of SO2 and NOx in the U.S. and found that the growth
of eastern red cedar trees (assessed through 100-year tree ring chronology) is explained best by
increases in atmospheric CO2 and NOx emissions and decreases in SO2 emissions. Although the
authors attributed the growth response to reductions in S02-associated acid deposition, and
related recovery from soil acidification, the relative roles of different pathways is unclear as a
historical deposition record was not available (ISA, Appendix 3, section 3.2). Other researchers
have suggested that the observed red cedar response was related to the fact that the trees were
growing on a limestone outcrop that could be well buffered from soil acidification (Schaberg et
al., 2014). This seems to suggest a somewhat faster recovery than might be expected from
deposition-related soil acidification which may indicate a relatively greater role for changes in
ambient air concentrations of SO2, in combination with changes in other gases than was
previously understood (ISA, Appendix 3, section 3.2 and Appendix 5, section 5.2.1.3).
The evidence base evaluated in the 1993 AQCD for Oxides of N included evidence of
phytotoxic effects of NO, NO2, and PAN on plants through decreasing photosynthesis and
induction of visible foliar injury (U.S. EPA, 1993). The 1993 AQCD additionally concluded that
concentrations of NO, NO2, and PAN in the atmosphere were rarely high enough to have
phytotoxic effects on vegetation. Little new information is available since that time on these
phytotoxic effects at concentrations currently observed in the U.S. (ISA, Appendix 3, section
3.3).
The evidence for HNO3 indicates a role in lichen species declines observed in the 1970s
in the Los Angeles basin (ISA, Appendix 3, section 3.3; Boonpragob and Nash 1991; Nash and
Sigal, 1999; Riddell et al., 2008). A 2008 resampling of areas shown to be impacted in the past
by HNO3 found community shifts, declines in the most pollutant-sensitive lichen species, and
increases in abundance of nitrogen-tolerant lichen species compared to 1976-1977, indicating
that these lichen communities have not recovered and had experienced additional changes (ISA,
Appendix 3, section 3.4; Riddell et al., 2011). The recently available evidence on this topic also
included a study of six lichen species that reported decreased chlorophyll content and
chlorophyll fluorescence, decreased photosynthesis and respiration, and increased electrolyte
leakage from HNO3 exposures for 2-11 weeks (daily peak levels near 50 ppb) in controlled
chambers. (ISA, Appendix 3, section 3.4; Riddell et al., 2012).
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4.2 ACID DEPOSITION-RELATED ECOLOGICAL EFFECTS
Deposited S and N compounds can both act as acidifying agents. Acidifying deposition
can affect biogeochemical processes in soils, with ramifications for terrestrial biota and for the
chemistry and biological functioning of associated surface waters (ISA, Appendix 7, section 7.1).
Soil acidification is influenced by the deposition of inorganic acids (HNO3 and H2SO4), and by
chemical and biological processes, which can also be influenced by atmospheric deposition of
other chemicals. For example, NH3 or NH4+ can stimulate soil bacteria that produce NO3" (ISA,
Appendix 4, section 4.3). In this process, hydrogen ions are produced and the extent to which
this changes soil acidity depends on the fate of the NO3". When NO3", or SO42", leach from soils
to surface waters, an equivalent number of positive cations, or countercharge, is also transported.
If the countercharge is provided by a base cation (e.g., calcium, [Ca2+], magnesium [Mg2+],
sodium [Na+], or potassium [K+]), rather than hydrogen (H+), the leachate is neutralized, but the
soil becomes more acidic from the H+ left behind and the base saturation of the soil is reduced by
the loss of the base cation. Depending on the relative rates of soil processes that contribute to the
soil pools of H+ and base cations, such as weathering, continued SO42" or NO3" leaching can
deplete the soil base cation pool which contributes to increased acidity of the leaching soil water,
and by connection, the surface water. Accordingly, the ability of a watershed to neutralize acidic
deposition is determined by a variety of biogeophysical factors including weathering rates,
bedrock composition, vegetation and microbial processes, physical and chemical characteristics
of soils, and hydrology (ISA Appendix 4, section 4.3).
This connection between SO2 and NOx emissions, atmospheric deposition of N and/or S,
and the acidification of acid-sensitive soils and surface waters is well documented with several
decades of evidence, particularly in the eastern U.S. (ISA, section IS.5; Appendix 8, section 8.1).
While there is evidence newly available since the 2008 ISA, in general, the fundamental
understanding of mechanisms and biological effects has not changed. Rather, the more recent
studies further support the 2008 ISA findings on these broad conclusions and provide updated
information on specific aspects. An overview of the ISA findings is provided for aquatic
acidification in section 4.2.1 below, and for terrestrial acidification in section 4.2.2 below.
4.2.1 Freshwater Ecosystems
Surface water processes integrate the chemicals deposited directly onto waterbodies with
those released from hydrologically connected terrestrial ecosystems as a result of deposition
within the watershed (ISA, Appendix 7, section 7.1). As was the case in the last review, the body
of evidence regarding such processes available in this review, including that newly available, is
sufficient to infer a causal relationship between N and S deposition and the alteration of
freshwater biogeochemistry (ISA, section IS.6.1). Additionally, based on the previously
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available evidence, the current body of evidence is also sufficient to conclude that a causal
relationship exists between acidifying deposition and changes in biota, including physiological
impairment and alteration of species richness, community composition, and biodiversity in
freshwater ecosystems (ISA, section IS.6.3).
In addition to the acidity of surface waters quantified over weeks or months, waterbodies
can also experience spikes in acidity in response to episodic events such as precipitation or rapid
snowmelt that may elicit a pulse of acidic leachate over shorter periods such as hours or days. In
these situations, sulfate and nitrate in snowpack (or downpours) can provide a surge or pulse of
drainage water, containing acidic compounds, that is routed through upper soil horizons rather
than the deeper soil horizons that usually would provide buffering for acidic compounds (ISA,
Appendix 7, section 7.1). During these episodes, N and S sources other than atmospheric
deposition, such as acid mine drainage or road salt applications can also be important. While
some streams and lakes may have chronic or base flow chemistry that provides suitable
conditions for aquatic biota, they may experience occasional acidic episodes with the potential
for deleterious consequences to sensitive biota (ISA, Appendix 8, section 8.5).
4.2.1.1 Nature of Effects and New Evidence
Longstanding evidence has well characterized the changes in biogeochemical processes
and water chemistry caused by N and S deposition to surface waters and their watersheds and the
ramifications for biological functioning of freshwater ecosystems (ISA, Appendix 8, section 8.1).
The 2020 ISA found that the newly available scientific research "reflects incremental
improvements in scientific knowledge of aquatic biological effects and indicators of acidification
as compared with knowledge summarized in the 2008 ISA" (ISA, Appendix 8, p. 8-80).
Previously and newly available studies "indicate that aquatic organisms in sensitive ecosystems
have been affected by acidification at virtually all trophic levels and that these responses have
been well characterized for several decades" (ISA, Appendix 8, p. 8-80). For example,
information reported in the previous 2008 ISA "showed consistent and coherent evidence for
effects on aquatic biota, especially algae, benthic invertebrates, and fish that are most clearly
linked to chemical indicators of acidification" (ISA, Appendix 8, p. 8-80). These indicators are
surface water pH, base cation ratios, acid neutralizing capacity (ANC), and inorganic aluminum
(Ali) concentration (ISA, Appendix 8, Table 8-9).
The effects of waterbody acidification on fish species are especially well understood in
the scientific literature, and many species have been documented to have experienced negative
effects from acidification (ISA, Appendix 8, section 8.3). Research conducted in fresh
waterbodies of Europe and North America before 1990 documented the adverse biological
effects on various fish species associated with acidification (ISA, Appendix 8, section 8.3.6).
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Some of the most frequently studied fish species are brown and brook trout, and Atlantic salmon,
among these species the earliest lifestages are most sensitive to acidic conditions. Many effects
of acidic surface waters on fish, particularly effects on gill function or structure, relate to the
combination of low pH and elevated dissolved inorganic A1 (ISA, Appendix 8, section 8.3.6.1).
Based on studies in the 1980s and 1990s of waterbodies affected by acidic deposition,
researchers have summarized the evidence of effects on fish populations in relation to the pH and
ANC of the studied waterbodies. Such effects include reduced presence of some species in
acidified lakes in the Adirondacks of New York or the Appalachian Mountains (ISA, Appendix
8, section 8.3.6). Such studies have been used to characterize ranges of ANC as to potential risk
to aquatic communities. The use of ANC as an indicator of waterbody acidification is described
in section 4.2.1.2 below.
Despite the reductions in acidifying deposition, as summarized in section 2.5 above,
aquatic ecosystems across the U.S. are still experiencing effects from historical contributions of
N and S (ISA, Appendix 8, section 8.6). Long-term monitoring programs in several acid-
sensitive regions of the U.S., including the Adirondacks and the northeastern U.S. have
documented temporal trends in surface water chemistry that include evidence for chemical
recovery in the northeastern and southeastern U.S. suggesting that full chemical recovery may
take many decades or not occur at all due to the dynamics of S adsorption and desorption and
long-term Ca depletion of soils (ISA, Appendix 7, section 7.1.5.1, Appendix 11, section 11.2 and
Appendix 16, section 16.3.4). As reported in the 2008 ISA, biological recovery of aquatic
systems lags chemical recovery due to a number of physical and ecological factors (including the
time for populations to recover), as well as other environmental stressors, which make the time
required for biological recovery uncertain (ISA, Appendix 8, section 8.4). Some recent studies
report on waterbodies showing signs of recovery from the impacts of many decades of
substantially elevated acidic deposition. One example is the successful reintroduction and re-
establishment of a naturalized native fish species (brook trout) in an Adirondack Lake from
which the species had been previously lost. Based on reconstruction of the historical record, the
study reported ANC had increased from -2 microequivalents per liter (|ieq/L) during the 1980s to
12 |aeq/L during the period 2010-2012 when the trout were reintroduced. By 2012, young fish
were observed, documenting successful reproduction in or in tributary streams near, the lake
(ISA, Appendix 8, section 8.4.4; Sutherland et al., 2015). Another recent study in the Adirondack
Lake region however, found no evidence of widespread or substantial brook trout recovery,
although water quality had improved, indicating the impact of the factors mentioned above that
can contribute to lags of biological recovery behind chemical recovery (ISA, Appendix 8,
sections 8.4 and 8.4.4).
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4.2.1.2 Freshwater Ecosystem Sensitivity
The effects of acid deposition on aquatic systems depend largely upon the ability of the
system to neutralize additional acidic inputs from the environment, whether from the atmosphere
or from surface inputs. There is a large amount of variability between freshwater systems in this
regard which reflects their underlying geology as well as previous acidic inputs. Accordingly,
different freshwater systems (e.g., in different geographic regions) respond differently to similar
amounts of acid deposition. The main factor in determining sensitivity is the underlying geology
of an area and its ability to provide soil base cations through weathering to buffer acidic inputs
(ISA, Appendix 8, section 8.5.1). As noted in the ISA, "[gjeologic formations having low base
cation supply, due mainly to low soil and bedrock weathering, generally underlie the watersheds
of acid-sensitive lakes and streams" (ISA, Appendix 8, p. 8-58). Consistent with this, studies
have indicated that the thickness of the till (the sediment layer deposited by action of receding
glaciers) "has been shown to be a key control on the pH and ANC of Adirondack lakes" (ISA,
Appendix 8, p. 8-58). Other factors identified as contributing to the sensitivity of surface waters
to acidifying deposition, include topography, soil chemistry and physical properties, land use and
history, and hydrologic flowpath, as well as impacts of historic, appreciably higher, deposition
(ISA, Appendix 8, p. 8-58).
Acid neutralizing capacity is commonly used to describe the potential sensitivity of a
freshwater system to acidification-related effects and has been found in various studies to be the
single best indicator of the biological response and health of aquatic communities in acid
sensitive systems (ISA, Appendix 8, section 8.6). The parameter ANC is an indicator of the
buffering capacity of natural waters against acidification. Although ANC does not directly affect
biota, it is a indicator of acidification that relates to pH and aluminum levels, and biological
effects in aquatic systems are primarily attributable to low pH and high inorganic aluminum
concentration (ISA, p. ES-14). Acid neutralizing capacity is parameter that can be measured in
water bodies. It is also often estimated for use in water quality modeling, as the molar sum of
strong base cations minus the molar sum of strong acid anions (specifically including SO42" and
NO3") (e.g., Driscoll et al., 1994). Water quality models are generally better at estimating ANC
than at estimating other indicators of acidification-related risk. While ANC is not the direct cause
of acidification-related effects on aquatic biota, it serves as an indicator of acidification-related
risk, since it has been related to the health of biota and to other surface water constituents like pH
and Al or watershed characteristics like base cation weathering (BCw) rate (ISA, Appendix 8,
sections 8.1 and 8.3.6.3). Waterbody pH largely controls the bioavailability of Al, which is toxic
to fish (ISA, Appendix 8, section 8.6.4). Values of ANC can also be influenced by high
concentrations of naturally occurring organic acids (Waller et al. 2012). In waters where that
occurs, ANC may not be a good indicator of risk to biota as the organic compounds can reduce
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bioavailability of Al, buffering effects usually associated with low pH and high total A1
concentrations (ISA, Appendix 8, section 8.3.6.4).
In its role as an indicator, ANC levels are commonly used to categorize waterbody
sensitivity. Waterbodies with annual average levels above 100 are generally not considered
sensitive or at risk of acidification-related effects. There is potential for risk at lower levels, at
which consideration of other factors can inform interpretation. National survey data dating back
to the early 1980s that were available for the 2008 ISA indicated acidifying deposition had
acidified surface waters in the southwestern Adirondacks, New England uplands, eastern portion
of the upper Midwest, forested Mid-Atlantic highlands, and Mid-Atlantic coastal plain (2008
ISA, section 4.2.2.3; ISA, Appendix 8, section 8.5.1). As noted in section 4.2.1 above, events
such as spring snowmelt and heavy rain events can contribute to episodic acidification events.
For example, in some impacted northeastern waterbodies, ANC levels may dip below zero for
hours to days or weeks in response to such events, while waterbodies labeled chronically acidic
have ANC levels below zero throughout the year (ISA, Appendix 6, section 6.1.1.1; Driscoll et
al., 2001). Accordingly, headwater streams in both the eastern and western U.S. tend to be more
sensitive to such episodes due to their smaller size (ISA, Appendix 8, section 8.5.1).
Fish and water quality surveys as well as in situ bioassays inform our understanding of
risk posed to fish species across a range of ANC. For example, surveys in the heavily impacted
Adirondack mountains found that waterbodies with ANC levels near/below zero2 and pH
near/below 5.0 generally had few or no fish species (Sullivan et al., 2006; ISA, Appendix 8,
section 8.6). Waterbodies with levels of ANC above zero differed in the types and numbers of
species present. At relatively lower ANC levels such as below 20 |ieq/L, comparatively acid
tolerant species such as brook trout can have healthy populations, but sensitive fish species such
as Atlantic salmon smolts, blacknose shiner, and other fish can be absent, or their population can
be greatly reduced. While most sensitive species were not lost from the aquatic system, their
fitness (population size and growth) declined; plankton and macroinvertebrate assemblages were
also impacted somewhat; and fish species richness in some areas was lower, with fewer of the
most sensitive species present. Some sites with ANC levels above 80 [j,eq/L have appeared
unimpaired (Bulger et al., 1999; Driscoll et al., 2001; Kretser et al., 1989; Sullivan et al., 2006).
An ANC level of 100 [j,eq/L is often identified as a benchmark at/below which waterbodies may
be considered at increased sensitivity.
Surveys conducted from the 1980s through 2004, available in the last review, indicated
that the surface waters in the southwestern Adirondacks, New England uplands, eastern portion
2 A survey of waterbodies in the Adirondacks in 1984-1987 found 27% of streams to have ANC values below zero,
with a minimum value of -134 |icq /L (Sullivan et al., 2006). Values of ANC below 20 in Shenandoah stream
sites were associated with fewer fish of sensitive species compared to sites with higher ANC (Bulger et al., 1999).
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of the upper Midwest, forested Mid-Atlantic highlands, and Mid-Atlantic coastal plain had been
acidified as a result of acidifying deposition (ISA, Appendix 8, section 8.5.1). A compilation of
historical water quality measurements of ANC from 1980 to 2011 (nearly 200,000 measurements
at nearly 20,000 spatially unique sites) is presented in Figure 4-1 below (Sullivan, 2017).3 As
described in the ISA, "[ajcidic waters were mostly restricted to northern New York, New
England, the Appalachian Mountain chain, upper Midwest, and Florida" (ISA, Appendix 8, p. 8-
60). Additionally, the figure indicates low, but positive, ANC values for these same regions, as
well as high-elevation western waterbodies (e.g., in the Sierra and Cascades mountains) and parts
of Arkansas and the Gulf states (Figure 4-1; ISA, Appendix 8, section 8.5.2). The findings for
high-elevation portions of the West and parts of Arkansas and the Gulf states are thought to
largely reflect base cation supply in soils, as levels of acidifying deposition have been low in
most areas of the West, and acidic surface waters there are rare (ISA, Appendix 8, section 8.5.2).
3 Samples expected to be strongly influenced by acid mine drainage, sea salt spray, or road salt application were
excluded. Among the full dataset, 6,065 sites had ANC < 100 (ieq/L.
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1> 300 600 K«ometet*N
I ¦> L1—s—h
ko 100 200 400 Mies ,
Puerto Rico
a?-
Alaska
>
Surface Water ANC
Entire U.S.
Projection: Lambert Conformal Conic, NAD 1983
Produced for National Park Sen/ice, Air Resources Division, 2011
Prepared by: E&S Environmental Chemistry
* ° Hawaii
-;W M
Note No ranking for American Affiliated
Parks in PACN Not Shown
Surface Water ANC
Median (peq/L)
• <0
• 0-20
20-50
• 50- 100
• > 100
Figure 4-1. Surface water ANC map, based on data compiled by Sullivan (2017) (ISA, Appendix 8, Figure 8-11).
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4.2.1.3 Key Uncertainties
In the longstanding evidence base for acidification effects of deposited S and N in aquatic
ecosystems, uncertainties remain. Key uncertainties include those associated with inputs to
models that simulate watershed chemistry and are employed to estimate waterbody buffering
capacity, such as base cation weathering rates and leaching of S and N compounds from
watershed soils. Uncertainties are associated with estimates of the response of waterbodies to
different deposition levels in areas for which site-specific data are not available because of the
high spatial variability of the factors that influence watershed sensitivity (ISA, Appendix 8,
section 8.5.1; McNulty et al., 2007). For example, there are uncertainties related to limitations in
water quality measurements, data on surface runoff characteristics, and other factors important to
characterizing watershed supplies of base cations related to weathering of bedrock and soils.
There are also uncertainties associated with our understanding of relationships between ANC and
risk to native biota, particularly in waterbodies in geologic regions prone to waterbody acidity.
These relate to the varying influences of site-specific factors other than ANC.
4.2.2 Terrestrial Ecosystems
There is longstanding evidence that changes in soil biogeochemical processes caused by
acidifying deposition of N and S to terrestrial systems are linked to changes in terrestrial biota,
with associated impacts on ecosystem characteristics. The currently available evidence, including
that newly available in this review, supports and strengthens this understanding (ISA, Appendix
5, section 5.1). Consistent with conclusions in the last review the current body of evidence is
sufficient to infer a causal relationship between acidifying deposition and alterations of
biogeochemistry in terrestrial ecosystems. Additionally, and consistent with conclusions in the
last review, the current body of evidence is sufficient to infer a causal relationship between
acidifying N and S deposition and the alteration of the physiology and growth of terrestrial
organisms and the productivity of terrestrial ecosystems. The current body of evidence is also
sufficient to conclude that a causal relationship exists between acidifying N and S deposition and
alterations of species richness, community composition, and biodiversity in terrestrial
ecosystems (2008 ISA, Appendix 4, sections 4.2.1.1 and 4.2.1.2; 2020 ISA, Appendix 4, section
4.1 and Appendix 5, sections 5.7.1 and 5.7.2).
4.2.2.1 Nature of Effects and New Evidence
Deposition of acidifying compounds to acid-sensitive soils can cause soil acidification,
increased mobilization of Al from soil to drainage water, and deplete the pool of exchangeable
base cations in the soil (ISA, Appendix 5, section 5.2 and Appendix 4, sections 4.3.4 and 4.3.5).
The physiological effects of acidification on terrestrial biota include slower growth and increased
mortality among sensitive plant species, which are generally attributable to physiological
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impairment caused by A1 toxicity (related to increased availability of inorganic A1 in soil water)
and a reduced ability of plant roots to take up base cations (ISA, Appendix 4, section 4.3 and
Appendix 5, section 5.2). The U.S. tree species most studied with regard to effects of acid
deposition are red spruce and sugar maple, although there is also evidence for other tree species
such as flowering dogwood (ISA, Appendix 5, section 5.2.1). The recently available evidence
includes Ca addition experiments in which Ca is added to acidic soils and physiological and
growth responses of red spruce and sugar maple are assessed to help understand the response of
these species to the soil changes induced by acid deposition (ISA, Appendix 5, Table 5-2). Other
recent studies have included addition or gradient studies evaluating relationships between soil
chemistry indicators of acidification (e.g., soil pH, Be: A1 ratio, base saturation, and Al) and
ecosystem biological endpoints, including physiological and community responses of trees and
other vegetation, lichens, soil biota, and fauna (ISA, Appendix 5, Table 5-6).
Since the last review of the NAAQS for oxides of S and N, and as described in detail in
Chapter 5 (and Appendix 5B), several observational studies have reported on statistical
associations between tree growth or survival, as assessed at monitoring sites across the U.S. and
estimates of average deposition of S or N compounds at those sites over time periods on the
order of 10 years (section 5.3.2.3 and Appendix 5B, section 5B.2.2 below; ISA, Appendix 5,
section 5.5.2 and Appendix 6, section 6.2.3.1; Dietze and Moorcroft, 2011; Thomas et al., 2010;
Horn et al., 2018). Negative associations were observed for survival and growth in several
species or species groups with S deposition metrics; positive and negative associations were
reported with N deposition (see section 5.3.2.3 and 5.3.4 below and Appendix 5B).
The physiological effects of acidifying deposition on terrestrial biota can also result in
changes in species composition whereby sensitive species are replaced by more tolerant species,
or the sensitive species that were dominant in the community become a minority. For example,
increasing soil cation availability (as in Ca addition or gradient experiments) was associated with
greater growth and seedling colonization for sugar maple while American beech was more
prevalent on soils with lower levels of base cations where sugar maple is less often found (ISA,
Appendix 5, section 5.2.1.3.1; Duchesne and Ouimet, 2009). In a study of understory species
composition, soil acid-base chemistry was found to be a predictor of understory species
composition (ISA, Appendix 5, section 5.2.2.1). Additionally, limited evidence, including a
recent S addition study and agricultural soil gradient study, indicated that soil acid-base
chemistry predicted and was correlated with diversity and composition of soil bacteria, fungi,
and nematodes (ISA, Appendix 5, section 5.2.4.1).
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4.2.2.2 Terrestrial Ecosystem Sensitivity
Underlying geology is the principal factor governing the sensitivity of both terrestrial and
aquatic ecosystems to acidification from S and N deposition. Geologic formations with low base
cation supply (e.g., sandstone, quartzite), due mainly to low weathering rates, generally underlie
these acid sensitive watersheds. Other factors also contribute to the overall sensitivity of an area
to acidifying nitrogen and sulfur deposition including topography, soil chemistry, land use, and
hydrology (ISA, Appendix 5, section 5.3). As observed in the ISA, "[a]cid-sensitive ecosystems
are mostly located in upland mountainous terrain in the eastern and western U.S. and are
underlain by bedrock that is resistant to weathering, such as granite or quartzite sandstone" (ISA,
Appendix 7, p. 7-45). Further, as well documented in the evidence, biogeochemical sensitivity to
deposition-driven acidification (and eutrophication [see section 4.3 below]) is the result of
historical loading, geologic/soil conditions (e.g., mineral weathering and S adsorption), and
nonanthropogenic sources of N and S loading to the system (ISA, Appendix 7, section 7.1.5).
Several different indicators are commonly used to identify areas at increased risk of
acidification processes (ISA, Appendix 5, Table 4-1). They include the ratio of the molar sum of
base cations to the molar amount of A1 (BC:A1) in soil solution. The BC:A1 ratio is commonly
used, particularly in mass balance modeling approaches, such as the simple mass balance
equation (SMBE; also referred to as the simple mass balance, SMB, model), that are intended to
assess the vulnerability of different areas to acidification as a result of atmospheric deposition of
N and S compounds. Higher values of this ratio indicate a lower potential for acidification-
related biological effects (ISA, Table IS-2). The ratio value can be reduced by release of base
cations from the soil (e.g., through the process of neutralizing drainage water acidity) which, in
turn, reduces the base saturation of the soil. Soil base saturation4 and changes to it can also be an
indicator of acidification risk (ISA, Appendix 4, section 4.3.4). The accelerated loss of base
cations through leaching, decrease in base saturation, and decreases in the BC:A1 ratio all serve
as indicators of soil acidification. This is because the input of base cations to soil solution, e.g.,
via soil weathering or base cation exchange, can neutralize inorganic and organic acids (ISA,
Appendix 4, section 4.3).
Although there has been no systematic national survey of U.S. terrestrial ecosystem soils,
several forest ecosystems are considered the most sensitive to terrestrial acidification from
atmospheric deposition. These include forests of the Adirondack Mountains of New York, Green
Mountains of Vermont, White Mountains of New Hampshire, the Allegheny Plateau of
Pennsylvania, and mountain top and ridge forest ecosystems in the southern Appalachians (2008
4 Soil base saturation expresses the concentration of exchangeable bases (Ca, Mg, potassium [K], sodium [Na]) as a
percentage of the total cation exchange capacity (which includes exchangeable H+ and inorganic Al).
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ISA, Appendix 3, section 3.2.4.2; ISA, Appendix 5, section 5.3). A number of modeling
analyses, including a national-scale analysis, have been performed to identify acid-sensitive
areas, generally through estimates of indicators such as BC:A1 (ISA, Appendix 5, sections 5.3,
5.4 and 5.5). In some cases, more recent analyses augment estimates from the previously
available national-scale analysis (McNulty et al., 2007), potentially providing updated estimates.
For example, a recent modeling analysis by Phelan et al. (2014) employed the PROFILE model
to estimate BCw in support of SMB modeling, a difference from the empirical approach (clay
correlation-substrate method) used by McNulty et al. (2007). This more recent analysis
suggested that Pennsylvania hardwood sites may not be as sensitive to acidifying deposition as
previously estimated (ISA, Appendix 5, section 5.4; Phelan et al., 2014). Another commonly
used indicator of acidification is soil base saturation (ISA, Appendix 4, Table 4-1). Values below
10% have been associated with areas experiencing acidification such as the eastern forests
recognized above (ISA, Appendix 4, section 4.3.4).
Recently available evidence includes some studies describing early stages of recovery
from soil acidification in some eastern forests. For example, studies at the Hubbard Brook
Experimental Forest in New Hampshire reported indications of acidification recovery in soil
solution measurements across the period from 1984 to 2011 (ISA, Appendix 4, section 4.6.1;
Fuss et al., 2015). Another study of 27 sites in eastern Canada and the northeastern U.S. reported
reductions in wet SO42" deposition to be positively correlated with changes in base saturation and
negatively correlated with changes in exchangeable Al between initial samplings in the mid-
1980s to early 1990s and a resampling in the period 2003-2014. That is, reductions in wet
deposition SO42" were associated with increases in soil base saturation and decreases in
exchangeable Al (ISA, Appendix 4, section 4.6.1; Lawrence et al., 2015). Modeling analyses
indicate extended timeframes for recovery are likely, as well as delays or lags related to
accumulated pools of S in forest soils (ISA, Appendix 4, section 4.6.1).
4.2.2.3 Key Uncertainties
Although the evidence clearly demonstrates that N and S deposition causes acidification
related effects in terrestrial ecosystems, uncertainties remain that are important to our
consideration of the evidence in this review. For example, there are uncertainties associated with
the various approaches for estimating sensitive ecosystems and for understanding and
characterizing long-term risks and processes against the backdrop of deposition reductions
occurring over the past several decades. As summarized in section 4.2.2.1 above, modeling
analyses are commonly employed, with several inputs recognized as contributing to overall
uncertainty.
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As noted in the ISA, the rate of base cation weathering "is one of the most influential yet
difficult to estimate parameters" in modeling (such as the SMB) that estimate indicators of
acidification as a function of deposition inputs (ISA, Appendix 4, section 4.5.1.1). Estimating
this parameter continues to be a major source of uncertainty in such modeling. For example, in
an analysis of uncertainties associated with simulating ANC in waterbodies of interest in
response to acid deposition over a broad spatial scale, the primary source of uncertainty was
identified to be from factors affecting base cation weathering and ANC, including BCw rates,
soil depth and soil temperature (ISA, p. IS-114; Li and McNulty, 2007). The authors concluded
that improvements in estimates of these factors are crucial to reducing uncertainty and successful
model application for broader scales (e.g., where site-specific information is limited), including
national scale (ISA, Appendix 4, section 4.6). Another analysis of major sources of uncertainty
related to estimating soil acidification also found the greatest uncertainty to be associated with
the BCw estimates, particularly citing the particle size class-based method commonly used to
estimate the total specific surface area upon which weathering reactions can take place
(Whitfield et al., 2018).
There are also more general sources of uncertainty associated with observational or
gradient studies that relate variation in biological/ecological indices to variation in deposition
metrics. For example, such studies may fail to account for influences such as variation in
biological and biogeochemical processes imposed by climate, geology, biota, and other
environmental factors. Further, observed variation in current or recent biological metrics may be
affected by the lags reported in the evidence, both in ecosystem response to acid deposition and
to ecosystem recovery from historic deposition. Additionally, biological measures in areas for
which recent values of deposition metrics are relatively low, may be influenced by impacts from
past deposition.
4.3 NITROGEN ENRICHMENT AND ASSOCIATED EFFECTS
The numerous ecosystem types that occur across the U.S. have a broad range of
sensitivity to N enrichment. Organisms in their natural environments are commonly adapted to
the nutrient availability in those environments. Historically, N has been the primary limiting
nutrient in many ecosystems. In such ecosystems, when the limiting nutrient, N, becomes more
available, whether from atmospheric deposition, runoff, or episodic events, the subset of species
able to most effectively utilize the higher nitrogen levels may out-compete other species leading
to a shift in the community composition that may be dominated by a smaller number of species
(i.e., a community with lower diversity) (ISA, sections IS.6.1.1.2, IS.6.2.1.1 and IS.7.1.1,
Appendix 6, section 6.2.4 and Appendix 7, section 7.2.6.6). Thus, change in the availability of
nitrogen in nitrogen-limited systems can affect growth and productivity, with ramifications on
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relative abundance of different species, and potentially further and broader ramifications on
ecosystem processes, structure, and function. The term, eutrophication, refers to such processes
that occur in response to enrichment of a system with nutrients. A common example of
eutrophi cation in aquatic ecosystems is when increased loading of the limiting nutrient (usually
N or phosphorus) results in rapid and appreciable algal growth. Decomposition of the plant
biomass from the subsequent algal die-off contributes to reduced waterbody oxygen which in
turn contributes to fish mortality (ISA, p. ES-18).
Both N oxides and reduced forms of nitrogen (Mix) can contribute to N enrichment. In
addition to atmospheric deposition, other sources of S and N can play relatively greater or lesser
roles in contributing to S and N inputs, depending on location. For example, many waterbodies
receive appreciable amounts of N from agricultural runoff and municipal or industrial
wastewater discharges. For many terrestrial and freshwater ecosystems, sources of N other than
atmospheric deposition, including fertilizer and waste treatment, contribute to ecosystem total N
with contributions that can be larger than that from atmospheric deposition (ISA Appendix 7,
sections 7.1 and 7.2). Additionally, the impacts of historic deposition in both aquatic and
terrestrial ecosystems pose complications to discerning the potential effects of more recent lower
deposition rates.
4.3.1 Aquatic and Wetland Ecosystems
Nitrogen additions, including from atmospheric deposition, to freshwater, estuarine and
near-coastal ecosystems can contribute to eutrophi cation which typically begins with nutrient-
stimulated rapid algal growth developing into an algal bloom that can, depending on various site-
specific factors, be followed by anoxic conditions associated with the algal die-off (ISA, section
ES.5.2). This reduction in dissolved oxygen can affect higher-trophic-level species (ISA, section
ES.5.2). The extensive body of evidence in this area is sufficient to infer causal relationships
between N deposition and the alteration of biogeochemistry in freshwater, estuarine and near-
coastal marine systems (ISA, Appendix 7, sections 7.1 and 7.2). Further, consistent with findings
in the last review, the current body of evidence is sufficient to infer a causal relationship between
N deposition and changes in biota, including altered growth and productivity, species richness,
community composition, and biodiversity due to N enrichment in freshwater ecosystems (ISA,
Appendix 9, section 9.1). The body of evidence is sufficient to infer a causal relationship
between N deposition and changes in biota, including altered growth, total primary production,
total algal community biomass, species richness, community composition, and biodiversity due
to N enrichment in estuarine environments (ISA, Appendix 10, section 10.1).
The impact of N additions on wetlands depends on the type of wetland and other factors.
More specifically, the type of wetland, as well as hydrological conditions and season, influence
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whether a wetland serves as a source, sink, or transformer of atmospherically deposited N (ISA,
section IS. 8.1 and Appendix 11, section 11.1). One of the transformations that may occur in
wetlands is denitrification which leads to the production of N2O, a greenhouse gas. This is a
normal process in anaerobic soils but can be increased with the introduction of additional N,
especially when in reduced forms such as NH4+(ISA, Appendix 4, section 4.3.6). Whether
wetlands are a source or a sink of N is extremely variable and depends on vegetation type,
physiography, and local hydrology, as well as climate. Studies generally show N enrichment to
decrease the ability of wetlands to retain and store N, which may diminish the wetland ecosystem
service of improving water quality (ISA, section IS.8.1). Consistent with the evidence available
in the last review, the current body of evidence is sufficient to infer a causal relationship between
N deposition and the alteration of biogeochemical cycling in wetlands. Newly available evidence
regarding N inputs and plant physiology, expands the evidence base related to species diversity.
The currently available evidence, including that newly available, is sufficient to infer a causal
relationship between N deposition and the alteration of growth and productivity, species
physiology, species richness, community composition, and biodiversity in wetlands (ISA,
Appendix 11, section 11.10).
4.3.1.1 Nature of Effects and New Evidence
As summarized above, N inputs and other factors contribute to nutrient enrichment which
contribute to eutrophication, the process of enriching a water body with nutrients resulting in
increased growth and change in the composition of primary producers (algae and/or aquatic
plants) which can also lead to low oxygen levels in the water body when these primary producers
decompose. Such nitrogen driven eutrophi cation alters freshwater biogeochemistry and can
impact physiology, survival, and biodiversity of sensitive aquatic biota (Figure 4-2).
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Figure 4-2. Conceptual model of the influence of atmospheric N deposition on freshwater
nutrient enrichment (ISA, Appendix 9, Figure 9-1).
Evidence newly available in this review provides insights regarding N enrichment and its
impacts in several types of aquatic systems, including freshwater streams and lakes, estuarine
and near-coastal systems, and wetlands. For example, studies published since the 2008 ISA
augment the evidence base for high-elevation waterbodies where the main source of N is
atmospheric deposition, including a finding that N deposition is correlated with a shift from N to
P limitation in certain waterbodies (ISA, Appendix 9, section 9.1.1.3). The newly available
evidence, including that from paleolimnological surveys, fertilization experiments, and gradient
studies continues to show effects of N loading to sensitive freshwater systems, including an
influence on the occurrence of harmful algal blooms (ISA, Appendix 9).
More specifically, the availability and form of N has been found to influence freshwater
algal bloom composition and toxicity (ISA, Appendix 9, section 9.2.6.1). Information available
in this review indicates that growth of some harmful algal species, including those that produce
microcystin, are favored by increased availability of N and its availability in dissolved inorganic
form (ISA, Appendix 9, p. 9-28). For example, studies in Lake Erie have indicated Microcystis
bloom growth and microcystin concentration were stimulated more frequently to N additions
than phosphorus additions (Davis et al., 2015). Further, inorganic N was also associated with
peak surface water concentrations of microcystin, a cyanobacteria produced toxin that is
enriched in N (Gobler et al., 2016).
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Evidence of the influence of availability and form of N on algal blooms is also available
in estuarine systems. For example, specific phytoplankton functional groups prefer reduced
forms of N (such as NH4+) over oxidized forms (such as NO3"), and in many parts of the U.S.,
including the Southeast and Mid-Atlantic, reduced N deposition has increased relative to
oxidized N deposition (ISA, Appendix 10, section 10.3.3). Very limited evidence suggests a role
for atmospheric N deposition in taxonomic shifts and declines in some invertebrates, although
"the effects attributed to N are difficult to separate from other stressors such as climate change
and invasive species" (ISA, Appendix 9, section 9.6).
Evidence in coastal waters has recognized a role of nutrient enrichment in acidification of
some coastal waters (ISA, Appendix 10, section 10.5). More specifically, nutrient-driven algal
blooms may contribute to ocean acidification, possibly through increased decomposition which
lowers dissolved oxygen levels in the water column and contributes to lower pH. Such nutrient-
enhanced acidification can also be exacerbated by warming (associated with increased microbial
respiration) and changes in buffering capacity (alkalinity) of freshwater inputs (ISA, Appendix
10, section 10.5).
4.3.1.2 Aquatic Ecosystem Sensitivity
Current evidence continues to support the conclusions of the previous review regarding
ecosystem sensitivity to nutrient enrichment.
4.3.1.2.1 Freshwater Ecosystems
Freshwater systems that are likely to be most impacted by nutrient enrichment due to
atmospheric deposition of N are remote, oligotrophic, high-elevation water bodies with limited
local nutrient sources and with low N retention capacity. Freshwater systems sensitive to N
nutrient enrichment include those in the Snowy Range in Wyoming, the Sierra Nevada
Mountains, and the Colorado Front Range. A portion of these lakes and streams where effects
are observed are in Class I wilderness areas (Williams et al., 2017a; Clow et al., 2015; Nanus et
al., 2012).
Recent research also supports the 2008 ISA findings that N limitation is common in
oligotrophic waters in the western U.S. (Elser et al., 2009b; Elser et al., 2009a). Shifts in nutrient
limitation, from N limitation, to between N and P limitation, or to P limitation, were reported in
some alpine lake studies reviewed in the 2008 ISA and in this review. Since the 2008 ISA,
several meta-analyses have reported an increase in P deposition to water bodies (Stoddard et al.,
2016; Brahney et al., 2015; Tipping et al., 2014) and highlight the need to account for how
sustained P deposition can modify the effects of anthropogenically emitted N deposition on
productivity. Even small inputs of N in these water bodies can increase nutrient availability or
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alter the balance of N and P, which can stimulate growth of primary producers and lead to
changes in species richness, community composition, and diversity.
The relative contribution of N deposition to total N loading varies among waterbodies.
For example, atmospheric deposition is generally considered to be the main source of new N
inputs to most headwater stream, high-elevation lake, and low-order stream watersheds that are
far from the influence of other N sources like agricultural runoff and wastewater effluent (ISA,
section ES5.2). In other fresh waterbodies, however, agricultural practices and point source
discharges have been estimated to be larger contributors (ISA, Appendix 7, section 7.1.1.1).
Since the 2008 ISA, several long-term monitoring studies in the Appalachian Mountains,
the Adirondacks, and the Rocky Mountains have reported temporal patterns of declines in
surface water NO3" concentration corresponding to declines in atmospheric N deposition (ISA,
Appendix 9, section 9.1.1.2). Declines in basin wide NO3" concentrations have also been reported
for the nontidal Potomac River watershed and attributed to declines in atmospheric N deposition
(ISA, Appendix 7, section 7.1.5.1). A study of water quality monitoring in a watershed in Rocky
Mountain National Park has also reported reductions in stream water NO3" concentrations of
more than 40% from peak concentrations in the mid-2000s, which corresponded to decreases in
NOx emissions and estimated N deposition (ISA, Appendix 7, section 7.1.5.1).
4.3.1.2.2 Estuarine and Coastal Ecosystems
Nutrient inputs to coastal and estuarine waters are important influences on the health of
these waterbodies. As long recognized, "N enrichment of marine and estuarine waters can alter
the ratios among nutrients such as P and Si and affect overall nutrient limitation" (ISA, Appendix
10, p. 10-6). Continued inputs of N, the most common limiting nutrient in estuarine and coastal
systems, have resulted in N over enrichment and subsequent alterations to the nutrient balance in
these systems (ISA, Appendix 10, p. 10-6). For example, the limiting nutrient may change (e.g.,
from phosphorus to N) as water moves from freshwater through the transition zones into
estuaries and marine waters (ISA, Appendix 10, section 10.1.3). Further, "[ljevels of N
limitations are also affected by seasonal patterns in estuaries, with N limited conditions likely
occurring during the peak of annual productivity in the summer" (ISA, Appendix 10, p. 10-6).
Moreover, the rate of N delivery to coastal waters is strongly correlated to changes in primary
production and phytoplankton biomass (ISA, Appendix 10, section 10.1.3; Paerl and Piehler,
2008).
In estuarine and near coastal systems, the prevalence and health of submerged aquatic
vegetation (SAV) has been identified as a biological indicator for estuarine condition (ISA,
Appendix 10, section 10.2.5). Previously available evidence indicated the role of N loading in
SAV declines in multiple U.S. estuaries through increased production of macroalgae or other
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algae which reduce sunlight penetration into shallow waters where SAVs are found (ISA,
Appendix 10, section 10.2.3). Newly available studies have reported findings of increased SAV
populations in two tributaries of the Chesapeake Bay corresponding to reduction in total N
loading from all sources since 1990 (ISA, Appendix 10, section 10.2.5). The newly available
studies also identify other factors threatening SAV, including increasing temperature related to
climate change (ISA, Appendix 10, section 10.2.5).
Algal blooms and associated die-offs can contribute to hypoxic conditions (most common
during summer months), which can contribute to fish kills and associate reductions in marine
populations. In the U.S., the documented incidence of hypoxia increased almost 30-fold from
1960 to 2008, at which time it was reported in more than 300 coastal areas (ISA, Appendix 10,
section 10.2.4; Jewett et al., 2010). Areas of eutrophication-related hypoxia are found along the
East coast, Gulf of Mexico coast and some areas of the Pacific coast (ISA, Appendix 10, Figure
10-5). In such low oxygen conditions, only tolerant organisms are present (Diaz et al., 2013;
Jewett et al., 2010).
Increased N loading to coastal areas (regardless of source) can affect dissolved oxygen
levels and lead to shifts in community composition, reduced biodiversity, and increased mortality
of biota (ISA, Appendix 10, section 10.3). Studies of these categories of effects describe shifts in
diatom communities over times of extremely low oxygen levels (ISA, AppendixlO, section
10.3.1), altered phytoplankton community composition with higher N inputs (ISA, AppendixlO,
section 10.3.2), as well as correlation of waterbody levels of nitrogen compounds with changes
to bacteria/archaea diversity (ISA, AppendixlO, section 10.3.4), benthic diversity (ISA,
AppendixlO, section 10.3.5), and fish diversity (ISA, AppendixlO, section 10.3.6). Further, the
form of available N (e.g., NH4+ or NO3") can influence phytoplankton community composition in
estuarine and marine environments (ISA, Appendix 10, section 10.3.3). In hypoxic areas,
mortality of stationary organisms and avoidance of hypoxic conditions by mobile organisms lead
to changes in biodiversity and loss of biomass (ISA, Appendix 10, section 10.3.3; Diaz and
Rosenberg, 2008) which can in turn affect energy transfer through the food web. The degree to
which these impacts are driven by atmospheric N deposition vary greatly and are largely unique
to the specific ecosystem.
Estimates of the relative contribution of atmospheric deposition to total N loading vary
among estuaries. Analyses based on data across two to three decades extending from the 1990s
through about 2010 estimate that most of the analyzed estuaries receive 15-40% of their N inputs
from atmospheric sources (ISA, section ES5.2; ISA, Appendix 7, section 7.2.1) though for
specific estuaries contributions can vary more widely. In areas along the West Coast, N sources
may include coastal upwelling from oceanic waters, as well as transport from watersheds.
Common N inputs to estuaries include those associated with freshwater inflows transporting N
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from agriculture, urban, wastewater sources, in addition to atmospheric deposition across the
watershed (ISA, IS2.2.2; ISA, Appendix 7, section 7.2.1).
Estimates of N loading to estuaries from atmospheric deposition has been estimated in
several recent modeling studies (ISA, Table 7-9). One analysis of estuaries along the Atlantic
Coast and the Gulf of Mexico, which estimated that 62-81% of N delivered to the eastern U.S
coastal zone is anthropogenic in source, also reported that atmospheric N deposition to
freshwater that is subsequently transported to estuaries represents 17-21% of the total N loading
into the coastal zone (McCrackin et al., 2013; Moore et al., 2011). In the Gulf of Mexico, 26% of
the N transported to the Gulf in the Mississippi/Atchafalaya River basin was estimated to be
contributed from atmospheric deposition (which may include volatilized losses from natural,
urban, and agricultural sources) (Robertson and Saad, 2013). Another modeling analysis
identified atmospheric deposition to watersheds as the dominant source of N to the estuaries of
the Connecticut, Kennebec, and Penobscot rivers. For the entire Northeast and mid-Atlantic
coastal region, it dropped to third largest source (20%), following agriculture (37%) and sewage
and population-related sources (28%) (ISA, Appendix 7, section 7.2.1). Estimates for West Coast
estuaries indicate much smaller contribution from atmospheric deposition. For example, analyses
for Yaquina Bay, Oregon, estimated direct deposition to contribute only 0.03% of N inputs;
estimated N input to the watershed from N fixing red alder (Alnus rubra) trees was a much larger
(8%>) source (ISA, Appendix 7, section 7.2.1; Brown and Ozretich, 2009).
4.3.1.2.3 Wetlands
With regard to wetland sensitivity to N deposition, in general, those wetlands receiving a
larger fraction of their total water budget in the form of precipitation are more sensitive to the
effects of N deposition. The relative contribution of atmospheric deposition to total wetland N
loading varies with wetland type, with bogs receiving the greatest contribution and accordingly
being most vulnerable to nutrient enrichment effects of N deposition (ISA, Appendix 11, section
11.1). For example, bogs (70-100%) of hydrological input from rainfall) are more sensitive to N
deposition than fens (55-83%> as rainfall), which are more sensitive than coastal wetlands
(10-20%) as rainfall) (ISA, Appendix 11, section 11.10). Nearly all N loading to ombrotrophic
bogs5 comes from atmospheric deposition because precipitation is the only source of water to
these wetlands. For freshwater fens, marshes, and swamps, inputs from ground and surface water
are often of similar order of magnitude as that from precipitation. Similarly, estuarine and coastal
wetlands receive water from multiple sources that include precipitation, ground and/or surface
water, and marine and/or estuarine waters (ISA, Appendix 11, section 11.1).
5 Ombrotrophic bogs develop in areas where drainage is impeded and precipitation exceeds evapotranspiration (ISA,
Appendix 11, section 11.1).
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4.3.1.3 Key Uncertainties
Models are used extensively to simulate the movement of N to sensitive receptors in
aquatic ecosystems, and to estimate indicators of eutrophication risk. In the case of estuarine and
near-coastal systems, the models are hydrodynamically complex and due to the need for inputs
particular to the waterbody to which they are applied, tend to be site specific (NRC, 2000; ISA,
Appendix 7, section 7.2.8.2). Other model uncertainties may arise from the difficulties in
disentangling N input sources and apportioning the source of N in the ecosystem correctly. This
leads to uncertainty in the role of atmospheric deposition in the N driven effects that are
observed.
Several uncertainties contribute to estimates of N deposition associated with certain water
body responses. These include a difficulty in estimating dry deposition of gaseous and particulate
N to complex surfaces; extremely limited data, particularly for arid, mountainous terrain; and
difficulties estimating deposition in areas with high snowfall, cloud water or fog (ISA, Appendix
9, section 9.5; Pardo et al., 2011). For example, "N deposition estimates at high-elevation sites
such as those in the Rocky and Sierra Nevada mountains are associated with considerable
uncertainty, especially uncertainty for estimates of dry deposition" (ISA, Appendix 9, p. 9-44;
Williams et al., 2017b). For estimates of N deposition associated with other sensitive responses,
such as shifts in phytoplankton communities in high-elevation lakes, "N deposition model bias
may be close to, or exceed, predicted [critical load] values" (ISA, Appendix 9, p. 9-44; Williams
etal., 2017b).
4.3.2 Terrestrial Ecosystems
It is long established thatN enrichment of terrestrial ecosystems increases plant
productivity (ISA, Appendix 6, section 6.1). Building on this, the currently available evidence,
including evidence that is longstanding, is sufficient to infer a causal relationship between N
deposition and the alteration of the physiology and growth of terrestrial organisms and the
productivity of terrestrial ecosystems (ISA, Appendix 5, section 5.2 and Appendix 6, section
6.2). Responsive ecosystems include those that are N limited and/or contain species that have
evolved in nutrient-poor environments. In these ecosystems the N-enrichment changes in plant
physiology and growth rates vary among species, with species that are adapted to low N supply
being readily outcompeted by species that have higher N demand. In this manner, the relative
representation of different species may be altered, and some species may be eliminated
altogether, such that community composition is changed and species diversity declines (ISA,
Appendix 6, sections 6.3.2 and 6.3.8). The currently available evidence in this area is sufficient
to infer a causal relationship between N deposition and the alteration of species richness,
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community composition, and biodiversity in terrestrial ecosystems (ISA, section IS.5.3 and
Appendix 6, section 6.3).
4.3.2.1 Nature of Effects and New Evidence
Previously available evidence described the role of N deposition in changing soil carbon
and N pools and fluxes, as well as altering plant and microbial growth and physiology in an array
of terrestrial ecosystems. This evidence supported our understanding in the last review of how N
deposition influences plant physiology, growth, and terrestrial ecosystem productivity. The
newly available evidence confirms these conclusions and improves our understanding of the
mechanisms that linkN deposition and biogeochemistry in terrestrial ecosystems. The new
evidence supports a more detailed understanding of how N influences terrestrial ecosystem
growth and productivity; community composition and biodiversity in sensitive ecosystems (ISA,
Appendix 6, section 6.2.1).
A supply of N is essential for plant growth and, as was clear in the last review, N
availability is broadly limiting for productivity in many terrestrial ecosystems (ISA, Appendix 6,
section 6.2.1). Accordingly, N additions contribute to increased productivity and can alter
biodiversity. Eutrophi cation, one of the mechanisms by which this can occur, comprises multiple
effects that include changes to the physiology of individual organisms, alteration of the relative
growth and abundance of various species, transformation of relationships between species, and
indirect effects on availability of essential resources other than N, such as light, water, and
nutrients (ISA, Appendix 6, section 6.2.1).
The currently available evidence for the terrestrial ecosystem effects of N enrichment,
including eutrophi cation, includes studies in a wide array of systems, including forests (tropical,
temperate, and boreal), grasslands, arid and semi-arid scrublands, and tundra (ISA, Appendix 6).
The organisms affected include trees, herbs and shrubs, and lichen, as well as fungal, microbial,
and arthropod communities. As recognized in section 4.1 above, lichen communities, which have
important roles in hydrologic cycling, nutrient cycling, and as sources of food and habitat for
other species, are also affected by atmospheric N (ISA, Appendix 6). The recently available
studies on the biological effects of added N in terrestrial ecosystems include investigations of
plant and microbial physiology, long-term ecosystem-scale N addition experiments, regional and
continental-scale monitoring studies, and syntheses.
The previously available evidence included N addition studies in the U.S. and N
deposition gradient studies in Europe that reported associations of N deposition with reduced
species richness and altered community composition for grassland plants, forest understory
plants, and mycorrhizal fungi (soil fungi that have a symbiotic relationship with plant roots)
(ISA, Appendix 6, section 6.3). Since 2008, new research techniques have been developed to
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understand community composition, additional communities have been surveyed, and new
studies have made it possible to isolate the influence of N deposition from other environmental
factors. In addition, new evidence has been developed for forest communities indicating thatN
deposition alters the physiology and growth of overstory trees, and thatN deposition has the
potential to change the community composition of forests (ISA, Appendix 6, section 6.6). Recent
studies on forest trees include analyses of long-term forest inventory data collected from across
the U.S. and Europe (ISA, Appendix 6, section 6.2.3.1). New research also expands the
understanding that N deposition can alter the physiology, growth, and community composition of
understory plants, lichens, mycorrhizal fungi, soil microorganisms, and arthropods (ISA,
Appendix 6, section 6.2.3 and 6.3.3).
The recent evidence includes findings of variation in forest understory and non-forest
plant communities with atmospheric N deposition gradients in the U.S. and in Europe. For
example, gradient studies in Europe have found higher N deposition to be associated with forest
understory plant communities with more nutrient-demanding and shade-tolerant plant species
(ISA, Appendix 6, section 6.3.3.2). A recent gradient study in the U.S. found forest understory
species richness to be highly dependent on soil pH, with negative associations of species richness
with N deposition rates above 11.6 kg N/ha-yr at sites with low soil pH but not at the sites with
basic soils (ISA, Appendix 6, section 6.3.3.2).
Among the new studies are investigations of effects of N on mycorrhizal fungi and
lichens. Studies indicate that increased N in forest systems can result in changes in mycorrhizal
community composition (ISA, Appendix 6, section 6.2). Forest microbial biomass and
community composition can also be affected, which can contribute to impacts on arthropod
communities (ISA, Appendix 6, section 6.3.3.4). Recent evidence includes associations of
variation in lichen community composition with N deposition gradients in the U.S. and Europe,
(ISA, Appendix 6, section 6.2.6; Table 6-23). Differences in lichen community composition have
been attributed to atmospheric N pollution in forests throughout the West Coast, in the Rocky
Mountains, and in southeastern Alaska. Differences in epiphytic lichen growth or physiology
have been observed along atmospheric N deposition gradients in the highly impacted area of
southern California, and also in more remote locations such as Wyoming and southeastern
Alaska (ISA, Appendix 6, section 6.3.7). Historical deposition may play a role in observational
studies of N deposition effects, complicating the disentangling of responses that may be related
to more recent N loading.
Newly available findings from N addition experiments expand on the understanding of
mechanisms linking changes in plant and microbial community composition to increased N
availability. Such experiments in arid and semi-arid environments indicate that competition for
resources such as water may exacerbate the effects of N addition on diversity (ISA, Appendix 6,
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section 6.2.6). A 25-year experiment with N additions ranging from 10 to 95 kg N/ha-yr (and
background wet deposition of N estimated at 6 kg N/ha-yr) observed grassland composition to
change from a high-diversity, native-dominated state to a low-diversity, non-native dominated
state (ISA, Appendix 6, section 6.3.5). The newly available evidence also includes studies in arid
and semiarid ecosystems, particularly in southern California, that have reported changes in plant
community composition, in the context of a long history of significant N deposition, with fewer
observations of plant species loss or changes in plant diversity (ISA, Appendix 6, section 6.3.6).
4.3.2.2 Terrestrial Ecosystem Sensitivity
In general, most terrestrial ecosystems are N limited and, consequently, sensitive to
effects related to N enrichment (ISA, Appendix 6, section 6.3.8). Factors identified as governing
the sensitivity of terrestrial ecosystems to nutrient enrichment from N deposition include "the
rates of N deposition, degree of N limitation, ecosystem productivity, elevation, species
composition, length of growing season, and soil N retention capacity" (ISA, Appendix 6, p. 6-
162). One example is that of alpine tundra ecosystems, which: (1) are typically strongly N
limited, and contain vegetation adapted to low N availability; (2) often have thin soils with
limited N retention capacity; and (3) have short growing seasons (ISA, Appendix 6, section
6.3.8). Given the evidence regarding sensitivity of lichens and ectomycorrhizal fungi to N
enrichment effects, it may be that ecosystems containing a large number and/or diversity of these
organisms, such as temperate and boreal forests and alpine tundra, could be considered
particularly sensitive to N deposition (ISA, Appendix 6, sections 6.2.3.2, 6.2.3.3, 6.2.4, and
6.3.8).
In the currently available evidence, studies conducted in grassland and coastal sage shrub
communities, and in arid ecosystems, such as the Mojave Desert, indicate sensitivity of those
communities. For example, N addition studies in Joshua Tree National Park have reported losses
in forb species richness (which make up most of the grassland biodiversity), greater growth of
grass species (which make up the majority of grassland biomass), and changes in reproductive
rates. Accordingly, the N limitation in grasslands and the dominance by fast-growing species that
can shift in abundance rapidly (in contrast to forest trees) contribute to an increased sensitivity of
grassland ecosystems to N inputs (ISA, Appendix 6, section 6.3.6). Studies in southern
California coastal sage scrub communities, including studies of the long-term history of N
deposition, which was appreciably greater in the past than recent rates, indicate impacts on
community composition and species richness in these ecosystems (ISA, Appendix 6, sections
6.2.6 and 6.3.6). In summary, the ability of atmospheric N deposition to override the natural
spatial heterogeneity in N availability in arid ecosystems, such as the Mojave Desert and
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California coastal sage scrub ecosystems in southern California, makes these ecosystems
sensitive toN deposition (ISA, Appendix 6, section 6.3.8).
The current evidence includes relatively few studies of N enrichment recovery in
terrestrial ecosystems. Among N addition studies assessing responses after cessation of
additions, it has been observed that soil nitrate and ammonium concentrations recovered to levels
observed in untreated controls within 1 to 3 years of the cessation of additions, but soil processes
such as N mineralization and litter decomposition were slower to recover (ISA, Appendix 6,
section 6.3.2; Stevens, 2016). A range of recovery times have been reported for mycorrhizal
community composition and abundance from a few years in some systems to as long as 28 or 48
years in others (ISA, Appendix 6, section 6.3.2; Stevens, 2016; Emmett et al., 1998; Strengbom
et al., 2001). An N addition study in the midwestern U.S. observed that plant physiological
processes recovered in less than 2 years, although grassland communities were slower to recover
and still differed from controls 20 years after the cessation of N additions (ISA, Appendix 6,
section 6.3.2; Isbell et al., 2013).
4.3.2.3 Key Uncertainties
Just as there are uncertainties associated with estimating N deposition associated with
ecological responses in aquatic systems (as summarized in section 4.3.1.3 above), such
uncertainties exist with terrestrial ecosystem analyses. For example, regarding wet deposition
measurements, there are uncertainties associated with monitoring instrumentation and
measurement protocols, as well as limitations in the spatial extent of existing monitoring
networks, especially in remote areas. Given limitations in our ability to estimate dry deposition,
estimates are often based on model predictions, for which there are various sources of
uncertainty, including model formulation and inputs for the simulation of chemistry and
transport processes. Other uncertainties are associated with an incomplete understanding of the
underlying scientific processes influencing atmospheric deposition that are not possible to
quantify. For example, uncertainties associated with deposition estimates (that may be utilized in
observational studies) include those associated with simulating effects of the tree canopy on N
oxides (including both bidirectional gas exchange and canopy reactions), bidirectional exchange
of NH3 with biota and soils, and processes determining transference ratios that relate average
concentration to deposition. (ISA, section IS. 14.1.3).
There is also uncertainty with regard to the relative importance of different N species in
effects of N enrichment on terrestrial ecosystem [ISA, Appendix 6, section 6.3.2], Although
there are few direct analyses comparing the impacts of oxidized and reduced forms of N
deposition on biodiversity, it is plausible that NO3" may be less likely to accumulate in soil, with
associated effects, due to its greater tendency to be more readily lost to both leaching and
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denitrification than NH4+ (ISA, Appendix 6, section 6.3.2). Further, while multiple meta-
analyses have generally not reported differences in the relationship of different N forms with
ecological and biogeochemical endpoints, such as plant productivity or microbial biomass,
several individual studies have observed differential effects on diversity of NH4+ versus NO3"
additions. For example, an experiment involving a nutrient-poor, Mediterranean site found that
while an NH4+ addition (40 kg N/ha-yr) increased plant richness, addition of the same amount of
N comprised of half NH4+ and half NO3" did not (ISA, Appendix 6, section 6.3.2).
With regard to ecological responses and impacts of concern, there are several key areas
of uncertainty. In observational studies, in addition to uncertainty regarding the role of historical
deposition, other confounding factors such as drought and ozone may also contribute to impacts
of concern. Further, there is wide variability in the response of plants to nitrogen inputs and the
impacts of spatially variable factors such as climate, geology and past deposition on that
response is generally unknown. Spatially, variation in biological and biogeochemical processes
imposed by climate, geology, biota, and other environmental factors may affect observed
associations of ecological metrics with deposition metrics.
Uncertainties also relate to time scales and lags. For example, while atmospheric
deposition responds dynamically to shifts in emissions and weather patterns, ecological
processes react to environmental stress at a variety of timescales, which due to intervening
ecosystem processes usually lag changes in deposition. There are also uncertainties related to the
role of historic patterns of deposition in ecosystem effects initially attributed to recent gradients
in deposition. These may loom larger for geographic regions, such as the northeastern U.S. or
southern California that have long and geographically extensive histories of elevated N
deposition.
4.4 OTHER DEPOSITION-RELATED EFFECTS
Additional categories of effects for which the current evidence is sufficient to infer causal
relationships include changes in mercury methylation processes in freshwater ecosystems,
changes in aquatic biota due to sulfide phytotoxicity, and ecological effects from PM deposition
(ISA, Table IS-1).
4.4.1 Mercury Methylation
The current evidence, including that newly available in this review, is sufficient to infer a
causal relationship between S deposition and the alteration of mercury methylation in surface
water, sediment, and soils in wetland and freshwater ecosystems. The process of mercury
methylation is influenced in part by surface water SO42" concentrations, as well as the presence
of mercury. Accordingly, in waterbodies where mercury is present, S deposition, particularly that
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associated with SOx has a role in production of methylmercury, which contributes to
methylmercury accumulation in fish (ISA, Appendix 12, section 12.8).
Newly available evidence has improved our scientific understanding of the types of
organisms involved in the methylation process, as well as the environments in which they are
found. Studies have also identified additional areas within the U.S. containing habitats with
conditions suitable for methylation, and species that accumulate methylmercury (ISA, Appendix
12, section 12.3). The evidence also contributes to our understanding of factors that can
influence the relationship between atmospheric S deposition and methylmercury in aquatic
systems; such factors include oxygen content, temperature, pH, and carbon supply, which
themselves vary temporally, seasonally, and geographically (ISA, Appendix 12, section 12.3).
4.4.2 Sulfide Toxicity
The evidence newly available in this review regarding non-acidifying sulfur effects on
biota expands upon that available for the 2008 ISA. The currently available evidence is sufficient
to infer a new causal relationship between S deposition and changes in biota due to sulfide
phytotoxicity, including alteration of growth and productivity, species physiology, species
richness, community composition, and biodiversity in wetland and freshwater ecosystems (ISA,
section IS.9). The currently available evidence indicates that the presence of sulfide, produced
through microbial transformation, interferes with nutrient uptake in roots of plants in wetlands
and other fresh waterbodies. Studies also report that elevated sulfide can result in decreased seed
mass, seed viability, seedling emergence rates, decreased seedling height, decreased seedling
survival rates, and reductions in total plant cover, all which have the potential to contribute to
shifts in plant community composition (ISA, Appendix 12, section 12.2.3). Sulfur deposition can
contribute to sulfide and associated phytotoxicity in freshwater wetlands and lakes. Recently
available studies indicate that sulfide toxicity can occur in wetland habitats and suggests that
sulfide toxicity can determine plant community composition in freshwater wetlands. These
studies indicate sulfide toxicity to have occurred in multiple wetland ecosystems in North
America (ISA, Appendix 12, sectionsl2.2.3 and 12.7.3).
4.4.3 Ecological Effects of PM Other Than N and S Deposition
Particulate matter includes a heterogeneous mixture of particles differing in origin, size,
and chemical composition. In addition to N and S and their transformation products, other PM
components, such as trace metals and organic compounds are also deposited to ecosystems and
may affect biota. Material deposited onto leaf surfaces can alter leaf processes and PM
components deposited to soils and waterbodies may be taken up into biota, with the potential for
effects on biological and ecosystem processes. The currently available evidence is sufficient to
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infer a likely causal relationship between deposition of PM and a variety of effects on individual
organisms and ecosystems (ISA, Appendix 15, section 15.1).
The effects of PM on ecological receptors can be both chemical and physical, and
particles that elicit effects on ecological receptors vary by size, origin, and chemical
composition. Studies involving ambient air PM have generally involved conditions that would
not be expected to meet the current secondary standards for PM, e.g., polluted locations in India
or Argentina (ISA, Appendix 15, sections 15.4.3 and 15.4.4). Similarly, reduced photosynthesis
has been reported for rice plants experiencing fly ash particle deposition of 0.5 to 1.5 grams per
square meter per day (g/m2-day), a loading which corresponds to greater than 1000 kg/ha-yr
(ISA, Appendix 15, sections 15.4.3 and 15.4.6). Further, studies of the direct effects of PM in
ambient air on plant reproduction in near roadway locations in the U.S. have not reported a
relationship between PM concentrations and pollen germination (ISA, Appendix 15, section
15.4.6). Rather, the evidence related to PM is that associated with deposition of its components,
as summarized in section 4.4.3 below.
Although in some limited cases, effects have been attributed to particle size (e.g., soiling
of leaves by large coarse particles near industrial facilities or unpaved roads), ecological effects
of PM have been largely attributed more to particle composition (Grantz et al., 2003; ISA,
Appendix 15, section 15.2). For example, exposure to a given mass-per-volume or -mass
concentration may result in quite different ecological effects depending on the PM components.
Depending on concentration, trace metals, some of which are biologically essential, can be toxic
in large amounts (ISA, Appendix 15, section 15.3.1). Depending on conditions, deposited PM
has been associated with effects on vegetation including effects on plant surfaces, foliar uptake
processes, gas exchange, physiology, growth, and reproduction. The evidence largely comes
from studies involving elevated concentrations such as near industrial areas or historically
polluted cities (ISA, Appendix 15, section 15.4). Recent assays have supported previously
available evidence that toxicity relates more to chemical components than total mass.
Additionally recent experiments have suggested that PM deposition can influence responses in
microbial communities (ISA, Appendix 15, section 15.8). Quantifying relationships between
ambient air concentrations of PM and ecosystem response are difficult and uncertain.
4.5 PUBLIC WELFARE IMPLICATIONS
The public welfare implications of the evidence regarding S and N related welfare effects
are dependent on the type and severity of the effects, as well as the extent of the effect at a
particular biological or ecological level of organization or spatial scale. We discuss such factors
here in light of judgments and conclusions made in NAAQS reviews regarding effects on the
public welfare.
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As provided in section 109(b)(2) of the CAA, the secondary standard is to "specify a
level of air quality the attainment and maintenance of which in the judgment of the
Administrator ... is requisite to protect the public welfare from any known or anticipated adverse
effects associated with the presence of such air pollutant in the ambient air." The secondary
standard is not meant to protect against all known or anticipated welfare effects related to oxides
of N and S, and particulate matter, but rather those that are judged to be adverse to the public
welfare, and a bright-line determination of adversity is not required in judging what is requisite
(78 FR 3212, January 15, 2013; 80 FR 65376, October 26, 2015; see also 73 FR 16496, March
27, 2008). Thus, the level of protection from known or anticipated adverse effects to public
welfare that is requisite for the secondary standard is a public welfare policy judgment made by
the Administrator. The Administrator's judgment regarding the available information and
adequacy of protection provided by an existing standard is generally informed by considerations
in prior reviews and associated conclusions.
• What does the available information indicate regarding the public welfare
implications of S and N deposition-related welfare effects?
The categories of effects identified in the CAA to be included among welfare effects are
quite diverse,6 and among these categories, any single category includes many different types of
effects that are of broadly varying specificity and level of resolution. For example, effects on
vegetation and effects on animals are categories identified in CAA section 302(h), and the ISA
recognizes effects of N and S deposition at the organism, population, community, and ecosystem
level, as summarized in sections 4.1 and 4.2 above (ISA, sections IS.5 to IS.9). As noted in the
last review of the secondary NAAQS for N oxides and SOx, while the CAA section 302(h) lists a
number of welfare effects, "these effects do not define public welfare in and of themselves" (77
FR 20232, April 3, 2012).
The significance of each type of effect with regard to potential effects on the public
welfare depends on the type and severity of effects, as well as the extent of such effects on the
affected environmental entity, and on the societal use of the affected entity and the entity's
significance to the public welfare. Such factors have been considered in the context of judgments
and conclusions made in some prior reviews regarding public welfare effects. For example, in
the context of secondary NAAQS decisions for ozone, judgments regarding public welfare
significance have given particular attention to effects in areas with special federal protections
6 Section 302(h) of the CAA states that language referring to "effects on welfare" in the CAA "includes, but is not
limited to, effects on soils, water, crops, vegetation, manmade materials, animals, wildlife, weather, visibility, and
climate, damage to and deterioration of property, and hazards to transportation, as well as effects on economic
values and on personal comfort and well-being" (CAA section 302(h)).
4-32
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(such as Class I areas),7 and lands set aside by states, tribes and public interest groups to provide
similar benefits to the public welfare (73 FR 16496, March 27, 2008; 80 FR 65292, October 26,
2015).8 In the 2015 O3 NAAQS review, the EPA recognized the "clear public interest in and
value of maintaining these areas in a condition that does not impair their intended use and the
fact that many of these lands contain Cb-sensitive species" (73 FR 16496, March 27, 2008).
Judgments regarding effects on the public welfare can depend on the intended use for, or
service (and value) of, the affected vegetation, ecological receptors, ecosystems and resources
and the significance of that use to the public welfare (73 FR 16496, March 27, 2008: 80 FR
65377, October 26, 2015). Uses or services provided by areas that have been afforded special
protection can flow in part or entirely from the vegetation that grows there or other natural
resources. Ecosystem services range from those directly related to the natural functioning of the
ecosystem to ecosystem uses for human recreation or profit, such as through the production of
lumber or fuel (Costanza et al., 2017; ISA, section IS.5.1). The spatial, temporal, and social
dimensions of public welfare impacts are also influenced by the type of service affected. For
example, a national park can provide direct recreational services to the thousands of visitors that
come each year, but also provide an indirect value to the millions who may not visit but receive
satisfaction from knowing it exists and is preserved for the future (80 FR 65377, October 26,
2015).
In the last review of the secondary NAAQS for N oxides and SOx, ecosystem services
were discussed as a method of assessing the magnitude and significance to the public of
resources affected by ambient air concentrations of oxides of nitrogen and sulfur and associated
deposition in sensitive ecosystems (77 FR 20232, April 3, 2012). That review recognized that
although there is no specific definition of adversity to public welfare, one paradigm might
involve ascribing public welfare significance to disruptions in ecosystem structure and function.
The concept of considering the extent to which a pollutant effect will contribute to such
7 Areas designated as Class I include all international parks, national wilderness areas which exceed 5,000 acres in
size, national memorial parks which exceed 5,000 acres in size, and national parks which exceed 6,000 acres in
size, provided the park or wilderness area was in existence on August 7, 1977. Other areas may also be Class I if
designated as Class I consistent with the CAA.
8 For example, the fundamental purpose of parks in the National Park System "is to conserve the scenery, natural
and historic objects, and wild life in the System units and to provide for the enjoyment of the scenery, natural and
historic objects, and wild life in such manner and by such means as will leave them unimpaired for the enjoyment
of future generations" (54 U.S.C. 100101). Additionally, the Wilderness Act of 1964 defines designated
"wilderness areas" in part as areas "protected and managed so as to preserve [their] natural conditions" and
requires that these areas "shall be administered for the use and enjoyment of the American people in such manner
as will leave them unimpaired for future use and enjoyment as wilderness, and so as to provide for the protection
of these areas, [and] the preservation of their wilderness character ..." (16 U.S.C. 1131 (a) and (c)). Other lands
that benefit the public welfare include national forests which are managed for multiple uses including sustained
yield management in accordance with land management plans (see 16 U.S.C. 1600(l)-(3); 16 U.S.C. 1601(d)(1)).
4-33
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disruptions has been used broadly by the EPA in considering effects. An evaluation of adversity
to public welfare might also consider the likelihood, type, magnitude, and spatial scale of the
effect, as well as the potential for recovery and any uncertainties relating to these considerations
(77 FR 20218, April 3, 2012).
The types of effects on aquatic and terrestrial ecosystems discussed in sections 4.1
through 4.4 above differ with regard to aspects important to judging their public welfare
significance. For example, in the case of effects on timber harvest, such judgments may consider
aspects such as the heavy management of silviculture in the U.S., while judgments for other
categories of effects may generally relate to considerations regarding natural areas, including
specifically those areas that are not managed for harvest. For example, effects on tree growth and
survival have the potential to be significant to the public welfare through impacts in Class I and
other areas given special protection in their natural/existing state, although they differ in how
they might be significant.
In this context, it may be important to consider that S and N deposition-related effects,
such as changes in growth and survival of plant and animal species, could, depending on
severity, extent, and other factors, lead to effects on a larger scale including changes in overall
productivity and altered community composition (ISA, section IS.2.2.1 and Appendices 5, 6, 8,
9, and 10). Further, effects on individual species could contribute to impacts on community
composition through effects on growth and reproductive success of sensitive species in the
community, with varying impacts to the system through many factors including changes to
competitive interactions (ISA, section IS.5.2 and Appendix 6, section 6.3.2).
With respect to aquatic acidification effects, because acidification primarily affects the
diversity and abundance of aquatic biota, it also affects the ecosystem services that are derived
from the fish and other aquatic life found in these surface waters (2011 PA, section 4.4.5). Fresh
surface waters support several cultural services, such as aesthetic and educational services; the
type of service that is likely to be most widely and significantly affected by aquatic acidification
is recreational fishing. Multiple studies have documented the economic benefits of recreational
fishing. While the freshwater rivers and lakes of the northeastern United States, surface waters
that have been most affected by acidification, are not a major source of commercially raised or
caught fish, they are a source of food for some recreational and subsistence fishers and for other
consumers (2009 REA, section 4.2.1.3). It is not known, however, if and how consumption
patterns of these fishers may have been affected by the historical impacts of surface water
acidification in the affected systems. Non-use services, which include existence (protection and
preservation with no expectation of direct use) and bequest values, are arguably a significant
source of benefits from reduced acidification (Banzhaf et al., 2006).
4-34
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Nitrogen loading in aquatic ecosystems, particularly large estuarine and coastal water
bodies, has and continues to pose risks to the services provided by those ecosystems, with clear
implications to the public welfare (2011 PA, section 4.4.2; ISA, Appendix 14, section 14.3.2).
For example, the large estuaries of the eastern U.S. are an important source of fish and shellfish
production, capable of supporting large stocks of resident commercial species and serving as
breeding grounds and interim habitat for several migratory species (2009 REA, section 5.2.1.3).
These estuaries also provide an important and substantial variety of cultural ecosystem services,
including water-based recreational and aesthetic services. And as noted for fresh waters above,
these systems have non-use benefits to the public (2011 PA, section 4.4.5). As discussed in
section 4.3.1.2.2 above, the relative contribution of atmospheric deposition to total N loading
varies widely among estuaries and has declined in more recent years.
A complication to consideration of public welfare implications that is specific to N
deposition in terrestrial systems is its potential to increase growth and yield of agricultural and
forest crops, which may be judged and valued differentially than changes in growth of some
species in natural ecosystems. As discussed further in section 4.3.2 above, N enrichment in
natural ecosystems can, by increasing growth of N limited plant species, change competitive
advantages of species in a community, with associated impacts on the composition of the
ecosystem's plant community. The public welfare implications of such effects may vary
depending on their severity, prevalence or magnitude, such as with only those rising to a
particular severity (e.g., with associated significant impact on key ecosystem functions or other
services), magnitude or prevalence considered of public welfare significance. Impacts on some
of these characteristics (e.g., forest or forest community composition) may be considered of
greater public welfare significance when occurring in Class I or other protected areas, due to the
value that the public places on such areas.9 Other ecosystem services that can be affected are
summarized below in Figure 4-310 (ISA, Appendix 14). In considering such services in past
reviews for secondary standards for other pollutants (e.g., O3), the Agency has given particular
attention to effects in natural ecosystems, indicating that a protective standard, based on
consideration of effects in natural ecosystems in areas afforded special protection, would also
"provide a level of protection for other vegetation that is used by the public and potentially
affected by O3 including timber, produce grown for consumption and horticultural plants used
for landscaping" (80 FR 65403, October 26, 2015).
9 Locations of the Class I areas identified under the Clean Air Act, section 162(1) are shown in Figure 4-4
(https://www.epa.gov/visibility/regional-haze-program).
10 The articulation of welfare effects in Figure 4-3 is intended to reflect the ISA causal determinations in an easier to
comprehend manner that also illustrates connections among effects.
4-35
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Although more sensitive effects are described with increasingly greater frequency in the
evidence base of effects related to ecosystem deposition of N and S compounds, the available
information does not yet provide a framework that can specifically tie various magnitudes or
prevalences of changes in a biological or ecological indicator (e.g., lichen abundance or
community composition) to broader effects on the public welfare. This gap creates uncertainties
when considering the public welfare implications of some biological or geochemical responses to
ecosystem acidification or N enrichment, and accordingly judgments on the potential for public
welfare significance. That notwithstanding, while shifts in species abundance or composition of
various ecological communities may not be easily judged with regard to public welfare
significance, at some level, such changes, especially if occurring broadly in specially protected
areas, where the public can be expected to place high value, might reasonably be concluded to
impact the public welfare. An additional complexity in the current review is the current much-
improved air quality and associated reduced deposition within the context of a longer history that
included appreciably greater deposition in the middle of the last century, the environmental
impacts of which may remain.
In summary, several considerations are recognized as important to judgments on the
public welfare significance of the array of welfare effects at different exposure conditions. These
include uncertainties and limitations that must be taken into account regarding the magnitude of
key effects that might be concluded to be adverse to ecosystem health and associated services.
Additionally, there are numerous locations vulnerable to public welfare impacts from S or N
deposition-related effects on terrestrial and aquatic ecosystems and their associated services.
Other important considerations include the exposure circumstances that may elicit effects and the
potential for the significance of the effects to vary in specific situations due to differences in
sensitivity of the exposed species, the severity and associated significance of the observed or
predicted effect, the role that the species plays in the ecosystem, the intended use of the affected
species and its associated ecosystem and services, the presence of other co-occurring
predisposing or mitigating factors, and associated uncertainties and limitations.
4-36
-------
Figure 4-3. Potential effects on the public welfare of ecological effects of N Oxides, SOx, and PM.
4-37
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Whitfield, CJ, Phelan, JN, Buckley, J, Clark, CM, Guthrie, S and Lynch, JA (2018). Estimating
base cation weathering rates in the USA: challenges of uncertain soil mineralogy and
specific surface area with applications of the profile model. Water Air Soil Pollut 229:61.
Williams, JJ, Chung, SH, Johansen, AM, Lamb, BK, Vaughan, JK and Beutel, M (2017b).
Evaluation of atmospheric nitrogen deposition model performance in the context of US
critical load assessments. Atmos Environ 150: 244-255.
Williams, JJ, Lynch, JA, Saros, JE and Labou, SG (2017a). Critical loads 1 of atmospheric N
deposition for phytoplankton nutrient limitation shifts in western U.S. mountain lakes.
Ecosphere 8: e01955.
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5 EXPOSURE CONDITIONS ASSOCIATED WITH
EFFECTS
In this review, we consider two categories of exposure conditions associated with welfare
effects. The first is the less complex consideration of the direct exposures to pollutants in
ambient air, which were the focus in the establishment of the standards. The second is the more
complex consideration of exposures related to atmospheric deposition associated with the
pollutants in ambient air. In our consideration in this chapter of exposure conditions associated
with effects, we have generally addressed the two categories in separate sections beginning with
the second category. This is done in the context of the following overarching question:
• To what extent does the available evidence include quantitative exposure and
response information that can inform judgments on air exposures and deposition
levels of concern and accordingly, the likelihood of occurrence of such effects in
response to air quality that meets the current standards?
With regard to the more complex consideration of deposition-related effects such as
ecosystem acidification and N enrichment, there is wide variation in the extent and level of detail
of the evidence available to describe the ecosystem characteristics (e.g., physical, chemical, and
geological characteristics, as well as atmospheric deposition history) that influence the degree to
which deposition of N and S associated with the oxides of S and N and PM in ambient air elicit
ecological effects. One reason for this relates to the contribution of many decades of
uncontrolled atmospheric deposition before the establishment of NAAQS for PM, oxides of S
and oxides of N, followed by the subsequent decades of continued deposition as standards were
implemented and updated. The impacts of this deposition history remain in soils of many parts of
the U.S. today (e.g., in the Northeast and portions of the Appalachian Mountains in both
hardwood and coniferous forests, as well as areas in and near the Los Angeles Basin), with
recent signs of recovery in some areas (ISA, Appendix 4, section 4.6.1; 2008 ISA, section
3.2.1.1). This backdrop and associated site-specific characteristics are among the challenges we
consider in our task of identifying deposition targets to provide protection going forward against
the array of effects for which we have evidence of occurrence in sensitive ecosystems as a result
of the deposition of the past.
With regard to aquatic systems, prior to the peak of S deposition levels that occurred in
the 1970s and early 1980s, surface water sulfate concentrations increased in response to S
deposition. Subsequently, and especially more recently, surface water sulfate concentrations
have generally decreased, particularly in the Northeast. Some waterbodies, however, continue to
exhibit little reduction in acidic ions, such as in the Blue Ridge Mountains region in Virginia,
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where surface water SO42" has remained relatively stable even as emissions declined. This is an
example of the competing role of changes in S adsorption on soils and the release of historically
deposited S from soils into surface water, which some modeling has suggested will delay
chemical recovery in those water bodies (ISA, Appendix 7, section 7.1.2.2).
In this chapter, we first consider aquatic acidification, a category of effects for which
quantitative assessment approaches for atmospheric deposition are well established. In the 2012
review of the oxides of N and S, quantitative analyses relating deposition in recent times (e.g.,
since 2000) to ecosystem acidification, and particularly aquatic acidification, were generally
considered to be less uncertain and the ability of those analyses to inform NAAQS policy
judgments more robust than analyses related to deposition and ecosystem nutrient enrichment, or
eutrophication (2011 PA). While quantitative assessment approaches for aquatic eutrophication
as a result of total N loading are also well established, and the evidence base regarding
atmospheric deposition and nutrient enrichment has expanded since the 2012 review (as
summarized in section 4.3 above), the significance of non-air N loading to rivers, estuaries and
coastal waters continues to complicate the assessment of nutrient enrichment-related risks
specifically related to atmospheric N deposition. Accordingly, the quantitative REA developed in
this review focused on aquatic acidification. This chapter, however, addresses both the
quantitative information available for aquatic acidification (section 5.1 summarizes the REA that
is described in Appendix 5A in detail) and aquatic nutrient enrichment (section 5.2), as well as
terrestrial and other effects of S and N deposition.
Critical loads are frequently used in studies that investigate associations between various
chemical, biological, ecological and ecosystem characteristics and a variety ofN or S deposition-
related metrics.1 These studies vary widely with regard to the specific ecosystem characteristics
being evaluated, as well as the benchmarks selected forjudging them, such as the deposition-
related metrics, their scope, method of estimation and time period. The specific details of these
various factors influence the strengths and limitations for different uses and have associated
uncertainties. Given the role of the PA both in focusing on the most policy-relevant aspects of
the currently available information (reviewed in the 2020 and 2008 ISAs and past AQCDs) and
in clearly describing key aspects, including limitations and associated uncertainties, this
1 The term, critical load, which in general terms refers to an amount (or a rate of addition) of a pollutant to an
ecosystem that is estimated to be at, or just below, that which would have an effect of interest, has multiple
interpretations or applications (ISA, p. IS-14). This multiplicity or variety in meanings stems, at least in part, from
differing judgments and associated identifications regarding the effect of interest, and judgment of its harm. There
is additionally the complication of the dynamic nature of ecosystem pollutant processing and the broad array of
factors that influence it. As a result, time is an important dimension, sometimes unstated, as in empirical or
observational analyses, sometimes explicit, as in steady-state or dynamic modeling analyses (ISA, section
IS.2.2.4).
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document is intended to reach beyond individual critical loads developed over a variety of
studies and ecosystems and consider the underlying study findings with regard to key aspects of
the environmental conditions and ecological characteristics studied. A more quantitative
variation of this is the methodology developed for the analyses of aquatic systems and
acidification, summarized in section 5.1.2 below. In these analyses, the concept of a critical load
is employed with steady-state modeling that relates deposition to waterbody acid neutralizing
capacity. This specific use of critical loads in the REA analyses in this review is explicitly
described in section 5.1.2.
While recognizing the inherent connections between watersheds and waterbodies, such as
lakes and streams, the organization of this chapter recognizes the more established state of the
information, tools and data for aquatic ecosystems for characterizing relationships between
atmospheric deposition and acidification and/or nutrient enrichment effects under air quality
associated with the current standards. Further, we recognize the relatively greater role of
atmospheric deposition in aquatic acidification compared to aquatic eutrophication, to which
surface water discharges in populated watersheds have long contributed. Therefore, with regard
to deposition-related effects, we focus first on the quantitative information for aquatic ecosystem
effects in sections 5.1 and 5.2. Section 5.3 discusses the available evidence regarding
relationships between deposition-related exposures and the occurrence and severity of effects on
trees and understory communities in terrestrial ecosystems. Section 5.4 discusses the currently
available information related to consideration of exposure concentrations associated with other
welfare effects of nitrogen and sulfur oxides and PM in ambient air.
5.1 AQUATIC ECOSYSTEM ACIDIFICATION
Changes in biogeochemical processes and water chemistry caused by deposition of
nitrogen and sulfur compounds to surface waters and their watersheds have been well
characterized for decades and have ramifications for biological functioning of freshwater
ecosystems, as summarized in section 4.2.1 above (ISA, Appendix 8, section 8.1). These
deposited acidic compounds infiltrate both terrestrial and aquatic systems and may contribute to
changes to soils and water that are harmful to biota (ISA, section IS.5.3). These changes are
dependent on a number of factors that influence the sensitivity of a system to acidification
including weathering rates, bedrock composition, topography, vegetation and microbial
processes, physical and chemical characteristics of soils and hydrology (ISA, Appendix 4,
section 4.3).
The quantitative assessment of aquatic acidification risk performed for this review
(described in detail in Appendix 5A) is based on established modeling approaches, extensive
databases of site-specific water quality measurements and a commonly recognized indicator of
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acidification risk, ANC. The extensive evidence, history of quantitative modeling and site-
specific model evaluation supports this assessment. The ability to characterize the role of
atmospheric deposition of the pollutants under review is also a factor in the decision to focus
quantitative analysis on acid deposition into aquatic ecosystems.
Key aspects of this REA and its results are summarized in the following subsections, with
details provided in Appendix 5A. Section 5.1.1 provides background information on the
evidence supporting the use of ANC as an indicator of acidification risk in the assessment. The
conceptual model and the analysis approach are summarized in section 5.1.2. Results for
analyses at three scales are presented in section 5.1.3, and a characterization of the analysis
uncertainty is summarized in section 5.1.4. Overall findings are summarized in section 5.1.5.
5.1.1 Role of ANC as Acidification Indicator
Several measures of surface water chemistry are commonly used in assessments of
aquatic acidification. These include surface water base cations, pH, inorganic A1 and ANC (ISA,
Table IS-3). Accordingly, risk to aquatic systems from acidifying deposition can be assessed as a
change in specific water quality metrics as a result of nitrogen and/or sulfur deposition. Changes
in surface water chemistry reflect the influence of acidic inputs from precipitation, gases, and
particles, as well as local geology and soil conditions. As described in section 4.2.1 above,
surface water chemical factors such as pH, Ca2+, ANC, ionic metals concentrations, and
dissolved organic carbon (DOC) are affected by acid deposition and, accordingly, are commonly
used indicators of acidification. Although ANC does not directly cause effects on biota, it relates
to pH and aluminum levels, and biological effects are primarily attributable to low pH and high
inorganic aluminum concentration (ISA, section ES.5.1). The most widely used measure of
surface water acidification, and subsequent recovery under scenarios with lower acidifying
deposition, is ANC (ISA, Appendix 7, section 7.1.2.6). This is for several reasons: (1) ANC is
associated with the surface water constituents that directly cause or reduce acidity-related stress,
in particular pH, Ca2+, and inorganic A1 concentrations; (2) ANC is generally a more stable (less
variable) measurement than pH; and (3) ANC reflects sensitivity and effects of acidification in a
linear fashion across the full range of ANC values (ISA, Appendix 7, section 7.1.2.6).
As summarized in section 4.2.1.2 above, the evidence of effects on biota from aquatic
acidification indicates a range of severity with varying pH and ANC levels. The evidence relates
to biota ranging from phytoplankton and invertebrates to fish communities. For example, a
review by Lacoul et al. (2011) of aquatic acidification effects on aquatic organisms in Atlantic
Canada observed that the greatest differences in phytoplankton species richness occurred across
a pH range of 4.7 to 5.5 (ANC range of 0 to 20 (J,eq/L), just below the range (pH 5.5 to 6.5) where
bicarbonate becomes rapidly depleted in the water (ISA, Appendix 8, section 8.3.1.1). Under
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acidifying conditions, these phytoplankton communities shifted from dominance by
chrysophytes, other flagellates, and diatoms to dominance by larger dinoflagellates. In benthic
invertebrates residing in sediments of acidic streams, A1 concentration is a key influence on the
presence of sensitive species. Studies of macroinvertebrate species have reported reduced species
richness at lower pH, with the most sensitive group, mayflies, absent at the lowest levels. Values
of pH below 5 (which may correspond to ANC levels below 0 (j,eq/L)2 were associated with the
virtual elimination of all acid-sensitive mayfly and stonefly species over the period from 1937-42
to 1984-85 in two streams in Ontario (Baker and Christensen, 1991). In a more recent study,
Baldigo et al. (2009) showed species richness of macroinvertebrate assemblages in the
southwestern Adirondack Mountains were severely impacted at median stream pH values below
5.1, moderately impacted at pH values from 5.1 to 5.7, slightly impacted at pH from 5.7 to 6.4
and usually unaffected above pH 6.4 (Figure 5-1). In Atlantic Canada, Lacoul et al. (2011) found
the median pH for sensitive invertebrate species occurrence was between 5.2 and 6.1 (ANC of 10
and 80 (J,eq/L), below which such species tended to be absent. For example, some benthic
macroinvertebrates, including several species of mayfly and some gastropods, are intolerant of
acid conditions and only occur at pH >5.5 (ANC 20 (j,eq/L) and >6, (ANC 50 (j,eq/L) respectively
(ISA, Appendix 8, section 8.3.3).
2 The citing of ANC values from studies that reported only pH, depended on relating pH and ANC to one another
using a generalized relationship based on the assumption of equilibrium with atmospheric CO2 concentration
(Cole and Prairie, 2010).
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40
35
30
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VI
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Of
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V
he
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o
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•
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y * 4,62* -1.49
RJ * 0*57
4.0
4.5
5.0
5.5 6.0
Median pH
6.5
7.0
7.5
Figure 5-1. Total macroinvertebrate species richness as a function of pH in 36 streams in
western Adirondack Mountains of New York, 2003-2005. From Baldigo et al.
(2009); see ISA, Appendix 8, section 8.3.3 and p. 8-12.
Responses among fish species and life stages to changes in ANC, pH and Al in surface
waters are variable (ISA, Appendix 8, section 8.3.6). Early life stages such as larvae and smolts
are more sensitive to acidic conditions than the young-of-the-year, yearlings, and adults (Baker,
et al., 1990; Johnson et al., 1987; Baker and Schofield, 1982). Some species and life stages
experienced significant mortality in bioassays at relatively high pH (e.g., pH 6.0-6.5; ANC 50-
100 [j,eq/L for eggs and fry of striped bass and fathead minnow [McCormick et al., 1989; Buckler
et al., 1987]), whereas other species were able to survive at quite low pH without adverse effects.
Many minnows and dace (Cyprinidae) are highly sensitive to acidity, but some common game
species such as brook trout, largemouth bass, and smallmouth bass are less sensitive (threshold
effects at pH <5.0 to near 5.5; ANC 20 and 50 (j,eq/L). A study by Nefif et al. (2008) investigated
the effects of two acid runoff episodes in the Great Smoky Mountains National Park on native
brook trout using an in-situ bioassay. The whole-body sodium concentrations differed before and
after the episodes. More specifically, the reduction in whole-body sodium when stream pH
dropped below 5.1 (ANC 0 (j,eq/L) indicated that the trout had lost the ability to ionoregulate
(ISA, Appendix 8, section 8.3.6.1). Field and laboratory bioassay studies indicate a wide
variation in optimal pH range among fish species (Figure 5-2).
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Critical pH Ranges of Fish
Central mudmnnc
Brown bulhead
Pumpkjreeed
-
Northern pike
Brook trout
Golden shiner
-
Atlantic salmon
Brown trout
Smallmouth bass
N. rebeMied dace
Biacknose daco
-
Blacknose shiner
4 0 50 6.0 pH 70
™ - Safe range, no ac id-related effects occur
Uncertain range, acid related effects may occur
Critical range, acid-rotated effects likely
Figure 5-2. Critical aquatic pH range for fish species. Notes: Baker and Christensen
(1991) generally defined bioassay thresholds as statistically significant
increases in mortality or by survival rates less than 50% of survival rates in
control waters. For field surveys, values reported represent pH levels
consistently associated with population absence or loss. Source: Fenn et al.
(2011) based on Baker and Christensen (1991). (ISA, Appendix 8, Figure 8-3)
As noted in the ISA, "[ajcross the eastern U.S., brook trout are often selected as a
biological indicator of aquatic acidification because they are native to many eastern surface
waters and because residents place substantial recreational and aesthetic value on this species,"
although compared to other fish species in Appalachian streams this species is relatively acid
tolerant (ISA, Appendix 8, p. 8-26). For example, "[in many Appalachian mountain streams that
have been acidified by acidic deposition, brook trout is the last fish species to disappear; it is
generally lost at pH near 5.0 (MacAvoy and Bulger, 1995), which usually corresponds in these
streams with ANC near 0 [ieq/L (Sullivan et al., 2003)" (ISA, Appendix 8, p. 8-21).
As described in section 4.2.1 above episodic acidification during storm events can pose
risks in low ANC streams. For example, streams with ANC around 20 peq/L or less at base flow
may be considered vulnerable to episodic acidification events that could reduce pH and ANC to
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levels potentially harmful to brook trout and other species. Streams with suitable habitat and
annual average ANC greater than about 50 [j,eq/L are often considered suitable for brook trout in
southeastern U.S. streams, and reproducing brook trout populations are expected (Bulger et al.,
2000). Streams of this type "provide sufficient buffering capacity to prevent acidification from
eliminating this species and there is reduced likelihood of lethal storm-induced acidic episodes"
(ISA, Appendix 8, p. 8-26). Results of a study by Andren and Rydin (2012) suggested a
threshold less than 20 ug/L Al and pH higher than 5.0 for healthy brown trout populations by
exposing yearling trout to a pH and inorganic Al gradient in humic streams in Scandinavia (ISA,
Appendix 8, section 8.3.6.2). Another recently available study that investigated the effects of
episodic pH shifts fluctuations in waterbodies of eastern Maine reported that episodes resulting
in pH dropping below 5.9 (ANC of -50 (j,eq/L) have the potential for harmful physiological
effects to Atlantic salmon smolts if coinciding with the smolt migration in eastern Maine rivers
(Liebich et al., 2011; ISA, Appendix 8, section 8.3.6.2).
Investigations of waterbody recovery from historic deposition have reported on episodic
acidification associated with the high SO42" remaining in watershed soils. For example,
monitoring data in the Great Smoky Mountains National Park indicated that while the majority
of SO42" entering the study watershed was retained, SO42" in wet deposition moved more directly
and rapidly to streams during large precipitation events, contributing to episodic acidification of
receiving streams and posing increased risk to biota (ISA, Appendix 7, section 7.1.5.1.4). High
flow episodes in historically impacted watersheds of the Appalachians have been reported to
appreciably reduce stream ANC (Lawrence et al., 2015).
There is often a positive relationship between pH or ANC and number of fish species, at
least for pH values between about 5.0 and 6.5, or ANC values between about 0 and 50 to 100
[j,eq/L (Cosby et al., 2006; Sullivan et al., 2006; Bulger et al., 1999). This is because energy cost
in maintaining physiological homeostasis, growth, and reproduction is high at low ANC levels
(Schreck, 1982; Wedemeyer et al., 1990). As noted in section 4.2.1.2 above, surveys in the
heavily impacted Adirondack mountains found that lakes and streams having an annual average
ANC < 0 [j,eq/L and pH near or below 5.0 generally support few or no fish species to no fish at
all, as illustrated in Figure 5-3 below (Sullivan et al., 2006; ISA, Appendix 8, section 8.3.6.3.
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w
®
u
4>
Ol
(0
UL
O
®
J2
£
-200
•100
109 200
ANC (ueq/L)
300
400
500
Figure 5-3. Number of fish species per lake versus acidity status, expressed as ANC, for
Adirondack lakes. Notes: The data are presented as the mean (filled circles) of
species richness within 10 ^ieq/L ANC categories, based on data collected by
the Adirondacks Lakes Survey Corporation. Source: Modified from Sullivan
et al. (2006) (ISA, Appendix 8, Figure 8-4).
The data presented in Figure 5-3 above suggest that there could be a loss of fish species
in these lakes with decreases in ANC below approximately 50 to 100 peq/L (Sullivan et al.,
2006). For streams in Shenandoah National Park, a statistically robust relationship between ANC
and fish species richness was also documented by Bulger et al. (2000). However, interpretation
of species richness relationship with ANC can be difficult and misleading, because more species
tend to occur in larger lakes and streams as compared with smaller ones, irrespective of acidity
(Sullivan et al., 2006) because of increased aquatic habitat complexity in larger lakes and streams
(Sullivan et al., 2003; ISA, Appendix 8, section 8.3.6.3).
Obseivations of effects in watersheds impacted by historic acidification can also reflect
the influence of episodic high flow events that lower pH and ANC appreciably below the
baseflow ANC (as described above). Studies described above are summarized below in the
context of ANC ranges: <0, 0-20, 20-50, 50-80, and >80 }ieq/L:
• At ANC levels <0 ueq/L, aquatic ecosystems have exhibited low to a near loss of aquatic
diversity and small population sizes. For example, planktonic and macroinvertebrates
communities shift to the most acid tolerant species (Lacoul et al., 2011), and mayflies can
be eliminated (Baker and Christensen, 1991). A near to complete loss of fish populations
can occur, including non-acid-sensitive native species such as brook trout {Salvelinus
fontinalis), northern pike (Esox Indus), and others (Sullivan et al., 2003, 2006; Bulger et
al., 2000), which is in most cases attributed to elevated inorganic monomeric Al
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concentration (Baldigo and Murdoch, 1997). At this level, aquatic diversity is at its
lowest (Bulger et al., 2000; Baldigo et al., 2009; Sullivan et al. 2006) with only
acidophilic species being present.
• In waterbodies with ANC levels between 0 and 20 [j,eq/L, acidophilic species dominate
other species (Matuszek and Beggs, 1988; Driscoll et al., 2001) and diversity is low
(Bulger et al., 2000; Baldigo et al., 2009; Sullivan et al., 2006). Plankton and
macroinvertebrate populations have been observed to decline, and acid-tolerant species
have outnumbered non-acid-sensitive species (Liebich et al., 2011). Sensitive species are
often absent (e.g., brown trout, common shiner) while non-sensitive fish species
populations may be reduced (Bulger et al., 2000). Episodic acidification events (e.g.,
inflow with ANC <0 [j,eq/L and pH< 5), may have lethal impacts on sensitive lifestages
of some biota, including brook trout and other fish species (Matuszek and Beggs, 1988;
Driscoll et al., 2001).
• Levels of ANC between 20 and 50 [j,eq/L have been associated with the loss and/or
reduction in fitness of aquatic biota that are sensitive to acidification in some waterbodies
of the Adirondacks and Appalachians. Such effects included reduced aquatic diversity
(Kretser et al., 1989; Lawrence et al., 2015; Dennis and Bulger, 1995) with some
sensitive species missing (Bulger et al., 2000; Sullivan et al., 2006). In historically
impacted watersheds, waterbodies with ANC below 50 [j,eq/L are more vulnerable to
increased potential for harm associated with episodic acidification (ISA, Appendix 8,
section 8.2). Comparatively, acid tolerant species, such as brook trout may have moderate
to healthy populations (Kretser et al., 1989, Lawrence et al., 2015; Dennis and Bulger,
1995).
• At an ANC between 50 and 80 [j,eq L, the fitness and population size of some sensitive
species have been affected in some historically impacted watersheds. Levels of ANC
above 50 [j,eq/L are considered suitable for brook trout and most fish species because
buffering capacity is sufficient to prevent the likelihood of lethal episodic acidification
events (Driscoll et al., 2001; Baker and Christensen 1991). However, depending on other
factors, the most sensitive species have been reported to experience a reduction in fitness
and/or population size in some waterbodies (e.g., blacknose shiner [Baldigo et al., 2009;
Kretser et al., 1989; Lawrence et al., 2015; Dennis and Bulger, 1995]). Fish species
richness has also been reported to be affected in some Adirondack streams at ANC 50
(Sullivan et al., 2006).
• Values of ANC >80 [j,eq/L have generally not been associated with harmful effects on
biota (Bulger et al., 1999; Driscoll et al., 2001; Kretser et al., 1989; Sullivan et al., 2006).
5.1.2 Conceptual Model and Analysis Approach
The impact of acidifying deposition on aquatic ecosystems across the U.S. was evaluated
in this review by developing analyses using a CL approach with ANC as the acidification
indicator. This approach provides a means of assessing risk to a group of lakes, streams, and
rivers (i.e., waterbodies) in a given area from various levels of N and/or S deposition. ANC was
used as the water quality metric where ANC targets (see description of the 5 categories above)
were used to correspond to different levels of acidification risk. This approach was used to
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characterize the risk of acidifying deposition on aquatic acidification across the contiguous U.S.
(CONUS) with a focus on acid-sensitive areas.
These linkages between acidifying deposition of nitrogen and sulfur; water chemistry
changes (reflected by changes in ANC and pH); and waterbody health and biodiversity are the
basis for the quantitative assessments that were performed in this review and provide the
foundation for describing the potential impacts from acidification across the U.S. The following
schematic (Figure 5-4) represents the conceptual model used in the analyses to link these factors.
Acidification of Freshwater Ecosystems
Natural Acidic Inputs
Nitrogen, Sulfur, OrganicAcids
Anthropogenic Acidic Inputs
Nitrogen, Sulfur, Ammonium
Geology, Base Cations
Analyses Considerations
Chemical Indicator
Spatial Scale
CL and Exceedance Definitions
Deposition Data Sources and
Contributions
Sensitivity and Recovery
Potential
Wet and Dry Deposition
Soil-
Vegetation,
Organic
Matter
£rosion
Surface Water and
Sediment
A ANC,
pH, Al, BC
Aq
tic
B
a
Reduced
growth,
survival, and
reproduction;
community
changes; loss
of biota
Figure 5-4. Conceptual model for aquatic acidification analyses.
In the analyses described below, waterbody estimates of deposition were compared to
atmospheric loading (CLs) estimated to support ANC levels equal to each of several targets
(described in section 5.1.2.2 below). Depending on the ANC target, low CL values may indicate
that the watershed has a limited ability to neutralize the addition of acidic anions and, hence, may
be susceptible to acidification as a result of acidic inputs. In general, the higher the CL value, the
greater the ability of a given watershed to neutralize additional acidic anions. Similarly, for any
specific ANC target, lower CL estimates are associated with more acid-sensitive waterbodies.
Further, given the negative relationship between acidic loading and ANC, the CL estimates for
any one waterbody are lower for the higher ANC targets.
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Key aspects of the assessments described in the subsections below include the spatial
scales of assessment (section 5.1.2.1), the chemical indicator (section 5.1.2.2), identification of
CL estimates for this assessment (section 5.1.2.3) and determining exceedances (section 5.1.2.4),
as well as sources of waterbody deposition estimates (section 5.1.2.5). Also discussed is the
approach for interpreting results, including regarding ecosystems with sensitivity to acidic
deposition, ecosystems for which factors other than deposition are critical influences on
waterbody ANC, and waterbodies for which CL estimates above zero cannot be derived for ANC
levels of interest. Results of the assessments are presented in section 5.1.3. The characterization
of uncertainty is described in section 5.1.4, and key findings are summarized in section 5.1.5.
5.1.2.1 Spatial Scale
For this assessment, we developed a multi-scale analysis to assess aquatic acidification at
three levels of spatial extent: national, ecoregion, and case study. The national assessment
included the CONUS only since there are insufficient data available for Hawaii, Alaska, and the
territories. The Omernik ecoregion classifications were used for the regional assessments, and
case study locations were areas likely to be most impacted and for which sufficient data were
available. Further discussion of these spatial scales can be found below. Since acidification of
waterbodies is controlled by local factors such as geology, hydrology, etc. the aquatic CLs for
acidification are unique to the waterbody itself, and information about the waterbody, like water
quality, is needed to determine its CL. For these reasons, CLs were determined at the waterbody
level and then summarized at the national, ecoregion, and case study level. The national
assessment is a combined summary of aquatic CLs across the CONUS.
It is important to note that aquatic ecosystems across the CONUS exhibit a wide range of
sensitivity to acidification because of multiple landscape factors, such as geology, hydrology,
soils, catchment scale, and vegetation characteristics, that control whether a waterbody will be
acidified by atmospheric deposition. Consequently, variations in ecosystem sensitivity must be
taken into account in order to characterize sensitive populations of waterbodies and relevant
regions across the CONUS. The EPA's Omernik Ecoregions classifications were used to define
ecologically relevant, spatially aggregated, acid-sensitive regions across the CONUS in order to
better characterize the regional difference in the impact of deposition-driven aquatic
acidification.
Ecoregions are areas of similarity regarding patterns in vegetation, aquatic, and terrestrial
ecosystem components. The Omernik ecoregion categorization scheme categorizes ecoregions
using a holistic, "weight-of-evidence" approach in which the relative importance of factors may
vary from region to region (Omernik, 1987). The method used to map ecoregions is described in
Omernik (1987) and classifies regions through the analysis of the patterns and the composition of
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biotic and abiotic phenomena that affect or reflect differences in ecosystem quality and integrity.
Factors include geology, physiography, vegetation, climate, soils, land use, wildlife, and
hydrology. Four hierarchical levels of ecoregions distinguish coarser (more general) and finer
(more detailed) categorization (Omernik and Griffith, 2014). Level I is the coarsest level,
dividing North America into 12 ecoregions. At level II, the continent is subdivided into 25
ecoregions and the contiguous U.S. (CONUS) into 20 ecoregions (Figure 5-5). Level III is a
further subdivision of level II and divides CONUS into 84 ecoregions. Level IV is a subdivision
of level III and divides CONUS into 967 ecoregions. For the analyses in this review, we used the
level III categorization to give the greatest sensitivity for variation in ecoregion response while
allowing us to aggregate available water quality data while maintaining its representativeness.
5.2: Mixed Wood Shield
5.3: Atlantic Highlands
6.2: Western Cordillera
7.1: Marine West Coast Forest
8.1: Mixed Wood Plains
8.2: Central USA Plains
8.3: Southeastern USA Plains
8.4: Ozark/Ouachita-
Appalachian Forests
8.5: Mississippi Alluvial and
Southeast USA Coastal Plains
9.2: Temperate Prairies
9.3: West-Central Semi-
Arid Prairies
9.4: South Central Semi-
Arid Prairies
n 9.5:
U Plain
Texas-Louisiana Coastal
9.6: Tamaulipas-Texas Semi-
Arid Plain
10.1: Cold Deserts
10.2: Warm Deserts
11.1: Mediterranean California
12.1: Western Sierra Madre
Piedmont
13.1: Upper Gila Mountains
15.4: Everglades
Figure 5-5. Level II ecoregions of the contiguous U.S.
In order to focus our analyses on those areas that were likely to be affected by
acidification and that were also driven primarily by deposition of N and S from ambient air, we
looked more closely at the ecoregions and their underlying characteristics. We also identified
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those ecoregions where, for various reasons, target ANC values could not be achieved. These
factors are discussed fully in the REA presented in Appendix 5A, and summarized below.
Based on this analysis, 30 ecoregions were identified as sensitive to acidification
(Appendix 5A, Table 5A-5). Of these 30 ecoregions, three were identified as having natural
acidity, based on DOC as an indicator of natural acidity (ISA, Appendix 7, section 7.1.2.5; 2008
ISA, section 3.2.4.2 and Annex B, p. B-35). The acid-sensitive ecoregions are most generally
areas with mountains, high elevation terrain or waterbodies in northern latitudes (northern areas
of Minnesota, Wisconsin, and Michigan; and New England). The northern, non-mountainous
regions that are sensitive to acidity share attributes (e.g., growing season, vegetation, soils, and
geology) similar to mountainous regions and typically are located in rural areas, often in tracts of
designated wilderness, park and recreation areas. The three naturally acidic ecoregions, located
on eastern coastal plain, were excluded from the analyses because of their natural acidity
indicated by high DOC values: (1) Middle Atlantic Coastal Plain (8.5.1), (2) Southern Coastal
Plains (8.5.3), and (3) Atlantic Coastal Pine Barrens (8.5.4). These ecoregions generally lie along
the Atlantic coast from New Jersey south to northern Florida (Figure 5-6). A more complete
discussion of ecoregion sensitivity can be found in Appendix 5A, section 5 A. 1.7.
Figure 5-6. Level III ecoregions grouped into acid sensitivity categories.
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The case study scale represents the smallest scale at which we performed our analyses
and is intended to give some insight into the local impact of aquatic acidification. Five case study
areas across the U.S. were examined: Shenandoah Valley, White Mountain National Forest,
Northern Minnesota, Sierra Nevada Mountains, and Rocky Mountain National Park. These areas
include several parks and national forests that vary in their sensitivity to acidification but
represent high value or protected ecosystems, such as Class 1 areas, wilderness, and national
forests (as further described in Appendix 5A, section 5A.2.3.2).
5.1.2.2 Chemical Indicator
The chemical indicator of acidification risk used in this assessment is ANC, as calculated
in model simulations (described in Appendix 5A, section 5A.1.5). Although biological effects
are primarily attributable to low pH and high inorganic aluminum concentration, ANC is more
commonly used for estimating CLs for N and S in the U.S. as it is a more stable and more easily
modelled, as described in Appendix 5A, section 5A.1 (ISA, section ES.5.1 and Appendix 7,
section 7.1.2.5). Additionally, CL estimates generally are linearly associated with ANC levels. In
our use of ANC, we have also looked most closely at those waterbodies for which deposition
was the main source of acidifying input and eliminated from consideration those waterbodies for
which either other sources of acidifying input were significant (for example, runoff) or for which
natural conditions were such that those waterbodies would be unable to reach specific ANC
thresholds.
For the analyses described below, we evaluated CLs for three different ANC targets: 20
[j,eq/L, 30 [j,eq/L and 50 [j,eq/L. Selection of these target ANC values reflects several
considerations. For example, most aquatic CL studies conducted in the U.S. since 2010 use an
ANC of 20 and/or 50 [j,eq/L, because 20 [j,eq/L has been suggested to provide protection for
"natural" or "historical" range of ANC and 50 [j,eq/L provides greater protection (Dupont et al.,
2005; McDonnell et al., 2012, 2014; Sullivan et al., 2012a, 2012b; Lynch et al., 2022; Fakhraei
et al., 2014; Lawrence et al., 2015). In the western U.S., lakes and streams vulnerable to
deposition-driven aquatic acidification are often found in the mountains where surface water
ANC levels are naturally low and typically vary between 0 and 30 [j,eq/L (Williams and Labou,
2017; Shaw et al., 2014). For these reasons, previous studies and the National Critical Load
Database (NCLD) uses an ANC threshold of 50 [j,eq/L for the eastern CONUS and 20 [j,eq/L for
the western CONUS (denoted as "50/20" (j,eq/L). With regard to higher ANC levels, such as 80
[j,eq/L, it was also recognized that many waterbodies, particularly in acid-sensitive regions of
CONUS never had an ANC that high and would never reach an ANC that high naturally
(Williams and Labou 2017; Shaw et al., 2014). Additionally, in conveying its advice in the 2012
review, the CASAC expressed its view that "[ljevels of 50 [j,eq/L and higher would provide
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additional protection, but the Panel has less confidence in the significance of the incremental
benefits as the level increases above 50 [j,eq/L" (Russell and Samet, 2010; pp. 15-16).
For the analyses included below, ANC target values of 20, 30 and 50 [j,eq/L were selected
for the following reasons:
ANC of 20 (ieq/L :
- In western high elevation sites, ANC is typically below 50 [j,eq/L (e.g., median
around 30 [j,eq/L in Sierra Nevada) even though acidifying deposition is low at
those sites (Shaw et al., 2014). Accordingly, a target of 20 [j,eq/L is commonly
considered an appropriate target for western sites.
- ANC levels below 20 [j,eq/L in sensitive Shenandoah/Adirondack waterbodies are
associated with significant/appreciable reduction in fish species (Bulger et al.,
2000; Sullivan et al., 2006). Thus, ANC of 20 [j,eq/L is considered a
minimum/lower bound target for such eastern mountain sites.
ANC of 30 [j,eq/L:
- While ecological effects occur at ANC levels at 30 [j,eq/L in some sensitive
ecosystems (based primarily on studies in Shenandoah/Adirondack waterbodies),
the degree and nature of those effects are less significant than at levels below 20
[j,eq/L.
- Research in New England, the Adirondacks and Northern Appalachian Plateau
indicates ANC of 30-40 [j,eq/L may protect from spring episodic acidification in
those watersheds (Driscoll et al., 2001; Baker and Christensen, 1991).
ANC of 50 [j,eq/L
- ANC of 50 [j,eq/L is is commonly cited as a target for eastern sites (Dupont et al.,
2005; McDonnell et al., 2012; McDonnell et al., 2014; Sullivan et al., 2012a;
Sullivan et al., 2012b; Lynch et al., 2022; Fakhraei et al., 2014; Lawrence et al.,
2015).
- In the 2012 review, ANC values at/above 50 [j,eq/L were concluded to provide
additional protection although with increasingly greater uncertainty for values
at/above 75 [j,eq/L (2011 PA, pp. 7-47 to 7-48).
5.1.2.3 Critical Load Estimates Based on ANC
Considerable new research on critical loads for acidification is available since the 2008
ISA and both steady-state and dynamic models have been used to generate ANC-based critical
loads for much of the U.S. (ISA, Appendix 8, section 8.5.4.1.2). Steady-state CLs are calculated
from mass-balance models under assumed or modeled equilibrium conditions based in part on
water quality measurements. While the models used to derive steady-state CLs vary in
complexity, fundamentally they rely on the calculation of elemental mass balances. Dynamic
models have also been used to develop CLs. These models simulate soil or water chemistry or
biological response to calculate a target within a specified time period, such as by the year 2100,
and they can also be used to calculate a CL comparable to a long-term steady-state CL by
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applying the model to a date in the distant future. Since the 2008 ISA, studies utilizing dynamic
modeling of CLs have generally been focused on the Adirondacks, the Appalachians, and the
Rocky Mountains or Sierra Nevada (ISA, Appendix 8, section 8.5.4.1.2.2).
Aquatic CLs used in this assessment came from the NCLD version 3.2.1 (Lynch et al.,
2022), and studies identified in the ISA (e.g., Shaw et al., 2014; McDonnell et al., 2014; Sullivan
et al., 2012a). The NCLD is comprised of CLs calculated from several common models,
including the steady-state mass-balance model (SMBE), Steady State Water Chemistry (SSWC)
model, and dynamic models such as the Model of Acidification of Groundwater In Catchment
(MAGIC) run out to year 2100 or 3000. The overwhelming majority of CLs (more than 90%) are
based on application of the SSWC model (as described in Appendix 5A, section 5A.1.5). Data in
the NCLD are focused on waterbodies that are typically impacted by deposition driven
acidification. A waterbody3 is represented as a single CL value. In many cases where more than
one CL value has been estimated for a waterbody (e.g., via different studies) the CL from the
most recent study was selected or, when the CL estimates are from publications of the same
timeframe, they were averaged for our analysis (see Appendix 5A, section 5A.1.5). The unique
locations for the 13,824 CLs used in this assessment are indicated in Figure 5-12 below.
There are several newly available studies using steady-state modeling. Sullivan et al.
(2012b) and McDonnell et al. (2012) developed an approach for deriving regional estimates of
base cation weathering to support steady-state CL estimates for the protection of southern
Appalachian Mountain streams against acidification. Calculated CL values were low at many
locations, suggesting high acidification sensitivity. In the Blue Ridge ecoregion, calculated CL
values to maintain stream ANC at 50 [j,eq/L were less than 500 equivalents per hectare per year
(eq/ha-yr) at one third of the study sites. In another model simulation for Appalachian Mountain
streams, McDonnell et al. (2014) calculated critical values, including steady-state aquatic CLs to
protect streams against acidification. They based the CLs on ANC thresholds of 50 [j,eq/L, and
nearly one-third of the stream length assessed in the study region had a CL for S deposition
below 500 eq/ha-yr (ISA, Appendix 8, section 8.6.8).
Critical loads have most frequently been developed for waterbodies concentrated in areas
that are acid sensitive, primarily, the eastern U.S. and the Rocky Mountain and Pacific Northwest
regions of the West. Not all waterbodies are sensitive to acidification. As noted in the ISA,
"acid-sensitive ecosystems are mostly located in upland mountainous terrain in the eastern and
western U.S. and are underlain by bedrock that is resistant to weathering, such as granite or
quartzite sandstone" (ISA, Appendix 7, p. 7-45). Small to median size lakes (>200 Ha) and lower
3 A waterbody for the purposes of our analyses is a unique stream or lake represented in the critical loads database.
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order-streams tend to be the waterbodies that are impacted by deposition driven acidification,
while rivers are not typically impacted (ISA, Appendix 7, section 7.1.2).
5.1.2.4 Critical Load-Based Analysis
In this analysis, we compared waterbody deposition estimates to critical loads derived for
the three ANC targets. As well documented in the evidence, deposition of both S and N
contributes to acid deposition and associated acidification risk of a waterbody. However, as not
all N deposition to a watershed will contribute to acidification, evaluating acidic deposition for N
and S together is complex. Nitrogen deposition inputs below what is removed by long-term N
processes in the soil and waterbody (e.g., N uptake and immobilization) do not contribute to
acidification, but the amount above this minimum will likely contribute to acidification.
Therefore, if N removal is greater than N deposition, only S deposition will contribute to the
acidification and thereby to any potential for exceedance of the acidification CL (ISA, Appendix
7, section 7.1.2.1). The analyses performed for this PA first investigated the contribution to
acidification from N deposition and, based on the finding of little appreciable contribution, then
focused on S only deposition (Appendix 5A, section 5A.2.1).
This analysis focused on the S component of acidic deposition due to the finding of little
appreciable contribution of N deposition to acidification beyond that associated with S
deposition. For 2014-2016 and 2018-2020 deposition estimates, very few CL exceedances were
driven by N. Thus, adding N from leaching to the critical load exceedances with S was not found
to substantially change the percent of waterbodies that exceed their CL. This was found for
national-scale analyses that compared the percentage of CL exceedances in waterbodies with
both N and S exceedance versus only S exceedances (see Appendix 5A, section 5A.2.1). The
results of these national-scale analyses support the assumption that most of the N deposition
entering the watersheds during the analyses' time periods was retained within the watershed
and/or converted to gaseous N (e.g., N2O, N2, etc.). Different methods have been developed to
determine the amount of N deposition that acidifies related to aquatic CL exceedances. There are
two common approaches in the studies that derived CLs used in this assessment: the first
approach is based on the amount of "N leaching" to the waterbody determined by the amount of
dissolved N in the water measured as the concentration of nitrite and runoff as presented in
Henriksen and Posch, (2001).4 The second approach is the use of a "set value" based on long-
term estimate of N immobilization and denitrification as described by McNulty et al. (2007).
Those methods and the details for calculating CL exceedance are also discussed in Appendix 5A,
section 5A. 1.6.2.
4 Analyses in the Appendix 5A, section 5A.3.2 evaluate uncertainty associated with the input data for this approach.
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However, it is important to take into account the uncertainty associated with the CL
estimates in the calculation of CL exceedances. Specifically, in the analyses for this REA, CLs
are exceeded when the S deposition estimates are greater than the CLs by at least a margin of
3.125 milliequivalents per square meter per year (meq S/m2-yr) or 0.5 kg S/ha-yr. An exceedance
was not concluded when the S deposition estimate was below the CL by less than 3.125 meq
S/m2-yr or 0.5 kg S/ha-yr. Estimates of S deposition that are within 3.125 meq S/m2-yr or 0.5 kg
S/ha-yr of the CL are described for the purpose of our analyses as being "at" the CL. This factor
is derived from the CL uncertainty analysis (see Appendix 5A, section 5A.3).
Estimates of CL less than zero indicate that a target ANC value is not expected to be
reached regardless of the level of acidifying deposition. Areas with negative CLs, by and large,
are those that, due to either base cation loss from past deposition or natural conditions, would not
be able to achieve the target ANC values of 20, 30 or 50 |ieq/L under any deposition scenario. In
our analyses, exceedances are reported separately for these areas from those areas with CL
estimates greater than zero (see Appendix 5 A, section 5A.2.1).
5.1.2.5 Waterbody Deposition Estimates
Estimates of waterbody deposition used in this assessment were based on the Total
Deposition (TDep) model.5 This model is discussed more fully in section 2.5. Both total N and S
deposition were estimated at a resolution of a 4 km grid cell for each stream reach or lake
location. For each waterbody, total N and S deposition were determined for each year from 2000
to 2020 and used to derive three-year averages for five periods: 2001-03, 2006-08, 2010-2012,
2014-16 and 2018-20. The extent of critical load exceedances across the waterbodies with CLs
was then calculated for each of these five periods and summarized nationally and by ecoregion
(sections 5.1.3.1 and 5.1.3.2).
5.1.3 Estimates for Achieving ANC Targets with Different Deposition Levels
The aquatic acidification assessments developed for this review are intended to estimate
the ecological exposure and risk posed to aquatic ecosystems from the acidification effects of S
and/or N deposition at varying levels to sensitive regions across the CONUS. They were
performed at three spatial scales of differing levels of complexity. The results of these analyses
are presented below. Section 5.1.3.1 presents the results of the national-scale analyses whereas
sections 5.1.3.2 and 5.1.3.3 present the results of the ecoregion-scale and case study analyses
respectively.
5 The TDep modeling approach was developed by Schwede and Lear (2014) and the recent iterations are
documented on the TDep website (https://nadp.slh.wisc.edu/committees/tdep/). Data were downloaded for
exceedance calculations on September 26, 2022.
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5.1.3.1 National-scale Analysis
A total of 13,824 unique waterbodies across the CONUS had calculated CLs available in
NCLD. Most of those waterbodies had CLs that were less than 18 kg S/ha-yr across all the target
ANC levels (Appendix 5 A, Table 5A-6). Note that as discussed above, for the purpose of this
analysis we focused here on CL estimates greater than zero (CL>0) and S only. The 50/20 values
reflect a threshold ANC of 50 |ieq/L in the eastern portion of the U.S. and one of 20 |ieq/L in the
west.6 For the waterbody sites with CL values above zero,7 Table 5-1 contains a summary of the
percent of waterbodies with CL exceedances for S only for annual average deposition in the five
3-year periods for the ANC thresholds for an ANC of 20, 30, 50, and 50/20 |ieq/L (additional
detail in Appendix 5 A, Table 5A-7).
Table 5-1. Percentage of waterbodies nationally for which annual average S deposition
during the five time periods assessed exceed the waterbody CL (for CLs>0) for
each of the ANC targets.
ANC
(ueq/L)
2018-20
2014-16
2010-12
2006-08
2001-03
20
1%
3%
5%
16%
22%
30
2%
4%
7%
19%
25%
50
4%
6%
11%
24%
28%
50/20
4%
6%
10%
23%
28%
The geographic distribution of the waterbodies for which S deposition during the five
time periods exceeded CLs for the target ANC values is shown in Figures 5-7 to 5-11. Most
exceedances occurred in New England, the Adirondacks, the Appalachian Mountain range (New
England to Georgia), the upper Midwest, Florida, and the Sierra Nevada mountains in California
as expected. As discussed above, waterbodies in Florida and other coastal plain ecoregions that
exceed the CL are likely not related to deposition of S, but instead are related to high levels of
natural acidity in these drainage waters. These drainage waters tend to be naturally high in
dissolved organic carbon, causing these systems to be acidic. Because these are waterbodies that
are highly sensitive to acidification and likely naturally acidic, they exceed the calculated CL at
any deposition amount. These three ecoregions (8.5.1, 8.5.3 and 8.5.4) are not included in the
6 Consistent with regional definitions based on groups of states that were employed in the last review, in analyses in
this PA, the West includes the states of ND, SD, CO, WY, MT, AZ, NM, UT, ID, CA, OR, WA (2009 REA,
Appendix 1, p. 1-21). Accordingly, an ecoregion is designated western if it intersects or overlaps with these ten
states, and eastern ecoregions are those not designated as western.
7 For ANC threshold of 50 |icq/L. there are 13,184 sites with CL values above zero, 13,649 for ANC of 30 |icq/L
and 13,771 for ANC of 20 |icq/L. For ANC of 50 (East) and 20 (West), 13,344 sites have CL values above zero.
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ecoregion-scale analyses (see section 5.1.3.2). For more information on these areas see Appendix
5A, section 5A.2.1.
b.
ANC = 50 peq/L
ANC = 50 \ieqlL East
ANC = 20 peq/L West
Figure 5-7. Waterbodies for which annual average S only deposition for 2001-03 exceed
CLs for ANC thresholds: a. 20, b. 30, c. 50, d, 50/20 jieq/L.
Exeed the Critical Load
Near the Critical Load (±3.125 meq/m2/yr)
d.
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ANC = 20 peq/L
Exeed the Critical Load
Nearthe Critical Load (±3.125 meq/m2/yr)
d.
ANC = 50 peq/L East
ANC = 20 peq/L West
Figure 5-8. Waterbodies for which annual average S only deposition for 2006-08 exceed
CLs for ANC thresholds: a. 20, b. 30, c. 50, d, 50/20 jieq/L.
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ANC = 30 peq/L
ANC = 50 (jeq/L
Exeed the Critical Load
Near the Critical Load (±3.125 meq/m2/yr)
d.
ANC = 50 peq/L East
ANC = 20 peq/L West
Figure 5-9. Waterbodies for which annual average S only deposition for 2010-12 exceed
CLs for ANC thresholds: a. 20, b. 30, c. 50, d, 50/20 jieq/L.
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ANC = 50 peq/L
Exeed the Critical Load
Nearthe Critical Load (±3.125 meq/m2/yr)
d.
ANC = 50 |jeq/L East
ANC = 20 peq/L West
Figure 5-10. Waterbodies for which annual average S only deposition for 2014-16 exceed
CLs for ANC thresholds: a. 20, b. 30, c. 50, d, 50/20 jxeq/L.
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ANC = 50 peq/L
Exeed the Critical Load
Nearthe Critical Load (±3.125 meq/m2/yr)
d.
ANC
ANC
= 50 peq/L East
= 20 |jeq/L West
Figure 5-11. Waterbodies for which annual average S only deposition for 2018-20 exceed
CLs for ANC thresholds: a. 20, b. 30, c. 50, d, 50/20 jieq/L.
The results of the national-scale analyses show a significant reduction in exceedances
over time as sulfur deposition has decreased (see section 2.5.4 above for temporal trends in
deposition across the U.S.). It also provides the foundation for the additional analyses below to
look at what impacts might be expected under different geographic scales and deposition
scenarios.
5.1.3,2 Ecoregion Analyses
The ecoregion-level analyses, summarized below, focused on level III ecoregion
delineations (from this point on the term ecoregions refers to ecoregions delineated to level III).
These analyses provide further characterization of both spatial variability of acid-sensitive
waterbodies across the U.S. and the extent of deposition driven acidification impacts. Since the
acidification of waterbodies is controlled by local factors such as geology and hydrology, aquatic
CLs for acidification are unique to the waterbody itself and information about the waterbody,
like water quality, is needed to determine its critical load. Unfortunately, not all waterbodies
within an ecoregion have sufficient data to calculate a CL. This is the case for many ecoregions,
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although generally ecoregions in historically recognized acid-sensitive areas have been heavily
sampled, and, hence, include many waterbodies for which CLs have been estimated (see Figure
5-12). These waterbodies tend to be in the eastern CONUS in such ecoregions as Central
Appalachian (8.4.2), the Northern Appalachian and Atlantic Maritime Highlands (5.3.1), and the
Blue Ridge (8.4.2). Areas in the Rocky Mountains (6.2.10 and 6.2.14) and Sierra Nevada
(6.2.12) also have been sampled extensively and include many waterbodies for which CLs have
been estimated. The Northern Appalachian and Atlantic Maritime Highlands ecoregion (5.3.1)
had the most waterbodies with a CL at 2,851 (see Appendix 5 A, Table 5A-15).
Having more waterbodies with CLs in an ecoregion helps to capture the spatial variability
of acid-sensitive areas across the landscape and provide a more accurate measurement of the
impact of deposition driven acidification. In ecoregions with few waterbodies for which CLs
have been developed, however, the spatial variability of acid-sensitive areas cannot be well
described, which in turn limits our confidence in the representativeness of the estimated percent
of exceedances for the ecoregion. For this reason, ecoregions with more than 50 CLs were the
focus of this analysis.
Across the CONUS there are a total of 84 level III ecoregions, with a subset of 69 in
which there is at least one waterbody with a CL estimated (Figure 5-12 and Appendix 5A, Table
5A-15). Ecoregions included in the analysis presented here are those for which there are at least
50 waterbodies with CLs and that (1) are not one of the three ecoregions identified as naturally
acidic (see section 5.1.2.1 above) and (2) are not one of ecoregions that, for all of the five time
periods, had no waterbodies with a CL exceedance for a CL greater than zero (based on ANC of
50 in the East and 20 in the West). There are 25 ecoregions that meet these criteria: 18 are in the
east and 7 in the west.
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Figure 5-12. Locations of aquatic critical loads (x's) within level III ecoregion boundaries.
For each of the 25 ecoregions in this analysis, median annual average S deposition8
declined across the five 3-year periods. The minimum to maximum range for median S
deposition in these ecoregions was 0.90-18.08 kg S/ha-yr for 2001-2003 and 0.54-3.64 kg S/ha-
yr for 2018 - 2020 (Table 5-2). Deposition for the 18 eastern ecoregions had a median value of
11.0 kg S/ha-yr in 2001-03 and 2.0 kg S/ha-yr in 2018-20 (Table 5-2). Deposition was lower for
the seven western ecoregions, with the median of ecoregion medians ranging from 1.14 kg S/ha-
yr in 2001-03 (highest median was 1.69 kg/ha-yr) to 0.71 kg S/ha-yr in 2080-20, when highest
median was 1.24 kg/ha-yr. For the period 2001-2003, 17 of the 25 ecoregions had a median total
S deposition over 10 kg S/ha-yr, while the highest ecoregion median in the period 2018-2020
was 3.64 kg S/ha-yr (South Central Plains ecoregion [8.3.7]) (Appendix 5A, Table 5A-11).
Among the 25 ecoregions in the analysis, the ones with the highest median S deposition were the
North Central Appalachians, Central Appalachians, Northern Piedmont, Southwestern
Appalachians, and Ridge and Valley, all in the Mid-Atlantic region of the eastern U.S (see
Appendix 5A, Table 5A-15).
8 The ecoregion medians summarized here are spatial medians derived by GIS zonal statistic. The median was
calculated across TDep grid cells, which are 4 km x 4 km, within each ecoregion.
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Table 5-2. Ecoregion median S deposition estimates derived as medians of all ecoregion
grid cell estimates (TDep).
Ecoregion Median* Total Sulfur Deposition (kg S/ha-yr)
2001-03
2006-08
2010-12
2014-16
2018-20
All 25 Ecoregions
Minimum
0.90
0.98
0.83
0.79
0.54
Maximum
18.1
15.1
7.24
4.70
3.64
Median
7.34
6.78
4.04
2.61
1.68
18 Eastern Ecoregions
Minimum
4.29
3.24
2.38
1.65
1.22
Maximum
18.1
15.1
7.24
4.70
3.64
Median
11.0
9.04
4.53
2.99
2.04
7 Western Ecoregions
Minimum
0.90
0.98
0.83
0.79
0.52
Maximum
1.69
1.66
1.41
1.51
1.24
Median
1.14
1.16
1.10
0.93
0.71
* The ecoregion medians for which descriptive statistics are presented are the medians of the 4 x 4 km TDep grid cells
within each ecoregion. The number of grid cells varies across ecoregions based on the size of the ecoregion.
Ecoregion median S deposition was also derived based on the TDep grid cells for
locations with a CL estimate in each ecoregion. Descriptive statistics for these ecoregion
medians are summarized in Table 5-3 below. For each of the 25 ecoregions, Figure 5-13 presents
the temporal trend in percentage of waterbody sites at which the TDep grid cell S deposition
estimates exceeded the CL estimates (Appendix 5A, section 5A.2.2.1).
Table 5-3. Summary of ecoregion medians derived as median of TDep S deposition
estimates at CL sites within each ecoregion.
Ecoregion Median* Total Sulfur Deposition (kg S/ha-yr)
2001-03
2006-08
2010-12
2014-16
2018-20
All 25 Ecoregions
Minimum
1.18
1.22
1.02
1.08
0.62
Maximum
17.27
14.44
7.25
4.58
3.88
Median
7.77
6.50
3.71
2.32
1.73
18 Eastern Ecoregions
Minimum
4.01
3.10
2.34
1.88
1.31
Maximum
17.27
14.44
7.25
4.58
3.88
Median
11.08
9.36
4.76
2.97
2.04
7 Western Ecoregions
Minimum
1.18
1.22
1.02
1.08
0.62
Maximum
1.94
1.83
1.47
1.56
1.19
Median
1.40
1.52
1.29
1.17
0.87
* The ecoregion medians for which descriptive statistics are presented here are medians of TDep estimates across each
ecoregion's waterbody sites with CL estimates.
5-28
-------
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8.4.2
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8.4.4
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2001-2003
2006-2008 2010-2012 2014-2016
Time Period (Years)
2018-2020
X CL
~
5.2.1
C
5.3.1
5.3.3
6.2.3
6.2.5
=
6.2.7
6.2.10
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E
6.2.14
]
6.2.15
J
8.1.1
~
8.1.3
8.1.4
1
¦
8.1.7
Figure 5-13. Percentage of waterbodies exceeding their CLs per ecoregion for ANC of 20
jieq/L, with shading indicating the maximum ecoregion percentage exceeding
CLs for ANC of 50 jieq/L (upper panel). Symbols on the upper line of the grey
shaded area indicate the ecoregion with this maximum. Ecoregion locations
are shown on map (lower panel), with bold indicating those designated as
"West" (N=7) and regular font indicating eastern ecoregions (N= 18).
5-29
-------
We summarize below the CL exceedance results for the 25 ecoregions analyzed, in terms
of number and percentage of waterbodies per ecoregion with CL exceedances in every
ecoregion-time period combination, using ecoregion deposition estimates (medians of deposition
estimates at waterbodies with CLs in each ecoregion) as the organizing parameter. For example,
Table 5-4 presents the CL exceedance results of the ecoregion level analyses for the three ANC
target levels, summarized by bins for different magnitudes of ecoregion median annual average S
deposition (regardless of the 3-year period in which it occurred). For each S deposition bin (e.g.,
S deposition at or below 5 kg S/ha-yr), Table 5-4 presents the number of ecoregi on-time period
combinations with more than 10, 15, 20, 25 and 30% of waterbodies exceeding their CL for the
specified ANC target level.
For example, among the eastern and western ecoregi on-time period combinations with S
deposition at or below 2 kg S/ha-yr across ecoregions and deposition periods, there are no
ecoregions that have more than 10% of their waterbodies exceeding their CLs for any of the
three ANC targets (Table 5-4). In contrast, for annual average S deposition at or below 10 kg
S/ha-yr, there are 22 of the 90 eastern ecoregi on-time period combinations with more than 10%
of their waterbodies exceeding their CLs for an ANC of 50 [j,eq/L, one of which had more than
30%) of its waterbodies exceeding their CLs. The lowest annual average S deposition level
associated with any ecoregi on-time period combinations having more than 30% of waterbodies
exceeding their CLs was 10 kg S/ha-yr, for which one ecoregion in one time period had more
than 30%) of the waterbodies exceeding their CLs for all three ANC targets.
5-30
-------
Table 5-4. Number of ecoregion-time period combinations with more than 10,15, 20, 25, and 30% of waterbodies exceeding
their CLs for three ANC targets as a function of ecoregion-level estimates of annual average S deposition.
S Deposition
(kg/ha-yr):
No. of
Eastern
Ecoregion-
Time
Periods
Number of eastern ecoregion-time periods with more than specified percent of
waterbodies exceeding their CLs
Number of western ecoregion-time
periods with more than 10% of
waterbodies exceeding their CLs
for ANC target of 20, 30 or 50 peq/L
10%
15%
20%
25%
30%
10%
15%
20%
25%
30%
10%
15%
20%
25%
30%
S Deposition
(kg/ha-yr)
No. ecoregion
-time periods
>10%
ANC target of 20 yeq/L
ANC target of 30 yeq/L
ANC target of 50 yeq/L
<2
10
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
<2
35
0
<3
29
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
<4
41
0
0
0
0
0
2
0
0
0
0
3
1
0
0
0
<5
51
2
1
0
0
0
4
1
0
0
0
9
3
2
1
0
None of the 35 western ecoregion-
time periods (7 ecoregions and 5 time
periods) in analysis had ecoregion S
deposition estimates above 2 kg S/ha-
yr
<6
59
4
1
0
0
0
7
1
0
0
0
13
4
2
1
0
<7
63
5
1
0
0
0
8
2
0
0
0
14
5
3
1
0
<8
67
9
4
0
0
0
12
6
1
0
0
18
9
5
3
0
<9
69
9
4
0
0
0
13
6
1
0
0
19
9
5
3
0
<10
73
11
6
1
1
1
16
8
2
1
1
22
11
6
4
1
<11
76
13
7
2
1
1
18
9
3
1
1
24
13
7
4
1
<12
79
15
9
4
3
2
21
11
5
3
3
27
15
9
6
3
<13
81
16
10
4
3
2
22
12
5
3
3
28
16
10
6
3
<14
84
19
12
6
4
3
25
14
7
5
4
31
18
12
8
5
<15
86
21
14
8
6
4
27
16
9
7
6
33
20
14
10
7
<16
88
22
15
9
7
5
28
17
10
8
7
34
21
15
11
8
<17
88
22
15
9
7
5
28
17
10
8
7
34
21
15
11
8
<18
90
24
17
11
9
7
30
19
12
10
9
36
23
17
13
10
5-31
-------
As none of the 7 western ecoregions had more than 10% of their waterbodies exceeding
their CLs for any of the ANC thresholds in any of the five time periods, we focus the remaining
presentations on the eastern ecoregions. We considered these ecoregion-scale results from the
perspective of the extent to which waterbodies within the eastern ecoregions were estimated to
achieve the various ANC targets across the S deposition levels for the 18 ecoregions and five
time periods. This can be considered the inverse of the presentation in Table 5-4 above, using
percentages instead of absolute counts in the presentation. For example, rather than the number
of ecoregion-time periods, with a particular range of S deposition estimates, that have more than
10% of waterbodies exceeding their CLs for an ANC target of 20 |ieq/L, Figure 5-14 presents the
percentage of ecoregion-time periods that have less than or equal to 10% (or 15, 20, 25 or 30%)
of waterbodies exceeding their CLs for each of the three ANC levels (20, 30 and 50 |ieq/L). The
same dataset is presented in Table 5-5, but in terms of percentage of waterbodies that are not
exceeding their CLs (i.e., that are estimated to achieve the ANC target). Results also presented in
Appendix 5A, section 5A.2.2.
5-32
-------
100%
90%
80%
70%
•g 60%
£ 50%
E
£ 40%
0
O)
1 30%
LU
^ 20%
10%
0%
100%
90%
80%
70%
» 60%
_o
S. 50%
a>
E
'T 40%
| 30%
O
LU
SS 20%
10%
100%
90%
80%
70%
60%
50%
' 40%
30%
20%
10%
0%
-<30% exceedances
<25% exceedances
<20% exceedances
-<15% exceedances
-<10% exceedances
4 8 12 16
Highest Ecoregion Median Sulfur Deposition (kg S/ha-yr
20
Figure 5-14. Percentage of ecoregion-time period combinations with less than or equal to
10,15, 20, 25 and 30% of waterbodies exceeding their CLs for ANC of 20
(top), 30 (middle) and 50 jieq/L (bottom) for 18 eastern ecoregions.
5-33
-------
Table 5-5. Percentage of ecoregion-time periods combinations with at least 90, 85, 80, 75 and 70% of waterbodies estimated
to achieve an ANC at/above the ANC targets of 20,30 and 50 jieq/L as a function of annual average S deposition
for 18 eastern ecoregions (90 ecoregion-time period combinations).
Total Sulfur
Deposition
(kg S/ha-yr)
at/below:
No. of
Ecoregi
on-Time
Periods
% Waterbodies per ecoregion-time period meeting s
pecified ANC target
90%
85%
80%
75%
70%
90%
85%
80%
75%
70%
90%
85%
80%
75%
70%
ANC target of 20 jjeq/L
ANC target of 30 jjeq/L
ANC target of 50 jjeq/L
2
10
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
3
29
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
97%
100%
100%
100%
100%
4
41
100%
100%
100%
100%
100%
95%
100%
100%
100%
100%
93%
98%
100%
100%
100%
5
51
96%
98%
100%
100%
100%
92%
98%
100%
100%
100%
82%
94%
96%
98%
100%
6
59
93%
98%
100%
100%
100%
88%
98%
100%
100%
100%
78%
93%
97%
98%
100%
7
63
92%
98%
100%
100%
100%
87%
97%
100%
100%
100%
78%
92%
95%
98%
100%
8
67
87%
94%
100%
100%
100%
82%
91%
99%
100%
100%
73%
87%
93%
96%
100%
9
69
87%
94%
100%
100%
100%
81%
91%
99%
100%
100%
72%
87%
93%
96%
100%
10
73
85%
92%
99%
99%
99%
78%
89%
97%
99%
99%
70%
85%
92%
95%
99%
11
76
83%
91%
97%
99%
99%
76%
88%
96%
99%
99%
68%
83%
91%
95%
99%
12
79
81%
89%
95%
96%
97%
73%
86%
94%
96%
96%
66%
81%
89%
92%
96%
13
81
80%
88%
95%
96%
98%
73%
85%
94%
96%
96%
65%
80%
88%
93%
96%
14
84
77%
86%
93%
95%
96%
70%
83%
92%
94%
95%
63%
79%
86%
90%
94%
15
86
76%
84%
91%
93%
95%
69%
81%
90%
92%
93%
62%
77%
84%
88%
92%
16
88
75%
83%
90%
92%
94%
68%
81%
89%
91%
92%
61%
76%
83%
88%
91%
17
88
75%
83%
90%
92%
94%
68%
81%
89%
91%
92%
61%
76%
83%
88%
91%
18
90
73%
81%
88%
90%
92%
67%
79%
87%
89%
90%
60%
74%
81%
86%
89%
5-34
-------
Overall, the S deposition levels in the 18 eastern ecoregions and five time periods
analyzed include a range from below 2 up to nearly 18 kg/ha-yr. Across all 90 eastern ecoregion-
time period combinations (including S deposition estimates up to near 18 kg/ha-yr), 73% of the
combinations had at least 90% of waterbodies per ecoregion estimated to achieve ANC at or
above 20 |ieq/L, and 60% had at least 90% of the waterbodies estimated to achieve ANC at or
above 50 |ieq/L. Less than half of the eastern ecoregion-time period combinations (and all of the
western combinations) had an S deposition estimate below 4 kg/ha-yr. Ninety percent of the
eastern combinations were at or below 13 kg/ha-yr. For the 75 western-time period
combinations, all of which had an S deposition estimate below 4 kg/ha-yr, at least 90% of
waterbodies per ecoregion were estimated to achieve an ANC at or above 50 |ig/L The results by
annual average S deposition bin are summarized below for the bins from 13 kg/ha-yr down to 5
kg/ha-yr (the bin that includes at least half of this dataset):
• For S deposition estimates at or below 13 kg/ha-yr, at least 90% of waterbodies per
ecoregion were estimated to achieve an ANC at or above 20, 30 and 50 |ieq/L in 80%,
73%) and 65% of all ecoregion-time period combinations, respectively.
• For S deposition at or below 11 kg/ha-yr, at least 90% of all waterbodies per ecoregion
were estimated to achieve ANC at or above 20, 30 and 50 |ieq/L in 83%, 77% and 68%
of all ecoregion-time period combinations, respectively.
• For S deposition at or below 9 kg/ha-yr, at least 90% of all waterbodies per ecoregion
were estimated to achieve ANC at or above 20, 30 and 50 |ieq/L in 87%, 81% and 12%
of combinations, respectively.
- At least 80%), 75% and 70% of waterbodies per ecoregion were estimated to
achieve ANC at or above 20, 30 and 50 |ieq/L, respectively, in all ecoregion-time
period combinations.
• For S deposition at or below 7 kg/ha-yr, at least 90% of waterbodies per ecoregion were
estimated to achieve ANC at or above 20, 30 and 50 |ieq/L in 92, 87 and 78% of
combinations, respectively.
- At least 80%), 80% and 70% of waterbodies per ecoregion were estimated to
achieve ANC at or above 20, 30 and 50 |ieq/L, respectively, in all ecoregion-time
period combinations.
• For S deposition at or below 5 kg/ha-yr, at least 90% of all waterbodies per region were
estimated to achieve ANC at or above 20, 30 and 50 |ieq/L in 96%, 92% and 82% of
combinations, respectively.
- At least 80%), 80% and 70% of waterbodies per ecoregion were estimated to
achieve ANC at or above 20, 30 and 50 |ieq/L, respectively, in all ecoregion-time
period combinations.
• For S deposition at or below 4 kg/ha-yr, at least 90% of all waterbodies per region were
estimated to achieve ANC at or above 20 in all 41 ecoregion-time period combinations
for that deposition bin, and to achieve ANC at or above 30 and 50 |ieq/L in 95% and 97%
5-35
-------
of those combinations, respectively. The number of ecoregion-time period combinations
in this deposition bin is less than half the full dataset for the 18 eastern ecoregions.
To further describe the results for recent conditions, we looked at S deposition for the 25
ecoregions in the two most recent time periods, 2014-2016 and 2018-2020, and the critical load
exceedances for the three ANC targets (Figures 5-15 and 5-16). Only one ecoregion had more
than 10% of its waterbodies exceeding a CL for any target ANC values in either time period.
This was the South-Central Plains ecoregion (8.3.7), which covers portions of eastern Texas,
western Louisiana and southwestern Arkansas, an area dominated by pine forest (which tend to
be in acidic soils). The median of the 18 eastern ecoregion median S deposition values for the
2014-2016 time period was 3.0 kg/ha-yr, dropping to 2.0 kg/ha-yr in the 2018 2020 time period.
Figure 5-17 through 5-19 show the eastern ecoregions with exceedances of target critical
loads under the two most recent time periods. Figure 5-20 shows the ecoregions with
exceedances for the entire U.S. for the most recent time periods using an ANC target of 50 ueq/L
for the east and 20 ueq/L for the west.
« gkf
4 A B
~ ANC 20 peq/L
• ANC 30 peq/L
A ANC 50 peq/L
100
90
c
o
CD
80
2?
o
70
0
1 1 1
LU
a3
60
CL
co
50
CD
"O
o
40
-Q
Jd
30
s
5
20
o°
10
0
0 1 2 3 4 5
Ecoregion Median Deposition (kg S/ha-yr)
Figure 5-15. Percentage of waterbodies in each of the 25 ecoregions estimated to achieve
ANC values of 20 (F.&W), 30 (E only) and 50 (E only) jxeq/L as a function of
ecoregion annual average S deposition for 2014-2016 (median across CL sites).
5-36
-------
~ ANC
20
jjeq/L
• ANC
30
|jeq/L
A ANC
50
jjeq/L
100
90
0 80
CO
§ 70
¦§ 60
-------
2018 -2020 Sulfur Deposition Ecoregion Exceedances
2014 - 2016 Sulfur Deposition Ecoregion Exceedances
Percent Exceedances
(ANC = 20 Heq/L)
I 0 -10%
10-15%
H >15%
Ecoregions where critical loads are < 50 values
|.~ ^ Ecoregions with high level of natural acidity
| Areas without critical loads
Figure 5-17. Map of critical load exceedances for S only deposition from 2018-20 (top) and
2014-16 (bottom) for ANC threshold of 20 fieq/L.
5-38
-------
2018 -2020 Sulfur Deposition Ecoregion Exceedances
2014 - 2016 Sulfur Deposition Ecoregion Exceedances
Percent Exceedances
(ANC = 30 Heq/L)
I 0 -10%
10-15%
H >15%
Ecoregions where critical loads are < 50 values
|.~ ^ Ecoregions with high level of natural acidity
| Areas without critical loads
Figure 5-18. Map of critical load exceedances for S only deposition from 2018-20 (top) and
2014-16 (bottom) for an ANC threshold of 30 jieq/L.
5-39
-------
2018 -2020 Sulfur Deposition Ecoregion Exceedances
2014 - 2016 Sulfur Deposition Ecoregion Exceedances
Percent Exceedances
(ANC = 50 (jeq/L)
| I 0 -10%
10-15%
H >15%
Ecoregions where critical loads are < 50 values
Ecoregions with high level of natural acidity
| Areas without critical loads
Figure 5-19. Map of critical load exceedances for S only deposition from 2018-20 (top) and
2014-16 (bottom) for an ANC threshold of 50 ^eq/L.
5-40
-------
2018 -2020 Sulfur Deposition Ecoregion Exceedances
2014 - 2016 Sulfur Deposition Ecoregion Exceedances
Percent Exceedances
(ANC = 50/20 |jeq/L)
0-10%
10-15%
H >15%
Ecoregions where critical loads are < 50 values
.;] Ecoregions with high level of natural acidity
| Areas without critical loads
Figure 5-20. Map of critical load exceedances for S only deposition from 2018-20 (top) and
2014-16 (bottom) for an ANC threshold of 50 jieq/L for East and 20 jieq/L
for the West.
5-41
-------
5.1.3.3 Case Study Analyses
The case study areas are geographically diverse acid-sensitive areas across the CONUS
that have sufficient data to complete the quantitative analyses. Five case study areas were
identified that meet the criteria (Figure 5-21): White Mountain National Forest (WHMT),
Shenandoah Valley Area (SHVA), Northern Minnesota (NOMN), Rocky Mountain National
Park (ROMO) and Sierra Nevada Mountains (SEME). Three of these areas are in the eastern U.S.
(NOMN, SHVA and WHMT) and two areas are in the western U.S. (ROMO and SINE). Class I
areas occur in three of the five case study areas (SHVA, ROMO and SINE). Additional aquatic
acidification analyses using the case studies can be found in Appendix 5A. A total of 523 CLs
were identified in four of the five case study areas, while the SHVA case study had complete
coverage, with 4977 CLs. The case studies, ROMO, SINE, NOMN, and WHMT, had 119, 139,
190, and 75 CLs, respectively. For this discussion, the analyses identified the calculated sulfur
deposition values at or below which the case study sites would likely be able to attain the target
ANC values of 50, 30 and 20 |ieq/L for the eastern case studies and 20 jaeq/L for the western
case studies.
The steady-state mass balance modeling results summarized in Table 5-6 indicate the
average CL for achieving a target ANC of 20 neq/L in the five study areas ranges from about 10
5-42
-------
to 12 kg/ha-yr. For 70 to 90% of sites to achieve an ANC of 20 |ieq/L, the estimated CL for S
deposition ranges from about 4 to 9 kg/ha-yr. The average CL to achieve an ANC value of 30
|aeq/L ranges from about 10 to 11 kg/ha-yr and for 70-90% of sites to achieve an ANC of 30
|ieq/L, the estimated CL for S deposition ranges from about 3 to 8 kg/ha-yr. For an ANC target
of 50 |ieq/L, the average CL for sites in the five case studies ranges from about 7 to 10 kg/ha-yr.
For 70 to 90% of the case study sites to achieve a target ANC of 50 |ieq/L, the estimated CL for
S deposition ranges between 3 to 4kg/ha-yr, except for White Mountain, which is extremely
sensitive. Overall, these findings are slightly lower than the ecoregion-scale results.
Table 5-6. Annual average S deposition at/below which modeling indicates an ANC of 20,
30 or 50 jieq/L can be achieved in the average, 70% and 90% of waterbodies
in each study area.
ANC
(Ijeq/L)
Based on average across all sites in
area
Based on 70% of sites achieving
Based on 90% of sites achieving
Eastern
— Western —
Eastern
— Western —
Eastern
— Western —
N.
Minn
White
Mtns
Shenan-
doah
Rocky
Mtn
NP
Sierra
Nev
Mtns
N.
Minn
White
Mtns
Shenan-
doah
Rocky
Mtn
NP
Sierra
Nev
Mtns
N.
Minn
White
Mtns
Shenan-
doah
Rocky
Mtn
NP
Sierra
Nev
Mtns
(kg/ha
-yr)
(kg/ha-
yr)
(kg/ha-
yr)
(kg/ha
-yr)
(kg/ha-
yr)
(kg/ha
-yr)
(kg/ha
-yr)
(kg/ha-
yr)
(kg/ha
-yr)
(kg/ha-
yr)
(kg/ha
-yr)
(kg/ha
-yr)
(kg/ha-
yr)
(kg/ha
-yr)
(kg/ha-
yr)
20
11
11
12
9.5
12
5.5
6.9
9.4
5.4
4.1
4.2
4.4
7.1
3.6
1.8
30
10
10
11
5.3
6.1
8.4
3.9
3.3
6.3
50
10
10
9.4
4.7
4.1
6.3
3.2
0.7
4.1
Note: Shaded boxes indicate that consistent with convention followed in the ecoregion analysis above, CLs are not presented for
ANC target values of 30 and 50 |jg/L in the West.
5.1.4 Characterization of Uncertainty
We have characterized the nature and magnitude of associated uncertainties and their
impact on the REA estimates based primarily on a mainly qualitative approach, informed by
several quantitative sensitive analyses, all of which are described in Appendix 5 A, section 5A.3.
The mainly qualitative approach used here and in quantitative analyses in other NAAQS reviews
is described by WHO (2008). Briefly, with this approach, we have identified key aspects of the
assessment approach that may contribute to uncertainty in the conclusions and provided the
rationale for their inclusion. Then, we characterized the magnitude and direction of the influence
on the assessment for each of these identified sources of uncertainty. Consistent with the WHO
(2008) guidance, we scaled the overall impact of the uncertainty by considering the degree of
uncertainty as implied by the relationship between the source of uncertainty and the exposure
and risk estimates. A qualitative characterization of low, moderate, and high was assigned to the
magnitude of influence and knowledge base uncertainty descriptors, using quantitative
observations relating to understanding the uncertainty, where possible. Where the magnitude of
uncertainty was rated low, it was judged that large changes within the source of uncertainty
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would have only a small effect on the assessment results (e.g., an impact of few percentage
points upwards to a factor of two). A designation of medium implies that a change within the
source of uncertainty would likely have a moderate (or proportional) effect on the results (e.g., a
factor of two or more). A characterization of high implies that a change in the source would have
a large effect on results (e.g., an order of magnitude). We also included the direction of
influence, whether the source of uncertainty was judged to potentially over-estimate ("over"),
under-estimate ("under"), or have an unknown impact to exposure/risk estimates.
A summary of the overall uncertainty characterization is provided in Appendix 5A, Table
5A-53. Two types of quantitative analyses that informed our understanding of the variability and
uncertainty associated with the CL estimates developed in this assessment and support the
uncertainty characterization are also presented in Appendix 5A, in sections 5A. 1.1 and 5A. 1.2.
The first type of analysis is a sensitivity analysis using Monte Carlo techniques to quantify CL
estimate uncertainty associated with several model inputs, and the second is an analysis of the
variation in CL estimates among the three primary modeling approaches on which the CLs used
in this assessment were based.
As overarching observations regarding uncertainty associated with this REA, we take
note of two overarching aspects of the assessment. The first relates to interpretation of specific
thresholds of ANC and the second to our understanding of the biogeochemical linkages between
deposition of S and N compounds and waterbody ANC (implemented in modeling used in this
assessment), and the associated estimation of CLs. While ANC is an established indicator of
aquatic acidification risk, there is uncertainty in our understanding of relationships between ANC
and risk to native biota, particularly in waterbodies in geologic regions prone to waterbody
acidity. Such uncertainties relate to the varying influences of site-specific factors other than
ANC. Uncertainty associated with our understanding of the biogeochemical linkages between
deposition and ANC and the determination of steady-state CLs is difficult to characterize and
assess. Uncertainty in CL estimates is associated with parameters used in the steady-state CL
models. While the SSWS and other CL models are well conceived and based on a substantial
amount of research and applications available in the peer-reviewed literature, there is uncertainty
associated with the availability of the necessary data to support certain model components.
The strength of the CL estimates and the exceedance calculation rely on the ability of
models to estimate the catchment-average base-cation supply (i.e., input of base cations from
weathering of bedrock and soils and air), runoff, and surface water chemistry. Key parameters in
this modeling include estimates of the catchment-average base-cation supply (i.e., input of base
cations from weathering of bedrock and soils and air), runoff, and surface water chemistry. The
uncertainty associated with runoff and surface water parameters relates to availability of
measurements; however, the ability to accurately estimate the catchment supply of base cations
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to a water body is still difficult, and uncertain (Appendix 5A, section 5A.3). This area of
uncertainty is important because the catchment supply of base cations from the weathering of
bedrock and soils is the factor with the greatest influence on the CL calculation and has the
largest uncertainty (Li and McNulty, 2007). For example, the well-established models generally
rely on input or simulated values for base cation weathering (BCw) rate, a parameter the ISA
notes to be "one of the most influential yet difficult to estimate parameters in the calculation of
critical acid loads of N and S deposition for protection against terrestrial acidification" (ISA,
section IS. 14.2.2.1). Obtaining accurate estimates of weathering rates is difficult because
weathering is a process that occurs over very long periods of time, and the estimates on an
ecosystem's ability to buffer acid deposition rely on accurate estimates of weathering. Although
the approach to estimate base-cation supply for the national case study (e.g., F-factor approach)
has been widely published and analyzed in Canada and Europe, and has been applied in the U.S.
(e.g., Dupont et al., 2005 and others), the uncertainty in this estimate is unclear and could be
large in some cases.
In light of the significant contribution of this input to the CL estimates, a quantitative
uncertainty analysis of CL estimates based on state-steady CL modeling was performed
(Appendix 5A, section 5A.3.1). This analysis, involving many model simulations for the more
than 14,000 waterbodies, drawing on Monte Carlo sampling, provided a description of the
uncertainty around the CL estimate in terms of the confidence interval for each waterbody mean
result. The size of the confidence interval ranged from 0.37 meq/m2-yr at the 5th percentile to
33.2 meq/m2-yr at the 95th percentile. Lower confidence intervals were associated with CLs
determined with long-term water quality data and low variability in runoff measurements.
Estimates of CL determined by one or very few water quality measurements, and in areas where
runoff is quite variable (e.g., the western U.S.) had larger confidence intervals, indicating greater
uncertainty. Critical load estimates with the lowest uncertainty were for waterbody sites in the
eastern U.S., particularly along the Appalachian Mountains, in the Upper Midwest, and in the
Rocky Mountains. Greater uncertainty is associated with CLs in the Midwest and South and
along the CA to WA coast. This uncertainty in the Midwest is associated with most of the CLs in
waterbodies in this area being based on one or a few water quality measurements, while the high
uncertainty for sites along the CA and WA coasts relates to variability in runoff values. On
average, the size of the confidence interval for all SSWC CLs was 7.68 meq S/m2-yr or 1.3 kg
S/ha-yr, giving a confidence level of ±3.84 meq/m2-yr or ±0.65 kg S/ha-yr. While a
comprehensive analysis of uncertainty has not been completed for these estimates prior to this
REA, expert judgment suggested the uncertainty for combined N and S CLs to be on average
about ±0.5 kg/ha-yr (3.125 meq/m2-yr), which is generally consistent with the range of
determined from this quantitative uncertainty analysis.
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At the ecoregion scale, fifty-one ecoregions had sufficient data to calculate the 5th to 95th
percentile (Appendix 5A, Table 5A-56). Smaller confidence intervals around the mean CL (i.e.,
lower uncertainty CLs) were associated with ecoregions in the Appalachian Mountains (e.g.,
Northern Appalachian and Atlantic Maritime Highlands (5.3.1), Blue Ridge (8.4.4), Northern
Lakes and Forests (5.2.1), and North Central Appalachians (5.3.3) and Rockies (e.g. Sierra
Nevada (6.2.14), Southern Rockies (6.2.14), and Idaho Batholith (6.2.15). Ecoregions with more
uncertain CLs included the Northeastern Coastal Zone (8.1.7), Cascades (6.2.7), Coast Range
(7.1.8), Interior Plateau (8.3.3), and Klamath Mountains/California High North Coast Range
(6.2.11).
Although the vast majority of CLs in this assessment were based on the SSWC model, an
analysis was conducted to understand differences in the CLs calculated with the different
methods. There are three main CL approaches all based on the watershed mass-balance approach
where acid-base inputs are balanced. The three approaches include: (1) SSWC model and F-
Factor that is based on quantitative relationships to water chemistry (Dupont et al., 2005; Scheffe
et al., 2014; Lynch et al., 2022), (2) Statistical Regression Model that extrapolated weathering
rates across the landscape using water quality or landscape factors (Sullivan et al., 2012a;
McDonnell et al., 2014), and (3) Dynamic Models (MAGIC or Pnet-BGC). Critical load values
were compared between these models to determine model biases. Results from the comparison
between different CL methods that were used to calculate the critical loads in the NCLD are
summarized in Appendix 5A, section 5A.3.3, for lakes in New England and the Adirondacks and
streams in the Appalachian Mountains. Overall, good agreement was found between the three
methods used to calculate CLs, indicating there was not a systematic bias between the methods
and that they should produce comparable results when used together as they were in these
analyses.
5.1.5 Summary of Key Findings
Quantitative analyses were performed to assess acidification risks of S deposition in
waterbodies across the U.S. using a critical load approach. Due to the finding of a negligible
influence of N deposition on acidification under the S deposition levels in this assessment, we
focused on S deposition solely (Appendix 5A, section 5A.2.1). In this assessment, ANC was
used as the water quality indicator of acidification, based on its longstanding use for this purpose
(ISA, Appendix 7, section 7.1.2.6). We also focused on acid-deposition-sensitive areas for which
the available CL modeling estimates indicated that the target ANC values of 50, 30 and 20 |ig/L
could be reached. Analyses were performed at three different spatial scales: nationwide,
ecoregion (level III), and case studies.
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Critical load estimates for specific waterbody sites across the contiguous U.S. were
drawn from the NCLD (version 3.2.1) for comparison to total deposition estimates in the same
locations from TDep for five time periods since 2000. Comparisons were only performed for
critical load estimates greater than zero. The results of these analyses are summarized with
regard to spatial extent and severity of deposition-related acidification effects and the protection
from these effects associated with a range of annual S deposition.
Between the three-year period 2000-2002, which was the analysis year for the 2009 REA,
and 2018-2020, the latest period considered in the present analyses, national average sulfur
deposition has declined by 68% across the U.S. This decline in deposition is reflected in the very
different aquatic acidification impact estimates for the two periods. Unlike the findings for 2000-
2002 in the last review (concluded in 2012), few waterbody sites are estimated to be receiving
deposition in excess of their critical loads for relevant ANC targets under recent deposition
levels. While recognizing inherent limitations and associated uncertainties of any such analysis,
the national-scale assessment performed as part of this review, indicates that under deposition
scenarios for the 2018-2020 time period, the percentage of waterbodies nationwide that might
not be able to maintain an ANC of 50 |ig/L in the east and an ANC of 20 |ig/L in the west would
be less than 5% (see Table 5-1).
The ecoregion-level analyses of ANC levels and deposition estimates for the five periods
from 2001-2003 through 2018 -2020 illustrate the spatial variability and magnitude of the
impacts that might be expected for several target ANC levels (50, 30 and 20 |ig/L) and the
temporal changes across the 20-year period. For example, during the two most recent 3-year
periods, the ecoregion median S deposition estimates in 2014-16 were below 5 kg/ha-yr in all
ecoregions and the estimates for 2018-20 were all below 4 kg/ha-yr. In this analysis, we
summarized the ecoregion-level exceedances of CLs for each of the ANC targets in each of the
five time periods. While recognizing limitations and associated uncertainties of these analyses,
we note several key observations.
Although the ecoregion S deposition estimates in the 18 eastern ecoregions analyzed
were all below 5 kg/ha-yr in the two most recent time periods (2014-16 and 2018-20), the full
dataset of five time periods ranges from below 2 up to nearly 18 kg/ha-yr. Across this dataset of
CL exceedances for the three ANC targets for all 90 eastern ecoregion-time period combinations,
73% of the combinations had at least 90% of waterbodies per ecoregion estimated to achieve
ANC at or above 20 |ieq/L, and 60% had at least 90% of the waterbodies estimated to achieve
ANC at or above 50 |ieq/L. In the early ecoregion-time period combinations fewer than half of
the eastern ecoregion-time period combinations (and all of the western combinations) had an S
deposition estimate below 4 kg/ha-yr.
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Ninety percent of the eastern ecoregion-time period combinations were for ecoregion
deposition estimates at or below 13 kg/ha-yr. For these combinations (at or below 13 kg/ha-yr),
at least 90% of waterbodies per ecoregion were estimated to achieve an ANC at or above 20, 30
and 50 |ieq/L in 80%, 73% and 65% of all ecoregion-time period combinations, respectively. For
S deposition estimates at or below 9 kg/ha-yr (approximately three quarters of the combinations),
at least 90% of all waterbodies per ecoregion were estimated to achieve ANC at or above 20, 30
and 50 |ieq/L in 87%, 81% and 72% of combinations, respectively. For S deposition estimates at
or below 5 kg S/ha-yr, these values are 96%, 92% and 82% of combinations. For the 75 western
ecoregion-time period combinations, all of which had an S deposition estimate below 4 kg/ha-yr,
at least 90% of waterbodies per ecoregion were estimated to achieve an ANC at or above 50
l-ig/L.
The case study analyses provide estimates of S deposition that might be expected to allow
these geographically diverse locations, including several Class I areas, to meet the three ANC
targets. In reviewing these estimates, we recognize inherent limitations and associated
uncertainties. Focusing on the three eastern case studies, the CL modeling indicates that at an
annual average S deposition of 9-10 kg/ha-yr, the sites in these areas, on average, might be
expected to achieve an ANC at or above 50 |ieq/L. At an annual average S deposition of about 6-
9 kg/ha-yr, 70% of the sites in the areas are estimated to achieve an ANC at or above 20 |ieq/L
and at about 5-8 kg/ha-yr, 70% are estimated to achieve an ANC at or above 30 |ieq/L. Lower S
deposition values are estimated to achieve higher ANC across more sites. Across the three
eastern areas, the CL estimates for each ANC target are lowest for the White Mountains National
Forest study area, and highest for the Shenandoah Valley study area.
5.2 NITROGEN ENRICHMENT IN AQUATIC ECOSYSTEMS
There are several other categories of effects to aquatic ecosystems from deposition of
nitrogen and sulfur for which there is significant scientific evidence and causality judgements, as
described in Chapter 4. These include N enrichment in various types of aquatic systems,
including freshwater streams and lakes, estuarine and near-coastal systems, and wetlands, as
described in section 4.3.1.9 Separate quantitative analyses were not performed for these
categories of effects in this review due to recognition of a number of factors, including modeling
and assessment complexities, and site- or waterbody-specific data requirements, as well as, in
some cases, issues of apportionment of atmospheric sources separate from other influential
sources.
9 Two other categories of effects assessed in the ISA (and for which causal determinations are made) are mercury
methylation, and sulfide toxicity (ISA, Appendix 12), as summarized in sections 4.4.1 and 4.4.2 above.
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5.2.1 Freshwater Wetlands
New information has become available since the 2008 ISA on N critical loads for U.S.
wetlands. While critical loads have previously been identified for European wetlands such as
bogs, fens, and intertidal wetlands for a variety of endpoints including plant growth and species
composition, peat and peat water chemistry, decomposition of organic material, and nutrient
cycling (Bobbink et al., 2003), recent studies have shown that CLs for Sphagnum moss effects in
European bogs may not be directly relevant or transferrable to North American and/or U.S.
wetlands (ISA, Appendix 11, section 11.3.1.6). With regard to North American freshwater
wetlands, some limited new information is available in this review. For example, a CL for
wetland C cycling, quantified as altered peat accumulation and net primary productivity, has
been estimated between 2.7 and 13 kg N/ha-yr based on four studies (Greaver et al., 2011; ISA,
Appendix 11, section 11.9.1). Additionally, N loading between 6.8-14 kg N/ha-yr has been
suggested by empirical evidence and modeling to be protective of populations of purple pitcher
plants (Sarraceniapurpurea) based on morphology and population dynamic endpoints (Gotelli
and Ellison, 2002, 2006). At the lowest experimental addition level (16 kg N/ha-yr), which has
been assessed in several studies, there are observations of altered C and N cycling and altered
biodiversity (ISA, Appendix 11). The endpoints affected include decreases in moss cover,
increased peat biomass, decreased N retention efficiency, and altered/damaged leaf stoichiometry
in vascular plants (ISA, Appendix 11, section 11.10.2).
5.2.2 Freshwater Lakes and Streams
Since the 2008 ISA, empirical and modeled critical loads for the U.S. have been
estimated based on surface water NO3" concentration, diatom community shifts, and
phytoplankton biomass growth nutrient limitation shifts. A critical load ranging from 3.5 to 6.0
kg N/ha-yr was identified for high-elevation lakes in the eastern U.S. based on the nutrient
enrichment inflection point (where NO3" concentrations increase in response to increasing N
deposition). Another critical load of 8.0 kg N/ha-yr was estimated by Pardo et al. (2011) for
eastern lakes based on the value of N deposition at which significant increases in surface water
NO3 concentrations occur. In both Grand Teton and Yellowstone national parks, critical loads
for total N deposition ranged from <1.5 ± 1.0 kg N/ha-yr to >4.0 ± 1.0 kg N/ha-yr (Nanus et al.,
2017; ISA, Appendix 9, section 9.5).
Additional critical loads have been identified since the 2008 ISA for eastern Sierra
Nevada lakes, Rocky Mountain lakes, the Greater Yellowstone Ecosystem, and Hoh Lake,
Olympic National Park (ISA, Appendix 9, Table 9-4). The identified values fall near or within
the range of 1.0 to 3.0 kg N/ha-yr for western lakes (Baron et al., 2011). An empirical critical
load of 4.1 kg/TN/ha-yr above which phytoplankton biomass P limitation is more likely than N
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limitation was identified by Williams et al. (2017) for the western U.S. using univariate
regression modeling of available water chemistry data from 2006-2011 for 208 western U.S.
mountain lakes, with prediction of a ratio of dissolved inorganic N to total phosphorus as the
response variable (ISA, Appendix 9, section 9.5); the lake-specific estimates ranged from 2.8 to
5.2 kg/TN/ha-yr. This evidence is geographically specific, perhaps even waterbody specific, and
is not available for most of the U.S.
Larger freshwater lakes, such as the Great Lakes, and freshwater portions of large river
systems are also susceptible to eutrophication from N loading (ISA, Appendix 9, section 9.1). In
these larger systems, atmospheric N from direct deposition, runoff, and leaching from terrestrial
ecosystems combines with other diffuse and point sources of N. The contribution from other
terrestrial sources ofN, such as fertilizer, livestock waste, septic effluent, and wastewater
treatment plant outflow, often becomes much more important in these large waterbodies than in
headwater and upland areas (ISA, Appendix 9, section 9.1.1.1). Further, N limitation appears to
have become increasingly common in freshwater systems, likely due to alteration of nutrient
dynamics from increased agricultural and urban P inputs (Appendix 9, section 9.1; Paerl et al.,
2016; Grantz et al., 2014; Paerl et al., 2014; Finlay et al., 2013).
5.2.3 Estuaries, Coastal Waters and Coastal Wetlands
Information newly available in this review includes new applications of models that have
quantified eutrophi cation processes in estuaries and near-coastal marine ecosystems (ISA,
section IS.7). These have included applications of N cycling or hypoxia models, as well as
modeling the apportionment of N loads in these systems.
In U.S. coastal wetlands, two studies are available that have considered N loads below
100 kg N/ha-yr. Wigand et al. (2003) observed associations of estimated N loading with plant
community structure in 10 saltmarsh sites around Narragansett Bay but indicated that
confounding effects of marsh physical characteristics made unclear the extent to which N
enrichment contributed to variation in plant structure. A N addition experiment in a Narragansett
Bay saltmarsh by Caffrey et al. (2007) provided evidence that 80 kg N/ha-yr can alter microbial
activity and biogeochemistry.
The relationship between N loading and algal blooms, and associated water quality
impacts, has led to numerous water quality modeling projects to inform water quality
management decision-making in multiple estuaries, including Chesapeake Bay, Narraganset Bay,
Tampa Bay, Neuse River Estuary and Waquoit (ISA, Appendix 7, section 7.2). These projects
often utilize indicators of nutrient enrichment, such as chlorophyll a, dissolved oxygen and
abundance of submerged aquatic vegetation, among others (ISA, section IS.7.3 and Appendix
10, section 10.6). For these estuaries, the available information regarding atmospheric deposition
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and the establishment of associated target loads varies across the various estuaries (ISA,
Appendix 7, Table 7-9).
The establishment of target loads is in many areas related to implementation of the total
maximum daily load (TMDL) requirements of section 303(d) of the Clean Water Act. Under the
CWA, section 303(d), every two years, states and other jurisdictions are required to list impaired
waterbodies not meeting water quality standards. For waterbodies on the list, a TMDL must be
developed that identifies the maximum amount of pollutant a waterbody can receive and still
meet water quality standards, e.g., standards for dissolved oxygen and chlorophyll a (which are
indicators of eutrophication).
Nutrient load allocation and reduction activities in some large estuaries predate
development of CWA 303(d) TMDLs. The multiple Chesapeake Bay Agreements signed by the
U.S. EPA, District of Columbia, and states of Virginia, Maryland, and Pennsylvania first
established the voluntary government partnership that directs and manages bay cleanup efforts
and subsequently included commitments for reduction of N and P loading to the bay. Efforts
prior to 2000 focused largely on point-source discharges, with slower progress for nonpoint-
source reductions via strategies such as adoption of better agricultural practices, reduction of
atmospheric N deposition, enhancement of wetlands and other nutrient sinks, and control of
urban sprawl (2008 ISA, section 3.3.8.3).
Studies since 2000 estimate atmospheric deposition to contribute substantially to the
overall N budget for Chesapeake Bay (ISA, Appendix 7, section 7.2.1; Howarth, 2008b; Boyer et
al., 2002). In the TMDL established for Chesapeake Bay in 2010, atmospheric deposition was
recognized as the major N source to the Chesapeake Bay watershed, greater than the other
sources of fertilizer, manures, or point sources (U.S. EPA, 2010). The TMDL modeling
estimated seventy-five percent of the atmospheric N loading to the Chesapeake watershed to
originate from sources within the Bay airshed (U.S. EPA, 2010). The 2010 TMDL included a
loading allocation for atmospheric deposition of N directly to tidal waters of 15.7 million
lbs/year (7.1 million kg/yr), which was projected to be achieved by 2020 based on air quality
progress under existing Clean Air Act regulations and programs (U.S. EPA, 2010). With that
projection in reduced atmospheric loading, water quality modeling was used to identify the
reductions across the subbasins and tributaries that were needed to enable water quality standards
for dissolved oxygen to be achieved in the mainstem of the Bay and the major tidal river
segments. The total additional N loading reduction is 185.93 million lbs/year, to be achieved by
actions of the seven jurisdictions in the Chesapeake Bay watershed, which includes six States
and the District of Columbia (U.S. EPA, 2010).
Jurisdictions for other U.S. estuaries have also developed TMDLs to address nutrient
loading causing eutrophication. For example, atmospheric deposition in 2000 was identified as
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the third largest source of N loading to Narragansett Bay (via the watershed and directly to the
water body), which, to Narragansett Bay in the year 2000, was atmospheric deposition (20%)
(ISA, Appendix 7, section 7.2.1). Similarly atmospheric deposition was estimated to account for
approximately a third of N input to several small- to medium-sized estuaries of southern New
England, with the percentage varying widely for individual estuaries (ISA, Appendix 7, section
7.2.1; Latimer and Charpentier, 2010). Another modeling study in the Waquoit Bay estuaries in
Cape Cod, MA, using data since 1990, estimated atmospheric deposition to have decreased by
about 41% while wastewater inputs increased 80% with a net result that total loads were
concluded to not have changed over that time period (ISA, Appendix 7, section 7.2.1). Another
well studied estuarine system is Tampa Bay, for which a 2013 study estimated atmospheric
sources to account for more than 70% of total N loading based on 2002 data (ISA, Appendix 7,
section 7.2.1). The TMDL for Tampa Bay allocates 11.8 kg/ha-yr N loading to atmospheric
deposition (ISA, Appendix 16, section 16.4.2; Janicki Environmental, 2013). The Neuse River
Estuary is another for which modeling work has investigated the role of atmospheric N
deposition nutrient enrichment and associated water quality indicators, including chlorophyll a
(ISA, Appendix 10, section 10.2).
Nitrogen loading to estuaries has also been considered with regard to impacts on
submerged aquatic vegetation. For example, eelgrass coverage was estimated to be markedly
reduced in shallow New England estuaries with N loading at or above 100 kg N/ha-yr (ISA,
Appendix 10, section 10.2.5). Another study estimated loading rates above 50 kg/ha-yr as a
threshold at which habitat extent may be impacted (ISA, Appendix 10, section 10.2.5; Latimer
and Rego, 2010). Factors that influence the impact of N loading on submerged vegetation
includes flushing and drainage in estuaries (ISA, Appendix 10, section 10.6).
5.2.4 Summary: Key Findings and Associated Uncertainties
The eutrophication of wetlands and other aquatic systems is primarily associated with
nitrogen inputs whether from deposition or other sources. The ranges of deposition associated
with these effects is very broad and ranges from levels on the order of a few kg N/ha-yr for
impacts to diatom communities in high elevation lakes to over 500 kg N/ha-yr for some effects
of interest in some wetland N addition studies. While the information available on these types of
impacts is sufficient for causal determinations it is often very localized and difficult to utilize
more broadly, such as for the purpose of quantitative assessment relating deposition to
waterbody response at an array of U.S. locations. Accordingly, in this review, this information
was considered from a more descriptive perspective in characterizing conditions reported in the
evidence as associated with various effects described in Chapter 4.
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There is also a wealth of information available for estuaries and coastal systems. Over the
past few decades, modeling analyses have been conducted in multiple estuaries and large river
systems to relate N loading to various water quality indicators, including chlorophyll a, dissolved
oxygen and also prevalence of habitat, such as SAV. While a focus is identification of total N
loading targets for purposes of attaining water quality standards for such indicators, the modeling
work also includes apportionment of sources, which vary by system. The assignment of targets to
different source types (e.g., groundwater, surface water runoff and atmospheric deposition) in
different waterbodies and watersheds the also varies for both practical and policy reasons.
Further, during the multi-decade time period across which these activities have occurred,
atmospheric deposition of N in coastal areas has declined. In general, however, atmospheric
deposition targets for N for the large systems summarized above have been on the order of 10
kg/ha-yr, with some somewhat lower and some somewhat higher.
5.3 EFFECTS OF S AND N DEPOSITION IN TERRESTRIAL
ECOSYSTEMS
As noted in the introduction to this chapter, analyses in the 2012 review that related
atmospheric deposition in recent times (e.g., since 2000) to terrestrial effects, or indicators of
terrestrial ecosystem risk, were generally considered to be more uncertain than conceptually
similar modeling analyses for aquatic ecosystems (e.g., "aquatic acidification is clearly the
targeted effect area with the highest level of confidence" (2009 REA, section 7.5; 2011 PA,
section 1.3). The terrestrial analyses in the 2012 review were comprised of a critical load-based
quantitative modeling analysis focused on BC: A1 ratio in soil (the benchmarks for which are
based on laboratory responses rather than field measurements) and a qualitative characterization
of nutrient enrichment (2009 REA). The more qualitative approach taken for nutrient enrichment
in the 2012 review involved describing deposition ranges identified from observational or
modeling research as associated with potential effects/changes in species, communities and
ecosystems, with recognition of uncertainties associated with quantitative analysis of these
depositional effects (2011 PA, section 3.2.3).
In this review, rather than performing new quantitative analyses focused on terrestrial
ecosystems, we draw on prior analyses (e.g., in the 2009 REA) and published studies recognized
in the ISA that provide information pertaining to deposition levels associated with effects related
to terrestrial acidification and N enrichment. This approach considers the available studies and
with investigation into various assessment approaches. Unlike aquatic acidification where a full
quantitative exposure and risk assessment has been conducted (see section 5.1) at multiple scales
because the available information, tools and assessment approaches provide strong support for
analyses that are targeted to the needs in this review, we determined that such an approach is not
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warranted for terrestrial effects related to N and S deposition in this review based on our
assessment of the available information and tools and current review needs. Therefore, this
section draws on the wealth of quantitative information relating deposition to consideration of
terrestrial ecosystem effects, as described below and in the following subsections.
Since the 2012 combined review of the secondary NAAQS for N oxides and SOx, in
addition to publications of analyses that apply steady-state (and dynamic) modeling to predict
future soil acidity conditions in various regions of the U.S. under differing atmospheric loading
scenarios (ISA, Appendix 4, section 4.6.2), several publications have analyzed large datasets
from field assessments of tree growth and survival, as well as understory plant community
richness, with estimates of atmospheric N and/or S deposition (ISA, Appendix 6, section 6.5).
These latter studies investigate the existence of associations of variations in plant community or
individual measures (e.g., species richness, growth, survival) with a metric for deposition during
an overlapping time period, generally of a decade or two in duration. Soil acidification modeling
and observational studies, as well as experimental addition studies, are, to various extents,
informative in considering N and S deposition levels of interest in the review.
In general, observational or gradient studies differ from the chemical mass balance
modeling approach in a number of ways that are relevant to their consideration and utilization for
our purposes in this review. One difference of note is the extent to which their findings reflect or
take into account the ecosystem impacts of historical deposition. Observational studies are
describing variation in indicators in the current context (with any ecosystem impacts, including
stores of deposited chemicals that remain from historical loading). Historical loading, and its
associated impacts, can also contribute to effects analyzed with estimates of more recent
deposition in observational studies. Mass balance modeling, in the steady-state mode that is
commonly used for estimating critical loads for acidification targets, does not usually address the
complication of historical deposition impacts that can play a significant role in timing of system
recovery. In this type of modeling, timelines of the various processes are not addressed. While
this provides a simple approach that may facilitate consideration unrelated to timelines, it cannot
address the potential for changes in influential factors that may occur over time with different or
changed deposition patterns.
For example, in considering the potential for terrestrial ecosystem impacts associated
with different levels of deposition, the simple mass balance models common for estimating
critical acid loads related to BC: A1 ratio are often run for the steady state case. Accordingly, the
underlying assumption is that while historic deposition, and the various ways it may affect soil
chemistry into the future (e.g., through the stores of historically deposited sulfur), may affect
time to reach steady state (e.g., as the system processes the past loadings), it would not be
expected to affect the steady state solution (i.e., the estimated critical load for the specified soil
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acidification indicator target). The complexities associated with site-specific aspects of
ecosystem recovery from historic depositional loading (which contribute uncertainties to
interpretation of steady-state solutions) become evident through application of dynamic models.
Observational studies, on the other hand, due to their focus on an existing set of
conditions, are inherently affected by the potential influence of historical deposition and any past
or remaining deposition-related impacts on soil chemistry and/or biota, in addition to other
environmental factors. The extent of the influence of historical deposition (and its ramifications)
on the associations reported in these studies with metrics quantifying more recent deposition is
generally not known. Where patterns of spatial variation in recent deposition are similar to those
for historic deposition, there may be potential for such influence. This is an uncertainty
associated with interpretation of the observational studies as to the deposition levels that may be
contributing to the observed variation in plant or plant community responses. Thus, while
observational studies contribute to the evidence base on the potential for N/S deposition to
contribute to ecosystem effects (and thus are important evidence in the ISA determinations
regarding causality), their uncertainties (and underlying assumptions) differ from those of
modeling analyses, and they may be somewhat less informative with regard to identification of
specific N and S deposition levels that may elicit ecosystem impacts of interest. Both types of
studies, as well as N addition experiments, which are not generally confounded by exposure
changes beyond those assessed yet may have other limitations (see section 5.3.4 below), are
considered in the sections below.
5.3.1 Soil Chemistry Response
Quantitative linkages between N and S deposition and soil chemistry responses vary
across the geography of the U.S. As summarized in sections 4.2 and 4.3 above, acidification and
N enrichment processes can alter the biogeochemistry in terrestrial ecosystems (ISA, Appendix
4). There are several indicators of acidification and N enrichment that also have linkages to
biological responses that are commonly used in quantitative analyses (ISA, Appendix 4, Table 4-
1). These indicators are soil characteristics strongly associated with specific aspects of soil
acidification or nutrient enrichment. Uncertainties in the estimates of these indicators in
quantitative analyses for specific areas will generally be associated with limitations in the
estimation approach and the associated parameter values for those locations.
A number of soil characteristic metrics have been identified to have relationships with
biological responses, making them useful indicators for assessing potential soil acidification
impacts. One commonly used indicator for quantitative modeling analyses of the effect of
acidifying deposition on forests (see section 5.2.2 below) is the ratio of base cations to aluminum
(BC:A1), with higher ratios indicating a lower potential for acidification-related biological effects
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(ISA, Table IS-2). The ratio in soil solution can be reduced by release of base cations from the
soil (e.g., through the process of neutralizing drainage water acidity), which reduces the base
saturation of the soil. Soil base saturation10 and changes to it can also be an indicator of
acidification risk (ISA, Appendix 4, section 4.3.4). The accelerated loss of base cations through
leaching can cause a decrease in base saturation and decreases in soil solution Ca: A1 ratio, which
are all indicators of soil acidification. Inorganic and organic acids can be neutralized by soil
weathering or base cation exchange, in addition to denitrification (ISA, Appendix 4, section 4.3).
Some studies have indicated soil base saturation to be a better indicator than BC: A1 ratio, and
one for which metrics associated with potential risk may have a more well-founded basis as a
more robust indicator for field assessment (e.g., Sullivan et al., 2013).
There are many indicators of N enrichment and potential eutrophication, including N
accumulation, e.g., increased soil N concentrations or decreased carbon to nitrogen (C:N) ratios
(ISA, section IS.5.1.1). The ratio of soil C to soil N can be indicative of ecosystem N status; it is
a "reliable and relatively straightforward measure for identifying forest ecosystems that may be
experiencing soil acidification and base leaching as a result of N input and increased
nitrification" (ISA, Appendix 4, p. 4-39). Accordingly, the C:N ratio can be useful in informing
assessments of the potential for accelerated nitrification and nitrate leaching (ISA Appendix 4,
section 4.3.6; Aber et al., 2003).
Increases in soil N can lead to nitrate leaching, potentially imposing a drain on base
cations and a potential for increased acidity (ISA, Appendix 4, section 4.3). Thus, nitrate
leaching can be an indicator of potential for increased aquatic acidity, as well as for terrestrial or
aquatic N enrichment. Studies in various locations throughout the eastern U.S. and in the Rocky
Mountains have reported estimates of N deposition associated with an onset of increased nitrate
leaching (ISA, Appendix 4, sections 4.3.2 and 4.6.2). For example, based on monitoring results
for an 8-year experimental addition experiment in an alpine dry meadow in the Rocky
Mountains, with annual additions of 20, 40 and 60 kg N/ha-yr (Bowman et al., 2006), Bowman
et al. (2014) reported 10 kg N/ha-yr to be associated with enhanced nitrate leaching at this
location (ISA, Appendix 4, section 4.6.2.2).
Thus, the response of a terrestrial system, and the associated biota, to N additions as
through atmospheric deposition, can be one of acidification and/or nutrient enrichment
depending on the geology and soil chemistry (e.g., base cation weathering rate or base cation
exchange capacity), residual impacts of historic deposition (e.g., S042~/N03~ stored in soil) and
organic content, as well as acid sensitivity or growth limitations of the resident species. With
10 As described in the ISA, "[s]oil base saturation expresses the concentration of exchangeable bases (Ca, Mg,
potassium [K], sodium [Na]) as a percentage of the total cation exchange capacity (which includes exchangeable
H+ and inorganic Al)" (ISA, Appendix 4, p. 4-27).
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regard to soil indicators of nutrient enrichment (i.e., levels associated with particular risk of harm
or degree of protection), there is little research in the U.S. on which to base target values for
indicators such as soil N accumulation or NO3" leaching (Duarte et al., 2013). This and
uncertainties associated with site-specific characteristics (e.g., carbon and organic content of
soils) may affect the use of soil modeling for identifying deposition targets aimed at controlling
nutrient enrichment.
5.3.2 Effects on Trees
In this section we summarize the findings related to quantitative evaluation of S and N
deposition effects on trees. While S deposition contributes to acidification and its associated
negative effects on terrestrial systems, N deposition, as described in Chapter 4 and section 5.3.1
above, may contribute to acidification and/or nutrient enrichment, with associated effects on tree
growth and survival that, for acidification, can be negative and, for nutrient enrichment, can be
positive or negative. While the response is influenced by site-specific characteristics, some
species-specific patterns have also been observed (ISA, Appendix 6, section 6.2.3.1). For
example, conifer species, particularly at high elevations, were more likely to exhibit negative
growth responses or mortality in response to added N and less likely to demonstrate increased
growth (ISA, Appendix 6, section 6.2.3.1; McNulty et al., 2005; Beier et al., 1998; Boxman et
al., 1998). Variation in response can also be related to site-specific factors contributing to
variations associated with location. For example, while some long-term N addition experiments
indicate that broadleaf species more commonly exhibit increased growth (than conifers), there is
variation across studies as seen in Appendix 5B (Table 5B-1). The extent to which species-
specific observations are related to the site-specific characteristics of areas where species are
distributed or to species-specific sensitivities is not clear.
In the subsections below, we draw on three main categories of studies: steady-state mass
balance modeling of soils, experimental addition studies, and observational or gradient studies of
trees. As noted in section 5.3. above, each of these categories of studies has associated strengths
and limitations/uncertainties for our purposes here. For example, while the mass balance
modeling studies are explicitly focused on acidic deposition effects, observational studies, given
their real-world settings, may reflect patterns of deposition contributing to both acidic deposition
and/or the effects of nutrient enrichment. Thus, the subsections below are organized by study
category within which the findings with regard to both types of effects are discussed.
5.3.2.1 Steady-State Mass Balance Modeling of Terrestrial Acidification
Consistent with assessment of aquatic acidification (see section 5.1 above), steady-state
mass balance modeling is also utilized to identify N/S deposition rates associated with conditions
posing differing risks to tree health. The evidence base evaluating such modeling, however, is
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less robust than for aquatic ecosystems, such that the foundation for identifying target conditions
for neutralizing acidification, and for identifying appropriate values for some model parameters,
is more limited and uncertain, as noted below.
As noted in section 5.3.1 above, an indicator commonly utilized to identify conditions
associated with protection from acidifying deposition risks to tree growth and survival is BC:A1
(ISA, Appendix 5, section 5.2.1). There are limitations, however, in the ability of this ratio for
indicating tree health risk. Accordingly, some more recent studies have emphasized other
indicators such as exchangeable Ca or soil base saturation (e.g., Sullivan et al., 2013).
Limitations associated with use of BC:A1 ratios include those related to their
interpretation. More specifically, the two meta-analyses often referenced to inform interpretation
of estimated BC:A1 ratios with regard to associated potential risks to tree health - Sverdrup and
Warfvinge (1993) and Cronan and Grigal (1995) - were largely based on soil solution
concentrations derived from laboratory and greenhouse studies (Sverdrup and Warfvinge, 1993;
Cronan and Grigal, 1995).11 For example, the literature review by Cronan and Grigal (1995),
which reported the Ca:Al ratios in 35 studies in which a response in seedling roots (e.g., change
in nutrient content) were reported, is also often cited as a basis for selection of a target BC: Al
value for use in simple mass balance models. Nearly all of the 35 studies were conducted in
hydroponic or sand systems, in which aluminum is generally more freely available than in a soil
substrate (Cronan and Grigal, 1995). As would be expected, there are limitations and
uncertainties associated with findings involving artificial substrates and growing conditions
(ISA, Appendix 5, section 5.2.1).12 In consideration of these analyses, the BC:A1 targets used in
the 2009 REA for identifying acidifying deposition loads that might provide different levels of
protection range from less than 1 to 10. Use of such target values (of 0.6, 1 and 10) in steady
state simple mass balance modeling in the last review resulted in the identification of acidifying
deposition loads ranging from 487 to 2009 eq/ha-yr, across two areas of the Northeast for BC: Al
target values differing by a factor of nearly 20 (Table 5-7 and Table 5-8).
11 Ratios of BC:A1 were identified using the cumulative percentage of experiments for tree seedling species grown in
solution reporting a 20% growth reduction (Sverdrup and Warfvinge, 1993). For example, at cumulative
percentage of 50% the BC:A1 ratio was 1.2, and at 100% the ratio was on the order of 8 (Sverdrup and
Warfyinger, 1993). The 2009 REA concluded that this analysis reported critical BC:A1 ratios ranging from 0.2 to
0.8 (2009 REA, p. 4-54).
12 Based on the distribution Ca: Al ratios in the studies, Cronan and Grigal (1995) estimated a 50% risk of tree
growth response for a molar ratio of 1.0 based on fact that 17 of the 35 studies had ratio at/above 1.0. The
percentage of studies with a ratio at/above 1.8 was 25%., and it was approximately 5% at a ratio of 5, based on
there being 33 of 35 or 94% of studies reporting a response for a Ca/Al ratio above 5. Only two of the 35 studies,
both in conifers, reported a response, a change to root nutrient content (Cronan and Grigal (1995). In this
assessment, "plant toxicity or nutrient antagonism was reported to occur at Ca/Al ratios ranging from 0.2 to 2.5"
(2009 REA, p. 4-54).
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Table 5-7. Acid deposition levels estimated for BC:A1 targets in 24-state range of red
spruce and sugar maple using steady-state simple mass balance model (2009
REA).
Critical Loads for Acid Deposition for
Target
BC:AI
Different BC:AI Targets
In terms of
S+N
(eq/ha-yr)
In terms ofS
In terms ofN
(kg S/ha-yr)
(kg N/ha-yr)
0.6
1237- 2009
40-64
17-28
1
892-1481
29-48
13-21
10
487-910
16-29
7-13
The 2009 REA (that informed the 2012 review of the NAAQS for N oxides and SOx
review) used the Simple Mass Balance (SMB) model for forest soil acidification, in steady-state
mode, to assess the extent to which atmospheric S and N deposition for the year 2002 might be
expected to contribute to soil acidification of potential concern (with BC:A1 ratio used as an
indicator) for the sensitive species of sugar maple and red spruce in areas of 24-states where they
are native (2011 PA, section 3.1.3; 2009 REA, section 4.3). The critical load analysis for the
three target BC:A1 ratio values (identified for different levels of risk for growth impacts) drawn
from an estimated relationship between tree growth effect for different species and BC:A1 ratio
yielded an array of estimates of acidifying deposition with potential to affect the health of at least
a portion of the sugar maple and red spruce growing in the United States (2009 REA, section 4.3
and Appendix 5; 2011 PA).
In addition to the uncertainty associated with characterization of risk for target BC: A1
ratio values, uncertainties were recognized in the SMB model calculations for the 2009 REA
analyses. For example, uncertainty recognized with the findings related to the use of default
values for several key parameters (e.g., denitrification, nitrogen immobilization, the gibbsite
equilibrium constant, and rooting zone soil depth), and dependence of the SMB calculations on
assumptions made in its application (2009 REA, section 4.3.9). Similarly, the ISA discussion of
SMB equations summarized findings of Li and McNulty (2007), who found uncertainty to come
primarily from components of the estimates for base cation weathering and acid-neutralizing
capacity (ISA, Appendix 4, section 4.5.1.2).
Since the 2009 REA, an updated approach to estimating one particularly influential
parameter in the soil BC:A1 modeling (cation weathering) has been reported (Phelan et al.,
2014). Use of the new approach at 51 forested sites in Pennsylvania yielded rates consistent with
soil properties and regional geology. The updated rates were generally higher, indicating a
greater buffering capacity for sites in this area to acidifying deposition than previously
determined (Phelan et al., 2014). The recent study by Duarte et al. (2013) also used updated
values for cation weathering for a study extending across New England and New York. For a soil
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BC:A1 target of 10, this study reported a range of deposition estimates slightly higher than those
from the 2009 REA (see Table 5-8 below).
Table 5-8. Acidic deposition levels estimated for several BC:A1 ratio targets by steady-
state mass balance modeling for sites in northeastern U.S.
Endpoint, Species, Location | Deposition/Addition (loading) Notes
Modeling Analyses - Steady-state mass balance
Range of risk for reduced growth (sugar maple and
red spruce) in areas of 24 states in Northeast,
based on soil BC:AI targets of 0.6,1 and 10
487 to 2009 eq/ha-yr (7-28 kg N/ha-yr or 16-64
kg S/ha-yr)
2009
REA
Soil BC:AI target of 10 for forest protection at
>4000 plots in New England and New York.
For a BC:AI target of 10, 850-2050 eq/ha-yr (27-
66 kg S/ha-yr or 12-29 kg N/ha-yr), range for
80% of sites (for a BC:AI target of 10) total range
was 11 to 6,540 eq ha-1yr-1, the lowest loads in
Maine, NH and VT
Duarte
et al.
(2013)
5.3.2.2 Experimental Addition Studies
A number of experimental addition studies, conducted primarily in the eastern U.S., have
reported mixed results for growth and survival (see Appendix 5B, Table 5B-1). The species
studied have included oaks, spruce, maples, and pines. (Magill et al., 2004; McNulty et al., 2005;
Pregitzer et al., 2008; Wallace et al., 2007). Some multiyear S or N addition experiments
(involving additions greater than 20 kg/ha-yr) with a small set of eastern species, including sugar
maple, aspen, white spruce, yellow poplar, black cherry, have not reported tree growth effects
(ISA, Appendix 5, section 5.5.1; Bethers et al., 2009; Moore and Houle, 2013; Jung and Chang,
2012; Jensen et al., 2014). Studies described in Appendix 5B are summarized here, including the
annual amounts of N added (in addition to the background deposition occurring during these
times):
• Additions of 25 to as high as 150 kg N/ha-yr for 8-14 years (dating back to 1988) were
associated with increased growth reported in sugar maple and oaks, at sites in MI, MA,
NY, ME.
• Additions of 15.7 and 31.4 kg N/ha-yr for 14 years (beginning in 1988) were associated
with reduced basal area (red spruce) or growth (red maple, tulip poplar and black cherry,
red pine) at sites in VT, MA, WV.
• Additions of 25 kg N/ha-yr for 13 years (beginning in 1989) were associated with
increased growth rates for sugar maple but not for red spruce.
The N deposition levels simulated in experimental addition studies that report tree effects,
including either increased or reduced growth, are generally greater than 10 kg N/ha-yr (Appendix
5B, Table 5B-1).
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5.3.2.3 Observational or Gradient Studies
Since the last review of the NAAQS for N oxides and SOx, several observational studies
have been published that investigate the existence of statistical associations between tree growth
or survival, as assessed at U.S. Forest Service, Forest Inventory and Analysis program
(USFS/FIA)13 sites across the U.S., and estimates of average deposition of S or N compounds
averaged over multiyear time periods (Appendix 5B, section 5B.2.2; ISA, Appendix 5, section
5.5.2 and Appendix 6, section.6.2.3.1; Dietze and Moorcroft, 2011; Horn et al., 2018). The
standardized protocols employed in the FIA program make the use of the FIA plot data a strength
of these studies. These studies generally utilized the tree measurement data collected by the
USFS from periodic assessments at each site, and data for other factors analyzed, including
metrics for atmospheric deposition (Table 5-9; Dietze and Moorcroft, 2011; Thomas et al., 2010;
Horn et al., 2018).
The study by Dietze and Moorcroft (2011) statistically evaluated the influence of a
number of factors, in addition to SC>42"and NO3" wet deposition (site-specific estimates of average
of 1994-2005 annual averages), on tree mortality (assessed over 5-15-year measurement intervals
within the period from 1970s through early 2000s) in groups of species characterized by
functional type (267 species categorized into 10 groups) at sites in the eastern and central U.S.
(Appendix 5B, section 5B.2.2.1; ISA, Appendix 5, section 5.5.2). The full range of average SO42"
deposition was 4 to 30 kg S/ha-yr (Dietze and Moorcroft, 2011). Other factors assessed (which
were all found to have statistically significant associations with more than one of the tree species
groups) were precipitation, minimum and maximum temperature, ozone, topographic factors
(elevation, slope and variation in solar radiation and soil moisture), and biotic interaction factors
(stand basal area and age, and focal-tree diameter at breast height). The authors reported that the
strongest effect on mortality was due to acidifying deposition (specifically SO42"), particularly in
the northeast sites (Dietze and Moorcroft, 2011). Negative associations were reported with tree
survival for 9 of the 10 functional groups. Survival for the same 9 groups was also negatively
associated with long-term average ozone concentrations. The third highest influence was for N
deposition (range across sites was 6 to 16 kg N/ha-yr), with mortality in all but one species group
having a negative association (i.e., lower probability of mortality with higher NO3" deposition).
Regarding the significant associations with S and N deposition, the authors recognized that "[t]he
impacts of both acidification and nitrogen deposition on tree mortality result from cumulative,
13 The FIA Program's forest monitoring component involves periodic assessments of an established set of plots
distributed across the U.S. This component includes collection of data at field sites (one for every 6,000 acres of
forest). The data include forest type, site attributes, tree species, tree size, and overall tree condition. At a subset
of the plots, a broader suite of forest health attributes including tree crown conditions, lichen community
composition, understory vegetation, down woody debris, and soil attributes are also assessed (USFS, 2005).
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long-term deposition, and the patterns presented [in their paper] should be interpreted in that
light" (Dietze and Moorcroft, 2011).
The study by Thomas et al. (2010) focused on relationships of tree growth and survival
(assessed at FIA plots from 1978 through 2001, with measurement interval ranging from 8.3 to
14.4 years) with N deposition (mean annual average for 2000-04) as the only pollutant included
in the statistical analyses (Appendix 5B, section 5B.2.2.2). Increased growth was associated with
higher N deposition in 11 of 23 species in northeastern and north-central U.S and with lower N
deposition in three species (Thomas et al., 2010). Eight species showed negative associations of
survival rates with N deposition and three showed positive associations. The other factors
analyzed included temperature, precipitation, and tree size, but did not include other pollutants
(Thomas et al., 2010).
The third study utilizing measurements at USFS plots, reported on statistical modeling of
tree growth and survival of 71 species at USFS plots across the U.S. with site-specific estimates
of average S and N deposition across the measurement interval (generally 10 years) within the
period from 2000-2013 (Horn et al., 2018; Appendix 5B, section 5B.2.2.3). The study focused on
71 of 94 species for which covariance between N and S deposition metric values and other
factors was a lower concern (Horn et al., 2018). Of the 71 species on which the analysis focused,
negative associations were reported for survival and growth with S deposition estimates for 40
and 31 species, respectively. Sulfur deposition at sites of these species ranged from a minimum
below 5 kg/ha-yr to a site maximum above 40 kg/ha-yr, with medians for these species generally
ranging from around 5 to 12 kg/ha-yr (Appendix 5B, section 5B.2.3).
The study by Horn et al. (2018) also reported associations of growth and survival with N
deposition estimates that varied positive to negative across the range of deposition at the
measurement plots for some species, and also among species (Horn et al., 2018). For the six
species, for which survival was negatively associated with the N deposition metric across the full
range of values, the site-specific deposition metric ranged from below 5 to above 50 kg/ha-yr,
with medians ranging from 8 to 11 kg N/ha-yr (Appendix 5B, Figure 5B-7). The median values
for the 19 other species with unimodal (or hump-shaped) associations that were negative at the
species median deposition value (and for which sites were not limited to the western U.S.)
ranged from 7 to 11 kg N/ha-yr. The deposition metric ranges were generally similar for the
species for which survival was positively associated with the metric (across full range or at the
median). Of the 39 species for which growth was significantly associated with N deposition, the
association was negative across the full range for two species (with sample sites predominantly
in the Atlantic coastal pine barrens and northern plains and forests, respectively). The median
deposition across sites for these two were nine and ten kg N/ha-yr (Appendix 5B, Figure 5B-5
and Attachment 2). The median deposition values for the two other species with hump shaped
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functions that were negative at the median were seven and eight kg N/ha-yr, respectively
(Appendix 5B, Figure 5B-5).
Observational studies newly available in this review include two smaller studies in the
Adirondacks of New York that investigated relationships of forest plot characteristics with N and
S deposition metrics. These locations are well documented to have received appreciable acidic
deposition over the past several decades. The studies report negative associations of forest health
metrics with N and/or S deposition metrics (see Appendix 5B, Table 5B-2). These include the
study by Sullivan et al. (2013), in which mean growth rates of sugar maple were positively
correlated with exchangeable Ca and base saturation at the watershed level, indicating the
influence of these soil acidification indicators. Also, newly available in this review are studies
that analyzed potential for associations of tree growth of sensitive species with temporal changes
in SOx and/or NOx emissions. For example, a study by Soule (2011) reported increased red
spruce growth in North Carolina to be associated with reductions in emissions of SOx and N
oxides from utilities in the southeastern U.S., among other factors, over the period from 1974 to
2007 (Soule, 2011; ISA, Appendix 5, section 5.5.1).
Another observational study newly available in this review documented recovery of a
stand of eastern redcedar (in the Appalachian Mountains of West Virginia) from historical S
pollution using an analysis of tree ring chronology from 1909 to 2008, and a multivariate
correlation analysis involving historical climate variables, atmospheric CO2 concentrations and
U.S. emissions estimates for SO2 and N oxides (ISA, Appendix 5, p. 5-18; Thomas et al., 2013).
Tree growth has increased significantly since 1970 and the analysis indicates it is explained by
increases in atmospheric CO2 and NOx emissions and reductions in SO2 emissions (ISA,
Appendix 5, section 5.2.1.3; Thomas et al., 2013). The authors described the response as an
indirect result of reductions in acid deposition, while other researchers have suggested that, given
the speed of the response, it may more likely be related to reduced gaseous SO2 than acid
deposition (ISA, Appendix 5, section 5.2.1.3; Schaberg et al., 2014).
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Table 5-9. Tree effects and associated S/N deposition levels from observational studies
using USFS/FIA data.
Endpoint, Species, Location Deposition/Addition Reference
S Deposition Metric Analyses
Survival in 7 of 10 species' groups in eastern and
central U.S. negatively associated with SCVdeposition
S042 wet deposition estimates (average, 1994-
2005) varied 4 to 30 kg S/ha-yr across all sites.
Dietze and
Moorcroft (2011)
Survival in 40 species across U.S. was negatively
associated with S deposition estimates.
Median average S deposition estimates (2000-16)
for these species: 3A to 12 kg S/ha-yr.
Horn etal. (2018)
Growth in 31 species across U.S. was negatively
associated with S deposition estimates.
Median S deposition estimates for these species
varied 4A to 12 kg S/ha-yr, when western species
are excluded.
N Deposition Metric Analyses
Mortality in 1 species' group in eastern/central U.S.
positively associated with NOx deposition
Mortality in 9 of 10 species' groups in eastern and
central U.S. negatively associated with NO3- deposition
(reduced mortality with increased NO3 )
NO3- wet deposition estimates (average, 1994-2005)
varied from 6 to 16 kg N/ha-yr across all sites
analyzed
Dietze and
Moorcroft (2011)
Survival of 8 species negatively associated with N
deposition. Survival of 3 species positively associated
with N deposition.
Growth of 3 species (all conifers) negatively
associated with N deposition,
Growth of 11 of 24 species positively associated with
N deposition,
Estimates of average N deposition across the full
set of study sites ranged from 3 to 11 kg N/ha-yr for
the period 2000-2004.
Thomas et al.
(2010)
Survival of 6 species was negatively associated with N
deposition across deposition ranges
Survival of 21 other species (2 limited to the West),
with hump-shape associations, also negatively
associated with N deposition at median deposition
across species' sites.
Survival of one species positively associated with N
deposition across deposition range
Survival of 4 other species, with hump-shape
associations, also positively associated with N
deposition at median deposition for species' sites.
For species with negative associations, median N
deposition estimates varied from 8 to 11 kg N/ha-yr.
For 19 species with negative association at median
deposition, western species excluded, median N
deposition varied 7 to 12 kg N/ha-yr.
For species with positive association, median N
deposition estimate was 11 kg N/ha-yr.
For species with positive association at median
deposition, median N deposition varied from 7 to 12
kg N/ha-yr.
Horn etal. (2018)
Growth of 2 species was negatively associated with N
deposition across all species' sites.
Growth of 2 other species (with hump-shape
associations) also negatively associated with N
deposition at the median deposition across sites
Growth of 20 species (17 nonwestern species) was
positively associated with N deposition across all
species' sites.
Growth of 15 other species with hump-shape
associations (14 nonwestern species) was also
positively associated with N deposition at the median
deposition across those species' sites.
The median average deposition estimates for the
measurement interval (during 2000-16) varied from
9 and 10 kg N/ha-yr.
The median estimates for the other 2 species were
7 and 8.
The 17 nonwestern species assessed at sites for
which the median average deposition estimate for
the measurement interval (during 2000-16) varied
from 7 to 12 kg N/ha-yr.
The median estimates for the other 14 nonwestern
species were 7 to 11 kg N/ha-yr.
A The two values below 5 kg S/ha-yr were for species with 60-80% of samples from the Northern Forests ecoregion.
Details of information summarized here are provided in Appendix 5B, section 5B.2.2.3 and Tables 5B-2 and 5B-6.
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5.3.3 Other Effects
The studies available that may inform consideration of S or N deposition levels of
potential interest for deposition-related effects on terrestrial biota other than trees include both
addition experiments and observational or gradient studies. In addition to effects on individual
species, these studies often report metrics related to changes in communities of particular plant
or lichen populations. Information from both types of studies and with regard to species-level or
community-level effects is discussed in the subsections below. The focus in these studies,
however, is predominantly on N deposition.
5.3.3.1 Effects on Herbs and Shrubs
Observational/Gradient Studies
Since the 2012 review, new observational studies have investigated relationships between
deposition and community composition for understory plants. One of the largest studies, Simkin
et al. (2016), investigated relationships between species richness (number of species) of
herbaceous plants14 and values of a N deposition metric at more than 15,000 forest, woodland,
shrubland and grassland sites across the U.S. (Appendix 5B, section 5B.3.2). The study grouped
the sites into open- or closed-canopy sites, with forest sites falling into the closed-canopy
category and the rest in open-canopy. The data for sites in each of the two categories were
analyzed for relationships of species richness (number of herbaceous species) with values of the
N deposition metric, soil pH, temperature, and precipitation (Simkin et al., 2016). The species
richness assessments were conducted across a 23-year period (1990-2013) by multiple
researchers, at sites clustered most prominently in portions of the 14-state study area, e.g., MN,
WA, OR, VA, NC and SC (Appendix 5B, Figure 5B-13). The N deposition metric for each site
was a 10-year average of dry N deposition (2002-2011) added to a 27-year average (1985-2011)
of wet deposition (Simkin et al., 2016; Appendix 5B, section 5B.3.2).
Different relationships among the analyzed factors were observed for the two categories
of sites, with a hump-shaped relationship of species richness with the deposition metric at open-
canopy sites and a strong influence of soil pH at the closed-canopy (forest) sites (Simkin et al.,
2016).
• At open-canopy sites, the association of herbaceous species richness with the N deposition
metric was somewhat dependent on soil pH, precipitation and temperature. Herbaceous
species richness was positively associated with the N deposition metric at the lower end
of the deposition range and negatively associated with N deposition at the higher end of
the deposition range, on average for metric values above 8.7 kg N/ha-yr (Simkin et al.,
2016).
14 Herbaceous plants are nonwoody vascular plants, including annuals, biannual and perennials.
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• At closed-canopy (forest) sites, the association of herbaceous species richness with the N
deposition metric was highly dependent on soil pH. Across sites with acid soil pH
at/above 4.5, species richness was negatively associated with N deposition metric values
greater than 11.6 kg N/ha-yr, but among sites with basic soils there was no point in the
data set at which N deposition had a negative effect on species richness (the analysis
included deposition values up to -20 kg N/ha-yr).
The long time period over which the N deposition estimates are averaged in this study
provides for an N deposition metric generally representative of long-term N deposition over a
time period of temporally changing rates, particularly in areas of the Midwest south to the Gulf
and eastward (e.g., ISA, Appendix 2, section 2.7). The impact of the differing time periods for
the wet versus dry deposition estimates, however, is unclear. Notably, the study did not consider
potential roles for other pollutants with a potential influence on the observations, including ozone
and S deposition. Overall, the study by Simkin et al. (2016) indicates an effect of N deposition
on herbaceous species richness, with a number of uncertainties that limit interpretations
regarding identification of specific deposition levels of potential concern with regard to impacts
on herbaceous species number.
Studies in southern California, particularly in grassland or coastal sage scrub
communities, have investigated the role of past N deposition in documented alterations of
community composition and increases in the presence of invasive species (ISA, Appendix 6,
section 6.3.6). In light of the changes in vegetation that have occurred in this area since the early
20th century, a recent study by Cox et al. (2014) utilized a landscape-level analysis in
investigating the risk of coastal sage scrub communities converting to exotic annual grasslands
and potential associations with N deposition. These analyses further considered the factors that
might influence or facilitate community recovery. Results of these analyses indicated that
recovery of coastal sage shrub communities15 from exotic grass invasion was most likely in sites
with N deposition below 11.0 kg N/ ha-yr (in 2002, based on CMAQ modeling) and that had
experienced relatively low invasion (Cox et al., 2014).
Experimental Addition Studies
Several addition studies have focused on California coastal sage scrub communities (ISA,
Appendix 6, section 6.3.6). A study of 13 years of 50 kg N/ha-yr additions reported no
significant effects on plant cover for the first 11 years of the 13-year period (ISA, Appendix 6, p.
6-81; Appendix 5B, Table 5B-7). Community composition was changed after five years,
reflecting changes in the relative abundance of dominant shrubs, and in the 11th through 13th
years, increases in an exotic plant and decreases in one of the native shrubs were reported
(Vourlitis, 2017; Vourlitis and Pasquini, 2009).
15 Coastal sage scrub is a shrubland community that occurs in Mediterranean-climate areas in southern California.
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Experimental addition experiments have also reported variable relationships between N
additions and impacts for herb or shrub communities (ISA, Appendix 6, section 6.3; Appendix
5B, section 5B.3.1). For example, a study by Bowman et al. (2012) in a dry sedge meadow in
Colorado reported no shifts in species richness or diversity in response to N additions of 5, 10 and
30 kg/ha-yr, but also found increases in cover of one species (Carex rupestris) that ranged from
34 to 125% across the treatments (ISA, Appendix 6, section 6.3.4). Changes in the relative
abundance of this species was the authors' basis for their CL estimate of 4 kg N/ha-yr.
At Joshua Tree National Park in the Mojave desert of California, non-native grass
biomass increased significantly at three of the four study sites receiving 30 kg N/ha-yr for two
years but experienced no significant change with an addition of 5 kg N/ha-yr (Allen et al., 2009).
No significant change in community composition or species richness was reported in a semi-arid
grassland in Utah in response to smaller additions of 2, 5 and 8 kg N/ha-yr over two years (ISA,
Appendix 6, Table 6-21; McHugh et al., 2017). Much higher additions, of 10, 20, 34, 54 and 95
kg N/ha-yr over 23 years, in prairie grasslands resulted in reduced species richness. Ceasing
those additions after 10 years resulted in recovery of species number back to control numbers16
after 13 years (Clark and Tillman, 2008).
5.3.3.2 Effects on Lichen
The available information on N, S or PM exposure conditions associated with effects on
lichen is primarily focused on nitrogen species (available evidence summarized in the ISA,
Appendix 6, section 6.5.2). Limited information regarding effects of SOx on lichen species is
summarized in section 5.4.1 below, and the extent to which the effects relate to airborne SOx or
dry deposition of SO2 O'.s associated acidic deposition) is not clear. Somewhat similarly, section
5.4.2 below summarizes the available information regarding N oxides exposure conditions,
including associated deposition, for which effects are reported on lichen species. We address
below several observational or gradient studies newly available in this review that analyzed
relationships between lichen community characteristics and N and/or S deposition metrics at
sites in the Northeast and Northwest (Table 5B-9; ISA, Appendix 5, section 5.5.1 and Appendix
6, section 6.5).
In the northeastern U.S., past studies have concluded that in areas distant from industrial
or urban sources, atmospheric deposition alters chemistry of tree bark (that provides substrate for
lichen species) through acidification or eutrophication (Cleavitt et al., 2011; van Herk, 2001;
ISA, Appendix 6, section 6.2.3.3). A study of relationships between lichen metrics and metrics
for annual and cumulative N and S deposition from 2000 to 2013 at plots in four Class I areas of
16 Species number changes in control plots contributed to this finding (Clark and Tillman, 2008; Isbell et al., 2013).
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the northeastern U.S. reported that "lichen metrics were generally better correlated with
cumulative deposition than annual deposition" (Cleavitt et al., 2015). Further, cumulative dry
deposition of S yielded the best fit to decreases in thallus condition, poorer community-based S
Index values, and absence of many S-sensitive species, indicating a stronger role for legacy of
historical deposition than recent deposition patterns (Cleavitt et al., 2015). Across the years
studied, annual S and N deposition in the four areas declined, from roughly 6-15 kg S/ha-yr to 3-
6 kg S/ha-yr and from roughly 4-15 kg N/ha-yr to 3-8 kg N/ha-yr (Cleavitt et al., 2015, Figure 4).
Two more recent studies involve sites in the Northwest and focus on assessing
relationships between metrics for lichen community composition and estimated N deposition.
The study by Geiser et al. (2010) related lichen air scores assigned based on relative abundance
of oligotrophic and eutrophic species in assessments (conducted from 1994 to 2002) to N
deposition metric values (based on 1990-99 average N deposition). The authors identified a
breakpoint between the third and fourth air scores which was associated with 33-43% fewer
oligotrophic species and 3 to 4-fold more eutrophic species than sites with scores in the "best"
bin; at sites reflecting this scoring breakpoint, total N deposition estimates ranged from 3 to 9 kg
N/ha-yr (Geiser et al., 2010). Using a different score or index to characterize lichen communities
(based on assessments 1993-2011), Root et al. (2015) analyzed particulate N estimated from
speciated PM2.5 monitoring data and throughfall N deposition estimated from lichen N content.
Several aspects of these studies complicate interpretation of exposure conditions and
identification of N deposition levels associated with particular risks to lichen communities. For
example, the methods for utilizing N deposition differ from current commonly accepted
methods. There is also uncertainty regarding the potential role of other unaccounted-for
environmental factors (including ozone, SO2, S deposition and historical air quality and
associated deposition). There is uncertainty concerning the independence of any effect of
deposition levels from residual effects of past N deposition. And there are few controlled N
addition experiments that might augment or inform interpretation of the findings of
observational/gradient studies (fumigation studies are summarized in section 5.4.2 below). Other
studies in Europe and Canada have not reported such associations with relatively large N
deposition gradients.
5.3.4 Summary: Key Findings and Associated Uncertainties
Key findings related to ambient air concentrations and S and N deposition levels
associated with terrestrial effects discussed in prior sections are summarized below.
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5.3.4.1 Deposition and Risks to Trees
Soil Acidification Analyses and Risk to Trees
Steady-state modeling analysis performed in the 2009 REA estimated annual amounts of
acid deposition at or below which one of three BC:A1 targets would be met in a 24-state area in
which the acid-sensitive species, red spruce and sugar maple, occur. While the two least
restrictive targets (BC:A1 of 0.6 and 1) differed by less than a factor of two, the two most
restrictive targets (BC:A1 of 1 and 10) differed by a factor of 10. A range of acid deposition was
estimated for each of the three targets. For a BC:A1 target of 0.6, the range was 1237-2009 eq/ha-
yr; for a BC:A1 target of 1, the range was 892-1481 eq/ha-yr; and for a BC: A1 target of 10, the
range was 487-910 eq/ha-yr. Estimates of total S and N deposition in regions of the U.S. for the
2019-2021 period appear to meet all but the most restrictive of these targets (e.g., section 2.5.3
above; ISA, Appendix 2, sections 2.6 and 2.7).
Uncertainties associated with these analyses include those associated with the limited
dataset of laboratory-generated data on which the BC:A1 targets are based. These data are
derived from an array of studies of tree seedlings in artificial substrates and responses ranging
from changes in plant tissue components to changes in biomass. In addition to the uncertainty
associated with the basis for the BC: A1 targets, there are uncertainties in the steady-state
modeling parameters, most prominently those related to base cation weathering and acid-
neutralizing capacity (2009 REA, section 4.3.9). As discussed in section 5.3.2.1 above, more
recent publications have employed a new approach to estimating these parameters, including the
weathering parameter, with reduced uncertainty. For the Pennsylvania study area where this was
tested, a greater buffering capacity was estimated, and for a larger study area of the Northeast,
the deposition estimates for the BC: A1 target of 10 were slightly higher than those for the 2009
REA (Phelan et al., 2014; Duarte et al., 2013).
Tree Growth and Survival in Experimental Addition Studies
Experimental addition studies of S, or S plus N, with additions greater than 20 kg/ha-yr,
have been performed in eastern locations and focused on a small set of species, including sugar
maple, aspen, white spruce, yellow poplar, black cherry; these studies generally have not
reported growth effects (Appendix 5B, section 5B.2.1). A study involving both S and N additions
greater than 20 kg/ha-yr for each substance reported increased growth rate for sugar maple but
not for the second species (Bethers et al., 2009), while another study of similar dosing of S and N
reported reduced growth in three species after 10 years that resolved in two of the species after
22 years (Jensen et al., 2014). In both situations background deposition contributions were also
appreciable (Appendix 5B, Table 5B-1).
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Uncertainties associated with these analyses include the extent to which the studies
reflect steady-state conditions. Given the variability in the durations across these studies and the
relatively short durations for some (e.g., less than five years), it might be expected that steady-
state conditions have not been reached, such that the S/N loading is within the buffering capacity
of the soils. A related limitation of some of these studies is the lack of information regarding
historic deposition at the study locations that might inform an understanding of the prior issue.
However, many of the studies have assessed soil characteristics and soil acidification indicators,
which also informs this issue.
With regard to N addition, the available studies have reported mixed results for growth
and survival for several eastern species including oaks, spruce, maples and pines (Table 5B-1;
Magill et al., 2004; McNulty et al., 2004; Pregitzer et al., 2008; and Wallace et al., 2007). Some
studies have suggested that this variation in responses is related to the dominant mycorrhizal
association of the species (e.g., Thomas et al., 2010). It is not clear the extent to which such
findings may be influenced by species-specific sensitivities or soils and trees already impacted
by historic deposition, or other environmental factors. Uncertainties for N addition experiments
and interpretation of their results include this complexity, as well as the uncertainties identified
above for S or S+N addition studies.
Observational/Gradient Studies of Tree Growth/ Survival
With regard to S deposition, the two large studies that analyzed growth and/or survival
measurements in tree species at sites in the eastern U.S. or across the country report negative
associations of tree survival and growth with the S deposition metric for nearly half the species
individually and negative associations of tree survival for 9 of the 10 species' functional type
groupings (Dietze and Moorcroft, 2011; Horn et al., 201817). Interestingly, survival for the same
9 species groups was also negatively associated with long-term average ozone (Dietze and
Moorcroft, 2011).
• The full range of average S042"deposition estimated for the 1994-2005 time period
assessed by Dietze and Moorcroft (2011) for the eastern U.S. study area was 4 to 30 kg S
ha^yr"1.
• Median S deposition (2000-13) estimated at sites (measurement interval average
[occurring within 2000-13]) of nonwestern species with negative associations with
growth or survival ranged from 5 to 12 kg S ha"1 yr"1, with few exceptions (Horn et al.,
2018).
The S deposition metrics for the two studies were mean annual average deposition
estimates for total S or sulfate (wet deposition) during different, but overlapping, time periods of
17 The study by Horn et al. (2018) constrained the S analyses to preclude a positive association with S.
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roughly 10-year durations. Additionally, S deposition in the U.S. across the full period of these
studies (1994-2013) generally exhibited a consistent pattern of appreciable declines. Further, the
study plots, particularly in the eastern U.S., have experienced decades of much higher S
deposition in the past. The extent to which the differences in growth or survival across sites with
different deposition estimates are influenced by historically higher deposition (e.g., versus the
magnitude of the average over the measurement interval) is unknown. There are few available
studies describing recovery of historically impacted sites (e.g., ISA, section IS.4.1, IS.5.1,
IS. 11.2).
Regarding N deposition, the three large studies that analyzed growth and/or survival
measurements in tree species at sites in the northeastern or eastern U.S., or across the country,
report associations of tree survival and growth with several N deposition metrics (Dietze and
Moorcroft, 2011; Thomas et al., 2010; Horn et al., 2018).
• Estimates of average N deposition across the full set of sites analyzed by Thomas et al.
(2010) in 19 states in the northeastern quadrant of the U.S. ranged from 3 to 11 kg N/ha-
yr for the period 2000-2004.
• The full range of average NO3" deposition estimated for the 1994-2005 time period
assessed by Dietze and Moorcroft (2011) for the eastern U.S. study area was 6 to 16 kg N
ha^yr"1.
• Median N deposition estimated (measurement interval average [falling within 2000-13])
at sites of nonwestern species for which associations with growth or survival were
negative (either over full range or at median for species) ranged from 7 to 12 kg N ha~'yr~
1 (Horn et al., 2018).
• Median N deposition estimated (measurement interval average [within 2000-13]) at sites
of nonwestern species for which associations with growth or survival were positive
(either over full range or at median for species) ranged from 7 to 12 kg N ha"1 yr"1 (Horn
et al., 2018).
The N deposition metrics for these three studies were mean annual average deposition
estimates for total N or nitrate (wet deposition) during different, but overlapping, time periods
that varied from 5 to more than 10 years and include areas that have experienced decades of
much higher deposition. Further, N deposition during the combined time period (1994-2013) has
changed appreciably at many sites across the country, with many areas experiencing declines and
a few areas experiencing increases in deposition of some N species and in total N deposition.
In considering what can be drawn from these studies with regard to identification of
deposition levels of potential concern for tree species effects, a number of uncertainties are
recognized. For example, several factors with potential influence on tree growth and survival
were not accounted for. For example, although ozone was analyzed in one of the three studies,
soil characteristics and other factors with potential to impact tree growth and survival (other than
climate) were not assessed, contributing uncertainty to their interpretations. Further, differences
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in findings for the various species (or species' groups) may relate to differences in geographic
distribution of sampling locations, which may contribute to differences in ranges of deposition
history, geochemistry etc. Additionally, as noted above, the extent to which associations reflect
the influence of historical deposition patterns and associated impact is unknown.
As summarized in Appendix 5B, Table 5B-6, there is a general similarity in findings
among the studies, particularly of Horn et al. (2018) and Dietze and Moorcroft (2011), even
though the time period and estimation approach for S and N deposition differ. Given the role of
deposition in causing soil conditions that affect tree growth and survival, and a general similarity
of spatial variation of recent deposition to historic deposition, an uncertainty associated with
quantitative interpretation of these studies is the extent to which the similarity in the two studies'
finding may indicate the two different metrics to both be reflecting geographic variation in
impacts stemming from historic deposition. Although the spatial patterns are somewhat similar,
the magnitudes of S and N deposition in the U.S. has changed appreciably over the time period
covered by these studies (e.g., Appendix 5B, Figures 5B-9 through 5B-12). The appreciable
differences in magnitude across the time periods also contribute uncertainty to interpretations
related to specific magnitudes of deposition associated with patterns of tree growth and survival.
5.3.4.2 Deposition Studies of Herbs, Shrubs and Lichens
The available studies that may inform our understanding of exposure conditions,
including N deposition levels, of potential risk to herb, shrub and lichen communities include
observational or gradient studies and experimental addition conducted in different parts of the
U.S. Among the studies of plant communities are observational studies of herbaceous species
richness at sites in a multi-state study area and of grassland or coastal sage scrub communities in
southern California, and experimental addition experiments in several western herb or shrub
ecosystems. The experimental addition studies indicate effects on community composition
associated with annual N additions of 10 kg N/ha-yr (in the context of background deposition on
the order of 6 kg N/ha-yr) and higher (section 5.3.3.1 above). Experiments involving additions of
5 kg N/ha-yr variously reported no response or increased cover for one species (in context of
background deposition estimated at 5 kg N/ha-yr). The landscape-level analysis of coastal sage
scrub community history in southern California observed a greater likelihood of recovery of sites
with relatively low invasion of exotic invasive grasses when the N deposition metric level was
below 11 kg N/ha-yr. Lastly, the multi-state analysis of herbaceous species richness reported a
negative association with N deposition metric values above 8.7 kg N/ha-yr at open-canopy sites
and above 11.6 kg N/ha-yr at forest sites with acidic soil pH at or above 4.5.
Limitations and associated uncertainties vary between the two types of studies
(experimental addition and observational). Both are limited with regard to consideration of the
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impacts of long-term deposition. While there are some experimental addition studies lasting
more than 20 years, many are for fewer than 10 years. Additionally, such studies are necessarily
limited with regard to the number and diversity of species and ecosystems that can be analyzed.
In the case of observational studies, the many decades-long history of S and N deposition, as
well as elevated levels of airborne pollutants, including ozone and nitrogen oxides, in the U.S. is
their backdrop, and its influence on associations observed with more recent deposition metrics is
generally unaccounted for. Further, given the very nature of observational studies as occurring in
real time, there is uncertainty associated with characterization, including quantification, of the
particular exposure conditions that may be eliciting patterns of ecosystem metrics observed.
The few studies of lichen species diversity and deposition-related metrics, while
contributing to the evidence that relates deposition, including acidic deposition in eastern
locations, to relative abundance of different lichen species, are more limited with regard to the
extent that they inform an understanding of specific exposure conditions in terms of deposition
levels that may be of concern. As summarized in section 5.3.3.2 above, a number of factors limit
such interpretations of the currently available studies. These factors include uncertainties related
to the methods employed for utilizing estimates of N deposition, the potential role of other
unaccounted-for environmental factors (including ozone, SO2, S deposition and historical air
quality and associated deposition), and uncertainty concerning the independence of any effect of
deposition levels from residual effects of past patterns of deposition. We additionally note the
summary in section 5.4.2 below, of information on exposure conditions associated with effects
on lichen species of oxides of N such as HNO3.
5.4 OTHER EFFECTS OF OXIDES OF N AND S AND OF PM IN
AMBIENT AIR
The evidence related to exposure conditions for other effects of SOx, N oxides and PM in
ambient air includes concentrations of SO2 and NO2 associated with effects on plants,
concentrations of NO2 and HNO3 associated with effects on plants and lichens and quite high
concentrations of PM that affect plant photosynthesis. The PM effects described in the evidence
are nearly all related to deposition. With regard to oxides of N and S, we note that some effects
described may be related to dry deposition of SO2 and HNO3 onto plant and lichen surfaces.
These exposure pathways would be captured in observational studies and could also be captured
in some fumigation experiments.
With regard to SO2 the evidence comes from an array of studies, primarily field studies
for the higher concentrations associated with visible foliar injury and laboratory studies for other
effects. With regard to oxides of N, the evidence indicates that effects on plants and lichens
occur at much lower exposures to HNO3 (than to NO2). The laboratory and field studies of
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oxides of N vary with regard to their limitations; field studies are limited with regard to
identification of threshold exposures for the reported effects and uncertainties associated with
controlled experiments include whether the conditions under which the observed effects occur
would be expected in the field. With regard to the latter, as described in section 5.4.2 below, the
elevated concentrations of NO2 and HNO3 in the Los Angeles area in the 1970s-90s is well
documented as is the decline of lichen species in the Los Angeles Basin during that time.18
5.4.1 Sulfur Oxides
As summarized in section 4.1 above, other welfare effects of SOx in ambient air include
effects on vegetation, such as foliar injury, depressed photosynthesis and reduced growth or
yield. Within the recently available information are observational studies reporting increased tree
growth in association with reductions in SO2 emissions. These studies, however, do not generally
report the SO2 concentrations in ambient air or account for the influence of changes in
concentrations of co-occurring pollutants such as ozone (ISA, Appendix 3, section 3.2). The
available data that include exposure concentrations are drawn from experimental studies or
observational studies in areas near sources, with the most studied effect being visible foliar
injury to various trees and crops (ISA, Appendix 3, section 3.2; 1982 AQCD, section 8.3). Based
on controlled laboratory exposures in some early studies (assessed in the 1982 AQCD),
concentrations greater than approximately 0.3 ppm SO2 for a few hours were required to induce
slight injury in seedlings of several pine species, with sensitive species exposed in conducive
conditions being more likely to show visible injury (1982 AQCD, section 8.3). With regard to
foliar injury, the current ISA states there to be "no clear evidence of acute foliar injury below the
level of the current standard" (ISA, p. IS-37). For effects on plant productivity and growth,
studies described in the 1982 AQCD that involve experimental exposures in the laboratory have
reported depressed photosynthesis by 20% or more from one week of continuous exposure to 0.5
ppm SO2 for 3 weeks to 3 hours/day at 0.5 ppm. Few studies report yield effects from acute
exposures, with the available ones reporting relatively high concentrations. For example, a study
with soybeans reported statistically significant yield reductions (more than 10%) after a 4.2-hour
exposure to concentrations greater than 1 ppm SO2 (1982 AQCD, section 8.3).
The evidence presented in the ISA also includes effects on lichen species, such as those
reported in laboratory fumigation experiments that have assessed effects on photosynthesis and
other functions in a few lichen groups (ISA, Appendix 3, section 3.2). For example, a study of
18 For example, concentrations of HNO3 reported in forested areas of California in the 1980s ranged up to 33 ug/m3,
and annual average NO2 concentrations in the Los Angeles area ranged from 0.078 ppm in 1979 to 0.053 ppm in
the early 1990s (section 5.4.2). Ambient air concentrations of HNO3 in the Los Angeles metropolitan area have
declined markedly, as can be seen from Figure 2-23 (in section 2.4.1), which compares concentrations at
CASTNET monitoring sites between 2019 and 1996.
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two lichens in Spain by Sanz et al. (1992) found photosynthesis to be significantly depressed in
the more sensitive species after 4 to 6 hours at 0.1 ppm SO2, with recovery occurring within 2
hours following exposure. After shorter exposures to 0.25, 0.5 and 0.9 ppm, photosynthesis
recovered within two weeks. After exposures to higher concentrations, photosynthesis in the
more sensitive species was significantly reduced and did not recover. The second species tested
was appreciably less sensitive to SO2 exposure (Sanz et al., 1992).
5.4.2 Nitrogen Oxides
The direct welfare effects of N oxides in ambient air include effects on plants and lichens.
For plants, studies reported in the ISA did not report effects on photosynthesis and growth
resulting from exposures of NO2 concentrations below 0.1 ppm (ISA, Appendix 3, section 3.3).
For example, five days of 7-hour/day exposures of soybean plants reduced photosynthesis at 0.5
ppm and increased photosynthesis at 0.2 ppm NO2 (ISA, Appendix 3, section 3.3). Exposures to
0.1 ppm NO2 continuously for eight weeks and for six hours/day over 28 days elicited reduced
growth of Kentucky blue grass and seedlings of three tree species, respectively (ISA, Appendix
3, section 3.3). A study of five California native grasses and forbs exposed to 0.03 ppm NO2
continuously for 16 weeks found no significant effects on shoot or root biomass, photosynthesis
or stomatal conductance (ISA, Appendix 3, section 3.3). Visible foliar injury has not been
reported to occur with NO2 exposure concentrations below 0.2 ppm except for exposures of
durations lasting 100 hours or longer (ISA, Appendix 3, section 3.3). The ISA notes that for most
plants, "injury occurred in less than 1 day only when concentrations exceeded 1 ppm" (ISA,
Appendix 3, p. 3-10). The information is more limited with regard to exposures to other oxides
of N. A study involving three 4-hr exposures to 30 ppb PAN on alternating days in a laboratory
setting reported statistically significant reduction in growth of kidney bean and petunia plants
(ISA, Appendix 3, section 3.3).
The evidence for HNO3 includes controlled exposure studies describing foliar effects on
several tree species. For example, 12-hour exposures to 50 ppb HNO3 (-75 |ig/m3) in light, and
to 200 ppb (-530 |ig/ m3) in darkness, affected ponderosa pine needle cuticle (ISA, Appendix 3,
section 3.4). Nitric acid has also been found to deposit on and bind to the leaf or needle surfaces.
Continuous 32- or 33-day chamber exposure of ponderosa pine, white fir, California black oak
and canyon live oak to 24-hour average HNO3 concentrations generally ranging from 10 to 18
|ig/m3 (moderate treatment) or 18 to 42 |ig/ m3 (high treatment), with the average of the highest
10% of concentrations generally ranging from 18 to 42 |ig/ m3 (30-60 |ig/ m3 peak) or 89 to 155
|ig/ m3 (95-160 |ig/ m3 peak), resulted in damage to foliar surfaces of the 1 to 2-year old plants
(ISA, Appendix 3, section 3.4; Padgett et al., 2009). The moderate treatment reflects exposure
concentrations observed during some summer periods in the Los Angeles Basin in the mid-
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1980s, including a high HNO3 concentration of 33 ug/ m3 in August 1986 (Padgett et al., 2009;
Bytnerowicz and Fenn, 1996), when annual average NO2 concentrations in the Basin ranged up
to 0.058 ppm (U.S. EPA, 1987).
The available evidence for lichens includes a recent laboratory study, involving daily
HNO3 exposures for 18 to 78 days, with daily peaks near 50 ppb (-75 |ig/ m3) that reported
decreased photosynthesis, among other effects (ISA, Appendix 6, section 6.2.3.3; Riddell et al.,
2012). Based on studies extending back to the 1980s, HNO3 has been suspected to have had an
important role in the dramatic declines of lichen communities that occurred in the Los Angeles
basin (ISA, Appendix 3, section 3.4; Nash and Sigal, 1999; Riddell et al., 2008; Riddell et al.,
2012). For example, lichen transplanted from clean air habitats to analogous habitats in the Los
Angeles basin in 1985-86 were affected in a few weeks by mortality and appreciable
accumulation of H+ and NO3" (ISA, Appendix 3, section 3.4; Boonpragob et al., 1989). As
described in Appendix 5B, section 5B.4.1, the Los Angeles metropolitan area experienced NO2
concentrations well in excess of the NO2 secondary standard during this period. For example,
annual average NO2 concentrations in Los Angeles ranged up to 0.078 ppm in 1979 and
remained above the standard level of 0.053 ppm into the early 1990s (Appendix 5B, section
5B.4.1). The magnitude and spatial extent of declines over the last several decades, in both dry
deposition of HNO3 and annual average HNO3 concentration in this area and nationally, are
illustrated in Figure 2-23 above (and the ISA, Appendix 2, Figure 2-60). As assessed in the ISA,
the evidence indicates NO2, and particularly, HNO3, as "the main agent of decline of lichen in
the Los Angeles basin" (ISA, Appendix 3, p. 3-15), thus indicating a role for the elevated
concentrations of nitrogen oxides documented during the 1970s to 1990s (and likely also
occurring earlier). More recent studies indicate variation in eutrophic lichen abundance to be
associated with variation in N deposition metrics (ISA, Appendix 6, section 6.2.3.3). The extent
to which these associations are influenced by residual impacts of historic air quality is unclear.
5.4.3 Particulate Matter
The extent to which quantitative information is available for airborne PM concentrations
associated with ecological effects varies for the various types of effects. The concentrations at
which PM has been reported to affect vegetation (e.g., through effects on leaf surfaces, which
may affect function or through effects on gas exchange processes) are generally higher than
those associated with conditions meeting the current standards and may be focused on specific
particulate chemicals rather than on the mixture of chemicals in PM occurring in ambient air. For
example, reduced photosynthesis has been reported for rice plants experiencing fly ash particle
deposition of 0.5 to 1.5 g/m2-day, which corresponds to loading greater than 1000 kg/ha-yr (ISA,
Appendix 15, sections 15.4.3 and 15.4.6). Studies involving ambient air PM have generally
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involved conditions that would not be expected to meet the current secondary standards, e.g.,
polluted locations in India or Argentina (ISA, Appendix 15, sections 15.4.3 and 15.4.4). Studies
in the U.S. that have looked at the effects of airborne PM on plant reproduction near roadway
locations in the U.S. have not reported a relationship between PM concentrations and pollen
germination (ISA, Appendix 15, section 15.4.6). In summary, little information is available on
welfare effects of airborne PM in exposure conditions likely to meet the current standards, and
that which is available does not indicate effects to occur under those conditions.
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Vourlitis, GL (2017). Chronic N enrichment and drought alter plant cover and community
composition in a Mediterranean-type semi-arid shrubland. Oecologia 184: 267-277.
Vourlitis, GL and Pasquini, SC (2009). Experimental dry-season N deposition alters species
composition in southern Californian mediterranean-type shrublands. Ecology 90: 2183-
2189.
Wallace, ZP, Lovett, GM, Hart, JE and Machona, B (2007). Effects of nitrogen saturation on tree
growth and death in a mixed-oak forest. For Ecol Manage 243: 210-218.
Wedemeyer, DA, Barton, BA and McLeary, DJ (1990). Methods for Fish Biology: Stress and
acclimation. American Fisheries Society. Bethesda, Maryland.
WHO (2008). World Health Organization Harmonization Project Document No. 6. Part 1:
Guidance Document on Characterizing and Communicating Uncertainty in Exposure
Assessment. Available at: https://www.who.int/publications/i/item/9789241563765.
Wigand, C, McKinney, RA, Charpentier, MA, Chintala, MM and Thursby, GB (2003).
Relationships of nitrogen loadings, residential development, and physical characteristics
with plant structure in new England salt marshes. Estuaries 26: 1494-1504.
Williams, J and Labou, S (2017). A database of georeferenced nutrient chemistry data for
mountain lakes of the Western United States. Sci Data 4: 170069.
Williams, JJ, Lynch, JA, Saros, JE and Labou, SG (2017). Critical loads 1 of atmospheric N
deposition for phytoplankton nutrient limitation shifts in western U.S. mountain lakes.
Ecosphere 8: e01955.
5-87
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6 RELATIONSHIPS OF DEPOSITION TO AIR
QUALITY METRICS
6.1 OVERVIEW
To address the framing questions that guide the scope of this review, this section
focuses on characterizing the relationship between deposition of S and N compounds and air
quality concentrations of S oxides, N oxides and PM2.5. This characterization is a key aspect of
the approach taken in this PA for assessing deposition-related effects and the adequacy of the
current secondary standards, as summarized in section 3.2 (Figure 6-1).
Figure 6-1. General approach for assessing the currently available information with
regard to consideration of protection provided against deposition-related
ecological effects on the public welfare.
While the ecological effects examined in this review include those associated with
deposition of S and N, the NAAQS are set in terms of pollutant concentrations. The goal of this
section is to examine the relationship between atmospheric deposition of S and N with ambient
air concentrations of criteria air pollutants, over a range of conditions (e.g., pollutants, regions,
time periods). An evaluation of this relationship can then help inform how changes in air
concentrations, and the emissions from which they result, could lead to changes in the amounts
of S and N deposited. This understanding can then help inform decisions on which air quality
metric(s) to consider for a standard designed to protect against S and N deposition-related
effects.
6-1
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However, there are fundamental difficulties in establishing quantitative relationships
between air quality concentrations and deposition, stemming from the complex atmospheric
processes that govern the lifecycle of pollutant emissions to eventual deposition to the surface.
As described in more detail below, multiple pollutants can contribute to S and N deposition.
Additionally, there are multiple deposition pathways (i.e., dry deposition and wet deposition) that
can influence the spatial and temporal scales at which deposition occurs and which can vary by
pollutant and pollutant phase. Further, deposition measurements are relatively limited and are
largely available only for wet deposition. There are relatively few sites that collect collocated air
concentration and pollutant deposition data. We can use air quality models to estimate deposition
where there is a lack of monitors, but these models are limited by our understanding of the
processes that influence deposition and have their own uncertainties and error.
6.1.1 Review of the Processes Affecting Atmospheric Deposition
Atmospheric deposition occurs when a pollutant is transferred from the atmosphere to the
earth's surface through dry deposition (settling onto the surface through direct contact) or wet
deposition (aqueous uptake, or scavenging by rain, clouds, snow, or fog). There are a variety of
factors that determine how much of the pollutant is deposited. For example, the rate at which a
pollutant dry deposits (i.e., the dry deposition velocity) depends on the physical properties of the
chemical compound, meteorological conditions, and the properties of the surface to which the
pollutant is being deposited. The rate of wet deposition is influenced by the chemical and
physical properties of the pollutant, the precipitation rate, and the vertical distribution of the
pollutant in the atmosphere.
For dry deposition, the physical properties of a chemical compound can be especially
important in determining its deposition velocity and the rates of deposition can vary substantially
across the nitrogen and sulfur containing compounds in the atmosphere (ISA, Appendix 2,
section 2.5). For example, NO2 can oxidize to form nitric acid (HNO3), which deposits more
readily than NO2. However, HNO3 can also partition into the particle phase in the presence of
ammonia to form ammonium nitrate (NH4NO3) which is deposited primarily through wet
processes. Fine particles have a slower dry deposition velocity and generally remain in the
atmosphere longer than gases, i.e., days for nitrate PM2.5 versus hours for HNO3 (Table 6-1; Xu
and Penner, 2012). On the other hand, HNO3 can also absorb onto larger, coarse particles, whose
dry deposition velocity is faster than PM2.5 (e.g., Zhang et al., 2001; Emerson et al., 2020). Thus,
differences in the chemical and physical forms of nitrogen and sulfur contribute variability in the
rate of deposition and in the relationship between total air concentrations and atmospheric
deposition. Furthermore, the dry deposition velocity is influenced by meteorological conditions
and interaction with the deposition surface properties (for example, the density of leaf area).
6-2
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Meteorological factors such as wind speed, humidity, atmospheric stability, and temperature all
affect the rate of settling for particles and gases. There are also micrometeorological parameters
that impact dry deposition rates of particles, such as friction velocity, roughness height, and
surface wetness (ISA, Appendix 2, section 2.5.2; Wesely, 2007).
For wet deposition, the amount of nitrogen and sulfur transferred to cloud water and
falling precipitation is largely driven by the air concentration. The vertical distribution of the
pollutant can influence deposition amounts. Air pollutant concentrations have historically been
measured near ground level where health and ecological effects occur. Sulfur and nitrogen higher
in the troposphere can be scavenged by clouds and falling precipitation via wet deposition. While
dry deposition is directly related to the ground-level concentration, it is important to recognize
that wet deposition is affected by concentrations throughout the troposphere (ISA, Appendix 2,
section 2.5.2) which highlights the role of atmospheric transport of pollution.
6.1.2 Scales of Influence for Depositional Pathways Amid a Changing Chemical
Environment
Near emission sources, where there is an abundance of nitrogen and sulfur compounds in
the gas phase prior to chemical conversion to products like PM2.5, it is anticipated that dry
deposition will have a relatively larger influence over total deposition. On the other hand, wet
deposition is expected to have a larger influence downwind following transport and
transformation of gaseous species into longer-lived aerosol forms. Changes in chemical regimes
and in the sensitivity of PM2.5 formation may affect when, where and how pollution deposits. For
example, NOx and SOx emission reductions over the past several decades have shifted the
sensitivity of PM2.5 toward an acid gas limitation, such that a greater portion of emitted NH3 now
remains in the gas phase. This will reduce the atmospheric lifetime of NHx and increase the
influence of NH3 dry deposition on local scales.
Atmospheric humidity and the frequency of precipitation is also influential. For example,
desert areas receive very little precipitation and hence contribution from wet deposition is low.
Much of the western U.S. has drought years that result in very low wet deposition amounts,
followed by years with higher amounts of precipitation and higher wet deposition. The eastern
U.S. has less interannual variability in rainfall. The frequency of precipitation affects the relative
contributions of wet and dry deposition and therefore can also cause variability in the
relationship between ground-level air concentrations and deposition.
Figure 6-2 is a simplified illustration of the primary pathways by which different
pollutants contribute to total deposition of S and N and is intended to summarize the discussion
above. This schematic differentiates the role of criteria pollutants and their indicator compounds
from the non-criteria pollutants (i.e., ammonia). Additionally, this illustration highlights the
6-3
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primary loss pathways for each pollutant on a generalized national basis. It is an overview
schematic focused on the relationships between the criteria pollutants under consideration, and in
this case does not illustrate the role for meteorology or of other atmospheric constituents (e.g.,
organic species). Table 6-1 provides a summary of the expected atmospheric lifetimes of various
N and S containing pollutants based on a literature review.
Figure 6-2. Primary pathways by which emitted pollutants are transformed and
deposited. Blue arrows indicate that chemical transformation can occur during
transport. Bold arrow indicates primary loss mechanism pathway. Bolded
pollutants are NAAQS indicators; grey font is for non-criteria pollutant (ammonia).
6-4
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Table 6-1. Estimated atmospheric lifetimes of S- and N-containing species based on
literature review.
Pollutant
Lifetime
Conditions
Reference
S02
19 ± 7 hours
Summer, Eastern USA
Lee et al., 2011
58 ± 20 hours
Winter, Eastern USA
Lee et al., 2011
CM
O
+
o
4 to 21 hours
Surface
Shah et al., 2020; Liu et al., 2016;
Laughner and Cohen, 2019
6 hours
Nighttime, Winter, Eastern USA
Kenagy et al., 2018
29 hours
Daytime, Winter, Eastern USA
Kenagy et al., 2018
nh3
~11 hours
Global troposphere
Xu and Penner, 2012
1.5 to 12 hours
Canadian wildfire plume
Adams et al., 2019
hno3
1.5 to 12 hours
Power plant plumes
Neuman et al., 2004
4.8 days
Global troposphere
Xu and Penner, 2012
NH4 aerosol
3.2 days
Global troposphere
Xu and Penner, 2012
NO3 aerosol
3.9 days
Global troposphere
Xu and Penner, 2012
SO4 aerosol
4.6 days
Global troposphere
Banks et al., 2022
Isoprene nitrates
One to a few
hours
Forests
Lockwood et al., 2010; Paulotet
al., 2009
First-generation
~2 hours
Southeastern USA
Zare et al., 2018
organic nitrates
Second-generation
3 hours to 5 days
Eastern USA
Mao et al. 2013; Zare et al., 2018
organic nitrates
6.1.3 Analyses in the 2012 Review (Transference Ratio)
The PA for the 2012 review of the NAAQS for N and S oxides introduced the term
"transference ratio" which was defined as the ratio of deposition to air concentration (2011 PA,
section 7.2.3). This was calculated from annual average values and was spatially averaged over
ecoregions that spanned distances on the order of 10,000 km2. While generally capturing the
average relationship between air concentrations and atmospheric deposition over larger areas of
the country, the transference ratio approach had some important limitations, especially at local
scales. For example, the transference ratio approach did not capture the spatial variability across
an area due to the proximity to sources, chemical composition, frequency of precipitation, and
vertical distribution of nitrogen and sulfur (ISA, Appendix 2, section 2.5.2.4). Studies completed
since the 2011 PA have examined how the use of different models to calculate concentration and
deposition can yield very different estimates of the transference ratio, despite having comparable
error statistics when compared to measurements of air concentrations and wet deposition (ISA,
Appendix 2, section 2.5.2.4). As noted earlier, there are fundamental difficulties in establishing
quantitative relationships between air quality concentrations and deposition; the analyses in this
review are designed to go beyond the transference ratio by considering these relationships across
multiple geographic scales and through multiple analytical approaches.
6-5
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6.1.4 Organization of this Chapter
The challenges noted above have been considered in the analyses performed in the
current review to investigate the relationships between criteria pollutant concentrations and
deposition rates. Section 6.2 below describes four separate analyses completed as part of this
review: a review of recent trends in air pollutant concentrations and deposition (section 6.2.1), an
assessment of concentrations and deposition amounts at collocated sites (specific Class I areas in
section 6.2.2; national SLAMS monitors in section 6.2.3), and a trajectory-based modeling
analysis that enables an assessment of the association between upwind concentrations and
downwind deposition (section 6.2.4). Key uncertainties associated with these analyses are
characterized in section 6.3, and the key observations from this work are summarized in section
6.4.
6.2 RELATING AIR QUALITY TO DEPOSITION
This PA recognizes the limitations mentioned above, and as described in Chapter 2, also
recognizes that emissions, air concentrations, and deposition have all declined for sulfur and
oxidized nitrogen in recent years. This assessment examines the historical record of observations,
multi-decadal CMAQ simulations, and hybrid model-measurement TDep estimates to assess the
relationship between air concentrations of a specific compound, or combination of compounds,
and estimates of N and S deposition in specific locations.
6.2.1 Historical Trend Analyses of Emissions, Concentrations, and Deposition
Total anthropogenic NOx emissions (as represented by emissions of NO and NO2) have
trended strongly downward across the U.S. between 2002 and 2022 (Figure 6-3). Nationwide
estimates indicate a 70% decrease in these emissions over this time as a result of multiple
regulatory programs implemented over the past two decades, as well as changes in economic
conditions and domestic energy production. This trend is an opportunity to consider how changes
in emissions, air concentrations and deposition levels are correlated. As seen in Figure 6-3, the
overall decrease in emissions of NO and NO2 has been driven primarily by decreases from the
three largest emissions sectors (i.e., highway vehicles, stationary fuel combustion, and non-road
mobile sources). Specifically, compared to the 2002 start year, estimates for 2022 (from the 2020
NEI) indicate an 84% reduction in emissions from highway vehicles, a 68% reduction in
emissions from stationary fuel combustion, and a 54% reduction in emissions from non-road
mobile sources. Similar to NOx, and for many of the same reasons, SO2 emissions have also
declined significantly since 2002. Figure 6-4 illustrates the emissions changes over the 2002-
2022 period. The data show an 87% decrease in total SO2 emissions over the period, driven by
reductions of 91% in EGU emissions and 96% in mobile source emissions.
6-6
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In contrast with NOx and SO2 emission trends, the annual rate of NH3 emissions
nationally has increased by about 15-20% since 2002 (Figure 6-5). The magnitude and direction
of the NH3 emissions change varies with location across the U.S. and is partly due to growth in
agricultural sources of NH3, which are largely unregulated at the national scale. Variability in
local management practices related to animal husbandry makes NH3 emissions more uncertain
than other pollutant emissions derived from, for example, a mobile source model or direct
measurements from EGU sources. The EPA has recently improved its models for simulating
both livestock waste emissions and the fertilizer application process to inform development of
the 2020 NEI, which is expected to have reduced these uncertainties (U.S. EPA, 2023a).
Inventory Year
Figure 6-3. Trends in NO + NO2 emissions by sector from 2002 to 2022 (U.S. EPA, 2023b).
6-7
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£
ui
o
in
Inventory Year
Figure 6-4. Trends in SO2 emissions by sector from 2002 to 2022 (U.S. EPA, 2023b).
¦¦
Livestock Waste
m
Fertilizer Application
Wildfires
Agricultural & Prescribed Fires
Waste Disposal
Mobile Sources
Stationary Fuel Combustion
¦¦
Other
Inventory Year
Figure 6-5. Trends in NH3 emissions by sector from 2002 to 2022.
As discussed in more detail in Chapter 2, coincident with the major reductions in NOx
and SO2 source emissions, ambient air monitoring data indicate that atmospheric concentrations
of NO2, SO2, and PM2.5 have also trended downward across the U.S. over the past two decades.
Figure 6-6 shows the national trends in the annual and 1-hour NO2 design values based on the
Stationary Fuel Combustion
Industrial and Other Processes
Transportation
Other Anthropogenic Sources
16000
14000-
12000-
10000-
8000-
6000-
4000
2000-
6-8
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209 sites (annual standard, primary and secondary) and the 135 sites (1-hour standard, primary
only) with continuously valid data over the 2000-2022 period. The national median of the annual
design values has decreased by 54% from about 15.7 ppb in 2000 to about 7.3 ppb in 2022. The
national median of the 1-hour design values has decreased by 38% from 60 ppb in 2000 to 37
ppb in 2022.
1 C'*."-90tr Pe'ce^tlli 1-hourD¥
vlec:3" >¦":>/ DV
- - - ioth/90th Percentile Annual DV
Median Annual DV
- - * N02 Annual NAAQS Level
m to «t w co
eo m o
~i 1 1 1 1 1 1 1 r
OOOOOOOOOOt-t-t-t-t-t-t-t-t-t-MCMM
ooooooooooooooooooooooo
-------
CM
-
10th/90th Percentile D
Median DV
\
\
\
—
— J
S02 NAAQS Level
1
OS I
¦V
V
%
-Q
Q-
Q.
\
\
s
\
V
\
c
O)
(/)
Time
Figure 6-7. Trends in design values for the primary SO2 standard (99th percentile of 1-
hour daily maximum concentrations, averaged over three years) (upper panel)
and in annual average SO2 concentrations at SLAMS in the U.S., excluding
Hawaii (lower panel).
6-10
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Multiple chemicals, including nitrates and sulfates, comprise PM2.5. Figure 6-8 shows the
national trends in the annual and 24-hour PM2.5 design values based on the 395 sites (annual
standard) and the 398 sites (24-hour standard) that had valid design values in at least 16 of the 21
three-year periods from 2000-2002 to 2020-2022. Both the annual and 24-hour PM2.5 design
values exhibited steady decreases from 2002 to 2016. In recent years, the median annual PM2.5
design value has remained relatively constant at about 8 |ig/m3 while the 10th and 90th percentile
trends have also remained relatively flat at about 6 |ig/m3 and 10 |ig/m3, respectively. The 10th
percentile and median of the 24-hour PM2.5 design values have also remained relatively constant
at about 15 |ig/m3 and 20 |ig/m3, respectively, since 2016. However, the 90th percentile of the 24-
hour PM2.5 design values has increased substantially in the past six years largely as a result of
increased wildfire activity in the western U.S.
10th-90*" Percentile Annual DV
vec 2" 4"v2: DV
'0ti-;S0t- Percentile 24-hour D\
:'v'ec;an 2-J-hci.rDV
~ ~ 1 "*v:2.o Na.aQ3 lswsIs
rO
<
Q CM
CM
o o o o o o o o
o o o o o o o o
o o o o
fN CM
-------
Figures 6-9 and 6-10 show trends in annual average concentrations for NO3" (nitrate
aerosol) and SO42" (sulfate aerosol) based on sites that collected data for at least 12 out of 16
years from 2006 to 2021. Broad national reductions in NOx emissions have resulted in
substantial decreasing trends in NO3" concentrations over most of the U.S., especially in areas
where NO3" concentrations were historically highest. Similarly, reductions in SO2 emissions have
resulted in significant reductions in SO42" concentrations nationally and especially in the eastern
U.S. While not shown here, trends in other PM2.5 components like elemental carbon and organic
carbon were more variable, with some sites showing substantial decreases and the remaining
sites having no clear trend.1 As discussed in Chapter 2, ammonium sulfate and ammonium nitrate
now make up less than one-third of the PM2.5 mass at the majority of sites, and only a few sites
have more than half of the PM2.5 mass from these two compounds.
~ Decreasing > 0.1 ug/mA3/yr (11 sites) 0 No Significant Trend (102 sites)
v Decreasing < 0.1 ug/mA3/yr (138 sites)
Figure 6-9. Trends in annual average concentrations of NO3", as measured at select
NCore, CSN, and IMPROVE sites from 2006 through 2021.
1 https://gispub.epa.gOv/air/trendsreport/2022/#introduction
6-12
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~ Decreasing > 0.1 ug/mA3/yr (108 sites) v Decreasing < 0,1 ug/mA3/yr (146 sites)
Figure 6-10. Trends in annual average concentrations of SO42" as measured at select NCore,
CSN, and IMPROVE sites from 2006 through 2021.
Air quality across the U.S. has changed substantially over the past two decades. In
response to emissions reductions of NOx, SO2, and PM precursor pollutants, concentrations of
NOx, SO2, and PM2.5, including nitrates and sulfates, have decreased sharply. Returning to the
examination of the relationship between air concentrations of criteria air pollutants and
atmospheric deposition of S and N, these changes in air quality provide an opportunity to assess
their simultaneous influence on S and N deposition levels across the U.S. In response to the
changes in emissions and air concentrations described above, total deposition of oxidized
nitrogen and sulfur have also decreased significantly since 2000 (Feng et al., 2020; McHale et
al., 2021). Between the two three-year periods of 2000-2002 and 2019-2021, national average
estimates of N deposition over the contiguous U.S. have declined by 15% and estimates of total
S deposition have declined by 68% (U.S. EPA, 2022). See Table 6-2 for a regional breakout of
trends in total S, total N, oxidized N, and reduced N deposition.
The change in total N deposition reflects a combination of declining oxidized N and
increasing reduced N, which is consistent with the trends in emissions and air concentrations
described above. As expected, the data suggest that dry deposition of nitric acid has decreased
significantly over the past two decades and is likely a key contributor to the decrease in total
nitrate deposition and decreasing trends in oxidized nitrogen deposition (ISA, Appendix 2,
6-13
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section 2.7 and Figure 2-60). Emissions decreases of NOx and downward trends in wet
deposition of nitrate have a positive correlation, but because the formation of ammonium is
related to the availability of nitrate and sulfate, the correlation between NH3 emissions and NH4+
wet deposition is more complicated (Tan et al., 2020). While dry deposition is more uncertain in
magnitude, both surface-based and remote-sensing measurements indicate increasing ammonia
concentrations, which is consistent with the increasing trend for ammonia dry deposition,
especially in areas with significant agricultural emissions in the Midwest and the Central Valley
of California where ammonia dry deposition has become the largest contributor to inorganic N
deposition (Li et al., 2016).
The next series of plots further illustrate the changes in deposition patterns across the
U.S. over the past two decades. As shown in Figure 6-11,2 S deposition has decreased sharply
across the U.S. over this period due to the significant decreases in sulfur emissions. The changes
in sulfur deposition in the Ohio River Valley region are particularly notable. When we restrict
the analysis to consider trends only at CASTNET sites, we observe a similar downward trend in
total, wet, and dry S deposition, both nationally and over the eastern U.S. (Figure 6-12).
As expected, Figure 6-13 shows that the trends in N deposition are more heterogeneous.
Total N deposition has decreased over parts of the Ohio River Valley and in downwind regions
such as the northeastern U.S., but there are parts of the country where increases in N deposition
are estimated to have occurred over the past two decades (e.g., Texas).
2 Figures 6-11 and 6-13 through 6-18 were downloaded from the EPA's CASTNET website
(https://www.epa.gov/castnet/maps-charts'). Figures 6-12 and 6-19 were downloaded from the EPA's CASTNET
data download website (https://www.epa.gov/castnet/download-data). Figures 6-11 through 6-19 are based on
TDep version v2022.02.
6-14
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Table 6-2. Regional changes in deposition between 2000-2002 and 2019-2021: (a) total S
deposition; (b) total, oxidized and reduced N deposition (U.S. EPA, 2022).
Change in total S deposition
Form of S Deposition
Region
2000-2002
2019-2021
% change
Mid-Atlantic
15.9
2.1
-87
Midwest
11.2
2.2
-80
North Central
3.5
1.5
-56
Total Deposition of Sulfur
Northeast
8.7
1.5
-83
(kg S ha-1)
Pacific
1.0
0.6
-38
Rocky Mountain
1.0
0.6
-46
South Central
5.4
2.8
-49
Southeast
10.3
2.6
-74
Change in total, oxidized and reduced N deposition
Form of N Deposition
Region
2000-2002
2019-2021
% change
Mid-Atlantic
13.4
8.5
-36
Midwest
12.2
9.8
-20
North Central
8.5
9.5
+11
Total Deposition of Nitrogen
Northeast
10.4
6.2
-40
(kg N ha-1)
Pacific
3.8
3.1
-18
Rocky Mountain
3.0
3.1
+3
South Central
7.8
9.0
+16
Southeast
10.8
8.4
-23
Mid-Atlantic
10.3
4.0
-62
Midwest
8.0
3.6
-54
North Central
4.1
2.6
-37
Total Deposition of Oxidized Nitrogen
Northeast
7.7
2.9
-62
(kg N ha-1)
Pacific
2.4
1.4
-42
Rocky Mountain
1.9
1.3
-35
South Central
5.0
3.1
-39
Southeast
7.7
3.4
-56
Mid-Atlantic
3.0
4.6
+51
Midwest
4.3
6.2
+45
North Central
4.4
6.9
+56
Total Deposition of Reduced Nitrogen
Northeast
2.7
3.3
+22
(kg N ha-1)
Pacific
1.4
1.7
+22
Rocky Mountain
1.1
1.8
+72
South Central
2.8
6.0
+111
Southeast
3.1
5.0
+63
The states included in each region are as follows: Mid-Atlantic: DE, MD, NJ, PA, VA, WV; Midwest: IL, IN, KY, Ml, OH, Wl; North
Central: IA, KS, MN, MO, ND, NE, SD; Northeast: CT, MA, ME, NH, NY, Rl, VT; Pacific: CA, NV, OR, WA; Rocky Mountain: AZ, CO,
ID, MT, NM, UT, WY; South Central: AR, LA, OK, TX; Southeast: AL, FL, GA, MS, NC, TN, SC.
6-15
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Figure 6-11. TDep-estimsited total S deposition: 2000-2002 (top) and 2019-2021 (bottom).
6-16
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30-
25-
o 20
o
CL
0
Q
CO
Q
~o
~o
0)
"CD
E
to
LLl
15-
10-
5-
0-
All CONUS, at CASTNET Sites
N=92
| Wet Sulfur Deposition
Dry Sulfur Deposition
• •
I i
t •
t
iiiiiiiiitiiiiiiiii;
30
25
East CASTNET Sites Only
N=63
03
_C
o 20
-------
Figure 6-13. TDep-estimated total N deposition: 2000-2002 (top) and 2019-2021 (bottom).
6-18
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Looking into the components of these trends in N deposition, it can be seen from Figure
6-14 that most of the widespread changes in N deposition across the U.S., both increases and
decreases, are due to changes in dry deposition of N. Figure 6-15 shows that while there have
been some changes in wet N deposition over the past 20 years (e.g., decreases near Lake Ontario;
increases in parts of southern MN), these levels and patterns have remained relatively unchanged
compared to dry N deposition.
6-19
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Figure 6-14. TDep-estimsited dry N deposition: 2000-2002 (top) and 2019-2021 (bottom).
Dry N
(kg-N/ha)
6-20
-------
Figure 6-15. TDep-estimated wet IN deposition: 2000-2002 (top) and 2019-2021 (bottom).
6-21
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The aggregate trends in dry deposition of N are driven by two largely opposing trends in
the dry deposition of oxidized nitrogen and reduced nitrogen. Two decades ago, there were large
amounts of dry oxidized N deposition (5-10 kg N/ha) over much of the eastern U.S. that are not
seen in the more current period (< 5 kg N/ha), as shown in Figure 6-16. Conversely, while there
were isolated hotspots of dry reduced N deposition in the 2000-2002 timeframe, the number and
magnitude of these hotspots has increased substantially in the more recent 2019-2021 period, as
shown by Figure 6-17, especially in places like AR, IA, MN, MO and TX. Figure 6-18 confirms
that the increases in dry deposition of reduced N are closely linked to increases in NFb
deposition.
6-22
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Dry oxN
(kg-N/ha)
Figure 6-16. Dry oxidized N deposition (TDep estimates): 2000-2002 (top) and 2019-2021
(bottom).
6-23
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Dry reN
(kg-N/ha)
Figure 6-17. TDep-estimsited dry reduced N deposition: 2000-2002 (top) and 2019-2021
(bottom).
Dry reN
(kg-N/ha)
6-24
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(kg-N/ha)
o.o
(kg-N/ha)
o.o
Figure 6-18. TDep-estiinated NH3 deposition: 2000-2002 (top) and 2019-2021 (bottom).
6-25
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Annual estimates of major components of N deposition at 92 CASTNET sites across the
U.S. (for which locations are shown in Figure 2-17) during the period from 2000 through 2021
further confirm the changing trends in the influence of oxidized and reduced N species, as shown
in Figure 6-19. Over this period, the relative presence of oxidized species has declined at these
monitors, tracking the trends in NO and NO2 emissions noted above. However, the relative
presence of NH3 has increased appreciably (Figure 6-19).
6-26
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100-
c
o
"co
O
Q.
CD
TD
O
90-
80-
Z 70
03
O
60-
50
"O
<0
N
T3
&•
~
Oxidized Nitrogen
Wet NH4*
Dry NH4"
Dry NH3
• .» • *1
l • •
+ t
t'U
• • • +
• « •
..f .
tJi
•. •
• • • : • - -n • •
• * *lt!4-:„.TTl !
ri
m
j
i :i •}
® • • • • ¦ " ® 8
i it i-
Qy5 C?5 Cv* ^ V5 ^ 0^
^ ^ # rf rf <$> cf rf ^ rf
-------
The longer-term trends in deposition of reduced nitrogen are more challenging to assess
because, before 2011, ambient air NH3 monitoring was rare. For particulate matter, the trend in
ammonium (NH4+) has followed the downward trends in sulfate and nitrate, because aerosol
partitioning to NH4+ requires the availability of acid gases, such as sulfur oxides and/or nitrogen
oxides, to neutralize NH3. This increased prevalence of gas phase NH3 also contributes to the
trend of increasing dry fraction of total nitrogen deposition. Satellite-based measurements and
chemical transport models have been used to augment the surface-based measurements of
ammonia and ammonium to better understand trends. These studies also show increasing
ammonia concentrations, especially in parts of the Midwest, Southeast, and West near
agricultural sources (Warner et al., 2016; Warner et al., 2017; Yu et al., 2018; Nair et al., 2019;
He et al., 2021). These trends are attributed to a combination of warmer temperatures causing
greater emissions, increasing agricultural activity, and less available sulfate and nitrate, shifting
the prevalence in reduced nitrogen partitioning from particle ammonium toward gas-phase
ammonia.
In summary, the analysis of air quality concentrations from criteria pollutants and
deposition data over the past two decades show similar trends in the following quantities,
implying that there is correlation between:
• SO2 concentrations and total S deposition
• NO2 concentrations and oxidizedN deposition
• PM2.5 sulfate concentrations and total S deposition.
However, the spatiotemporal trends between ambient air NO2 concentrations and total N
deposition are inconsistent; due to increases in NH3 emissions and associated increases in
reduced N deposition. Similarly, the trends data show a mixed relationship between PM2.5 nitrate
concentrations and total N deposition. The subsequent analyses presented in this section are
designed to expand upon this simple observation of correlation.
6.2.2 Class I Area Sites - Relationships Between Air Concentrations and Deposition
As a second type of analysis to evaluate the relationship between air quality metrics of
interest and the deposition of S and N, we evaluated observational data at 27 sites in 27 remote
Class I areas. These areas tend to be further away from emissions sources and are of particular
interest for ecological reasons related to the secondary standards, as well. Class I areas have
some special federal protections (e.g., focus of efforts to reduce regional haze).3
3 Areas designated as Class I receive special protection status under the CAA, and include all international parks,
national wilderness areas that exceed 5,000 acres in size, national memorial parks that exceed 5,000 acres in size,
and national parks that exceed 6,000 acres in size, provided the park or wilderness area was in existence on
August 7, 1977.
6-28
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In this section, we first evaluate concentration-to-deposition relationships at these
locations from CMAQ simulations (section 6.2.2.1) to consider how the terms are associated
within a chemical transport model simulation. We then analyze ambient air observations from
CASTNET and IMPROVE sites and measured wet deposition from NADP/NTN sites in these
same Class I areas (section 6.2.2.2) to identify S and N-containing compounds for which air
concentrations are closely related to S and N deposition. This section also considers TDep
estimated total S and N deposition and how total deposition relates to measured ambient air
concentrations from CASTNET and IMPROVE monitor sites (section 6.2.2.3). Noting the many
factors that can lead to variability in estimated deposition, including frequency of precipitation,
and micrometeorological factors relevant to the dry deposition velocity, the analyses focus on
multiple years of data to better assess these local relationships. The averaging time for all these
comparisons is one year.
The set of 27 Class I areas with co-located CASTNET monitoring stations, chemically-
speciated PM2.5 from the IMPROVE network, and NADP/NTN wet deposition monitors are
identified in Table 6-3 and shown in the map in Figure 6-20. Figure 6-21 shows the distribution
of TDep-estimated wet and dry deposition amounts across these 27 areas for the 2017-2019
period. At these locations, N deposition tends to be much greater than S deposition, with both
quantities lower than national average values, likely because most of these locations are in the
western U.S. Consistent with the national trends, S deposition has also declined more than N
deposition over the last few decades at these locations. For nitrogen, at these sites during this
time period, dry deposition comprises approximately 60% and wet deposition approximately
40% of total deposition estimates (Figure 6-21). In contrast, for sulfur, wet deposition comprises
approximately 60%, on average, and dry deposition, 40% (Figure 6-21). While this presentation
reflects estimates at this specific set of sites, we note that patterns here may differ from patterns
at other sites across the U.S.
6-29
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Table 6-3. Collocated CASTNET, NADP/NTN, and IMPROVE monitoring stations used
in this analysis of air concentration and deposition.
Class 1 Area Name
CASTNET
NADP
IMPROVE
Acadia
ACA416
ME98
ACAD1
Big Bend
BBE401
TX04
BIBE1
Canyonlands
CAN407
UT09
CANY1
Chiricahua
CHA467
AZ98
CHIR1
Death Valley
DEV412
CA95
DEVA1
Dinosaur National Monument
DIN431
C015
DIN01
Everglades
EVE419
FL11
EVER1
Glacier
GLR468
MT05
GLAC1
Great Basin
GRB411
NV05
GRBA1
Grand Canyon
GRC474
AZ03
GRCA2
Great Smokey Mountains
GRS420
TN11
GRSM1
Joshua Tree
JOT403
CA67
JOSH1
Mt. Lassen
LAV410
CA96
LAV01
Mammoth Cave
MAC426
KY10
MACA1
Mesa Verde
MEV405
C099
MEVE1
Cascades
NCS415
WA19
NOCA1
Olympic
OLY421
WA14
OLYM1
Petrified Forest
PET427
AZ97
PEF01
Pinnacles
PIN414
CA66
PINN1
Rocky Mountain
ROM406
C019
R0M01
Sequoia
SEK430
CA75
SEQU1
Shenandoah
SHN418
VA28
SHEN1
Theodore Roosevelt
THR422
ND00
THR01
Voyageurs
VOY413
MN32
VOYA2
Wind Cave
WNC429
SD04
WICA1
Yellowstone
YEL408
WY08
YELL2
Yosemite
YOS404
CA99
YOSE1
Monitored Parameters
Included in this Analysis
Gas: S02and HNO3
Particulate: SO4, NO3-,
nh4+
[TN03= HNO3 + pN03"]
Wet deposition of:
S compounds
(SO42-) and N
compounds (NOy,
NH4+)
PM2 s(total mass)"
PM2.5(S042-);
PM2.5(N03-);
PM25(NH4+)
PM2.5(N03-+NH4+)
* PM2.5 mass monitors at IMPROVE sites employ methods other than FRM/FEM sites (Hand et al., 2023).
6-30
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VE419
.CA416
Figure 6-20. Locations of co-located CASTNET, NADP/NTN, and IMPROVE monitoring
sites, denoted by CASTNET site identifier. The NADP/NTN and IMPROVE
station identifiers are listed in Table 6-3.
N Dry
N Wet S Dry
deposition type
SWet
Figure 6-21. Annual average TDep-estiinated dry and wet deposition of N and S (2017-
2019) at Class I area NADP sites in Table 6-3. Boxes indicate interquartile
range.
6-31
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In the following subsections, the analyses focus on assessing relationships between: (1)
simulated air concentrations and simulated total deposition using output from a chemical
transport model (CMAQ) that reflects known physical and chemical processes, and (2) measured
air concentrations (IMPROVE and CASTNET), measured wet deposition (NADP/NTN), and
estimated total deposition (TDep). These sets of measured and predicted variables are compared
using linear regression, which allows a more detailed assessment of the uncertainty and
variability. There are several ways to assess how well one variable relates to the other, such as by
calculating the correlation between variables (r), creating linear regressions for pairs of variables,
and calculating the significance of those analyses (p value). The correlation coefficients used in
this chapter are Spearman's Rank Correlation.4 While the correlation coefficients are useful in
evaluating the relative strength of a concentration-deposition association, it is also important to
visually consider the relationship via a scatterplot. Such figures are also provided in this section.
6.2.2.1 Relationships in Chemical Transport Model Simulations
Since dry deposition flux is not routinely measured, models are often used to inform
deposition estimates and to examine the relationship between air concentration and total
deposition. The CMAQ is a numerical air quality model that relies on scientific first principles to
simulate the concentration of airborne gases and particles and the deposition of these pollutants
back to Earth's surface under user-prescribed scenarios. We utilize the results of a 21-year
CMAQ simulation, as described in Zhang et al. (2018), to further analyze relationships between
air concentrations and deposition of S- and N-related compounds as part of this review. One of
the inherent advantages of evaluating the concentration to deposition relationship in a chemical
transport model is that one is not limited by measurement technology (e.g., absence of
widespread dry deposition data or challenges in measuring certain pollutant concentrations).
However, an important caveat is that the model-estimated relationships will be affected by
imperfect parameterizations as the model necessarily simplifies highly complex real-world
processes in its simulations.
For model grid cells across the contiguous U.S., CMAQ-estimated annual average SOx
and N oxides concentrations, total S and N deposition, and the associated deposition-to-
concentration ratios are presented in Figures 6-22 and 6-23. For SOx (Figure 6-22), most of the
U.S. generally exhibits deposition:concentration ratios of 1 to 5, especially in areas where local
and regional sources of SO2 are prevalent. However, as an air parcel moves further away from
emissions sources, the more rapidly-depositing pollutants are removed, and pollutants are diluted
by being mixed vertically in the atmosphere. In these locations, the deposition-to-concentration
4 All linear regressions in this chapter were derived using R, version 4.3.1, and correlations were calculated using
Spearman's rank correlation.
6-32
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ratios for S oxides are higher (i.e., > 5). Such locations include parts of the northeastern U.S. and
high elevation sites in the western U.S. These areas are generally further away from sources and
ground-level air concentrations in these regions are relatively low. For N oxides the spatial
patterns are similar, however the ratios are slightly lower over most of the U.S. (i.e., ratios range
from 1 to 3, Figure 6-23). The spatial consistency in the simulated deposition-to-concentration
ratios in the model, at least over the annual averaging time considered here, indicates some
general association between local deposition rates and local ambient air concentrations of S and
N oxides. However, this general rule has clear exceptions (e.g., high altitude sites) and there is
some variability within the typical range of ratios.
6-33
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0 1 2 3 4 5 6 7 8 9 10 0 4 8 12 16 20 24 28 32 36 40 t 2 3 4 5 6 7
Figure 6-22. Annual average SOx concentration, ppb, (left), total S deposition, kg/ha-yr, (middle), and associated
depositionrconcentration ratios (right), estimated from a 21-year (1990-2010) CMAQ simulation.
0 12 3 4 5 6 7 8 9 10 0 2 4 6 8 10 12 14 16 18 20 1 2 3 4 5 6 7
Figure 6-23. Annual average N oxides concentration, ppb (left), total N deposition, kg/ha-yr, (middle), and associated
depositionrconcentration ratios (right), as estimated from a 21-year (1990-2010) CMAQ simulation.
6-34
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To further assess potentially influential chemical predictors of S and N deposition rates,
we considered the CMAQ model results in more detail, evaluating data from the grid cells
containing the 27 Class I area monitoring sites identified in Table 6-3. For Figures 6-24, 6-25,
and 6-26, a histogram of each deposition or concentration variable is shown in a diagonal
running from the top left to lower right. Below that diagonal are scatter plots and linear
regressions for each pair of variables. Above that diagonal are the correlations between pairs of
variables, with asterisks indicating p-value thresholds (*** = p<0.001). Each data point marks
the annual average air concentration and annual total deposition for individual years at that
location from the 21-year CMAQ simulation (1990-2010).
Figure 6-24 presents analyses of relationships between CMAQ estimates of annual
average concentrations of SO2 and S deposition in the same grid cells. These analyses indicate
moderate correlations of SO2 concentrations and total S deposition (r = 0.57, p<0.001). Figure 6-
24 also illustrates the relationship between annual average PM2.5 and S deposition in the same
cells. A weaker correlation is seen for PM2.5 concentrations with total S deposition (r = 0.36,
pO.OOl).
6-35
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100
o>
TO
O
50
CMAQ: Total PM25
JkflflflBh
CMAQ: S02
Corr:
0.66***
XI
D.
Q.
5-
4-
3-
O 2-
w
1 -
0-
y = - 0.34 + 0.16 x •
Tl rv. n
-n-TK.
>
t
to
JZ
c/5
D)
Q_
<
• • H M •
* •
t/« %
•
x
A .
•
pi
r* •
• •
5 10 15 20 25
Total PM2 5 (|jg/m3)
2 3
S02 (ppb)
CMAQ: Total S Dep
Corr:
0.36***
Corr:
0.57
***
•
•
II
O
-vl
+ 5 x
s
\yr
•
•
A,
Jl{ •
jC •
•
W*
J
r*
*
Tm I i I n-rrn-|-rQ=i
0 10 20
Total S Dep (kg S/ha-yr)
Figure 6-24. Scatter plot matrix of annual average CMAQ-siinulated total S deposition
versus annual average CMAQ-simulated concentrations of SO2 and PM2.5 for
27 grid cells in Class 1 areas from a 21-year simulation (1990-2010).
Using the same CMAQ simulations described in the above paragraph, Figure 6-25
illustrates the model relationships between total deposition of oxidized nitrogen (i.e., NO, NO2,
and NO3") and air concentrations of NO2 and particulate nitrate (NO3") at the 27 Class 1 areas.
Total oxidized nitrogen deposition has moderate correlations with both NO2 (r = 0.64, p<0.001)
and particulate nitrate (r = 0.61, p<0.001). The similarity in correlations among these variables is
expected given chemical transformation of NO2 to NO3" in the atmosphere. All the univariate
6-36
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linear regressions generated with the CMAQ simulations in Figure 6-25 have positive
correlations.
To better understand some of the patterns seen in Figure 6-25, we added an additional
parameter in Figure 6-25 with the CMAQ-estimated NH3 concentration. Concentrations of NH3
are represented by color coding the data points. One notable feature of panels A, B, and C in
Figure 6-25 are the existence of distinct groups of points in the data. In panel A, it can be
observed that the particulate nitrate and NO2 relationship varies as a function NH3
concentrations, with a sharp divide at 1 |ig/m3 of particulate nitrate. In grid cells where the NH3
concentrations are low, particulate nitrate is also low and largely independent of how much NO2
exists. In grid cells where ammonia is higher (i.e., Shenandoah VA and Mammoth Cave KY)
there is a strong relationship between particulate nitrate and NO2. This contributes to the bi- and
trifurcations in panels B and C. Panel B appears to show at least two, and possibly three groups
of points with similar slopes. This suggests that site-specific correlations are likely higher than
when all 27 areas are combined, and in particular, that NO2 is strongly correlated with oxidized
N deposition at the site-level. Panel C shows the relationship between particulate nitrate and total
oxidized nitrogen deposition is strongest at sites associated with high NH3 concentrations, and
weaker at sites with lower NH3.
6-37
-------
2-
O CO**
Z E
m O)
1 -
0
10.0
£75
"o -r
O CD
.N SI
^ z
O|5.0
jS ^
O (D
"5 2.5
y = -0.084 + 0.3 x
#
r TffTn-i_
Corr:
0.61***
y = 1.1 +0.84 x
y = 1.6 + 2.2 x
• ••
t •
%
> t >—, •
NH3((jg/m3)
2.0
1 15
WmLifi I r I
I 10
If-**- L2J
¦ 0.5
Tn-mTLruTT-n^ r,
2 4
N02 (ppb)
1 2 3
P^2.5 (N03") (^g/m3)
2.5 5.0 7.5 10.0
Total Oxidized N Dep (kg N/ha-yr)
Figure 6-25. Scatter plot matrix of annual average CMAQ-siinulated total oxidized
nitrogen deposition versus annual average CMAQ-siinulated concentrations of
NO2 and particulate nitrate for 27 Class 1 areas from 1990-2010. Colors in
scatterplots indicate NH3 concentrations.
Finally, Figure 6-26 indicates relationships between total reduced N deposition (i.e., NH3
+ NH4') and NFI3 and ammonium in the same CMAQ simulations used for Figures 6-24 and 6-
25. The correlation between total reduced N deposition and PM2.5 (NH4 ) is moderate (r = 0.71,
p<0.001). The correlation between reduced N deposition and NH3 is weaker (r = 0.50, p<0.001).
All of the linear regressions generated with the CMAQ simulations in Figure 6-26 have positive
correlations. As in Figure 6-25, there are bi-and trifurcations in the scatterplots (panels A, B, and
C) and to better understand the causes of those different patterns, each data point in Fi gure 6-26
was color coded in terms of the model NO2 concentration to investigate further. Panel B of
Figure 6-26 illustrates that while the correlation between NH4+ and reduced N deposition is
relatively higher than other concentration:deposition associations (r = 0.71), there is considerable
scatter in that relationship when NO2 concentrations are high (lighter colors). Conversely, in
6-38
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Panel C of Figure 6-26, we see stronger associations between NH3 and reduced N deposition
when NO2 is high.
250-
200-
+
^ ^ 150 -
m O)
c\i =L
2 —100-
CL
50-
0-
CMAQ: PM2 5(NH4+)
tk.
¦ n-n-u
E
CT>
I
z
2.0-
1.5-
1.0-
0.5-
0.0-
6-
4-
-o ^
§ I
"O 2
DC S
-------
In summary, a CMAQ-based analysis of simulated air quality concentrations and
deposition data over a 21-year simulation indicates that there is evidence of a moderate positive
correlation at the 27 collocated sites between:
• Total S deposition and SO2 concentrations at the sites
• Oxidized N deposition and NO2 and particulate nitrate
• Reduced N deposition and ammonium and NH3 concentrations.
Conversely, we see weaker association between PM2.5 and S deposition in the CMAQ
simulations. Further, there is evidence that the strength of the associations can vary by location
and can be influenced by concentrations of other pollutants contributing to N or S deposition.
Additionally, it is important to remember that the model simulations are highly parameterized
and are developed to be simplified approximations of highly complex processes. In that regard,
these CMAQ comparisons of concentration and deposition are best viewed as informative
associations based on modeled physics.
6.2.2.2 Relationships between Air Quality and Wet Deposition Observations
This section evaluates how wet deposition measurements from the NADP monitoring
network relate to ambient air measurements from the IMPROVE and CASTNET monitoring
networks, at the sites listed in Table 6-3 above. Parameters considered from the NADP network
include wet deposition of SO42", NO3" and NH4+, as well as the sum of NO3" and NH4+ (as wet
deposition of N). For Figures 6-27 through 6-30, a histogram of each deposition or concentration
variable is shown in a diagonal running from the top left to lower right. Below that diagonal are
scatter plots for each pair of variables. Above that diagonal are the correlations between pairs of
variables, with asterisks indicating p-value thresholds (*** = p<0.001).
Figure 6-27 shows the relationship between wet S deposition (NADP) and ambient air
concentrations of S04+2 (IMPROVE) and of total S ambient air concentrations (SO2 + S04+2;
CASTNET). Concentrations of S04+2 from IMPROVE have a strong positive relationship with
CASTNET total S concentrations (r = 0.76, p<0.001). Both total S and S04+2 also indicate a
moderate relationship with wet deposition of S measured at the same sites (r = 0.52, p<0.001 for
CASTNET total S and r = 0.59, p<0.001 for IMPROVE S04+2).
6-40
-------
CASTNET: Total Sulfur
"h-rrw
IMPROVE: S042
Corr:
0.76***
NADP: Wet S Dep
0e+00 3e-08 6e-08 9e-08 0e+00 1e-08 2e-08 3e-08 4e-08 5e-08
S02 + S04 2- (mol S/m3), CASTNET S04 2~ (mol S/m3), IMPROVE
Corr:
0.52***
Corr:
0.59***
iTlTTil-UT^ri-ri-rn i
0 2 4 6
Wet S Dep (kg S/ha-yr), NADP
Figure 6-27. Scatter plot matrix of annual average wet S deposition (NADP) with annual
average concentrations of SO-t2" (IMPROVE) and total S (SO2 + SO42",
CASTNET) concentrations for 27 Class 1 areas (2000-2019).
6-41
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For nitrogen, Figure 6-28 shows a strong relationship of CASTNET TNO3 (HNO3 and
particulate NO3") with IMPROVE NO3" (r = 0.86, p<0.001). This is not unexpected given that
one parameter (NO3") is a subset of the other (TNO3). The relationship of N wet deposition with
those parameters includes considerable scatter, with low correlations (r = 0.32, p<0.001 for NO3"
alone [IMPROVE] and r = 0.22, p<0.001 for TNO3). This may reflect the influence of reduced N
compounds on wet N deposition, which may vary among the Class I sites
y= 1.6 + 0.79 x
* *
% • 2 1 •
IMPROVE: NO3-
Corr:
0.86***
¦
1" H-n-rru-, ^
y= 1.5 + 1.3 x
NADP: Total Wet N Dep
Corr:
0 22***
Corr:
0 02***
~fT"h~h n o_=
0.0 0.5 1.0 1.5 2.0 2.5 0.0
CASTNET: TN03(|jg/m3)
0.5 1.0 1.5 2.0 0 2 4 6
IMPROVE: NO3- (|jg/m3) Total Wet N Dep (kg N/ha-yr)
Figure 6-28. Scatter plot matrix of annual average wet N deposition (NADP) with annual
average TNO3 (CASTNET) and NO3" (IMPROVE) concentrations for 27 Class
1 areas (2000-2019).
6-42
-------
Figure 6-29 shows relationships between wet deposition of N and wet deposition of each
of its primary particulate components (M 1^ and NO.r) based on NADP monitoring data. Both
wet deposition of NFLf and NO3" are highly correlated with wet deposition of the sum of the two
(r = 0.96, p<0.001 for NH4" and r = 0.93, p<0.001 for NO3). The correlation between NH4 wet
deposition and NO3" wet deposition is slightly weaker but still strong (r = 0.79, p<0.001).
40-
20-
0
15
10-
5-
0-
6-
4-
2-
0-
NADP: NH4 + Wet Dep
NADP: N03-Wet Dep
Corr:
0.79***
NADP: Total Wet N Dep
Corr:
0.96***
/ = 0.92 +2.2 x
IthTHTI
Corr:
0.93***
• J
•
s v
•*
y = 0.21
+ 1.3 x
y = 0.27 +0.43 x
k n o-n
0 2 4
NADP: NH4+ Wet Dep (kg
NH4+/ha-yr)
0 5 10
NADP: NCV Wet Dep (kg
NQ3/ha-yr)
15 0 2 4 6
Total Wet N Dep (kg N/ha-yr)
Figure 6-29. Scatter plot matrix of annual average wet N deposition (NADP) with annual
average wet deposition of NFLi+ and NO3" (NADP) deposition for 27 Class I
areas (2000-2019).
6-43
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Figure 6-30 presents wet deposition data for N and S (from NADP) and ambient air
concentrations of PM2.5 (IMPROVE) at the 27 Class I area sites. Somewhat weak but statistically
significant correlations are observed for wet deposition of both N and S with PM2.5 (IMPROVE)
concentrations (r = 0.38, p<0.001 for wet S deposition and r = 0.37, p<0.001 for wet N
deposition). The existence of a positive correlation likely reflects the presence of particulate S
and N compounds in PM2.5, and the variability in this relationship may reflect variation in PM2.5
composition across the 27 Class I areas. The high correlation between wet deposition of nitrogen
and of sulfur (r = 0.84, p<0.001) may be related to the role of precipitation rate in wet deposition.
IMPROVE: Total PM,
Th-nfl m-n
n=i
•
y= -0.89 +0.49 X
t
•
•
• • *
• • • . •
•V •
'-y
y = 0.43 + 0.36 X
• %•*
.• -
NADP: Total Wet S Dep
Corr:
0.38***
: 1.1 + 0.7 X
2.5
5.0 7.5 10.0
Total PM2 5 (Mg/m3)
12.5 0 2 4 6 8
Total Wet S Dep (kg S/ha-yr)
NADP: Total Wet N Dep
Corr:
0.37***
TlTrfl-un^n4-|-rn
n
Corr:
0.84***
Tn-l n—n n
2 4 6
Total Wet N Dep (kg N/ha-yr)
Figure 6-30. Scatter plot matrix of annual average wet deposition of N and S (NADP) with
annual average PM2.5 (IMPROVE) for 27 Class 1 areas (2000-2019).
6-44
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6.2.2.3 Relationships between Observed Air Quality and TDep Estimates of
Deposition
We next consider the extent of relationships between TDep-estimated total S deposition
and PM2.5 (total mass), particulate SO42" and total S (SO2 plus particulate SO42") using
IMPROVE and CASTNET data at the 27 Class I area sites from 2000-2019 in Figure 6-31.5 This
figure indicates that air quality concentrations are lower at these sites in recent periods relative to
the past. Additionally, as noted above for wet deposition, total S deposition appears to have only
weak association with PM2.5 (IMPROVE, r = 0.33, p<0.05), and the correlation of TDep
estimated sulfur deposition and measured PM2.5 is not as strong as that of TDep estimated sulfur
deposition and SO42" (r = 0.55, p<0.05). The strongest associations are seen for S deposition with
total sulfur (SO42" + SO2) from CASTNET monitors (r = 0.61, p<0.05, Figure 6-31).
For total nitrogen deposition, concentrations of annual average PM2.5 (IMPROVE),
annual average NO3" (IMPROVE), and TNO3 (CASTNET) are all associated with TDep-
estimated total N deposition.6 Of the ambient air concentrations from IMPROVE and CASTNET
shown in Figure 6-32, NO3" had the strongest correlations with TDep estimates of total N
deposition (r = 0.63, p<0.05). TDep estimates of total N deposition had the weakest correlation
with PM2.5 measurements from IMPROVE (r = 0.53, p<0.05). All three ambient air
concentrations had positive correlations with TDep estimates of total N deposition. Linear
regressions run on all three ambient air concentrations and their associated TDep N depositions
were positive and significant.
5 Deposition estimates in Figure 6-31 are based TDep version v2018.02, downloaded on March 7, 2021.
6 Deposition estimates in Figures 6-32 and 6-33 are based TDep version v2018.02, downloaded on March 7, 2021
6-45
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15-
0-
15-
10-
5-
0-
15
10
5
0
Time Penod
•
2000
•
2010
•
2001
~
2011
•
2002
~
2012
•
2003
~
2013
~
2004
•
2014
•
2005
~
2015
•
2006
#
2016
•
2007
~
2017
~
2008
2018
•
2009
2019
Region
• Eass
* Wess
IMPROVE: Annual Average PM2 5 IMPROVE: Annual Average SO(mol Sfrn3) CASTNET:S (mol S/m3)
Figure 6-31. Total S deposition (TDep) versus annual average ambient air concentrations (2000-2019) of PM2.5 (left;
IMPROVE), SO42" (center; IMPROVE) and total S (right; CASTNET) at 27 Class I area sites. Linear regressions
are shown as black lines.
6-46
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IMPROVE: Annual Average PMj 5 (ng/rr?) IMPROVE: Annual Average NOf (pg/nr?) CASTNETTNQ (pg/rr?)
Figure 6-32. Total N deposition (TDep) versus annual average ambient air concentrations (2000-2019) of PM2.5 (left;
IMPROVE), annual average NO3" (center; IMPROVE), and TNO3 (right; CASTNET) at 27 Class I area sites.
Linear regressions are shown as black lines.
6-47
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Estimated total N deposition (TDep) at the 27 Class I area sites are related to air
concentrations of N species (IMPROVE and CASTNET) at those sites (Figure 6-33). Total N
deposition estimates are moderately correlated with IMPROVE total N (NH4+ and NO3")
concentrations (r = 0.62, p<0.05), CASTNET total N (NH4+ and NO3") concentrations (r = 0.62,
p<0.05), and CASTNET NH4+ concentrations (r = 0.62, p<0.05). Note that IMPROVE
ammonium is estimated assuming that the nitrate and sulfate are fully neutralized by ammonia.
Although IMPROVE NH4+ is not directly measured, the total N deposition-concentration
correlation using IMPROVE is similar to that of CASTNET.
Greater scatter is observed in the relationships between wet N deposition (NADP) and
IMPROVE total N concentrations, measured CASTNET total N concentrations, and CASTNET
NH4+ concentrations (Figure 6-34). Accordingly, the correlation coefficients are lower, ranging
from 0.31 for wet N deposition (NADP) with CASTNET total N concentrations to 0.47 for wet
N deposition (NADP) with CASTNET NH4+ concentrations (Figure 6-34).
6-48
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SjVJ- sv N
IMPROVE: TotaIN (NH4+ ~ NO3-; mol N/m3)
CASTNET TotaIN (NH4* - NO5-; mol N/m3)
CASTNET :NHj* (mol N/m3)
Time Period
•
2000
~
2010
~
2001
•
2011
~
2002
•
2012
•
2003
~
2013
•
2004
•
2014
•
2005
•
2015
~
2006
#
2016
~
2007
2017
•
2008
2018
«
2009
2019
Region
~
East
~
West
Figure 6-33. Total N deposition (TDep) versus annual average ambient air concentrations (2000-2019) of total particulate N
(left; IMPROVE), total particulate N (center; CASTNET), and NHj+ (right; CASTNET) at 27 Class I area sites.
Linear regressions are shown as black lines.
6-49
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100
r= 0.41 (p<0.05)
r= 0.47 (p<0 05)
<3^ v
IMPROVE: TotaIN (NH4* ~ NOj"; mol N/m3)
CASTNET:TotalN (NH4* + NOj-; mol N/m3)
CASTNET :NH4* (mol N/m3)
Tine Period
• 2000
•
2010
• 2001
~
2011
• 2002
•
2012
• 2003
•
2013
• 2004
•
2014
« 2005
•
2015
• 2006
•
2016
• 2007
•
2017
• 2008
2018
• 2009
2019
Region
• Eas?
a WesJ
Figure 6-34. Wet N deposition (NADP) versus annual average ambient air concentrations (2000-2019) of total particulate N
(right; IMPROVE), total particulate N (center; CASTNET), and NH-f (right; CASTNET) at 27 Class I area sites.
Linear regressions are shown as black lines.
6-50
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6.2.2.4 Conclusions
The above analyses focus on characterizing relationships between various chemical
species that are the air quality components of S and N deposition and of S and N air
concentrations over longer time periods (e.g., annual observations or 21-year CMAQ
simulations) in more rural locations by assessing various forms of available information
collocated (measured and estimated) at 27 sites in Class I areas. Assessment of these various
forms of information generally show consistency in the observed relationships. For air
concentrations of S compounds (SO42" or SO2+SO42") and deposition of S, the analyses suggest
that in more rural locations, such as those represented by these 27 Class I areas, S deposition is
moderately associated with measurements of particulate SO42" (r = 0.59, Figure 6-27) and the
combination of SO2+SO42" (r = 0.52, Figure 6-27). There is a slightly weaker association
between wet S deposition and PM2.5 (r = 0.38, Figure 6-30) in these rural locations, marked by
more variability. This variability and generally lower association likely relates to the fact that
some percentage of the PM2.5 mass is expected to be composed of compounds other than sulfate.
These results suggest that total S deposition in rural areas is mostly resulting from deposition of
sulfate and SO2. This is consistent with our understanding of the chemical properties and
physical transport of these compounds. For example, we know that fine particles, such as PM2.5,
have a much slower dry deposition velocity and remain in the atmosphere longer (Table 6-1).
Thus, it is not surprising to see that sulfur can be transported as PM2.5 in these rural locations.
These results also indicate that among PM2.5 (IMPROVE), SO42" (IMPROVE), and total S
(SO2+SO42", CASTNET), total S (CASTNET) shows the strongest relationship with total sulfur
deposition (Figure 6-31). For nitrogen, these results suggest that wet deposition of N in these
rural areas has little association with air concentrations of TNO3 (r = 0.22, Figure 6-28) while
having a strong correlation with particulate nitrate (r = 0.86, Figure 6-28). Lower, somewhat
moderate correlations are observed for total N deposition in these locations with PM2.5
(IMPROVE, r = 0.53), NO3" (IMPROVE, r = 0.63), and TNO3 (CASTNET, r = 0.57, Figure 6-
32).
6.2.3 National SLAMS Network - Relationships Between Air Concentrations and
Deposition
In this section, we consider ambient air concentrations and deposition estimates for the
period 2001 to 20207 at the SLAMS monitors that employ FRM/FEM and collect data for
NAAQS surveillance purposes. As with the analyses in the sections 6.2.1 and 6.2.2, the analyses
7 Deposition estimates in Figures 6-35 to 6-39 and Tables 6-4 to 6-7 are based on TDep version: v2022.02. TDep
data for Figures 6-35 and 6-37 through 6-39 were downloaded on September 7, 2022. TDep data for 6-36 were
downloaded on August 30, 2023.
6-51
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here focus on "local" concentrations and "local" deposition. This analysis will be most
illustrative of concentration and deposition relationships where the deposition results primarily
from local sources of the pollutants. Further, this analysis incorporates a national-scale
consideration of criteria pollutant concentrations measured at the ambient air monitors used to
judge attainment of the current secondary NAAQS for oxides of nitrogen, oxides of sulfur and
PM2.5. The locations for the SLAMS that were active in the 2019-2021 period are shown in
Figures 2-11, 2-12 and 2-13 forNCh, SO2 and PM2.5, respectively.
Figure 6-35 illustrates the relationship between SO2 annual average concentrations
(averaged over 3 years) across the U.S. and the S deposition at these locations.8 When looking at
the five time periods used in Chapter 5, there is a strong positive, significant association between
annual SO2 in the eastern9 U.S. and the S deposition at those locations (Figure 6-35, r = 0.79,
p<0.05, slope= 1.84). The figure suggests, however, that this association has become weaker
over the most recent 3-year averages as SO2 levels have decreased sharply across the eastern
U.S. This is also reflected in the correlation coefficients for total sulfur deposition and SO2
annual average concentrations at eastern SLAMS monitors (Table 6-4) for 2014-2016 (r = 0.40,
p<0.05) and 2018-2020 (r = 0.28, p<0.05).
8 There are two outlier SO2 data points in the 2018-2020 period which have been removed from the plots and
correlation calculations involving S deposition in this section. These data are driven by a location in southeastern
MO where annual average SO2 has exceeded 20 ppb in recent years. A preliminary analysis suggests that these
SO2 measurements reflect a relatively recent source that was not modeled in the CMAQ simulation that informed
the TDep estimates of deposition. As there is no deposition monitor in the immediate vicinity of the source it is
unlikely that the TDep estimates are capturing the impacts of this source. For that reason, we concluded it
appropriate to exclude these data from evaluations of the concentration-deposition relationship.
9 The East and West categorization of sites in this section is the same as that used in the aquatic acidification REA in
section 5.1. That is, sites in ND, SD, CO, WY, MT, AZ, NM, UT, ID, CA, OR, WA (2009 REA, Appendix 1, p.
1-21) are designated West, and all other sites (which are in locations from the eastern U.S. out into the Great
Plains) are designated East.
6-52
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Table 6-4. Correlation coefficients for TDep-estimated S deposition and annual average
SO2 concentrations (averaged over three years) at SLAMS sites, by time
period and region.
Sulfur Deposition
SLAMS
Wet
Dry
and SO2
Total
Deposition
Deposition
Deposition
Annua! DV-M
Correlation
Annual DV-AII
r = 0.66*
Annual DV-AII
r = 0.72*
Ecoregions
Coefficient (r)=
0.70*
Ecoregions
Ecoregions
Year
r
Year
r
Year
r
2001 - 2003
0.64*
2001 - 2003
0.67*
2001 - 2003
0.62*
2006 - 2008
0.72*
2006 - 2008
0.70*
2006 - 2008
0.66*
2010-2012
0.54*
2010 -2012
0.55*
2010-2012
0.48*
2014-2016
0.37*
2014-2016
0.31*
2014-2016
0.40*
2018-2020
0.19*
2018-2020
0.06
2018-2020
0.27*
Annual DV-East
r = 0.79*
Annual DV-
r = 0.79*
Annual DV-
—?
II
O
OO
*
Ecoregions
East
Ecoregions
East
Ecoregions
Year
r
Year
Year
r
2001 - 2003
0.58*
2001 - 2003
0.61*
2001 - 2003
0.57*
2006 - 2008
0.65*
2006 - 2008
0.62*
2006 - 2008
0.59*
2010-2012
0.52*
2010 -2012
0.51*
2010-2012
0.47*
2014-2016
0.40*
2014-2016
0.36*
2014-2016
0.42*
2018-2020
0.28*
2018-2020
0.18*
2018-2020
0.37*
Annual DV-West
r = 0.29*
Annual DV-
—?
II
O
O
Annual DV-
—?
II
O
CO
0
*
Ecoregions
West
Ecoregions
West
Ecoregions
Year
r
Year
r
Year
r
2001 - 2003
-0.03
2001 - 2003
-0.18
2001 - 2003
0.06
2006 - 2008
0.25*
2006 - 2008
-0.13
2006 - 2008
0.38*
2010-2012
0.03
2010 -2012
0.02
2010-2012
0.16
2014-2016
0.33*
2014-2016
0.18
2014-2016
0.30*
2018-2020
0.20
2018-2020
0.27*
2018-2020
0.09
*p<0.05
Correlations are Spearman's Rank correlation.
6-53
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to
0
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CD
03
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CD
O
Q_
0
Q
2001-2003
2006-2008
2010-2012
2014-2016
2018-2020
Region
o East
A West
r= 0.70 (p<0.05)
slope= 1.84 (p<0.05)
0 5 10 15 20
Annual Average S02 concentration (ppb), averaged over 3 years
Time Period
• 2001-2003
• 2006-2008
• 2010-2012
• 2014-2016
2018-2020
Region
o East
r= 0.79 (p<0.05)
slope= 1.84 (p<0.05)
0 5 10 15 20
Annual Average S02 concentration (ppb), averaged over 3 years
Figure 6-35. TDep estimated S deposition and annual average SO2 concentrations (3-year
average) at SLAMS across the CONUS (upper) and in the East (lower).
6-54
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Figures 6-36 explores the relationship between annual average SO2 concentrations and S
deposition for dry, and wet deposition separately. As discussed in section 6.1.2, in the generally
more arid areas of the West dry deposition tends to dominate, while wet deposition plays a larger
role in the East. The correlation coefficients in Table 6-4 provide some indication of this as the
correlation coefficient for wet S deposition and SChin the eastern United States (r = 0.79,
p<0.05) is much higher than the correlation coefficient in the western United States (r = 0.10,
p<0.05). For the full dataset of sites across the CONUS, the correlation coefficient for wet S
deposition (r = 0.66, p<0.05) is similar to that for dry S deposition (r = 0.72, p<0.05).
Figure 6-37 presents scatterplots for TDep-estimated S deposition and design values for
the current secondary standard (annual second maximum 3-hour concentration), averaged over
three years. Correlation coefficients are presented in Table 6-5. A moderate correlation is
observed, although somewhat weaker than for annual average SO2 concentrations (r = 0.66
compared to r = 0.70 for all sites and r = 0.71 compared to r = 0.79 at eastern sites).
6-55
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301
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CO
0
Q
£
Q
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10
Time Period
• 2001-2003 • 2014-2016
• 2006-2008 2018-2020
• 2010-2012
Region
o East
A West
r= 0.71 (p<0.05)
slope= 1.21 (p<0.05)
0 5 10 15 20
Annual Average S02 concentration (ppb), averaged over three years
ro
j=
CO
U)
Q)
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03
>>
CO
-------
Table 6-5. Correlation coefficients for TDEP-estimated S deposition and annual second
highest 3-hr SO2 concentration (averaged over three years), at SLAMS in the
CONUS by region and time period.
Sulfur Deposition
and S02(3 hr
Standard)
SLAMS
Total
Deposition
3-hr DV-AII Ecoregions
Correlation
Coefficient (r) =
0.66*
3-hr DV-East Ecoregions
r = 0.71*
3-hr DV-West
Ecoregions
r = 0.37*
Year
r
Year
r
Year
r
2001 - 2003
0.57*
2001 - 2003
0.52*
2001 - 2003
-0.005
2006 - 2008
0.60*
2006 - 2008
0.46*
2006 - 2008
0.44*
2010-2012
0.53*
2010-2012
0.55*
2010 -2012
0.24
2014-2016
0.52*
2014-2016
0.58*
2014-2016
0.27
2018-2020
0.40*
2018-2020
0.42*
2018-2020
0.36*
*p< 0.05
Correlations are Spearman's Rank correlation.
6-57
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ro
JZ
C/D 30-
(D
CD
CO
20-
10-
o
Q-
10
(D
CI
7
Time Period
• 2001-2003
• 2006-2008
• 2010-2012
• 2014-2016
2018-2020
• • •
r= 0.66 (p<0.05)
slope= 0.08 (p<0.05)
0 100 200 300 400
Secondary S02 Design Value, 3-yr average, at monitor sites (ppb)
Time Period
• • •
r= 0.71 (p<0.05)
slope= 0.09 (p<0.05)
0 100 200 300 400
Secondary S02 Design Value, 3-yr average, at monitor sites (ppb)
Figure 6-37. TDep estimated total S deposition and design values for the SO2 secondary
standard (annual second maximum 3-hour concentration), averaged over
three years, at SLAMS across COM S (upper) and in East (lower).
6-58
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Figure 6-38 presents analyses of relationships between TDep N deposition estimates and
NO2 concentrations at SLAMS across the CONUS. Analysis of this dataset for the five time
periods indicates a positive but weak association of nitrogen deposition with NO2 (r = 0.38,
p<0.05), with scatter in the relationship across both eastern and western monitor sites. Unlike for
sulfur deposition and SO2, the time period with the highest correlation coefficient for N
deposition and NO2 is 2014-2016, rather than the earliest time periods (Table 6-6). Also, unlike
sulfur deposition versus SO2 concentrations, there is a stronger correlation between N deposition
and NO2 at western SLAMS monitors (r = 0.63, p<0.05) than eastern sites (r = 0.44, p<0.05,
Table 6-6). Indeed, all of the correlation coefficients are stronger in western sites over all time
periods (Table 6-6).
6-59
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CT)
CO
0
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25
20
15-
10
2 5
0
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25
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0
CD
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15
0
ra 10
>%
CO
S 5
0
a
c
0
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0-
r= 0.38 (p<0.05)
slope= 0.19 (p<0.05)
«•
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^'
-^o®' Ta7a. A
a A. A ^ a.-A a
A A.
A
A
A A
Time Period
• 2001-2003
• 2006-2008
• 2010-2012
• 2014-2016
2018-2020
Region
O East
A West
10
20
30
40
50
Annual Average N02 (DV), 3-yr average (ppb)
r= 0.44 (p<0.05)
a slope= 0.16 (p<0.05)
•*
• • *
• «© • 0
. •« 0 • • -
„» • • • » •
,0 °S °0 •
o
-------
Table 6-6. Correlation coefficients for N deposition (TDep) and annual average NO2
concentrations (averaged over three years) at SLAMS in the CONUS by
region and time period.
Nitrogen
Deposition and NO2
SLAMS
Total
Deposition
Annual DV (averaged
over three years)
-All Ecoregions
Correlation
Coefficient
(r) = 0.38*
Annual DV (averaged
over three years)-
East Ecoregions
r = 0.44*
Annual DV (averaged
over three years)
-West Ecoregions
r = 0.63*
Year
r
Year
r
Year
r
2001 - 2003
0.31*
2001 -2003
0.18*
2001 - 2003
0.63*
2006 - 2008
0.10
2006 - 2008
0.35
2006 - 2008
0.44*
2010-2012
0.36*
2010-2012
0.23
2010-2012
0.65*
2014-2016
0.60*
2014-2016
0.36
2014-2016
0.76*
2018-2020
0.13
2018-2020
0.10
2018-2020
0.64*
*p<0.05
Correlations are Spearman's Rank Correlations.
In addition to an assessment of N deposition and NO2 ambient air concentrations, we
assessed the relationship between N deposition and annual average PM2.5 concentrations at
SLAMS across the CONUS (Figure 6-39). Although there is substantial scatter, the correlation is
moderate and statistically significant (r = 0.57, p<0.05). As with S deposition and SO2 air
concentrations, the correlation between N deposition and PM2.5 concentrations is lower in the
later years (Table 6-7). For example, across the CONUS, the correlation forN deposition and
PM2.5 concentrations is much higher for the 2001-2003 period (r = 0.61, p<0.05) than for the
2018-2020 period (r = 0.20, p<0.05), although both are significant (Table 6-7). This pattern of
decreasing correlation coefficients with later years is also observed for the eastern and western
subsets.
6-61
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cb
100
75
CD
O)
2
0
>
w
o
Q_
0)
Q
c
0
CJ)
O
50
25
0-
Time Period
r= 0.57 (p<0.05)
slope= 0.59 (p<0.05)
2001-2003
2006-2008
2010-2012
2014-2016
2018-2020
Region
o East
A West
0 10 20 30
Annual Average PM2 5 (DV), 3-yr average (|jg/m3)
03
100
75
"O
0)
ro
E
0
CD
2
0
>
cc
w
o
CL
0
Q
c
0
CD
o
50
25
0-
r= 0.56 (p<0.05)
slope= 0.48 (p<0.05)
Time Period
2001-2003
2006-2008
2010-2012
2014-2016
2018-2020
Region
o East
0 10 20 30
Annual Average PM2 5 (DV), 3-yr average (|jg/m3)
Figure 6-39. N deposition (TDep) and annual average PM2.5 concentration (averaged over
three years) at SLAMS across the CON US (upper), and in East (lower).
6-62
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Table 6-7. Correlation coefficients for TDep-estimated N deposition and annual average
PM2.5 concentrations (averaged across three years) at SLAMS in the CONUS.
Nitrogen
Deposition
and PM2.5
SLAMS
Annua! DV-M
Ecoregions
Correlation
Coefficient (r) = 0.57*
Annual DV-East
Ecoregions
r = 0.56*
Annual DV-West
Ecoregions
r = 0.45*
Year
r
Year
r
Year
r
2001 - 2003
0.61*
2001 - 2003
0.46*
2001 - 2003
0.63*
2006 - 2008
0.47*
2006 - 2008
0.24*
2006 - 2008
0.50*
2010-2012
0.46*
2010-2012
0.24*
2010 -2012
0.40*
2014-2016
0.38*
2014-2016
0.24*
2014-2016
0.24*
2018-2020
0.20*
2018-2020
0.33*
2018-2020
0.39*
*p<0.05
Correlations are Spearman's Rank Correlations.
In summary, the analyses described here expand on the Class 1 area analyses in section
6.2.2 above to consider deposition-concentration relationships at SLAMS regulatory monitors
across the U.S., which are generally closer to sources than are the Class I monitors. At SLAMS
locations, S deposition is strongly correlated with SO2 concentrations, particularly in eastern sites
and during the earliest period (2001-2003). This association is weaker in later periods and at
western sites. Overall, the correlations are weaker for N deposition with NO2 concentrations than
those for S deposition and SO2. Additionally, in contrast to what is seen for S and SO2,
correlations between N deposition and NO2 concentrations are strongest at sites in the West.
Nitrogen deposition and PM2.5 concentrations have similar, low to moderate, correlation
coefficients at sites in the East and West, with much weaker correlations in earlier time periods.
6.2.4 National-scale Sites of Influence Analyses
One limitation of the collocated analyses (Class 1 areas and at SLAMS monitors; "local"
concentrations vs. "local" deposition) presented above is their inability to account for the role of
upwind emissions, transport and chemical transformation in deposition. This section presents the
results from a trajectory-based methodology that first identifies "sites of influence" that have the
potential to contribute to deposition in a downwind location. Then, as a second step, considers
the relationships between "upwind" air quality concentrations and "downwind" deposition in
impacted ecoregions.
6.2.4.1 Methodology
We used the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) air
parcel trajectory model to examine the potential transport of pollutants from source to receptor
(see Appendix 6A for more detailed information). We generated forward trajectories from all
6-63
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N02, SO2, and PM2.5 ambient air monitor locations with valid air quality data (i.e., from the
SLAMS network described in Chapter 2). This was done to estimate how pollution observed at
certain locations (referred to here as "sites of influence") could potentially be transported to
downwind ecoregions. In a source-receptor framework, this analysis is considering the
monitored values to be the "source" and downwind ecoregions as the "receptor." By identifying
which air quality monitors are potentially representative of the air quality that contributes to
deposition in a particular ecoregion, one can potentially better understand the relationship
between upwind ambient air concentrations and downwind deposition rates.
After identifying the upwind geographic areas from which emissions potentially
contribute to N and S deposition in each ecoregion,10 we aggregated air quality concentrations
within each ecoregion's set of sites of influence to estimate a weighted-average air quality
metric, where the value of each site was weighted by how often the forward HYSPLIT trajectory
crossed into the ecoregion (i.e., sites with more frequent trajectory intersections with the
ecoregion are weighted higher). In addition to the weighted-average metric, we also extracted the
area-wide maximum monitored concentration across the area contributing to deposition in each
ecoregion. Both the weighted-averages and area-wide maximum air quality metrics were
estimated for each ecoregion and for three separate pollutants: NO2, SO2, and PM2.5.11 For SO2,
we estimated two sets of metrics, one based on an annual average and one based on the 2nd high
3-hour max within the year. For NO2 and PM2.5, the data are based on annual average
concentrations. These data are intended to provide a perspective of air quality levels in the
upwind regions that potentially contribute to downwind deposition levels. For ease of reference,
we have established the term Ecoregion Air Quality Metric, or EAQM, as shorthand for these
metrics. The two types of metrics are referred to as EAQM-max and EAQM-weighted. All
EAQM estimates were calculated for five 3-year periods: 2001-2003, 2006-2008, 2010-2012,
2014-2016, and 2018-2020 (i.e., further averaging the annual data).
To better understand the differences between these two types of air quality metrics,
consider the following simplified hypothetical example for annual SO2. The trajectory analysis
suggests that there are four upwind monitoring sites where emissions contributing to the
concentrations at those locations could also be contributing to S or N deposition in a specific
downwind ecoregion. Other sites can also impact the downwind ecoregion but they do so less
frequently (i.e., below some identified threshold) and therefore do not get included in the EAQM
calculation.
10 As in Chapter 5 above, we focused on level III ecoregions.
11 We focused on the metric for the annual PM2.5 standard because this averaging timescale is more relevant to
assessing accumulated deposition than a standard with a form set to reduce peak concentrations (i.e., PM2 5 24-hour
standard with its 98th percentile form).
6-64
-------
• Site A - contributes 2% of the total ecoregion "hits;" 3-year average annual SO2 =10
ppb
• Site B - contributes 1% of the total ecoregion "hits;" 3-year average annual SO2 = 8
ppb
• Site C - contributes 0.5% of the total ecoregion "hits;" 3-year average annual SO2 =
12 ppb
• Site D - contributes 0.5% of the total ecoregion "hits;" 3-year average annual SO2 =
10 ppb.
The EAQM-weighted metric for SO2 for this ecoregion-year would be: [(2*10) + (1*8) +
(0.5*12) + (0.5*10)] / [2 + 1 + 0.5 + 0.5] = 9.75 ppb. The EAQM-max metric in this example
would be 12 ppb (from Site C). The EAQM-max metric offers insight into the highest design
value associated with a particular deposition level, while the EAQM-weighted metric is useful in
assessing how well upwind air quality is correlated with estimated S and N deposition. Used
together, the assessment of these two metrics is intended to help further inform conclusions
regarding the association between upwind regional air quality concentrations and downwind S
and N deposition.12
6.2.4.2 Results
Starting with SO2, we observe a strong positive correlation between SO2 EAQM-
weighted values across upwind sites of influence and S deposition in impacted ecoregions across
the eastern U.S. As shown in Figure 6-40, higher S deposition values are associated with higher
weighted-average SO2 at upwind sites of influence (r = 0.85, slope = 2.22, p<0.05). This
association holds across all five time periods, although there is more scatter in the 2018-2020
period than the others. As expected, EAQM-weighted annual SO2 (averaged over 3 years) has
decreased with time as have the S deposition amounts across the eastern U.S. The figure
reaffirms the decreasing trends in ambient air SO2 concentrations and S deposition discussed
elsewhere in the PA. Prior to the 2010-2012 period, it was not uncommon for ecoregions to
experience median S deposition exceeding 5 kg/ha-yr. However, since the 2014-2016 period, no
regions have experienced median S deposition above 5 kg/ha-yr (Figure 6-40). Turning attention
to the western U.S. (Figure 6A-63), the data suggest that the relationship between upwind SO2
and downwind S deposition is less certain (r = 0.19, slope = 0.14, p<0.05). Annual SO2 EAQM
levels have decreased across the periods, but the S deposition in western ecoregions has been
12 In these analyses S and N deposition are ecoregion medians derived from grid-cell estimates based on TDEP
version: v2020.02, downloaded on September 7, 2022. These estimates are also presented in Figures 6-53 and 6-
55.
6-65
-------
relatively low (i.e., less than 2.5 kg/ha-yr) and exhibits smaller changes than what was observed
in the EAQM-based air quality concentrations.
The EAQM-weighted data tend to be slightly better correlated with deposition than the
EAQM-max data (Table 6-8). Figure 6-41 compares EAQM-max annual SO2 values across the
identified sites of influence against the downwind ecoregion S deposition for the eastern U.S.13
There is some suggestion across the 20 years of data of a relationship between the concentration
and deposition terms (r = 0.65, slope = 0.95, p<0.05), but it is largely driven by the older time
periods when S deposition was higher. Figure 6-41 shows that there is no significant relationship
between EAQM-max annual SO2 at upwind sites of influence and S deposition in the western
U.S. However, it should be noted that S deposition is relatively low in the western U.S.
ecoregions.
13 There are several outlier points in this comparison where the EAQM-max annual average SO2 value exceeds 20
ppb in the 2018-2020 period. These points have been removed from this plot. These data are driven by a monitor
in southeastern MO where annual average SO2 has exceeded 20 ppb in recent years. Any downwind ecoregion
that is linked to this upwind monitor will have an EAQM-max with this value. A preliminary analysis suggests
that these observed SO2 data are due to a new source that was not modeled in the CMAQ simulation that
informed the TDep estimates of deposition. As there is no deposition monitor in the immediate vicinity of the
source it is unlikely that the TDep estimates are capturing the impacts of this source. For that reason, the EPA
concluded it was appropriate not to consider these data in our evaluation of the concentration-deposition
relationship.
6-66
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0-
Monitor Inclusion Criterion: 0.5%
r= 0.48 (p<0.05)
slope= 0.45 (p<0.05)
Time Period
• 2001-2003
• 2006-2008
• 2010-2012
• 2014-2016
2018-2020
5 10 15 20
Annual Average S02 (Max), 3-yr average (ppb)
Time Period
• 2001-2003
• 2006-2008
• 2010-2012
• 2014-2016
2018-2020
Monitor Inclusion Criterion: 0.5%
r= 0.65 (p<0.05)
slope= 0.95 (p<0.05)
5 10 15 20
Annual Average S02 (Max), 3-yr average (ppb)
Figure 6-41. TDep-estimated median S deposition in all ecoregions (upper) and eastern
ecoregions (lower) versus upwind annual SO2 EAQM-max values.
6-68
-------
Table 6-8. Correlation coefficients of TDep-estimated S deposition and annual SO2
EAQMs by time period and region.
Sulfur Deposition and
S02
Annual Max-All Ecoregions-
Monitor Inclusion Criteria: 0.5%
Correlation
Coefficient
(r) = 0.49*
Annual Max-East
Ecoregions- Monitor
Inclusion Criteria: 0.5%
r = 0.65*
Annual Max-West
Ecoregions- Monitor
Inclusion Criteria: 0.5%
—?
II
O
O
Year
r
Year
r
Year
r
2001 -2003
0.62*
2001 -2003
0.78*
2001 -2003
-0.11
2006 - 2008
0.69*
2006-2008
0.59*
2006-2008
0.12
2010-2012
0.28*
2010-2012
-0.43*
2010-2012
-0.07
2014-2016
-0.05
2014-2016
-0.44*
2014-2016
-0.06
2018-2020
0.10
2018-2020
-0.13
2018-2020
0.03
Weighted Annual Average-All
Ecoregions- Monitor Inclusion
Criteria: 0.5%
r = 0.56*
Weighted Annual
Average-East
Ecoregions- Monitor
Inclusion Criteria: 0.5%
r = 0.85*
Weighted Annual
Average-West
Ecoregions- Monitor
Inclusion Criteria: 0.5%
r = 0.19*
Year
r
Year
r
Year
r
2001 -2003
0.77*
2001 -2003
0.89*
2001 -2003
0.04
2006 - 2008
0.81*
2006-2008
0.9*
2006-2008
-0.07
2010-2012
0.71*
2010-2012
0.75*
2010-2012
-0.12
2014-2016
0.16
2014-2016
0.19
2014-2016
-0.14
2018-2020
0.22*
2018-2020
0.30*
2018-2020
0.04
*p< 0.05
Considering the current secondary SO2 NAAQS has an averaging time of 3 hours and a
level of 0.5 ppm (500 ppb) that is not to be exceeded more than once per year, we next evaluate
the concentration-deposition relationship for the 2nd highest 3-hour SO2 EAQM values (weighted
and max, again averaged over 3 years). Figure 6-42 suggests that there is strong association
between S deposition and the weighted 3-hour EAQM (r = 0.83, slope = 0.16, p<0.05) across
eastern U.S. ecoregions where higher values of downwind S deposition are associated with
higher values of the weighted EAQM and roughly equivalent to the strong association reported
for the annual SO2 (Figure 6-40). However, as shown in Figure 6-43, there is a weaker
association (r = 0.42, slope = 0.02, p<0.05) between EAQM-max and downwind deposition
across eastern U.S. ecoregions for the 3-hour form of the standard. This is not a surprising result
given that deposition is accumulated over several years, with pollution contributed by multiple
locations, that may not be captured by simply looking at a short-term metric (2nd highest, 3-
hour). This suggests that any revised SO2 standard designed to protect against deposition-related
effects would benefit from a longer averaging time. There is little significant association between
the EAQM value and S deposition in the western U.S. for the current secondary SO2 standard
(Table 6-9).
6-69
-------
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ra
15-
10'
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CL
0-
Time Period
2001-2003
2006-2008
2010-2012
2014-2016
2018-2020
Region
o East
Monitor Inclusion Criterion: 0 5%
r= 0.42 (p<0.05)
slope- 0.02 (p<0.05)
0 200 400
Secondary S02 Design Value (Max), 3-yr average (ppb)
Figure 6-43. TDep-estimated median S deposition in all ecoregions (upper) and eastern
ecoregions (lower) versus upwind 3-hour SO2 EAQM-max values.
6-71
-------
Table 6-9. Correlation coefficients of TDep-estimated ecoregion median S deposition and
3-hr SO2 EAQM values at upwind site of influence by time period and region.
Sulfur Deposition and
SO2 (3-hour Standard)
3-hr Max-All Ecoregions-
Monitor Inclusion Criteria:
0.5%
Correlation
Coefficient
(r) =0.51*
3-hr Max-East
Ecoregions- Monitor
Inclusion Criteria: 0.5%
r = 0.52*
3-hr Max-West
Ecoregions- Monitor
Inclusion Criteria: 0.5%
r = 0.07
Year
r
Year
r
Year
r
2001 - 2003
0.49*
2001 - 2003
0.49*
2001 - 2003
-0.06
2006 - 2008
0.69*
2006 - 2008
0.69*
2006 - 2008
0.18
2010-2012
0.25*
2010 -2012
0.25*
2010-2012
-0.09
2014-2016
0.23*
2014-2016
0.23*
2014-2016
-0.09
2018-2020
0.54*
2018-2020
0.40*
2018-2020
0.10
3-hr Weighted Average-All
Ecoregions- Monitor Inclusion
Criteria: 0.5%
—?
II
O
O
*
3-hr Weighted Average-
East Ecoregions-
Monitor Inclusion
Criteria: 0.5%
r = 0.83*
3-hr Weighted Average-
West Ecoregions-
Monitor Inclusion
Criteria: 0.5%
r = 0.20*
Year
r
Year
r
Year
r
2001 - 2003
0.86*
2001 - 2003
0.86*
2001 - 2003
0.15
2006 - 2008
0.89*
2006 - 2008
0.78*
2006 - 2008
0.31
2010-2012
0.77*
2010 -2012
0.76*
2010-2012
0.06
2014-2016
0.38*
2014-2016
0.24
2014-2016
-0.16
2018-2020
0.54*
2018-2020
0.41*
2018-2020
0.15
*p< 0.05
Similar analyses were completed assessing the relationship between upwind EAQM
values in the form of the current secondary NO2 standard (annual mean), averaged over three
years, against downwind N deposition. Based on the results of section 6.2.1, one would expect it
to be less likely that the upwind annual NO2 EAQM values would be strongly correlated with N
deposition due to the multiple pathways forN deposition and including ammonia-related
sources. This is borne out as shown in Figure 6-44 for NO2 EAQM-weighted values and
ecoregion median N deposition in the East. The data indicate that the ecoregions with higher N
deposition are associated with higher annual average NO2 EAQM values in the older time
periods. However, the correlation is much weaker than for annual average SO2 EAQM for the
eastern ecoregions (r = 0.48 versus r = 0.85 for the weighted metrics), and no association is
observed between the upwind NO2 concentrations and downwind N deposition in the more
current periods or in the western ecoregions (Table 6-10 and Figure 6-44). The pattern of
findings are generally similar for the NO2 EAQM-max metric (Table 6-10 and Figure 6-45).
6-72
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Time Period
• 2001-2003
• 2006-2008 °
• 2010-2012 °
• 2014-2016
2018-2020
Monitor Inclusion Criterion: 0.5%
i
|o*V
Region
o East
A
West A .
A A
oo£
0$*cPf
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0 5 10 15 20
Annual Average N02 Design Value (Weighted), 3-yr average (ppb)
* !••/
Time Period
Q 0
• 2001-2003
• 2006-2008
• 2010-2012
• 2014-2016
2018-2020
q\y©3jfc®
Region
/jW
O
/ 0°® 0 0
/
o East
e
<5> ® ®
*
0 ©
Monitor Inclusion Criterion: 0.5%
r= 0.48 (p<0.05)
slope= 0.38 (p<0.05)
0 10 20 30 40
Annual Average N02 Design Value (Weighted), 3-yr average (ppb)
Figure 6-44. TDep-estimated median N deposition in all ecoregions (upper) and eastern
ecoregions (lower) versus upwind annual NOi EAQM-weighted values.
6-73
-------
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CD
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m
Q
c
£
o
o
10
5-
0-
Monitor Inclusion Criterion: 0.5%
r= -0.17 (p<0.05)
slope= -0.09 (p<0.05)
Time Period
• 2001-2003
• 2006-2008
• 2010-2012
• 2014-2016
2018-2020
Region
o East
A West
W. M * ' ,
85 I
^8 A
°
°A aA
A 1&A aa i
0 10 20 30 40
Annual Average N02 Design Value (Max), 3-yr average (ppb)
15-
¦
Time Period
• 2001-2003
• 2006-2008
• 2010-2012
• 2014-2016
2018-2020
• • • £
n • •* t 1
A '* -
* .
Monitor Inclusion Criterion: 0.5% o o
r= 0.35 (p<0.05)
stope= 0 12 (p<0.05)
10
20
30
40
Annual Average NOz Design Value (Max), 3-yr average (ppb)
Figure 6-45. TDep-estimated median N deposition in all ecoregions (upper) and eastern
ecoregions (lower) versus upwind annual NO2 EAQM-max values.
6-74
-------
Table 6-10. Correlation coefficients of ecoregion N deposition and upwind NO2 annual
EAQM values by time period and region.
Nitrogen Deposition and
N02
Annual Max-All Ecoregions-
Monitor Inclusion Criteria: 0.5%
Correlation
Coefficient
(r) = -0.17*
Annual Max-East
Ecoregions- Monitor
Inclusion Criteria: 0.5%
r = 0.35*
Annual Max-West
Ecoregions- Monitor
Inclusion Criteria: 0.5%
—?
II
O
O
Year
r
Year
r
Year
r
2001 - 2003
-0.31*
2001 - 2003
0.24*
2001 - 2003
-0.12
2006 - 2008
0.05
2006 - 2008
0.35*
2006 - 2008
-0.05
2010-2012
-0.26*
2010 -2012
0.15
2010-2012
0.02
2014-2016
-0.41*
2014-2016
0.03
2014-2016
-0.19
2018-2020
-0.58*
2018-2020
0.02
2018-2020
-0.25
Weighted Annual Average-All
Ecoregions- Monitor Inclusion
Criteria: 0.5%
r = -0.06
Weighted Annual
Average-East
Ecoregions- Monitor
Inclusion Criteria: 0.5%
r = 0.48*
Weighted Annual
Average-West
Ecoregions- Monitor
Inclusion Criteria: 0.5%
r = -0.17*
Year
r
Year
r
Year
r
2001 - 2003
-0.1
2001 - 2003
0.61*
2001 - 2003
-0.22
2006 - 2008
-0.21
2006 - 2008
0.39*
2006 - 2008
-0.23
2010-2012
-0.14
2010 -2012
0.32*
2010-2012
-0.34*
2014-2016
-0.20
2014-2016
0.21
2014-2016
-0.28
2018-2020
-0.37*
2018-2020
-0.03
2018-2020
-0.26
*p< 0.05
Correlations are Spearman's Rank Correlation
Regarding upwind PM2.5 EAQM values and N deposition in downwind ecoregions, as the
composition of PM2.5 over much of the U.S. is dominated by species that will not contribute to N
deposition (e.g., organic carbon, elemental carbon), a strong relationship between PM2.5 and N
deposition is not expected, even though the ammonium component of PM2.5 can contribute.
Figures 6-46 and 6-47 show the results for the EAQM-weighted and EAQM-max, respectively.
The PM2.5 EAQM values have decreased over the past two decades and that the association
between concentrations and N deposition is not significant over the western U.S. where
deposition values are generally lower (Table 6-11). However, there is a moderate association
between EAQM-weighted annual PM2.5 and N deposition in eastern ecoregions (Figure 6-46; r =
0.62, slope = 0.63, p<0.05). As was the case for the S deposition and the SO2 EAQMs, the
EAQM-weighted values for annual PM2.5 tend to be slightly better correlated with N deposition
than the EAQM-max values (r = 0.53, slope = 0.44, p<0.05), but it is largely driven by the older
time periods when N deposition was higher (Figure 6-47, Table 6-11).
6-75
-------
15
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2
0
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03
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CL
0
Q
c
0
O)
o
10
Time Period
• 2001-2003
• 2006-2008
• 2010-2012
• 2014-2016
2018-2020
A
K A Monitor Inclusion Criterion: 0.5%
0.45 (p<0.05)
slope= 0.76 (p<0.05)
0 10 20 30
Annual Average PM2 5 Design Value (Weighted), 3-yr average (|jg/m3)
15
z
CD
0
CD
2
0
>
cc
o
Q.
0
Q
c
0
CD
O
10
Time Period
0
0
d
• 2001-2003
• 2006-2008
• 2010-2012
• 2014-2016
2018-2020
o°«&
Region
Ms?.
y&W*
0 East
/o°
/o°
/ 9
/ CO •
/ 0 0
•«
©
Monitor Inclusion Criterion:
r= 0.62 (p<0.05)
slope= 0.63 (p<0.05)
0.5%
0 10 20 30
Annual Average PM2 5 Design Value (Weighted), 3-yr average (|jg/m3)
Figure 6-46. TDep-estiniated median N deposition in all ecoregions (upper) and eastern
ecoregions (lower) versus upwind annual PM2.5 EAQM-weighted values.
6-76
-------
15
(D
O)
CO
a5
>
03
>*
CO
O
Ql
• 2001-2003
• 2006-2008
0^0
0
• 2010-2012
0 A
• 2014-2016
A
/A
2018-2020
tr
0 0
Region
o East
A West
15-
10
CO
>.
CO
o
Q_
Q)
o
c
-------
Table 6-11. Correlation coefficients of PM2.5 EAQM values with TDep-estimated median
N deposition in downwind ecoregions.
Nitrogen Deposition and
PM2.5
Annual Max-All Ecoregions-
Monitor Inclusion Criteria: 0.5%
Correlation
Coefficient
(r) = -0.22*
Annual Max-East
Ecoregions- Monitor
Inclusion Criteria: 0.5%
r = 0.53*
Annual Max-West
Ecoregions- Monitor
Inclusion Criteria: 0.5%
r = -0.12
Year
r
Year
r
Year
r
2001 -2003
-0.12
2001 -2003
0.64*
2001 -2003
-0.18
2006 - 2008
-0.30*
2006-2008
0.34*
2006-2008
-0.22
2010-2012
-0.14
2010-2012
0.46*
2010-2012
-0.13
2014-2016
-0.46*
2014-2016
0.27
2014-2016
-0.24
2018-2020
-0.49*
2018-2020
0.26
2018-2020
-0.07
Weighted Annual Average-All
Ecoregions- Monitor Inclusion
Criteria: 0.5%
r = 0.45*
Weighted Annual
Average-East
Ecoregions- Monitor
Inclusion Criteria: 0.5%
r = 0.62*
Weighted Annual
Average-West
Ecoregions- Monitor
Inclusion Criteria: 0.5%
r = 0.02
Year
r
Year
r
Year
r
2001 -2003
0.65*
2001 -2003
0.85*
2001 -2003
-0.03
2006 - 2008
0.64*
2006-2008
0.67*
2006-2008
-0.14
2010-2012
0.75*
2010-2012
0.60*
2010-2012
0.09
2014-2016
0.45*
2014-2016
0.42*
2014-2016
-0.16
2018-2020
-0.09
2018-2020
0.27
2018-2020
-0.02
*p< 0.05
Correlations are Spearman's Rank Correlations
Regarding PM2.5 EAQM values and median S deposition in downwind ecoregions, the
correlations for both the max and weighted metrics for the full datasets (all time periods and both
regions) were nearly identical with those for N deposition (Table 6-12). The correlations for both
metrics with deposition in the eastern ecoregions were appreciably stronger for S than for N
deposition (r = 0.83 and r = 0.90 versus r = 0.53 and r = 0.62). Little correlation was observed in
the western ecoregions for either N or S deposition (Figures 6-48 and 6-49, Table 6-12).
6-78
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15
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Monitor Inclusion Criterion: 0.5%
r= 0.48 (p<0.05)
slope= 1.1 (p<0.05)
0 10 20 30
Annual Average PM2 5 Design Value (Weighted), 3-yr average (|jg/m3)
Time Period
• 2001-2003
• 2006-2008
• 2010-2012
• 2014-2016
2018-2020
Region
o East
Monitor Inclusion Criterion: 0.5%
r= 0.90 (p<0.05)
slope= 1.11 (p<0.05)
0 10 20 30
Annual Average PM2 5 Design Value (Weighted), 3-yr average (pg/m3)
Figure 6-48. TDep-estimated median S deposition in all ecoregions (upper) and eastern
ecoregions (lower) versus upwind annual PM2.5 EAQM-weighted values.
6-79
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Time Period
• 2001-2003
• 2006-2008
• 2010-2012
• 2014-2016
2018-2020
Region
o East
A West
» ©
• #
• •
8*
%
Monitor Inclusion Criterion: 0.5%
r= -0.22 (p<0.05)
0 10 20 30
Annual Average PM25 Design Value (Max), 3-yr average (^ig/m3)
20
>.
TO
-C
CO
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ro
CO
10
d)
Q
Time Period
• 2001-2003
• 2006-2008
• 2010-2012
• 2014-2016
2018-2020
Region
o East
Monitor Inclusion Criterion: 0.5%
r= 0.83 (p<0.05)
slope= 1.12 (p<0.05)
0 10 20 30
Annual Average PM2 5 Design Value (Max), 3-yr average (^g/m3)
Figure 6-49. TDep-estimated median S deposition in all ecoregions (upper) and eastern
ecoregions (lower) versus upwind annual PM2.5 EAQM-max values.
6-80
-------
Table 6-12. Correlation coefficients of TDep-estimated median S deposition and upwind
PM2.5 E AQM values.
Sulfur Deposition and
PM2.5
Annual Max-All Ecoregions-
Monitor Inclusion Criteria:
0.5%
Correlation
Coefficient
(r) = -0.22*
Annual Max-East
Ecoregions- Monitor
Inclusion Criteria: 0.5%
r = 0.83*
Annual Max-West
Ecoregions- Monitor
Inclusion Criteria: 0.5%
r = -0.33*
Year
r
Year
r
Year
r
2001 -2003
-0.21
2001 -2003
0.73*
2001 -2003
-0.48*
2006 - 2008
-0.37*
2006 - 2008
0.53*
2006-2008
-0.61*
2010-2012
-0.22*
2010-2012
0.70*
2010-2012
-0.56*
2014-2016
-0.53*
2014-2016
0.43*
2014-2016
-0.53*
2018-2020
-0.54*
2018-2020
0.53*
2018-2020
-0.37*
Weighted Annual Average-All
Ecoregions- Monitor Inclusion
Criteria: 0.5%
r = 0.48*
Weighted Annual
Average-East
Ecoregions- Monitor
Inclusion Criteria: 0.5%
r = 0.90*
Weighted Annual
Average-West
Ecoregions- Monitor
Inclusion Criteria: 0.5%
r = -0.22
Year
r
Year
r
Year
r
2001 -2003
0.55*
2001 -2003
0.88*
2001 -2003
-0.44*
2006 - 2008
0.56*
2006 - 2008
0.86*
2006-2008
-0.62*
2010-2012
0.70*
2010-2012
0.84*
2010-2012
-0.40*
2014-2016
0.43*
2014-2016
0.65*
2014-2016
-0.55*
2018-2020
-0.07
2018-2020
0.69*
2018-2020
-0.25
*p< 0.05
Correlations are Spearman's Rank Correlations.
6.2.4.3 Conclusions
For SO2 downwind ecoregion S deposition, we examined both the 3-hour and annual
average metrics. The results indicate that both metrics are correlated with S deposition, with the
strongest correlations observed in the earliest time periods and for S deposition in the eastern
ecoregions. As would be expected given its derivation, there is somewhat higher correlation for
the EAQM-weighted metric. The figures for SO2 also indicate median ecoregion S deposition to
be above approximately 10 kg/ha-yr in the first two time periods assessed and generally
approximately 5 kg/ha-yr in the most recent period. However, the SO2 figures also indicate that
there can be high measured SO2 concentrations associated with low S deposition (i.e., < 5 kg
S/ha-yr), in both the eastern and western U.S., and that there is generally more scatter in the data
at lower deposition values. Both of these observations could be driven by uncertainties in the
TDep calculations and/or uncertainties in our EAQM assessment methodology.
For NO2, the correlations between the annual NO2 EAQM values and N deposition are
not nearly as strong as they are between metrics for SO2 concentrations and S deposition. This
could be because oxidized nitrogen only contributes to part of the total N deposition estimate
(Figure 6-18), and as discussed in sections 2.5.3 and 6.2.1, the contribution of reduced nitrogen
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to total N deposition has grown over the last few decades (e.g., Li et al., 2016). The NO2 EAQM
analysis, like others in this section, suggest that, based on patterns of and trends in N species in
recent air quality, NO2 may not be a good indicator for total N deposition and consideration of
related effects.
For PM2.5, the results show some limited correlation between the measurements of annual
average PM2.5 and estimates of N deposition, particularly in the east and in the earlier time
periods. The findings of association may relate to measurements at PM2.5 monitors where both
oxidized and reduced forms of N (i.e., NO3 and NH4+) are copious. However, PM2.5 mass is also
comprised of many components unrelated to N or N deposition. Further, the scatterplot of annual
PM2.5 EAQM-max and N deposition shows that the range of ecoregion deposition values
associated with individual annual average PM2.5 concentrations is broad. For example, for annual
average PM2.5 concentrations (averaged over three years) from approximately 15 to 10 |ig/m3,
downwind N deposition ranges from less than 5 to somewhat above 12 kg/ha-yr (Figure 6-47).
The analyses for PM2.5 and ecoregion S deposition indicate a somewhat tighter relationship.
Except for a few datapoints prior to 2010, S deposition was below 10 kg/ha-yr when upwind
annual average PM2.5 concentrations (averaged over three years) were at or below 15 |ig/m3
(Figure 6-49).
Regarding the EAQM approach, we take note of certain assumptions and limitations that
are discussed in detail in Appendix 6A. We emphasize here that the EAQM-based relationships
between concentrations and deposition in downwind ecoregions are not intended to represent
predictive associations that can determine what the downwind deposition will be as a function of
upwind air quality. In fact, the scatter in the data (e.g., same concentrations can lead to different
deposition levels, same deposition levels can result from different upwind pollutant
concentrations) argues just the opposite. The findings of the EAQM analysis suggest that among
the three criteria pollutants, SOx will have the closest relationships between concentrations and
eventual S deposition, particularly for a concentration metric with a longer-term averaging time,
such as a 3-year average of annual average hourly data.
6.3 LIMITATIONS AND UNCERTAINTIES
A summary of key limitations and associated uncertainties of the data and analyses
described in this chapter is provided below. This summary is based on the characterization of
uncertainties presented in section 6.3.1 and is followed by sensitivity analyses in section 6.3.2
that provide additional support to the characterization of uncertainty associated with the
trajectory-based analyses discussed in section 6.2.4 above. The mainly qualitative approach to
uncertainty characterization that is presented in section 6.3.1 and used for air quality, exposure
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and risk assessments performed in other NAAQS reviews,14 is also informed by quantitative
sensitivity analyses, as described by WHO (2008).
The linkage between air concentration and deposition can vary based on site-specific
conditions, including the chemical form of nitrogen and sulfur, frequency of precipitation, and
micrometeorological factors relevant to the dry deposition velocity. The analyses above attempt
to provide insight into these relationships and variability for multiple measured air quality
metrics. As with any assessment, there are uncertainties and limitations associated with the work,
most of which are discussed above in the context of each the analyses. In this section, we
summarize some of the overarching uncertainties and limitations.
In section 6.2.2, multiple forms of data were analyzed using co-located information in a
subset of Class I areas. While there are uncertainties in each of the different sets of modeled and
measured data analyzed, the fact that the assessment saw consistent results across these different
forms of data reduces the concern with these potential data-related issues. The biggest limitation
of the assessment in section 6.2.2 is the limited geographical coverage of the Class I areas that
were included. Although these areas are in different parts of the country and were chosen based
on the availability of co-located air quality (i.e., IMPROVE, CASTNET) and NADP/NTN
monitors, most were located in the western U.S., where terrain, emissions and air quality
chemistry can look different from other parts of the country. This analysis may neglect or
underestimate the role of large ammonia emission sources in the Midwest and large nitrogen
oxide emission sources in the eastern U.S. Additionally, these selected Class I areas include
greater representation of the West (20 of the 27 sites are in the ecoregions designated West) than
is the case for the locations that were quantitatively assessed in Chapter 5 for potential aquatic
acidification effects (e.g., only 8 of the 25 ecoregions are in the West).
In section 6.2.4, an analysis using the HYSPLIT model was included to assess the
linkage between TDep estimates of N and S deposition and measured air quality concentrations
of NO2, SO2 and PM2.5. There are uncertainties in the HYSPLIT application itself, including the
use of one year of meteorological data to estimate multiple years of transport. Additionally, this
analysis included judgments on the percentage of trajectory impacts warranting inclusion in an
ecosystem's sites of influence. It is unclear how much and in what way these uncertainties and
assumptions might impact the results. Although increasing the geographic scope of the sites of
influence could lead to higher maximum values, there are also uncertainties in the TDep
estimates, which are discussed in more detail in section 2.5. There is also uncertainty as to
whether only SO2, NO2 and PM2.5 concentrations at the monitor site influence the designated
14 This approach to uncertainty characterization has been utilized in welfare and health RE As for reviews of the
ozone, NO2, SO2, and carbon monoxide NAAQS (e.g., U.S. EPA 2014, 2018).
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receiving ecoregion deposition. An additional uncertainty that should also be considered is the
application of HYSPLIT to somewhat large areas of the country (i.e., ecoregions), which may
have substantial spatial variability in deposition levels.
6.3.1 Characterization of Uncertainty
Briefly, with this approach, we have identified key aspects of the assessment approach
that may contribute to uncertainty in the conclusions and provided the rationale for their
inclusion (Table 6-13). Then, we characterized the magnitude and direction of the influence on
the assessment for each of these identified sources of uncertainty. Consistent with the WHO
(2008) guidance, we scaled the overall impact of the uncertainty by considering the degree of
uncertainty as implied by the relationship between the source of uncertainty and interpretations
drawn from the air quality analyses. A qualitative characterization of low, moderate, and high
was assigned to the magnitude of influence and knowledge base uncertainty descriptors, using
quantitative observations relating to understanding the uncertainty, where possible. Where the
magnitude of uncertainty was rated low, it was judged that large changes within the source of
uncertainty would have only a small effect on the assessment results (e.g., upwards to a factor of
two). A designation of medium implies that a change within the source of uncertainty would
likely have a moderate (or proportional) effect on the results (e.g., a factor of two or more). A
characterization of high implies that a change in the source would have a large effect on results
(e.g., an order of magnitude). We also included the direction of influence, whether the source of
uncertainty was judged to potentially over-estimate ("over"), under-estimate ("under"), or have
an unknown impact on the analyses designed to assess relationships between air quality
concentrations and deposition (Table 6-13).
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Table 6-13. Characterization of key uncertainties in analyses that relate air quality to deposition.
Sources of Uncertainty
Uncertainty Characterization
Influence of Uncertainty on
Analyses
Knowledge-
base
Uncertainty
Comments
Category
Element
Direction
Magnitude
Ambient Air
Concentrations at
IMPROVE Monitors
Ambient Air
Concentration
Measurements
Unknown
Low
High
IMPROVE monitoring of PM2.5 uses a gravimetric assessment of aerosol mass collected on a
Teflon filter, which may have biases outside of the sampling relative humidity range (30-40%).
Sulfate and nitrate mass are calculated by assuming that either is fully neutralized by ammonia.
The bias in this estimate would be affected by the actual composition of sulfate or nitrate (e.g., in
organic vs. inorganic forms). A comparison of the IMPROVE gravimetric PM2.5 and reconstructed
mass methods suggests that they generally co-vary on a seasonal to annual basis (R2 = 0.93-
0.96; Malm et al., 2011), such that we do not anticipate that uncertainties in either measurement
method will alter the conclusions drawn from our assessment of the correlation between
deposition and IMPROVE concentration measurements.
Air Quality System
(AQS) Database
Quality
Unknown
Unknown
High
See above
Spatial
Representation
Low
Medium
Medium
Overall, IMPROVE sites are in national parks and Federal Class 1 locations, which are generally
remote and relatively pristine ecosystems. There is a higher density in the west versus eastern
USA, and there is a dearth of monitors in the midwest. More analysis would be needed to assess
the extent to which our collocated assessment using IMPROVE is extendable to areas with fewer
collocated monitors and differing environmental conditions (e.g., urban). IMPROVE monitors at
remote locations help reduce uncertainty in HYSPLIT estimated concentrations.
Temporal
Representation
Low
Low
Medium
IMPROVE collects samples on a 24-h basis every three days. There may be some uncertainty
associated with non-continuous sampling.
Ambient Air
Concentrations at
CASTNET Monitors
(part of NADP)
Air Concentration
Measurements
Unknown
Low
High
The precision of CASTNET measured ammonium, nitric acid and nitrate are estimated as 3.0%,
5.5% and 7.8%, respectively (Sickles and Shadwick, 2002). The volatility of ammonium nitrate can
contribute biases in nitrate (low bias) and nitric acid (high bias), while the total nitrate
concentration (NO3- + HNO3) is conserved (Lavery et al., 2009; Zhang et al., 2009). Although
volatility-related bias in ammonium concentrations was not the focus of these studies, this bias
should be lower where ammonium is generally associated with sulfate (Walker et al., 2019).
Spatial
Representation
Low
Medium
High
CASTNET monitors are located in remote or rural areas. CASTNET-measured concentrations
may be representative of pollution levels that affect sensitive or pristine ecosystems. CASTNET
follows the legacy of acid rain, so that most sites are located in the eastern USA. There is more
uncertainty in the western and midwest USA due to a relative sparsity in measurements.
Temporal
Representation
Low
Low
Medium
CASTNET measures the total mass of HNO3, SO2 and PM on a weekly basis. This level of
temporal resolution should be sufficient for inferring annual, cumulative impacts from ecosystem
exposure.
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Sources of Uncertainty
Uncertainty Characterization
Influence of Uncertainty on
Analyses
Knowledge-
base
Uncertainty
Comments
Category
Element
Direction
Magnitude
Wet Deposition
Measurements (NADP)
Sample Collection
Methods
Unknown
Low
High
Collocated collectors suggest that the median absolute error of NTN precipitation measurements
of ammonium and nitrate is 11 % and 5.0%, respectively (Wetherbee et al., 2005). Precipitation
sampled ammonium is biased low by approximately 10% (Gilliland et al., 2002; Walker et al.,
2012).
Chemical Analysis
Unknown
Low
High
Since 2018, NADP chemical analysis has been conducted by the Wisconsin State Laboratory of
Hygiene. Previous assessment of inter-lab measurements a significant difference in sulfate,
although with a small median difference of 0.048 mg/L. There were not significant differences in
the measurement of ammonium or nitrate (Wetherbee et al., 2010).
Precipitation
measurements
Unknown
Low
High
Differences across precipitation monitors varied by 4.1 to 8% at several NTN study sites between
2007-2009. The precision among precipitation measurements was found to be between 0.6 to
2.2%. Although these differences were statistically significant, the magnitude of biases was small
enough to be considered negligible (Wetherbee et al., 2010).
Spatial
Representation
Low
Medium
High
Wet deposition is currently measured at approximately 250 sites in the NADP/NTN network. NTN
monitors are mainly located away from urban areas and pollution sources.
Temporal
Representation
Low
Low
Medium
Wet deposition is currently measured on a weekly average by the NADP/NTN network.
TDep - Continuous
estimate of deposition
Spatial interpolation to
estimate 4km wet
deposition
Unknown
Medium
Low
Estimates of wet deposition are interpolated using inverse distance weighting (NADP,
https://nadp.slh.wisc.edu/networks/national-trends-network/). While there are other methods for
spatial interpolation, to our knowledge potential differences among them have not been tested for
wet deposition. Areas having a relatively lower density of monitor sites may have greater
uncertainty than other areas.
CMAQ estimates of
gas phase NO2, SO2,
and NH3 and
particulate SO42", NO3"
and NH4+
Unknown
Medium
Medium
CMAQ air concentration biases may be substantial, varying by season and region. These biases
will affect the interpolated estimates of deposition from TDep, with a larger extent of influence
further from monitor locations.
Bias correction with
CASTNET
measurements of gas
phase SO2 and NOy,
and particulate SO42-,
NO3- and NH4+
Unknown
Low
Low
There are several potential approaches for bias correction that have not been evaluated as part of
our analysis. However, we anticipate that the selection of bias correction method will have a
smaller influence than the measurement uncertainty.
Effective dry
deposition velocity
estimates
Unknown
High
Low
Affected by meteorology, surface conditions (e.g., complex terrain), elevation and land cover.
Although NH3 fluxes are bi-directional, there is not yet a way to represent this feature more
dynamically in TDep.
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Uncertainty Characterization
bources ot uncertainty
Influence of Uncertainty on
Analyses
Knowledge-
base
Comments
Category
Element
Direction
Magnitude
Uncertainty
Dry deposition
interpolation to
estimate dry
deposition at 4km grid
Unknown
Medium
Low
Additional measurements would be needed to evaluate TDep interpolation. The interpolation may
obscure fine resolution variability distant from monitors.
Use of a single year
(2016) in HYSPLIT to
be representative of
long term
meteorological
patterns
Unknown
Medium
Low
HYSPLIT analyses require meteorological data to identify the trajectories of air parcel transport
from a source to a receptor. A single year was chosen to keep the analyses manageable and for
consistency with prior EPA trajectory analysis. 2016 was selected for consistency with prior EPA
trajectory analyses and because it appears to represent typical meteorological conditions.
However, the use of only one year of meteorological data adds uncertainty to the identification of
potential upwind sites of influence as the true frequency of wind directions over the 20-year study
period (2000-2020) may differ from what was determined based on 2016 alone.
Resolution of
HYSPLIT
meteorological inputs
Low
Low
High
Sensitivity analyses compared how associations between pollutants and deposition differed when
the trajectories were based off of 12-km data instead of 32-km data (see Appendix 6A). The
results suggest the impact of meteorological input resolution is small.
Assignment of monitors
to ecoregion zones of
influence with HYSPLIT
modeling
Duration of HYSPLIT
trajectories (120
hours)
Low
Low
High
Sensitivity analyses compared how associations between pollutants and deposition differed when
the trajectories that identified the upwind sites of influence were developed using 120-hour
trajectories, as opposed to 48-hour trajectories (see Appendix 6A). The results suggest the impact
of trajectory duration is small.
Extent to which
monitors represent
areas with air quality
of interest
Unknown
Medium
Low
The air quality data on which the EAQM calculations are based are from the SLAMS network. The
presumption is that U.S. air quality monitoring networks for NO2, SO2, and PM2.5 are robust
enough to enable one to use these data to establish a meaningful representation of the air quality
that may contribute to downwind deposition. It is beneficial that NO2, SO2, and PM2.5 monitors are
often located near sources or in highly-populated areas (e.g., source-oriented monitoring, near-
road monitoring). However, there are some background-oriented sites included in this analysis
which may influence conclusions. Additionally, there are likely some source of pollutants that
eventually impact deposition which are not captured. This is most likely to impact the EAQM-max
metric.
Monitor inclusion
criteria
Unknown
High
Moderate
Sensitivity analyses assessed how different choices about which upwind monitors should be
considered as potential sites of influence (and therefore part of the EAQM calculation) impacted
both the spatial extent of the upwind influence and the eventual assessments of the strength of
relationships between pollutant metrics and deposition. The lower the threshold for inclusion
resulted in a larger areal extent of sites of influence. This, in turn, affected the strength of several
associations (as measured by R2 and slope).
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Sources of Uncertainty
Uncertainty Characterization
Influence of Uncertainty on
Analyses
Knowledge-
base
Uncertainty
Comments
Category
Element
Direction
Magnitude
Variation in ecoregion
size and shape, as
well as topographic,
geologic and other
features
Unknown
Medium
Moderate
Smaller ecoregions are more likely to have fewer sites of influence, as most of the trajectory hits
come from monitors within the ecoregion itself, making it less likely that sites outside the
ecoregion will reach the 0.5% criteria for fraction of total trajectory hits.
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6.3.2 Sensitivity Analyses Related to Aspects of Trajectory-Based Assessment
As described in more detail in Appendix 6A, we conducted sensitivity testing on three
aspects of the analytical methodology used to calculate EAQM values. Specifically, we
examined two durations for the forward parcel trajectories (48 hours and 120 hours), two
different meteorological input data sets (NARR-32 and NAM-12) with differing resolution, and
three different monitor inclusion criteria ranging from 1% of total hits in an ecoregion to 0.1% of
total hits in an ecoregion. Each of these methodological changes, when moving from the original
analysis to the final analysis, had the effect of allowing more distant upwind sites to be included
in the EAQM calculations of air quality across potential sites of influence.
Figure 6-50 shows the association between annual SO2 EAQM values and S deposition
across the 84 ecoregions and 5 time periods, based on a 48-hour duration for the trajectory
analysis, the NARR-32 inputs, and a monitor inclusion criterion of 1%. Figure 6-51 shows the
association between annual SO2 EAQM values and S deposition across the 84 ecoregions and 5
time periods, based on 120-hour duration for the trajectory analysis, the NAM-12 input data, and
a minimum hit rate of 0.5% for monitoring site inclusion criterion. In both analyses, similar
themes emerge. It is clear from both figures that the SO2 EAQM and TDep-estimated S
deposition association is strongest in the eastern U.S., and essentially non-existent in western
U.S. locations. In both cases, we can conclude that the relationship between upwind air quality
and downwind deposition was stronger in the earlier periods than it is in the most recent, 2018-
2020, period. It is also noted that the R-value increases slightly with the inclusion of more distant
sites, from 0.45 to 0.56. Figures 6A-23 and 6A-24 in Appendix 6A present the same types of
plots for ecoregion S deposition and SO2 EAQM-max but for data limited to the eastern
ecoregions limit the comparisons to sites in the eastern U.S. and the associations are equally
strong in both iterations of the methodology (r = 0.85, slope ~ 2.2, p<0.05). In sum, we
concluded that the overall strength of association between upwind air quality and downwind
deposition are not strongly affected by the choice of trajectory length, meteorological inputs, or
monitor inclusion criteria.
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Annual Average S02 (Weighted), 3-yr average (ppb)
Figure 6-51. Plot of annual SO2 EAQM values against TDep total S deposition across 84
ecoregions. The individual pairs are color-coded by 3-year periods and the
symbols differentiate between sites in the eastern U.S. and western U.S. This
figure is based on EAQM data using 120-hour trajectories, the NAM-12
meteorological data, and a monitor inclusion criterion of 0.5%.
6.4 KEY OBSERVATIONS
Based on the information above, this section discusses how well various air quality
metrics relate to S and N deposition. We used five separate approaches to evaluate these
relationships. The first approach consisted of a simple comparison of AQ and deposition trends
over the past two decades, as a type of "real-world experiment," to determine how these two
terms have correlated over recent periods. The strength of this approach is that it relies entirely
on monitoring data and the observed trends. A limitation of this is that while one can observe
correlation between the downward trends in emissions, air quality concentrations, and deposition
in nitrogen and sulfur, more analyses are needed to determine that the trends in emissions and
SO2 and NOx concentrations caused the decrease in deposition.
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The second approach assessed how air quality concentrations and resultant deposition
levels are related within a chemical-transport model (CMAQ) both nationally and then at certain
Class 1 areas. The advantage of this particular approach is that it allowed comparison of air
quality and deposition without some of the monitoring limitations that constrain other types of
concentration to deposition relationships (e.g., could be assessed at every model grid cell, model
estimates dry, wet, and total deposition which allows for more detailed comparisons). These
comparisons have the disadvantage of being subject to model input errors or imperfect model
parameterizations.
The third approach focused exclusively on a subset of monitoring sites where detailed air
quality data (CASTNET, IMPROVE) were collocated with wet deposition measurements
(NADP). Comparisons at these 27 Class 1 sites allowed for an evaluation of the association
between wet deposition of N and S against concentrations of multiple gaseous and particulate Isl-
and S-containing chemicals and PM2.5. The strength of this analysis is that it is entirely based on
monitoring data (i.e., no contribution from air quality modeling as in analyses involving TDep
estimates). The primary limitations of this approach are (1) that the collocated measurements are
only available in certain locations (mostly in the western U.S.) and thus any associations may not
be representative of national conditions; and (2) that the deposition data do not include dry
deposition.
The fourth approach looked at the associations between measured air quality
concentrations (SO2, NO2, and PM2.5) and TDep estimates of deposition at all sites that measure
those pollutants across the U.S. This allows for a robust comparison of local concentrations and
local deposition across the U.S. This analysis is particularly relevant given that the current
standards (both primary and secondary) are judged using design value metrics based on
measurements at the current SO2, NO2 and PM2.5 SLAMS monitors. Many of these monitors are
in the areas of higher pollutant concentrations, and many are sited near sources of SOx and N
oxides emissions. For example, as discussed in section 2.3, many SO2 monitors are sited near
large point sources of SO2 (e.g., electric generating units) and for NO2, larger urban areas are
required to site NO2 monitors near larger roadways with a focus on mobile source emissions.
One limitation of this approach is that it does not account for deposition associated with the
transport of pollutants emitted some distance upwind.
The fifth approach assesses relationships between a composite air quality metric (EAQM)
and TDep estimates of deposition within downwind ecoregions. This approach provides a way to
account for the air quality data at upwind locations with the potential to influence downwind
deposition. One limitation of this approach is that it is challenging to identify the upwind sites
with the potential to influence downwind deposition. The fourth and fifth approaches may be
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affected by biases in the model simulations and uncertainties in the interpolation method used to
create the TDep product.
No single approach to assessing the relationship between concentrations and deposition is
perfect. Each of them is informative to our understanding. The conclusions discussed below are
based on an evaluation of the results from all five approaches, with higher weighting assigned to
the fourth, fifth, and first approaches, in that order.
6.4.1 SO2 Metrics
As introduced in Chapter 2, and discussed earlier in this chapter, S tends to deposit as
SO2 close to sources of SO2 emissions but as SO42" in areas further away, including more rural
areas of the country. In the western U.S., where S tends to be low, S may deposit more equally
from SO2 and SO42". Section 6.2 considers the current form and averaging time of the SO2
secondary NAAQS (i.e., the 2nd highest 3-hour daily maximum for a year) in the deposition to air
quality analyses. Additionally, given that the deposition-related impacts examined in this review
are associated with deposition over some longer period of time (e.g., growing season, year,
multi-year), this chapter also assesses an SO2 air quality metric in the form of an annual average.
Additionally, noting the many factors that can lead to variability in the deposition, including
frequency of precipitation, and micrometeorological factors relevant to the dry deposition
velocity, the analyses focus on a 3-year average for each of the air quality and deposition metrics
and include multiple years of data to better assess more typical relationships.
Starting with the annual SO2 metric and the fourth approach, we observe that the
comparisons of annual SO2 concentrations (averaged over 3 years) and deposition estimates at
the SLAMS for the same time periods indicate that the two entities are strongly correlated (r =
0.70). This is especially true for the earlier periods of the record (e.g., 2001-2003) and across the
eastern U.S. (Figures 6-35). While there are exceptions, there is a general association of SLAMS
with higher annual average SO2 concentrations with higher local S deposition estimates. The
EAQM analyses in the fifth approach then extend the conclusion that annual SO2 is also likely a
good indicator for regional S deposition levels. The EAQM-weighted comparisons of annual
average SO2 (averaged over 3 years) in the eastern U.S. exhibit a high degree of correlation (r =
0.85 and 0.65, Figures 6-40 and 6-41). Finally, per the first approach, we note that the observed
declines in national levels of S deposition over the past two decades has occurred during a period
in which emissions of SO2 have also declined sharply (Figures 6-52).
Figure 6-52 displays the trend in SO2 emissions (averaged over 3 years) nationally for
five time periods from 2001 through 2020. Figure 6-53 displays the distributions of median S
deposition estimates for the 84 ecoregions in the CONUS for the same five time periods. The
two parameters (annual average SO2 emissions and S deposition) have exhibited consistent
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decreases across the 20-year period, suggesting again that SO2 may be a good indicator for a
secondary standard associated with S deposition.
The decline in ambient air SO2 concentrations observed at SLAMS monitors (Figure 6-7)
is not as sharp as the decline in SO2 emissions or in ecoregion median S deposition. This is likely
due to the direct relationship of emissions with atmospheric loading of S compounds, which is
then directly related to S deposition. The ambient air monitoring dataset is not limited to only
monitors with consistent monitoring across the time period examined, and the monitor locations
are not uniformly distributed across the U.S. and/or are not necessarily sited adjacent to all
significant sources operating in all of the time periods examined. Even so, the ambient air
concentration declines are consistent over the period and exhibit correlations with the declining
trend in S deposition.
The different approaches for examining relationships between SO2 concentrations and S
deposition indicate associations between the two variables, locally, regionally, and nationally.
We also find that the CMAQ comparisons (second approach) and the Class I areas analyses
(third approach) also indicate annual average SO2 concentrations to generally be associated with
S deposition. We note, however, that many of the relationships between SO2 concentrations and
S deposition values become much weaker when S deposition levels are less than 5 kg/ha-yr (e.g.,
across the western U.S., more recent S deposition levels in the eastern U.S.). There is also
substantial scatter at these lower deposition values, calling into question the ability to identify a
specific SO2 concentration and metric that might be consistently associated with deposition
below approximately 5 kg/ha-yr.
In addition to the annual average SO2 metric, the current 3-hour SO2 metric also appears
to relate to S deposition. The correlations for S deposition with this metric and the annual metric
vary across all the approaches, with one or the other having a somewhat higher correlation than
the other. Overall, the metrics demonstrate similar strength in correlation, with r values for the
full datasets ranging from about 0.5 to 0.7. Thus, as the focus in this review is on annual
deposition across sensitive regions, we conclude that the SO2 annual average, averaged over
three years, would likely be the better metric for consideration of policy options to address S
deposition-related effects.
6-94
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E
LU
o
CO
2001-2003
2006-2008
2010-2012
2014-2016
2018-2020
Figure 6-52. Estimated annual SO2 emissions, nationally (NEI), averaged over three years,
from 2001-2020.
Figure 6-53. TDep estimates of ecoregion median S deposition. Whiskers mark 5th and 95th
percentiles; estimates above 95th percentiles are black dots.
6-95
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6.4.2 NOi and PM2.5 Metrics
Both NO2 and certain components of PM2.5 can contribute to N deposition. As was the
case for SO2 and S deposition, there are multiple pathways for N deposition (dry and wet), and
multiple scales of N deposition (local and regional). However, there are some additional
complications in the consideration of how air quality concentrations (i.e., NO2 and PM2.5 mass)
are associated with eventual N deposition. First, not all N deposition is caused by the criteria
pollutants. As discussed in Chapter 2, ammonia emissions also lead to N deposition, especially
through dry deposition at local scales. Second, only certain components of PM2.5 mass contribute
to N deposition (i.e., nitrate and ammonium). As a result of these two complications, there is
reason to expect that the association between NO2 concentrations and N deposition, and PM2.5
concentrations and N deposition will be less robust than what we observed for SO2.
Considering NO2, we note that the current form and averaging time of the NO2 secondary
NAAQS is the annual average NO2 concentration. As in the assessments of SO2 metrics, these
analyses focus on a 3-year average of NO2 and N deposition and include multiple years of data to
better assess more typical relationships. At the SLAMS, there was only weak association (r =
0.38) between NO2 concentrations, and the N deposition levels at those locations (Table 6-6,
Figure 6-38). The associations were stronger in the western U.S., which are generally less
affected by ammonia. The comparisons of collocated NO2 and N deposition at the 27 Class 1
sites (mostly western U.S.) confirmed this conclusion. The regional EAQM comparisons confirm
that the associations between NO2 and N deposition are much smaller than what was observed
for SO2 and S deposition, but the regional signals are different than what was observed with the
local SLAMS comparisons, i.e., some weak positive association (r = 0.48 and r = 0.35, Figures
6-44 and 6-45) in the eastern U.S., but no association in the western U.S. When considering
national trends over the past 20 years, we note that sharp declines in NO2 emissions and
concentrations are linked in time with sharp declines in oxidized N deposition (Table 6-2), but
the same is not true when considering total atmospheric N deposition. For the five time periods,
figure 6-54 displays the distributions of annual average NO2 concentrations (averaged over 3
years) at SLAMS monitors with valid data. Figure 6-55 displays the distributions of median N
deposition amounts at the 84 ecoregions across the same time periods. In the earliest two periods
(2001-2003, 2006-2008) both parameters exhibited decreases. However, since 2010, NO2
concentrations have continued to drop while N deposition has remained steady. In sum, the
evidence suggests that NO2 would be a weak indicator of total atmospheric N deposition,
especially in areas where ammonia is prevalent.
6-96
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Figure 6-54. Estimated annual NO2 emissions, nationally (NEI), averaged over three years,
from 2001-2020.
-------
Given this finding that NO2 would be a poor indicator of total atmospheric N deposition
and our understanding about these relationships, we also evaluated PM2.5 annual average,
averaged over three years, recognizing that it captures particulate ammonium. Analyses at the
SLAMS suggest some moderate correlation (r = 0.57) between the two parameters (Figure 6-39).
The shallowness of this association and the variation (or scatter) in both parameters, however,
indicates that PM2.5 would not be expected to provide a useful or effective indicator for a policy
option for limiting N deposition.
Consistent with the SO2 and NO2 comparisons against deposition, the associations
between PM2.5 and N deposition were stronger in the earlier time periods. The more regionally
focused EAQM results confirm this moderate correlation (r = 0.62), but only at the eastern U.S.
sites (Figure 6-46 and 6-47), with near-zero correlation in the western U.S. Again however, it is
important to recognize that any PM2.5 to N deposition associations will be affected by the fact
that some parts of the PM2.5 total mass do not contribute to N deposition (e.g., organic carbon,
elemental carbon). Figure 6-56 shows the fraction of total PM2.5 that is attributable to the sum of
particulate nitrate and ammonium at CSN sites for the 2020-2022 average. The median across
sites is less than 20%site and the highest fraction is in Riverside County, CA where the value is
30%. Further, this fraction has declined since the 2006-2008 period, the first for which these data
are available. In sum, the evidence suggests that PM2.5 would be a weak indicator of total
atmospheric N deposition, especially in areas where other components of the PM2.5 total are
dominant.
6-98
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2020-2022
% of PM2.5
that is nitrate
<$P'
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3°
n?
cV
r{V
,ov
Figure 6-56. Fraction of total PM2.5 at CSN sites that is either NO.?" or NIIj' in 2020-2022
(upper) and across five time periods at consistently sampled sites (lower).
6-99
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7 REVIEW OF THE STANDARDS
In considering what the currently available evidence and exposure/risk information
indicate with regard to the current secondary SO2, NO2 and PM standards, the initial overarching
question we address is:
• Do the currently available scientific evidence and air quality and exposure analyses
support or call into question the adequacy of the protection afforded by the current
secondary standards?
To assist us in interpreting the currently available scientific evidence and quantitative
information, including results of recent and past quantitative analyses to address this question,
we have focused on a series of more specific questions. In considering the scientific and
technical information, we consider both the information previously available and information
newly available in this review which has been critically analyzed and characterized in the current
ISA, the 2008 ISA for the oxides of N and S, the 2009 ISA for PM, and prior AQCDs for all
three criteria pollutants. In so doing, an important consideration is whether the information
newly available in this review alters the EPA's overall conclusions from the last reviews
regarding ecological effects associated with oxides of N and S and with PM in ambient air. We
also consider the currently available quantitative information regarding environmental exposures
(characterized by the pertinent metric) likely to be associated with the air quality metric
representing the current standards. Additionally, we consider the significance of these exposures
with regard to the potential for ecological effects, their potential severity and any associated
public welfare implications.
Within this chapter, sections 7.1 and 7.2 discuss the evidence and exposure-based
questions regarding policy-relevant aspects of the currently available information on welfare
effects, public welfare implications, the current standards and as appropriate, consideration of
potential alternatives. Section 7.1 addresses the questions in the context of effects other than
those related to ecosystem deposition of S and N compounds and, in similar fashion, section 7.2
addresses policy-relevant questions in the context of deposition-related effects. Advice received
from the CASAC on the standards is summarized in section 7.3. Staff conclusions derived from
the evaluations presented in this PA are described in section 7.4. Section 7.5 identifies key
uncertainties and areas for future research.
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7.1 EVIDENCE AND EXPOSURE/RISK BASED CONSIDERATIONS
FOR EFFECTS OTHER THAN ECOSYSTEM DEPOSITION-
RELATED EFFECTS OF S AND N
In considering the currently available evidence and quantitative information pertaining to
ecological effects of oxides of N and S and PM in ambient air other than those associated with
ecosystem deposition of S and N, we focus on addressing several questions (listed below).
Included in this consideration is what this information indicates regarding effects, and associated
public welfare implications, that might be expected to occur under air quality meeting the
existing standards.
• To what extent has the newly available information altered our scientific
understanding of the ecological effects of oxides of S and N and PM in ambient air?
• To what extent does the currently available information indicate the potential for
exposures associated with ecological effects under air quality meeting the existing
standards? If so, might such effects be of sufficient magnitude, severity, extent
and/or frequency such that they might reasonably be judged to be adverse to public
welfare?
• To what extent have important uncertainties identified in past reviews been reduced
and/or have new uncertainties emerged?
Framed by these questions, we consider the evidence and quantitative information for the three
criteria pollutants in the subsections below.
7.1.1 Sulfur Oxides
As summarized in section 4.1 above, the previously available evidence base describes the
direct effects of SOx in ambient air on vegetation, and very little of the currently available
information is newly available in this review. Among the SOx — which can include SO, SO2,
SO3, and S2O — only SO2 is present in the lower troposphere at concentrations relevant for
environmental considerations (ISA, Appendix 2, section 2.1). Sulfate is the prominent S oxide
present in the particulate phase. The available evidence, largely comprising studies focused on
SO2, documents the effects of SO2 on vegetation, including foliar injury, depressed
photosynthesis and reduced growth or yield (ISA, Appendix 3, section 3.2). The newer studies
continue to support the determination also reached in the last review that the evidence is
sufficient to infer a causal relationship between gas-phase SO2 and injury to vegetation (ISA,
Appendix 3, section 3.6.1).
The SO2 effects evidence derives from a combination of laboratory studies and
observational studies. In general, effects on plants occur at SO2 exposures higher than a 3-hour
average concentration of 0.5 ppm. For example, a recent laboratory study reports some transient
effects on lichen photosynthesis for short exposures, with more long-lasting effects only
7-2
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observed for exposures of nearly 1 ppm SO2, as summarized in section 5.4.1 above. With regard
to the sensitive effect of foliar injury, the current ISA states there to be "no clear evidence of
acute foliar injury below the level of the current standard" (ISA, p. IS-37). Further, the "limited
new research since 2008 adds more evidence that SO2 can have acute negative effects on
vegetation but does not change conclusions from the 2008 ISA regarding ... the SO2 levels
producing these effects" (ISA, p. IS-37).
Uncertainties associated with the current information are generally similar to those
existing at the time of the last review. In large part these uncertainties relate to limitations of
experimental studies in reflecting the natural environment and limitations of observational
studies in untangling effects of SO2 from those related to other pollutants that may have
influenced the analyzed effects. Regardless of these uncertainties, we note that the evidence from
either type of study indicates exposures associated with effects to generally be associated with air
concentrations and durations which would not be expected to occur when the current standard is
met.
7.1.2 Nitrogen Oxides
The currently available information on direct effects of N oxides in ambient air, which
generally concerns effects on plants and lichens (as summarized in section 4.1 above), is
comprised predominantly of studies of NO2 and HNO3, and also of PAN. The very few studies
newly available in this review do not alter our prior understanding of effects of these N oxides,
which include visible foliar injury and effects on photosynthesis and growth at exposures
considered high relative to current levels in ambient air (ISA, Appendix 3, section 3.3). Thus, as
in the last review, the ISA again concludes that the body of evidence is sufficient to infer a
causal relationship between gas-phase NO, NO2, and PAN and injury to vegetation (ISA, section
IS.4.2).
Regarding another N oxide compound, HNO3, the previously available evidence included
experimental studies of leaf cuticle damage in tree seedlings, a finding confirmed in a more
recent study, as well as effects on lichens, as summarized in section 5.4.2 above. Effects of
HNO3 may be related to vapor exposures and gaseous uptake or, given the very high deposition
velocity of HNO3, to direct contact via deposition on surfaces (ISA, Appendix 2, section 2.5.2.1
and Appendix 3, section 3.4). Among other studies, the evidence includes studies of effects
related to historic conditions in the Los Angeles basin, although no such studies are available for
other areas of the U.S. A more recent 2008 reassessment of an area in the Los Angeles basin in
which there was a significant decline in lichen species in the late 1970s found that lichen
communities have not recovered from the damage evident in the 1970s, as described in section
5.4.2 above (ISA, Appendix 3, section 3.4). The newer studies continue to support the findings
7-3
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of the 2008 ISA, such that as in the last review, the ISA again concludes "the body of evidence is
sufficient to infer a causal relationship between gas-phase HNO3 and changes to vegetation"
(ISA, Appendix 3, p. 3-17).
With regard to the exposure concentrations, we note that the ISA concludes that for NO2
"[w]ith few exceptions, visible injury has not been reported at concentrations below 0.20 ppm,
and these exceptions occurred when the cumulative duration of exposures extended to 100 hours
or longer" (ISA, Appendix 3, p. 3-8). Effects on plant photosynthesis and growth have resulted
from multiday exposures of six or more hours per day to NO2 concentrations above 0.1 ppm,
with a newly available study documenting effects at exposures of 4 ppm NO2, effects that the
ISA finds to be "consistent with past studies of plants with relatively high NO2 exposure" (ISA,
Appendix 3, p. 3-12). Regarding PAN, there is "little evidence in recent years to suggest that
PAN poses a significant risk to vegetation in the U.S." (ISA, Appendix 3, p. 3-13).
The recently available information for HNO3 includes effects on tree foliage under
controlled 12-hour exposures to 50 ppb HNO3 (approximately 75 |ig/m3). Foliar damage was
also reported in longer, 32- or 33-day exposures in which peak HNO3 concentrations for the
"moderate" treatment (30-60 |ig/m3) encompassed the range reported in summers during the
1980s in the Los Angeles Basin, as described in section 5.4.2 above (ISA, Appendix 3, section
3.4). During that period, NO2 concentrations in the Basin ranged up to 0.058 ppm, exceeding the
secondary standard (U.S. EPA, 1987). Effects on lichen photosynthesis have been reported from
6.5-hour daily varying exposures with peaks near 50 ppb (approximately 75 |ig/m3) that extend
beyond 18 days (ISA, Appendix 6, section 6.2.3.3; Riddell et al., 2012).
In considering the potential for concentrations of N oxides associated with welfare effects
to occur under air quality conditions meeting the current NO2 standard, we consider the air
quality information summarized in section 2.4.1 above. In so doing, we note that air quality at all
sites in the contiguous U.S. has met the existing secondary NO2 standard since around 1991
(Figure 2-22). During the period 1983 to 1991, the 99th percentile of annual mean NO2
concentrations at sites nationwide was near the level of the standard (Figure 2-22). Further,
hourly NO2 concentrations during this time indicate little likelihood of an occurrence of a 6-hour
concentration of magnitude for which plant growth effects were reported from experimental
studies (as described in section 5.4.2), as the 98th percentile of 1-hour concentrations rarely
exceeded 0.2 ppm, as shown in Figure 2-21.
In considering the potential for HNO3 concentrations of a magnitude to pose risk of
effects to occur in conditions that meet the current NO2 secondary standard, we recognize, as
summarized in section 5.4 above, that the evidence indicates N oxides, and particularly, HNO3,
as "the main agent of decline of lichen in the Los Angeles basin" (ISA, Appendix 3, p. 3-15),
where elevated concentrations of N oxides were documented during the 1970s to 1990s (and
7-4
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likely also occurred earlier). Based on a limited number of studies extending back to the 1980s,
HNO3 has been suspected to have had an important role in these declines, as summarized in
section 5.4.2 above. During that time period the Los Angeles metropolitan area experienced NO2
concentrations in excess of the NO2 secondary standard (e.g., annual average NO2 concentrations
up to 0.078 ppm in 1979 and above 0.053 ppm into the early 1990s). Surveys in 2008, when NO2
concentrations were well below the standard, reported that the impacts documented on lichen
communities in the 1970s still remained (ISA, Appendix 3, section 3.4). Although the extent to
which this relates to lag in recovery or concurrent air pollutant concentrations is unknown, we
take note of the risk posed from HNO3 contact with plant and lichen surfaces. This risk likely
relates to the direct exposure of these surfaces to air pollutants, the high deposition velocity of
HNO3 (ISA, Appendix 2, section 2.5.2.1) and its acidity. Given these factors, we recognize that
the risk of HNO3 effects to lichens may be from both direct and deposition-related exposure
related to direct contact of the chemical to the lichen surfaces.
In summary, the currently available information is somewhat limited with regard to the
extent to which it informs conclusions as to the potential for exposures associated with
ecological effects under air quality meeting the existing NO2 secondary standard. More recent
studies extending into more recent periods indicate variation in eutrophic lichen abundance to be
associated with variation in metrics representing N deposition (ISA, Appendix 6, section
6.2.3.3). The extent to which these associations are influenced by residual impacts of the historic
air quality is unclear.
While new uncertainties have not emerged, uncertainties remain in our interpretation of
the evidence, including those related to limitations of the various study types. For example, the
various types of studies in the evidence for welfare effects of the different N oxides vary with
regard to their limitations, and associated uncertainties. Field studies are limited with regard to
identification of threshold exposures for the reported effects and uncertainties associated with
controlled experiments include whether the conditions under which the observed effects occur
would be expected in the field. A key uncertainty affecting interpretation of studies of historic
conditions in the LA Basin relates to the extent to which other air pollutants or local conditions
(unrelated to N oxides) may have contributed to the observations of effects, and whether such
effects would be expected in response to N oxides in other locations in the U.S. (and the extent to
which the conditions unrelated to N oxides differ in other locations). With regard to the risk
posed by N oxides, and particularly HNO3, the evidence, as summarized in sections 5.4.2 and
5.3.3 above indicates the potential for effects of air quality occurring during periods when the
current secondary standard was not met. The evidence is limited, however, with regard to
support for conclusions related to conditions meeting the current standard.
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7.1.3 Particulate Matter
As summarized in section 5.4.3 above, the evidence for ecological effects of PM is
consistent with that available in the last review. The ISA causal determinations with regard to
ecological effects of PM in the 2012 PM review and in this review focused on deposition-related
effects, rather than direct effects of PM in ambient air. In this review, as in the last one, the
ecological effects evidence was found to be sufficient to conclude there is likely to exist a causal
relationship between deposition of PM and a variety of effects on individual organisms and
ecosystems (ISA, Appendix 15; 2009 PM ISA, section 9.4).
With regard to direct effects of PM in ambient air, the associated information on ambient
air concentrations associated with effects is well in excess of the existing secondary standards.
While some uncertainties remain, new uncertainties have not emerged since the last review. In
summary, little information is available on the assessment of direct effects of PM in exposure
conditions likely to meet the current standards, and the limited available information does not
indicate direct effects to occur under those conditions.
7.2 EVIDENCE AND EXPOSURE/RISK-BASED CONSIDERATIONS
FOR S AND N DEPOSITION-RELATED EFFECTS
In this section, we consider the evidence and quantitative exposure/risk information
related to ecological effects of N and S deposition associated with S oxides, N oxides and PM in
ambient air. We do this in the larger context of evaluating the protection from such effects
provided by the existing standards and potential alternative standards. The potential for the three
criteria pollutants to all contribute to particular ecosystem effects while also having a potential
for independent effects poses challenges to the organization of the discussion. A particular focus
of this chapter is on considering quantitative aspects of the relationships between deposition and
ecosystem effects that can inform decisions on standards that provide the appropriate control of
deposition for the desired level of protection from adverse environmental effects. As recognized
in Chapter 5 and the associated appendices, the availability of quantitative information for
relating atmospheric deposition to specific welfare effects varies across the categories of effects.
We consider here the extent to which such information is available that might support
characterization of the potential for effects, and of the protection that might be afforded for such
effects, under different air quality conditions.
While recognizing there are multiple organizations that could be applied, we have
adopted one that focuses first on consideration of the evidence for welfare effects associated with
atmospheric deposition of both S and N compounds, including the nature of effects and
associated uncertainties in the evidence (section 7.2.1) and then consideration of the quantitative
information and risk estimates particular to first S deposition (section 7.2.2) and then N
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deposition (section 7.2.3). Further, within sections 7.2.2 and 7.2.3, we first consider the evidence
regarding deposition effects and the quantitative information or analysis results for effects of
potential public welfare significance, and then consider relationships between relevant air quality
metrics and deposition levels that may be appropriate to target when considering the appropriate
degree of public welfare protection that the secondary standards should afford.
7.2.1 Evidence of Ecosystem Effects of S and N deposition
A long-standing evidence base documents the array of effects of acidic deposition in
aquatic and terrestrial ecosystems and the effects associated with ecosystem N enrichment. The
evidence for acidic deposition effects, extending back many decades, has accrued in part through
study of ecosystem acidification that has resulted from many decades of acidifying deposition
(ISA, section ES.5.1 and Appendix 4, section 4.6). As discussed in prior chapters, both S and N
compounds have contributed to ecosystem acidification, with relative contributions varying with
emissions, air concentrations and atmospheric chemistry, among other factors. Ecological effects
have been documented comprehensively in waterbodies of the Adirondack and Appalachian
Mountains, and forests of the Northeast, at the organism to ecosystem scale. With regard to N
enrichment, research on its effects in estuaries and large river systems across the U.S. extends
back at least four decades (2008 ISA, section 3.3.2.4; Officer et al., 1984). Further, the evidence
base on the effects of N enrichment on terrestrial ecosystems, primarily in grassland and forested
ecosystems, extends back to the last review (e.g., 2008 ISA, sections 3.3.3 and 3.3.5). We
consider the evidence of these effects, and others more recently understood, as characterized in
the ISA and summarized in Chapter 4, in the context of the following questions.
• To what extent has the newly available information altered our scientific
understanding of the ecological effects of atmospheric deposition of N and S
compounds?
The current evidence, including that newly available in this review, supports, sharpens
and expands somewhat on the conclusions reached in the 2008 ISA for the review completed in
2012. The long-standing evidence continues to support determinations of causal relationships
between acidifying deposition of N and S compounds and N deposition and an array of effects in
terrestrial and aquatic ecosystems, as in the last review (ISA, Table ES-1).
A wealth of scientific evidence, spanning many decades, demonstrates effects of
acidifying deposition, associated with N and S compounds, in aquatic and terrestrial ecosystems
(ISA, sections ES.5.1, IS.5.1, IS.5.3, IS.6.1 and IS.6.3; 2008 ISA, section 3.2; U.S. EPA, 1982,
Chapter 7). Accordingly, consistent with the evidence in the last review, the currently available
evidence describes an array of acidification-related effects on ecosystems. The current evidence
base, which includes an abundance of longstanding evidence, supports conclusions also reached
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in the last review of causal relationships between N and S deposition and alteration of soil and
aquatic biogeochemistry, alteration of the physiology and growth of terrestrial organisms and of
associated productivity, changes in aquatic biota, including physiological impairment, and
alteration of species richness, community composition and biodiversity in both aquatic and
terrestrial ecosystems (ISA, Table ES-1).
Similarly, a robust evidence base demonstrates ecosystem effects of N enrichment. In
both estuarine and freshwater ecosystems, the current evidence, including a wealth of
longstanding evidence, also supports conclusions reached in the last review of a causal
relationship between N deposition and changes in biota, including altered growth and
productivity, and alteration of species richness, community composition and biodiversity due to
N enrichment (ISA, sections ES.5.2, IS.6, and IS.7, and Table ES-1). In addition to evidence in
freshwater systems, this evidence base also includes longstanding evidence of effects in estuaries
along the East and Gulf Coasts of the U.S., as summarized in more detail in Chapters 4 and 5
(ISA, Appendix 7, section 7.2.9). Additional effects of N deposition in wetlands, also recognized
in the last review, include alteration of biogeochemical cycling, growth, productivity, species
physiology, species richness, community composition and biodiversity.
In terrestrial ecosystems, as in the last review, the evidence supports determination of a
causal relationship between N deposition and alteration of species richness, community
composition and biodiversity. The ISA additionally determines there to be a causal relationship
for alteration of the physiology and growth of terrestrial organisms and associated productivity, a
category of effects not included in the 2008 ISA (ISA, Table ES-1). The studies available since
the last review provide further evidence that addition of N to sensitive ecosystems "alters plant
physiological processes, stimulates the growth of most plants and broadly increases productivity"
(ISA, Appendix 6, p. 6-188). Further there is evidence of effects on soil microbes and symbiotic
mycorrhizal, with the evidence as a whole indicating the sensitivity of plants, microorganisms
and ecosystem productivity to N availability (ISA, Appendix 6, section 6.6.1). With regard to
species richness, community composition and biodiversity, the evidence base is expanded from
the last review, with regard to observational studies and N addition studies in grass and shrub
communities of the Southwest, as summarized in sections 4.3.2.2 and 5.3.3.1 above.
Other evidence of effects causally associated with S deposition in wetland and freshwater
ecosystems includes that related to chemical transformation and associated toxicity. This
includes alteration of mercury methylation, which was also recognized in the last review. The
other category of effects, which was not included in the last review, is that related to sulfide
phytotoxicity, and its associated effects in wetland and freshwater ecosystems (ISA, Table ES-1).
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• To what extent have previously identified uncertainties in the evidence been reduced
or do important uncertainties remain?
The evidence base has expanded since the last review, as summarized above, and
continues to be strong in documenting roles of SOx, N oxides and PM (including N and S
compounds) in aquatic acidification, nutrient enrichment and other effects. Some uncertainties
associated with the evidence in the last review remain, and some additional uncertainties are
important. In addition to uncertainties related to the specific air quality circumstances associated
with effects (e.g., magnitude, duration and frequency of NO2 and HNO3 concentrations
associated with effects), there are also uncertainties associated with the effects of N and S
deposition expected under changing environmental circumstances, including reduced
atmospheric loading with associated changes to soil and waterbody biogeochemistry and
meteorological changes associated with changing climate (ISA, section IS. 12).
Further, there are important uncertainties associated with the various assessment
approaches employed by different study types. For example, uncertainties associated with
observational studies include uncertainty regarding the potential influence of historical
deposition on species distribution, richness and community composition observed in recent times
(ISA, section IS. 14.2.1). Further, there are uncertainties contributed by variation in physical,
chemical and ecological responses to N and S deposition, and by the potential influence of
unaccounted-for stressors on response measures. Uncertainties associated with addition
experiments include, among others, those related to the potential for effects to occur over longer
periods than those assessed in those studies (section 5.3.4.1). Lastly uncertainties associated with
studies reporting atmospheric deposition associated with effects include authors' judgments on
magnitude of responses identified as effects, as well as a lack of clarity as to references or
baselines from which responses are assessed and with regard to judgments associated with
reference or baseline conditions. Additionally, variability in physical, chemical and ecological
characteristics of ecosystems contribute uncertainty to such judgments. As noted in the ISA,
"[t]he majority of studies that evaluate terrestrial N CLs for N enrichment effects are based on
observed response of a biological receptor to N deposition (or N addition as a proxy for
deposition), without a known soil chemistry threshold that causes the biological effect" (ISA, p.
IS-113).
7.2.2 S Deposition and S Oxides
To inform conclusions in this review related to the SOx secondary standard, we consider
a series of questions below that are intended to facilitate the evaluation of the linkages of SOx in
ambient air with S deposition and associated welfare effects. In considering these questions, we
draw on the available welfare effects evidence described in the current ISA, the 2008 ISA for
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oxides of N and S, the 2009 ISA for PM, and past AQCDs, and summarized in chapter 4. We do
this in combination with the available information from quantitative analyses (and summarized
in Chapters 5 and 6 above), both analyses recently developed and those available from the 2009
REA and considering the information now available.
7.2.2.1 Quantitative Information for Ecosystem Risks Associated with S Deposition
The currently available information provides modeling approaches for quantitatively
analyzing linkages between S deposition, geochemical processes in soils and waterbodies, and
indicators of aquatic and terrestrial ecosystem acidification risk. The use of such modeling
approaches for characterizing potential risk of aquatic and terrestrial acidification is well
established. Since the last review, aspects of the modeling approaches that quantify processes
that are the major determinants of the indicators have been expanded and improved. Further,
modeling approaches vary in their complexity, precision, and limitations, and the extent to which
they inform different questions.
As recognized in Chapter 5 above, although the approaches and tools for assessing
aquatic acidification have often been utilized for S and N deposition in combination, the
approach taken in the aquatic acidification REA, summarized in section 5.1 above and described
in detail in Appendix 5A, is focused on S deposition. This focus is supported by analyses (as
summarized in section 5.1.2.4 above) indicating the relatively greater role of S deposition under
the more recent air quality conditions that are the focus of this review. The aquatic acidification
REA utilizes available site-specific water quality modeling that relates atmospheric deposition to
ANC in a CL-based approach, as described in more detail in Appendix 5A. The site-specific
modeling applications and associated estimates of CLs for different ANC thresholds or targets
are publicly available in the NCLD.1 The modeling applications most frequently utilize mass
balance modeling tools for watershed processes (e.g., fluxes that affect watershed concentrations
of anions and cations), although in some areas, dynamic modeling applications are prevalent
(e.g., in the Adirondacks). In summary, the aquatic acidification assessment has utilized well-
established site-specific water quality modeling applications with a widely recognized indicator
of aquatic acidification, ANC.
Quantitative tools are also available for the assessment of terrestrial acidification related
to S deposition, as they were in the last review (section 5.3.2.1; 2009 REA, section 4.3).
Recently available studies have addressed a particular area of uncertainty identified for this
approach in the last review (related to model inputs for base cation weathering). While updated
analyses of terrestrial acidification have not been performed in this review, the findings from the
1 The surface water acidification CLs used in the REA are from version 3.2.1 of the NCLD (Lynch et al., 2022).
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analyses presented in the 2009 REA have been considered in the context of more recently
available evidence (section 5.3.2.1; 2009 REA, section 4.3). Quantitative tools and approaches
are not well developed for other ecological effects associated with atmospheric deposition of S
compounds, such as mercury methylation and sulfide toxicity (summarized in sections 4.4.1 and
4.4.2 above).
In summary, as in the last review, we give primary attention to the quantitative
approaches and tools for assessment of aquatic acidification (including particularly that
attributable to S deposition). While recognizing the uncertainties associated with results of
analyses utilizing these tools in the aquatic acidification REA, as summarized in section 5.1.5
above, we recognize these results to be informative to our purposes in identifying S deposition
benchmarks associated with potential for aquatic acidification effects of concern. As described in
section 3.3.2 above, this assessment of quantitative linkages between S deposition and potential
for aquatic acidification is one component of the approach implemented in this PA for informing
judgments on the likelihood of occurrence of such effects under differing air quality conditions.
• To what extent does the available evidence support the use of waterbody ANC for
purposes of judging a potential for ecosystem acidification effects?
As described in section 4.2.1.2 above, ANC is an indicator of susceptibility or risk of
acidification-related effects in waterbodies. Accordingly, the evidence generally indicates that
the higher the ANC, the lower the potential for acidification and related waterbody effects, and
the lower the ANC, the higher the potential. The support for this relationship is strongest in
aquatic systems low in organic material, and the evidence comes predominantly from impacted
waterbodies in the eastern U.S. (e.g., in the Adirondack Mountains) and Canada. In waterbodies
with relatively higher levels of dissolved organic material (e.g., dissolved organic carbon),
however, while the organic acid anions contribute to reduced pH, these anions create complexes
with the dissolved aluminum, protecting resident biota against aluminum toxicity (ISA,
Appendix 8, section 8.3.6.2). Accordingly, biota in such systems tolerate lower ANC values than
biota in waterbodies with low dissolved organic carbon. Thus, while the evidence generally
supports the use of ANC as an acidification indicator, the relationship with risk to biota differs
depending on the presence of naturally occurring organic acids. Further, such natural acidity
affects the responsiveness of ANC to acidifying deposition in these areas. As noted in section 5.1
above, the ecoregions in which ANC is less well supported as an indicator for acidic deposition-
related effects due to the prevalence of waterbodies with high dissolved organic material include
the Middle Atlantic Coastal Plain (ecoregion 8.5.1), Southern Coastal Plains (ecoregion 8.5.3),
and Atlantic Coastal Pine Barrens (ecoregion 8.5.4). The evidence does, however, support the
use of waterbody ANC in other areas for purposes of judging a potential for ecosystem
acidification effects (section 5.1.2.2).
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As summarized in sections 4.2.1.2 and 5.1.1 above, there is longstanding evidence of an
array of impacts on aquatic biota and species richness reported in surface waters with ANC
values below zero, and in some historically impacted waterbodies with ANC values below 20
|ieq/L. The severity in impact is greatest for the ANC values below zero. This evidence derives
primarily from lakes and streams of the Adirondack Mountains and areas along the Appalachian
Mountains. The evidence base additionally indicates a potential for some increased risk to
resident biota, depending on site-specific factors, of ANC levels between 20 and 50 |ieq/L. As
recognized in the last review, in addition to providing protection during base flow situations,
ANC is a water quality characteristic that affords protection against the likelihood of decreased
pH from episodic events in impacted watersheds. For example, waterbodies with ANC below 20
|aeq/L have been generally associated with increased probability of low pH events, that,
depending on other factors as noted above, have potential for reduced survival or loss of fitness
of sensitive biota or lifestages (2008 ISA, section 5.1.2.1). In general, the higher the ANC level
above zero, the lower the risk presented by episodic acidity. In summary, the available evidence
provides strong support for the consideration of ANC for purposes of making judgments
regarding risk to aquatic biota in streams impacted by acidifying deposition, and for
consideration of the set of targets analyzed in the aquatic acidification REA: 20, 30, and 50
|ieq/L (section 5.1 above).
• What do the quantitative exposure/risk estimates indicate about acidification risks in
freshwater streams and lakes for S deposition levels over the past two decades
(including the time since the last review)? What are the important uncertainties
associated with these quantitative risk estimates?
In considering this question, we focus on the results of the aquatic acidification REA, as
summarized in section 5.1 above (and described in detail in Appendix 5A). In summarizing the
acidification risk estimates in Chapter 5, the different scales of analysis make use of water
quality modeling-based CLs derived for three different ANC targets (20, 30 and 50 |ieq/L). In
this way we recognize both the differing risk that might be ascribed to the different ANC targets,
as well as the variation in ANC response across waterbodies that may be reasonable to expect
with differences in geology, history of acidifying deposition and different patterns of S
deposition, and also recognize limitations and uncertainties in the use of ANC as an indicator for
model-based risk assessments (section 5.1).
The national-scale analysis involved the 13,824 waterbody sites for which a CL based on
ANC target was available (PA, section 5.1.3). As an initial matter, we note the appreciable
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reduction in risk over the 20-year period of analysis.2 For the 2001-03 period, more than 20% of
waterbodies analyzed nationally were estimated to be unable to achieve an ANC of 20 |ieq/L or
greater based on S deposition estimates (Table 5-1). This percentage declines significantly by the
2010-12 period, and by the 2018-20 period, only 1% and 4% of waterbodies analyzed nationally
were estimated to be unable to achieve or exceed ANC targets of 20 |ieq/L and 50 |ieq/L,
respectively (Table 5-1).
The ecoregion-scale analyses focus on the 25 level III ecoregions (18 East and 7 West) in
which there are at least 50 waterbody sites with CL estimates. This set of 25 ecoregions is
dominated by ecoregions categorized as acid sensitive (Table 5A-5) and excludes the three
ecoregions identified as having natural acidity related to organic acids (section 5.1.2.1). The
ecoregion-scale results across the 20-year period reflect the results at the national scale, but the
percentages of waterbodies not able to meet the ANC targets are higher than the national
percentages due to the dominance of the acid-sensitive ecoregions among the 25. Specifically, in
the most affected ecoregion (ecoregion 8.4.2, Central Appalachians), more than 50% of
waterbodies were estimated to be unable to achieve an ANC of 20 |ieq/L or greater based on S
deposition estimates for the 2001-03 period; the percentage was close to 60% for an ANC target
of 50 |ieq/L (Figure 5-13). By the 2018-20 period, less than 10% of waterbodies in any of the 25
ecoregions (and less than 5% in all but one) were estimated to be unable to achieve an ANC of
20 |aeq/L and less than 15% of waterbodies in the most affected waterbody were estimated to be
unable to achieve an ANC of 50 |ieq/L (Figure 5-13).
In considering this information we also note the uncertainties associated with such
estimates. We recognize uncertainty associated with two overarching aspects of the assessment.
The first relates to interpretation of specific thresholds of ANC with regard to aquatic
acidification risk and the second relates to our understanding of the biogeochemical linkages
between deposition of S and N compounds and waterbody ANC (which is reflected in the
modeling employed), and the associated estimation of CLs.
With regard to the first, while ANC is an established indicator of aquatic acidification
risk, there is uncertainty in our understanding of relationships between ANC and risk to native
biota, particularly in waterbodies in geologic regions prone to waterbody acidity. Such
uncertainties relate to a number of factors, including the varying influences of site-specific
factors other than ANC. Such factors include prevalence of organic acids in the watershed, as
well as historical loading to watershed soils that can influence acidity of episodic high-flow
events.
2 The aquatic acidification risk analyses conducted in the last review focused on the earliest part of this time period
(e.g., deposition estimates derived using CMAQ modeling with 2002 wet deposition measurements and 2002
emissions (2009 REA, Appendix 4, p. 4-26).
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With regard to the second aspect of the assessment, associated uncertainties are difficult
to characterize and assess. Uncertainty in CL estimates is associated with parameters used in the
steady-state CL models. While the SSWS and other CL models are well conceived and based on
a substantial amount of research and applications available in the peer-reviewed literature, there
is uncertainty associated with the availability of the necessary data to support certain model
components. Such uncertainties contribute to uncertainty in estimation of the ANC levels that
individual waterbodies might be expected to achieve under different rates of S deposition. This
estimation is based on site-specific steady-state water quality modeling,3 with associated
limitations and uncertainties. For example, as recognized in sections 4.2.1.3 and 5.1.4 above, the
data to support the site-specific model inputs for some areas are more limited than others, with
associated greater uncertainties. Further, there are additional uncertainties associated with the
estimates of S deposition for use in the analyses of CL exceedances, such as those for the
national- and ecoregion-scale analyses (section 6.3.1, Table 6-13).
The strength of the CL estimates and the exceedance calculation rely on the ability of
models to estimate the catchment-average base-cation supply (i.e., input of base cations from
weathering of bedrock and soils and air), runoff, and surface water chemistry. The uncertainty
associated with runoff and surface water parameters relates to availability of measurements,
which varies among waterbodies. Further, the ability to accurately estimate the catchment supply
of base cations to a water body is difficult, and uncertain (Appendix 5A, section 5A.3). This area
of uncertainty is important because the catchment supply of base cations from the weathering of
bedrock and soils is the factor with the greatest influence on the CL calculation and has the
largest uncertainty (Li and McNulty, 2007).The ISA recognizes the model input for this (base
cation weathering [BCw] rate) to be "one of the most influential yet difficult to estimate
parameters in the calculation of critical acid loads of N and S deposition for protection against
terrestrial acidification" (ISA, section IS. 14.2.2.1). Although the approach to estimate base-
cation supply for the national case study (e.g., F-factor approach) has been widely published and
analyzed in Canada and Europe, and has been applied in the U.S. (e.g., Dupont et al., 2005 and
others), the uncertainty in this estimate is unclear and could be large in some cases.
The REA included a quantitative analysis of uncertainty in CL estimates related to state-
steady CL modeling inputs that involved many model simulations for the more than 14,000
waterbodies (in 51 ecoregions), drawing on Monte Carlo sampling of model input values, which
provides a description of the uncertainty around the CL estimate in terms of the confidence
interval for each waterbody mean result. Lower confidence intervals (indicating lower
3 A small subset of waterbody CLs in Adirondacks region is based on dynamic modeling, simulating response in
year 2100 or 3000 based on water quality parameter inputs from the somewhat recent past (Appendix 5A, section
5A.1.5).
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uncertainty associated with model inputs) were associated with CLs determined with more
extensive and longer-term water quality datasets and low variability in the runoff measurements.
Critical load estimates for waterbody sites in the eastern U.S., particularly along the Appalachian
Mountains, in the Upper Midwest, and in the Rocky Mountains, had smaller confidence intervals
while larger intervals (greater uncertainty) were found for CLs in the Midwest and South and
along the CA to WA coast (Appendix 5A, section 5A.3.1).
Consideration of such uncertainties informs the weighing of the findings of the
quantitative analyses. For example, in light of the variation in uncertainty associated with CLs
among the more to less well studied areas may indicate the appropriateness of a greater emphasis
on the former and/or less emphasis on estimates for the upper end of the distribution. This
information additionally informs interpretation of the potential risk associated with the different
ANC targets.
7.2.2.2 General Approach for Considering Public Welfare Protection
In light of the available evidence, air quality and exposure/risk information, we discuss
here key considerations in judging public welfare protection from S deposition in the context of
the review of the secondary standard for SOx.
• What do the quantitative estimates of aquatic acidification risk indicate about
deposition conditions under which waterbodies in sensitive ecoregions might be
expected to achieve ANC levels of interest?
In considering this question, we focus on the results of the aquatic acidification REA at
three scales: national-scale, ecoregion-scale and the more localized case study-scale, as described
in section 5.1 above. We give particular focus to the ecoregion and case-study analyses, which
utilize the waterbody-specific comparisons of estimated deposition and waterbody CLs to
provide ecoregion wide and cross-ecoregion summaries of estimated waterbody responses to
ecoregion estimates of deposition. In so doing, we have considered the extent to which
waterbodies in each ecoregion analyzed were estimated to achieve ANC levels at or above each
of the three targets. In this way we recognize the variation in ANC response across waterbodies
in an ecoregion that may be reasonable to expect based on both differences in watersheds that
can affect sensitivity to S deposition and with different spatial or geographic patterns of S
deposition. As summarized in section 6.1.1 above, S deposition levels will vary spatially or
geographically due to differences in a number of factors including those related to upwind
emissions of S-containing compounds and atmospheric chemistry, as well as patterns of other
chemicals that can influence S deposition.
At the national-scale, as summarized in section 7.2.2.1 above, unlike the case for the
2000-2002 period, which was also analyzed in the last review, few waterbodies are estimated to
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be receiving deposition in excess of their critical loads for relevant ANC targets under recent
deposition levels. More specifically, for S deposition estimates for the most recent time period
(2018-2020), only 4% of waterbodies nationally were estimated to exceed CLs for an ANC of 50
|ieq/L and 1% for an ANC of 20 |ieq/L (Table 5-1). In this time period (2018-2020), median
estimates of deposition in all but one of the 69 ecoregions that are represented in these national-
scale percentages (ecoregions with at least one site with a CL estimate) are below 4 kg S/ha-yr
(Tables 5A-15 and 5A-11).
In the case study analyses, CL estimates for ANC targets of 20, 30 and 50 |ieq/L are
summarized for waterbodies in five sensitive areas, three areas in the eastern U.S. and two in the
western U.S. (Table 5-6). The most well studied of these, the Shenandoah Valley Area case
study, includes a Class I area (Shenandoah National Park) and waterbodies in each of three
ecoregions (8.4.1, 8.4.4 and 8.3.1). The number of waterbody sites with CLs available in the
NCLD for this study area (4977) is nearly an order of magnitude greater than the total for the
four other areas combined (524). In the Shenandoah Valley Area, 70% of the waterbodies are
estimated to be able to achieve an ANC at or above 20 |ieq/L when annual average S deposition
is at or below 9.4 kg/ha-yr; the comparable value for 90% of the waterbodies in this case study
area is 7.1 kg/ha-yr. The 70th percentile for achieving an ANC at or above 50 |ieq/L is 4 kg/ha-yr
(Table 5-6). The S deposition estimates for the 70th and 90th percentile of waterbody CLs for the
other, less-well-studied case study areas, for which there are appreciably fewer waterbody sites
for which modeling has been performed to estimate CLs (and accordingly greater uncertainty),
were consistently lower. Yet, the case study area averages of waterbody CLs for achieving ANC
at or above each of the three targets (20, 30 or 50 |ieq/L) is quite similar across the five case
studies, ranging from 9.4 kg/ha-yr for an ANC of 50 |ieq/L in Shenandoah Valley Area to 12
kg/ha-yr for an ANC of 20 |ieq/L in both Shenandoah and Sierra Nevada Mtns case study areas
(Table 5-6).
The ecoregion analyses focused on 25 ecoregions (18 East and 7 West), nearly all of
which are considered acid sensitive. Based on waterbody-specific deposition and CL estimates
percentages of waterbodies per ecoregion expected to achieve each of the three ANC targets
were derived for five deposition time periods from 2001-03 to 2018-20. The ecoregion-specific
information has then been summarized in two different ways: (1) in terms of ecoregion median
deposition regardless of time period or ecoregion (ecoregion-time period combinations), and (2)
in terms of temporal trends in S deposition and waterbody percentages achieving ANC targets.
The first summarization approach relies on the dataset of 125 pairs of ecoregion median S
deposition and percentages of waterbodies estimated to achieve ANC at or above one of the three
ANC targets based on waterbody-specific deposition estimates. This dataset is compiled from
estimates for the five time periods from 2001-03 to 2018-20 and 25 ecoregions (18 East and 7
7-16
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West), as described in section 5.1.3.2 above. The ecoregion-time period combinations (totaling
90 for the 18 eastern ecoregions) were distributed into bins distinguished by the maximum
ecoregion median deposition in the grouping (e.g., <15 kg S/ha-yr, <10 kg S/ha-yr, <5 kg S/ha-
yr). In recognition of the increased uncertainty associated with analyses relying on a smaller
portion of the full dataset, we focused primarily on the results for the deposition bins
representing half or more of the full dataset (which were those in which the highest ecoregion
median included is at least 5 kg/ha-yr). Based on this organization, the estimates for the eastern
ecoregions indicate that for ecoregion median S deposition at or below 18 kg/ha-yr, at least 90%
of waterbodies per ecoregion were estimated to achieve ANC at or above 20 |ieq/L in only 73%
of the ecoregion-time period combinations (80% of waterbodies per ecoregion in 83% of
combinations), and at or above 50 |ieq/L in only 60% of the combinations (Tables 5-5 and 7-1).
This summary contrasts with that for the 76 combinations in the bin for S deposition at or below
11 kg/ha-yr, for which at least 90% of waterbodies per ecoregion were estimated to achieve ANC
at or above 20 |ieq/L in 83% of the combinations, and with the bin for at or below 9 kg/ha-yr, for
which at least 80% of waterbodies per ecoregion were estimated to achieve ANC at or above 20
|ieq/L in all of the combinations in that bin (Tables 5-5 and 7-1).
As shown in Table 7-1,4 results for ecoregion median deposition at or below 11 kg/ha-yr
(and for the bins for lower values) in eastern ecoregions indicate the likelihood of appreciably
more waterbodies achieving the acid buffering capacity targets compared to that estimated for
the set of ecoregion-time periods reflecting deposition estimates up to 18 kg/ha-yr. More
specifically, this reflects an appreciably greater number of waterbodies in more ecoregions
achieving ANC at or above 20 |ieq/L, at or above 30 |ieq/L and also at or above 50 |ieq/L (Table
7-1). Additionally, these percentages increase across the bins for the lower deposition estimates,
although while also based on smaller proportions of the supporting dataset (i.e., fewer ecoregion-
time period combinations in each subsequently lower deposition bin). For example, for the 69
combinations for S deposition at or below 9 kg/ha-yr, at least 90% of waterbodies per ecoregion
were estimated to achieve an ANC at or above 20 |ieq/L in 87% of the combinations, and at or
above 50 |ieq/L in 72% of the combinations (Table 7-1). Although fewer ecoregion-time period
combinations are associated with still lower S deposition estimates, contributing to increased
uncertainty, we also note that for the 63 ecoregion-time periods for which S deposition is
estimated at or below 7 kg/ha-yr, at least 90% of waterbodies per ecoregion were estimated to
achieve an ANC at or above 20 |ieq/L in 92% of the combinations, and at or above 50 |ieq/L in
78%) of the combinations (Table 7-1). Lastly, for the lowest bin that comprises at least half of the
4 Table 7-1 summarizes aspects of the more detailed results presented in Table 5-5 for the 90 eastern ecoregion-time
period combinations.
7-17
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full eastern ecoregion dataset (51 ecoregion-time periods with S deposition estimates at or below
5 kg/ha-yr), 90% of waterbodies per ecoregion were estimated to achieve an ANC at or above 20
|aeq/L in 96% of the combinations, and at or above 50 |ieq/L in 82% of the combinations.
Table 7-1. Summary of the eastern ecoregion and time period combinations achieving
different ANC targets with estimated S deposition at or below different values.
s
% of
% of Eastern ecoregion-time period combinations** with at least
deposition
combinations
90%, 80% or 70% waterbodies per ecoregion achieving ANC target
(kg/ha-yr)*
included
>90% of waterbodies
>80% of waterbodies
>70% of waterbodies
ANC (jjeq/L) at/below:
20
30
50
20
30
50
20
30
50
<18
100%
73%
67%
60%
88%
87%
81%
92%
90%
89%
<13
90%
80%
73%
65%
95%
94%
88%
98%
96%
96%
<11
84%
83%
76%
68%
97%
96%
91%
99%
99%
99%
<9
77%
87%
81%
72%
100%
99%
93%
100%
100%
100%
<7
70%
92%
87%
78%
100%
100%
95%
100%
100%
100%
<6
66%
93%
88%
78%
100%
100%
97%
100%
100%
100%
<5
57%
96%
92%
82%
100%
100%
96%
100%
100%
100%
* These values are ecoregion median estimates across all waterbody sites in an ecoregion with a CL estimate.
** These percentages are drawn from the more extensive presentation of results in Table 5-5.
We turn now to consideration of the quantitative acidification risk information from a
temporal perspective. Given the decreasing temporal trend in S deposition across all ecoregions
(section 6.2.1), we also consider the aquatic acidification results at the ecoregion scale across the
20 years represented by the five time periods (2001-03, 2006-08, 2010-12, 2014-16, 2018-20).
With regard to percentages of waterbodies per ecoregion estimated to achieve the three ANC
targets, an appreciable improvement is observed for the latter three time periods compared to the
initial two time periods. By the 2010-2012 time period, more than 70% of waterbodies in all 25
ecoregions are estimated to achieve an ANC at or above 50 |ieq/L and at least 85% are able to
achieve an ANC at or above 20 |ieq/L. By the 2014-2016 period, the percentages are 85% and
nearly 90%, respectively. The median deposition for the CL sites in each of the 18 eastern
ecoregions during the latter three time periods range from 1.3 kg S/ha-yr to 7.3 kg S/ha-yr (Table
7-2 and Figure 7-2).
As seen in Table 7-2, with each reduction in S deposition in each subsequent time period,
more waterbodies in each of the eastern ecoregions are estimated to be able to achieve the ANC
targets. Nearly 90% of the 18 eastern ecoregions are estimated to have at least 90% of their
waterbodies achieving an ANC of 20 |ieq/L in the 2010-12 period and achieving an ANC of 50
|ieq/L in the 2014-16 period. When the 7 western ecoregions are included in a summary based on
7-18
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ANC targets of 20 |ieq/L for the West and 50 |ieq/L for the East,5 over 70% of the full set of
ecoregions are estimated to have at least 90% of their waterbodies achieving the ANC targets by
the 2010-12 period. More than 90% of the ecoregions are estimated to have at least 90% of their
waterbodies achieving the ANC targets by the 2014-16 period (Table 7-2).
Table 7-2. Ecoregions estimated to have different percentages of waterbodies achieving
different ANC targets for the five deposition periods analyzed.
Time
period
% (n) of ecoregions
with specified percentage of waterbodies per ecoregion achieving specified ANC
ANC: 20 |jeq/L
30 |jeq/L
50 |jeq/L
Ecoregion
median S
deposition
(kg/ha-yr)
Min | Max
Percent of waterbodies
per ecoregion
90% 80% 70%
Percent of waterbodies
per ecoregion
90% 80% 70%
Percent of waterbodies
per ecoregion
90% 80% 70%
East
Of 18 Eastern Ecoregions
2001-03
4.0
17.3
39%
(7)
67%
(12)
72%
(13)
28%
(5)
61%
(11)
72%
(13)
22%
(4)
50%
(9)
72%
(13)
2006-08
3.1
14.4
44%
(8)
72%
(13)
89%
(16)
33%
(6)
72%
(13)
78%
(14)
33%
(6)
67%
(12)
72%
(13)
2010-12
2.3
7.3
89%
(16)
100%
(18)
100%
(18)
83%
(15)
100%
(18)
100%
(18)
61%
(11)
89%
(16)
100%
(18)
2014-16
1.9
4.6
94%
(17)
100%
(18)
100%
(18)
94%
(17)
100%
(18)
100%
(18)
89%
(16)
100%
(18)
100%
(18)
2018-20
1.3
3.9
100%
(18)
100%
(18)
100%
(18)
94%
(17)
100%
(18)
100%
(18)
94%
(17)
100%
(18)
100%
(18)
All
Of
Ecoregions (18 East, 7 Wes
y
2001-03
1.2
17.3
56%
(14)
76%
(19)
80%
(20)
48%
(12)
72%
(18)
80%
(20)
44%
(11)
64%
(16)
80%
(20)
2006-08
1.2
14.4
60%
(15)
80%
(20)
92%
(23)
52%
(13)
80%
(20)
84%
(21)
52%
(13)
76%
(19)
80%
(20)
2010-12
1.0
7.3
92%
(23)
100%
(25)
100%
(25)
88%
(22)
100%
(25)
100%
(25)
72%
(18)
92%
(23)
100%
(25)
2014-16
1.1
4.6
96%
(24)
100%
(25)
100%
(25)
96%
(24)
100%
(25)
100%
(25)
92%
(23)
100%
(25)
100%
(25)
2018-20
0.62
3.9
100%
(25)
100%
(25)
100%
(25)
96%
(24)
100%
(25)
100%
(25)
96%
(24)
100%
(25)
100%
(25)
Note: Estimates for ANC of 50 peq/L (East) and 20 peq/L (West) are identical to those for 50 in all 25 ecoregions.
5 This combination of targets recognizes the naturally and typically low ANC levels observed in western
waterbodies while also including a higher target for the East, as described in section 5.1.2.2.
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The temporal trends in percentage of waterbodies estimated to achieve the target ANC
levels for each of the 25 individual ecoregions (mapped in Figure 5-13 above) document a large
difference between the time periods prior to 2010 and subsequent time periods (Figure 7-1). For
the S deposition estimated for the 2010-2012 time period, more than 70% of waterbodies are
estimated to be able to achieve an ANC of 50 ueq/L in all 25 ecoregions (Figure 7-1, left panel),
and 85% to 100% of waterbodies in all ecoregions are estimated to be able to achieve an ANC of
20 ueq/L (Figure 7-1, right panel).
Given the dependency of the ANC estimates on S deposition estimates, this distinction
between the period prior to 2010 and the subsequent decade is also seen in the ecoregion
deposition estimates (Figure 7-2). The distribution of deposition estimates at waterbody sites
assessed in each ecoregion, and particularly the pattern for the higher percentile sites, as
presented in Figure 7-2, illustrate the deposition estimates that are driving the REA estimates.
For example, among the 25 East and West ecoregions during the two periods prior to 2010, the
medians of the ecoregion 90th percentile deposition estimates ranged from approximately 14 to
17 kg/ha-yr, with maximum values above 20 kg/ha-yr. This contrasts with the deposition
estimates during the 2010-2020 period when, among all 25 ecoregions, the medians of the
ecoregion 90th percentile deposition estimates ranged from approximately 2 to 5 kg/ha-yr, with
all ecoregion 90th percentile estimates below 8 kg/ha-yr. The contrast is much less sharp for the
ecoregion medians, as the median is a statistic much less influenced by changes in the magnitude
of values at the upper end of the distribution (Figure 7-2).
7-20
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2001-2003 2006-2008 2010-2012 2014-2016 2018-2020 2001-2003 2006-2008 2010-2012 2014-2016 2018-2020
Time Period (Years) Time Period (Years)
Figure 7-1. Percent of water bodies per ecoregion estimated to achieve ANC at or above 50 fieq/L (left panel) or 20 fieq/L
(right panel). Western ecoregions in bold font and solid lines (versus regular font and dashed lines for Eastern
ecoregions).
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25 Ecoregions
90lh Percentile
75th Percentile
Median
iii
2001-2003 2006-2008 2010-2012 2014-2016 2018-2020
Year
25- 18 Ecoregions
20-
15-
10-
5- *-*-
90th Percentile
75th Percentile
Median
S ia
2001-2003 2006-2008 2010-2012 2014-2016 2018-2020
Year
Figure 7-2. Ecoregion 90th, 75th and 50th percentile S deposition estimates at REA waterbody sites summarized for all 25
ecoregions (left) and the 18 eastern ecoregions (right).
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In summary, the array of CL-based analyses provides a general sense of the ANC values
that waterbodies in sensitive regions across the continental U.S. may be able to achieve,
including for areas heavily affected by a long history of acidifying deposition, such as
waterbodies in Shenandoah Valley. In the case study for that well studied area (4977 sites in
three different ecoregions), 90% of waterbody sites are estimated to be able to achieve an ANC
at or above 20 |ieq/L (focusing on S deposition only) with S deposition of 7.1 kg/ha-yr and 70%
with S deposition of 9.4 kg/ha-yr. For an ANC target at or above 50 |ieq/L in the Shenandoah
Valley case study, the corresponding deposition estimates are 4.1 and 6.3 kg/ha-yr. These
estimates are somewhat similar to the findings of the ecoregion analysis. For example, in that
analysis, at least 90% of waterbody sites in 87% of the eastern ecoregion-time period
combinations are estimated to be able to achieve an ANC at or above 20 |ieq/L with ecoregion
median S deposition at or below 9 kg/ha-yr and in 96% of those combinations for S deposition at
or below 5 kg/ha-yr. Further, 70% of waterbody sites in all 18 eastern ecoregions are estimated
to achieve an ANC at or above 50 |ieq/L with ecoregion median S deposition at or below 9
kg/ha-yr. Lastly, consideration of the temporal trend indicates that during the latter half of the
20-year period analyzed (i.e., by the 2010-2012 period), by which time all 25 ecoregions were
estimated to have more than 70% of waterbodies able to achieve an ANC at/above 50 |ieq/L (and
at least 85% able to achieve an ANC at/above 20 |ieq/L), median deposition in 95% of the
ecoregions was below 8 kg S/ha-yr. By the 2014-2016 and 2018-2020 periods, 24 of the 25
ecoregions were estimated to have more than 90% of waterbodies able to achieve an ANC
at/above 50 |ieq/L, and median S deposition in all 25 ecoregions was below 5 kg/ha-yr (Figures
7-1 and 7-2).
In considering identification of S deposition levels that may be associated with a desired
level of ecosystem protection for a secondary standard, we take note of the implications of the
temporal trend in estimated water quality improvements indicated by the increased percentages
of waterbodies estimated to achieve more protective ANC levels. The pattern of estimated
improving water quality is paralleled by the pattern of declining deposition over the 20-year
study period, which is more clear in the upper percentiles of the distribution of values per
ecoregion (Figure 7-2). This pattern indicates appreciable difference between the first and second
decades of the period in terms of S deposition (at upper percentiles as well as at the median of
sites within the 25 ecoregions) and associated aquatic acidification risk. The ecoregion with the
highest S deposition in the latter decade had 90th percentile estimates ranging from
approximately 8 kg/ha-yr to just below 5 kg/ha-yr (and median estimates with a very similar
range) across this decade (Figure 7-2). As noted immediately above, the risk estimates associated
with the deposition estimates of this decade indicate generally high percentages of waterbodies
per ecoregion as able to achieve or exceed the three ANC targets. Similarly, the ecoregion-time
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period binning summary also indicates generally high percentages of waterbodies for ecoregion
median S deposition at or below about 8 or 9 kg/ha-yr (Table 7-1). Lastly, the case study CL
estimates also indicate appreciable portions of the case study areas that might be expected to
attain the 3 ANC targets with deposition below 9 kg/ha-yr. Thus, a range of S deposition, on an
areawide basis, that falls below approximately 10-5 kg/ha-yr, or 8-5 kg/ha-yr, appears to be
associated with potential to achieve acid buffering capacity levels of interest in appreciable
portion of sensitive areas.
• What does the available information indicate for considering the potential public
welfare protection from S deposition-related effects in aquatic ecosystems?
As an initial matter, we note the integral role of watersheds in aquatic ecosystem health.
In so doing, we also recognize the effects of acidic deposition on forested areas that are distinct
from effects in water bodies. As recognized in section 4.5 above, given the array of benefits of
forested areas to the public, there are public welfare implications of acidifying deposition effects
on the natural resources in these areas, with the public welfare significance dependent on the
severity and extent of such effects. In light of the more extensive quantitative analyses for
aquatic acidification in this review, we focus particularly on the public welfare implications of S
deposition-related effects in aquatic ecosystems recognizing their relevance to decision-making
in this review.
As recognized in the 2012 review, aquatic ecosystems provide a number of services
important to the public welfare, ranging from recreational and commercial fisheries to
recreational activities engaged in by the public (77 FR 20232, April 3, 2012). As summarized
briefly in section 4.5 above, because aquatic acidification primarily affects the diversity and
abundance of aquatic biota, it also affects the ecosystem services that are derived from the fish
and other aquatic life found in these surface waters (section 4.5). Fresh surface waters support
several cultural services, such as aesthetic and educational services; the type of service that is
likely to be most widely and significantly affected by aquatic acidification is recreational fishing,
with associated economic and other benefits. Other potentially affected services include
provision of food for some recreational and subsistence fishers and for other consumers, as well
as non-use services, including existence (protection and preservation with no expectation of
direct use) and bequest values (section 4.5).
In light of the considerations above, we recognize that some level of S deposition and
associated risk of aquatic acidification, including those associated with past decades of
acidifying deposition in the Northeast, can impact the public welfare and thus might reasonably
be judged adverse to the public welfare. Depending on magnitude and the associated impacts,
there are many locations in which S deposition and associated aquatic acidification can adversely
affect the public welfare. For example, there is evidence in some waterbodies, as summarized in
7-24
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section 5.1.1 above, that aquatic acidification resulting in reduced acid buffering capacity can
adversely affect waterbodies and associated fisheries, which in addition to any commercial
ramifications, can also have ramifications on recreational enjoyment of affected areas. The
evidence is less clear as to what level of risk to an aquatic system, in terms of estimates for
achieving various ANC targets across sites within an ecoregion, that might be judged of public
welfare significance.
In other secondary NAAQS reviews, the EPA's consideration of the public welfare
significance of the associated effects has recognized a particular importance of Class I areas and
other similarly protected areas. Accordingly, we note that waterbodies that have been most
affected by acidic deposition are in the eastern U.S. and include several Class I areas and a
number of other national and state parks and forests (section 5.1.2.1).6 Such areas were among
two of the case studies in the aquatic acidification REA (section 5.1.3.3 above). Thus, while
assuring continued improvement of affected waterbodies throughout the U.S. (e.g., through
lower S deposition than the levels of the past) may reasonably be considered to be of public
welfare importance, such assurance in Class I and similarly protected areas would seem to be of
particular importance.
For the purposes of considering the potential public welfare significance of aquatic
acidification effects of differing levels of S deposition, we take note of the approach taken in
Appendix 5A and section 5.1 to summarize the REA ecoregion-scale results, i.e., in terms of
percentages of ecoregions in which differing percentages of waterbodies are estimated to achieve
the three acid buffering capacity targets. The presentations above are summarized in such a way
in identifying a range of S deposition less than approximately 10 to 5 kg/ha-yr or 8 to 5 kg/ha-yr
that may be appropriate to consider for potential alternative standard options in light of REA
estimates for achieving the three acid buffering capacity targets. In considering the question
below with regard to terrestrial acidification, we also focus on consideration of quantitative
information with a similar objective in mind.
• What does the quantitative information regarding S deposition and terrestrial
acidification indicate regarding deposition levels of relatively greater and lesser
concern as to the potential for acidification-related effects? What are associated
uncertainties?
As recognized in Chapter 5, the quantitative tools for characterizing waterbody response
to acidic deposition are well established and/or have been extensively applied in a greater variety
of locations. Further, there is appreciable availability of site-specific water quality measurements
in sensitive areas across the U.S. The available quantitative information related to terrestrial
6 A comparison of Figures 4-4 and 5-6 indicates multiple Class I areas in ecoregions considered acid sensitive.
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acidification summarized in Chapter 5 (and presented in more detail in Appendix 5B) includes
discussion of soil chemistry modeling analyses (both those described in published studies and an
analysis performed in the 2009 REA), studies involving experimental additions of S compounds
to defined forestry plots, and observational studies of potential relationships between terrestrial
biota assessments and metrics for S deposition (section 5.3). We consider each here in
consideration of the questions posed above.
With regard to soil chemistry modeling, we note first the quantitative analyses, performed
in the last review, of soil acidification in areas of the northeastern U.S. in which two sensitive
tree species, sugar maple and red spruce, are widely distributed. These analyses yielded estimates
of acidic deposition CLs associated with three different values for a well-studied indicator of soil
acidification, BC:A1 ratio7 (2009 REA, section 4.3). These estimates indicated a range of annual
deposition rates (under which ratios were at or above the intermediate target value of 1) that were
well above the CL estimates associated with achieving various ANC targets in the aquatic
acidification analyses discussed above.8 Thus, a focus on aquatic acidification might reasonably
be expected to also provide protection from soil acidification effects on terrestrial biota. As
concluded in the 2009 REA, an important source of uncertainty in the simple mass balance
model used in the analysis is the soil weathering parameter (as is also the case in water quality
modeling). In this context, we additionally note that studies published since the 2009 REA,
including one focused on areas of Pennsylvania, have utilized different estimates for this
parameter intended to reduce the associated uncertainty and have reported somewhat higher CL
estimates when the updated approach is used (as described more fully in section 5.3.2.1).
With regard to the information available from studies involving S additions to
experimental forested areas, the number of tree species that have been included in such
experiments is somewhat limited. Although limited in number, the more widely recognized
sensitive species (based on field observations) have been included in such studies. We note that
the available studies have not reported effects on the trees analyzed plots with additions below
20 kg/ha-yr (in addition to the atmospheric deposition occurring during the experiment).
The recently available quantitative information regarding S deposition and terrestrial
acidification also includes two observational studies that report associations of tree growth
and/or survival metrics with various air quality or S deposition metrics, providing support to
conclusions regarding the role of acidic S deposition on tree health in the U.S., most particularly
7 Given the toxicity of some forms of aluminum in soil solution, the ratio of base cations to aluminum ions in soil
(BC: A1 ratio) has commonly been used in assessing risk of acidifying deposition to terrestrial systems (ISA,
Appendix 4, section 4.3.5 and Appendix 5).
8 These deposition rates were also above all of the ecoregion estimates (across the five time periods from 2001
through 2020) considered in the aquatic acidification analyses (Table 5-7).
7-26
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in regions of the eastern U.S. (summarized in section 5.3.2.3 and described more fully in
Appendix 5B, section 5B.3.2). The metrics utilized in the two largest studies include site-specific
estimates of average SO42" deposition and of average total S deposition over the interval between
tree measurements, generally on the order of 10 years (Dietze and Moorcroft, 2011; Horn et al.,
2018). In the study that used SO42" as the indicator of acidic S deposition, and for which the
study area was the eastern half of the contiguous U.S., site-specific average SO42" deposition
(1994-2005) ranged from a minimum of 4 kg/ha-yr to a maximum of 30 kg/ha-yr (Dietze and
Moorcroft, 2011). Review of the study area for this study and a map indicating geographic
patterns of deposition during the period of the deposition data indicate the lowest deposition
areas to be the farthest western, northeastern and southeastern areas of the eastern U.S. (in which
S deposition in the 2000-2002 period is estimated to fall below 8 kg/ha-yr), and the highest
deposition areas to be a large area extending from New York through the Ohio River valley
(Appendix 5B, Figures 5B-1 and 5B-11). In the second study, deposition at the sites with species
for which growth or survival was negatively associated with S deposition ranged from a
minimum below 5 kg/ha-yr to a site maximum above 40 kg/ha-yr, with medians for these species
generally ranging from around 5 to 12 kg/hr-yr (Appendix 5B, sections 5B.2.2.3 and 5B.2.3;
Horn et al., 2018).
As discussed in section 5.3.2 and Appendix 5B, the history of appreciable acidic
deposition in the eastern U.S., with its associated impacts on soil chemistry, has the potential to
be exerting a legacy influence on tree growth and survival more recently. Further, at a national-
scale, the geographic deposition patterns (e.g., locations of relatively greater versus relatively
lesser deposition) more recently appear to be somewhat similar to those of several decades ago
(e.g., sections 2.5.4 and 6.2.1). This similarity in patterns has the potential to influence findings
of observational studies that assess associations between variation in tree growth and survival
with variation in levels of a metric for recent deposition at the tree locations. This indicates an
uncertainty with regard to interpretation of these studies with regard to a specific magnitude of
deposition that might be expected to elicit specific tree responses, such as those for which
associations have been found. As recognized in the study by Dietze and Moorcroft (2011), which
grouped species into plant functional groups, acidification impacts on tree mortality result from
cumulative long-term deposition, and patterns reported by their study should be interpreted with
that in mind.
7.2.2.3 Relating SOx Air Quality Metrics to Deposition of S Compounds
Analyses in Chapter 6 examine the relationships between air concentrations, in terms of
various air quality metrics (including design values for the current standards), and S deposition
in areas near or removed from ambient air monitoring sites. Analyses include air quality metrics
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based on S compounds measured in three different monitoring networks. These include the
SLAMS network for SO2 NAAQS surveillance, as well as the IMPROVE network of PM2.5
monitors (which report on particulate S compounds) and the CASTNET network that reports
measurements of SO2 and particulate SO42". The latter two networks do not employ FRM/FEMs
established for SO2 or PM2.5 NAAQS surveillance and are generally focused on monitoring in
rural or remote areas.
While the data from the CASTNET and IMPROVE networks support analyses of S
compounds other than SO2 (i.e., particulate SO42" or summed airborne S compounds), the
analyses based on data from NAAQS surveillance monitors are particularly relevant given that
the current standards are judged using design value metrics based on measurements at existing
FRM/FEM monitor locations, which are mostly located in areas of higher pollutant
concentrations near emissions sources. For example, many ambient air SO2 monitors are sited
near large point sources of SO2 (e.g., electric generating units). Accordingly, information from
these monitoring sites can help inform an understanding of how changes in SO2 emissions,
reflected in ambient air concentrations, may relate to changes in deposition and, correspondingly,
what secondary standard options might best regulate ambient air concentrations such that
deposition in areas of interest is maintained at or below certain levels.
Analyses of relationships between S deposition and ambient air concentrations of S
compounds were conducted using ecoregion median S deposition and upwind monitoring site
concentrations (in the trajectory-based analyses), as well as S deposition in TDep grid cells with
ambient air concentrations at SLAMS monitors in the same grid cells, and TDep total S
deposition or NADP wet S deposition at Class I area sites of collocated IMPROVE and
CASTNET monitors with ambient air concentrations of SO2, particulate SO42" or both in
combination. Information is also analyzed from a 21-year CMAQ simulation. Details of these
analyses are described in Chapter 6. In addressing the questions below, we consider the findings
of those analyses specific to S deposition associated with SOx and PM in ambient air.
• What do the information and air quality analyses available in this review indicate
regarding relationships between air quality metrics related to the existing standards,
and potential alternatives, and S deposition? What are the uncertainties in
relationships using such metrics?
As characterized in the ISA and summarized in Chapters 2 and 6, S deposition has
declined appreciably over the past 20 years (e.g., Figure 6-11). This decline tracks closely with
the parallel decline in SO2 emissions, as discussed in sections 2.5.4 and 6.2.1, above. In the more
recent years, the areas of relatively higher S deposition estimates are generally within the Ohio
River Valley (southeastern Ohio, West Virginia, and western Pennsylvania), the Gulf Coast
(Texas and Louisiana), and a few very small areas in North Dakota and northern California
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(Figure 6-11). In addition to source emissions, there are many factors contributing to temporal
and spatial variability in S deposition, including frequency of precipitation, and
micrometeorological factors relevant to the dry deposition velocity. For example, S deposition in
arid areas, such as the Southwest, and in areas close to sources of SO2 emissions tends to be
predominantly dry deposition of SO2, while S deposition more distant from sources, and in less
arid areas, tends to be predominantly wet deposition of SO42" (ISA, Appendix 2, section 2.6.5).
Thus, in areas of the more arid western U.S., where S tends to be low, S may deposit more from
SO2, while in the wetter eastern U.S., S deposition may be more influenced by wet deposition of
S042".
The analyses in Chapter 6 assess SO2 concentrations using a metric based on the current
form and averaging time of the secondary SO2 NAAQS, which is the second highest 3-hour daily
maximum in a year, as well as an annual average SO2 air quality metric. With regard to the
annual average metric, we focused on the annual average SO2 concentration, averaged over three
years. In light of the many factors contributing variability to S deposition, the analyses focus on a
3-year average of all of the air quality and deposition metrics and include multiple years of data,
generally on the order of 20 years and covering a period of declining concentrations and
deposition. Of the two metrics analyzed (annual average and second maximum annual 3-hour
average), we focus primarily on the annual average of SO2 concentrations, averaged over 3
years, given the greater stability of the metric and our focus on control of long-term S deposition.
The data and analyses presented in Chapter 6 indicate a significant association of S
deposition with SO2 concentrations with statistically significant correlation coefficients ranging
from approximately 0.50 up to above 0.70. These include associations of estimated total and
measured wet S deposition with annual average SO2 concentrations (at same location) in 27
Class I areas based on a 21-year CMAQ simulation (1990-2010) or collocated NADP and
CASTNET monitors (2000-2019). These associations are also observed for TDep estimated total
S deposition with SO2 concentrations at SLAMS monitors in the same TDep grid cell, as well as
for ecoregion median total S deposition based on TDep with SO2 concentrations at upwind sites
of influence monitors (SLAMS) identified by trajectory-based analyses.
At SLAMS monitor locations, the correlation coefficient for S deposition with annual
average SO2 (averaged over three years) has a value of 0.70 for the full dataset across the five
time periods, with similar correlations for dry and wet deposition (r=0.72 and r=0.66,
respectively) and an even higher r value (0.79) for the eastern sites. In the complete dataset and
the subset of eastern sites, the statistically significant correlation coefficient ranges from 0.52 to
0.72 in the first three time periods (through 2010-2012) and is much reduced in the latter two
time periods. Little correlation is observed in the subset of western sites.
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In considering the findings of the trajectory-based analyses, we note the somewhat
stronger correlations observed for the weighted metric (which provides for proportional
weighting of air concentrations from locations projected to contribute more heavily to a
particular ecoregion), compared to the maximum EAQM, particularly for the first two to three
time periods of the 20-year period. For example, across all sites, the correlation coefficients for
the weighted metric range from 0.71 to 0.81 for the first three periods, while the corresponding
coefficients for the maximum metric range from 0.28 to 0.69. This difference is related to the
extent to which monitor concentrations can be indicative of atmospheric loading. Conceptually,
the weighted maximum EAQM is representing the atmospheric loading for the locations (and
associated sources) of the contributing (sites of influence) monitors. We note that this metric,
however, is not directly translatable to a standard level which is an upper limit on concentrations
in individual areas. Conversely, unweighted concentrations (even from the maximum
contributing monitor) are limited in the extent to which they can reflect atmospheric loading due
to a number of factors, including monitor and source distribution and magnitude of emissions.
The lower correlations observed between deposition and the maximum EAQM in areas of lower
concentrations are an indication of this complexity. Across a broad enough range in deposition
(e.g., as occurring in the earlier time periods and in the East), a rough correlation is observed,
which breaks down across smaller ranges in deposition, as evidenced by the much lower r values
for the more recent period with its much lower magnitude of deposition and much smaller range
in deposition.
In the context of identifying a range of annual average SO2 EAQM levels that may be
associated with an acceptable level of S deposition, such as ecoregion median S deposition of 5-
10 kg S/ha-yr, as discussed above, we take note of several important considerations. First,
monitor concentrations of SO2 can vary substantially across the U.S., reflecting the distribution
of sources, and other factors such as meteorology, complicating consideration of how the
maximum contributing monitor (as identified in the HYSPLIT analysis described in section 6.2.4
above) relates to S deposition levels in downwind ecosystems. Another consideration is the
substantial scatter in the relationship between S deposition estimates and measured SO2
concentrations with ecoregion median S deposition values below 5 kg/ha-yr. This scatter in the
relationship between measured SO2 concentration and S deposition estimates at these lower
deposition levels, contributes increased uncertainty to conclusions regarding potential secondary
standard SO2 metric levels intended to relate to ecoregion median deposition levels at or below 5
kg/ha-yr.
In identifying levels for consideration for a potential annual average SO2 metric, we
consider first the SO2 concentrations at ecoregion sites of influence identified in the trajectory-
based analyses (of the 84 ecoregions in CONUS) across different ranges of downwind ecoregion
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S deposition estimates. Figure 7-3 presents the pairs of median deposition estimates and
associated upwind sites of influence EAQM-max SO2 concentrations from the trajectory-based
analysis in section 6.2.4 above (specifically, the combined datasets presented in Figures 6-40 and
6-41). In this dataset for all 84 ecoregions, the maximum annual average SO2 concentrations,
averaged over three years, at sites of influence to downwind ecoregions with median S
deposition ranging below 9 kg S/ha-yr to 6 kg/ha-yr, were all below 15 ppb, and 75% of the
monitor sites of influence concentrations were at or below 10 ppb. For ecoregions with median S
deposition below 6 kg/ha-yr, EAQM-max SO2 concentrations at associated sites of influence
were all below approximately 10 ppb (Figure 7-3). In considering this presentation, we note that
9-10 kg/ha-yr is the approximate upper end of the range identified for ecoregion median (or
areawide) deposition in section 7.2.2.2 above, and 5 kg/ha-yr is the lower end.
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deposition estimates (across the waterbody sites assessed in the REA in each ecoregion). This
presentation indicates that for the three highest ecoregion median bins (at or above 9 kg/ha-hr),
all of the EAQM-max concentrations are greater than 10 ppb (Figure 7-4, left panel). The next
lower bin (for deposition below 9 down to 6 kg/ha-yr), has more than half of the EAQM-max
concentrations below 10 ppb. And all of the EAQM-max concentrations associated with
ecoregion median deposition in the lowest bins (S deposition below 6 kg/ha-yr) were below 10
ppb. The pattern of declining frequency of EAQM-max concentrations above 10 ppb with lower
deposition estimates is also seen for the ecoregion 90th percentile deposition estimates (Figure 7-
4, right panel). This pattern suggests that when the highest EAQM-max concentration is
somewhat below 15 ppb and down to 10 ppb, the ecoregion median deposition is below 9 kg/ha-
yr and the 90th percentile deposition 13 kg/ha-yr. when the highest EAQM-max concentrations is
at approximately 11 ppb, or 10 ppb, both the median and 90th percentile deposition are below 9
kg/ha-yr (Figure 7-4).
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Figure 7-4. Distributions of maximum annual average SO2 concentrations (3-year average) at ecoregion sites of influence
identified in trajectory-based analyses for multiple levels of ecoregion median (left) and 90th percentile (right) S
deposition in the 25 RE A ecoregions for the five time periods (2001-2003, 2006-2008, 2010-2012, 2014-2016, 2018-
20). Ecoregion medians and 90th percentiles derived from TDep estimates at sites with CLs in the ecoregion.
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Given the declining trend in S deposition across the five time periods in the aquatic
acidification analysis for which there were corresponding estimates of increasing ANC in
sensitive ecoregions (as discussed above), we also consider the annual average SO2
concentration at monitor sites during these same five time periods (Figure 7-5). In so doing, we
focus on the most recent time periods analyzed (i.e., since 2010) when, as noted in section
7.2.2.2 above, the REA indicated appreciably improved levels of acid buffering capability in the
waterbodies of the 25 analyzed ecoregions in which ANC targets were met or exceeded in a high
percentage of water bodies across a high percentage of ecoregions. This presentation indicates
that during the most recent time periods (in which ecoregion median S deposition estimates for
the 25 REA ecoregions were below 10 kg/ha-yr), the highest 3-year average annual SO2
concentrations were generally somewhat above 10 ppb (with some exceptions during the 2019-
2021 period), and 95% of the concentrations in each of the three most recent periods are just at
or below 5 ppb (Figure 7-5, left panel).9 The distributions of annual average SO2 concentrations
exhibit a similar pattern of concentrations to that for the 3-year averages, suggesting there to be
little year-to-year variability in this metric (Figure 7-5).
9 The outlier annual SO2 concentration values above 10 ppb during 2019-2021 in Figure 7-5 are at two sites in
southern Missouri where the design values for the primary standard are more than three times the level of the
primary standard.
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2001-2003 2006-2008 2010-2012 2014-2016
Time Period
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Lastly, although there are significant correlations between SO2 concentrations and S
deposition, there is variability in relationships between SO2 concentrations at SLAMS monitors
and nearby and/or downwind S deposition. This variability relates to the complexity of the
atmospheric chemistry, pollutant transport and deposition processes, as summarized in sections
2.1.1 and 2.5 above. There is also uncertainty in these relationships which relates to a number of
factors, described more fully in section 6.3 and Table 6-13. These factors include uncertainty in
our estimates of S deposition (section 2.5.2)., as well as spatial distribution of monitor sites and
their representation of significant SO2 emissions sources, as well as elements of the trajectory-
based analysis, e.g., inclusion criteria for identifying monitoring sites of influence (Table 6-
13).These various uncertainties in the data and analyses, and the inherent variability of the
physical and chemical processes involved, contribute uncertainty to conclusions concerning
ambient air SO2 concentrations related to S deposition estimates at different scales, although it is
unclear, however, how much and in what way each of these uncertainties might impact those
conclusions.
In recognition of such uncertainty and variability, REA aquatic acidification analyses and
discussion of S deposition levels above have focused on statistics for deposition estimates
representing large areas (e.g., ecoregion median and 90th percentile, and case study area average
or 70th and 90th percentile CLs). In considering median estimates, however, we have also
recognized that it is the higher points on the distribution of deposition estimates within an
ecoregion (e.g., 90th percentile) which will contribute most to aquatic acidification risk. In light
of this, it is noteworthy, however, that the distribution of S deposition estimates within
ecoregions has collapsed in the more recent years of the 20-year analysis period, with 90th
percentile estimates falling much close to the medians than in the first decade of the period
(Figure 7-2).
• What do the available information and air quality analyses indicate regarding
relationships between air quality metrics based on indicators other than those of the
existing standards and S deposition? What are the uncertainties in relationships
using such metrics?
We also assessed relationships between collocated estimates of total S deposition (or wet
deposition measurements) and measurements of indicators of atmospheric S-containing
pollutants (particulate SO42" and the sum of S in SO2 and particulate SO42") in 27 Class I areas,
mostly located in the western U.S (section 6.2.2). The correlations of deposition with the two
indicators assessed are moderate and similar for S deposition as NADP wet deposition and as
TDep estimates. For example, the correlation coefficients for either total S deposition (TDep) or
wet S deposition (NADP) with either SO42" in PM2.5 (IMPROVE) or total S (CASTNET) in those
locations vary from 0.52 to 0.61 (Figures 6-27 and 6-31). These coefficient values, while
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somewhat comparable to those for the trajectory-based analysis of downwind deposition and
upwind annual average SO2 concentrations (r=0.49 and 0.56; Table 6-8), they are somewhat
lower than the correlation observed between S deposition (TDep) and SO2 concentrations at
SLAMS monitors. For example, for annual average SO2 concentrations, averaged over three
years at SLAMS, the correlation coefficients are 0.70, 0.66 and 0.72 for total, wet and dry S
deposition, respectively (Table 6-4).
The analyses for these Class I area sites also indicate poor correlation of total S
deposition (TDep) with annual average IMPROVE PM2.5 (r=0.33, Figure 6-31). This is not
dissimilar to the correlations observed for ecoregion S deposition estimates with annual average
PM2.5 (3-year average) at upwind sites of influence from the trajectory-based analysis (r=0.22
and 0.48, Table 6-12). While the correlations in this for deposition in eastern ecoregions were
much higher (r=0.83 and 0.90), the coefficients were negative for deposition in western
ecoregions. The fact that most of the Class I area sites are in the West (20 of the 27 sites) may be
an influence on the low correlation observed for that dataset.
In summary, the analyses involving total S deposition and ambient air SO42"
concentrations are at remote Class I area locations, distant from sources of SO2 emissions, and
the relationship of SO42" with S deposition is no stronger than that for SO2 at SLAMS, which are
near sources and which monitor SO2 (the source for atmospheric S042") As a result, we find that
this analysis does not indicate a clear advantage for an indicator based on S042~measurements (or
SO42" and SO2 combined), such as is currently collected at CASTNET sites, over options for a
potential annual average standard metric focused on SO2 concentrations (based on FRM/FEMs),
as discussed above. It is also of note that use of SO42" measurements, alone or in combination
with SO2 concentrations, as an indicator for a new standard would entail development of sample
collection and analysis FRM/FEMs and of a surveillance network.
7.2.3 N Deposition and N Oxides and PM
To inform conclusions in this review related to the N oxides and PM secondary
standards, we consider the information supporting quantitative evaluation of the linkages
between N oxides and PM in ambient air with N deposition and associated ecological effects. In
considering the questions below, we draw on the available welfare effects evidence described in
the current ISA, the 2008 ISA for oxides of N and S, the 2009 ISA for PM, and past AQCDs for
all three pollutants, and summarized in Chapter 4 above. We do this in combination with the
available quantitative information summarized in chapters 5 and 6 above.
7.2.3.1 Quantitative Information for Ecosystem Risks Associated with N Deposition
The currently available evidence, including that previously available, documents aquatic
and terrestrial effects of N deposition, as summarized in Chapter 4 and described in detail in the
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ISA. As recognized in sections 7.2.1 and 7.2.2.1 above, N deposition has played a role in acidic
deposition in both terrestrial and aquatic ecosystems and associated effects in the U.S., although
analyses in the aquatic acidification REA indicate a reduced role for N deposition in the time
period analyzed (section 5.1.2.4 and Appendix 5A, section 5A.2.1). Additionally, the evidence is
extensive and longstanding as to the role of N loading of waterbodies and associated
eutrophication. Further, the evidence previously available, with noteworthy additions from the
more recently available evidence, describes the role of N deposition in terrestrial N enrichment
and associated ecosystem effects.
We consider here the available information that quantitatively relates atmospheric
deposition of N to effects on soil and surface water chemistry and relates those effects to specific
ecological effects for the different types of ecosystems and categories of effects. Our focus with
regard to N deposition is on N enrichment-related effects in light of the relatively greater role
played by S in acidic deposition, in recent years (section 5.1.2.4 above). In focusing on N
enrichment-related effects, we note the varying directionality of some of these effects,
particularly in terrestrial ecosystems, such that the effects of N enrichment can in particular
ecosystems and for particular species, seem beneficial (e.g., to growth or survival of those
species), although in a multispecies system, effects are more complex with potential for
alteration of community composition. Our consideration below of the availability of quantitative
information relating atmospheric N deposition to N enrichment-related effects in aquatic and
terrestrial ecosystems is in the context of the following question.
• What does the available evidence base indicate regarding air quality and
atmospheric deposition and risk or likelihood of occurrence of ecosystem effects
under differing conditions? What are limitations and associated uncertainties in this
evidence?
With regard to acidification-related effects of N deposition, we recognize the approaches
and tools referenced in section 7.2.2.1 above could be utilized for S and N deposition in
combination, but we focused the analysis of aquatic ecosystem acidification summarized in
section 5.1 above on S deposition, based on analyses indicating the relatively greater role of S
deposition under the more recent air quality conditions (section 5.1.1.4). Discussion of analyses
relating acidifying deposition to terrestrial acidification indicators is presented in section 5.3
above.
In evaluating the available information for the purposes of quantitatively relating N
deposition associated with N oxides and PM to waterbody responses (most particularly
waterbody eutrophication), we first take note of the appreciable evidence base documenting
assessments of N loading to waterbodies across the U.S. (ISA, Appendix 7). In so doing, we note
the waterbody-specific nature of such responses and the relative role played by atmospheric
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deposition, among other N sources. For example, as recognized in the ISA and Chapters 4 and 5
above, the relative contribution to such loading from atmospheric deposition compared to other
sources (e.g., agricultural runoff and wastewater discharges), which varies among waterbody
types and locations, can be a complicating factor in quantitative analyses. Additionally,
characteristics of resident biota populations and other environmental factors are influential in
waterbody responses to N loading (e.g., temperature, organic microbial community structure,
aquatic habitat type, among others), as discussed in the ISA (ISA, Appendix 7).
Based on identification of eutrophication as a factor in impacts on important fisheries in
some estuaries across the U.S., multiple government and nongovernment organizations have
engaged in research and water quality management activities over the past multiple decades in
large and small estuaries and coastal waters across the U.S. These activities have generally
involved quantitative modeling of relationships between N loading and water quality parameters
such as dissolved oxygen (ISA, Appendix 7, section 7.2). As summarized in section 5.2.3 above,
this research documents both the impacts of N enrichment in these waterbodies and the
relationships between effects on waterbody biota, ecosystem processes and functions, and N
loading. The evidence base recognizes N loading to have contributions from multiple types of
sources to these large waterbodies, and their associated watersheds, including surface and ground
water discharges, as well as atmospheric deposition. Accordingly, loading targets or reduction
targets identified for these systems have generally been identified in light of policy and
management considerations related to the different source types, as discussed further in section
7.2.3.2 below.
Focused assessments in freshwater lakes, including alpine lakes, where atmospheric
deposition may be the dominant or only source of N loading, also provide evidence linking N
loading with seemingly subtle changes, as summarized in section 5.2.2. above. Such changes
include with regard to whether P or N is the limiting nutrient and shifts in phytoplankton
community composition, for which public welfare implications are less clear. This evidence has
included observational studies of freshwater lakes of the western U.S. involving statistical
modeling, and studies which have utilized NO3" concentrations as an indicator of N enrichment
(e.g., ISA, Appendix 9). Among the recent evidence in the ISA are long-term monitoring studies
of lakes in several mountainous regions, including the Appalachian Mountains, the Adirondacks,
and the Rocky Mountains, that have documented reduced surface water NO3" concentrations
corresponding to decreases in atmospheric N deposition since the 1980s and 1990s (ISA,
Appendix 7, section 7.1.5.1).
An additional type of aquatic ecosystem effect recognized in the available evidence for N
loading, particularly to freshwaters, relates to an increase in the toxicity of exudates associated
with harmful algal blooms (ISA, Appendix 9, section 9.2.6.1). Information available in this
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review indicates that growth of some harmful algal species, including those that produce
microcystin, are favored by increased availability of N and its availability in dissolved inorganic
form (ISA, Appendix 9, p. 9-28). Although this is an active research area, few if any datasets are
currently available that quantitatively relate N loading to risk of harmful blooms, including those
that may distinguish roles for different deposition components such as deposition of oxidized N
or of particulate reduced N distinguished from that of N loading via dry deposition of reduced N.
With regard to terrestrial ecosystems and effects on trees and other plants, we recognize
the complexity, referenced above, that poses challenges to approaches for simulating terrestrial
ecosystem responses to N deposition across areas diverse in geography, geology, native
vegetation, deposition history, and site-specific aspects of other environmental characteristics. In
general, limitations particular to the different types of quantitative analyses contribute associated
uncertainty to our interpretations. Uncertainties associated with the soil acidification modeling
analyses include uncertainties associated with the limited dataset of lab oratory-generated data on
which the BC:A1 targets are based, as well as the steady-state modeling parameters, most
prominently those related to base cation weathering and acid-neutralizing capacity (section
5.3.4.1). Uncertainties associated with experimental addition analyses include the extent to
which the studies reflect steady-state conditions, with a related limitation of some of these
studies associated with a lack of information regarding historic deposition at the study locations
that might inform an understanding of the prior issue (section 5.3.4.1). Several aspects of
observational or gradient studies of tree growth and survival (or of species richness for herbs,
shrubs and lichens) contribute uncertainties to identification of deposition levels of potential
concern for tree species effects, including unaccounted-for factors with potential influence on
tree growth and survival (e.g., ozone and soil characteristics), as well as the extent to which
associations may reflect the influence of historical deposition patterns and associated impact.
Thus, while the evidence is robust as to the ability for N loading from deposition to contribute to
changes in plant growth and survival, and associated alterations in terrestrial plant communities,
there are a variety of factors, including the history of deposition and variability of response
across the landscape, that complicate our ability to quantitatively relate specific N deposition
rates, associated with various air quality conditions, to N enrichment-related risks of harm to
forests and other plant communities in areas across the U.S. (section 5.3.4).
7.2.3.2 General Approach for Considering Public Welfare Protection
As an initial matter, we note that the effects of acidification on plant growth and survival,
at the individual level, are generally directionally harmful, including reduced growth and
survival. In contrast, the effects of N enrichment can, in particular ecosystems and for particular
species, be beneficial or harmful (e.g., to growth or survival of those species). Accordingly, there
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is added complexity to risk management policy decisions for this category of effects, including
the lack of established risk management targets or objectives, particularly in light of historical
deposition and its associated effects that have influenced the current status of terrestrial
ecosystems, their biota, structure and function.
Further, we recognize the contribution to N deposition of atmospheric pollutants other
than the criteria pollutants N oxides and PM, most significantly the contribution of NH3 (as
described in section 6.2.1 above). This contribution has increased since the last reviews of the
NO2 and PM secondary standards, as seen in Figures 6-17 and 6-18, reflecting increases in NH3
emissions over that time period. These trends of increased NH3 emissions and reduced N
deposition coincide with decreasing trends in N oxides emissions and associated contributions of
oxidized N to total N deposition (Figures 6-3 and 6-19). The TDep estimates of different types of
N being deposited at the 92 CASTNET sites indicate that since about 2015, reduced N
compounds comprise a greater proportion of total N deposition than do oxidized compounds,
with reduced N in recent years generally accounting for more than 50% of total N deposition
(Figure 6-19). Further, dry deposition of NH3 as a percentage of total N deposition at CASTNET
sites ranges up to a maximum of 65% at the highest site in 2021 (Figure 6-19). The 75th
percentile for these sites is greater than 30%, a noteworthy value given that these sites are
generally in the West, with few in the areas of highest NH3 emissions (Figures 6-20 and 2-9).
As a result of the contrasting temporal trends for emissions of oxidized and reduced N
compounds, the influence of ambient air concentrations of N oxides and PM on N deposition
appears to have declined over the past 20 years, complicating our consideration of the protection
from N deposition-related effects that can be provided by secondary NAAQS for these
pollutants. Thus a complicating factor in considering policy options related to NAAQS for
addressing ecological effects related to N deposition is NH3, which is not a criteria pollutant and
its contribution to total N deposition, particularly in parts of the U.S. where N deposition is
highest (e.g., Figure 6-18 and 6-13).
• What does the available information indicate for considering the potential public
welfare protection from N deposition-related effects in aquatic and terrestrial
ecosystems?
As discussed in section 4.5 above, effects of N deposition in both aquatic and terrestrial
ecosystems have potential public welfare implications. For example, in the case of eutrophication
in large estuaries and coastal waters of the eastern U.S., the public welfare significance of effects
related to decades of N loading is illustrated by the large state, local and national government
investments in activities aimed at reducing the loading. This significance relates both to the
severity of the effects and the wide-ranging public uses dependent on these waters. These
waterbodies are important sources of fish and shellfish production, capable of supporting large
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stocks of resident commercial species and serving as breeding grounds and interim habitat for
several migratory species, and also provide an important and substantial variety of cultural
ecosystem services, including water-based recreational and aesthetic services. Further, these
systems have non-use benefits to the public. The relative contribution of atmospheric deposition
to total N loading, however, varies widely among estuaries, and has declined in more recent
years, contributing a complexity to considerations in this review. While, such complications may
not affect smaller, more isolated fresh waterbodies for which N loading is from atmospheric
deposition, the evidence with regard to public welfare significance of any small deposition-
related effects in these systems is less clear and well established. For example, the public welfare
implications of relatively subtle effects of N enrichment in aquatic systems, such as shifts in
phytoplankton species communities in remote alpine lakes, are not clear. Additionally, the public
welfare implications of HNO3 effects on lichens (which might be considered direct effects or the
result of deposition) are also not clear, and might depend on the extent to which they impact
whole communities, other biota or ecosystem structure and function.
With regard to N enrichment in terrestrial ecosystems, the associated effects may vary
with regard to public welfare implications. As noted above with regard to impacts of aquatic
acidification, we recognize that some level of N deposition and associated effects on terrestrial
ecosystems can impact the public welfare and thus might reasonably be judged adverse to the
public welfare. Depending on magnitude and the associated impacts, there are situations in
which N deposition and associated nutrient enrichment-related impacts might reasonably be
concluded to be significant to the public welfare. For example, to the extent forest ecosystem
community structures are altered in ways that appreciably affect use and enjoyment of those
areas by the public, implication for the public welfare are more obvious.
A complication to consideration of public welfare implications that is specific to N
deposition in terrestrial systems is its potential to increase growth and yield of agricultural and
forest crops, which may be judged and valued differentially than changes in growth of some
species in natural ecosystems. Nitrogen enrichment in natural ecosystems can, by increasing
growth of N limited plant species, change competitive advantages of species in a community,
with associated impacts on the composition of the ecosystem's plant community. The public
welfare implications of such effects may vary depending on their severity, prevalence or
magnitude, such as with only those rising to a particular severity (e.g., with associated significant
impact on key ecosystem functions or other services), magnitude or prevalence considered of
public welfare significance.
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• What does the currently available quantitative information regarding terrestrial
ecosystem responses to N deposition indicate about levels of N deposition that may be
associated with increased concern for adverse effects?
Focusing first on the evidence for effects of N deposition on trees, we note that the
available quantitative information related to effects on plants, including trees, from N deposition
summarized in Chapter 5 (and presented in more detail on Appendix 5B) includes soil chemistry
modeling analyses for an indicator of soil acidification, as well as studies involving experimental
additions of N compounds to defined field plots, and observational studies of potential
relationships between tree growth and survival and metrics for N deposition. We consider the
latter two types of studies here, as in Chapter 5 above, with regard to what each provides to
inform the question posed above. Estimates from the array of studies indicates N deposition with
a range of 7 to 12 kg/ha-yr, on a large area basis, may be a reasonable summary of conditions for
which statistical associations have been reported for terrestrial effects, such as tree growth and
survival and species richness of herbs and shrubs.
With regard to the information available from experimental addition tree studies, the
ranges of N additions that elicited increased tree growth overlapped with those that elicited
reduced growth and increased mortality. In considering these studies, we note that while some
report observations based on additions over just a few years, others extend over a decade or
more. In general, these studies inform our understanding of the effects on tree populations of
increased N in forested areas, which can vary, influenced in part by other environmental factors,
as well as by species-specific effects on population dynamics. The lowest forest N addition that
elicited effects was 15 kg N/ha-yr over a 14-year period occurring from 1988-2002 (Appendix
5B, Table 5B-1; McNulty et al., 2005).
Among the available observational or gradient studies of N deposition and tree growth
and survival (or mortality) are three recently available studies that utilized the USFS/FIA dataset
of standardized measurements at sites across the U.S. (Dietze and Moorcroft, 2011; Thomas et
al., 2010; Horn et al., 2018). These studies cover overlapping areas of the U.S. (see Appendix
5B, Figure 5B-1) and report associations of tree growth and/or survival metrics with various N
deposition metrics, which provides support to conclusions regarding a role for N deposition in
affecting tree health in the U.S., most particularly in regions of the eastern U.S., where
confidence in the study associations is greatest (see summaries in section 5.3.2.3 and Appendix
5B, section 5B.3.2). The metrics utilized include site-specific estimates of average NO3"
deposition and of average total N deposition over three different time periods (Dietze and
Moorcroft, 2011; Thomas et al., 2010; Horn et al., 2018). In considering information from these
studies discussed in section 5.3.2 and Appendix 5B, we note the history of N deposition in the
eastern U.S. and the similarity between geographic patterns of historical deposition and more
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recent deposition patterns in the U.S., which may influence the findings of observational studies,
contributing an uncertainty to estimates of a specific magnitude of deposition rate that might be
expected to elicit specific tree responses, such as increased or decreased growth or survival.
With regard to tree survival, Dietze and Moorcroft (2011) reported negative associations
of mortality in multi-species groups (positive associations for survival) with average NO3"
deposition at sites across the eastern half of the contiguous U.S. (i.e., higher survival rates in
areas of higher NO3" deposition estimates). Site-specific average NO3" deposition in the analysis
(1994-2005) ranged from a minimum of 6 kg/ha-yr to a maximum of 16 kg/ha-yr (Dietze and
Moorcroft, 2011). Among 23 species in the northeastern and north-central U.S, the study by
Thomas et al. (2010) reported negative and positive associations of N deposition (mean annual
average for 2000-04) with survival for eight and three species, respectively. Positive and
negative associations were reported with tree growth for 11 and 3 species, respectively. Site-
specific average N deposition estimates in the analysis (2000-2004) ranged from a minimum of 3
kg/ha-yr to a maximum of 11 kg/ha-yr (Thomas et al., 2010). The other factors analyzed (e.g.,
temperature, precipitation, and tree size) did not include pollutants other than N deposition
(Thomas et al., 2010).
The much larger study by Horn et al. (2018) of 71 species reported associations of tree
survival and growth with N deposition that varied from positive to negative across the range of
deposition at the measurement plots for some species, and also varied among species (Appendix
5B, section 5B.3.2.3). The median deposition values across the sample sites for species with
significant positive or negative associations generally ranged from 7 to 11 kg N/ha-yr, as
described in more detail in section 5.3.2 and Appendix B, section 5B.3.2.3. For species for which
the association varied from negative to positive across deposition levels, this range includes
those species for which the association was negative at the median deposition value (and for
which sample sites were not limited to the western U.S.). Of the six species with negative
associations of survival with the N deposition metric across the full range of the N deposition
metric, the median deposition values ranged from 8 to 11 kg N ha ha"1 yr"1 (Appendix 5B, Figure
5B-7). The median deposition values for the 19 other species with hump-shaped (or humped)
functions that were negative at the median deposition value (and for which sample sites were not
limited to the western U.S.) ranged from 7 to 11 kg N ha"1 yr"1,
With regard to studies of herb and shrub community response, a number of recently
available studies report on addition experiments, as summarized in section 5.3.3.1 and Appendix
5B, section 5B.3.1. The lowest N additions for which community effects have been reported
include 10 kg N/ha-yr. With an addition of 10 kg N/ha-yr over a 10-year period, grassland
species numbers declined; in a subset of plots for which additions then ceased, relative species
numbers increased, converging with controls after 13 years (Appendix 5B, Table 5B-7; Clark
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and Tilman, 2008). Recent gradient studies of coastal sage scrub in southern California have
indicated N deposition above 10 or 11 kg/ha-year to be associated with increased risk of
conversion to non-native grasslands or reduced species richness (Appendix 5B; section 5B.3.2;
Cox et al., 2014; Fenn et al., 2010). A larger observational study of herb and shrub species
richness in open- and closed-canopy communities using a database of site assessments conducted
over a 23-year period and average N deposition estimates for a 26-year period reported
significant influence of soil pH on the relationship between species richness and N deposition
metric. A negative association was observed for acidic (pH 4.5) forested sites with N deposition
estimates above 11.6 kg N/ha-yr and for low pH open canopy sites (woods, shrubs and grasses)
with N deposition estimates above 6.5 kg N/ha-yr (section 5.3.3.1).
Lastly, the evidence base includes observational studies that have analyzed variation in
lichen community composition in relation to indicators of N deposition (section 5.3.3.2 and
Appendix 5B, section 5B.4.2). A recent study focused on relating metrics for community
composition to estimated N deposition across sites in the Northwest reported an association of
total N deposition in the range of 3 to 9 kg N/ha-yr with areas having 33-43% fewer species that
grow well in low N environments and 3 to 4-fold more species that thrive in high N
environments (Geiser et al., 2010). In addition to limitations with regard to interpretation,
uncertainties associated with these studies include alternate methods for utilizing N deposition
estimates as well as the potential influence of unaccounted-for environmental factors (e.g.,
ozone, SO2 and historical air quality and associated deposition), as noted in section 5.3.3.2
above.
• What does the currently available quantitative information regarding aquatic
ecosystem responses to N deposition indicate about levels of N deposition that may be
associated with increased concern for adverse effects?
With regard to the evidence for effects of N deposition in aquatic ecosystems, we
recognize several different types of information and evidence. This information includes the
observational studies utilizing statistical modeling to estimate critical loads, such as those related
to subtle phytoplankton species shifts in western lakes. This also includes the four to five
decades of research on the impacts and causes of eutrophication in large rivers and estuaries. In
considering this diverse evidence base, we take note of the robust evidence base on N loading
and eutrophi cation, with its potentially significant impacts on submerged aquatic vegetation and
fish species, particularly in large river systems, estuaries and coastal systems. As noted above,
the public attention, including government expenditures, that has been given to N loading and
eutrophi cation in several estuarine and coastal systems are indicative of the recognized public
welfare implications of related impacts.
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In large aquatic systems across the U.S., the relationship between N loading and algal
blooms, and associated water quality impacts (both short- and longer-term), has led to numerous
water quality modeling projects to inform water quality management decision-making in
multiple estuaries, including Chesapeake Bay, Narraganset Bay, Tampa Bay, Neuse River
Estuary and Waquoit (ISA, Appendix 7, section 7.2). These projects often utilize indicators of
nutrient enrichment, such as chlorophyll a, dissolved oxygen, and abundance of submerged
aquatic vegetation, among others (ISA, section IS.7.3 and Appendix 10, section 10.6). For these
estuaries, the available information regarding atmospheric deposition and the establishment of
associated target loads varies across the various estuaries (ISA, Appendix 7, Table 7-9). Further,
in many cases atmospheric loading has decreased since the initial modeling analyses.
As summarized in section 5.2.3 above, analyses in multiple East Coast estuaries -
including Chesapeake Bay, Tampa Bay, Neuse River Estuary and Waquoit Bay - have
considered atmospheric deposition as a source of N loading (ISA, Appendix 7, section 7.2.1).
Total estuary loading or loading reductions were established in TMDLs developed under the
Clean Water Act for these estuaries. Levels identified for allocation of atmospheric N loading in
the first three of these estuaries were 6.1, 11.8 and 6.9 kg/ha-yr, and atmospheric loading
estimated to be occurring in the fourth was below 5 kg/ha-yr (section 7.3 below).
7.2.3.3 Relating Air Quality Metrics to N Deposition-related Effects of N Oxides and PM
Analyses in Chapter 6 explored how well various air quality metrics relate to S and N
deposition. The analyses examine the relationships between air concentrations, in terms of
various air quality metrics (including design values for the current standards), and N deposition
in areas near or removed from the ambient air monitoring sites. The analyses utilizing data from
NAAQS surveillance monitors are particularly relevant given that the current standards are
judged using design values derived from FRM/FEM measurements at existing SLAMS. Given
their role in surveillance for NAAQS violations, most or many of these monitors are located in
areas of relatively higher pollutant concentrations, such as near large sources of NO2 or PM.
Accordingly, information from these monitoring sites can help inform how changes in NO2
and/or PM emissions, reflected in ambient air concentrations, relate to changes in deposition and,
correspondingly, what secondary standard options might best regulate ambient air concentrations
such that deposition in sensitive ecosystems of interest is maintained at or below certain levels.
In addressing the questions below, we consider the findings of those analyses specific to N
deposition associated with N oxides and PM.
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• What do the available information and air quality analyses indicate regarding
relationships between air quality metrics related to the existing standards and N
deposition? What are the uncertainties in relationships using such metrics?
In considering the information and analyses regarding relationships between N deposition
and N oxides and PM in ambient air, we consider the current forms and averaging times of the
secondary PM and NO2 NAAQS. For N oxides, the current secondary standard is the annual
average of NO2, and that for PM is the average of three consecutive years of annual averages. As
in the assessments of S deposition and air quality metrics, the analyses here focus on 3-year
average metrics (e.g., annual average NO2 and N deposition, averaged over three years) and
include multiple time periods of data to better assess more typical relationships. For consistency
and simplicity, most analyses in Chapter 6 focus on the five 3-year periods also used for S
deposition and SOx: 2001-03, 2006-08, 2010-12, 2014-16 and 2018-20.
As an initial matter, we note that, as discussed in section 6.4.2 above, relationships
between N deposition and NO2 and PM air quality are affected by NH3 emissions and non-N-
containing components of PM. Further, the influence of these factors on the relationships has
varied across the 20-year evaluation period and varies across different regions of the U.S.
(section 6.2.1). Both of these factors influence relationships between total N deposition and NO2
and PM air quality metrics.
For total N deposition estimated for grid cells with collocated SLAMS monitors, the
correlations with annual average NO2 concentrations, averaged over three years, are low across
all sites and in the East, although somewhat better for the West, with coefficient values of 0.38
and 0.44 for all sites and in the East, respectively, and 0.63 for West (Table 6-6). As noted in
section 6.4.2, this likely reflects the relatively greater role of NH3 in N deposition in the East
(which for purposes of the analyses in this PA extends across the Midwest). For N deposition
and NO2 at upwind monitoring sites of influence, the correlation between estimates of total N
deposition (wet plus dry) in eastern ecoregions and annual average NO2 concentrations at
monitor sites of influence (identified via trajectory-based modeling) for the five periods from
2001-2020 is low to moderate (0.35 and 0.48 for EAQM-max and EAQM-weighted,
respectively), with the earlier part of the 20-year period, when NO2 concentrations were higher
and NH3 emissions were lower (as indicated by Figures 6-6 and 6-5) having relatively higher
correlation than the later part. The correlation is negative or near zero for the western ecoregions,
as described in section 6.2.4 above.
As described in section 6.2.1 above, the reductions in NO2 emissions over the past 20
years have been accompanied by a reduction in deposition of oxidized N. However, increases in
NH3 emissions, particularly in the latter 10 years of the period analyzed (2010-2020), have
modified the prior declining trend in total N deposition. That is, coincident with the decreasing
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trends in NO2 emissions and in deposition of oxidized N in the past 10 years there is a trend of
increased NH3 and increased deposition of reduced N, most particularly in areas of the Midwest,
Texas, Florida and North Carolina (Figures 6-16 and 6-17). This indicates that while, in the
earlier years of the assessment period, controls on NO2 emissions may have resulted in
reductions in deposition of oxidized N, in more recent years they have much less influence on
total N deposition (sections 6.2.1 and 6.4). In terms of ecoregion median statistics, Figure 7-6
illustrates a decreasing trend in ecoregion median total N deposition across the period from 2001
through 2012. From 2012 onward, it can be seen that deposition increases, most particularly in
ecoregions in which the median % of total deposition that is reduced exceeds 50% (Figure 7-6,
left and center panels).
The impact of increasing deposition of reduced N on the 20-year trend in total N
deposition is also illustrated by TDep estimates at the nearly 100 CASTNET sites. At these sites,
the median percentage of total N deposition comprised by oxidized N species, which is driven
predominantly by N oxides, has declined from more than 70% to less than 45% (Figure 6-19).
Examination of the components of reduced N deposition indicates the greatest influence on the
parallel increase in N deposition percentage comprised of reduced N is the increasing role of
NH3 dry deposition. The percentage of total N deposition at the CASTNET sites has increased,
from a median below 10% in 2000 to a median above 25% in 2021 (Figure 6-19).
Recognizing limitations in the extent to which CASTNET sites can provide information
representative of the U.S. as a whole, we have also analyzed TDep estimates for the most recent
period (2018-2020) with regard to total N deposition percent of total represented by reduced N
across the U.S. Figure 7-7 illustrates that in areas with ecoregion median total N deposition
above 9 kg/ha-yr (upper panel), the ecoregion median percentage of total N deposition comprised
of reduced N is greater than 60% (lower panel). Further, in Figure 7-8, recent (2019-2021) TDep
estimates across individual TDep grid cells provide a similar picture showing that areas of the
U.S. where total N deposition is highest and is greater than potential targets identified in section
7.2.3.2 above (Figure 7-8, upper) are also the areas with the greatest deposition of NH3 (Figure
7-8, lower), comprising more than 30% of total N deposition. That is, NH3 driven deposition is
greatest in regions of the U.S. where total deposition is greatest.
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Reduced N dep >60%.
N=32
Reduced N dep <50%,
N=15
2001-2003 2006-2008 2010-2012 2014-2016 2018-2020
2001-2003 2006-2008 2010-2012 2014-2016 2018-2020
Time Period
2001-2003 2006-2008 2010-2012 2014-2016 2018-2020
Time Period
Figure 7-6. Temporal trend in ecoregion median estimates of total N deposition in ecoregions for which 2018-2020 TDep
estimated reduced N deposition is >60% (left), 50-60% (middle) and <50%(right).
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Median N Deposition
Figure 7-7. Ecoregion median total N deposition (upper panel) and percentage of total
comprised of reduced N (lower panel) based on TDep estimates (2018-2020).
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Figure 7-8. Estimated total N deposition (upper panel) and percentage of total comprised
by NHs deposition (lower panel) based on TDep grid cells (2018-2020).
Regarding ecoregion median N deposition and PM2.5 concentrations at upwind sites of
influence, as with NO2 concentrations, the correlation for eastern ecoregions (r=0.53 [max] and
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0.62 [weighted]) is better than in western ecoregions, for which there is no correlation at all
(section 6.2.4). For N deposition and PM2.5 concentrations at SLAMS, as described in section
6.2.3 above, a low to moderate correlation is observed between total N deposition and annual
average PM2.5 concentrations (r=0.57 across all sites, 0.56 in East, 0.45 in West). In considering
the two factors mentioned above, we note, as described in section 6.1 above, some NH3
transforms to NH4+, which is a component of PM2.5. As noted above, however, in the areas of
greatest N deposition, the portion represented by deposition of gaseous NH3 generally exceeds
30%. Additionally, while NH3 emissions have been increasing over the past 20 years, the
proportion of PM2.5 that is comprised of N compounds has declined. As discussed in section
6.4.2 above, the median % of PM2.5 comprised by N compounds at CSN sites declined from
about 25% in 2006-2008 to about 17% in 2020-2022 and the highest percentage across sites
declined from over 50% to 30% (Figure 6-56). Further this percentages varies regionally, with
sites in the nine southeast states having less than 10% of PM2.5 mass comprised of N compounds
(Figure 6-56).
In summary, in recent years, NH3, which is not a criteria pollutant, contributes
appreciably to total N deposition, particularly in parts of the country where N deposition is
highest (as illustrated by comparison of Figures 6-13 and 6-18). This situation, of an increasing,
and spatially variable, portion of N deposition not being derived from N oxides or PM,
complicates our assessment of policy options for protection against ecological effects related to
N deposition associated with N oxides and PM, and on secondary standards for those pollutants
that may be considered to be associated with a desired level of welfare protection. That
notwithstanding, we have also considered analyses of SLAMS air quality data with regard to
trends in annual average NO2 concentrations (Figure 7-9) and relationships between annual
average NO2 concentrations (in a single year and averaged over three years) and design values
for the existing primary standard (Figure 7-10).
From the temporal trend figures for N deposition and NO2 concentrations, it can be seen
that subsequent to 2011-2012, when median N deposition levels in 95% of the eastern ecoregions
of the continental U.S.10 have generally been at/below 11 kg N/ha-yr, annual average NO2
concentrations, averaged across three years, have been at/below 35 ppb (Figures 7-6 and 7-9).
Recognizing that among the NO2 primary and secondary NAAQS, the 1-hour primary standard
(established in 2010) may be the more controlling on ambient air concentrations, we considered
the relationship among the two metrics (1-hr and annual). Figure 7-10 (left panel) below
illustrates the relationship between 1-hour and annual design values for the existing primary and
secondary NO2 standards. Figure 7-10 (right panel) indicates that single-year annual average
10As noted earlier the eastern designation used throughout PA includes areas generally considered the Great Plains.
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NO: concentrations, averaged over three years, in areas that meet the current 1-hour primary
standard have generally been below approximately 35 to 40 ppb.
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Given this information and these relationships, there may be some potential for a standard
set as an NO2 annual average (annual or averaged over three years), if reduced below the level of
the existing standard, to contribute to a degree of control of N deposition (control of N
deposition specifically associated with N oxides). However, this information also suggests the
potential for future reductions in N oxide-related N deposition to be negated by increasing
reduced N deposition. The results also suggest that the PM2.5 annual average standard may
provide some control of N deposition associated with PM and N oxides. We note, however, that
PM2.5 monitors, while capturing some compounds that contribute to S and N deposition across
the U.S., also capture other non-S and non-N related pollutants as part of the PM2.5 mass.
Variation in the amounts of each category of compounds varies regionally (and seasonally), and
as noted above, N-compounds generally comprise less than 30% of total PM2.5 mass.
Uncertainties associated with this variation and other uncertainties in the analyses are noted in
Chapter 6, along with a characterization of the extent to which each of these uncertainties might
impact interpretation of the various analyses (section 6.3).
• What do the available information and air quality analyses indicate regarding
relationships between air quality metrics based on indicators other than those of the
existing standards and N deposition? What are the uncertainties in relationships
using such metrics?
As discussed above, Chapter 6 also assessed relationships for collocated measurements
and modeled estimates of N compounds other than NO2 with N deposition in a subset of 27
CASTNET sites located in 27 Class I areas, the majority of which (21 of 27) are located in the
western U.S. The analyses indicated some correlations between concentrations of other air
quality metrics and N deposition levels in these locations. For example, these results suggest that
total N deposition (TDep) in these rural areas has a moderate correlation with annual average air
concentrations of nitric acid and particulate nitrate for the 20-year dataset (2000-2020) (Figure 6-
32, r=0.57 for TNO3, r=0.63 for NO3). These values are comparable to the correlation of NO2
with total N dep (TDep) at western SLAMS, a not unexpected observation given that more than
75% of the 27 CASTNET sites are in the West. A much lower correlation was observed at
SLAMS in the East, and with the trajectory-based dataset. As noted in section 6.4.2 above,
deposition at the western U.S sites is generally less affected by NH3. Further, the observed trend
of increasing contribution to N deposition of NH3 emissions over the past decade suggests that
such correlations of N deposition with oxidized N may be still further reduced in the future.
Thus, the evidence does not provide support for the oxidized N compounds (as analyzed at the
27 Class I sites) as indicators of total atmospheric N deposition, especially in areas where NH3 is
prevalent.
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As similarly recognized above for S deposition and SOx, the analyses involving total N
deposition and ambient air NO3" (or NO3" plus HNO3) concentrations are at remote locations,
distant from sources of N oxides emissions, and the SLAMS, which monitor NO2 (the primary
precursor for atmospheric NO3") are generally in areas near sources. Thus, these analyses do not
indicate an advantage or benefit for an indicator based on NO3" measurements such as is
currently collected at CASTNET sites, over options based on NO2 as the indicator.
The analyses involving N deposition and N-containing PM components, also performed
at the 27 Class I area sites yield similar correlation coefficients as those for 3-year average N
deposition (TDep) and PM2.5 at SLAMS monitors. For example, the correlation coefficients for
annual total N deposition estimates with annual particulate NH4+ and NO3" combined, or
particulate NH4+ alone, are all 0.62 (Figure 6-33), which is comparable to the correlation
coefficient 0.57 for PM2.5 mass design values observed at all U.S. SLAMS (Figure 6-39, upper
panel), and also not much different from the value of 0.53 for PM2.5 mass (IMPROVE) at the
same 27 Class I area sites (Figure 6-32, left panel). Further the graphs of total N deposition
estimates versus total N at the 27 Class I area sites indicate the calculated correlations (and
slopes) likely to be appreciably influenced by the higher concentrations occurring in the first
decade of the 20-year (Figure 6-33). Thus, the available analyses of N-containing PM2.5
components at the small dataset of sites remote from sources, also do not indicate an overall
benefit or advantage over consideration of PM2.5 (discussed in section 7.4 below).
As a whole, the limited dataset with varying analytical methods and monitor locations,
generally distant from sources, does not clearly support a conclusion that such alternative
indicators might provide better control of N deposition related to N oxides and PM over those
options discussed above (and used for the existing standards). It is also of note that use of the
NO3" or particulate N measurements analyzed with deposition estimates at the 27 Class I area
sites, alone or in combination with NO2, as an indicator for a new standard would entail
development of sample collection and analysis FRM/FEMs11 and of a surveillance network.
7.3 CASAC ADVICE AND PUBLIC COMMENTS
In our consideration of the adequacy of the current secondary standards for SOx, N
oxides and PM, in addition to evidence and air quality/exposure/risk-based information discussed
above, we have considered the advice and recommendations of the CASAC, based on their
review of the ISA and the earlier draft of this PA, as well as comments from the public on the
earlier draft of this PA. A limited number of public comments have been received in the docket
11 For example, sampling challenges have long been recognized for particulate NH4+ (e.g., ISA, Appendix 2, sections
2.4.5; 2008 ISA, section 2.7.3).
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for this review12 to date, including just a few comments on the draft PA, and they were primarily
focused on technical analyses and information, which we've considered in developing the final
PA (section 1.4 above). The few public commenters that addressed the adequacy of the current
secondary standards or potential alternative options to achieve appropriate public welfare
protection expressed the view that the available evidence does not indicate the need for revision
of the existing standards. The remainder of this section focuses on advice and recommendations
from the CASAC regarding the standards review based on review of the draft PA.
The CASAC provided its advice regarding the current secondary standard in the context
of its review of the draft PA (Sheppard, 2023). As an initial matter, the CASAC recognized that
"translation of deposition-based effects to an ambient concentration in air is fraught with
difficulties and complexities" (Sheppard, 2023, pp. 1-2). Further, the CASAC expressed its view
that, based on its interpretation of the Clean Air Act, NAAQS could be in terms of atmospheric
deposition, which it concluded "would be a cleaner, more scientifically defensible approach to
standard setting" and accordingly recommended that direct atmospheric deposition standards be
considered in future reviews (Sheppard, 2023, pp. 2 and 5). The CASAC then, as summarized
below, provided recommendations regarding standards based on air concentrations, consistent
with EPA's interpretations for NAAQS.
With regard to protection from effects other than those associated with ecosystem
deposition of S and N compounds, the CASAC concluded that the existing SO2 and NO2
secondary standards provide adequate protection for direct effects of those pollutants on plants
and lichens, recommending that these standards can be retained without revision for this purpose
(Sheppard, 2023, p. 5 of letter and p. 23 of Response to Charge Questions). With regard to
deposition-related effects of S and N compounds, the CASAC members did not reach consensus.
Advice conveyed from both groups of members concerning deposition-related effects is
summarized here.
With regard to deposition-related effects of S and standards for SOx, the majority of
CASAC members recommended a new annual SO2 standard with a level in the range of 10 to 15
ppb, which these members concluded would generally maintain ecoregion median S deposition
below 5 kg/ha-yr13 based on consideration of the trajectory-based SO2 analyses (and associated
figures) in the draft PA (Sheppard, 2023, Response to Charge Questions, p. 25). They concluded
12 The docket for this review of the secondary standards for SOx, N oxides and PM is EPA-HQ-OAR-2014-0128,
accessible from www.regulations.gov.
13 Although the CASAC letter does not specify the statistic for the 5kg/ha-yr value, the analyses referenced in citing
that value, both the trajectory analyses and the ecoregion-scale summary of aquatic acidification results, focus on
ecoregion medians. So that is how it is interpreted here.
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that such a level of S deposition would afford protection for tree and lichen species,14 and aquatic
ecosystems. Regarding aquatic ecosystems, these members cite the ecoregion-scale estimates
(from the aquatic acidification REA) associated with median S deposition bins for the 90
ecoregi on-time period combinations (summarized in section 5.1.3.2 above) in conveying that for
S deposition below 5 kg/ha-yr, 80%, 80% and 70% of waterbodies per ecoregion are estimated to
achieve an ANC at or above 20, 30 and 50, respectively, in all ecoregion-time period
combinations (Sheppard, 2023, Response to Charge Questions, p 25).15 In recommending an
annual standards with a level in the range of 10-15 ppb, these members stated that such a
standard would "preclude the possibility of returning to deleterious deposition values as observed
associated with the emergence of high annual average SO2 concentrations near industrial sources
in 2019, 2020, and 2021," citing Figure 2-25 of the draft PA16 (Sheppard, 2023, Response to
Charge Questions, p. 24).
One CASAC member dissented from this recommendation for an annual SO2 standard17
and instead recommended adoption of a new 1-hour SO2 secondary standard identical in form,
averaging time, and level to the existing primary standard based on the conclusion that the
ecoregion 3-year average S deposition estimates for the most recent periods are generally below
5 kg/ha-yr and that those periods correspond to the timing of the existing primary SO2 standard
(established in 2010), indicating the reduced deposition to be a product of current regulatory
requirements (Sheppard, 2023, Appendix A, p. A-2).
With regard to N oxides and protection against deposition-related welfare effects of N,
the majority of CASAC members recommended revision of the existing annual NO2 standard to
a level "<10 - 20 ppb" (Sheppard, 2023, Response to Charge Questions, p.24). The justification
these members provide is related to their consideration of the relationship presented in the draft
14 In making this statement, these CASAC members cite two observational data studies with national-scale study
areas published after the ISA: one study is on lichen species richness and abundance and the second is on tree
growth and mortality (Geiser et al., 2019; Pavlovic et al., 2023). The lichen study by Geiser et al. (2019) relies on
lichen community surveys conducted at USFS sites from 1990 to 2012. The tree study by Pavlovic et al. (2023)
utilizes machine learning models with the dataset from the observational study by Horn et al. (2018) to estimate
confidence intervals for CLs for growth and survival for 108 species based on the dataset first analyzed by Horn
et al. (2018).
15 As seen in Table 7-1, these levels of protection are also achieved in ecoregion-time period combinations for which
the ecoregion median S deposition estimate is at or below 7 kg/ha-yr.
16 This figure is the priorversion of Figure 2-28 in section 2.4.2 of this final PA. The figure presents temporal trend
in distribution (box and whiskers) of annual average SO2 concentrations at SLAMS.
17 Also dissenting from this advice was a member of the CASAC Oxides of Nitrogen, Oxides of Sulfur and
Particulate Matter Secondary NAAQS Panel who was not also a member of CASAC (Sheppard, 2023, Response
to Charge Questions, p. 23).
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PA of median ecosystem N deposition with the weighted18 annual average NO2 metric
concentrations, averaged over three years, at monitoring sites linked to the ecosystems by
trajectory-based analyses and a focus on total N deposition estimates at or below 10 kg/ha-yr
(Sheppard, 2023, Response to Charge Questions, p. 24). These members additionally recognize,
however, that "when considering all ecoregions, there is no correlation between annual average
NO2 and N deposition" (Sheppard, 2023, Response to Charge Questions, p. 24). A focus on total
N deposition estimates at or below 10 kg/ha-yr appears to relate to consideration of total
maximum daily load19 analyses in four East Coast estuaries: Chesapeake Bay, Tampa Bay,
Neuse River Estuary and Waquoit Bay (Sheppard, 2023, Response to Charge Questions, pp. 12-
14 and 29). Levels identified for allocation of atmospheric N loading in the first three of these
estuaries were 6.1, 11.8 and 6.920 kg/ha-yr, and atmospheric loading estimated in the fourth was
below 5 kg/ha-yr (Sheppard, 2023, Response to Charge Questions, pp. 12-14). Another
consideration may be these members' conclusion that 10 kg N/ha-yr is "at the middle to upper
end of the N critical load threshold for numerous species effects (e.g., richness) and ecosystem
effects e.g., tree growth) in U.S. forests grasslands, deserts, and shrublands (e.g., Pardo et al.,
2011; Simkin et al., 2016) and thus 10 kg N/ha-yr provides a good benchmark for assessing the
deposition-related effects of NO2 in ambient air" (Sheppard, 2023, Response to Charge
Questions, p. 23).
One CASAC member disagreed with revision of the existing annual NO2 standard and
instead recommended adoption of a new 1-hour NO2 secondary standard identical in form,
averaging time and level to the existing primary standard based on the conclusion that the N
deposition estimates for the most recent periods generally reflect reduced deposition that is a
product of current regulatory requirements, including the existing primary standards for NO2 and
PM (Sheppard, 2023, Appendix A). This member additionally notes that bringing into attainment
the areas still out of attainment with the existing primary standard will provide further reductions
in N deposition. This member also notes his analysis of NO2 annual and 1-hour design values for
18 As described in section 6.2.4 above, the weighted metric is constructed by applying weighting to concentrations to
the monitors identified as sites of influence, with the weighting equal to the relative contribution of air from the
monitor location to the downwind ecoregion based on the trajectory analysis (section 6.2.4). Values of this metric
are not directly translatable to individual monitor concentrations or to potential standard levels.
19 Total Maximum Daily Loads or TMDLs are an approach under the Clean Water Act for allocating loading to a
waterbody that is projected to allow the waterbody to meet its water quality standards, as described further in
section 5.2.3 above.
20 The CASAC letter states that the Neuse River Estuary TMDL specified a 30% reduction from the 1991-95
loading estimate of 9.8 kg/ha-yr, yielding a remaining atmospheric load target of 6.9 kg/ha-yr (Sheppard, 2023,
Response to Charge Questions, p. 13).
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the past 10 years (2013-2022) that indicates the current primary NO2 standard to provide
protection for annual average NO2 concentrations below 31 ppb (Sheppard, 2023, Appendix A).
With regard to PM and effects related to deposition of N and S, the CAS AC focused on
the PM2.5 standards, and made no recommendations regarding the PM10 standard. In considering
the annual PM2.5 standard, the majority of CAS AC members recommended revision of the
annual secondary PM2.5 standard to a level of 6 to 10 |ig/m3. In describing their justification for
this range, these members focus on rates of total N deposition at/below 10 kg/ha-yr and total S
deposition at/below 5 kg/ha-yr - as in their advice regarding SO2 and NO2 standards
(summarized above) - that they state would "afford an adequate level of protection to several
species and ecosystems across the U.S." (Sheppard, 2023, Response to Charge Questions, p. 23).
In reaching this conclusion for protection from N deposition, the CAS AC majority cites studies
of U.S. forests, grasslands, deserts and shrublands that are included in the ISA. For S deposition,
the CASAC majority notes the Pavlovic et al. (2023) analysis of the dataset used by Horn et al.
(2018). Conclusions of the latter study, which is characterized in the ISA and discussed in
sections 5.3.2.3 and 7.2.2.2 above (in noting median deposition of 5-12 kg S/ha-yr in ranges of
species for which survival and/or growth was observed to be associated with S deposition), is
consistent with the more recent analysis in the 2023 publication (ISA, Appendix 6, sections 6.2.3
and 6.3.3).
As justification for their recommended range of annual PM2.5 levels (6-10 |ig/m3), this
group of CASAC members makes several statements regarding annual PM2.5 concentrations and
estimates of S and N deposition for which they cite several figures in the draft PA. Citing figures
in the draft PA with TDep deposition estimates and IMPROVE and CASTNET monitoring data,
they state "[i]n remote areas, IMPROVE PM2.5 concentrations in the range of 2-8 |ig/m3 for the
periods 2014-2016 and 2017-2019 correspond with total S deposition levels <5 kg/ha-yr (Figure
6-12), with levels generally below 3 kg/ha-yr, and with total N deposition levels <10 kg/ha-yr
(Figure 6-13)" (Sheppard, 2023, Response to Charge Questions, p. 23). With regard to S
deposition, these members additionally cite a figure in the draft PA as indicating ecosystem
median S deposition estimates at/below 5 kg/ha-yr occurring with PM2.5 EAQM-max values in
the range of 6 to 12 |ig/m3 (Sheppard, 2023, Response to Charge Questions, pp. 23-24). These
members additionally cite figures in the draft PA as indicating that areas of 2019-2021 total N
deposition estimates greater than 15 kg/ha-yr (in California, the Midwest and the East)
correspond with areas where the annual PM2.5 design values for 2019-2021 range from 6 to 12
|ig/m3, and other figures (based on trajectory analyses) as indicating ecosystem median N
deposition estimates below 10 kg N/ha-yr occurring only with PM2.5 weighted EAQM values
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below 6 |ig/m3,21 and PM2.5 EAQM-max values below 8 |ig/m3 (Sheppard, 2023, Response to
Charge Questions, pp. 23-24). The CASAC also notes that the correlation coefficient forN
deposition with the weighted EAQM is 0.52, while the correlation coefficient with the EAQM-
max is near zero (0.03). The bases for the N and S deposition levels targeted in this CASAC
majority recommendation are described in the paragraphs above.
One CASAC member recommended revision of the annual secondary PM2.5 standard to a
level of 12 |ig/m3 based on his interpretation of figures in the draft PA that present S and N
deposition estimates for five different 3-year time periods from 2001 to 2020. This member
observes that these figures indicate ecoregion median S and N deposition estimates in the last 10
years below 5 and 10 kg/ha-yr, respectively. This member concludes this to indicate that the
current primary annual PM2.5 standard provides adequate protection against long-term annual S
and N deposition-related effects (Sheppard, 2023, Appendix A).
Regarding the existing 24-hour PM2.5 secondary standard, the majority of CASAC
members recommend revision of the level to 25 ug/m3 or revision of the indicator and level to
deciviews and 20 to 25, respectively (Sheppard, 2023, Response to Charge Questions, p 25).
These members variously cite "seasonal variabilities" of "[ejcological sensitivities," describing
sensitive lichen species to be influenced by fog or cloud water from which they state S and N
contributions to be highly episodic, and visibility impairment (Sheppard, 2023, Response to
Charge Questions, p 25). These members do not provide further specificity regarding their
reference to lichen species and fog or cloud water. With regard to visibility impairment, these
members describe the EPA solicitation of comments that occurred with the separate EPA action
of reconsidering the 2020 decision on the secondary PM2.5 standard in providing requisite
protection from visibility effects as the basis for the specific recommendations they make
(Sheppard, 2023, Response to Charge Questions, p 25; 88 FR 5562-5663, January 27, 2023).22
21 As noted earlier in this section, weighted EAQM values are not directly translatable to concentrations at
individual monitors or to potential standard levels.
22 The context for solicitation of comment regarding the 24-hour PM2 5 secondary standards and the associated target
level of visibility protection is provided in the Federal Register notice for that action (88 FR 5558, January 27,
2023), a quotation from which is provided here:
With regard to visibility effects, while the Administrator notes that the CASAC did not recommend
revising either the target level ofprotection for the visibility index or the level of the current
secondary 24-hour PM2.5 standard, the Administrator recognizes that, should an alternative level
be considered for the visibility index, that the CASAC recommends also considering revisions to
the secondary 24-hour PM2.5 standard. In considering the available evidence and quantitative
information, with its inherent uncertainties and limitations, the Administrator proposes not to
change the secondary PM standards at this time, and solicits comment on this proposed decision.
In addition, the Administrator additionally solicits comment on the appropriateness of a target
level ofprotection for visibility below 30 dv and down as low as 25 dv, and of revising the level of
the current secondary 24-hour PM2.5 standard to a level as low as 25 jug/m3.
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One CASAC member dissented from this view and supported retention of the existing 24-hr
PM2.5 standard.
Among the CASAC comments on the draft PA regarding and recommendations for
revising the PA23 was the comment that substantial new evidence has been published since
development of the 2020 ISA that supports changes to the draft PA conclusions on N deposition
effects. More specifically, the CASAC noted new literature regarding taxonomic groups affected
by elevated N deposition, national-scale data documenting adverse ecological effects of elevated
N, and lower levels of N deposition and associated quantified ecological effects (Sheppard, 2023,
Response to Charge Questions, p. 7). The CASAC raised the issue of more recent studies in the
context of its comments on chapters 4 and 5 (Sheppard, 2023, Response to Charge Questions, pp.
7-17). In these comments, the CASAC cites a number of studies published after May 2017 and
not included in the ISA, along with many previously available studies that are described in the
ISA. The array of topics on which the CASAC recommended updates to the PA includes effects
of atmospheric N deposition on various aspects of managed terrestrial ecosystems (including
recognition of benefits and disbenefits) and on freshwater and coastal aquatic ecosystems;
indicators of acidification and ecosystem N status; comparisons of steady state and dynamic
environmental modeling; the influence of climate change; and temporal changes in atmospheric
deposition (and associated changes in soil and water quality parameters) on the ecological effects
of N and S deposition (Sheppard, 2023, Response to Charge Questions, pp. 8-17). As noted in
section 1.4 above, a number of aspects of chapters 4 and 5 in this final PA are revised from the
draft PA in consideration of the information that was emphasized by the CASAC in this way
while also referring to the ISA and studies considered in it.24
7.4 SUMMARY OF STAFF CONCLUSIONS
This section summarizes staff findings and identifies policy options for the
Administrator's consideration in this review of the secondary NAAQS for SOx, N oxides and
PM. These conclusions are based on consideration of the assessment and integrative synthesis of
the evidence, as summarized in the ISA, and the 2008 ISA, the 2009 PM ISA and AQCDs from
prior reviews, and the quantitative information on exposure and air quality summarized above, as
well as the advice of the CASAC. Taking into consideration the discussions above in this
chapter, this section addresses the following overarching policy question.
23 Consideration of CASAC comments and areas of the PA in which revisions have been made between the draft and
this final document are described in section 1.4 above.
24 More recent studies cited by the CASAC generally concern effects described in the ISA based on studies available
at that time. While the newer studies include additional analyses and datasets, the ISA and studies in it also
generally support the main points raised and observations made by the CASAC.
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• Do the current evidence and quantitative analyses call into question the adequacy of
protection from ecological effects afforded by the SO2, NO2 and PM secondary
standards? What alternate standards may be appropriate to consider with regard to
protection from ecological effects of SOx, N oxides and PM?
In considering this question, we first recognize what the CAA specifies with regard to
protection to be provided by the secondary standards. Under section 109(b)(2) of the CAA, the
secondary standard is to "specify a level of air quality the attainment and maintenance of which
in the judgment of the Administrator ... is requisite to protect the public welfare from any known
or anticipated adverse effects associated with the presence of such air pollutant in the ambient
air." The secondary standard is not meant to protect against all known or anticipated SO2 related
welfare effects, but rather those that are judged to be adverse to the public welfare, and a bright-
line determination of adversity is not required in judging what is requisite (78 FR 3212, January
15, 2013; 80 FR 65376, October 26, 2015; see also 73 FR 16496, March 27, 2008). Thus, our
consideration of the currently available information regarding welfare effects of the oxides of
sulfur and nitrogen and of PM is in this context, while recognizing that the level of protection
from known or anticipated adverse effects to public welfare that is requisite for the secondary
standard is a public welfare policy judgment to be made by the Administrator.
The general approach in a review of a secondary NAAQS, and accordingly in associated
PAs, involves, first, evaluation of the currently available information with regard to key
considerations for assessing risk of or protection for the effects of the criteria pollutant of focus.
In this evaluation, the PA considers the welfare effects of the pollutant, associated public welfare
implications, and also the quantitative information, such as that regarding exposure-response
relationships, and associated tools or metrics, as well as associated limitations and uncertainties.
The quantitative tools (e.g., metrics for effects and metrics for summarizing exposures) allow for
identification and assessment of exposures of concern and, correspondingly, of exposures
appropriate for focus in assessing protection afforded by the existing standard(s), and as
appropriate, in assessing potential alternatives. The latter part of the general approach in a review
and a PA is then consideration of the extent to which the existing standard(s) provides air quality
that would be expected to achieve such protection and, as appropriate, potential alternative
options (e.g., standard or standards) that could be expected to achieve this desired air quality.
This consideration goes beyond a focus on the key exposure metrics and concentrations of
potential concern to whether the indicator, form, averaging time, and level of the standard (or
suite of standards), together, provide the requisite protection.
As in NAAQS reviews in general, the extent to which the protection provided by the
current secondary standards for SOx, N oxides and PM are judged to be adequate depends on a
variety of factors, including science policy judgments and public welfare policy judgments.
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These factors include public welfare policy judgments concerning the appropriate benchmarks
on which to place weight, as well as judgments on the public welfare significance of the effects
that have been observed at the exposures evaluated in the welfare effects evidence. The factors
relevant to judging the adequacy of each standard also include the interpretation of, and
decisions as to the weight to place on, different aspects of the quantitative analyses of air quality,
exposure and risk, and any associated uncertainties. Additionally, to the extent multiple policy
options are identified that might be expected to achieve a desired level of protection, decisions
on the approach to adopt fall within the scope of the Administrator's judgment. In the end, the
Agency's decisions on the adequacy of the current secondary standard and, as appropriate, on
any potential alternative standards considered in a review, are largely public welfare policy
judgments made by the Administrator. Accordingly, the Administrator's conclusions regarding
the adequacy of the current standard will depend in part on public welfare policy judgments, on
science policy judgments regarding aspects of the evidence and exposure/risk estimates, and on
judgments about the level of public welfare protection that is requisite under the Clean Air Act.
Thus, the Administrator's final decisions draw upon the scientific information and analyses
about welfare effects, environmental exposures and risks, and associated public welfare
significance, as well as judgments about how to consider the range and magnitude of
uncertainties that are inherent in the scientific evidence and analyses.
In the discussion below, we address first the SO2 standard, and its adequacy with regard
to protection of the public welfare from effects of SOx in ambient air other than those associated
with ecosystem deposition of S compounds. Next, we address the extent of protection provided
by the SO2 standard from S deposition-related effects of SOx in ambient air, and consideration of
alternate standards for this purpose. In so doing, we focus primarily on the contribution of SOx
in ambient air to ecosystem acidification and particularly aquatic acidification. After addressing
SOx in this way, we next consider the NO2 standard and its adequacy with regard to protection of
the public welfare from effects of N oxides in ambient air other than those associated with
ecosystem N deposition, as well as the extent of protection provided by the NO2 standard from
deposition related effects of N oxides in ambient air and consideration of alternate standards for
this purpose. Lastly, we address the PM standards and the extent of their protection of the public
welfare from ecological effects. In each case, we recognize limitations in the available
information and tools and associated uncertainties, which vary in specificity and significance.
The existing SO2 secondary standard is 0.5 ppm, as a 3-hour average concentration not to
be exceeded more than once per year. The evidence of welfare effects at the time this standard
was established in 1971 indicated the effects of SOx on vegetation, most particularly effects on
foliar surfaces. The currently available information continues to document the occurrence of
visible foliar injury as a result of acute or short exposures (e.g., of a few hours), with greater
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exposures (repeated and/or of longer duration) affecting plant growth and yield. As summarized
in the ISA, there is "no clear evidence of acute foliar injury below the level of the current
standard" (ISA, section IS.4.1, p. IS-37).
We additionally note that across all sites (outside Hawaii, where air quality can be
influenced by volcanic emissions) during all years from 2000 through 2021, with the exception
of one occurrence in 2010, all design values for the existing 3-hour standard (not to be exceeded
more than once in a year) are below the standard level of 0.5 ppm. Further, 95% of values have
been below 0.2 ppm in each year of the 22-year period and below 0.1 ppm since 2011 (Figure 2-
27). As summarized in section 5.4.1 above, the available evidence does not indicate effects on
plants or lichens for short-term air concentrations within this distribution. Thus, as the available
evidence does not indicate ecological effects associated with the pattern of concentrations
allowed by the existing standard, we find that the currently available information, including that
newly available in this review, does not call into question the adequacy of protection provided by
the existing SO2 standard from the direct effects of SOx in ambient air. Further, we note that the
CAS AC unanimously made a similar conclusion that the current 3-hour standard provides
adequate protection against such direct effects on plants and lichens and should be retained
(Sheppard, 2023, p. 23). In light of these considerations summarized immediately above, we
conclude that the information available in this review does not call into question the adequacy of
the existing standard in providing protection against effects related to the direct action of SOx on
plants and lichens.
With regard to deposition-related effects, we note the range of ecoregion median
deposition estimates across U.S. ecoregions analyzed during the 20-year period from 2001
through 2020 extended up through 10 kg S/ha-yr to as high as 20 kg S/ha-yr during years when
the existing SO2 standard was met in all but one occasion (in 2011) in contiguous U.S., and when
design values for the standard (second highest 3-hour average in a year) ranged well below 500
ppb (as discussed in section 6.2.1 above). For example, in the earliest 3-yr period (2001-03),
when virtually all design values for the existing 3-hour standard were below 400 ppb and the 75th
percentile of design values was below 100 ppb (Figure 2-27), total S deposition was estimated to
be greater than 14 kg/ha-yr across the Ohio River valley and Mid-Atlantic states, ranging above
20 kg/ha-yr in portions of this area (Figure 6-11). The magnitude of S deposition estimates at the
90th percentile per ecoregion at sites assessed in the aquatic acidification REA was at or above 15
kg/ha-yr in half of the 18 eastern ecoregions and ranged up to nearly 25 kg/ha-yr during this time
period (Figure 7-2). The aquatic acidification risk estimates indicate, as illustrated in Figure 5-13
above, that this pattern of S deposition is associated with 20% to more than 50% of waterbody
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sites in affected eastern ecoregions25 being unable to achieve the lowest of the three acid
buffering capacity targets (ANC of 20 |ieq/L), indicating risks of potential public welfare
significance. Considering that these aquatic acidification risk estimates are associated with S
deposition during periods when the existing standard has been met (e.g., 2000-2002), it is
reasonably concluded that the current evidence and quantitative analyses call into question the
adequacy of the existing standard with regard to S deposition-related effects such as aquatic
acidification. Thus, we have evaluated options for potential alternative standards that may be
more appropriately associated with protection of welfare effects.
For the purposes of evaluating options for potential alternative standards for deposition-
related effects of SOx, we draw on the quantitative analyses and information described in
Chapter 5 and summarized in section 7.2.2 above. In this context and for our purposes within this
PA, we primarily focus on the aquatic acidification risk estimates, and particularly the ecoregion-
scale analyses. In focusing on the aquatic acidification risk estimates for our consideration of
acidification risks, we also note the linkages between watershed soils and waterbody
acidification, as well as terrestrial effects. Such linkages indicate that protecting waterbodies
from reduced acid buffering capacity (with ANC as the indicator) will also, necessarily, provide
protection for watershed soils, and may reasonably be expected to also contribute protection for
terrestrial effects. That notwithstanding, we recognize there to be limitations of the quantitative
analyses and associated uncertainties in their interpretation, as referenced in Chapter 5.
Accordingly, in focusing on specific ranges of deposition that may provide protection for
waterbody acid buffering capacity for our purposes here, we note there to be relatively greater
uncertainty associated with the lower deposition levels. Moreover, we recognize that, in the end,
judgments inherent in identification of such a range, include judgments related to the weighing
of uncertainties, as well as the consideration of the appropriate targets for public welfare
protection, and fall within the purview of the Administrator.
In focusing on the ecoregion-scale findings of the aquatic acidification REA, with
particular attention to the 18 well studied, acid-sensitive eastern ecoregions, we consider the
ecoregion median S deposition values at and below which the associated risk estimates indicated
a high proportion of waterbodies in a high proportion of ecoregions to achieve ANC values at or
above the three targets (20, 30 and 50 |ieq/L), as summarized in Tables 7-1 and 5-5, above. As
an initial matter, we note the approach taken by the CAS AC majority in considering these
estimates (summarized in section 7.3 above). These members considered the ecoregion-scale
analysis summary in Table 5-5 and took note of estimated achievement of ANC at or above the
25 Aquatic acidification risk estimates for the 2001-2020 deposition estimates in the eight western ecoregions
indicated ANC levels achieving all three targets in at least 90% of all sites assessed in each ecoregion (Table 5-4).
Ecoregion median deposition estimates were at or below 2 kg/ha-yr in all eight western ecoregions (Table 5-3).
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three ANC targets in 80% (for ANC of 20 and 30) or 70% (for ANC of 50) of waterbody CL
sites in all ecoregion-time periods for which the ecoregion median S-deposition was below 5
kg/ha-yr (Sheppard, p. 25 of the Response to Charge Questions). We note that the results for
ecoregi on-time period combinations for median S-deposition in the 18 eastern ecoregi ons at or
below 7 kg/ha-yr also achieve these percentages of waterbodies achieving the three ANC targets
(as seen in Tables 7-1 and 5-5 above).26 The results for median S deposition at or below 7 kg/ha-
yr further indicate that 90% of waterbodies per ecoregion achieve ANC at/above targets of 20, 30
and 50 in 96%, 92% and 82%, respectively, of eastern ecoregion-time period combinations. For
median S deposition at or below 9 kg/ha-yr, the percentages of ecoregions meeting or exceeding
the ANC targets declines to 87%, 81% and 72% (as summarized in section 7.2.2.2., above).
We additionally consider the temporal trend or pattern of ecoregion-scale risk estimates
across the five time periods in relation to the declining S deposition estimates for those periods.
In so doing, we note the estimates of appreciably improved acid buffering capacity (increased
ANC) by the third time period (2010-2012) and so consider the REA risk and deposition
estimates for these and subsequent periods. The S deposition estimated to be occurring in the
2010-2012 time period included ecoregion medians (based on CL sites) ranging from 2.3 to 7.3
kg/ha-yr in the 18 eastern ecoregions and extending down below 1 kg/ha-year in the 7 western
ecoregions; the highest ecoregion 90th percentile was approximately 8 kg/ha-yr (Table 7-2,
Figure 7-2). For this pattern of deposition, more than 70% of waterbodies per ecoregion are
estimated to be able to achieve an ANC of 50 ueq/L in all 25 ecoregions (Figure 7-1, left panel),
and more than 80% of waterbodies per ecoregion in all ecoregions are estimated to be able to
achieve an ANC of 20 ueq/L (Figure 7-1, right panel). Further, by the 2014-2016 period, when
both median and 90th percentile S deposition in all 25 ecoregions was estimated to be at or below
5 kg/ha-yr, more than 80% of waterbodies per ecoregion are estimated to be able to achieve an
ANC of 50 ueq/L in all 25 ecoregions (more than 90% in 23 of the 25 ecoregions) and more than
90% of waterbodies per ecoregion in all ecoregions are estimated to be able to achieve an ANC
of 20 ueq/L (Figure 7-1, right panel).
The estimates of acid buffering capacity achievement for the 2010-12 period deposition
— achieving the ANC targets in at least 70% to 80% (depending on the target) of waterbodies
per ecoregion — are consistent with the objectives identified by the CAS AC (in considering
estimates for the 18 eastern ecoregions). The advice from the CASAC emphasized ecoregion
ANC achievement estimates of 70%, 80% and 80% for ANC targets of 50, 30 and 20 ueq/L,
respectively. The estimates for the later time period are somewhat better, with all ecoregions
26 Ecoregion median deposition was below 2 kg S/ha-yr in all 35 ecoregion-time period combinations for the eight
western ecoregions (Table 5-4).
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estimated to achieve the ANC targets in at least 80% to 90% (depending on the target) of
waterbodies per ecoregion. Both of these ecoregion-scale ANC achievement results (70% to 80%
and 80%) to 90%) may be reasonable to consider with regard to acid buffering capacity objectives
for the purposes of protecting ecoregions from aquatic acidification risk of a magnitude with
potential to be considered of public welfare significance.
In considering the aquatic acidification risk estimates at the ecoregion-scale for the
purpose of identifying a range of ecoregion deposition estimates on which to focus in identifying
options for potential secondary standards, we consider both sets of potential objectives for acid
buffering capacity intended to provide an appropriate degree of protection from S deposition-
related effects related to aquatic acidification. With regard to deposition levels, we consider
estimates for both the median and for an upper percentile on the distribution of values at sites
analyzed in each ecoregion (e.g., the 90th percentile). In so doing, we recognize that the sites
estimated to receive the higher levels of deposition are those most influencing the extent to
which the potential objectives for aquatic acidification protection are or are not met. With this in
mind, we note the appreciable reduction in the 90th percentile deposition estimates, as well as the
median, for REA sites in each of the 25 ecoregions analyzed. Although the ecoregion 90th
percentile and median estimates ranged up to 22 and 15 kg/ha-yr in the 2001-2003 time period,
both types of estimates fall below approximately 5 to 8 kg/ha-yr by the 2010-2012 period, and
below 5 kg/ha-yr in later years (Figure 7-2).
Based on all of the above, including the ecoregion-scale acid buffering objectives
identified by the CASAC (more than 70% to 80% of waterbody sites in all ecoregions assessed
achieving or exceeding the set of ANC targets), the temporal trends in REA aquatic acidification
estimates and the temporal trend in ecoregion S deposition, we estimate that such objectives
might be expected to be met when ecoregion median and upper (90th) percentile deposition
estimates at sensitive ecoregions are generally at and below about 5 to 8 kg/ha-yr. In so doing,
we additionally recognize uncertainties associated with the deposition estimates at individual
waterbody sites, and with the associated estimates of aquatic acidification risk, as summarized in
section 5.1.4 above. As noted in section 7.2.2.2 above, consideration of the case study analyses
as well as the ecoregion-scale results for both the ecoregion-time period and temporal
perspectives, indicates a range of S deposition below approximately 5 to 8 or 10 kg/ha-yr, on an
areawide basis, to be associated with a potential to achieve acid buffering capacity levels of
interest in an appreciable portion of acid sensitive areas. Based on this identification of
deposition rates at and below about 5 to 8 or 10 kg/ha-yr, we next consider the information
regarding patterns of monitoring site SO2 concentrations associated with these patterns of S
deposition.
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In considering options for a standard focused on S deposition, we have focused on an
averaging time longer than the three hours of the current standard. In so doing, we recognize, in
light of the second maximum form of the existing standard and its relatively short averaging
time, that this option might reasonably be considered a less than optimal approach for controlling
long-term atmospheric deposition of S compounds (and we note the majority of CAS AC advice
regarding an annual average metric for this purpose). As discussed in section 7.2.2.3 above, the
analyses described in Chapter 6 also indicates moderate to strong correlations for S deposition
with an annual air quality metric. Accordingly, we conclude it may be more appropriate to
consider adoption of a new SO2 standard with a longer averaging time and more stable form, as
well as level, such as a standard with an averaging time of one year, and a form of the average of
annual averages across three consecutive years.27
In considering options for an annual secondary standard based on consideration of S
deposition-related effects, we first note the complexity of identifying a national ambient air
quality standard focused on protection from national patterns of atmospheric deposition of
concern to the public welfare (rather than on protection from patterns of ambient air
concentrations of concern). For example, atmospheric deposition (ecosystem loading) of S, is, in
a simple sense, the product of atmospheric concentrations of S compounds, factors affecting S
transfer from air to surfaces, and time. Further, atmospheric concentrations in an ecosystem are,
themselves, the result of emissions from multiple, distributed sources (near and far), atmospheric
chemistry, and transport. Accordingly, consideration of the location of source emissions and
expected pollutant transport (in addition to the influence of physical and chemical processes) is
important to understanding relationships between SO2 concentrations at ambient air monitors and
S deposition rates in sensitive ecosystems of interest. Further, we recognize that to achieve a
desired level of S deposition control in sensitive ecosystems, SO2 emissions must be controlled
at their sources. Accordingly, it is reasonable to consider surveillance for a secondary standard to
be at regulatory SO2 monitors generally sited near large SO2 sources.
Recognizing the variation across the U.S. in locations and magnitude of sources of SOx,
as well as the processes that govern that transformation of source emissions to eventual
deposition of S compounds, we consider the key findings from the suite of analyses summarized
in Chapter 6. These include consideration of relationships between S deposition estimates and
SO2 concentrations near SO2 monitors (both in remote Class I areas and at NAAQS surveillance
monitors which are often near large sources) as well as relationships between ecoregion S
deposition estimates and SO2 concentrations at upwind sites of influence, identified by trajectory
27 Standards established in the last one to two decades have generally utilized 3-year forms in recognition of the
importance of stability in air quality management programs (e.g., 88 FR 3198, January 15, 2013).
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analyses to account for the relationship between upwind concentrations near sources and
deposition in areas more distant (sections 6.2.2 through 6.2.4, above). As evidence of the
influence of SO2 in ambient air on S deposition, all of these analyses demonstrated there to be an
association between SO2 concentrations and nearby or downwind S deposition. The correlation
coefficients are strongest in the East and in the earliest two to three time periods when deposition
rates and air concentrations were much higher compared to the West and to more recent years,
when deposition rates and concentrations are much lower, as described in Chapter 6.
As discussed in section 7.2.2.3 above, we recognize that the trajectory-based analyses and
the stronger correlations for the EAQM-weighted compared to the EAQM-max illustrate the fact
that atmospheric loading is a primary determinant of atmospheric deposition, as well as the
complexity of how to consider concentrations at individual monitors, with variable spatial
distribution, in relation to deposition rates. We additionally consider the parallel temporal trends
in SO2 emissions, annual SO2 concentrations, and annual average estimates of S deposition over
the 20-year time period from 2000-2020 (section 6.2.1). These trends additionally document the
expected strong correlation of SO2 emissions with S deposition. With regard to monitor
concentrations, we note the appreciably flatter distribution of concentrations prevalent in the
latter 10 years of the period in comparison to the initial years, and take note of the fact that the S
deposition rates during this time period are appreciably reduced from those in the earlier decade
(Figure 7-5). These parallel patterns indicate the role of the central part of the distribution of U.S.
monitor concentrations as a potential influence on the higher deposition levels of the past. The
much higher atmospheric loading in the first decade (evidenced by the deposition estimates prior
to 2010) is associated with a different distribution of ambient air SO2 concentrations than in the
second decade. The distribution in the first decade is characterized by a more broad or normal
distribution, while the distribution in the latter decade is more narrow or skewed. Further, we
take note of the parallel temporal trends of ecoregion S deposition estimates and the REA aquatic
acidification risk estimate across the five time periods analyzed (as discussed above). With all of
these linkages in mind, we have considered what the current information indicates regarding
options for a standard that may provide protection from aquatic acidification-related risks of S
deposition in sensitive ecoregions.
For an annual average standard, based on the air quality analyses and recognizing the
various limitations and associated uncertainties, we identify a range of levels extending down
from 15 ppb to a level as low as 5 ppb, based on a recognition of the pattern of ambient air SO2
concentrations across the U.S. in recent times. As discussed above, the current pattern involves a
much compressed distribution of concentrations with the bulk of the distribution well below this
range of levels. We additionally recognize that the more recent distribution of concentrations is
associated with the more recent deposition patterns and the corresponding aquatic acidification
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analysis results as discussed in section 7.2.2.2 above (Figures 7-1 and 7-2 above). The
information providing support across this broad range, as discussed in sections 7.2.2.2 and
7.2.2.3, varies. Identification of levels in the upper part of the range, generally from 10-15 ppb
places greater weight on an objective of ecoregion median and 90th percentile S deposition
values below approximately 5 to 8 kg/ha-yr, and on consideration of the trajectory-based
analyses of the 20-year dataset of ecoregion S deposition and SO2 concentrations (SO2 annual
EAQM-max [Figure 7-4]) at upwind sites of influence, and also on uncertainties associated with
potential limitations in the data analyzed (including with regard to representation of source
locations in the earlier years). Consideration of potential levels in the lower part of the range,
generally from 10 down to 5 ppb, would place greater weight on an objective of ecoregion
median and 90th percentile S deposition values below 5 kg/ha-yr, and on consideration of the
trend analyses that indicate 3-year average annual SO2 concentrations since 2010 and 2014 were
nearly all below 10 ppb. Given the much reduced correlation of S deposition estimates with SO2
concentrations in the more recent years (e.g., Table 6-4 above), however, we recognize
appreciably greater uncertainty associated with interpretation of relationships between S
deposition and ambient air SO2 concentrations below 10 ppb (and with related conclusions
regarding deposition levels that might be expected to be associated with such concentrations) and
thus with a potential level in the lower part of the range.
In identifying this broad range of levels for consideration with a new annual average SO2
secondary standard, we take note of a number of limitations in our information that contribute
uncertainties that vary in magnitude and type across this range. In general, we recognize
uncertainty in identifying a level within this range for a standard that may be expected to achieve
a particular degree of S deposition-related protection for ecological effects. This uncertainty is
coupled with the uncertainty associated with estimates of aquatic acidification risk in
waterbodies across the U.S. associated with specific deposition levels, including with regard to
interpretation of risk associated with different levels of acid buffering capacity. Together, we
consider there to be greater uncertainty associated with identification of levels in the lower part
of the broad range identified here.
We additionally take note of the advice from the CASAC on options for a secondary
standard to provide protection from S deposition-related ecological effects. As described in
section 7.3 above, the majority of the CASAC recommended adoption of an annual SO2 standard
with a level within the range of 10 to 15 ppb. In so doing, the CASAC majority noted the
ecoregion median deposition levels below 5 kg/ha-yr in the periods 2014-2016 and 2018-2020,
and conveyed that a standard level in this range (10-15 ppb) would afford protection to tree and
lichen species as well as waterbodies, further stating that such a standard would "preclude the
possibility of returning to deleterious deposition values" that these members indicate to be
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associated with outlier SO2 concentrations observed in 2019-2021 near a location of industrial
sources (Sheppard, Response to Charge Questions, pp. 24-25). The minority of the CASAC
recommended adoption of a new 1-hour secondary standard identical in all respects to the
existing primary standard form (Sheppard, 2023, pp. 24-25 and Appendix A).
In considering the CASAC advice on levels for an annual average SO2 standard, we note
that the range we have identified above for the option of a new annual SO2 standard includes the
range of levels (10-15 ppb) recommended by the majority of the CASAC (as summarized in
section 7.3 above). We additionally note that, as is generally the case here, the information
considered by the CASAC majority in drawing its conclusion also focused on an annual average
SO2 metric with a form that involved averaging over three consecutive years.28 Further, we note
an air quality similarity of the identified range for a new annual average standard with the
recommendation of the CASAC minority (to establish a 1-hour secondary standard identical to
the primary standard) based on observations regarding the relationship between annual average
SO2 concentrations and design values for the 1-hour primary standard indicating that annual
average concentrations are generally at or below 10 ppb in areas meeting the current 1-hour
primary standard (Figure 2-29).
We additionally consider the extent of control for short-term concentrations (e.g., of three
hours duration) that might be expected to be provided by an annual secondary SO2 standard. In
so doing, we note that in areas and periods when the annual SO2 concentration (annual average,
averaged over three years) is below 5-15 ppb, design values for the existing 3-hour standard are
well below the standard level of 0.5 ppm (Figure 2-29). Thus, we note that in considering
adoption of a new annual standard, it may be appropriate to consider this as an additional
secondary SO2 standard or to consider it in replacement of the existing 3-hour standard given
that peak concentrations are currently controlled to lower concentrations, likely in response to
the primary standard. We recognize, however, that which of these options — replacing or
augmenting the 3-hour standard (with an annual standard) — is concluded to be appropriate and
what value within the ranges of levels identified for an annual standard might be appropriate, are
in the end decisions made by the Administrator, in light of judgments associated with weighing
of the differing aspects of the evidence and air quality information and how to consider their
associated uncertainties and limitations.
Turning to consideration of the secondary standard for oxides of N, we note that the
existing secondary standard for oxides of N is 53 ppb, as an annual mean in a single year. The
28 A 3-year form is common to NAAQS adopted over the more recent past. This form provides a desired stability to
the air quality management programs which is considered to contribute to improved public health and welfare
protection (e.g., 78 FR 3198, January 15, 2013; 80 FR 65352, October 26, 2015; 85 FR 87267, December 31,
2020).
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evidence of welfare effects at the time this standard was established in 1971 indicated the direct
effects of N oxides on vegetation, most particularly effects on foliar surfaces. The currently
available information continues to document such effects, as summarized in sections 4.1 and
5.4.2 above. With regard to NO2 and NO, the evidence does not indicate effects associated with
ambient air concentrations allowed by the existing standard, as summarized in section 7.1.2
above. Accordingly, the evidence related to the N oxides, NO2 and NO, does not call into
question the adequacy of protection provided by the existing standard.
With regard to the N oxide, HNO3, however, we recognize the evidence of effects
associated with air concentrations and associated HNO3 dry deposition on plant and lichen
surfaces, and that there is uncertainty as to the extent to which exposures associated with such
effects may be allowed by the existing NO2 standard, as discussed in section 7.1.2 above (section
5.4.2 and Appendix 5B, sections 5B.4). The limited evidence, however, is not clear as to the
potential for such effects to have been elicited by air quality that met the standard. Thus, the
available information — while documenting the potential for HNO3 in ambient air to cause harm
— is not clear as to the extent to which it may call into question or support the adequacy of
protection provided by the current NO2 standard. The experimental evidence also does not
provide clear indication of ecological effects associated with exposure concentrations that might
be allowed by the current standard. We note, however, that depending on judgments as to the
weight to place on specific aspects of the evidence and air quality analyses, and associated
uncertainties, it may be judged appropriate to consider a more restrictive NO2 standard that
might also be considered to offer the potential for some additional protection from effects related
to ecosystem N deposition (as discussed below), and also the potential for increased protection
from effects related to airborne nitric acid effects on biota surfaces for which the quantitative
evidence is less clear. With regard to the latter, we take note of the relatively high dry deposition
velocity of HNO3, relative to other N-containing compounds and the evidence from field surveys
indicating its potential for damage (section 7.1.2). Accordingly, in addition to concluding it is
appropriate to consider retaining the existing NO2 standard, we additionally identify a revision
option for the secondary standard forN oxides in consideration of HNO3-related effects in
combination with consideration of ecosystem deposition-related effects discussed below.
In considering options for revision of the secondary standard for N oxides, we have also
evaluated the larger information base of effects related to N deposition in ecosystems. In this
context, we recognize that ecosystem N deposition is influenced by air pollutants other than N
oxides. More specifically, as discussed in sections 6.1 and 6.2.1 above, NH3 (which is not a CAA
criteria pollutant) also contributes to N deposition. The extent of this contribution varies
appreciably across the U.S. and has increased during the past 20 years. Thus, we take note of the
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fact that a secondary standard for N oxides cannot be expected to effectively control total N
deposition.
With regard to N deposition associated with N oxides, the historical trend analyses in
section 6.2.1 document the reductions in N deposition that correspond with reductions in
emissions of N oxides. These analyses additionally document the increasing role of NH3 in N
deposition since approximately 2010 and the co-occurring tempering of N deposition reductions
such that the declining trend that is observed from 2000 through 2010 appears to have leveled off
in the more recent years. Further, the areas of highest N deposition appear to correspond to the
areas with the greatest deposition of NH3 (Figure 7-8 above). This associated lessening influence
of N oxides on total N deposition is also evidenced by the poor correlations between N
deposition and annual average NO2 concentrations (reported in sections 6.2.3 and 6.2.4 above),
most particularly in more recent years and at eastern sites. It may be the result of increasing
emissions of NH3 in more recent years and at eastern sites (section 2.2.3 and Figure 6-5).
Together, this finding, particularly since 2010 (and in more localized areas prior to that),
complicates our evaluation of the current information with regard to protection from N
deposition-related effects that might be afforded by the secondary standard for N oxides. That is,
while the information regarding recent rates of ecoregion N deposition may in some individual
areas (particularly those for which reduced N, specifically NH3, has a larger role) indicate rates
greater than the range of values identified above for consideration (e.g., 7-12 kg/ha-yr based on
the considerations in section 7.2.3 and the benchmark of 10 kg/ha-yr, as conveyed in the advice
from the CASAC), the extent to which this occurrence relates to the existing NO2 secondary
standard is unclear.
That notwithstanding, we additionally consider the currently available information related
to deposition-related effects of N oxides on ecosystems, as discussed in section 7.2.3 above. In
so doing, we recognize the complexities and challenges associated with quantitative
characterization of N enrichment-related effects in terrestrial or aquatic ecosystems across the
U.S. that might be expected to occur due to specific rates of atmospheric deposition of N over
prolonged periods, and the associated uncertainties. Some complexities associated with terrestrial
deposition are similar to those for aquatic deposition, such as untangling the impacts of historic
deposition from what might be expected from specific annual deposition rates absent that history,
while others related to available quantitative information and analyses differ. Further, with
regard to many aquatic systems with non-air contributing sources, we recognize the complexity
of estimating the portion of N inputs, and associated contribution to effects, derived from
atmospheric sources.
Additionally, there are complexities in risk management and policy decisions, including
with regard to identifying risk management targets or objectives for an ecosystem stressor like N
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enrichment, for which as the CASAC recognized, in terrestrial systems, there are both "benefits
and disbenefits" (Sheppard, 2023, p. 8). As noted by the CASAC, "[bjenefits include fertilization
of crops and trees and the potential for improved sequestration of carbon in soils and plant
biomass" (Sheppard, 2023, p. 8). This also complicates conclusions regarding the extent to
which some ecological effects may be judged adverse to the public welfare. Further, with regard
to aquatic systems, identification of appropriate risk management targets or objectives for
consideration of the relative protection of secondary standards is complicated by the effects of
historical deposition that have influenced the current status of soils, surface waters, associated
biota, and ecosystem structure and function. For example, changes to ecosystems that have
resulted from past, appreciably higher levels of atmospheric deposition have the potential to
affect how the ecosystem responds to current, lower levels of deposition or to still further
reduced N inputs in the future.
In exploring the potential for a secondary standard to limit N deposition associated with
N oxides, we take note of the trends of ecoregion N deposition which differ for ecoregions in
which N deposition is driven by reduced N compared to those where reduced N comprises less of
the total (e.g., Figures 7-6 and 7-7). The N deposition trends in the latter ecoregions, which
include reductions in the upper part of the distribution of ecoregion medians, as well as lower N
deposition in the second as compared to the first decade of the 20-year period (corresponding to
the decline in NO2 emissions), appear to document the influence that NO2 emissions and
concentrations have had on N deposition. In light of this relationship and of the recognition that
recent levels of N deposition associated with N oxides are much lower than they were in the
early part of the 20-year period, we consider the option of a revision of the existing NO2 standard
level to maintain some associated protection from deposition-related effects of N oxides.
With regard to this option, we note the mixed advice from the CASAC regarding an NO2
annual standard in consideration of N deposition effects (section 7.3 above). The CASAC
majority recommended revision of the existing annual NO2 standard level to a value below 10 to
20 ppb (Sheppard, 2023, p. 24). As described in section 7.3 above, however, the basis for this
advice relates to a graph in the draft PA of the dataset of results from the trajectory-based
analyses for the weighted annual NO2 metric (annual NO2 EAQM-weighted). These CASAC
members additionally recognized that these results found no correlation between the ecoregion
deposition and the EAQM-weighted values at upwind locations, and as described in section
6.2.4.3 above the correlation coefficients are negative for N deposition with both annual NO2
EAQMs (-0.17 and -0.06; Table 6-10). While the correlation for the eastern ecoregions and the
weighted metric is as high as 0.61 in the 2001-2003 period, it declines for each subsequent time
period, and is negative for the most recent period. Further, as noted in section 7.2.2.3 above, the
weighted metric values from the trajectory-based analyses are not directly translatable to
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individual monitor concentrations or to potential standard levels. Accordingly, the information
highlighted by these members for relating N deposition levels to ambient air concentrations
cannot reasonably be concluded to provide support for the identified levels. The minority
CASAC member recommended revision of the secondary NO2 standard to be identical to the
primary standard based on their conclusion that the recent N deposition levels meet desired
targets and that the primary standard is currently the controlling standard (Sheppard, 2023,
Appendix A).
The air quality information regarding annual average NO2 concentrations at SLAMS
monitors indicates more recent NO2 concentrations are well below the existing standard level of
53 ppb. As noted in section 7.2.3.3 above, the temporal trend figures indicate that, subsequent to
2011-2012, when median N deposition levels in 95% of the eastern ecoregions of the continental
U.S. have generally been at/below 11 kg N/ha-yr, annual average NO2 concentrations, averaged
across three years, have been at/below 35 ppb. Recognizing that among the NO2 primary and
secondary NAAQS, the 1-hour primary standard (established in 2010) may currently be the
controlling standard for ambient air concentrations, we note that annual average NO2
concentrations, averaged over three years, in areas that meet the current 1-hour primary standard
have generally been below approximately 35 to 40 ppb. We note that an annual standard with a
level within this range would appear to have conceptual consistency with the advice from the
CASAC minority. Thus, for an option to reflect the recent pattern in NO2 concentrations, and any
associated influence on N deposition, as well as to provide additional protection from HNO3"
related effects that may be associated with higher NO2 concentrations, it may be appropriate to
consider an option for revision of the secondary NO2 standard to an annual standard (averaged
across three consecutive years) with a level below the current level of 53 ppb, within a range
extending down to 40-35 ppb.
While characterization of such an option as providing some level of protection from N
deposition related to N oxides is supported by the quantitative air quality analyses and
information regarding air quality and atmospheric chemistry (as discussed in chapter 6), and
accordingly, such a standard might be expected to provide some degree of protection from
deposition related effects associated with N oxides, we recognize significant uncertainty in
understanding the level of protection that would be provided. In addition to the complexity
associated with a judgment on the appropriate target level of protection for a national standard
for nitrogen, given its contribution to benefits and disbenefits, as well as its multiple sources
other than atmospheric deposition (discussed in section 7.2.3 above), this uncertainty relates
prominently to the influence of NH3 on total N deposition separate from that of N oxides, and
which in some areas of the U.S. appears to be dominant (as discussed in section 7.2.3.3 above).
Further, the extent to which the relative roles of these two pollutants (N oxides and NH3) may
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change in the future is not known. These factors together affect the extent of support for, and
contribute significant uncertainty to, a judgment as to a level of N oxides in ambient air that
might be expected to provide requisite protection from N deposition-related effects on the public
welfare. Thus, the revision option identified here would involve several judgments on the
weighing of information and associated uncertainties in several areas. These areas include, but
are not limited to the extent to which effects related to HNO3 may be expected to occur as a
result of NO2 concentrations above the existing standard, and the public welfare significance of
such effects; the extent to which a lower annual NO2 standard could be expected to affect total N
deposition across the U.S.; and, the extent of the evidence related to welfare effects associated
with deposition related specifically to N oxides. Accordingly, while an option for revision has
been identified, in light of considerations raised above, the support for this option is not strong.
Lastly, we turn to consideration of the existing standards for PM2.5. As an initial matter,
and in light of the discussion in section 7.1.3 above, we do not find the available information to
call into question the adequacy of protection afforded by the secondary PM2.5 standards from
direct effects and deposition of pollutants other than S and N compounds. The evidence indicates
such effects to be associated with conditions associated with concentrations much higher than the
existing standards.
Regarding S deposition, we note the findings of the air quality analyses in Chapter 6 that
indicate appreciable variation in associations between S deposition and PM2.5, and generally low
correlations and also note the varying composition of PM at sites across the U.S. which may be a
factor in the variability in associations. We additionally take note of the atmospheric chemistry
which indicates the dependency of S deposition on airborne SOx, as evidenced by the parallel
trends of SO2 emissions and S deposition. Based on all of these considerations, we find that
protection of sensitive ecosystems from S deposition may be more effectively achieved through a
revised SO2 standard than a standard for PM.
With regard to N deposition, as discussed in section 7.2.3.3 above, and in more detail in
Chapter 6, air quality analyses of relationships found low to barely moderate correlations
between N deposition estimates and annual average PM2.5 concentrations at nearby or upwind
locations based on the full 20-year dataset, with higher correlations for the early years of the 20-
year period and low or no correlation in the later years. We also note the variable composition of
PM2.5 across the U.S. which contributes to geographic variability in the relationship between N
deposition and PM2.5 concentrations. For example, as discussed in section 6.4.2, an appreciable
percentage of PM2.5 mass does not contribute to N deposition, and the highest percentage of
PM2.5 represented by N compounds at CSN sites in 2020-2022 is 30% (Riverside County, CA).
In fact, at an appreciable number of CSN sites, the fraction of PM2.5 represented by N
compounds is less than 10%. This variability in percentage of PM2.5 represented by N (or S)
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containing pollutants contributes a high level of uncertainty to our understanding of the potential
effect of a PM2.5 standard on patterns of N deposition. In light of these considerations and the
conclusions above regarding potential for control of S and N deposition from SOx and N oxides
standards, we conclude that the available evidence, as evaluated in this PA, is reasonably judged
insufficient to provide a basis for revising the PM2.5 annual standard with regard to effects of S
and N deposition related to PM.
With regard to options for the annual PM2.5 standard, we note that the CAS AC did not
reach consensus, and provided two sets of recommendations for a revised annual PM2.5 standard
(section 7.3 above). The CASAC majority recommended revision of the standard level to a value
within the range from 6 to 10 |ig/m3, although we note that the specific rationale for the ends of
this range is unclear. The justification provided includes observations regarding annual average
PM2.5 concentrations in locations for which total N (and S) deposition falls within, and falls
above, the preferred deposition ranges identified (Sheppard, 2023, pp. 23-24). For example, the
range of annual average PM2.5 concentrations these members identify to be associated with
deposition within their preferred N deposition range (PM2.5 concentrations from 2 to 8 |ig/m3 and
total N deposition at/below 10 kg/ha-yr, based on draft PA graphs of 2014-16 and 2017-19
values) overlaps with the concentration range they identify as being associated with deposition
above that range (PM2.5 concentrations from 6 to 12 |ig/m3 and total N deposition above 15
kg/ha-yr in "hotspots" of California, the Midwest and the East, based on draft PA maps depicting
2019-21 deposition estimates and annual PM2.5 design values).29 We note that this overlap
indicates a weakness in the associations of N deposition with PM concentrations (and scatter in
the dataset) in some areas of the U.S.30 Further, the expanded air quality analyses in this final PA
indicate only low correlation for total N deposition estimates with annual average PM2.5 design
values in the last 10 years (e.g., r values are less than 0.40 for 2014-16 and 2018-20 at SLAMS
[Table 6-7]). Among other factors, this reduction in correlation may relate to the reduced
presence of N compounds in PM2.5 mass in the more recent period, as discussed in section 6.4.2
above. In total, we take note of the appreciable uncertainty regarding relationships of N (and S)
deposition with PM2.5 concentrations across the U.S. The minority CASAC member
recommended revision of the secondary annual PM2.5 standard level to equal the primary
29 For example, the justification provided for the range of levels recommended by the CASAC majority for a revised
PM2 5 annual standard (6 to 10 |ig/m3) refers both to annual average PM2 5 concentrations (3-yr averages) ranging
from 2 to 8 |ig/m3 in 27 Class I areas (as corresponding to N deposition estimates at or below 10 kg/ha-yr) and to
annual average PM2 5 concentrations (3-year averages) ranging from 6 to 12 |ig/m3 (at design value sites in areas
of N deposition estimates greater than 15 kg/ha-yr), as summarized in section 7.3 above.
30 As discussed in section 6.2.1 above, these areas of highest N deposition estimates coincide with areas of the U.S.
in which NH3 deposition is also the highest (Figure 6-13, bottom and Figure 6-18, bottom), and also where NH3
deposition is estimated to comprise the majority of total N deposition (Figure 7-8; 7.2.3.3).
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standard level of 12 |ig/m3 based on their conclusion that the recent N (and S) deposition levels
meet desired targets and that the primary annual PM2.5 standard is currently the controlling
standard for annual PM2.5 concentrations (Sheppard, 2023, Appendix A).
Although we recognize there to be appreciable uncertainty associated with a basis for a
revised annual PM2.5 standard related to effects of S and N deposition related to PM, as discussed
above, we also recognize that decisions on the NAAQS also draw on judgements with regard to
the weight to place on various uncertainties, and so, in light of all the considerations described
above, and based on the air quality information that suggests some low to moderate correlation
of N-deposition with the annual PM2.5 metric, that is stronger in the West (as summarized in
section 7.2.3.3 above), we suggest that it may be appropriate to consider some revision of the
level of the PM2.5 annual secondary standard. For this option, it may be appropriate to consider
levels below the current level of 15 |ig/m3, such as a level of 12 |ig/m3 (the level of the currently
controlling primary standard), recognizing uncertainty with regard to the extent of N deposition-
related control and associated protection that might be achieved. In so doing, we note that this
option is that recommended by the CASAC minority.
With regard to other PM standards, we take note of the lack of information that would
call into question the adequacy of protection afforded by the existing PM10 secondary standard
for ecological effects, and thus conclude it is appropriate to consider retaining this standard
without revision. As to the 24-hour PM2.5 standard, we note the advice of the majority of
CASAC, summarized in section 7.3 above, with regard to revision of this standard to a lower
level or to an indicator of deciviews. In conveying these recommendations, these CASAC
members generally expressed the view that the existing standard was not adequate to protect
against short-term events. In justifying this view, the members make the general statements that
there are "seasonal variabilities" in "ecological sensitivities," and that sensitive lichen species are
dependent on fog or cloud water-related deposition, in which the members state S and N
contributions can be highly episodic. These members do not, however, provide further specificity
regarding the basis for these references to lichen species and fog or cloud water. While the
available evidence as characterized in the ISA recognizes there to be N deposition associated
with cloud water or fog (ISA, Appendix 2), it does not provide estimates of this deposition or
describe associated temporal variability, or specifically describe related effects on biota. Thus,
we do not find that the evidence available in this review, as documented in the ISA, or cited by
the CASAC, calls into question the adequacy of protection provided by the 24-hour PM2.5
standard from ecological effects. Further, with regard to their specific revision recommendations
for a revised level or indicator of the 24-hour PM2.5 secondary standard, the CASAC members
cite discussion in the January 2023 proposal to revise the PM2.5 secondary standard to protect
against visibility effects. We note that considerations as to the adequacy of protection provided
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by the PM2.5 standard from visibility effects are being addressed in the review of the PM
NAAQS Reconsideration (88 FR 5558, January 27, 2023), and are not included in the review
that is the subject of this PA.
In summary, based on the current evidence and quantitative air quality, exposure and risk
information, with associated limitations and uncertainties, in light of all of the considerations
above, we identify several options that may be appropriate for the Administrator to consider. The
potential policy options that could inform the Administrator's decisions on the NAAQS
providing the "requisite" public welfare protection and that are supported by the science include
both options to address protection for direct effects of the pollutants in ambient air and options to
address protection for effects related to S deposition and to N deposition. A summary of these
options is shown in Table 7-3 and described below.
To address protection of the public welfare from effects of SOx in ambient air, we
recognize options appropriate to consider for protection from both direct and deposition-related
effects. With regard to protection against the direct effects of SOx in ambient air, we conclude it
is appropriate to consider retaining the current secondary standard. To address protection of the
public welfare from effects related to S deposition, we conclude it is appropriate to consider
adoption of a new annual SO2 standard. This option involves establishing a SO2 annual mean
standard, averaged across three years, with a level within the range of levels extending below 15
to 5 ppb. In light of the extent to which peak concentrations (e.g., 3-hour averages) may be
otherwise controlled as discussed above, it may also be appropriate to consider adoption of such
an annual standard as a replacement for the current 3-hour standard.
With regard to protection from effects of N oxides and/or PM and N deposition, three
options are identified in consideration of: limitations in the available evidence, and associated
uncertainties related to interpretation of the evidence and air quality information; relationships
between the two pollutants and associated effects; and connections of effects elicited by N oxides
in ambient air and deposited onto biota surfaces. One option is to retain the existing NO2 and PM
standards, based on the judgment that the current evidence does not call into question the
adequacy of protection of the public welfare from both direct effects of N oxides and PM in
ambient air and effects related to N deposition associated with these pollutants. To the extent
different judgments are made, two options for revision are also identified that might be
appropriate to consider with regard to both protection from direct effects of N oxides in ambient
air and some increased protection from N deposition associated with N oxides and PM.
The option to retain the existing NO2 and PM standards is based on judgments that the
evidence for direct effects of N oxides and PM does not call into question the adequacy of
protection provided by these standards and also judgments that weigh heavily the limitations and
associated uncertainties associated with the available information. These limitations and
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associated uncertainties relate to the evidence base for ecosystem effects related to N deposition
associated with N oxides and PM, and with the air quality information related to the limited
potential for control of N deposition in areas across the U.S., in light of variation in the
composition of both oxides of N and of PM. The first set of limitations and uncertainties relates
to quantitative relationships between N deposition and ecosystem effects, based on which
differing judgments may be made in decisions regarding protection of the public welfare. In the
case of protection of the public welfare from adverse effects associated with nutrient enrichment,
we additionally recognize the complexity associated with identification of appropriate protection
objectives in the context of changing conditions in aquatic and terrestrial systems as recent
deposition has declined from the historical rates of loading. The second set of limitations and
uncertainties relates to relatively lower correlations in more recent time periods of air quality
metrics for N oxides with N deposition in ecosystems and the variation in PM composition
across the U.S., particularly that between the eastern and western U.S. This latter set of
limitations is considered to relate to the emergence of NH3 as a greater influence on N deposition
than N oxides and PM over the more recent years. Further, this influence appears to be exerted in
areas with some of the highest N deposition estimates for those years.
For N oxides, the options of retention or revision of the existing standard are based on
consideration of the air quality information that suggests control of N-deposition associated with
N oxides with an annual NO2 standard, and taking into account limitations in the available
evidence and associated uncertainties related to interpretation of the evidence of terrestrial biota
effects of nitric acid, which may be the direct effects most sensitive to oxides of N in ambient air.
We note that such effects may be considered to be both direct effects and also deposition-related
effects as they relate to direct contact with biota surfaces by dry deposition (e.g., ISA, Appendix
3, section 3.4, Appendix 5, section 5.2.3 and Appendix 6, section 6.3.7). The options, as
described earlier in this section, include retaining or revising the current secondary NO2
standard. For the revision option, it may be appropriate to consider levels below 53 ppb and
extending down to approximately 40-35 ppb.
With regard to the annual PM2.5 secondary standard, based on the air quality information
that suggests some correlation of N-deposition with the annual PM2.5 metric, which is stronger in
the West, it may be appropriate to consider revision of the level of the PM2.5 annual secondary
standard. For this option, it may be appropriate to consider levels below the current level of 15
|ig/m3, such as a level of 12 |ig/m3 (the level of the currently controlling primary standard),
recognizing uncertainty with regard to the extent of N deposition-related control and associated
protection that might be achieved. We note that this option is that recommended by the CASAC
minority.
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In addition to the options identified above, we recognize the potential value in
consideration of a standard or suite of standards with alternate indicator(s) that may target
specific chemicals that deposit N and S (e.g., NO3", SO42", NH4+). In so doing, however, we note
a number of information gaps that would need to be filled to inform identification of specific
options of this type. One example relates to the depth of our understanding of the distribution of
these chemicals in ambient air, including relationships between concentrations near sources and
in areas of deposition, such as protected areas. In this context we recognize that, depending on
the indicator selected, the relationship exhibited between concentrations of the indicator and N or
S deposition at the same location may not be expected to hold for concentrations of the indicator
in more distant locations, including locations near emissions sources. Additionally, we recognize
the practical considerations associated with establishing new standards with new indicators
related to establishment of regulatory measurement methods and surveillance networks, that
would yield effective implementation of the standards. Thus, while we note the potential value in
such approaches, as also recognized by the CASAC, we also recognize the additional data
collection and analysis needed to develop a foundation that might support their adoption.
We additionally note that the Administrator's decisions regarding secondary standards, in
general, are largely public welfare judgments, as described above. We note that different public
welfare policy judgments could lead to different conclusions regarding the extent to which the
current and various alternative standards might be expected to provide the requisite protection of
the public welfare. Such public welfare judgments include those related to identification of
effects of public welfare significance, as well as with regard to the appropriate weight to be
given to differing aspects of the evidence and air quality information, and how to consider their
associated uncertainties and limitations. For example, different judgments might give greater
weight to more uncertain aspects of the evidence. There are, additionally, judgments with regard
to the appropriate objectives for the requisite protection of the public welfare. Such judgments
are left to the discretion of the Administrator. Thus, in identifying a broad array of options for
consideration above (summarized in Table 7-3 below), we also note that decisions on the
approach to take in achieving the desired air quality and public welfare protection fall within the
scope of the Administrator's judgment.
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Table 7-3. Summary of current standards and range of potential policy options for
consideration.
Current Secondary Standards
Pollutant
Indicator
Averaging
Time
Level
Form
Basis
S0X
S02
3 hours
0.5 ppm
Not to be exceeded more than
once per year
Direct effects on vegetation
N Oxides
N02
1 year
53 ppb
Annual
Direct effects on vegetation
PM
PM2.5
1 year
15 |jg/m3
Annual, averaged over three
years
Ecological effects related
to deposition, as well as
effects on visibility and
climate, and materials
damage (with only the
former considered in this
review)
24 hours
35 |jg/m3
98th percentile, averaged over
three years
PM10
24 hours
150
|jg/m3
Not to be exceeded more than
once per year on average over
three years
Policy Options for Consideration: For protection from both direct effects of the pollutants on biota and from
ecological effects of ecosystem deposition of N and S associated with the pollutants.
SOx
Adoption of an annual average SO2 standard, averaged over three years, with a level within the range
of levels below 15 ppb down to 5 ppb, and retention of the existing 3-hour SO2 standard
Or
Replacement of existing 3-hour standard with an annual average SO2 standard based on air quality
data indicating annual standard to also provide the pertinent control for short-term concentrations.
N Oxides
Retention of the existing annual NO2 standard
Or
Revision of the level of the existing standard to within a range below 53 ppb to as low as 40-35 ppb,
in combination with consideration of a form averaged over three years
PM
Retention of the existing suite of standards
Or
Revision of the current annual PM2.5 standard level to within a range below 15 pg/m3 to that of the
current primary standard (12 |jg/m3)
Potential Options for Consideration in Future Reviews
SOx,
N Oxides
and PM
Alternate
indicator(s)
The potential for establishment of a revised standard or suite of standards with
alternate indicator(s) that may target specific chemicals that deposit N and S (e.g.,
particulate NO3-, SO42", NH4+) is associated with a number of uncertainties and
complications that include uncertainties in relationships between concentrations near
sources and in areas of deposition, as well as complications related to establishment of
measurement methods and design of regulatory monitoring networks.
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7.5 AREAS FOR FUTURE RESEARCH RELATED TO KEY
UNCERTAINTIES
In this section, we highlight several key uncertainties associated with reviewing and
establishing the secondary standards for SOx, oxides of N and PM, and additionally recognize
that research in these areas, and perhaps others not highlighted here, may additionally be
informative to the development of more efficient and effective control strategies. Accordingly,
areas highlighted for future welfare effects and atmospheric chemistry research include model
development, and data collection activities to address key uncertainties and limitations in the
current scientific evidence. These areas are similar to those highlighted in past reviews, such as
those that follow:
• Data and tools to relate concentrations of specific pollutants in ambient air with
deposition. This could include expansion of existing monitoring networks (either in
number or in the number of pollutants measured) to enable more geographically
representative comparisons of local deposition and local air quality concentrations.
• Research to further develop and improve modeling tools that relate atmospheric
deposition of specific compounds to changes in soil conditions, which influence
watershed aquatic impacts as well as effects on resident vegetation, in areas characterized
by different soil types and geology.
• Improved understanding of the relationship between wildfires and deposition of SOx, N
oxides and PM.
• Continued refinement of the TDep methodology to estimate national total deposition. This
could include efforts to continually evaluate and improve the air quality model simulation
inputs to TDep.
• Additional work to improve accuracy of estimates of BCw, a critical parameter in
modeling to characterize risks associated with aquatic and terrestrial acidification.
• To address uncertainty associated with characterizing risks associated with terrestrial
acidification, additional research might contribute to an improved understanding of
effects on sensitive vegetation of various levels of BC:A1 in different soil types.
• Improved understanding of relationships between soil N and carbon to N ratios, as
indicator metrics, and effects on key ecological receptors.
• Although addition or exposure studies are somewhat limited, studies assessing important
tree species included in Horn et al. 2018 would help improve confidence.
• Research to improve understanding of the linkages between deposition, geochemical
metrics and ecological effects of freshwater ecosystem eutrophication. Currently
available studies of waterbodies in the western U.S. have included investigations of
nutrient limitation and diatom assemblages. Studies in eastern lakes and streams have
primarily focused on NO3" leaching. Information is limited for relationships between
additional ecological endpoints (e.g., effects on fish and invertebrate communities) and
NO3" concentrations (or other chemical indicators).
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Research relating specific indicators of acidification or nutrient enrichment to ecological
effects and to ecosystem services (e.g., fish harvest, recreation, etc).
Research to address key limitations and uncertainties in modeling watershed N loading,
including atmospheric deposition to indicators of eutrophication (e.g., disolved oxygen
and chlorophyll A). For example, data to better estimate estuary-specific parameters (e.g.,
as used in Evans and Scavia Model); improved modeling tools that combine watershed
loading and influence on estuarine indicators.
Information is limited relating N deposition to specific endpoints in wetlands. Additional
research would contribute to an improved understanding of relationships between N
deposition and chemical and ecological responses across a range of wetland types and
across geographic regions.
Regarding aquatic eutrophication, research in several areas would advance assessment
approaches. These include research on appropriate endpoints or indicators; important
mediating factors (e.g., drought, temperatures, seasonality, dissolved organic carbon,
recovery from acidification) and characterization of their role in key processes, as well as
on the extent of differences among N compounds with regard to their role in key
processes.
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Cox, RD, Preston, KL, Johnson, RF, Minnich, RA and Allen, EB (2014). Influence of landscape
scale variables on vegetation conversion to exotic annual grassland in southern
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Dietze, MC and Moorcroft, PR (2011). Tree mortality in the eastern and central United
States:Patterns and drivers. Glob Change Biol 17(11): 3312-3326.
Dupont, J, Clair, TA, Gagnon, C, Jeffries, DS, Kahl, JS, Nelson, SJ and Peckenham, JM (2005).
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Canada. Environ Monit Assess 109(1): 275-291.
Fenn, ME, Allen, EB, Weiss, SB, Jovan, S, Geiser, LH, Tonnesen, GS, Johnson, RF, Rao, LE,
Gimeno, BS, Yuan, F, Meixner, T and Bytnerowicz, A (2010). Nitrogen critical loads and
management alternatives forN-impacted ecosystems in California. J Environ Manage 91:
2404-2423.
Geiser, LH, Jovan, SE, Glavich, DA and Porter, MK (2010). Lichen-based critical loads for
atmospheric nitrogen deposition in Western Oregon and Washington Forests, USA.
Environ Pollut 158: 2412-2421.
Geiser, LH, Nelson, PR, Jovan, SE, Root, HT and Clark, CM (2019). Assessing ecological risks
from atmospheric deposition of nitrogen and sulfur to us forests using epiphytic
macrolichens. Diversity 11(6): 87.
Horn, KJ, Thomas, RQ, Clark, CM, Pardo, LH, Fenn, ME, Lawrence, GB, Perakis, SS,
Smithwick, EA, Baldwin, D, Braun, S and Nordin, A (2018). Growth and survival
relationships of 71 tree species with nitrogen and sulfur deposition across the
conterminous U.S. PLoS ONE 13(10): e0205296.
Li, H and McNulty, SG (2007). Uncertainty analysis on simple mass balance model to calculate
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Lynch, JA, Phelan, J, Pardo, LH, McDonnell, TC, Clark, CM, Bell, MD, Geiser, LH and Smith,
RJ (2022). Detailed Documentation of the National Critical Load Database (NCLD) for
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APPENDICES
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APPENDIX 5A
RISK AND EXPOSURE ASSESSMENT FOR AQUATIC
ACIDIFICATION
TABLE OF CONTENTS
5A.1 Aquatic Acidification and Overview of Analyses 5A-1
5A. 1.1 Analysis Scales 5A-3
5A. 1.2 Method - Aquatic Critical Load Approach 5A-6
5A. 1.3 Ecological Risk and Response 5A-6
5 A. 1.4 Chemical Criterion and Critical Threshold 5A-12
5A. 1.4.1 Natural Acidic Waterbodies 5A-13
5 A. 1.5 Critical Load Data 5 A-14
5A.1.5.1 Steady-State Water Chemistry Model and F-Factor 5A-15
5A. 1.5.2 MAGIC Model and Regional Linear Regression Models for Estimating BCW
Input to SSWC 5A-17
5A. 1.5.3 MAGIC model and Hurdle Modeling for Estimating BCW Input to SSWC
5A-18
5 A. 1.6 Critical Load Exceedance 5A-19
5 A. 1.6.1 Deposition 5A-21
5A. 1.6.2 Acidifying Contribution of Nitrogen Deposition 5A-21
5 A. 1.7 Ecoregions Sensitivity to Acidification 5A-27
5A.2 Analysis Results 5A-32
5A.2.1 Results of National Scale Assessment of Risk 5A-32
5A.2.2 Ecoregion Analyses 5A-59
5A.2.2.1 Ecoregion Critical Load Exceedances - Sulfur Only 5A-68
5 A.2.2.2 Ecoregion Summary - Percent Exceedances as a Function of Total S
deposition 5A-98
5A.2.3 Case Study Analysis of Acidification Risk 5A-121
5A.2.3.1 Descriptive Information for Case Study Areas 5A-122
5A.2.3.2 Case Study Air Quality 5A-133
5A.2.3.2.1 Correlation of Deposition and Air Quality 5A-137
5A.2.3.2.2 Air Quality Scenarios 5A-138
5A.2.3.3 Critical Loads Analysis 5A-140
5A-i
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5A.2.3.3.1 Case Study Waterbody Critical Loads 5A-140
5A.2.3.3.2 Case Study Critical Load Exceedances 5A-143
5 A.3 Key Uncertainties 5A-144
5A.3.1 Quantitative Uncertainty Analyses on Model Inputs 5A-149
5A.3.1.1 Method 5A-149
5A.3.1.2 Results 5A-151
5A.3.2Uncertainty Analysis for N Leaching Estimates 5A-154
5A.3.3Variation in Critical Load Estimates Associated with Modeling Approach 5A-155
5A.3.3.1 Method 5A-155
5A.3.3.2 Results 5A-156
References 5A-160
5 A-ii
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TABLE OF TABLES
Table 5A-1. Multiple regression equations to estimate BCW from either water chemistry and
landscape variables or from landscape variables alone, stratified by
ecoregion 5A-18
Table 5A-2. Average annual nitrate concentrations for the EPA's Long-term Monitoring
program for lakes and streams 5 A-23
Table 5A-3. Regional aggregation of N leaching for ecoregion II and III, based on water quality
data for sites in NCLD, version 3.2 5A-26
Table 5A-4. Acid sensitive categories and criteria used to define each one 5A-29
Table 5A-5. Level III ecoregion categorization for acid sensitivity 5A-30
Table 5A-6. Percent of waterbodies with critical loads less than 2, 6, 12, and 18 kg S/ha-yr
based on ANC thresholds of 20, 30, and 50 |ieq/L 5A-33
Table 5A-7. Summary of CL exceedances, nationally, by ANC thresholds and deposition
periods 5A-33
Table 5A-8. Comparison of estimated deposition to CLs nationally based on all CL values by
ANC thresholds and deposition periods 5A-34
Table 5A-9. National aquatic CL exceedances based on CLs greater than 0 by ANC thresholds
and deposition periods 5A-36
Table 5A-10. Summary of median deposition estimates during five time periods for the 84
ecoregions in the CONUS. Deposition based on TDEP; median determined by
GIS zonal statistic 5A-59
Table 5A-11. Median sulfur deposition for the 84 ecoregions in the CONUS determined by GIS
zonal statistic based on TDEP estimates 5A-60
Table 5A-12. Summary of sulfur only CLs (kg S/ha-yr) for ANC thresholds of 20 and 30 |ieq/L
for ecoregions with at least 10 CL values 5A-63
Table 5A-13. Summary of sulfur only CLs (kg S/ha-yr) for ANC thresholds of 50 and 50/20
|ieq/L for ecoregions with at least 10 CL values 5A-65
Table 5A-14. Summary of total S deposition (kg S/ha-yr) estimates (based on TDEP) at CL
locations for 69 ecoregions with at least one CL 5A-66
Table 5A-15. Median total sulfur deposition (based on TDEP estimates at CL locations) for the
69 ecoregions with at least one CL 5A-67
Table 5A-16. Summary of CL values for those that have been exceeded for each ANC threshold
and time period for the 58 ecoregions with 10 or more values 5A-70
Table 5A-17. Percent exceedances of aquatic CLs for S only and ANC threshold of 20 |ieq/L for
deposition years of 2018-20 and 2014-16 in 69 ecoregions 5A-71
Table 5A-18. Percent exceedances of CLs for S only and ANC threshold of 20 |ieq/L for
deposition years of 2010-12, 2006-08 and 2001-03 in 69 ecoregions 5A-73
5 A-iii
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Table 5A-19. Percent exceedances of aquatic CLs for S only and ANC threshold of 30 |ieq/L for
deposition years of 2018-20 and 2014-16 in 69 ecoregions 5A-75
Table 5A-20. Percent exceedances of CLs for S only and ANC threshold of 30 |ieq/L for
deposition years of 2010-12, 2006-08 and 2001-03 in 69 ecoregions 5A-77
Table 5A-21. Percent exceedances of aquatic CLs for S only and ANC threshold of 50 |ieq/L for
deposition years of 2018-20 and 2014-16 in 69 ecoregions 5A-79
Table 5A-22. Percent exceedances of CLs for S only and ANC threshold of 50 |ieq/L for
deposition years of 2010-12, 2006-08 and 2001-03 in 69 ecoregions 5A-81
Table 5A-23. Percent exceedances of aquatic CLs for S only and ANC threshold of 50/20 |ieq/L
for deposition years of 2018-20 and 2014-16 in 69 ecoregions 5A-83
Table 5A-24. Percent exceedances of CLs for S only and ANC threshold of 50/20 |ieq/L for
deposition years of 2010-12, 2006-08 and 2001-03 in 69 ecoregions 5A-85
Table 5A-25. Minimum, maximum, and median S deposition for 25 ecoregions in analysis.
Ecoregion deposition values are medians of deposition at sites with CLs in the
ecoregion 5A-99
Table 5A-26. Number of ecoregion-time period combinations with more than 10, 15, 20, 25 and
30% of waterbodies exceeding their CLs for ANC target of 50 |ieq/L. Includes 18
ecoregions in the eastern U.S 5A-100
Table 5A-27. Cumulative percentage of ecoregion-time period combinations with less than 10,
15, 20, 25, and 30% of waterbodies per ecoregion exceeding their CLs for the
ANC target of 50 |ieq/L as a function of total S deposition. 100% indicates there
were no ecoregion-time period combinations that had percent exceedances above
specified value. For the 18 eastern U.S. ecoregions and five deposition periods
(2001-03, 2006-08, 2010-12, 2014-06, 2018-20) (See Table 5A-26 for data) 5A-
101
Table 5A-28. Number of ecoregion-time period combinations with >10, >15, >20, >25, >30% of
waterbodies exceeding their CLs for ANC target of 30 |ieq/L as a function of total
S deposition across all 5 deposition periods (2001-03, 2006-08, 2010-12, 2014-06,
2018-20). Includes 18 ecoregions in the eastern U.S 5A-103
Table 5A-29. Cumulative percent of ecoregion-time period combinations with less than 10, 15,
20, 25 and 30% of waterbodies per ecoregion exceeding their CLs for the ANC
target of 30 |ieq/L as a function of total S deposition. 100% indicates there were
no ecoregion-time period combinations that had percent exceedances above the
specified values. Critical load exceedances for 18 eastern U.S. ecoregions and five
deposition periods (2001-03, 2006-08, 2010-12, 2014-06, 2018-20) (See Table 5A-
28 for data) 5A-104
Table 5A-30. Number of ecoregion-time period combinations with >10, >15, >20, >25, >30% of
waterbodies exceeding their CLs for ANC target of 20 |ieq/L as a function of total
S deposition across all 5 deposition periods (2001-03, 2006-08, 2010-12, 2014-06,
2018-20). Includes 18 ecoregions in the eastern U.S 5A-106
5A-iv
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Table 5A-31. Cumulative percent of ecoregion-time period combinations with less than 10, 15,
20, 25 and 30% of waterbodies per ecoregion exceeding their CLs for the ANC
target of 20 |ieq/L as a function of total S deposition. 100% indicates there were
no ecoregion-time period combinations that had percent exceedances above the
specified values. Critical load exceedances for 18 eastern ecoregions and five
deposition periods (2001-03, 2006-08, 2010-12, 2014-06, 2018-20) (See Table 5A-
30 for data) 5A-107
Table 5A-32. Number of ecoregion-time period combinations with >10, >15, >20, >25, >30% of
waterbodies exceeding their CLs for ANC target of 20 |ieq/L as a function of total
S deposition across all 5 deposition periods (2001-03, 2006-08, 2010-12, 2014-06,
2018-20) for 7 ecoregions in the western U.S 5A-109
Table 5A-33. Cumulative percent of waterbodies in ecoregions meeting the target ANC values as
a function of total S deposition across all 5 deposition periods (2001-03, 2006-08,
2010-12, 2014-06, 2018-20). 100% indicates there were no ecoregions that had
percent exceedances above >10, >15, >20, >25, >30% for a given deposition level.
Critical load exceedances based on ANC target of 20 |ieq/L for the western U.S.
(See Table 5A-32 for data) 5A-109
Table 5A-34. Number of ecoregion-time period combinations with >10, >15, >20, >25, >30% of
waterbodies exceeding their CLs for ANC target of 50 |ieq/L for the east and 20
|aeq/L for the west as a function of total S deposition across all 5 deposition
periods (2001-03, 2006-08, 2010-12, 2014-06, 2018-20). Includes 25 ecoregions
across the U.S 5A-110
Table 5A-35. Cumulative percent of ecoregion-time period combinations with less than 10, 15,
20, 25 and 30% of waterbodies per ecoregion exceeding their CLs for the ANC
target of 50 |ieq/L for the east and 20 |ieq/L for the west as a function of total S
deposition. 100% indicates there were no ecoregion-time period combinations that
had percent exceedances above the specified values. Critical load exceedances for
18 eastern and 7 western ecoregions and five deposition periods (2001-03, 2006-
08, 2010-12, 2014-06, 2018-20) (See Table 5A-34 for data) 5A-111
Table 5A-36. Number of ecoregion-time period combinations with >10, >15, >20, >25, >30% of
waterbodies exceeding their CLs for ANC target of 30 |ieq/L for the east and 20
|aeq/L for the west as a function of total S deposition across all 5 deposition
periods (2001-03, 2006-08, 2010-12, 2014-06, 2018-20). Includes 25 ecoregions
across the U.S 5A-113
Table 5A-37. Cumulative percent of ecoregion-time period combinations with less than 10, 15,
20, 25 and 30% of waterbodies per ecoregion exceeding their CLs for the ANC
target of 30 |ieq/L for the east and 20 |ieq/L for the west as a function of total S
deposition. 100% indicates there were no ecoregion-time period combinations that
had percent exceedances above the specified values. Critical load exceedances for
the 18 eastern and 7 western ecoregions and five deposition periods (2001-03,
2006-08, 2010-12, 2014-06, 2018-20) (See Table 5A-36 for data) 5A-114
Table 5A-38. Number of ecoregion-time period combinations with >10, >15, >20, >25, >30% of
waterbodies exceeding their CLs for ANC target of 20 |ieq/L for both the east and
5A-v
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west as a function of total S deposition across all 5 deposition periods (2001-03,
2006-08, 2010-12, 2014-06, 2018-20). Includes 25 ecoregions across the
U.S 5A-116
Table 5A-39. Cumulative percent of ecoregion-time period combinations with less than 10, 15,
20, 25 and 30% of waterbodies per ecoregion exceeding their CLs for the ANC
target of 20 |ieq/L as a function of total S deposition. 100% indicates there were no
ecoregion-time period combinations that had percent exceedances above the
specified values. Critical load exceedances for 18 eastern and 7 western ecoregions
and five deposition periods (2001-03, 2006-08, 2010-12, 2014-06, 2018-20) (See
Table 5A-38 for data) 5A-117
Table 5A-40. Estimated annual deposition in five case study areas (2018-2020 average)
5A-122
Table 5A-41. Distribution of land cover types in WHMT based on the 2016 NLCD 5A-124
Table 5A-42. Distribution of land cover types in SHVA based on the 2016 NLCD 5A-126
Table 5A-43. Distribution of land cover types in NOMN based on the 2016 NLCD 5A-128
Table 5A-44. Distribution of land cover types in ROMO based on the 2016 NLCD 5A-130
Table 5A-45. Distribution of land cover types in SINE based on the 2016 NLCD 5A-133
Table 5A-46. Air quality and wet deposition monitors used to assess the relationship between air
concentration and deposition, and trends 5A-134
Table 5A-47. Correlation coefficients for TDEP total deposition estimates with annual average
concentrations at the PM2.5 over the period 2000-2019 5A-138
Table 5A-48. The 3-year historical periods used for each case study area 5A-139
Table 5A-49. For each 3-year period described in Table 5A-48, this is the estimated 3-year
average annual average deposition, based on spatial averaging of TDEP dataset
estimates across the case study area, for N and S deposition 5A-140
Table 5A-50. Average, 10th and 30th percentile of CLs for kg S in each case study area.... 5A-141
Table 5A-51. Average, 10th and 30th percentile of CLs for meq S in each case study area 141
Table 5A-52. Number and percent of case study waterbodies estimated to exceed their CLs for
specified ANC values and air quality scenario 5A-143
Table 5A-53. Characterization of key uncertainties in exposure and risk analyses for aquatic
acidification 5A-145
Table 5A-54. Parameters varied in the Monte Carlo analysis 5A-150
Table 5A-55. Results of the Monte Carlo analysis for uncertainty broken down by confidence
interval 5A-152
Table 5A-56. Results of the Monte Carlo analysis for uncertainty by ecoregion 5A-152
Table 5A-57. Uncertainty analysis of NO3" flux estimates based on data from EPA's Long-term
Monitoring Program 5A-155
5A-vi
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TABLE OF FIGURES
Figure 5A-1. Three scales of the analysis: national, level III ecoregion, and case study 5A-4
Figure 5A-2. Level II ecoregions with level III subdivisions 5A-5
Figure 5A-3. Total macroinvertebrate species richness as a function of pH in 36 streams in
western Adirondack Mountains of New York, 2003-2005. From Baldigo et al.
(2009); see ISA, Appendix 8, section 8.3.3, and p. 8-12 5A-8
Figure 5A-4. Critical aquatic pH range for fish species. Notes: Baker and Christensen (1991)
generally defined bioassay thresholds as statistically significant increases in
mortality or by survival rates less than 50% of survival rates in control waters. For
field surveys, values reported represent pH levels consistently associated with
population absence or loss. Source: Fenn et al. (2011) based on Baker and
Christensen (1991) (ISA, Appendix 8, Figure 8-3) 5A-9
Figure 5A-5. Number of fish species per lake versus acidity status, expressed as ANC, for
Adirondack lakes. Notes: The data are presented as the mean (filled circles) of
species richness within 10 [j,eq/L ANC categories, based on data collected by the
Adirondacks Lakes Survey Corporation. Source: Modified from Sullivan et al.
(2006). (ISA, Appendix 8, Figure 8-4) 5A-11
Figure 5A-6. Unique waterbody locations with CL estimates used in this assessment. Lower
values are red and orange; the lowest bin includes CLs of zero (section
5 A. 1.6) 5A-15
Figure 5A-7. Surface water quality alkalinity (a) and ANC (b) across the CONUS based on
measurements collected prior to 1988 through 2018 5A-28
Figure 5A-8. Level III ecoregions grouped into acid sensitivity categories 5A-29
Figure 5A-9. Percent CL exceedances by ANC thresholds and deposition periods 5A-38
Figure 5A-10. Critical load exceedance (Ex) for S only total deposition from 2001-03 for an
ANC threshold of 20 [j,eq/L: a) waterbodies with sulfur deposition below the CL
and uncertainty (Ex < -3.125 meq/m2-yr), and b) waterbodies with sulfur
deposition above or near the CL 5A-39
Figure 5A-11. Critical load exceedance (Ex) for S only total deposition from 2001-03 for an
ANC threshold of 30 [j,eq/L: a) waterbodies with sulfur deposition below the CL
and uncertainty (Ex < -3.125 meq/m2-yr), and b) waterbodies with sulfur
deposition above or near the CL 5 A-40
Figure 5A-12. Critical load exceedance (Ex) for S only total deposition from 2001-03 for an
ANC threshold of 50 [j,eq/L: a) waterbodies with sulfur deposition below the CL
and uncertainty (Ex < -3.125 meq/m2-yr), and b) waterbodies with sulfur
deposition above or near the CL 5A-41
Figure 5A-13. Critical load exceedance (Ex) for S only total deposition from 2001-03 for an
ANC threshold of 50 for the eastern and 20 [j,eq/L for Western CONUS: a)
waterbodies with sulfur deposition below the CL and uncertainty (Ex < -3.125
meq/m2-yr), and b) waterbodies with sulfur deposition above or near the CL. 5 A-42
5A-vii
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Figure 5A-14. Critical load exceedance (Ex) for S only total deposition from 2006-08 for an
ANC threshold of 20 [j,eq/L: a) waterbodies with sulfur deposition below the CL
and uncertainty (Ex < -3.125 meq/m2-yr), and b) waterbodies with sulfur
deposition above or near the CL 5 A-43
Figure 5A-15. Critical load exceedance (Ex) for S only total deposition from 2006-08 for an
ANC threshold of 30 [j,eq/L: a) waterbodies with sulfur deposition below the CL
and uncertainty (Ex < -3.125 meq/m2-yr), and b) waterbodies with sulfur
deposition above or near the CL 5 A-44
Figure 5A-16. Critical load exceedance (Ex) for S only total deposition from 2006-08 for an
ANC threshold of 50 [j,eq/L: a) waterbodies with sulfur deposition below the CL
and uncertainty (Ex < -3.125 meq/m2-yr), and b) waterbodies with sulfur
deposition above or near the CL 5 A-45
Figure 5A-17. Critical load exceedance (Ex) for S only total deposition from 2006-08 for an
ANC threshold of 50 for the eastern and 20 [j,eq/L for Western CONUS: a)
waterbodies with sulfur deposition below the CL and uncertainty (Ex < -3.125
meq/m2-yr), and b) waterbodies with sulfur deposition above or near the CL. 5 A-46
Figure 5A-18. Critical load exceedance (Ex) for S only total deposition from 2010-12 for an
ANC threshold of 20 [j,eq/L: a) waterbodies with sulfur deposition below the CL
and uncertainty (Ex < -3.125 meq/m2-yr), and b) waterbodies with sulfur
deposition above or near the CL 5 A-47
Figure 5A-19. Critical load exceedance (Ex) for S only total deposition from 2010-12 for an
ANC threshold of 30 [j,eq/L: a) waterbodies with sulfur deposition below the CL
and uncertainty (Ex < -3.125 meq/m2-yr), and b) waterbodies with sulfur
deposition above or near the CL 5 A-48
Figure 5A-20. Critical load exceedance (Ex) for S only total deposition from 2010-12 for an
ANC threshold of 50 [j,eq/L: a) waterbodies with sulfur deposition below the CL
and uncertainty (Ex < -3.125 meq/m2-yr), and b) waterbodies with sulfur
deposition above or near the CL 5 A-49
Figure 5A-21. Critical load exceedance (Ex) for S only total deposition from 2010-12 for an
ANC threshold of 50 for the eastern and 20 [j,eq/L for Western CONUS: a)
waterbodies with sulfur deposition below the CL and uncertainty (Ex < -3.125
meq/m2-yr), and b) waterbodies with sulfur deposition above or near the CL. 5 A-50
Figure 5A-22. Critical load exceedance (Ex) for S only total deposition from 2014-16 for an
ANC threshold of 20 [j,eq/L: a) waterbodies with sulfur deposition below the CL
and uncertainty (Ex < -3.125 meq/m2-yr), and b) waterbodies with sulfur
deposition above or near the CL 5A-51
Figure 5A-23. Critical load exceedance (Ex) for S only total deposition from 2014-16 for an
ANC threshold of 30 [j,eq/L: a) waterbodies with sulfur deposition below the CL
and uncertainty (Ex < -3.125 meq/m2-yr), and b) waterbodies with sulfur
deposition above or near the CL 5 A-52
Figure 5A-24. Critical load exceedance (Ex) for S only total deposition from 2014-16 for an
ANC threshold of 50 [j,eq/L: a) waterbodies with sulfur deposition below the CL
5A-viii
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and uncertainty (Ex < -3.125 meq/m2-yr), and b) waterbodies with sulfur
deposition above or near the CL 5A-53
Figure 5A-25. Critical load exceedance (Ex) for S only total deposition from 2014-16 for an
ANC threshold of 50 for the eastern and 20 [j,eq/L for Western CONUS: a)
waterbodies with sulfur deposition below the CL and uncertainty (Ex < -3.125
meq/m2-yr), and b) waterbodies with sulfur deposition above or near the CL. 5 A-54
Figure 5A-26. Critical load exceedance (Ex) for S only total deposition from 2018-20 for an
ANC threshold of 20 [j,eq/L: a) waterbodies with sulfur deposition below the CL
and uncertainty (Ex < -3.125 meq/m2-yr), and b) waterbodies with sulfur
deposition above or near the CL 5A-55
Figure 5A-27. Critical load exceedance (Ex) for S only total deposition from 2018-20 for an
ANC threshold of 30 [j,eq/L: a) waterbodies with sulfur deposition below the CL
and uncertainty (Ex < -3.125 meq/m2-yr), and b) waterbodies with sulfur
deposition above or near the CL 5A-56
Figure 5A-28. Critical load exceedance (Ex) for S only total deposition from 2018-20 for an
ANC threshold of 50 [j,eq/L: a) waterbodies with sulfur deposition below the CL
and uncertainty (Ex < -3.125 meq/m2-yr), and b) waterbodies with sulfur
deposition above or near the CL 5A-57
Figure 5A-29. Critical load exceedance (Ex) for S only total deposition from 2018-20 for an
ANC threshold of 50 for the eastern and 20 [j,eq/L for Western CONUS: a)
waterbodies with sulfur deposition below the CL and uncertainty (Ex < -3.125
meq/m2-yr), and b) waterbodies with sulfur deposition above or near the CL.5A-58
Figure 5A-30. Critical load exceedance for S only deposition from 2018-20 for four ANC
thresholds: a. 20, b. 30, c. 50, d. 50/20 [j,eq/L 5A-59
Figure 5 A-31. Locations of aquatic critical loads mapped across level III ecoregions 5 A-62
Figure 5A-32. Percent of CLs exceeded per ecoregion for S only deposition from 2018-20
(top) and 2014-16 (bottom) for an ANC threshold of 20 [j,eq/L. The Southern
Coastal Plan (8.5.3) and Atlantic Coastal Pine Barrens (8.5.4) ecoregions are cross
hatched to indicate natural high level of acidity 5A-87
Figure 5A-33. Percent of CLs exceeded per ecoregion for S only deposition from 2010-12
(top) and 2006-08 (bottom) for an ANC threshold of 20 [j,eq/L. The Southern
Coastal Plan (8.5.3) and Atlantic Coastal Pine Barrens (8.5.4) ecoregions are cross
hatched to indicate natural high level of acidity 5A-88
Figure 5A-34. Percent of CLs exceeded per ecoregion for S only deposition from 2001-02 for
an ANC threshold of 20 [j,eq/L. The Southern Coastal Plan (8.5.3) and Atlantic
Coastal Pine Barrens (8.5.4) ecoregions are cross hatched to indicate natural high
level of acidity 5A-89
Figure 5A-35. Percent of CLs exceeded per ecoregion for S only deposition from 2018-20
(top) and 2014-16 (bottom) for an ANC threshold of 30 [j,eq/L. The Southern
Coastal Plan (8.5.3) and Atlantic Coastal Pine Barrens (8.5.4) ecoregions are cross
hatched to indicate natural high level of acidity 5A-90
5A-ix
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Figure 5A-36. Percent of CLs exceeded per ecoregion for S only deposition from 2010-12
(top) and 2006-08 (bottom) for an ANC threshold of 30 [j,eq/L. The Southern
Coastal Plan (8.5.3) and Atlantic Coastal Pine Barrens (8.5.4) ecoregions are cross
hatched to indicate natural high level of acidity 5A-91
Figure 5A-37. Percent of CLs exceeded per ecoregion for S only deposition from 2001-03 for
an ANC threshold of 30 [j,eq/L. The Southern Coastal Plan (8.5.3) and Atlantic
Coastal Pine Barrens (8.5.4) ecoregions are cross hatched to indicate natural high
level of acidity 5A-92
Figure 5A-38. Percent of CLs exceeded per ecoregion for S only deposition from 2018-20
(top) and 2014-16 (bottom) for an ANC threshold of 50 [j,eq/L. The Southern
Coastal Plan (8.5.3) and Atlantic Coastal Pine Barrens (8.5.4) ecoregions are cross
hatched to indicate natural high level of acidity 5A-93
Figure 5A-39. Percent of CLs exceeded per ecoregion for S only deposition from 2010-12
(top) and 2006-08 (bottom) for an ANC threshold of 50 [j,eq/L. The Southern
Coastal Plan (8.5.3) and Atlantic Coastal Pine Barrens (8.5.4) ecoregions are cross
hatched to indicate natural high level of acidity 5A-94
Figure 5A-40. Percent of CLs exceeded per ecoregion for S only deposition from 2001-03 for
an ANC threshold of 50 [j,eq/L. The Southern Coastal Plan (8.5.3) and Atlantic
Coastal Pine Barrens (8.5.4) ecoregions are cross hatched to indicate natural high
level of acidity 5A-95
Figure 5A-41. Percent of CLs exceeded per ecoregion for S only deposition from 2018-20
(top) and 2014-16 (bottom) for an ANC threshold of 50 [j,eq/L for East and 20
[j,eq/L for the West. The Southern Coastal Plan (8.5.3) and Atlantic Coastal Pine
Barrens (8.5.4) ecoregions are cross hatched to indicate natural high level of
acidity 5A-96
Figure 5A-42. Percent of CLs exceeded per ecoregion for S only deposition from 2010-12
(top) and 2006-08 (bottom) for an ANC threshold of 50 [j,eq/L for East and 20
[j,eq/L for the West. The Southern Coastal Plan (8.5.3) and Atlantic Coastal Pine
Barrens (8.5.4) ecoregions are cross hatched to indicate natural high level of
acidity 5A-97
Figure 5A-43. Percent of CLs exceeded per ecoregion for S only deposition from 2001-03 for
an ANC threshold of 50 [j,eq/L for East and 20 [j,eq/L for the West. The Southern
Coastal Plan (8.5.3) and Atlantic Coastal Pine Barrens (8.5.4) ecoregions are cross
hatched to indicate natural high level of acidity 5A-98
Figure 5A-44. Cumulative percentage of ecoregion-time period combinations with CL
exceedances below 10, 15, 20, 25, or 30%. 100% indicates there was no ecoregion
that had a percent exceedance above 10, 15, 20, 25, or 30% for that deposition
level bin. Critical load exceedances based on ANC target of 50 |ieq/L for the 18
eastern U.S. ecoregions and five deposition periods (2001-03, 2006-08, 2010-12,
2014-06, 2018-20) (See Table 5A-27 for values) 5A-102
Figure 5A-45. Cumulative percent of ecoregion-time period combinations with CL
exceedances below 10, 15, 20, 25, 30%. 100% indicates there was no ecoregion
that had a percent exceedance above 10, 15, 20, 25, or 30% for a given deposition
5A-x
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level bin. Critical load exceedances based on ANC target of 30 |ieq/L for the 18
eastern U.S. ecoregions five deposition periods (2001-03, 2006-08, 2010-12, 2014-
06, 2018-20) (See Table 5A-29 for values) 5A-105
Figure 5A-46. Cumulative percent of ecoregion-time period combinations with CL
exceedances below 10, 15, 20, 25, or 30%. 100% indicates there was no ecoregion
that had a percent exceedance above 10, 15, 20, 25, or30%. Critical load
exceedances based on ANC target of 20 |ieq/L for the 18 eastern U.S. ecoregions
across all 5 deposition periods (2001-03, 2006-08, 2010-12, 2014-06, 2018-20)
(See Table 5A-31 for values) 5A-108
Figure 5A-47. Cumulative percent of ecoregion-time period combinations with CL
exceedances below 10, 15, 20, 25, or 30%. 100% indicates there was no ecoregion
that had a percent exceedance above 10, 15, 20, 25, or 30% for a given deposition
level bin. Critical load exceedances based on ANC target of 50 |ieq/L for the 18
east ecoregions and 20 |ieq/L for the 7 west ecoregions and five deposition periods
(2001-03, 2006-08, 2010-12, 2014-06, 2018-20) (See Table 5A-35 for
values) 5 A-112
Figure 5A-48. Cumulative percent of ecoregion-time period combinations with CL
exceedances belowlO, 15, 20, 25, or 30%. 100% indicates there was no ecoregion
that had a percent exceedance above 10, 15, 20, 25, or 30% for a given deposition
level bin. Critical load exceedances based on ANC target of 30 |ieq/L for the 18
east ecoregions and 20 |ieq/L for the 7 west ecoregions and five deposition periods
(2001-03, 2006-08, 2010-12, 2014-06, 2018-20) (See Table 5A-37 for
values) 5A-115
Figure 5A-49. Cumulative percent of ecoregion-time period combinations with CL
exceedances below 10, 15, 20, 25, or 30%. 100% indicates there was no ecoregion
that had a percent exceedance above 10, 15, 20, 25, or 30% for a given deposition
level bin. Critical load exceedances based on ANC target of 20 |ieq/L for the 18
east ecoregions and 7 west ecoregions and five deposition periods (2001-03, 2006-
08, 2010-12, 2014-06, 2018-20) (See Table 5A-39 for values) 5A-118
Figure 5A-50. Percentage of waterbodies per each of the 25 ecoregions that were estimated to
achieve ANC values of 20 (E&W), 30 (E only) and 50 (E only) |ieq/L based on
CLs greater than zero and annual average S deposition for 2018-2020 (upper) and
2014-2016 (lower) by ecoregion median (across sites with CLs) 5A-119
Figure 5 A-51. Percent of waterbodies per ecoregion estimated to achieve ANC of 20 [j,eq/L
(top left), 30 [j,eq/L (top right), 50 [j,eq/L (bottom left) and 50/20 [j,eq/L in E/W
(2001-2020). Bold text, solid lines indicate western ecoregions 5A-120
Figure 5A-52. Location of the case study areas 5A-121
Figure 5A-53. Level III ecoregions in which WHMT occurs 5A-123
Figure 5A-54. Types of land cover in WHMT based on 2016 NLCD 5A-124
Figure 5A-55. Level III ecoregions in which SHVA occurs 5A-125
Figure 5A-56. Types of land cover in SHVA based on the 2016 NLCD 5A-126
5A-xi
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Figure 5A-57. Level III ecoregions in which NOMN occurs 5A-127
Figure 5A-58. Types of land cover in NOMN based on the 2016 NLCD 5A-128
Figure 5A-59. Types of land cover in ROMO based on the 2016 NLCD 5A-129
Figure 5A-60. Level III ecoregions in which SINE occurs 5A-131
Figure 5A-61. Types of land cover in SINE based on the 2016 NLCD 5A-132
Figure 5A-62. Monitoring sites used for NOMN to analyze relationships and trends 5A-135
Figure 5A-63. Monitoring sites used for ROMO to analyze relationships and trends 5A-135
Figure 5A-64. Monitoring sites used for SHVA to analyze relationships and trends 5A-136
Figure 5A-65. Monitoring sites used for SINE to analyze relationships and trends 5A-136
Figure 5A-66. Monitoring sites used for WHMT to analyze relationships and trends 5A-137
Figure 5A-67. Case study area CL maps for sulfur (meq/m2-yr) using an ANC threshold of 20
|ieq/L (upper) and 50 |ieq/L (lower) 5A-142
Figure 5A-68. Critical load uncertainty analysis for 14,943 values across the CONUS of the
SSWC model. Blue and green dots have the lowest confidence interval and
orange, and red dots have the highest confidence interval 5A-151
Figure 5A-69. Critical load comparison between values based on MAGIC (y-axis) and values
based on the SSWC F-factor (Lynch et al., 2022) for New England lakes (a.) and
Adirondack lakes (b.). Units are meq/m2-yr 5A-157
Figure 5A-70. Critical load comparison between values based on Regional Regression model
(Sullivan et al., 2014) (y-axis) and values based on the SSWC F-factor model
(Lynch et al., 2022) for the full range of CLs (a) and for the range from 0 to 150
meq/m2-yr (b). Units are meq/m2-yr 5A-158
Figure 5A-71. Critical load comparisons: (a.) between values based on MAGIC (y-axis) and
values based on the SSWC F-factor model (Lynch et al., 2022) (x-axis); and (b.)
between values based on Regional Regression model (McDonnell et al., 2014) (y-
axis) and values based on the SSWC F-factor model (Lynch et al., 2022) (x-axis).
Units are meq/m2-yr 5A-159
5A-xii
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5A. 1 AQUATIC ACIDIFICATION AND OVERVIEW OF
ANALYSES
Air emissions of sulfur oxides (SOx), oxides of nitrogen, and reduced forms of nitrogen
(NHx) react in the atmosphere through a complex mix of reactions and thermodynamic processes
in gaseous, liquid, and solid phases to form various acidifying compounds. These compounds are
removed from the atmosphere through wet (e.g., rain, snow), cloud and fog, or dry (e.g., gases,
particles) deposition. Deposition of SOx, oxides of nitrogen, and NHx leads to ecosystem
exposure to acidification. The 2020 ISA concludes that the body of evidence is sufficient to infer
a causal relationship between acidifying deposition and adverse changes in freshwater biota (see
ISA, Appendix 8). Freshwater systems of the U.S. include lakes, rivers, streams, and wetlands.
Changes in biogeochemical processes and water chemistry caused by deposition of nitrogen (N)
and sulfur (S) to surface waters and their watersheds have been well characterized for decades
and have ramifications for biological functioning of freshwater ecosystems.
When S or N deposition leaches from soils to surface waters in the form of sulfate (SO42")
or nitrate (NO3"), an equivalent number of positive cations, or countercharge, is also transported.
This maintains electroneutrality. If the countercharge is provided by base cations such as calcium
(Ca2+), magnesium (Mg2+), sodium (Na+), or potassium (K+), rather than hydrogen (H+) and
aluminum (Ab+), the acidity of the soil water is neutralized, but the base saturation of the soil is
reduced. Continued SO42" and/or NO3" leaching can deplete available base cation pools in the
soil. As the base cations are removed, continued deposition and leaching of SO42" and/or NO3" -
(with H+ and Ab+) leads to acidification of soil water, and by connection, surface water. Loss of
soil base saturation is a cumulative effect that increases the sensitivity of the watershed to further
acidifying deposition.
These chemical changes in water quality can occur over both long- and short-term
timescales. Short-term (i.e., hours or days), often termed episodic, periods of increased acidity
can also have significant biological effects. Episodic chemistry refers to conditions during
precipitation or snowmelt events when proportionately more drainage water is routed through
upper soil horizons that tends to provide less acid neutralizing than deeper soil horizons. Surface
water chemistry has lower pH and acid neutralizing capacity (ANC) during these events than
during baseflow conditions. Acid neutralizing capacity is a water quality measurement of a
waterbody's ability to neutralize acid inputs or its "buffering capacity against acidification"
(ISA, p. ES-14). Models often simulate calculated ANC, e.g., as the difference between the total
amount of strong base ions (sum of base cations, SBC) and the total amount of strong acid anions
5A-1
-------
(sum of acid anions, SAA).1 In this assessment, the calculation is performed as in equation. 5A-
1):
ANC = SBC - SAA = (Ca2+ + Mg2+ + K+ + Na+ + NH4+) - (S042" + N03" + CI") (5A-1)
Acid neutralizing capacity and pH are related to one another as they both are measures of
acidity in surface waters and low pH values correspond to low ANC values. However, pH in
natural waters is dependent on the amount of carbon dioxide, organic acids, and aluminum
solubility, which impacts the relationships between the two parameters. The amount of carbon
dioxide (CO2) dissolved in surface waters is affected by biological activity and temperature,
which decreases pH but does not impact ANC. Dissolved organic carbon (DOC), which includes
organic acids (e.g., fulvic and humic acids, carboxylic acids, and amino acids), also lowers pH
values in surface waters and changes the relationship between pH and ANC (ISA, Appendix 4,
section 4.3.9).
The principal factor governing the sensitivity of aquatic ecosystems to acidification from
acidifying deposition is geology (particularly surficial geology; [Greaver et al., 2012]). Levels of
acidifying deposition are generally low in the western contiguous U.S. (western CONUS) but
can be higher in the eastern CONUS (ISA Appendix 8, section 8.5.1). In the eastern CONUS,
acid-sensitive ecosystems are generally located in upland, mountainous terrain underlain by
weathering resistant bedrock. Surface waters most sensitive to acidification are largely found in
the Northeast, southern Appalachian Mountains, Florida, the Upper Midwest, and the
mountainous West. (ISA, Appendix 8, section 8.5.1).
Acidification of freshwater ecosystems occurs in response to either N or S deposition
alone or in combination. This is because both N and S deposition can act as acidifying agents.
The effects of acidifying deposition on biogeochemical processes in soils have ramifications for
the water chemistry and biological functioning of associated surface waters. Surface water
chemistry integrates direct air-to-water deposition with deposition impacts on soil chemistry of
hydrologically connected terrestrial ecosystems within the watershed (ISA, Appendices 4, 7 and
8). Acid-sensitive freshwater systems can either be chronically acidified or subject to occasional
episodes of decreased pH, decreased ANC, and increased inorganic Al concentration (ISA,
Appendix 7, section 7.1).
In this assessment, the impact of N and/or S deposition on aquatic acidification was
evaluated using a critical load (CL) approach. This CL approach provides a means of gauging
whether a group of lakes, streams, and rivers (i.e., waterbodies) in each area receives a level of N
1 The two measures (measured or titrated ANC and calculated ANC) can differ greatly, depending mainly on the
amount of organic acidity and dissolved Al in the water (ISA, Appendix 7, p. 7-23).
5A-2
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and/or S deposition that corresponds to that associated with a specified value for the water
quality metric used as indicator of acidification. For this analysis, ANC was used as the
indicator, with target levels identified to correspond to different levels of acidification-related
risk to biota. Depending on the ANC target, low CL values may mean that the watershed has a
limited ability to neutralize the addition of acidic anions and, hence, is susceptible to
acidification. The greater the CL value, the greater the ability of the watershed to neutralize
additional acidic anions.
5A.1.1 Analysis Scales
A multi-scale analysis was completed that assessed aquatic acidification at three levels of
spatial extent: national, ecoregion, and case study (Figure 5A-1). The national-scale assessment
focused within the contiguous U.S. (CONUS) due to insufficient availability data for Hawaii,
Alaska, and the territories. The Omernik ecoregion classifications (level III) were used for the
ecoregion-scale analyses. Case study locations were areas likely to be most impacted and for
which sufficient data were available. Further discussion of these spatial scales can be found
below. Since acidification of waterbodies is controlled by local factors such as geology,
hydrology, etc. the aquatic CLs for acidification are unique to the waterbody itself and
information about the waterbody, like water quality, is needed to determine its critical load. For
these reasons, CLs were determined at the waterbody level and then summarized at the national,
ecoregion, and case study level. The national assessment is a combined summary of aquatic CLs
across the CONUS.
5A-3
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Figure 5A-1. Three scales of the analysis: national, level III ecoregion, and case study.
It is important to note that aquatic ecosystems across the CONUS exhibit a wide range of
sensitivity to acidification because of a host of landscape factors, such as geology, hydrology,
soils, catchment scale, and vegetation characteristics that control whether a waterbody will be
impacted by acidifying deposition. Consequently, variations in ecosystem sensitivity must be
considered in order to characterize sensitive populations of waterbodies and relevant regions
across the CONUS. The EPA's Omernik ecoregions classifications was used to define
ecologically relevant, spatial aggregated, acid sensitive regions across the CONUS in order to
better characterize the regional differences in the impact of deposition driven acidification.
Ecoregions are areas of similarity regarding patterns in vegetation, aquatic, and terrestrial
ecosystem components. Available ecoregion categorization schemes include the EPA's Omernik
classifications (Omernik, 1987). Omernik's ecoregions are categorized using a holistic, "weight-
of-evidence" approach in which the relative importance of factors may vary from region to
region. The method used to map ecoregions is described in Omernik (1987) and classifies
regions through the analysis of the patterns and the composition of biotic and abiotic
characteristics that affect or reflect differences in ecosystem quality and integrity. Factors
C3 Example of Ecoregion III
5A-4
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include geology, physiography, vegetation, climate, soils, land use, wildlife, and hydrology.
Three hierarchical levels were developed to distinguish coarser (more general) and finer (more
detailed) categorization. Level I is the coarsest level, dividing the CONUS into 12 ecoregions. At
level II, the continent is subdivided into 25 ecoregions and the contiguous U.S. into 20 (Figure
5A-2). Level III is a further subdivision of level II and divides North America into 105
ecoregions with 84 in the CONUS. Level IV is a subdivision of level III into 967 ecoregions for
the CONUS.
D
!
Figure 5A-2. Level II ecoregions with level III subdivisions.
The case study scale represents the smallest scale at which we performed our analyses
and is intended to give some insight into the local impact of aquatic acidification. Five case study
areas across the U.S. were examined: Shenandoah Valley Area, White Mountain National Forest,
Northern Minnesota, Sierra Nevada Mountains, and Rocky Mountain National Park (section
5A.2.3). These areas include a number of parks and national forests that vary in their sensitivity
to acidification, but represent high value or protected ecosystems, such as Class 1 areas,
wilderness, and national forests.
11.1: Mediterranean California
12.1: Western Sierra Madre
Piedmont
13.1: Upper Gila Mountains
15.4: Everglades
5.2: Mixed Wood Shield
5.3: Atlantic Highlands
6.2: Western Cordillera
7.1: Marine West Coast Forest
8.1: Mixed Wood Plains
8.2: Central USA Plains
8.3: Southeastern USA Plains
8.4: Ozark/Ouachita-
Appalachian Forests
IB 8.5: Mississippi Alluvial and
I Southeast USA Coastal Plains
9.2: Temperate Prairies
¦ 9.3: West-Central Semi-
1 Arid Prairies
9.4: South Central Semi-
Arid Prairies
9.5: Texas-Louisiana Coastal
Plain
9.6: Tamaulipas-Texas Semi-
Arid Plain
10.1: Cold Deserts
10.2: Warm Deserts
5A-5
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5A.1.2 Method - Aquatic Critical Load Approach
The impacts of N and/or S deposition on aquatic ecosystems were evaluated using a CL
approach. The CL approach was used to characterize the risk of N and/or S deposition on aquatic
acidification across the CONUS with a focus on acid sensitive areas. In this assessment, the CL
approach provides a means of gauging whether an individual or group of waterbodies (i.e., lake
or stream) in a given area receives an amount of deposition that results in the waterbody not
being able to achieve the target ANC level (as described in 5 A. 1.3). Critical load exceedances
were summarized at the national, ecoregion III, and case study levels to understand the spatial
extent of deposition-driven acidication across the CONUS. Special consideration was given to
naturally occuring aquatic acidification in order to focus the analysis on deposition-driven
impacts to aquatic biota. Uncertainty associated with the CL estimate was also estimated and
factored into the CL exceedance determination.
5A.1.3 Ecological Risk and Response
The biological impact of acidifying deposition is mediated through changes in water
quality that in turn impact biota (ISA, Appendices 7 and 8). Deposition of N and/or S can effect
biogeochemical changes in aquatic systems that may induce biologically harmful effects.
Surface water chemistry is then a good indicator of the risk of acidification on the biotic integrity
of freshwater ecosystems, because it integrates soil and water processes that occur within a
watershed. Changes in surface water chemistry reflect the influence of acidic inputs from
precipitation, gases, and particles, as well as local geology and soil conditions. Surface water
chemical factors such as pH, Ca2+, ANC, base cations, ionic metals concentrations, and DOC are
affected by acid deposition and, accordingly, are commonly used indicators of acidification.
Although ANC does not directly cause effects on biota, it relates to pH and aluminum levels, and
biological effects are primarily attributable to low pH and high inorganic aluminum
concentration (ISA, section ES.5.1).
The most widely used measure of surface water acidification, and subsequent recovery
under reduced acid deposition, is ANC (ISA, Appendix 7, section 7.1.2.6). This is because ANC
is associated with the surface water constituents that directly cause or reduce acidity-related
stress, in particular pH, Ca2+, and inorganic A1 concentrations and ANC is generally a more
stable measurement than pH, and it reflects sensitivity and effects of acidification in a linear
fashion across the full range of ANC values (ISA, Appendix 7, section 7.1.2.6). These water
quality parameters are indicators of aquatic acidification for which there is evidence of effects on
aquatic systems including physiological impairment, reduced fitness or survival, alteration of
species richness, community composition and structure, and biodiversity in freshwater
ecosystems.
5A-6
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The evidence of effects on biota from aquatic acidification indicates a range of severity
with varying levels of ANC, pH and inorganic Al, with effects on biota ranging from
phytoplankton and invertebrates to fish communities (ISA, Appendix 8, section 8.5). For
example, a review by Lacoul et al. (2011) of aquatic acidification effects on aquatic organisms in
Atlantic Canada observed that the greatest differences in phytoplankton species richness
occurred across a pH range of 4.7 to 5.5 (ANC range of 0 to 20 (J,eq/L), just below the range (pH
5.5 to 6.5) where bicarbonate becomes rapidly depleted in the water (ISA, Appendix 8, section
8.3.1.1). Under acidifying conditions, these phytoplankton communities shifted from dominance
by chrysophytes, other flagellates, and diatoms to dominance by larger dinoflagellates. In benthic
invertebrates residing in sediments of acidic streams, Al concentration is a key influence on the
presence of sensitive species. Studies of macroinvertebrate species have reported reduced species
richness at lower pH, with the most sensitive group, mayflies, absent at the lowest levels. Values
of pH below 5 (which may correspond to approximant ANC concentrations below 0 (j,eq/L)2 were
associated with the virtual elimination of all acid sensitive mayfly and stonefly species over the
period from 1937-42 to 1984-85 in two streams in Ontario (Baker and Christensen, 1991). In a
more recent study, Baldigo et al. (2009) showed macroinvertebrate assemblages in the
southwestern Adirondack Mountains were severely impacted at pH <5.1, moderately impacted at
pH from 5.1 to 5.7, slightly impacted at pH from 5.7 to 6.4 and usually unaffected above pH 6.4
(Figure 5A-3). In Atlantic Canada, Lacoul et al. (2011) found the median pH for sensitive
invertebrate species occurrence was between 5.2 and 6.1 (ANC of 10 and 80 (J,eq/L), below
which such species tended to be absent. For example, some benthic macroinvertebrates,
including several species of mayfly and some gastropods are intolerant of acid conditions and
only occur at pH >5.5 (ANC 20 (j,eq/L) and >6, (ANC 50 (j,eq/L) respectively. (ISA, section
8.3.3).
2 pH and ANC were related to one another using a generalized relationship base on equilibrium with atmospheric
CO2 concentration (Cole and Prairie, 2010).
5A-7
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40
35
30
25
y
tt.
jg 20
u
a>
2-1S
"ro
O 10
non-impacted
• *
• * •
• ^
#
*• •
m m
• • •
slight impact *
•
•
• •
•
%•
•
•
moderate impact
severe impact
y = 4.62* - 1.49
RJ« 0.57
4.0
4.5
5.0
5.5 6.0
Median pH
6.5
7.0
7.5
Figure 5A-3. Total macroinvertebrate species richness as a function of pH in 36 streams
in western Adirondack Mountains of New York, 2003-2005. From Baldigo et
al. (2009); see ISA, Appendix 8, section 8.3.3, and p. 8-12.
Responses among fish species and life stages to changes in ANC, pH and Al in surface
waters are variable. Early life stages such as larvae and smolts are more sensitive to acidic
conditions than the young-of-the-year, yearlings, and adults (Baker et al., 1990; Johnson et al.,
1987; Baker and Schofield 1985). Studies showed a loss of fish whole-body sodium in trout
when stream pH drops below 5.1 (ANC 0 (j,eq/L) indicating loss of the ability to ionoregulate.
Some species and life stages experienced significant mortality in bioassays at relatively high pH
((e.g., pH 6.0-6.5; ANC 50-100 [j,eq/L for eggs and fry of striped bass and fathead minnow)
(McCormick et al., 1989; Buckler et al., 1987)), whereas other species were able to survive at
quite low pH without adverse effects. Many minnows and dace (Cyprinidae) are highly sensitive
to acidity, but some common game species such as brook trout, largemouth bass, and
smallmouth bass are less sensitive. A study by Neff et al. (2008), investigated the effects of two
acid runoff episodes in the Great Smoke Mountains National Park on native brook trout using an
in-situ bioassay. The resulting whole-body sodium concentrations before and after the episodes
showed negative impacts on physiology. More specifically, the reduction in whole-body sodium
when stream pH dropped below 5.1 (ANC 0 (j,eq/L) indicated that the trout had lost the ability to
ionoregulate (ISA, Appendix 8, section 8.3.6.1). Field and laboratory bioassay studies indicate
variation in pH ranges among fish species (Figure 5A-4).
5A-8
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Critical pH Ranges of Fish
Yellow p-erch
Brown bullhead
Pumpkiris&ed
Largemouth bass
Northern pike
Brook trout
White sucker
Rock bass
Golden shiner
Arctic char
Atlantic salmon
z
Creek chub
SrnallmouHh bass
Walleye
N. rebellied dace
*«-
Fathead minnow
Bluntnose minnow
Blacknose shiner
o
4>o 5-o 6.0 pH 7
Safe range, no acid-related effects occur
Uncertain range, acid related effects may occur
Critical range, acid-rotated effects likely
Figure 5A-4. Critical aquatic pH range for fish species. Notes: Baker and Christensen
(1991) generally defined bioassay thresholds as statistically significant increases
in mortality or by survival rates less than 50% of survival rates in control waters.
For field surveys, values reported represent pH levels consistently associated
with population absence or loss. Source: Fenn et al. (2011) based on Baker and
Christensen (1991) (ISA, Appendix 8, Figure 8-3).
As noted in the ISA, "[ajcross the eastern U.S., brook trout are often selected as a
biological indicator of aquatic acidification because they are native to many eastern surface
waters and because residents place substantial recreational and aesthetic value on this species"
(ISA, Appendix 8, p. 8-26). Compared to other fish species in Appalachian streams, this species
is relatively pH sensitive. For example, "[in many Appalachian mountain streams that have been
acidified by acidic deposition, brook trout is the last fish species to disappear; it is generally lost
at pH near 5.0 (MacAvoy and Bulger, 1995), which usually corresponds in these streams with
ANC near 0 peq/L (Sullivan et al., 2003)" (ISA, Appendix 8, p. 8-21).
As described in section 4.2.1 episodic acidification during storm events can pose risks in
low ANC streams. For example, streams with ANC around 20 ueq/L or less at base flow may be
5A-9
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considered vulnerable to episodic acidification events that could reduce pH and ANC to levels
potentially harmful to brook trout and other species. Streams with suitable habitat and annual
average ANC greater than about 50 [j,eq/L are often considered suitable for brook trout in
southeastern U.S. streams and reproducing brook trout populations are expected (Bulger et al.,
2000). Streams of this type provide "sufficient buffering capacity to prevent acidification from
eliminating this species and there is reduced likelihood of lethal storm-induced acidic episodes"
(ISA, Appendix 8, p. 8-26). Results of a study by Andren and Rydin (2012) suggested a
threshold of Al less than 20 ug/L and pH higher than 5.0 for healthy brown trout populations by
exposing yearling trout to a pH and inorganic Al gradient in humic streams in Scandinavia (ISA,
Appendix 8, section 8.3.6.2). Another recently available study that investigated the effects of
episodic pH shifts fluctuations in waterbodies of eastern Maine reported that episodes resulting
in pH dropping below 5.9 (ANC of -50 (j,eq/L) have the potential for harmful physiological
effects to Atlantic salmon smolts if coinciding with the smolt migration in eastern Maine rivers
(Liebich et al., 2011; ISA, Appendix 8, section 8.3.6.2).
Investigations of waterbody recovery from historic deposition have reported on episodic
acidification associated with the high SO42" remaining in watershed soils. For example,
monitoring data in the Great Smoky Mountains National Park indicated that while the majority
of SO42" entering the study watershed was retained, SO42" in wet deposition moved more directly
and rapidly to streams during large precipitation events, contributing to episodic acidification of
receiving streams and posing increased risk to biota (ISA, Appendix 7, section 7.1.5.1.4). High
flow episodes in historically impacted watersheds of the Appalachians have been reported to
appreciably reduce stream ANC (Lawrence et al., 2015).
There is often a positive relationship between pH or ANC and number of fish species, at
least for pH values between about 5.0 and 6.5, or ANC values between about 0 and 50 to 100
[j,eq/L (Cosby et al., 2006; Sullivan et al., 2006; Bulger et al., 1999). This is because energy cost
in maintaining physiological homeostasis, growth, and reproduction is high at low ANC levels
(Sullivan et al., 2003; Wedemeyer et al., 1990). As noted in section 4.2.1.1.2, surveys in the
heavily impacted Adirondack mountains found that lakes and streams having an annual average
ANC < 0 [j,eq/L and pH near or below 5.0 generally support few or no fish species to no fish at
all, as illustrated in Figure 5-3 below (Sullivan et al., 2006; ISA, Appendix 8, section 8.3.6.3.
5 A-10
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14 -
12 ¦
W JA
(0 10
"5
a 8
to
» 6
UL
O 4
Is
JD 2
£
z 0
•2 -
•4 -I
•200 -100 0 100 200 300 400 500
ANC (ueq/L)
Figure 5A-5. Number of fish species per lake versus acidity status, expressed as ANC, for
Adirondack lakes. Notes: The data are presented as the mean (filled circles) of
species richness within 10 (ieq/L ANC categories, based on data collected by the
Adirondacks Lakes Survey Corporation. Source: Modified from Sullivan et al.
(2006). (ISA, Appendix 8, Figure 8-4)
The data presented in Figure 5A-5 above suggest that there could be a loss of fish species
in these lakes with decreases in ANC below approximately 50 to 100 peq/L (Sullivan et al.,
2006). For streams in Shenandoah National Park, a statistically robust relationship between ANC
and fish species richness was also documented by Bulger et al. (2000). However, interpretation
of species richness relationship with ANC can be difficult and misleading, because more species
tend to occur in larger lakes and streams as compared with smaller ones, irrespective of acidity
(Sullivan et al., 2006) because of increased aquatic habitat complexity in larger lakes and streams
(Sullivan et al., 2003; ISA, Appendix 8, section 8.3.6.3).
Observations of effects in watersheds impacted by historic acidification can also reflect
the influence of episodic high flow events that lower pH and ANC appreciably below the
baseflow ANC (as described above). Studies described above are summarized below in the
context of ANC ranges: <0, 0-20, 20-50, 50-80, and >80 }ieq/L:
• At ANC levels <0 ueq/L, aquatic ecosystems have exhibited low to a near loss of aquatic
diversity and small population sizes. For example, planktonic and macroinvertebrates
communities shift to the most acid tolerant species (Lacoul et al., 2011) and mayflies can
be eliminated (Baker and Christensen, 1991). A near to complete loss of fish populations
can occur, including non-acid sensitive native species such as brook trout {Salvelimis
fontinalis), northern pike (Esox Indus), and others (Sullivan et al., 2003, 2006; Bulger et
al., 2000), which is in most cases attributed to elevated inorganic monomeric Al
Acute
if
3
8
Low
«r
a»
a
m
•
f •-V.V -•
S
V.ad-
f
> » I » I
5 A-11
-------
concentration (Baldigo and Murdoch, 1997). At this level, aquatic diversity is at its
lowest (Bulger et al., 2000; Baldigo et al., 2009; Sullivan et al., 2006) with only
acidophilic species being present.
• In waterbodies with ANC levels between 0 and 20 [j,eq/L, acidophilic species dominate
other species (Matuszek and Beggs, 1988; Driscoll et al., 2001) and diversity is low
(Bulger et al., 2000; Baldigo et al., 2009; Sullivan et al., 2006). Plankton and
macroinvertebrate populations have been observed to decline, and acid-tolerant species
have outnumbered non-acid sensitive species (Liebich et al., 2011). Sensitive species are
often absent (e.g., brown trout, common shiner, etc.) while non-sensitive fish species
populations may be reduced (Bulger et al., 2000). Episodic acidification events (e.g.,
inflow with ANC <0 [j,eq/L and pH< 5), may have lethal impacts on sensitive lifestages
of some biota, including brook trout and other fish species (Matuszek and Beggs, 1988;
Driscoll et al., 2001).
• Levels of ANC between 20 and 50 [j,eq/L have been associated with the loss and/or
reduction in fitness of aquatic biota that are sensitive to acidification in some waterbodies
of the Adirondacks and Appalachians. Such effects included reduced aquatic diversity
(Kretser et al., 1989; Lawrence et al., 2015; Dennis and Bulger, 1995) with some
sensitive species missing (Bulger et al., 2000; Sullivan et al., 2006). In historically
impacted watersheds, waterbodies with ANC below 50 [j,eq/L are more vulnerable to
increased potential for harm associated with episodic acidification (ISA, Appendix 8,
section 8.2). Comparatively, acid tolerant species, such as brook trout may have moderate
to healthy populations, (Kretser et al., 1989; Lawrence et al., 2015; Dennis and Bulger,
1995).
• At an ANC between 50 and 80 [j,eq L-l, the fitness and population size of some sensitive
species have been affected in some historically impacted watersheds. Levels of ANC
above 50 [j,eq/L are considered suitable for brook trout and most fish species because
buffering capacity is sufficient to prevent the likelihood of lethal episodic acidification
events (Driscoll et al.; 2001; Baker and Christensen; 1991). However, depending on other
factors, the most sensitive species have been reported to experience a reduction in fitness
and/or population size in some waterbodies (e.g., blacknose shiner [Baldigo et al., 2009;
Kretser et al., 1989; Lawrence et al., 2015; Dennis and Bulger, 1995]). Fish species
richness has also been reported to be affected in some Adirondack streams at ANC 50
(Sullivan et al., 2006).
• Values of ANC >80 [j,eq/L have generally not been associated with harmful effects on
biota (Bulger et al., 1999; Driscoll et al., 2001; Kretser et al., 1989; Sullivan et al., 2006).
5A.1.4 Chemical Criterion and Critical Threshold
Most aquatic CL studies conducted in the U.S. use surface water ANC as the principal
metric of water quality change in response to changes in a N and/or S deposition, which is
known as the chemical criterion. The ANC is generally a more stable measurement than pH
because ANC is insensitive to changes in CO2 and it reflects sensitivity and effects of
acidification in a linear fashion across the full range of ANC values. The critical threshold is then
the value of the chemical criterion (ANC) beyond which it is negatively impacted. For the
5 A-12
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analyses in this assessment, CLs were evaluated with respect to three different ANC thresholds
to account for variation in waterbodies with regard to risk of episodic acidification events,
associated uncertainties, and potential for differing science policy judgments on these
uncertainties: 20 [j,eq/L, 30 [j,eq/L and 50 [j,eq/L based on section 5A. 1.3. Most aquatic CL
studies conducted in the U.S. since 2010 use an ANC of 20 and/or 50 [j,eq/L, because 20 [j,eq/L is
considered by the authors to provide protection for "natural" or "historical" range of ANC and
50 [j,eq/L provides overall ecosystem protection (DuPont et al., 2005; McDonnell et al., 2012,
2014; Sullivan et al., 2012a, 2012b; Lynch et al., 2022; Fakhraei et al., 2014; Lawrence et al.,
2015). In the Mountain west, vulnerable lakes and streams to deposition driven aquatic
acidification are often found in the mountains where surface water ANC levels are low and
typically vary between 0 and 30 [j,eq/L (Williams and Labou, 2017; Shaw et al., 2014). For these
reasons, various studies, including some represented in the National Critical Loads Database
(NCLD), have used an ANC threshold of 50 [j,eq/L for the eastern and 20 [j,eq/L for the western
CONUS (denoted as "50/20" (j,eq/L). In the analyses in this assessment, we have calculated CL
exceedances for ANC thresholds of 20, 30 and 50 [j,eq/L across the CONUS, and also for the
50/20 (E/W)3 application of ANC thresholds. An ANC of 80 [j,eq/L was considered; however, it
was determined that many waterbodies, particularly, in acid sensitive regions of CONUS never
had an ANC that high and would never reach an ANC that high naturally.
5A.1.4.1 Natural Acidic Waterbodies
Some waterbodies are naturally acidic because of multiple factors, but most commonly
due to acidic rock within the waterbodies watershed, low base cation weathering rates linked to
the type of bedrock, and high DOC with the surface waters. Natural or historical level of ANC
concentration are typically above 20 [j,eq/L (Sullivan et al., 2012a; Shaw et al., 2014). Sullivan et
al. (2012a) using Model of Acidification of Groundwater in Catchment (MAGIC) simulations for
pre-industrial (1850), suggested that in pre-industrial times, there were no acidic lakes (ANC < 0
(j,eq/L) and only -6% of modeled lakes had ANC < 20 [j,eq/L in the Adirondack mountains, NY.
For these reasons, most recent CL studies (since 2010) use 20 [j,eq/L as a minimum ANC
threshold. For waterbodies where their natural or historical level of ANC is lower than the
selected ANC threshold, the calculated CL is invalid or not achievable at any level of deposition.
In those cases, the CL was evaluated, but was not included in the results and summary
assessments.
3 Consistent with regional definitions based on groups of states that were employed in the last review, in this REA
for this current review, the West includes the states of ND, SD, CO, WY, MT, AZ, NM, UT, NV, ID, CA, OR,
and WA (2009 REA, Appendix 1, p. 1-21). Accordingly, an ecoregion is designated western if it intersects or
overlaps with these ten states, and eastern ecoregions are those not designated as western.
5A-13
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5A.1.5 Critical Load Data
Aquatic CLs used in this assessment came from the National Critical Load Database
version 3.2 (Lynch et al., 2022) and include recent studies identified in the ISA (e.g., Lawrence
et al., 2015; Fakhraei et al., 2014; Sullivan et al., 2012a; Fakhraei et al., 2016). The NCLD is
comprised of CLs calculated from several common models: (1) steady-state mass-balance
models such as the Steady-State Water Chemistry (SSWC), (2) dynamic models such as MAGIC
(Cosby et al., 1985) or Photosynthesis EvapoTranspiration Biogeochemical model (PnET-BGC)
(Zhou et al., 2015) run out to year 2100 or 3000 to model steady-state conditions and (3) regional
regression models that use results from dynamic models to extrapolate to other waterbodies
(McDonnell et al., 2012; Sullivan et al., 2012a). These approaches differ in the way watershed
base cation weathering was determined (e.g., F-Factor or dynamic model).
Figure 5A-6 shows the unique locations for 13,000+ CLs used in this assessment. Critical
load waterbodies are concentrated in areas that are acid sensitive in the eastern U.S. and the
Rocky Mountain and Pacific Northwest regions of the west. Not all waterbodies are sensitive to
acidification. Small to medium size lakes size (>200 Ha) and streams (1-3 orders) tend to be the
waterbodies that are impacted by deposition driven acidification. Rivers are not typically
impacted by deposition driven acidification. Data in the NCLD are generally focused on
waterbodies impacted by deposition-driven acidification. A waterbody is represented as a single
CL value. In many cases, a waterbody has more than one CL value calculated for it because
different studies determined a value for the same waterbody. When more than one CL exists, the
CL from the most recent study was selected or averaged when the publications are from the same
timeframe.
5 A-14
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Low Sensivity: 101 - 200
• Not Sensitive: > 200
Figure 5A-6. Unique waterbody locations with CL estimates used in this assessment. Lower
values are red and orange; the lowest bin includes CLs of zero (section 5A.1.6).
5A.1.5.1 Steady-State Water Chemistry Model and F-Factor
Critical loads derived with the Steady State Water Chemistry (SSWC) model used
available water chemistry data, and 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 pre-selected level (Scheffe et al., 2014; Miller, 2011; Dupont et
al.. 2005; and Vermont Department of Environmental Conservation (VDEC), 2003, 2004, 2012).
The SSWC model assumes a mass balance and that all SO42" in runoff originates from sea salt
spray and anthropogenic deposition. The acidity CL can be defined for S only (CLS) and S and
N (CLSN). A CI for S only is calculated based on the principle that the acid load should not
exceed the non-marine, base cation inputs minus a nutrient base cation uptake and ANC buffer to
protect selected biota from being damaged (Eq. 5A-2):4
CLS = BC*deP + BCW - Bcu - nANCcdt (5A-2)
Where:
4 The F-factor approach to the SSWC model uses an integrated watershed estimates of the base cation inputs of
BC*dep, BCw, and Bcu defined as base cation flux (BC*o). e.g., BC*o = BC*deP + BCW - Bcu.
5A-15
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BC*deP (BC; Ca+Mg+K+Na) = the sea-salt corrected non-anthropogenic deposition of
base cations (the asterisk denotes the correction for base
cations of marine origin [Henriksen et al., 2002]);
BCW (BC; Ca+Mg+K+Na) = the average watershed weathering flux;
Bcu (Be: Ca+Mg+K) = the net long-term average uptake of base cations in the biomass
(i.e., the annual average removal of base cations due to harvesting);
nANCcrit = the lowest ANC-flux that protects the biological communities.
BCU = zero for these CLs.
For these CLs based on both S and N, the SSWC model was modified to incorporate a
simplified N framework whereby N components that account for nitrogen removal from long-
term nitrogen immobilization and denitrification were included in the model (Eq. 5A-3):5
CLSN = BC*dep + BCW + Nu+ Ni + Nde - Bcu - nANCcrit (5A-3)
Where:
Nu = N removal through removal of trees with harvesting;
Ni = N removal from long-term N immobilization;
Nde = N removal from the soil through microbial denitrification.
The sum of Nu, Ni, and Nde defines the minimum CL for N (CLNmin) as the amount of N
deposition that does not lead to acidification in the watershed. The variable, Ni, was set equal to
4.30 meq/m2-yr (McNulty et al., 2007) and Nde was set equal to 7.14 meq/m2-yr (Ashby et al.,
1998) for sites in the east. For western states, a combined value of Ni+Nde = 11.0 eq/ha-yr was
used based on Nanus et al. (2012). For Sullivan et al. (2012b), Nu also includes removal of N via
uptake by tree boles that were harvested, which was based on literature values summarized by
McNulty et al. 2007. Nitrogen removal can also be incorporated into the acidity CL and CL
exceedances (Ex) using the using the NO3" leaching flux, Nie, (Henriksen and Posch, 2001) (Eq.
5A-4):
Ex = Sdep + Nie - CLS (5A-4)
Where:
Nie = the sum of the measured concentrations of nitrate (NO3" eq/L) and ammonia (NH4+
eq/L) in the runoff (Qs m/yr) as ([N03~]+[ NH4+])*QS.
5 The F-factor approach to the SSWC model uses an integrated watershed estimates of the base cation inputs of
BC*dep, BCw, and Bcu defined as base cation flux (BC*o), e.g., BC*o = BC*dep + BCW - Bcu.
5A-16
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See sections 5A. 1.6 "Critical Load Exceedance" and 5A. 1.6.2 "Acidifying Contribution of
Nitrogen Deposition" below regarding how exceedances of CLs of S and of S and N combined
are calculated and how Nie was determined.
5A.1.5.2 MAGIC Model and Regional Linear Regression Models for
Estimating BCw Input to SSWC
Sullivan et al. (2012b) CLs used a modified form of the SSWC model (see Eq. 5A-3)
where base cation weathering was derived using a new method based on MAGIC model
estimates of BCW and regional linear regression models (see Sullivan et al., 2012b and
McDonnell et al., 2012), rather than the F-factor method for estimating BCW.
The MAGIC model was used to calculate watershed-specific BCW for input to regional
linear regression models that estimated BCW in all watersheds, including those without MAGIC
values. The BCw estimates were then used as input to the SSWC model. 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., 1985). The model simulates soil solution
chemistry, weathering rates, and surface water chemistry to predict the monthly and annual
average concentrations of the major ions in these waters (see Cosby et al., 1985 for more details
about the model itself). The base cation weathering terms in MAGIC represent the catchment-
average weathering rates for the soil compartments. In a one soil-layer application of MAGIC,
the weathering rates in MAGIC thus reflect the catchment-average net supply of base cations to
the surface waters draining the catchment. The sum of the MAGIC weathering rates for the
individual base cations is therefore identical in concept to the base cation weathering term, BCW,
in the SSWC CL model (Eq. 5A-2). Base cation weathering rates in MAGIC are calibrated
parameters. The calibration procedure uses observed deposition of base cations, observed (or
estimated) base cation uptake in soils, observed stream water base cation concentrations, and
runoff (Qs). These observed input and output data provide upper and lower limits for internal
sources of base cations in the catchment soils. The two most important internal sources of base
cations in catchment soils are modeled explicitly by MAGIC: primarily mineral weathering and
soil cation exchange. During the calibration process, observed soil base saturation for each base
cation and observed soil chemical characteristics are combined with the observed input and
output data to partition the inferred net internal sources of base cations between weathering and
base cation exchange.
The watershed-specific BCW values calculated by the MAGIC and input to a regional
regression model provided for watershed specific BCW values for 500+ monitoring locations in
the Appalachian Mountains of Virginia and West Virginia. Water chemistry and landscape
variables were used as the predictor variables in regression analyses to extrapolate BCW. Each of
5A-17
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the calibrated MAGIC study watersheds was placed in an ecoregion category based on which
ecoregion contained most of the watershed area and three separate regression models were
developed for each ecoregion (Table 5A-1). Watershed averages were used to represent the
spatial variability within each watershed for the landscape characteristics, except for watershed
area. Regression models were established using stepwise linear regression using 'best subsets' to
evaluate candidate models and constrain the number of independent predictor variables during
model selection. Water quality predictor data were collected during several regional surveys, as
compiled by Sullivan and Cosby, 2004). These surveys included the National Stream Survey
(NSS), Environmental Monitoring and Assessment Program (EMAP), Virginia Trout Stream
Sensitivity Study (VTSSS), and stream surveys conducted in Monongahela National Forest. One
water quality sample, generally collected during the spring between 1985 and 2001, was used to
characterize each watershed (Sullivan and Cosby, 2004).
Table 5A-1. Multiple regression equations to estimate BCw from either water chemistry
and landscape variables or from landscape variables alone, stratified by
ecoregion.
Ecoregion
n
Equation
r2
Central Appalachian
24
BCW = -37.5 + 0.6 (SBC) + 0.9 (N03) +
0.006 (WS Area)
0.93
Ridge and Valley
42
BCW= 107.0+0.5 (SBC)-0.06
(Elevation) - 3.2 (Slope)
0.86
Blue Ridge
26
BCW = 27.1 +0.6 (CALK)+0.6 (N03)
0.90
Note: These equations are presented in Table S2 of the Supplemental Materials for Sullivan et al. (2012b). SBC is
the sum of base cations; CALK is calculated ANC. The r2 values are for correlation of BCW predicted by the
regression equations with the BCw calculated by MAGIC based on the site-specific water chemistry data for these
sites, as presented in Table 2 and Figure 3 of McDonnell et al. (2012).
5A.1.5.3 MAGIC model and Hurdle Modeling for Estimating BCw Input to
SSWC
For McDonnell et al. (2014) and Povak et al. (2014) CLs used a modified form of the
SSWC model that excluded the N terms. Building on the framework of Sullivan et al. (2012b)
and McDonnell et al. (2012), McDonnell et al. (2014) and Povak et al. (2014) expanded the
study area and developed new statistical models to better predict BCW and evaluate CLs of S.
Their studies expanded the area to include the full Southern Appalachian Mountain region and
surrounding terrain from northern Georgia to southern Pennsylvania, and from eastern Kentucky
and Tennessee to central Virginia and western North Carolina.
As with Sullivan et al. (2012b) and McDonnell et al. (2012), the MAGIC model was used
to calculate watershed-specific BCW for 140 stream locations containing both measured soil
chemistry and water chemistry data (see section above for a description of MAGIC). In addition,
McDonnell et al. (2014) aggregated all known water quality data that totaled 933 sample
5A-18
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locations in order to develop a statistical model to predict ANC and BCW for all streams in the
Southern Appalachian Mountain region. Water chemistry data were collected between 1986 and
2009, with stream ANC calculated as the equivalent sum of the base cation concentrations (Ca2+,
Mg2+, K+, Na+, ammonium [NH4+]) minus the sum of the mineral acid anion concentrations
(chloride [CI"], NO3", SC>42~) Base cation weathering flux, BCW, was estimated as the net internal
source of base cations between weathering and base cation exchange for the watershed based on
the MAGIC model calibrations, which used observed stream and soil chemistry data, and
atmospheric deposition estimates to simulate surface water and soil solution chemistry
(McDonnell et al., 2014).
A random forest regression modeling technique was used to generate estimates of BCW
and ANC for the region. This was based on a suite of initial candidate predictor variables chosen
to represent potential broad- to fine-scale climatic, lithologic, topoedaphic, vegetative, and S
deposition variables that have the potential to influence ANC and BCW. To represent the
landscape conditions that influence specific locations along a stream, all candidate landscape
predictor variables were expressed on a 30 m grid basis across the Southern Appalachian
Mountain domain This resolution allowed for the creation of "flowpaths" for the development of
a topographically determined stream network. All data values for each target grid cell and
upslope grid cells were averaged based on the technique described in McDonnell et al. (2012).
A total of 140,504 watersheds were represented (i.e., delineated) with the use of a
hydrologically conditioned digital elevation model derivatives drawn from the National Ihdr"8raphv
Dataset (NHD+) (https://www.epa.gov/waterdata/nhdplus-national-hydrography-(Jataset-plus) The CLs from McDonnell et
al. (2014) and Povak et al. (2014) were then calculated with the SSWC model (Henriksen and
Posch, 2001) using the estimates of BCdep, BCW, Bcu, Qs and the ANC criterion set to a value of
50 |aeq/L for each stream node. See McDonnell et al. (2014) and Povak et al. (2014) for
additional methods detail.
5A.1.6 Critical Load Exceedance
A critical load exceedance (Ex) is when deposition is greater than the critical load.
Critical Load exceedances define when the benchmark for which the CL is derived is likely to be
exceeded. Uncertainty associated with the CL estimates were taken into account in the
calculation of CL exceedances. Specifically, based on preliminary analyses, a 6.25 meq S/m2-yr
or 1 kg S/ha-yr range of uncertainty was used in the exceedance calculation.6'7 For that reason,
6 Based on the CL uncertainty analysis (see section 5A. 3), on average the magnitude of the uncertainty for aquatic
CLs is 4.29 meq S/m2-yr or 0.69 kg S/ha-yr and a confidence interval of ±2.15 meq/m2-yr or ±0.35 kg S/ha-yr.
7 Critical load estimates have been converted from meq/m2-yr to kg S/ha-hr by dividing by 6.25. This takes into
account conversions from milliequivalents to equivalents, equivalents to kg S, and m2 to ha.
5A-19
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we conclude that CLs are exceeded where deposition is above 3.125 meq S/m2-yr or 0.5 kg S/ha-
yr and are not exceeded where deposition is below 3.125 meq S/m2-yr or 0.5 kg S/ha-yr. The
exceedances that fall within this range are described as being "at" the CL.8 This factor is
generally confirmed by the CL uncertainty analysis (see section 5A.3). For comparisons of
deposition to CL falling within this range, it is judged unclear whether the CL is exceeded.9
Aquatic CL exceedances can be considered with respect to S and combined N and S
deposition. When considering only S deposition (i.e., N deposition is zero), the exceedance is
expressed as the difference between the CL of S, total S deposition, and an uncertainty of ±3.125
meq S/m2-yr or ±0.5 kg S/ha-yr (Eq. 5A-6).
Exceedance (Ex) = (Total S deposition - CLS) > 3.125 meq S/m2-yr (5A-6)
In most cases, deposition of both S and N contributes to the exceedance. Calculating a
combined S and N Ex is more complex because both S and N contribution to acidification needs
to be factored in the exceedance. Given that not all N deposition to a watershed will contribute to
acidification, the N deposition removed by long-term N processes in the soil and waterbody (e.g.,
N uptake and immobilization) defines a "minimum" CL for N, noted as CLNmin. Nitrogen
deposition inputs below what is removed do not acidify, but the amount above this minimum will
likely contribute to acidification.
Exceedance of both N and S is a two-step calculation where if N removal is greater than
N deposition, only S deposition contributes to the Ex (Eq. 5A-8). However, if deposition of N is
greater than what is removed, the amount is not removed (Eq. 5A-9):
When minimum CLNmin > Total N deposition, then
8 The approach used here is generally consistent with the approach described in Chapter VII: Exceedance
Calculation (2015) of CLRTAP (2014-2021) Manual on Methodologies and Criteria for Modeling and Mapping
Critical Loads and Levels and Air Pollution Effects Risks and Trends (available at:
https://www.nmweltbniidesamt.de/en/cce-mannal).
9 The approach used here is generally consistent with the approach described in Chapter VII: Exceedance
Calculation (2015) of CLRTAP (2014-2021) Manual on Methodologies and Criteria for Modeling and Mapping
Critical Loads and Levels and Air Pollution Effects Risks and Trends (available at:
https://www.nmweltbniidesamt.de/en/cce-mannal).
Ex(N±S) = Total S deposition - CLS
When minimum CLNmin < Total N deposition, then
(5A-8)
Ex(N±S) = Total S + N deposition - CLS ±CLNmin
(5A-9)
5A-20
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There are different methods for determining the contribution of N deposition to aquatic
acidification. Section 5 A. 1.6.2 below described the two most common methods and how they are
handled in the CL exceedance calculations.
5A.1.6.1 Deposition
The amount of deposition used in the critical load exceedance calculation was determined
from the Total Deposition (TDep) model (https://nadp.slh.wisc.edu/committees/tdep/) (Schwede
and Lear (2014). See section 2.5.1 for more details. Both total N and S deposition were
determined to be the deposition level at the grid cell of the stream reach or lake location. For
each waterbody total N and S deposition was determined for each year from 2000 to 2020.
Three-year averages were calculated for these periods: 2001-03, 2006-08, 2010-12, 2014-16 and
2018-20 to be used in the different analyses. Critical load exceedances were then calculated for
each of these five periods and summed nationally and by level III ecoregion.
5A.1.6.2 Acidifying Contribution of Nitrogen Deposition
Unlike sulfur, not all N deposition leads to acidification. In fact, in some systems, none of
the entering N deposition acidifies because it is retained in biomass (terrestrial and aquatic) and
soils or is lost to the atmosphere by denitrification (ISA, Appendix 7, section 7.1.2.1). The
contribution of N deposition that acidifies is difficult to estimate and uncertain because the
underlying processes that store and release N in a watershed are complex, making them hard to
measure or model. Different methods have been developed to determine the amount of N
deposition that acidifies related to aquatic CL exceedances. There are two common approaches
in the studies that derived CLs used in this assessment: the first approach is based on the amount
of "N leaching" to the waterbody determined by the amount of dissolved N in the water
measured as the concentration of nitrite and runoff as presented in Henriksen and Posch (2001).
The second approach is the use of a "set value" based on long-term estimate of N immobilization
and denitrification as described by McNulty et al. (2007).
While the majority of atmospherically deposited N is either denitrified or accumulates in
watershed soils, vegetation, or groundwater (Galloway et al., 2008), the relative partitioning of N
loss via denitrification versus watershed storage is poorly known (Galloway et al., 2003). The
amount of N leaching to a waterbody that is not retained within the waterbody's biota is the
actual amount that contributes to acidification in the surface water. This depends on the amount
of N immobilized in the watershed, the amount exported to the drainage waters from the soils,
and how much uptake there is within the waterbody itself (Bergstrom, 2010; ISA Appendix 9,
section 9.1.1.2). As the different forms of N deposition enter a watershed, they undergo many
biogeochemical changes that result in N being stored in the soil and vegetation and being
released to the drainage water. As N deposition enters the watershed it can be quickly taken up
5A-21
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by the microorganisms in the soils and vegetation (particularly NH3) and incorporated into
biomass. This is the amount of N immobilized in the watershed. Nitrogen immobilization or
accumulation is the conversion of inorganic N to organic N. The amount that is immobilized can
be variable, but in most upland forest areas in the U.S. most of the atmospheric deposition is
retained in the soil (Nadelhoffer et al., 1999). Lovett et al. (2000) found immobilization of N to
be 49% to 90% of the atmospheric input based on N measured in stream water because of factors
such as vegetation type, age of vegetation, soil type, soil condition, the amount of nitrification,
management activities, etc. that control the amount of N accumulating. Several different data
compilations indicate also that 80 to 100% of N deposition is retained or denitrified within
terrestrial ecosystems that receive less than about 10 kg N/ha-yr (2008 ISA,, section 3.3.2.1;
2020 ISA, Appendix 4, section 4.6.2.2). Using compiled data collected during the mid- to late
1990s and focusing on lakes and streams in 83 forested watersheds of the Northeast, Aber et al.
(2003) suggested that in northeastern watersheds that receive less than about 8 to 10 kg N/ha-yr,
nearly all N deposition is retained or denitrified (ISA, Appendix 4, section). In the West, a study
of mixed conifer forests of the Sierra Nevada and San Bernardino Mountains estimated 17 kg
N/ha-yr as the N deposition load associated with the onset of NO3 leaching (Fenn et al., 2011).
Two studies in the Rocky Mountains indicated that the onset of NO3 leaching in alpine
catchments occurs at approximately 10 kg N/ha-yr (Baron et al., 1994; Williams and Tonnesen,
2000).
Nitrogen is removed or exported from the watershed by being volatilized in fires,
denitrified or leached to drainage waters (ISA, Appendix 4, sections 4.3 and 4.7). Denitrification
is the process by which nitrate is converted into gaseous N, most commonly in water saturated
soil, and returned to the atmosphere. Like with immobilization, many factors control the rate of
denitrification, making it difficult to estimate on a site-by-site basis without directly measuring
it. Accordingly, rates vary widely across sites. For example, Groffman (1994) observed rates of
denitrification of 4 to 135 kg N/ha-yr in very poorly drained soils on nutrient-rich parent material
and rates of 1.2 to 5.3 kg N/ha-yr in soils that were better-drained or less nutrient-rich (2008 ISA,
section 3.3.2.1; 2020 ISA, Appendix 4, Table 4-7). The N remaining, that isn't volatilized,
denitrified, or immobilized, can be leached in drainage water as nitrate or dissolved organic
nitrogen (DON) and has the potential to acidify surface waters. Nitrate concentrations or
concentrations of DON in streams impacted by acidification (typically 1-3 order streams) are
often very low, near zero, during the growing season when the N entering the watershed is
incorporated into soil or vegetation (Campbell et al., 2000; MacDonald et al., 2002; Dise et al.,
2009).
Recent studies from some regions of the U.S. (e.g., Eshleman et al., 2013; Driscoll et al.,
2016; Strock et al., 2014; Eshleman and Sabo, 2016; ISA, Appendix 7, section 7.1.5.1) showed
5A-22
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declines in concentrations of NO3 in surface waters that are consistent with declines in N
deposition. Using the Lake Multi-Scaled Geospatial and Temporal Database of the Northeast
Lakes of the U.S. (LAGOS-NE) containing water quality data from 2,913 lakes, Oliver et al.
(2017) identified atmospheric deposition as the main driver of declines in total N (TN)
deposition and lake TN:total P (TP) ratios from 1990 to 2011. In additional, monitored lakes and
streams as part of the EPA's Long-term Monitoring (LTM) program have average annual nitrate
concentrations of 9.5 and 7.64 |ieq/L, respectively, from 1990 to 2018 (Table 5A-2).10 Average
annual nitrate concentrations have decreased during the past decade to 7.19 and 4.40 |ieq/L.
These areas receive 5 to 8 kg N/ha-yr deposition annually.
Table 5A-2. Average annual nitrate concentrations for the EPA's Long-term Monitoring
program for lakes and streams.
Areas
Years
Average (95% CI)
(Meq/L)
New England Lakes
1990-2018
2.36 (2.155 - 2.565)
1990- 1999
2.33 (1.947-2.713
2000 - 2009
2.45(2.165-2.745)
2010-2018
0.56 (0.46-0.66)
Adirondacks Lakes
1990-2018
16.64 (15.966-17.318)
1990- 1999
18.48 (17.183-19.779)
2000 - 2009
16.70 (15.602-17.796)
2010-2018
13.82 (12.736-14.907)
Appalachian Streams
1990-2018
7.64 (7.092-8.187)
1990- 1999
11.50 (10.334-12.675)
2000 - 2009
6.59 (5.774-7.40)
2010-2018
4.40 (3.744-5.049)
We recognize that estimating the contribution of N deposition to acidification of surface
waters is difficult and uncertain because N cycling in an ecosystem is inherently variable and
data are limited across the U.S. to model it, however, it is important to the review that an
estimate be determined for aquatic acidification. Given the availability of data and what was
used in the 2008 review, we chose the N leaching method to estimate the contribution of total N
deposition to acidification that uses water quality and runoff data to estimate the amount of total
N deposition leaching to the drainage water that acidify (Henriksen and Posch, 2001).
This method is based on Henriksen and Posch (2001) where the exceedance for these CLs
is determined using the Nle (see Eq. 5 A-10):
111 The EPA's Long-Term Monitoring program tracks changes in surface water chemistry in the four regions shown
below, known to be sensitive to acid rain: New England, the Adirondack Mountains, the Northern Appalachian
Plateau, and the central Appalachians (https://www.epa.gov/power-sector/monitoring-surface-water-
chemistrv#tab-6). Data from this program are available at: https://doi.org/10.23719/1518546.
5A-23
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Ex(N+S) = Total S deposition + Nle - CLS (5A-10)
Where:
Nle = the sum of the measured concentrations of nitrate (NO3" |ieq/L) and ammonia
(NH4+ |ieq/L) in the runoff (Qs m/yr) as ([N03~]+[ NH4+])*Qs.
Factoring in the CL uncertainty, Eq. 5A-11 is:
Ex(N+S) = ((Total S deposition + Nle)- CLS) > 3.125 meq /m2-yr (5A-11)
The advantage of using the leaching estimate, Nle, (units of meq/m2-yr) is that for some
waterbodies it is based on measured water quality parameters that integrate all the N processes
occurring in the watershed. However, it is an indicator of conditions at the time of the
measurement, which may or may not be representative of long-term leaching. Steady-state CLs
are intended to represent the long-term leaching amount, which may or may not be well
represented under current conditions. For example, if a forest is a watershed is young, it would
be growing fast, and be able to immobilize most of the N deposition. However, that would not be
the case for old growth forests, which leach N at a much higher rate than younger forests
(Goodale et al., 2000). Old growth forests are thought of as the steady-state condition. If future
forests are older, then the leaching estimate based on current water quality would under-estimate
the acidification affect. But if future forests are like today's forests, then the leaching value
would better represent acidification impacts. Further studies in other old-growth forests are
needed to better understand the mechanisms causing long-term change in N cycling with forest
development (ISA Appendix 4, sections 4.3.2 and 4.3.6).
The Nle estimate, which is used for calculating the contribution to acidification from N
deposition is based on the calculated flux of N to the waterbody. This is estimated by multiplying
the concentration of nitrate as N within the waterbody by the annual surface water runoff to the
waterbody. Nitrogen leaching measurements are not typically collected across the U.S. For that
reason, annual leaching is estimated as a function of annual runoff (eq 5 A-10), which we
recognize is a source of uncertainty for this estimated value. We chose to use an annual runoff
(based on 30-year "Normals"11) that is included as a catchment parameter in the national
hydrology dataset developed by the U.S. EPA and U.S. Geological Survey (NHDplus, version
11 A "normal" is the 30-year average of a particular variable's measurements, calculated for a uniform time period.
Climate normals are derived from weather and climate observations captured by weather stations. The official
normals are calculated by the National Centers for Environmental Information at the U.S. NOAA for a uniform
30 year period, and consist of annual/seasonal, monthly, daily, and hourly averages and statistics of temperature,
precipitation, and other climatological variables from almost 15,000 U.S. weather stations.
(https://www.ncei.noaa.gov/products/land-based-station/us-climate-
normals#:~:text=A%20%22normal%22%20is%20the%2030,observations%20captured%20by%20weather%20sta
tions.).
5A-24
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2).12 Site-specific catchment annual runoff values were used for each waterbody with a CL. We
decided to use these annual runoff values because they are expected to better reflect long-term
and temporal patterns in runoff relevant to the mass-balance steady-state CL approach.
Some nitrate measurements used to estimate the Nle value date back to the 1980-1990s
and for that reason may not reflect more recent N leaching rates. Also, many of the waterbodies
with CLs have no nitrate measurements, hence, no way to calculate the leaching directly.
Another limitation is that a single water quality measurement is from a single sample which
cannot reflect the variability of nitrate during the year and for that reason may over or under-
estimate Nle. For waterbodies with no or few nitrate measurements, a "regional approach,"
described immediately below, was used to estimate Nle values in equation 5A-11. We recognize
this regional approach provides additional uncertainty to the leaching estimate, as recognized in
section 5A.3.2 below; however, it provides an integrated regional average estimate that is based
on numerous available water quality data and long-term runoff data at the catchment level where
the waterbody is located. We recognize that multiple water quality measurements over many
years for each waterbody and waterbody specific runoff or flow would be more desirable to
estimate the contribution of N deposition that is acidifying deposition, however, those data are
not readily available.
The regional aggregation was done for level III and level II and level I ecoregions. Water
quality data associated with the CLs was drawn from the NCLD, version 3.2, and was
supplemented with data from EPA's LTM program.13 We decide to focus on the water quality
data within the NCLD, version 3.2, because they represent the type of waterbodies (i.e., small
lakes/ponds, 1-3 order streams, etc.) that the CLs are based on. Measurements from within each
ecoregion III, II, and I were averaged to create three different values from which a single
aggregated value was chosen to replace the measured value for the CL. The ecoregion average
for level III was used unless there were fewer than 30 water quality measurements, in which case
the level II ecoregion average was used, and if there were fewer than 30 measurements in level
II, the level I ecoregion average was used. See Table 5A-3 for the number of measured used in
the aggregation and Nle value.
12 This dataset is available at: https://www.epa.gov/waterdaki/nhdpliis-national-ltvdrograpltv-dataset-pliis.
13 These data, equaling 16,900+ measurements across the CONUS were downloaded from
https://www.epa.gOv/power-sector/mon.itoring-siiiface-water-cheniistre#tab-6 in February 2020.
5A-25
-------
Table 5A-3. Regional aggregation of N leaching for ecoregion II and III, based on water
quality data for sites in NCLD, version 3.2.
Name
Code
No.Sites
Average N
Leaching
(meq/m2-yr)
Ecoregion II
Northern Appalachian and Atlantic Maritime Highlands
5.3.1
3729
0.7
Blue Ridge
8.4.4
2703
1.7
Southern Rockies
6.2.14
444
1.2
Ridge and Valley
8.4.1
1719
3.0
Middle Rockies
6.2.10
552
1.3
Sierra Nevada
6.2.12
566
1.0
Northern Lakes and Forests
5.2.1
894
0.6
Acadian Plains and Hills
8.1.8
630
0.5
Piedmont
8.3.4
573
4.8
Northeastern Coastal Zone
8.1.7
526
1.8
Central Appalachians
8.4.2
495
3.0
Idaho Batholith
6.2.15
212
8.8
Cascades
6.2.7
229
1.4
Southeastern Plains
8.3.5
413
5.6
Northern Piedmont
8.3.1
265
16.1
Wasatch and Uinta Mountains
6.2.13
114
1.7
Atlantic Coastal Pine Barrens
8.5.4
263
5.2
North Central Appalachians
5.3.3
230
2.7
Northern Allegheny Plateau
8.1.3
224
3.3
North Cascades
6.2.5
169
1.0
South Central Plains
8.3.7
157
0.6
Southwestern Appalachians
8.4.9
127
2.4
Columbia Mountains/Northern Rockies
6.2.3
96
0.9
Southern Coastal Plain
8.5.3
149
1.6
Middle Atlantic Coastal Plain
8.5.1
118
13.5
Coast Range
7.1.8
119
4.0
Eastern Great Lakes Lowlands
8.1.1
92
1.5
Klamath Mountains
6.2.11
85
1.2
North Central Hardwood Forests
8.1.4
101
1.7
Interior Plateau
8.3.3
89
7.2
Blue Mountains
6.2.9
65
0.3
Ozark Highlands
8.4.5
61
4.0
Eastern Cascades Slopes and Foothills
6.2.8
32
0.6
Ouachita Mountains
8.4.8
51
3.2
Strait of Georgia/Puget Lowland
7.1.7
39
4.5
Mississippi Valley Loess Plains
8.3.6
41
0.4
Arkansas Valley
8.4.7
39
1.7
5A-26
-------
Name
Code
No.Sites
Average N
Leaching
(meq/m2-yr)
Arizona/New Mexico Mountains
13.1.1
27
NA
California Coastal Sage, Chaparral, and Oak Woodlands
11.1.1
25
NA
Central Basin and Range
10.1.5
17
NA
Western Allegheny Plateau
8.4.3
37
2.4
Northern Basin and Range
10.1.3
20
NA
Southern Michigan/Northern Indiana Drift Plains
8.1.6
36
0.4
Canadian Rockies
6.2.4
32
1.4
Cross Timbers
9.4.5
31
0.8
Ecoregion II
Atlantic Highlands
5.3
3960
0.85
Mixed Wood Plains
8.1
1639
1.51
Ozark/Ouachita-Appalachian Forests
8.4
5259
2.34
Southeastern USA Plains
8.3
1568
6.55
Mississippi Alluvial and Southeast USA Coastal Plains
8.5
551
5.85
Mixed Wood Shield
5.2
896
0.62
Temperate Prairies
9.2
51
1.57
Western Cordillera
6.2
2596
1.78
South Central Semi-Arid Prairies
9.4
48
0.65
Upper Gila Mountains
13.1
27
2.23
Mediterranean California
11.1
49
0.70
Marine West Coast Forest
7.1
182
4.72
Cold Deserts
10.1
46
2.83
5A.1.7 Ecoregions Sensitivity to Acidification
The CONUS areas that have been described as sensitive to aquatic acidification include
the Northeast, Southeast, and upper Midwest, and to lesser extent, some areas of the Rocky
Mountains, Sierra Nevada Mountains, and the Pacific Northwest (Figure 5A-7; ISA, Appendix 8,
section 8.5). Area of the Appalachian Mountains (which extend from Maine to Georgia) are
particularly sensitive (ISA, Appendix 8, section 8.5). Ecoregions are used here as the unit of
spatial aggregation to characterize the level of acidification in sensitive areas across the CONUS.
National patterns of surface water alkalinity in the conterminous U.S. based on data collected
prior to 1988 (U.S. EPA, 2012) and modern ANC water quality measurements were used to
define which ecoregions were considered acid sensitive. The EPA's Total Alkalinity GIS layer
was developed in 1980's using water quality data to define regions of acid sensitivity, as shown
in Figure 5A-7a (Omernik and Powers 1983). Additionally, over 15,000 water quality ANC
measurements, collected across the CONUS for the period from 1990 to 2018 by multiple water
quality networks, programs, and research groups, have been summarized in Figure 5A-7b.
5A-27
-------
a. Total Alkalinity
W ».
£
*%
i
>*?/
b. ANC
-i
v*c-
v Si
J«r-
*)
\
\
r
< \*
r\ jT^ •%
*r\' '¦•;
_-%v
|ieq/L
$
Most Sensitive < 50
H 50-100
100-200
200-400
Not Sensitive > 400
CI 131
I
Figure 5A-7. Surface water quality alkalinity (a) and ANC (b) across the CON l;S based on
measurements collected prior to 1988 through 2018.
Water quality measurements of ANC and total alkalinity (Omernik and Powers, 1983)
were used to classify the 84 CONUS ecoregion Ills into four acid sensitive classes: (1) most acid
sensitive (<50 jaeq/L), (2) acid sensitive (50-100 |aeq/L), (3) moderately acid sensitive (100-200
ueq/L), and (4) low or no acid sensitivity (>200 |.ieq/L). The four categories are based on what
Omernik and Powers (1983) and Greaver et al. (2012) used in their assessment (Table 5A-4). A
total of 24 ecoregions III were acid sensitive and another 6 ecoregions were moderately acid
sensitive for a total of 30 (Table 5A-5). Fifty-four ecoregions had low or no evidence of acid
5A-28
-------
sensitivity across the CONUS (Table 5A-5 and Figure 5A-8). The acid sensitive ecoregions
generally are areas with mountains, high elevation terrain or water bodies in northern latitudes
(northern areas of Minnesota, Wisconsin and Michigan; and New England). The northern, non-
mountainous regions share attributes similar to mountainous regions (e.g., growing season,
vegetation, soils, geology) and are typically in rural areas, often in designated wilderness, park
and recreation areas. Of the 30 acid sensitive ecoregions, the following three ecoregions are
located on eastern coastal plain: (1) Middle Atlantic Coastal Plain (8.5.1), (2) Southern Coastal
Plains (8.5.3), and (3) Atlantic Coastal Pine Barrens (8.5.4). Waterbodies in these ecoregions
tend to have higher DOC values >10 mg/L, which is indicative of natural acidity (ISA, Appendix
7, section 7.1.2.5; 2008 ISA, section 3.2.4.2 and Annex B, p. B-35).
Table 5A-4. Acid sensitive categories and criteria used to define each one.
Acid Sensitive Category
Criteria
Most Acid Sensitive Ecoregions
>25 ANC values* less than 100 peq/L, > 75 ANC values from 100-
200 peq/L and have total alkalinity areas < 50 peq/L
Acid Sensitive Ecoregions
>10 ANC values less than 100 peq/L, > 40 ANC values from 100-
200 peq/L and have total alkalinity areas < 100 peq/L
Moderately Sensitive Ecoregions
>5 ANC values less than 100 peq/L, > 20 ANC values from 100-
200 peq/L and have total alkalinity areas < 200 peq/L
Low or Non-sensitive Ecoregions
<5 ANC values less than 100 peq/L, < 20 ANC values from 100-
200 peq/L and have total alkalinity areas >200 peq/L
* The four categories are based on what Omernik and Powers (1983) and Greaver et ai. (2012) used in their assessment.
Acid Sensitive Category
Low or Non-Sensitive
Moderately Sensitive
Acid Sensitive
Most Acid Sensitive
High level of natural acidity
Figure 5A-8, Level III ecoregions grouped into acid sensitivity categories.
5A-29
-------
Table 5A-5. Level III ecoregion categorization for acid sensitivity.
Ecoregion III
Code
No.
Critical
Loads
Total No.
ANC
Values
No. ANC
values
<100
|jeq/L
No. ANC
values
<200
|jeq/L
Total
Alkalinity
Area
(Meq/L)
Acid Sensitive Category
5.3.1
2851
2053
901
1302
50
Most Acid Sensitive Ecoregion
8.4.4
1972
1136
619
916
50
Most Acid Sensitive Ecoregion
8.4.1
1292
1394
459
733
50
Most Acid Sensitive Ecoregion
5.2.1
839
1074
398
535
50
Most Acid Sensitive Ecoregion
8.1.7
565
488
88
201
50
Most Acid Sensitive Ecoregion
6.2.10
496
323
61
127
50
Most Acid Sensitive Ecoregion
8.1.8
494
492
197
316
50
Most Acid Sensitive Ecoregion
8.3.5
390
432
141
211
50
Most Acid Sensitive Ecoregion
8.4.2
372
420
229
282
50
Most Acid Sensitive Ecoregion
6.2.14
372
327
56
107
50
Most Acid Sensitive Ecoregion
6.2.12
353
359
224
279
50
Most Acid Sensitive Ecoregion
8.5.4
234
130
78
100
50
Most Acid Sensitive Ecoregion
5.3.3
216
242
113
177
50
Most Acid Sensitive Ecoregion
6.2.7
179
244
80
129
50
Most Acid Sensitive Ecoregion
8.5.3
142
228
115
132
50
Most Acid Sensitive Ecoregion
8.3.4
508
455
28
84
50
Acid Sensitive Ecoregion
8.1.3
199
223
13
42
50
Acid Sensitive Ecoregion
6.2.15
188
164
60
95
50
Acid Sensitive Ecoregion
6.2.5
162
155
40
80
50
Acid Sensitive Ecoregion
8.3.7
153
165
17
41
50
Acid Sensitive Ecoregion
6.2.13
96
139
26
61
100
Acid Sensitive Ecoregion
6.2.3
86
147
13
31
50
Acid Sensitive Ecoregion
8.4.8
42
73
17
44
50
Acid Sensitive Ecoregion
8.4.9
117
64
19
32
50
Moderately Sensitive Ecoregion
8.5.1
105
183
14
37
50
Acid Sensitive Ecoregion
8.1.4
94
162
12
21
50
Moderately Sensitive Ecoregion
8.4.7
31
59
9
25
100
Moderately Sensitive Ecoregion
8.3.1
231
211
3
6
50
Low or Non-Sensitive Ecoregion
7.1.8
115
154
4
13
200
Low or Non-Sensitive Ecoregion
8.1.1
83
97
1
2
50
Low or Non-Sensitive Ecoregion
6.2.11
81
105
5
11
50
Moderately Sensitive Ecoregion
8.3.3
71
114
0
2
200
Low or Non-Sensitive Ecoregion
6.2.9
63
91
5
16
50
Moderately Sensitive Ecoregion
8.4.5
56
111
0
0
>200
Low or Non-Sensitive Ecoregion
8.3.6
41
61
2
13
200
Low or Non-Sensitive Ecoregion
7.1.7
38
51
3
7
50
Low or Non-Sensitive Ecoregion
8.4.3
35
114
0
2
50
Low or Non-Sensitive Ecoregion
5A-30
-------
Ecoregion III
Code
No.
Critical
Loads
Total No.
ANC
Values
No. ANC
values
<100
|jeq/L
No. ANC
values
<200
|jeq/L
Total
Alkalinity
Area
(Meq/L)
Acid Sensitive Category
8.1.6
33
131
0
0
>200
Low or Non-Sensitive Ecoregion
6.2.4
31
42
3
5
100
Low or Non-Sensitive Ecoregion
6.2.8
27
43
0
1
50
Low or Non-Sensitive Ecoregion
9.4.5
26
96
0
0
>200
Low or Non-Sensitive Ecoregion
9.2.3
26
180
0
0
>200
Low or Non-Sensitive Ecoregion
13.1.1
25
64
0
3
>200
Low or Non-Sensitive Ecoregion
7.1.9
24
28
0
0
>200
Low or Non-Sensitive Ecoregion
8.4.6
23
31
3
20
100
Moderately Sensitive Ecoregion
11.1.3
22
19
0
0
>200
Low or Non-Sensitive Ecoregion
9.2.4
21
114
0
0
>200
Low or Non-Sensitive Ecoregion
11.1.1
21
57
0
0
>200
Low or Non-Sensitive Ecoregion
10.1.3
20
80
0
4
>200
Low or Non-Sensitive Ecoregion
8.5.2
19
91
0
0
200
Low or Non-Sensitive Ecoregion
8.3.2
18
115
5
5
50
Low or Non-Sensitive Ecoregion
9.5.1
16
36
0
0
200
Low or Non-Sensitive Ecoregion
10.1.5
16
87
0
2
200
Low or Non-Sensitive Ecoregion
8.1.5
15
80
1
1
>200
Low or Non-Sensitive Ecoregion
8.1.10
14
63
0
0
100
Low or Non-Sensitive Ecoregion
8.2.4
14
96
0
0
>200
Low or Non-Sensitive Ecoregion
8.3.8
10
27
0
0
200
Low or Non-Sensitive Ecoregion
8.2.1
10
38
1
1
>200
Low or Non-Sensitive Ecoregion
9.4.4
7
34
0
0
>200
Low or Non-Sensitive Ecoregion
9.4.2
5
144
2
3
>200
Low or Non-Sensitive Ecoregion
10.1.4
3
56
0
1
200
Low or Non-Sensitive Ecoregion
9.4.7
3
25
0
0
>200
Low or Non-Sensitive Ecoregion
5.2.2
2
26
1
1
200
Low or Non-Sensitive Ecoregion
10.1.8
2
11
0
0
200
Low or Non-Sensitive Ecoregion
11.1.2
2
14
0
0
>200
Low or Non-Sensitive Ecoregion
10.1.2
2
32
0
0
>200
Low or Non-Sensitive Ecoregion
8.2.3
2
42
0
0
>200
Low or Non-Sensitive Ecoregion
9.3.1
2
114
0
0
>200
Low or Non-Sensitive Ecoregion
10.1.6
1
51
0
0
200
Low or Non-Sensitive Ecoregion
10.2.1
0
6
0
0
>200
Low or Non-Sensitive Ecoregion
9.3.4
0
22
0
0
>200
Low or Non-Sensitive Ecoregion
9.4.6
0
19
0
0
>200
Low or Non-Sensitive Ecoregion
9.4.1
0
54
0
0
>200
Low or Non-Sensitive Ecoregion
9.2.1
0
66
0
0
>200
Low or Non-Sensitive Ecoregion
9.4.3
0
55
0
0
>200
Low or Non-Sensitive Ecoregion
5A-31
-------
Ecoregion III
Code
No.
Critical
Loads
Total No.
ANC
Values
No. ANC
values
<100
|jeq/L
No. ANC
values
<200
|jeq/L
Total
Alkalinity
Area
(Meq/L)
Acid Sensitive Category
10.1.7
0
21
0
0
>200
Low or Non-Sensitive Ecoregion
9.3.3
0
270
3
3
>200
Low or Non-Sensitive Ecoregion
12.1.1
0
11
0
0
>200
Low or Non-Sensitive Ecoregion
8.2.2
0
27
0
0
>200
Low or Non-Sensitive Ecoregion
15.4.1
0
5
0
0
>200
Low or Non-Sensitive Ecoregion
9.2.2
0
20
0
0
>200
Low or Non-Sensitive Ecoregion
10.2.4
0
14
0
0
>200
Low or Non-Sensitive Ecoregion
10.2.2
0
15
0
0
>200
Low or Non-Sensitive Ecoregion
9.6.1
0
7
1
1
>200
Low or Non-Sensitive Ecoregion
Note: Ecoregion III code in bold indicates the 25 ecoregions that are the focus o
described in section 5A.2.2.
: the ecoregion analyses
5A.2 ANALYSIS RESULTS
The aquatic acidification assessment is intended to estimate the ecological exposure and
risk posed to aquatic ecosystems from the acidification effects of S and/or N deposition to
sensitive regions across the CONUS. The CL itself indicates how sensitive the waterbody is to
inputs of acidic deposition of S and/or N. In Figure 5A-6, a CL indicates the amount of acidic
input of total S and/or N deposition that a waterbody can neutralize and still maintain an ANC of
50 [j,eq/L. Watersheds with CL values less than 100 meq/m2-yr (red and orange circles) are most
sensitive to surface water acidification, whereas watersheds with values greater than 100
meq/m2-yr (yellow and green circles) are the least sensitive sites. Most sensitive waterbodies are
located along the Appalachian Mountains range, the upper Mid-west, and the Rocky Mountain
range in the west, which correspond to the same regions as the acid sensitive ecoregions (Figure
5A-7).
5A.2.1 Results of National Scale Assessment of Risk
A total of 13,824 unique waterbodies across the CONUS had calculated CLs available in
NCLD v3.2. Table 5A-6 summarizes the percent of waterbodies with CLs that are less than 2, 6,
12, 18 kg S/ha, indicating most CLs used in this analysis are less than 18 kg S/ha. Table 5A-7
contains a summary of CL exceedances for S only and S and N combined for average annual
deposition estimates for 2018-20, 2014-16, 2010-12, 2006-08, and 2001-03. An exceedance
indicates that the estimated deposition for a period is greater than the amount of deposition the
waterbodies are estimated to be able to neutralize and still maintain the ANC thresholds of 20,
30, and 50 [j,eq/L.
5A-32
-------
Table 5A-6.
Percent of waterbodies with critical loads less than 2, 6,12, and 18 kg S/ha-yr
based on ANC thresholds of 20, 30, and 50 jieq/L.
Critical Load,
Percent of Waterbodies with CL for specified
kg/ha-yr (meq/m2-yr)
ANC thresholc
below 2, 6,12 and 18 kg/ha-hr
20 |jeq/L
30 |jeq/L
50 peq/L
<2 (12.5)
3%
5%
11%
<6 (37.5)
14%
17%
25%
<12(75)
36%
39%
45%
<18(112.5)
52%
55%
58%
Table 5A-7. Summary of CL exceedances, nationally, by ANC thresholds and deposition
periods.
ANC
Threshold
S Only CL Exceedances*
'S and N' CL Exceedances*
All Values6
CL>0 Values Onlyc
All Values6
CL>0 Values Onlyc
Deposition estimates for 2018-20
20
2%
1%
2%
2%
30
3%
2%
4%
2%
50
9%
4%
9%
5%
50/20
7%
4%
8%
4%
Deposition estimates for 2014-16
20
3%
3%
3%
3%
30
5%
4%
5%
4%
50
11%
6%
12%
7%
50/20
10%
6%
10%
7%
Deposition estimates for 2010-12
20
5%
5%
6%
5%
30
8%
7%
9%
7%
50
15%
11%
16%
11%
50/20
14%
10%
15%
11%
Deposition estimates for 2006-08
20
17%
16%
18%
17%
30
21%
19%
21%
20%
50
28%
24%
29%
25%
50/20
27%
23%
28%
24%
Deposition estimates for 2001-03
20
22%
22%
23%
23%
30
26%
25%
27%
25%
50
33%
28%
33%
29%
50/20
31%
28%
32%
28%
A An exceedance is deposition above the CL and error of 3.125 meq/m2-yr.
B "All Values" includes all critical loads.
c "CL>0 Values" includes only critical loads greater than 0.
5A-33
-------
Table 5A-8 includes both numbers and percent exceedances for the CONUS for the four-
deposition time periods and three ANC thresholds. Exceedance rates (e,g, percent of
waterbodies that exceed the CL) are lowest for the ANC threshold of 20 [j,eq/L and highest for
the ANC threshold of 50 [j,eq/L. For the most recent deposition period of 2018-20, 2%, 3%, and
9% of the modeled waterbodies received levels of total S deposition that exceeded their CL with
CL thresholds of 20, 30, and 50 [j,eq/L, respectively. The percentage of waterbodies exceeding a
CL for combined total S and N are slightly higher than S only percentages at 2%, 4%, and 9% of
the modeled waterbodies for CL thresholds of 20, 30, and 50 [j,eq/L based on Nle. This indicates
that most of the N deposition entering the watershed is retained within the watershed and/or
converted to gaseous N (e.g., N2O, N2, etc.). For all other deposition time periods, exceedance
rates are similar or only slightly higher (1-2%) when considering both N and S deposition
compared to just S deposition only.
Table 5A-8. Comparison of estimated deposition to CLs nationally based on all CL values
by ANC thresholds and deposition periods.
ANC
Threshold
Class
Sulfur Only CLs
Sulfur and Nitrogen CLs
No.
Percent
No.
Percent
De
position estimates for 2018-20
20
>CL
234
2%
266
2%
CL
452
3%
496
4%
CL
1203
9%
1262
9%
CL
1023
7%
1075
8%
CL
423
3%
465
3%
CL
680
5%
724
5%
CL
1512
11%
1591
12%
CL
1324
10%
1400
10%
-------
ANC
Threshold
Class
Sulfur Only CLs
Sulfur and Nitrogen CLs
No.
Percent
No.
Percent
De
position estimates for 2010-12
20
>CL
748
5%
798
6%
CL
1122
8%
1192
9%
CL
2114
15%
2215
16%
CL
1918
14%
2013
15%
CL
2328
17%
2433
18%
CL
2845
21%
2962
21%
CL
3911
28%
4035
29%
CL
3710
27%
3825
28%
CL
3064
22%
3191
23%
CL
3587
26%
3694
27%
CL
4504
33%
4611
33%
CL
4313
31%
4410
32%
CL) is where deposition for the modeled water
Dodies is above the
CL and error of 3.125 meq/m2-yr.
"at CL" indicates estimated deposition is within 3.125 meq/m2-yr of the CL.
This summary includes CLs below zero
Table 5A-9 includes both numbers of waterbodies and percent exceedances for the
CONUS for the four-deposition time periods and four ANC thresholds where CLs less than or
5A-35
-------
equal to zero were removed from the exceedance counts and percentages. Sites with CLs less
than or equal to zero are very sensitive waterbodies that naturally could not meet the ANC
threshold at any level of deposition. When zero and negative CLs are excluded, in the most
recent deposition period of 2018-2020, 1%, 2%, 4%, and 4% of the modeled waterbodies
received levels of total S deposition that exceeded CLs for ANC thresholds of 20, 30, 50, and
50/20 [j,eq/L, respectively (Table 5A-9). The percent exceedances for combined total S and/or N
CLs are only slightly higher than for S only CLs at 2%, 2%, 5%, and 4% of the modeled
waterbodies for ANC thresholds of 20, 30, 50, and 50/20 [j,eq/L based on Nle (Table 5 A-9). For
the deposition period of 2001-2003, exceedance percentages for Sulfur only were much higher
than in the 2018-2020 period, at 22%, 25%, 29, and 29% for ANC thresholds of 20, 30, 50, and
50/20 [j,eq/L (Table 5A-9). The percent of modeled waterbodies with negative CLs is the lowest
for an ANC threshold of 20 [j,eq/L at 0.4% and the highest for an ANC threshold of 50 [j,eq/L at
4.6% (Figure 5A-9).
Table 5A-9. National aquatic CL exceedances based on CLs greater than 0 by ANC
thresholds and deposition periods.
ANC
Threshold
Class
Sulfur Only CLs
Sulfur and Nitrogen CLs
No.
Percent
No.
Percent
Deposition estimates for 2018-20
20
>CL
182
1%
214
2%
CL
279
2%
323
2%
CL
566
4%
624
5%
CL
544
4%
596
4%
CL
371
3%
413
3%
CL
506
4%
550
4%
CL
873
7%
952
7%
CL
845
6%
921
7%
-------
ANC
Sulfur Only CLs
Sulfur and Nitrogen CLs
Threshold
Class
No.
Percent
No.
Percent
Deposition estimates for 2010-12
20
>CL
696
5%
746
5%
CL
948
8%
1018
7%
CL
1475
15%
1576
12%
CL
1439
14%
1534
11%
CL
2276
17%
2381
17%
CL
2671
20%
2788
20%
CL
3272
25%
3396
26%
CL
3231
24%
3346
25%
CL
3012
22%
3139
23%
CL
3413
25%
3520
26%
CL
3865
29%
3972
30%
CL
3834
29%
3931
29%
CL) is where deposition for the modeled waterbodies is above the CL and
error of 3.125 meq/m2-yr.
* "at CL" indicates that estimated deposition is within 3.125 meq/m2-yr of the waterbody CL
Zero and negative CLs were excluded from this summary.
5A-37
-------
Nationwide
35%
2020-18 2014-16 2010-12 2006-08 2001-03
¦ 20 ¦ 30 ¦ 5020 ¦ 50 ueq/L m Percent of waterbodies wih zero or negative critical loads
Figure 5A-9. Percent CL exceedances by ANC thresholds and deposition periods.
Figures 5A-10 to 5A-29 show locations of estimated CL exceedances across the CONUS
for S only and for ANC thresholds of 20, 30, 50, and 50/20 [j,eq/L for positive CLs only. Figure
5A-30 highlights the locations of waterbodies that have calculated negative CLs (grey dots).
These are waterbodies that are highly sensitive to acidification and likely naturally acidic as
indicated by their zero or negative CL. These waterbodies exceed the calculated CL at any
deposition amount. For these reasons, these sites have been removed from the assessment.
Exceedance maps for S and/or N combined are not included here because they show the same
pattern of exceedances as for S only and because exceedance rates are only slightly higher for
combined N and/or S deposition. Most exceedances occur in New England, the Adirondacks, the
Appalachian Mountain range (New England to Georgia), the upper Midwest, Florida, and the
Sierra Nevada mountains in California. Waterbodies in Florida and other coastal plain
ecoregions that exceed the CL are likely not related to deposition of S, but instead are related to
high levels of natural acidity in these drainage waters. These drainage waters tend to be naturally
high in dissolved organic carbon, causing these systems to be acidic (2008 ISA, section 3.2.4.2).
5A-38
-------
a- Critical Load Exeedance for Sulfur
O
Near the Critical Load (±3.125 meq/m2/yr)
Exceeds the Critical Load
Figure 5A-10. Critical load exceedance (Ex) for S only total deposition from 2001-03 for
an ANC threshold of 20 jieq/L: a) waterbodies with sulfur deposition below
the CL and uncertainty (Ex < -3.125 ineq/m2-yr), and b) waterbodies with
sulfur deposition above or near the CL.
5A-39
-------
Near the Critical Load (±3.125 meq/m2/yr)
• Exceeds the Critical Load
Critical Load Exeedance for Sulfur
Figure 5A-11. Critical load exceedance (Ex) for S only total deposition from 2001-03 for
an ANC threshold of 30 jieq/L: a) waterbodies with sulfur deposition below
the CL and uncertainty (Ex < -3.125 meq/m2-yr), and b) waterbodies with
sulfur deposition above or near the CL.
5A-40
-------
e Near the Critical Load (±3.125 meq/m2/yr)
• Exceeds the Critical Load
Critical Load Exeedance for Sulfur
Figure 5A-12. Critical load exceedance (Ex) for S only total deposition from 2001-03 for
an ANC threshold of 50 jieq/L: a) waterbodies with sulfur deposition below
the CL and uncertainty (Ex < -3.125 ineq/m2-yr), and b) waterbodies with
sulfur deposition above or near the CL.
5 A-41
-------
a.
Critical Load Exeedance for Sulfur
2001-2003
b.
ANC = 50 peq/L East
ANC = 20 peq/L West
• Does not Exceed the Critical Load
Near the Critical Load (±3.125 meq/m2/yr)
• Exceeds the Critical Load
Figure 5A-13. Critical load exceedance (Ex) for S only total deposition from 2001-03 for
an ANC threshold of 50 for the eastern and 20 fieq/L for Western CON I IS:
a) waterbodies with sulfur deposition below the CL and uncertainty (Ex < -
3.125 nieq/m2-yr), and b) waterbodies with sulfur deposition above or near
the CL.
5A-42
-------
o Near the Critical Load (±3.125 meq/m2/yr)
• Exceeds the Critical Load
Critical Load Exeedance for Sulfur
Figure 5A-14. Critical load exceedance (Ex) for S only total deposition from 2006-08 for
an ANC threshold of 20 jieq/L: a) waterbodies with sulfur deposition below
the CL and uncertainty (Ex < -3.125 ineq/m2-yr), and b) waterbodies with
sulfur deposition above or near the CL.
5A-43
-------
Critical Load Exeedance for Sulfur
G
Near the Critical Load (±3.125 meq/m2/yr)
Exceeds the Critical Load
Figure 5A-15. Critical load exceedance (Ex) for S only total deposition from 2006-08 for
an ANC threshold of 30 jieq/L: a) waterbodies with sulfur deposition below
the CL and uncertainty (Ex < -3.125 ineq/m2-yr), and b) waterbodies with
sulfur deposition above or near the CL.
5A-44
-------
a- Critical Load Exeedance for Sulfur
© Near the Critical Load (±3.125 meq/m2/yr)
• Exceeds the Critical Load
Figure 5A-16. Critical load exceedance (Ex) for S only total deposition from 2006-08 for
an ANC threshold of 50 fieq/L: a) waterbodies with sulfur deposition below
the CL and uncertainty (Ex < -3.125 meq/m2-yr), and b) waterbodies with
sulfur deposition above or near the CL.
5A-45
-------
a Critical Load Exeedance for Sulfur
ANC
ANC
•
O
= 50 peq/L East
= 20 peq/L West
Does not Exceed the Critical Load
Nearthe Critical Load (+3.125 meq/m2/yr)
Exceeds the Critical Load
Figure 5A-17. Critical load exceedance (Ex) for S only total deposition from 2006-08 for
an ANC threshold of 50 for the eastern and 20 jieq/L for Western CONUS:
a) waterbodies with sulfur deposition below the CL and uncertainty (Ex < -
3.125 meq/m2-yr), and b) waterbodies with sulfur deposition above or near
the CL.
5A-46
-------
Critical Load Exeedance for Sulfur
G
Near the Critical Load (±3.125 meq/m2/yr)
Exceeds the Critical Load
Figure 5A-18. Critical load exceedance (Ex) for S only total deposition from 2010-12 for
an ANC threshold of 20 jieq/L: a) waterbodies with sulfur deposition below
the CL and uncertainty (Ex < -3.125 ineq/m2-yr), and b) waterbodies with
sulfur deposition above or near the CL.
5A-47
-------
O Near the Critical Load (±3.125 meq/m2/yr)
• Exceeds the Critical Load
Critical Load Exeedance for Sulfur
Figure 5A-19. Critical load exceedance (Ex) for S only total deposition from 2010-12 for
an ANC threshold of 30 jieq/L: a) waterbodies with sulfur deposition below
the CL and uncertainty (Ex < -3.125 meq/m2-yr), and b) waterbodies with
sulfur deposition above or near the CL.
5A-48
-------
o Near the Critical Load (±3.125 meq/m2/yr)
• Exceeds the Critical Load
Critical Load Exeedance for Sulfur
Figure 5A-20. Critical load exceedance (Ex) for S only total deposition from 2010-12 for
an ANC threshold of 50 jieq/L: a) waterbodies with sulfur deposition below
the CL and uncertainty (Ex < -3.125 meq/m2-yr), and b) waterbodies with
sulfur deposition above or near the CL.
5A-49
-------
a.
Critical Load Exeedance for Sulfur
2010-2012
b.
ANC = 50 |jeq/L East
ANC = 20 peq/L West
• Does not Exceed the Critical Load
o Near the Critical Load (±3.125 meq/m2/yr)
• Exceeds the Critical Load
Figure 5A-21. Critical load exceedance (Ex) for S only total deposition from 2010-12 for
an ANC threshold of 50 for the eastern and 20 jieq/L for Western CONUS:
a) waterbodies with sulfur deposition below the CL and uncertainty (Ex < -
3.125 meq/m2-yr), and b) waterbodies with sulfur deposition above or near
the CL.
5A-50
-------
a- Critical Load Exeedance for Sulfur
Near the Critical Load (±3.125 meq/m2/yr)
Exceeds the Critical Load
Figure 5A-22. Critical load exceedance (Ex) for S only total deposition from 2014-16 for an
ANC threshold of 20 jieq/L: a) waterbodies with sulfur deposition below the
CL and uncertainty (Ex < -3.125 meq/m2-yr), and b) waterbodies with sulfur
deposition above or near the CL.
5A-51
-------
Critical Load Exeedance for Sulfur
O
Near the Critical Load (±3.125 meq/m2/yr)
Exceeds the Critical Load
Figure 5A-23. Critical load exceedance (Ex) for S only total deposition from 2014-16 for
an ANC threshold of 30 jieq/L: a) waterbodies with sulfur deposition below
the CL and uncertainty (Ex < -3.125 meq/m2-yr), and b) waterbodies with
sulfur deposition above or near the CL.
5A-52
-------
a- Critical Load Exeedance for Sulfur
© Near the Critical Load (±3.125 meq/m2/yr)
• Exceeds the Critical Load
Figure 5A-24. Critical load exceedance (Ex) for S only total deposition from 2014-16 for
an ANC threshold of 50 jieq/L: a) waterbodies with sulfur deposition below
the CL and uncertainty (Ex < -3.125 meq/m2-yr), and b) waterbodies with
sulfur deposition above or near the CL.
5A-53
-------
a.
Critical Load Exeedance for Sulfur
2014-2016
b.
ANC = 50 peq/L East
ANC = 20 peq/L West
• Does not Exceed the Critical Load
Near the Critical Load (±3.125 meq/m2/yr)
• Exceeds the Critical Load
Figure 5A-25. Critical load exceedance (Ex) for S only total deposition from 2014-16 for
an ANC threshold of 50 for the eastern and 20 jieq/L for Western CONUS:
a) waterbodies with sulfur deposition below the CL and uncertainty (Ex < -
3.125 meq/m2-yr), and b) waterbodies with sulfur deposition above or near
the CL.
5A-54
-------
a- Critical Load Exeedance for Sulfur
Near the Critical Load (±3.125 meq/m2/yr)
Exceeds the Critical Load
Figure 5A-26. Critical load exceedance (Ex) for S only total deposition from 2018-20 for
an ANC threshold of 20 fieq/L: a) waterbodies with sulfur deposition below
the CL and uncertainty (Ex < -3.125 meq/m2-yr), and b) waterbodies with
sulfur deposition above or near the CL.
5A-55
-------
Critical Load Exeedance for Sulfur
Near the Critical Load (±3.125 meq/m2/yr)
Exceeds the Critical Load
Figure 5A-27. Critical load exceedance (Ex) for S only total deposition from 2018-20 for
an ANC threshold of 30 jieq/L: a) waterbodies with sulfur deposition below
the CL and uncertainty (Ex < -3.125 ineq/m2-yr), and b) waterbodies with
sulfur deposition above or near the CL.
5A-56
-------
a- Critical Load Exeedance for Sulfur
Near the Critical Load (±3.125 meq/m2/yr)
Exceeds the Critical Load
Figure 5A-28. Critical load exceedance (Ex) for S only total deposition from 2018-20 for
an ANC threshold of 50 jieq/L: a) waterbodies with sulfur deposition below
the CL and uncertainty (Ex < -3.125 meq/m2-yr), and b) waterbodies with
sulfur deposition above or near the CL.
5A-57
-------
a.
Critical Load Exeedance for Sulfur
2018-2020
b.
ANC = 50 [jeq/L. East
ANC = 20 peq/L West
• Does not Exceed the Critical Load
© Near the Critical Load (±3.125 meq/m2/yr)
• Exceeds the Critical Load
Figure 5A-29. Critical load exceedance (Ex) for S only total deposition from 2018-20 for
an ANC threshold of 50 for the eastern and 20 fieq/L for Western CONUS:
a) waterbodies with sulfur deposition below the CL and uncertainty (Ex < -
3.125 meq/m2-yr), and b) waterbodies with sulfur deposition above or near
the CL.
5A-58
-------
ANC = 50 (Jeq/L
ANC = 50 neq/L East
ANC = 20 jjeq/L West
Figure 5A-30. Critical load exceedance for S only deposition from 2018-20 for four ANC
thresholds: a. 20, b. 30, c. 50, d. 50/20 jieq/L.
5A.2.2 Ecoregion Analyses
There are 84 level III ecoregions across the CONUS. As seen in Tables 5A-10 and 5A-11
below, S deposition has declined in all of them since the 2001-03 time period.
Exeed the Critical Load
Near the Critical Load (±3.125 meq/m2/yr)
Critical loads £ 0
d.
Table 5A-10. Summary of median deposition estimates during five time periods for the
84 ecoregions in the CONUS. Deposition based on TDEP; median
determined by GIS zonal statistic.
Total Sulfur
Deposition
Number of ecoregio
s
ns with median deposition within
pecified range
kg S/ha-yr
2001-03
2006-08
2010-12
2014-16
2018-20
>10
16
11
0
0
0
7-10
10
10
5
0
0
5-7
11
14
10
0
0
2-5
13
14
31
45
33
<2
34
35
38
39
51
5A-59
-------
Table 5A-11. Median sulfur deposition for the 84 ecoregions in the CONUS determined
by GIS zonal statistic based on TDEP estimates.
Ecoregion III
Median Total Su
fur Deposition (kg SI
ha-yr)
Code
Name
E/W
2001-03
2006-08
2010-12
2014-16
2018-20
10.1.2
Columbia Plateau
W
0.46
0.42
0.43
0.50
0.29
10.1.3
Northern Basin and Range
w
0.34
0.37
0.53
0.48
0.29
10.1.4
Wyoming Basin
w
0.64
0.67
0.52
0.56
0.42
10.1.5
Central Basin and Range
w
0.49
0.45
0.47
0.52
0.34
10.1.6
Colorado Plateaus
w
0.74
0.74
0.56
0.61
0.32
10.1.7
Arizona/New Mexico Plateau
w
0.82
0.80
0.64
0.57
0.33
10.1.8
Snake River Plain
w
0.48
0.66
0.59
0.59
0.38
10.2.1
Mojave Basin and Range
w
0.58
0.41
0.42
0.41
0.30
10.2.10
Chihuahuan Deserts (also 10.2.4)
w
1.21
1.12
1.11
1.22
0.86
10.2.2
Sonoran Basin and Range
w
0.54
0.46
0.45
0.44
0.29
11.1.1
Southern and Central California
Chaparral and Oak Woodlands
w
1.12
0.95
0.94
0.84
0.74
11.1.2
Central California Valley
w
1.09
0.92
0.82
0.80
0.66
11.1.3
Southern California Mountains
w
1.23
1.08
1.07
0.98
0.83
12.1.1
Madrean Archipelago
w
1.16
1.14
0.92
0.94
0.49
13.1.1
Arizona/New Mexico Mountains
w
1.43
1.41
1.17
1.03
0.60
15.4.1
Southern Florida Coastal Plain
E
5.96
5.16
4.20
4.34
3.76
5.2.1
Northern Lakes and Forests
E
4.29
3.24
2.44
1.89
1.33
5.2.2
Northern Minnesota Wetlands
E
2.28
2.12
1.45
1.13
0.86
5.3.1
Northern Appalachian and Atlantic
Maritime Highlands
E
6.46
5.78
3.01
1.99
1.34
5.3.3
North Central Appalachians
E
18.08
15.05
7.24
4.09
2.40
6.2.10
Middle Rockies
W
1.04
1.14
0.93
0.86
0.71
6.2.11
Klamath Mountains
W
0.90
1.05
1.02
1.07
0.93
6.2.12
Sierra Nevada
W
1.32
1.14
1.24
1.14
0.98
6.2.13
Wasatch and Uinta Mountains
W
1.36
1.38
1.18
1.27
0.77
6.2.14
Southern Rockies
W
1.14
1.18
0.92
0.85
0.54
6.2.15
Idaho Batholith
W
0.90
1.16
1.10
0.93
0.60
6.2.3
Northern Rockies
W
0.90
0.98
0.83
0.79
0.52
6.2.4
Canadian Rockies
W
1.22
1.35
0.97
0.97
0.78
6.2.5
North Cascades
W
1.64
1.55
1.28
1.39
1.09
6.2.7
Cascades
W
1.69
1.66
1.41
1.51
1.24
6.2.8
Eastern Cascades Slopes and Foothills
W
0.44
0.49
0.47
0.55
0.47
6.2.9
Blue Mountains
W
0.46
0.50
0.52
0.61
0.36
7.1.7
Puget Lowland
W
2.13
1.63
1.37
2.11
1.25
7.1.8
Coast Range
W
2.39
2.14
2.00
2.03
1.50
7.1.9
Willamette Valley
W
1.61
1.48
1.43
1.71
1.08
8.1.1
Eastern Great Lakes Lowlands
E
10.97
8.82
4.04
2.71
1.64
8.1.10
Erie Drift Plain
E
18.39
15.10
8.07
4.99
2.81
5A-60
-------
Ecoregion III
Median Total Su
fur Deposition (kg SI
ha-yr)
Code
Name
E/W
2001-03
2006-08
2010-12
2014-16
2018-20
8.1.3
Northern Allegheny Plateau
E
11.92
10.24
4.81
2.79
1.68
8.1.4
North Central Hardwood Forests
E
4.57
3.42
2.63
2.01
1.39
8.1.5
Driftless Area
E
5.39
5.00
3.37
2.61
1.95
8.1.6
Southern Michigan/Northern Indiana
Drift Plains
E
9.62
8.34
5.32
3.25
2.16
8.1.7
Northeastern Coastal Zone
E
9.57
8.42
3.82
2.40
1.87
8.1.8
Acadian Plains and Hills
E
4.46
4.61
2.38
1.65
1.22
8.2.1
Southeastern Wisconsin Till Plains
E
7.02
6.37
3.98
2.74
2.02
8.2.2
Huron/Erie Lake Plains
E
9.86
8.59
5.22
3.15
2.11
8.2.3
Central Corn Belt Plains
E
9.78
8.96
5.42
4.11
2.45
8.2.4
Eastern Corn Belt Plains
E
14.84
11.98
7.08
4.11
2.59
8.3.1
Northern Piedmont
E
14.94
12.58
5.30
3.32
2.12
8.3.2
Interior River Valleys and Hills
E
10.55
9.30
6.20
4.29
3.03
8.3.3
Interior Plateau
E
13.52
10.96
6.24
4.16
2.73
8.3.4
Piedmont
E
11.71
9.58
4.34
2.62
1.89
8.3.5
Southeastern Plains
E
9.68
8.05
4.34
3.48
2.63
8.3.6
Mississippi Valley Loess Plains
E
8.64
6.69
4.60
3.96
3.18
8.3.7
South Central Plains
E
7.34
6.78
4.91
4.70
3.64
8.3.8
East Central Texas Plains
E
6.41
5.14
3.82
4.45
3.62
8.4.1
Ridge and Valley
E
14.10
11.86
5.31
3.23
2.14
8.4.2
Central Appalachians
E
16.20
13.28
7.05
4.12
2.32
8.4.3
Western Allegheny Plateau
E
20.35
16.36
8.26
4.76
2.89
8.4.4
Blue Ridge
E
11.12
9.26
4.41
2.61
1.95
8.4.5
Ozark Highlands
E
6.31
5.84
4.65
3.19
2.59
8.4.6
Boston Mountains
E
5.98
5.72
4.48
3.33
2.79
8.4.7
Arkansas Valley
E
5.54
5.20
4.15
3.38
2.97
8.4.8
Ouachita Mountains
E
6.20
5.82
4.67
4.09
3.52
8.4.9
Southwestern Appalachians
E
14.71
11.56
5.47
3.46
2.61
8.5.1
Middle Atlantic Coastal Plain
E
10.52
9.34
5.09
3.43
2.36
8.5.2
Mississippi Alluvial Plain
E
7.37
6.06
4.22
3.91
3.17
8.5.3
Southern Coastal Plain
E
7.94
6.02
4.43
3.95
3.23
8.5.4
Atlantic Coastal Pine Barrens
E
14.03
12.27
5.61
3.80
2.75
9.2.1
Northern Glaciated Plains
W
2.04
2.08
1.74
1.33
1.22
9.2.2
Lake Agassiz Plain
W
1.97
1.99
1.44
1.19
1.07
9.2.3
Western Corn Belt Plains
E
4.52
4.25
2.98
2.56
1.93
9.2.4
Central Irregular Plains
E
5.81
5.34
4.13
2.98
2.27
9.3.1
Northwestern Glaciated Plains
W
1.57
1.62
1.38
1.20
1.09
9.3.3
Northwestern Great Plains
W
1.20
1.33
1.01
0.88
0.82
9.3.4
Nebraska Sand Hills
W
1.67
1.99
1.48
1.36
1.36
9.4.1
High Plains
W
1.60
1.52
1.27
1.33
0.98
9.4.2
Central Great Plains
E
3.06
2.99
2.16
2.19
1.84
5A-61
-------
Ecoregion III
Median Total Su
fur Deposition (kg SI
ha-yr)
Code
Name
E/W
2001-03
2006-08
2010-12
2014-16
2018-20
9.4,3
Southwestern Tablelands
W
1,30
1.24
0.99
1.12
0.65
9.4.4
Flint Hills
E
4.44
4,03
2.85
2,46
1.93
9,4.5
Cross Timbers
E
4.58
3,96
3,02
3,05
2.61
9.46
Edwards Plateau
E
3.07
2,76
2,21
2.54
2.10
9.4,7
Texas Blackland Prairies
E
6,15
4,87
3,85
4,02
3.39
9,5.1
Western Gulf Coastal Plain
E
6.95
5,64
4,31
4.74
4.33
9.6.1
Southern Texas Plains
E
3.72
3.03
2.54
3,09
2.36
Acidification of waterbodies is controlled by local factors such as geology, hydrology,
etc. For this reason, aquatic CLs for acidification are unique to the waterbody itself and
information about the waterbody, like water quality, is needed to determine its CL.
Unfortunately, not all waterbodies within an ecoregion have sufficient data to calculate a CL.
This is the case for many level III ecoregions (from this point on level III ecoregions will be
referred to as ecoregions), except for ones that historically are known to be in acid sensitive
areas. Acid sensitive areas typically have been heavily sampled, and, hence, contain many
waterbody sites with estimated CLs (see Figure 5 A-31). These areas tend to be in the eastern
CONUS in such ecoregions as Central Appalachians (8.4.2), Northern Appalachian and Atlantic
Maritime Highlands (5.3.1), and the Blue Ridge (8.4.4). Areas in the Rockies and Sierra Nevada
5A-62
-------
More CLs in an ecoregion helps to capture the spatial variability of acid sensitive areas
across the landscape and provide a more accurate measurement of the impact of deposition
driven acidification. Ecoregions with few CLs, however, fail to capture the spatial variability of
acid sensitive areas, which in turn reduces the accuracy of the percentile CL value and limits our
confidence in the estimated percent of exceedances. For this reason, although CL exceedances
were derived for all ecoregions with a CL in NCLD, ecoregions containing greater than 50 CLs
were the primary focus of the ecoregion-scale assessment summary (section 5A.2.2.2).
There are 84 ecoregions across the CONUS, 69 of which had at least one CL available.
The Northern Appalachian and Atlantic Maritime Highlands ecoregion had the most CLs at
2,851 (see Table 5A-10). Eleven ecoregions had 9 or fewer CLs and 58 ecoregions had 10 or
more. Of the 58 ecoregions only 32 had 50 or more CLs. Three of the 32 are recognized to have
acidity heavily influenced by natural acids (see section 5A.2.2.1).
The 10th to 30th percentile S only CL estimates for an ecoregion varied greatly among
ecoregions from 1.2 to 136.1 kg/ha-yr (7.4 to 850.6 meq/m2-yr) with an ANC threshold of 20
|ieq/L to 0.1 to 134.9 kg/ha-yr (0.625 to 843.1 meq/m2-yr) with an ANC threshold of 50 |ieq/L
(Tables 5A-12 and 5A-13). The lower values indicate ecoregions of higher sensitivity, in terms
of risk of exceeding CLs based on the ANC threshold of 50 |ieq/L. The most sensitive
ecoregions include Sierra Nevada, Southern Coastal Plain, Idaho Batholith, Atlantic Coastal Pine
Barrens, Blue Ridge, Middle Rockies, Wasatch and Uinta Mountains, Southern Rockies, and
Central Appalachian and Atlantic Maritime Highlands. See Tables 5A-12 and 5A-13 for 10th,
30th percentile, minimum S only CL estimates for each of the 58 ecoregions with at least 10 CLs.
Table 5A-12. Summary of sulfur only CLs (kg S/ha-yr) for ANC thresholds of 20 and 30
jieq/L for ecoregions with at least 10 CL values.
Ecoregion III
ANC of 20 |jeq/
L
ANC ol
30 |jeq/L
Name
Code
No. Sites
30th
10th
Min.
30th
10th
Min.
Northern Appalachian and Atlantic
Maritime Highlands
5.3.1
2851
9.7
4.8
0.0
8.7
3.6
0.0
Ridge and Valley
8.4.1
1292
11.5
5.9
0.0
10.7
5.0
0.0
Blue Ridge
8.4.4
1972
9.1
5.3
0.0
7.6
4.0
0.0
Northern Lakes and Forests
5.2.1
839
5.1
3.0
0.0
4.7
2.6
0.0
Northeastern Coastal Zone
8.1.7
565
16.3
8.1
0.0
15.2
7.1
0.0
Middle Rockies
6.2.10
496
9.2
5.2
0.5
8.1
4.1
0.0
Acadian Plains and Hills
8.1.8
494
11.2
5.2
0.0
10.3
4.2
0.0
Piedmont
8.3.4
508
16.0
8.7
0.9
14.9
7.7
0.0
Southern Rockies
6.2.14
372
7.4
3.8
0.0
6.2
2.7
0.0
Central Appalachians
8.4.2
372
8.4
5.0
0.0
7.3
3.8
0.0
Sierra Nevada
6.2.12
353
4.7
1.6
0.0
3.4
0.1
0.0
Southeastern Plains
8.3.5
390
13.9
4.4
0.0
13.1
3.3
0.0
Atlantic Coastal Pine Barrens
8.5.4
234
6.2
2.0
0.0
5.4
1.3
0.0
Northern Piedmont
8.3.1
231
40.0
16.8
1.5
39.2
15.8
1.0
5A-63
-------
Ecoregion III
ANC of 20 |jeq/
ANC ol
30 |jeq/L
Name
Code
No. Sites
30th
10th
Min.
30th
10th
Min.
North Central Appalachians
5.3.3
216
14.6
8.3
1.9
13.5
7.2
0.8
Northern Allegheny Plateau
8.1.3
199
22.2
11.8
0.2
21.1
10.9
0.0
Idaho Batholith
6.2.15
188
10.3
5.6
0.0
8.9
4.1
0.0
Cascades
6.2.7
179
13.4
3.3
0.0
12.2
1.9
0.0
North Cascades
6.2.5
162
26.3
11.5
0.0
23.9
9.9
0.0
Southern Coastal Plain
8.5.3
142
4.2
1.5
0.0
3.8
1.1
0.0
Coast Range
7.1.8
115
48.6
15.7
6.1
47.0
15.0
5.9
Middle Atlantic Coastal Plain
8.5.1
105
15.3
8.1
0.0
14.5
7.2
0.0
Wasatch and Uinta Mountains
6.2.13
96
11.0
7.7
2.1
10.4
6.7
1.6
North Central Hardwood Forests
8.1.4
94
23.0
5.8
2.8
22.0
4.8
0.1
Columbia Mountains/Northern Rockies
6.2.3
86
19.5
6.9
0.0
18.6
6.1
0.0
Eastern Great Lakes Lowlands
8.1.1
83
50.5
17.5
0.0
50.1
16.1
0.0
Klamath Mountains
6.2.11
81
27.6
12.4
7.3
26.5
11.6
6.2
Interior Plateau
8.3.3
71
66.8
10.9
5.3
65.8
9.8
3.2
Blue Mountains
6.2.9
63
18.1
8.6
3.6
16.7
7.5
2.5
South Central Plains
8.3.7
153
10.9
3.9
0.0
9.9
2.9
0.0
Ozark Highlands
8.4.5
56
48.3
13.5
2.8
47.4
12.5
1.7
Southwestern Appalachians
8.4.9
117
14.3
10.3
6.4
13.2
9.2
5.3
Ouachita Mountains
8.4.8
42
13.1
7.2
6.3
12.2
6.3
4.5
Strait of Georgia/Puget Lowland
7.1.7
38
28.9
10.5
4.6
28.3
9.2
3.6
Western Allegheny Plateau
8.4.3
35
18.7
8.2
5.0
17.7
7.0
4.3
Southern Michigan/Northern Indiana Drift
Plains
8.1.6
33
11.5
5.8
2.1
10.8
4.4
1.3
Arkansas Valley
8.4.7
31
14.9
6.3
3.4
14.1
5.4
2.6
Canadian Rockies
6.2.4
31
41.8
8.3
3.5
40.4
7.8
1.6
Western Corn Belt Plains
9.2.3
26
14.8
5.8
4.6
14.0
4.6
2.9
Cross Timbers
9.4.5
26
11.3
7.1
2.9
10.0
5.2
0.8
Eastern Cascades Slopes and Foothills
6.2.8
27
21.5
6.6
3.6
20.9
5.9
2.5
Arizona/New Mexico Mountains
13.1.1
25
20.3
11.7
10.1
19.7
10.8
9.4
Willamette Valley
7.1.9
24
65.2
25.5
8.3
63.2
24.9
7.4
Boston Mountains
8.4.6
23
20.3
9.3
6.4
19.5
8.6
5.4
Southern & Baja California Pine-Oak Mtns
11.1.3
22
25.2
3.4
1.1
24.5
2.7
0.0
Central Irregular Plains
9.2.4
21
14.2
5.4
4.5
13.0
4.1
2.5
California Coastal Sage, Chaparral, and
Oak Woodlands
11.1.1
21
34.9
4.6
2.7
34.4
3.8
2.3
Northern Basin and Range
10.1.3
20
19.1
10.1
3.2
18.7
8.9
1.7
Mississippi Alluvial Plain
8.5.2
19
12.5
4.7
0.6
11.1
3.5
0.0
Interior River Valleys and Hills
8.3.2
18
39.1
5.8
5.8
37.5
4.7
4.4
Driftless Area
8.1.5
15
54.5
25.0
17.8
54.2
24.2
17.0
Western Gulf Coastal Plain
9.5.1
16
52.2
20.6
10.3
51.6
19.7
9.4
Central Basin and Range
10.1.5
16
45.4
21.5
8.7
44.4
20.3
6.4
Eastern Corn Belt Plains
8.2.4
14
14.4
4.5
3.8
13.2
3.6
3.1
Erie Drift Plain
8.1.10
14
18.6
5.8
4.1
17.6
4.8
2.8
Mississippi Valley Loess Plains
8.3.6
41
14.5
4.2
1.6
13.4
3.1
0.5
East Central Texas Plains
8.3.8
10
16.6
1.2
0.3
15.4
0.8
0.0
Southeastern Wisconsin Till Plains
8.2.1
10
136.1
16.7
15.0
135.7
14.8
13.6
5A-64
-------
Table 5A-13. Summary of sulfur only CLs (kg S/ha-yr) for ANC thresholds of 50 and
50/20 jieq/L for ecoregions with at least 10 CL values.
Ecoregion III
ANC of 50 |jeq/
_
ANC of 50/20 |jeq/L
Name
Code
No.
Sites
30th
10th
Min.
30th
10th
Min.
Northern Appalachian and Atlantic
Maritime Highlands
5.3.1
2851
6.5
1.1
0.0
6.5
1.1
0.0
Ridge and Valley
8.4.1
1292
8.9
3.3
0.0
8.9
3.3
0.0
Blue Ridge
8.4.4
1972
4.7
1.1
0.0
4.7
1.1
0.0
Northern Lakes and Forests
5.2.1
839
3.8
1.5
0.0
3.8
1.5
0.0
Northeastern Coastal Zone
8.1.7
565
13.4
5.3
0.0
13.4
5.3
0.0
Middle Rockies
6.2.10
496
6.1
2.4
0.0
9.2
5.2
0.5
Acadian Plains and Hills
8.1.8
494
8.5
2.2
0.0
8.5
2.2
0.0
Piedmont
8.3.4
508
12.7
5.4
0.0
12.7
5.4
0.0
Southern Rockies
6.2.14
372
3.9
0.6
0.0
7.4
3.8
0.0
Central Appalachians
8.4.2
372
5.2
1.3
0.0
5.2
1.3
0.0
Sierra Nevada
6.2.12
353
0.7
0.0
0.0
4.7
1.6
0.0
Southeastern Plains
8.3.5
390
11.5
1.8
0.0
11.5
1.8
0.0
Atlantic Coastal Pine Barrens
8.5.4
234
3.8
0.0
0.0
3.8
0.0
0.0
Northern Piedmont
8.3.1
231
37.6
13.2
0.0
37.6
13.2
0.0
North Central Appalachians
5.3.3
216
11.5
4.9
0.0
11.5
4.9
0.0
Northern Allegheny Plateau
8.1.3
199
19.1
8.7
0.0
19.1
8.7
0.0
Idaho Batholith
6.2.15
188
7.2
1.0
0.0
10.3
5.6
0.0
Cascades
6.2.7
179
9.8
0.0
0.0
13.4
3.3
0.0
North Cascades
6.2.5
162
21.8
6.1
0.0
26.3
11.5
0.0
Southern Coastal Plain
8.5.3
142
2.9
0.2
0.0
2.9
0.2
0.0
Coast Range
7.1.8
115
42.5
14.0
4.9
48.6
15.7
6.1
Middle Atlantic Coastal Plain
8.5.1
105
13.2
5.3
0.0
13.2
5.3
0.0
Wasatch and Uinta Mountains
6.2.13
96
8.8
5.0
0.0
11.0
7.7
2.1
North Central Hardwood Forests
8.1.4
94
20.1
3.5
0.0
20.1
3.5
0.0
Columbia Mountains/Northern Rockies
6.2.3
86
16.8
4.0
0.0
19.5
6.9
0.0
Eastern Great Lakes Lowlands
8.1.1
83
49.7
13.1
0.0
49.7
13.1
0.0
Klamath Mountains
6.2.11
81
24.2
10.0
4.1
27.6
12.4
7.3
Interior Plateau
8.3.3
71
63.6
7.7
0.0
63.6
7.7
0.0
Blue Mountains
6.2.9
63
14.5
6.2
0.3
18.1
8.6
3.6
South Central Plains
8.3.7
153
8.2
1.0
0.0
8.2
1.0
0.0
Ozark Highlands
8.4.5
56
45.6
10.5
0.0
45.6
10.5
0.0
Southwestern Appalachians
8.4.9
117
10.9
7.0
3.1
10.9
7.0
3.1
Ouachita Mountains
8.4.8
42
10.2
4.8
0.4
10.2
4.8
0.4
Strait of Georgia/Puget Lowland
7.1.7
38
26.9
7.1
0.0
28.9
10.5
4.6
Western Allegheny Plateau
8.4.3
35
15.7
5.1
3.0
15.7
5.1
3.0
Southern Michigan/Northern Indiana Drift
Plains
8.1.6
33
9.1
2.4
0.0
9.1
2.4
0.0
Arkansas Valley
8.4.7
31
12.5
4.7
1.2
12.5
4.7
1.2
Canadian Rockies
6.2.4
31
37.8
6.5
0.0
41.8
8.3
3.5
Western Corn Belt Plains
9.2.3
26
12.4
2.6
0.0
12.4
2.6
0.0
Cross Timbers
9.4.5
26
7.1
1.0
0.0
7.1
1.0
0.0
Eastern Cascades Slopes and Foothills
6.2.8
27
19.7
4.7
0.5
21.5
6.6
3.6
5A-65
-------
Ecoregion III
ANC of 50 |jeq/
ANC of 50/20 |jeq/L
Name
Code
No.
Sites
30th
10th
Min.
30th
10th
Min.
Arizona/New Mexico Mountains
13.1.1
25
18.4
9.1
7.8
20.3
11.7
10.1
Willamette Valley
7.1.9
24
59.2
23.5
5.5
65.2
25.5
8.3
Boston Mountains
8.4.6
23
15.9
7.1
3.4
15.9
7.1
3.4
Southern & Baja California Pine-Oak Mtns
11.1.3
22
23.2
1.3
0.0
25.2
3.4
1.1
Central Irregular Plains
9.2.4
21
11.0
1.5
0.0
11.0
1.5
0.0
California Coastal Sage, Chaparral, and
Oak Woodlands
11.1.1
21
33.4
2.4
1.4
34.9
4.6
2.7
Northern Basin and Range
10.1.3
20
18.0
6.4
0.0
19.1
10.1
3.2
Mississippi Alluvial Plain
8.5.2
19
8.2
0.4
0.0
8.2
0.4
0.0
Interior River Valleys and Hills
8.3.2
18
35.3
2.4
1.7
35.3
2.4
1.7
Driftless Area
8.1.5
15
53.5
22.5
15.4
53.5
22.5
15.4
Western Gulf Coastal Plain
9.5.1
16
50.3
17.9
7.6
50.3
17.9
7.6
Central Basin and Range
10.1.5
16
42.4
17.9
1.9
45.4
21.5
8.7
Eastern Corn Belt Plains
8.2.4
14
10.9
1.7
1.7
10.9
1.7
1.7
Erie Drift Plain
8.1.10
14
15.4
2.7
0.3
15.4
2.7
0.3
Mississippi Valley Loess Plains
8.3.6
41
11.3
1.0
0.0
11.3
1.0
0.0
East Central Texas Plains
8.3.8
10
12.9
0.6
0.0
12.9
0.6
0.0
Southeastern Wisconsin Till Plains
8.2.1
10
134.9
11.0
10.7
134.9
11.0
10.7
For the 69 ecoregions with at least one CL, the minimum, maximum and average total S
deposition in each of the five deposition periods at each of the CL locations are summarized in
Table 5A-14. The minimum to maximum range of total S deposition across these locations was
0.32 - 32.20 kg S/ha-yr for 2001-2003 and 0.27 - 7.59 kg S/ha-yr for 2018 - 2020. Average
values ranged from 1.77 to 8.63 kg S/ha-yr for 2018-2020 to 2001-2003, respectively (Table 5A-
14).
Table 5A-14. Summary of total S deposition (kg S/ha-yr) estimates (based on TDEP) at
CL locations for 69 ecoregions with at least one CL.
otal Sulfur Deposition (kg S/ha-yr)
2001-03
2006-08
2010-12
2014-16
2018-20
Minimum
0.32
0.31
0.36
0.52
0.27
Maximum
32.20
25.97
12.75
9.38
7.59
Average
8.63
7.39
3.76
2.55
1.77
The medians of the TDEP deposition estimates at all CL locations in each ecoregion with
any CLs are presented in Table 5A-15. Ecoregions with the highest median total S deposition
estimates were Western Allegheny Plateau (8.4.3), Erie Drift Plain (8.1.10), North Central
Appalachians (5.3.3), Central Appalachians (8.4.2), Northern Piedmont (8.3.1), Eastern Corn
Belt Plains (8.2.4), Southwestern Appalachians (8.4.9), and Ridge and Valley (8.4.1), all in the
Mid-Atlantic region of the eastern U.S (5A-15).
5A-66
-------
Table 5A-15. Median total sulfur deposition (based on TDEP estimates at CL locations)
for the 69 ecoregions with at least one CL.
Ecoregion Name
Code
E/W
No.
CLs
2001-03
(kg/ha-yr)
2006-08
(kg/ha-yr)
2010-12
(kg/ha-yr)
2014-16
(kg/ha-yr)
2018-20
(kg/ha-yr)
Columbia Plateau
10.1.2
W
2
0.84
0.83
0.76
0.63
0.38
Northern Basin and Range
10.1.3
w
20
0.93
1.05
1.02
1.04
0.75
Wyoming Basin
10.1.4
w
3
0.77
0.76
0.70
0.68
0.59
Central Basin and Range
10.1.5
w
16
0.86
0.66
0.67
0.76
0.57
Colorado Plateaus
10.1.6
w
1
1.32
1.44
1.25
1.33
0.84
Snake River Plain
10.1.8
w
2
0.80
0.93
0.98
0.79
0.55
Southern and Central California Chaparral
and Oak Woodlands
11.1.1
w
21
1.65
1.20
1.26
0.98
1.06
Central California Valley
11.1.2
w
2
2.17
1.70
1.54
1.46
1.19
Southern California Mountains
11.1.3
w
22
1.45
1.21
1.24
1.04
0.86
Arizona/New Mexico Mountains
13.1.1
w
25
2.07
2.58
1.96
1.47
0.81
Northern Lakes and Forests
5.2.1
E
839
4.01
3.10
2.34
1.84
1.31
Northern Minnesota Wetlands
5.2.2
E
2
2.19
2.21
1.51
1.20
0.91
Northern Appalachian and Atlantic
Maritime Highlands
5.3.1
E
2851
7.29
6.12
3.12
2.22
1.48
North Central Appalachians
5.3.3
E
216
15.73
13.37
5.83
3.17
2.17
Middle Rockies
6.2.10
W
496
1.48
1.53
1.33
1.06
0.87
Klamath Mountains
6.2.11
W
81
0.92
1.07
1.06
0.99
0.84
Sierra Nevada
6.2.12
W
353
1.40
1.24
1.27
1.17
1.01
Wasatch and Uinta Mountains
6.2.13
W
96
1.75
1.92
1.64
1.72
1.11
Southern Rockies
6.2.14
W
372
1.63
1.70
1.29
1.10
0.74
Idaho Batholith
6.2.15
W
188
1.21
1.52
1.39
1.14
0.72
Northern Rockies
6.2.3
W
86
1.18
1.22
1.02
0.93
0.62
Canadian Rockies
6.2.4
W
31
1.27
1.43
1.08
0.99
0.79
North Cascades
6.2.5
W
162
1.94
1.83
1.47
1.48
1.19
Cascades
6.2.7
W
179
1.25
1.51
1.25
1.23
1.07
Eastern Cascades Slopes and Foothills
6.2.8
W
27
0.66
0.75
0.73
0.74
0.62
Blue Mountains
6.2.9
W
63
0.63
0.68
0.72
0.85
0.46
Puget Lowland
7.1.7
W
38
2.28
1.94
1.55
2.25
1.36
Coast Range
7.1.8
W
115
2.49
2.31
2.07
2.09
1.52
Willamette Valley
7.1.9
W
24
1.71
1.44
1.45
1.76
1.08
Eastern Great Lakes Lowlands
8.1.1
E
83
8.04
6.50
3.26
2.16
1.44
Erie Drift Plain
8.1.10
E
14
18.62
15.49
7.83
5.14
2.84
Northern Allegheny Plateau
8.1.3
E
199
11.69
10.45
4.69
2.70
1.73
North Central Hardwood Forests
8.1.4
E
94
5.30
3.72
2.86
2.12
1.48
Driftless Area
8.1.5
E
15
6.16
5.34
3.56
2.76
2.11
Southern Michigan/Northern Indiana Drift
Plains
8.1.6
E
33
10.36
8.99
5.41
3.35
2.37
Northeastern Coastal Zone
8.1.7
E
565
9.29
8.28
3.71
2.30
1.91
Acadian Plains and Hills
8.1.8
E
494
4.98
5.42
2.83
1.95
1.44
5A-67
-------
Ecoregion Name
Code
E/W
No.
CLs
2001-03
(kg/ha-yr)
2006-08
(kg/ha-yr)
2010-12
(kg/ha-yr)
2014-16
(kg/ha-yr)
2018-20
(kg/ha-yr)
Southeastern Wisconsin Till Plains
8.2.1
E
10
6.94
5.71
3.93
2.74
1.96
Central Corn Belt Plains
8.2.3
E
2
10.64
9.79
5.98
4.44
2.50
Eastern Corn Belt Plains
8.2.4
E
14
17.43
13.48
7.90
4.76
2.87
Northern Piedmont
8.3.1
E
231
15.18
12.94
5.63
3.33
2.21
Interior River Valleys and Hills
8.3.2
E
18
12.59
11.03
6.54
4.25
2.94
Interior Plateau
8.3.3
E
71
13.11
9.84
5.58
4.01
2.74
Piedmont
8.3.4
E
508
12.26
10.14
4.24
2.69
2.03
Southeastern Plains
8.3.5
E
390
10.88
9.14
4.83
3.49
2.41
Mississippi Valley Loess Plains
8.3.6
E
41
9.40
7.66
4.72
4.44
3.57
South Central Plains
8.3.7
E
153
7.77
7.15
5.03
4.69
3.88
East Central Texas Plains
8.3.8
E
10
6.36
6.37
4.65
4.78
3.79
Ridge and Valley
8.4.1
E
1292
14.18
11.93
5.71
3.33
1.94
Central Appalachians
8.4.2
E
372
17.03
13.98
7.25
4.09
2.43
Western Allegheny Plateau
8.4.3
E
35
17.08
14.12
7.59
4.19
2.56
Blue Ridge
8.4.4
E
1972
11.29
9.58
4.41
2.70
2.06
Ozark Highlands
8.4.5
E
56
6.95
6.18
4.87
3.24
2.66
Boston Mountains
8.4.6
E
23
6.25
5.90
4.60
3.43
2.78
Arkansas Valley
8.4.7
E
31
5.70
5.38
4.24
3.35
2.91
Ouachita Mountains
8.4.8
E
42
6.09
5.71
4.65
4.05
3.58
Southwestern Appalachians
8.4.9
E
117
17.27
14.44
5.59
4.17
2.93
Middle Atlantic Coastal Plain
8.5.1
E
105
14.10
12.07
5.35
3.58
2.41
Mississippi Alluvial Plain
8.5.2
E
19
7.02
5.45
4.06
3.67
3.05
Southern Coastal Plain
8.5.3
E
142
8.70
5.92
4.56
4.18
3.35
Atlantic Coastal Pine Barrens
8.5.4
E
234
13.88
12.01
5.40
3.89
2.84
Western Corn Belt Plains
9.2.3
E
26
4.72
4.01
2.85
2.35
1.99
Central Irregular Plains
9.2.4
E
21
5.55
5.12
3.99
2.95
2.29
Northwestern Glaciated Plains
9.3.1
E
2
0.67
0.74
0.54
0.56
0.46
Central Great Plains
9.4.2
E
5
4.32
4.67
2.86
2.73
2.44
Flint Hills
9.4.4
E
7
4.45
4.36
2.91
2.57
2.27
Cross Timbers
9.4.5
E
26
4.89
4.47
3.25
3.17
2.72
Texas Blackland Prairies
9.4.7
E
3
6.51
5.95
4.47
4.37
3.66
Western Gulf Coastal Plain
9.5.1
E
16
7.59
6.99
4.92
5.31
4.34
5A.2.2.1 Ecoregion Critical Load Exceedances - Sulfur Only
Of the 69 ecoregions that had at least one CL, 58 ecoregions had 10 or more values. We
evaluated CL exceedances and summarize sites with CLs in each ecoregion based on two
categories of CLs: (1) all CLs and (2) only CLs with positive values. Exceedances were
evaluated with respect to 2001-2003, 2006-2008, 2012-2014, 2014-2016, and 2018-2020 TDEP
deposition estimates for S only. Exceedances were calculated for ANC thresholds of 20, 30, 50
|ieq/L and combined 50 |ieq/L in the East and 20 |ieq/L in the West (denoted as 50/20 |ieq/L).
See section 5 A. 1.6 above for a description of how exceedances were calculated. Information
5A-68
-------
about S only exceedances in the 58 ecoregions with 10 or more CL sites are summarized in
Table 5A-16 for each ANC threshold and time period.
Results of S only exceedances per ecoregion for the 69 ecoregions with at least a single
CL estimate included in Tables 5A-17 through 5A-24. For ANC thresholds of 20 and 30 |ieq/L,
and the most recent years (2018-2020 and 2014-2016), there were few exceedances in either the
58 ecoregions with at least 10 CLs or the 69 ecoregions with at least a single CL estimate. Of the
69 ecoregions (and focusing on the CLs greater than zero), 48 and 40 had no exceedances for an
ANC threshold of 20 |ieq/L for 2018-2020 and 2014-2016, respectively. Of the remaining 21 and
29 ecoregions, only 6 and 9 had greater than 5% exceedance and 3 and 5 had greater than 10%
for ANC thresholds of 20 |ieq/L for the two deposition periods. For the following three
deposition periods 2010-2012, 2006-2008, and 2001-2003, the number of ecoregions without an
exceedance decreased to 35, 31, and 29 while the number with greater than 10% exceedance
increased to 8, 21, and 23, respectively (Tables 5A-17 and 5 A-18). There were slightly more
exceedances for CLs based on an ANC threshold of 30 |ieq/L across all deposition periods
(Tables 5A-19 and 5A-20). Critical loads determined for ANC thresholds of 50 and 50/20 |ieq/L
were exceeded in more sites within ecoregions and there were more ecoregions with exceedances
particularly for the early deposition periods of 2010-2012, 2006-2008, and 2001-2003 (Tables
5A-21 to 5A-24 and Figures 5A-38 to 5A-43). For CLs using an ANC threshold of 50 |ieq/L, 31,
25, 21, 21, and 21 of the 58 ecoregions had no CL exceedances for the 5 deposition periods
2018-2020, 2014-2016, 2010-2012, 2006-2008, and 2001-2003. Of the remaining ecoregions,
13,17, 36, 43, and 44 had greater than 5% exceedances and 8, 9, 25, 33, and 35 ecoregions had
exceedance percentage greater than 10%.
The Southeastern Plains (code 8.3.5), Southern Coastal Plain (code 8.5.3), and Atlantic
Coastal Pine Barrens (code 8.5.4) are ecoregions known to have naturally acidic surface waters
and the high exceedances calculated for these ecoregions are likely not driven by air pollution
deposition but instead by natural acidity linked to DOC, hydrology, and natural biogeochemical
processes (2008 ISA, section 3.2.4.2; Baker et al., 1991; Herlihy et al., 1991). Central
Appalachians (8.4.2), Acadian Plains and Hills (8.1.8), and Northern Appalachian and Atlantic
Maritime Highlands (5.3.1) are ecoregions know to be acid sensitive (Table 5A-5).
5A-69
-------
Table 5A-16. Summary of CL values for those that have been exceeded for each ANC
threshold and time period for the 58 ecoregions with 10 or more values.
CL Value*
S Deposition
estimates for CLs
that exceed**
Average
percentage of
sites/ecoregion
exceeding their
CLs
Number of ecoregions with more
than the specified percentage of
sites exceeding their CLs
Time
Period
Average
(5th - 95th percentile)
kg S/ha-yr
Average
(5th - 95th percentile)
kg S/ha-yr
# >5%
EX
#>10%
EX
#>15%
EX
#>25%
EA
ANC Threshold = 50/20 jueq/L
2018-2020
1 (0.1-2.4)
2.1 (1.2-3.6)
3.1
12
7
2
0
2014-2016
1.5(0.2-3.4)
3(1.7-5)
4.0
16
8
3
0
2010-2012
2.4 (0.2-5.4)
2.4 (0.2-5.4)
6.1
24
16
7
3
2006-2008
4.9(0.5-11.2)
9.5(3.3-15)
12.1
33
24
20
10
2001-2003
5.7 (0.5-13)
11.4(4.3-17.9)
14.4
33
27
20
13
ANC Threshold = 50 /jeq/L
2018-2020
1 (0.1-2.4)
2.1 (1-3.6)
3.2
12
7
0
0
2014-2016
1.5(0.2-3.4)
3(1.5-5)
4.3
16
8
3
0
2010-2012
2.3 (0.2-5.4)
4.5 (2-7.7)
6.3
25
16
7
3
2006-2008
4.9(0.4-11.1)
9.4(3.1-15)
12.4
33
24
20
10
2001-2003
5.6(0.5-12.9)
11.3(4-17.9)
14.6
33
27
20
13
ANC Threshold = 30 /jeq/L
2018-2020
1.3(0.1-3)
2.2 (0.9-3.4)
1.9
5
4
1
0
2014-2016
1.7(0.2-3.9)
3.1 (1.3-5.3)
3.0
11
5
2
1
2010-2012
2.7 (0.3-5.7)
4.6 (1.7-7.8)
4.7
20
11
4
1
2006-2008
5.6(0.8-11.5)
9.7(3.2-15.1)
10.3
30
23
17
8
2001-2003
6.6(0.9-13.7)
11.5(4.2-17.9)
13.1
31
24
20
12
ANC Threshold = 20 /jeq/L
2018-2020
1.4(0.2-3.2)
2.3 (0.9-4.3)
1.5
6
3
1
0
2014-2016
1.9(0.3-4.1)
3.4 (1.5-5.4)
2.7
8
4
3
2
2010-2012
2.9(0.5-6.1)
4.8(2.1-8.1)
4.2
17
7
3
2
2006-2008
6.1 (1.1-11.8)
9.8(4.1-15.2)
9.4
27
20
17
8
2001-2003
7.1 (1.3-14.1)
11.8(4.5-18.1)
12.3
28
22
21
11
* This summarizes the magnitude of the CL values of those that were exceeded by the deposition estimated in these time
periods. This summary is based on CL values greater than zero.
** This summarizes the magnitude of deposition estimates that yielded CL exceedances in these time periods.
5A-70
-------
Table 5A-17. Percent exceedances of aquatic CLs for S only and ANC threshold of 20
jieq/L for deposition years of 2018-20 and 2014-16 in 69 ecoregions.
Ecoregion (n=69)
Sulfur only - ANC = 20 ueq/L
Number of CLs
% Exceedances
Total
Number
CL<0
2018-20
2014
1-16
Name
Code
n
% of total
All
CL>0
All
CL>0
Northern Appalachian and Atlantic
Maritime Highlands
5.3.1
2851
11
0.4
1.6
1.2
2.7
2.4
Blue Ridge
8.4.4
1972
3
0.2
0.9
0.7
1.8
1.6
Ridge and Valley
8.4.1
1292
2
0.2
0.9
0.8
4.7
4.6
Northern Lakes and Forests
5.2.1
839
1
0.1
0.5
0.4
1.2
1.1
Northeastern Coastal Zone
8.1.7
565
1
0.2
0.2
0.0
0.9
0.7
Piedmont
8.3.4
508
0
0.0
0.2
0.2
0.6
0.6
Middle Rockies
6.2.10
496
0
0.0
0.2
0.2
0.6
0.6
Acadian Plains and Hills
8.1.8
494
2
0.4
2.0
1.6
2.6
2.2
Southeastern Plains
8.3.5
390
3
0.8
4.9
4.1
6.9
6.2
Central Appalachians
8.4.2
372
4
1.1
3.8
2.7
5.6
4.6
Southern Rockies
6.2.14
372
1
0.3
0.5
0.3
1.9
1.6
Sierra Nevada
6.2.12
353
11
3.1
4.2
1.1
5.9
2.8
Atlantic Coastal Pine Barrens
8.5.4
234
1
0.4
12.4
12.0
17.9
17.5
Northern Piedmont
8.3.1
231
0
0.0
0.0
0.0
0.4
0.4
North Central Appalachians
5.3.3
216
0
0.0
0.0
0.0
2.3
2.3
Northern Allegheny Plateau
8.1.3
199
0
0.0
0.5
0.5
1.0
1.0
Idaho Batholith
6.2.15
188
1
0.5
0.5
0.0
0.5
0.0
Cascades
6.2.7
179
4
2.2
3.9
1.7
5.0
2.8
North Cascades
6.2.5
162
1
0.6
0.6
0.0
0.6
0.0
South Central Plains
8.3.7
153
2
1.3
9.8
8.5
12.4
11.1
Southern Coastal Plain
8.5.3
142
1
0.7
20.4
19.7
27.5
26.8
Southwestern Appalachians
8.4.9
117
0
0.0
0.0
0.0
0.0
0.0
Coast Range
7.1.8
115
0
0.0
0.0
0.0
0.0
0.0
Middle Atlantic Coastal Plain
8.5.1
105
1
1.0
2.9
1.9
4.8
3.8
Wasatch and Uinta Mountains
6.2.13
96
0
0.0
0.0
0.0
0.0
0.0
North Central Hardwood Forests
8.1.4
94
0
0.0
0.0
0.0
0.0
0.0
Columbia Mountains/Northern Rockies
6.2.3
86
1
1.2
1.2
0.0
2.3
1.2
Eastern Great Lakes Lowlands
8.1.1
83
1
1.2
1.2
0.0
1.2
0.0
Klamath Mountains
6.2.11
81
0
0.0
0.0
0.0
0.0
0.0
Interior Plateau
8.3.3
71
0
0.0
0.0
0.0
0.0
0.0
Blue Mountains
6.2.9
63
0
0.0
0.0
0.0
0.0
0.0
Ozark Highlands
8.4.5
56
0
0.0
0.0
0.0
1.8
1.8
Ouachita Mountains
8.4.8
42
0
0.0
0.0
0.0
0.0
0.0
Mississippi Valley Loess Plains
8.3.6
41
0
0.0
2.4
2.4
9.8
9.8
Strait of Georgia/Puget Lowland
7.1.7
38
0
0.0
0.0
0.0
0.0
0.0
Western Allegheny Plateau
8.4.3
35
0
0.0
0.0
0.0
0.0
0.0
5A-71
-------
Ecoregion (n=69)
Sulfur only - ANC = 20 ueq/L
Number of CLs
% Exceedances
Total
Number
CL<0
2018-20
2014
1-16
Name
Code
n
% of total
All
CL>0
All
CL>0
Southern Michigan/Northern Indiana
Drift Plains
8.1.6
33
0
0.0
0.0
0.0
3.0
3.0
Arkansas Valley
8.4.7
31
0
0.0
0.0
0.0
0.0
0.0
Canadian Rockies
6.2.4
31
0
0.0
0.0
0.0
0.0
0.0
Eastern Cascades Slopes and Foothills
6.2.8
27
0
0.0
0.0
0.0
0.0
0.0
Cross Timbers
9.4.5
26
0
0.0
0.0
0.0
0.0
0.0
Western Corn Belt Plains
9.2.3
26
0
0.0
0.0
0.0
0.0
0.0
Arizona/New Mexico Mountains
13.1.1
25
0
0.0
0.0
0.0
0.0
0.0
Willamette Valley
7.1.9
24
0
0.0
0.0
0.0
0.0
0.0
Boston Mountains
8.4.6
23
0
0.0
0.0
0.0
0.0
0.0
Southern and Baja California Pine-Oak
Mountains
11.1.3
22
0
0.0
0.0
0.0
4.5
4.5
Central Irregular Plains
9.2.4
21
0
0.0
0.0
0.0
0.0
0.0
California Coastal Sage, Chaparral,
and Oak Woodlands
11.1.1
21
0
0.0
0.0
0.0
0.0
0.0
Northern Basin and Range
10.1.3
20
0
0.0
0.0
0.0
0.0
0.0
Mississippi Alluvial Plain
8.5.2
19
0
0.0
5.3
5.3
5.3
5.3
Interior River Valleys and Hills
8.3.2
18
0
0.0
0.0
0.0
0.0
0.0
Western Gulf Coastal Plain
9.5.1
16
0
0.0
0.0
0.0
0.0
0.0
Central Basin and Range
10.1.5
16
0
0.0
0.0
0.0
0.0
0.0
Driftless Area
8.1.5
15
0
0.0
0.0
0.0
0.0
0.0
Erie Drift Plain
8.1.10
14
0
0.0
0.0
0.0
0.0
0.0
Eastern Corn Belt Plains
8.2.4
14
0
0.0
0.0
0.0
0.0
0.0
East Central Texas Plains
8.3.8
10
0
0.0
10.0
10.0
10.0
10.0
Southeastern Wisconsin Till Plains
8.2.1
10
0
0.0
0.0
0.0
0.0
0.0
Flint Hills
9.4.4
7
0
0.0
14.3
14.3
28.6
28.6
Central Great Plains
9.4.2
5
0
0.0
0.0
0.0
0.0
0.0
Wyoming Basin
10.1.4
3
0
0.0
0.0
0.0
0.0
0.0
Texas Blackland Prairies
9.4.7
3
0
0.0
0.0
0.0
0.0
0.0
Northern Minnesota Wetlands
5.2.2
2
0
0.0
0.0
0.0
50.0
50.0
Snake River Plain
10.1.8
2
0
0.0
0.0
0.0
0.0
0.0
Central California Valley
11.1.2
2
0
0.0
0.0
0.0
0.0
0.0
Columbia Plateau
10.1.2
2
0
0.0
0.0
0.0
0.0
0.0
Central Corn Belt Plains
8.2.3
2
0
0.0
0.0
0.0
0.0
0.0
Northwestern Glaciated Plains
9.3.1
2
0
0.0
0.0
0.0
0.0
0.0
Colorado Plateaus
10.1.6
1
0
0.0
0.0
0.0
0.0
0.0
5A-72
-------
Table 5A-18. Percent exceedances of CLs for S only and ANC threshold of 20 jieq/L for
deposition years of 2010-12, 2006-08 and 2001-03 in 69 ecoregions.
Ecoregion (n=69)
Sulfur only - ANC = 20 ueq/L
Number of CLs
% Exceedances
Total
Number
C
L<0
2010-2012
2006-2008
2001
-2003
Name
Code
n
% of
total
All
CL>0
All
CL>0
All
CL>0
Northern Appalachian and
Atlantic Maritime Highlands
5.3.1
2851
11
0.4
4.5
4.1
14.9
14.5
19.9
19.5
Blue Ridge
8.4.4
1972
3
0.2
6.1
5.9
32.3
32.2
43.7
43.5
Ridge and Valley
8.4.1
1292
2
0.2
9.8
9.6
29.3
29.2
38.5
38.3
Northern Lakes and Forests
5.2.1
839
1
0.1
2.9
2.7
8.8
8.7
16.0
15.9
Northeastern Coastal Zone
8.1.7
565
1
0.2
2.1
1.9
8.3
8.1
10.1
9.9
Piedmont
8.3.4
508
0
0.0
1.8
1.8
11.8
11.8
16.1
16.1
Middle Rockies
6.2.10
496
0
0.0
0.8
0.8
0.8
0.8
0.8
0.8
Acadian Plains and Hills
8.1.8
494
2
0.4
4.7
4.3
10.5
10.1
10.1
9.7
Southeastern Plains
8.3.5
390
3
0.8
9.5
8.7
20.5
19.7
24.1
23.3
Central Appalachians
8.4.2
372
4
1.1
15.9
14.8
44.4
43.3
53.8
52.7
Southern Rockies
6.2.14
372
1
0.3
2.2
1.9
2.7
2.4
2.7
2.4
Sierra Nevada
6.2.12
353
11
3.1
5.1
2.0
5.1
2.0
6.2
3.1
Atlantic Coastal Pine Barrens
8.5.4
234
1
0.4
23.9
23.5
45.3
44.9
53.8
53.4
Northern Piedmont
8.3.1
231
0
0.0
1.7
1.7
6.5
6.5
7.8
7.8
North Central Appalachians
5.3.3
216
0
0.0
5.6
5.6
24.1
24.1
31.5
31.5
Northern Allegheny Plateau
8.1.3
199
0
0.0
2.0
2.0
7.5
7.5
9.5
9.5
Idaho Batholith
6.2.15
188
1
0.5
0.5
0.0
0.5
0.0
0.5
0.0
Cascades
6.2.7
179
4
2.2
3.9
1.7
3.9
1.7
3.9
1.7
North Cascades
6.2.5
162
1
0.6
0.6
0.0
0.6
0.0
0.6
0.0
South Central Plains
8.3.7
153
2
1.3
13.7
12.4
19.0
17.6
20.3
19.0
Southern Coastal Plain
8.5.3
142
1
0.7
28.9
28.2
35.2
34.5
49.3
48.6
Southwestern Appalachians
8.4.9
117
0
0.0
0.0
0.0
25.6
25.6
41.0
41.0
Coast Range
7.1.8
115
0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Middle Atlantic Coastal Plain
8.5.1
105
1
1.0
5.7
4.8
20.0
19.0
24.8
23.8
Wasatch and Uinta Mountains
6.2.13
96
0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
North Central Hardwood Forests
8.1.4
94
0
0.0
0.0
0.0
0.0
0.0
3.2
3.2
Columbia Mountains/Northern
Rockies
6.2.3
86
1
1.2
3.5
2.3
3.5
2.3
3.5
2.3
Eastern Great Lakes Lowlands
8.1.1
83
1
1.2
1.2
0.0
2.4
1.2
6.0
4.8
Klamath Mountains
6.2.11
81
0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Interior Plateau
8.3.3
71
0
0.0
5.6
5.6
8.5
8.5
12.7
12.7
Blue Mountains
6.2.9
63
0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Ozark Highlands
8.4.5
56
0
0.0
3.6
3.6
3.6
3.6
3.6
3.6
Ouachita Mountains
8.4.8
42
0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Mississippi Valley Loess Plains
8.3.6
41
0
0.0
12.2
12.2
17.1
17.1
19.5
19.5
5A-73
-------
Ecoregion (n=69)
Sulfur only - ANC = 20 ueq/L
Number of CLs
% Exceedances
Total
Number
C
L<0
2010-2012
2006-2008
2001
-2003
Name
Code
n
% of
total
All
CL>0
All
CL>0
All
CL>0
Strait of Georgia/Puget Lowland
7.1.7
38
0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Western Allegheny Plateau
8.4.3
35
0
0.0
2.9
2.9
20.0
20.0
28.6
28.6
Southern Michigan/Northern
Indiana Drift Plains
8.1.6
33
0
0.0
6.1
6.1
15.2
15.2
21.2
21.2
Arkansas Valley
8.4.7
31
0
0.0
3.2
3.2
3.2
3.2
3.2
3.2
Canadian Rockies
6.2.4
31
0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Eastern Cascades Slopes and
Foothills
6.2.8
27
0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Cross Timbers
9.4.5
26
0
0.0
0.0
0.0
3.8
3.8
7.7
7.7
Western Corn Belt Plains
9.2.3
26
0
0.0
0.0
0.0
0.0
0.0
3.8
3.8
Arizona/New Mexico Mountains
13.1.1
25
0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Willamette Valley
7.1.9
24
0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Boston Mountains
8.4.6
23
0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Southern and Baja California
Pine-Oak Mountains
11.1.3
22
0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Central Irregular Plains
9.2.4
21
0
0.0
0.0
0.0
4.8
4.8
4.8
4.8
California Coastal Sage,
Chaparral, and Oak Woodlands
11.1.1
21
0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Northern Basin and Range
10.1.3
20
0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Mississippi Alluvial Plain
8.5.2
19
0
0.0
5.3
5.3
5.3
5.3
21.1
21.1
Interior River Valleys and Hills
8.3.2
18
0
0.0
5.6
5.6
16.7
16.7
22.2
22.2
Western Gulf Coastal Plain
9.5.1
16
0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Central Basin and Range
10.1.5
16
0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Driftless Area
8.1.5
15
0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Erie Drift Plain
8.1.10
14
0
0.0
7.1
7.1
21.4
21.4
28.6
28.6
Eastern Corn Belt Plains
8.2.4
14
0
0.0
14.3
14.3
28.6
28.6
28.6
28.6
East Central Texas Plains
8.3.8
10
0
0.0
10.0
10.0
10.0
10.0
10.0
10.0
Southeastern Wisconsin Till
Plains
8.2.1
10
0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Flint Hills
9.4.4
7
0
0.0
28.6
28.6
28.6
28.6
28.6
28.6
Central Great Plains
9.4.2
5
0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Wyoming Basin
10.1.4
3
0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Texas Blackland Prairies
9.4.7
3
0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Northern Minnesota Wetlands
5.2.2
2
0
0.0
50.0
50.0
50.0
50.0
50.0
50.0
Snake River Plain
10.1.8
2
0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Central California Valley
11.1.2
2
0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Columbia Plateau
10.1.2
2
0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Central Corn Belt Plains
8.2.3
2
0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Northwestern Glaciated Plains
9.3.1
2
0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Colorado Plateaus
10.1.6
1
0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
5A-74
-------
Table 5A-19. Percent exceedances of aquatic CLs for S only and ANC threshold of 30
jieq/L for deposition years of 2018-20 and 2014-16 in 69 ecoregions.
Ecoregion (n=69)
Sulfur only - ANC = 30 ueq/L
Number of CLs
% Exceedances
Total
Number
CL<0
2018-2020
2014-2016
Name
Code
n
% of
total
All
CL>0
All
CL>0
Northern Appalachian and Atlantic
Maritime Highlands
5.3.1
2851
40
1.4
3.0
1.6
4.9
3.5
Blue Ridge
8.4.4
1972
13
0.7
2.3
1.6
3.9
3.2
Ridge and Valley
8.4.1
1292
8
0.6
2.4
1.8
5.8
5.2
Northern Lakes and Forests
5.2.1
839
1
0.1
1.0
0.8
2.4
2.3
Northeastern Coastal Zone
8.1.7
565
1
0.2
1.2
1.1
1.6
1.4
Piedmont
8.3.4
508
1
0.2
1.0
0.8
1.2
1.0
Middle Rockies
6.2.10
496
4
0.8
1.4
0.6
1.6
0.8
Acadian Plains and Hills
8.1.8
494
9
1.8
3.8
2.0
4.7
2.8
Southeastern Plains
8.3.5
390
9
2.3
7.2
4.9
9.5
7.2
Central Appalachians
8.4.2
372
10
2.7
5.6
3.0
8.3
5.6
Southern Rockies
6.2.14
372
8
2.2
3.0
0.8
4.0
1.9
Sierra Nevada
6.2.12
353
29
8.2
11.9
3.7
16.4
8.2
Atlantic Coastal Pine Barrens
8.5.4
234
9
3.8
17.9
14.1
22.2
18.4
Northern Piedmont
8.3.1
231
0
0.0
1.3
1.3
1.3
1.3
North Central Appalachians
5.3.3
216
0
0.0
1.4
1.4
2.3
2.3
Northern Allegheny Plateau
8.1.3
199
1
0.5
1.0
0.5
1.5
1.0
Idaho Batholith
6.2.15
188
3
1.6
2.1
0.5
2.7
1.1
Cascades
6.2.7
179
11
6.1
6.7
0.6
7.3
1.1
North Cascades
6.2.5
162
1
0.6
0.6
0.0
0.6
0.0
South Central Plains
8.3.7
153
3
2.0
14.4
12.4
16.3
14.4
Southern Coastal Plain
8.5.3
142
4
2.8
22.5
19.7
30.3
27.5
Southwestern Appalachians
8.4.9
117
0
0.0
0.0
0.0
0.0
0.0
Coast Range
7.1.8
115
0
0.0
0.0
0.0
0.0
0.0
Middle Atlantic Coastal Plain
8.5.1
105
1
1.0
2.9
1.9
4.8
3.8
Wasatch and Uinta Mountains
6.2.13
96
0
0.0
0.0
0.0
0.0
0.0
North Central Hardwood Forests
8.1.4
94
0
0.0
1.1
1.1
1.1
1.1
Columbia Mountains/Northern Rockies
6.2.3
86
3
3.5
3.5
0.0
3.5
0.0
Eastern Great Lakes Lowlands
8.1.1
83
1
1.2
1.2
0.0
1.2
0.0
Klamath Mountains
6.2.11
81
0
0.0
0.0
0.0
0.0
0.0
Interior Plateau
8.3.3
71
0
0.0
0.0
0.0
0.0
0.0
Blue Mountains
6.2.9
63
0
0.0
0.0
0.0
0.0
0.0
Ozark Highlands
8.4.5
56
0
0.0
3.6
3.6
3.6
3.6
Ouachita Mountains
8.4.8
42
0
0.0
0.0
0.0
0.0
0.0
Mississippi Valley Loess Plains
8.3.6
41
0
0.0
9.8
9.8
14.6
14.6
Strait of Georgia/Puget Lowland
7.1.7
38
0
0.0
0.0
0.0
0.0
0.0
5A-75
-------
Ecoregion (n=69)
Sulfur only - ANC = 30 ueq/L
Number of CLs
% Exceedances
Total
Number
CL<0
2018-2020
2014-2016
Name
Code
n
% of
total
All
CL>0
All
CL>0
Western Allegheny Plateau
8.4.3
35
0
0.0
0.0
0.0
0.0
0.0
Southern Michigan/Northern Indiana
Drift Plains
8.1.6
33
0
0.0
3.0
3.0
3.0
3.0
Arkansas Valley
8.4.7
31
0
0.0
0.0
0.0
3.2
3.2
Canadian Rockies
6.2.4
31
0
0.0
0.0
0.0
0.0
0.0
Eastern Cascades Slopes and
Foothills
6.2.8
27
0
0.0
0.0
0.0
3.7
3.7
Cross Timbers
9.4.5
26
0
0.0
3.8
3.8
3.8
3.8
Western Corn Belt Plains
9.2.3
26
0
0.0
0.0
0.0
0.0
0.0
Arizona/New Mexico Mountains
13.1.1
25
0
0.0
0.0
0.0
0.0
0.0
Willamette Valley
7.1.9
24
0
0.0
0.0
0.0
0.0
0.0
Boston Mountains
8.4.6
23
0
0.0
0.0
0.0
0.0
0.0
Southern and Baja California Pine-Oak
Mountains
11.1.3
22
1
4.5
4.5
0.0
4.5
0.0
Central Irregular Plains
9.2.4
21
0
0.0
0.0
0.0
4.8
4.8
California Coastal Sage, Chaparral,
and Oak Woodlands
11.1.1
21
0
0.0
0.0
0.0
0.0
0.0
Northern Basin and Range
10.1.3
20
0
0.0
0.0
0.0
0.0
0.0
Mississippi Alluvial Plain
8.5.2
19
1
5.3
5.3
0.0
5.3
0.0
Interior River Valleys and Hills
8.3.2
18
0
0.0
0.0
0.0
5.6
5.6
Western Gulf Coastal Plain
9.5.1
16
0
0.0
0.0
0.0
0.0
0.0
Central Basin and Range
10.1.5
16
0
0.0
0.0
0.0
0.0
0.0
Driftless Area
8.1.5
15
0
0.0
0.0
0.0
0.0
0.0
Erie Drift Plain
8.1.10
14
0
0.0
0.0
0.0
7.1
7.1
Eastern Corn Belt Plains
8.2.4
14
0
0.0
0.0
0.0
0.0
0.0
East Central Texas Plains
8.3.8
10
1
10.0
10.0
0.0
10.0
0.0
Southeastern Wisconsin Till Plains
8.2.1
10
0
0.0
0.0
0.0
0.0
0.0
Flint Hills
9.4.4
7
1
14.3
28.6
14.3
28.6
14.3
Central Great Plains
9.4.2
5
0
0.0
0.0
0.0
0.0
0.0
Wyoming Basin
10.1.4
3
0
0.0
0.0
0.0
0.0
0.0
Texas Blackland Prairies
9.4.7
3
0
0.0
0.0
0.0
0.0
0.0
Northern Minnesota Wetlands
5.2.2
2
0
0.0
0.0
0.0
50.0
50.0
Snake River Plain
10.1.8
2
0
0.0
0.0
0.0
0.0
0.0
Central California Valley
11.1.2
2
0
0.0
0.0
0.0
0.0
0.0
Columbia Plateau
10.1.2
2
0
0.0
0.0
0.0
0.0
0.0
Central Corn Belt Plains
8.2.3
2
0
0.0
0.0
0.0
0.0
0.0
Northwestern Glaciated Plains
9.3.1
2
0
0.0
0.0
0.0
0.0
0.0
Colorado Plateaus
10.1.6
1
0
0.0
0.0
0.0
0.0
0.0
5A-76
-------
Table 5A-20. Percent exceedances of CLs for S only and ANC threshold of 30 jieq/L for
deposition years of 2010-12, 2006-08 and 2001-03 in 69 ecoregions.
Ecoregion (n=69)
Sulfur only - ANC = 30 ueq/L
Number of CLs
% Exceedances
Total
Number
CL<0
2010-2012
2006-2008
2001-2003
Name
Code
n
%
All
CL>0
All
CL>0
All
CL>0
Northern Appalachian and
Atlantic Maritime Highlands
5.3.1
2851
40
1.4
7.5
6.1
18.7
17.3
24.2
22.8
Blue Ridge
8.4.4
1972
13
0.7
10.7
10.0
40.6
40.0
51.1
50.5
Ridge and Valley
8.4.1
1292
8
0.6
11.4
10.8
33.3
32.7
42.4
41.8
Northern Lakes and Forests
5.2.1
839
1
0.1
5.6
5.5
11.3
11.2
20.0
19.9
Northeastern Coastal Zone
8.1.7
565
1
0.2
3.0
2.8
10.6
10.4
12.9
12.7
Piedmont
8.3.4
508
1
0.2
3.3
3.1
14.4
14.2
19.7
19.5
Middle Rockies
6.2.10
496
4
0.8
2.2
1.4
2.4
1.6
2.4
1.6
Acadian Plains and Hills
8.1.8
494
9
1.8
6.9
5.1
13.6
11.7
11.7
9.9
Southeastern Plains
8.3.5
390
9
2.3
11.8
9.5
22.1
19.7
25.1
22.8
Central Appalachians
8.4.2
372
10
2.7
20.7
18.0
48.9
46.2
57.8
55.1
Southern Rockies
6.2.14
372
8
2.2
4.6
2.4
5.4
3.2
5.4
3.2
Sierra Nevada
6.2.12
353
29
8.2
16.4
8.2
16.1
7.9
17.3
9.1
Atlantic Coastal Pine Barrens
8.5.4
234
9
3.8
26.9
23.1
47.4
43.6
55.6
51.7
Northern Piedmont
8.3.1
231
0
0.0
3.0
3.0
7.4
7.4
8.7
8.7
North Central Appalachians
5.3.3
216
0
0.0
7.4
7.4
26.4
26.4
33.3
33.3
Northern Allegheny Plateau
8.1.3
199
1
0.5
2.5
2.0
9.0
8.5
10.6
10.1
Idaho Batholith
6.2.15
188
3
1.6
2.7
1.1
2.7
1.1
2.7
1.1
Cascades
6.2.7
179
11
6.1
7.3
1.1
7.3
1.1
7.8
1.7
North Cascades
6.2.5
162
1
0.6
1.2
0.6
1.2
0.6
1.2
0.6
South Central Plains
8.3.7
153
3
2.0
17.0
15.0
19.6
17.6
20.9
19.0
Southern Coastal Plain
8.5.3
142
4
2.8
31.0
28.2
35.2
32.4
51.4
48.6
Southwestern Appalachians
8.4.9
117
0
0.0
0.9
0.9
30.8
30.8
47.0
47.0
Coast Range
7.1.8
115
0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Middle Atlantic Coastal Plain
8.5.1
105
1
1.0
6.7
5.7
21.9
21.0
28.6
27.6
Wasatch and Uinta Mountains
6.2.13
96
0
0.0
0.0
0.0
1.0
1.0
0.0
0.0
North Central Hardwood Forests
8.1.4
94
0
0.0
1.1
1.1
2.1
2.1
3.2
3.2
Columbia Mountains/Northern
Rockies
6.2.3
86
3
3.5
3.5
0.0
3.5
0.0
3.5
0.0
Eastern Great Lakes Lowlands
8.1.1
83
1
1.2
1.2
0.0
4.8
3.6
6.0
4.8
Klamath Mountains
6.2.11
81
0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Interior Plateau
8.3.3
71
0
0.0
7.0
7.0
8.5
8.5
14.1
14.1
Blue Mountains
6.2.9
63
0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Ozark Highlands
8.4.5
56
0
0.0
3.6
3.6
3.6
3.6
5.4
5.4
Ouachita Mountains
8.4.8
42
0
0.0
0.0
0.0
4.8
4.8
4.8
4.8
Mississippi Valley Loess Plains
8.3.6
41
0
0.0
14.6
14.6
19.5
19.5
19.5
19.5
Strait of Georgia/Puget Lowland
7.1.7
38
0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
5A-77
-------
Western Allegheny Plateau
8.4.3
35
0
0.0
2.9
2.9
20.0
20.0
28.6
28.6
Southern Michigan/Northern
Indiana Drift Plains
8.1.6
33
0
0.0
9.1
9.1
15.2
15.2
27.3
27.3
Arkansas Valley
8.4.7
31
0
0.0
3.2
3.2
6.5
6.5
9.7
9.7
Canadian Rockies
6.2.4
31
0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Eastern Cascades Slopes and
Foothills
6.2.8
27
0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Cross Timbers
9.4.5
26
0
0.0
3.8
3.8
7.7
7.7
7.7
7.7
Western Corn Belt Plains
9.2.3
26
0
0.0
3.8
3.8
3.8
3.8
3.8
3.8
Arizona/New Mexico Mountains
13.1.1
25
0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Willamette Valley
7.1.9
24
0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Boston Mountains
8.4.6
23
0
0.0
0.0
0.0
0.0
0.0
4.3
4.3
Southern and Baja California
Pine-Oak Mountains
11.1.3
22
1
4.5
4.5
0.0
4.5
0.0
4.5
0.0
Central Irregular Plains
9.2.4
21
0
0.0
4.8
4.8
9.5
9.5
9.5
9.5
California Coastal Sage,
Chaparral, and Oak Woodlands
11.1.1
21
0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Northern Basin and Range
10.1.3
20
0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Mississippi Alluvial Plain
8.5.2
19
1
5.3
5.3
0.0
15.8
10.5
21.1
15.8
Interior River Valleys and Hills
8.3.2
18
0
0.0
11.1
11.1
22.2
22.2
22.2
22.2
Western Gulf Coastal Plain
9.5.1
16
0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Central Basin and Range
10.1.5
16
0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Driftless Area
8.1.5
15
0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Erie Drift Plain
8.1.10
14
0
0.0
14.3
14.3
21.4
21.4
28.6
28.6
Eastern Corn Belt Plains
8.2.4
14
0
0.0
14.3
14.3
28.6
28.6
28.6
28.6
East Central Texas Plains
8.3.8
10
1
10.0
10.0
0.0
10.0
0.0
10.0
0.0
Southeastern Wisconsin Till
Plains
8.2.1
10
0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Flint Hills
9.4.4
7
1
14.3
28.6
14.3
28.6
14.3
28.6
14.3
Central Great Plains
9.4.2
5
0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Wyoming Basin
10.1.4
3
0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Texas Blackland Prairies
9.4.7
3
0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Northern Minnesota Wetlands
5.2.2
2
0
0.0
50.0
50.0
50.0
50.0
50.0
50.0
Snake River Plain
10.1.8
2
0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Central California Valley
11.1.2
2
0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Columbia Plateau
10.1.2
2
0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Central Corn Belt Plains
8.2.3
2
0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Northwestern Glaciated Plains
9.3.1
2
0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Colorado Plateaus
10.1.6
1
0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
5A-78
-------
Table 5A-21. Percent exceedances of aquatic CLs for S only and ANC threshold of 50
jieq/L for deposition years of 2018-20 and 2014-16 in 69 ecoregions.
Ecoregion (n=69)
Sulfur only - ANC = 50 ueq/L
Number of CLs
% Exceedances
Total
Number
CL<0
2018-2020
2014-2016
Name
Code
No.
%
All
CL>0
All
CL>0
Northern Appalachian and Atlantic
Maritime Highlands
5.3.1
2851
153
5.4
9.7
4.3
11.9
6.6
Blue Ridge
8.4.4
1972
103
5.2
12.7
7.5
15.1
9.8
Ridge and Valley
8.4.1
1292
28
2.2
5.3
3.1
10.1
8.0
Northern Lakes and Forests
5.2.1
839
11
1.3
4.9
3.6
9.3
8.0
Northeastern Coastal Zone
8.1.7
565
9
1.6
2.8
1.2
3.4
1.8
Piedmont
8.3.4
508
6
1.2
3.5
2.4
4.5
3.3
Middle Rockies
6.2.10
496
16
3.2
4.4
1.2
5.0
1.8
Acadian Plains and Hills
8.1.8
494
29
5.9
7.9
2.0
8.9
3.0
Southeastern Plains
8.3.5
390
21
5.4
12.1
6.7
13.8
8.5
Central Appalachians
8.4.2
372
22
5.9
11.6
5.6
19.6
13.7
Southern Rockies
6.2.14
372
30
8.1
9.4
1.3
11.3
3.2
Sierra Nevada
6.2.12
353
90
25.5
28.3
2.8
30.0
4.5
Atlantic Coastal Pine Barrens
8.5.4
234
28
12.0
24.8
12.8
27.8
15.8
Northern Piedmont
8.3.1
231
3
1.3
1.7
0.4
2.6
1.3
North Central Appalachians
5.3.3
216
5
2.3
4.2
1.9
6.9
4.6
Northern Allegheny Plateau
8.1.3
199
4
2.0
2.5
0.5
2.5
0.5
Idaho Batholith
6.2.15
188
9
4.8
5.9
1.1
9.6
4.8
Cascades
6.2.7
179
21
11.7
13.4
1.7
12.8
1.1
North Cascades
6.2.5
162
4
2.5
3.7
1.2
3.1
0.6
South Central Plains
8.3.7
153
8
5.2
19.0
13.7
19.6
14.4
Southern Coastal Plain
8.5.3
142
12
8.5
29.6
21.1
33.1
24.6
Southwestern Appalachians
8.4.9
117
0
0.0
0.0
0.0
0.9
0.9
Coast Range
7.1.8
115
0
0.0
0.0
0.0
0.0
0.0
Middle Atlantic Coastal Plain
8.5.1
105
3
2.9
5.7
2.9
5.7
2.9
Wasatch and Uinta Mountains
6.2.13
96
1
1.0
1.0
0.0
1.0
0.0
North Central Hardwood Forests
8.1.4
94
1
1.1
2.1
1.1
3.2
2.1
Columbia Mountains/Northern
Rockies
6.2.3
86
4
4.7
5.8
1.2
5.8
1.2
Eastern Great Lakes Lowlands
8.1.1
83
1
1.2
2.4
1.2
2.4
1.2
Klamath Mountains
6.2.11
81
0
0.0
0.0
0.0
0.0
0.0
Interior Plateau
8.3.3
71
2
2.8
7.0
4.2
7.0
4.2
Blue Mountains
6.2.9
63
0
0.0
0.0
0.0
1.6
1.6
Ozark Highlands
8.4.5
56
1
1.8
3.6
1.8
3.6
1.8
Ouachita Mountains
8.4.8
42
0
0.0
2.4
2.4
2.4
2.4
Mississippi Valley Loess Plains
8.3.6
41
1
2.4
19.5
17.1
19.5
17.1
Strait of Georgia/Puget Lowland
7.1.7
38
1
2.6
2.6
0.0
5.3
2.6
5A-79
-------
Ecoregion (n=69)
Sulfur only - ANC = 50 ueq/L
Number of CLs
% Exceedances
Total
CL<0
2018-2020
2014-2016
Name
Code
Number
No.
%
All
CL>0
All
CL>0
Western Allegheny Plateau
8.4.3
35
0
0.0
0.0
0.0
2.9
2.9
Southern Michigan/Northern Indiana
Drift Plains
8.1.6
33
1
3.0
6.1
3.0
9.1
6.1
Arkansas Valley
8.4.7
31
0
0.0
6.5
6.5
3.2
3.2
Canadian Rockies
6.2.4
31
1
3.2
3.2
0.0
3.2
0.0
Eastern Cascades Slopes and
Foothills
6.2.8
27
0
0.0
0.0
0.0
3.7
3.7
Cross Timbers
9.4.5
26
2
7.7
11.5
3.8
11.5
3.8
Western Corn Belt Plains
9.2.3
26
1
3.8
3.8
0.0
3.8
0.0
Arizona/New Mexico Mountains
13.1.1
25
0
0.0
0.0
0.0
0.0
0.0
Willamette Valley
7.1.9
24
0
0.0
0.0
0.0
0.0
0.0
Boston Mountains
8.4.6
23
0
0.0
0.0
0.0
0.0
0.0
Southern and Baja California Pine-
Oak Mountains
11.1.3
22
1
4.5
4.5
0.0
9.1
4.5
Central Irregular Plains
9.2.4
21
1
4.8
9.5
4.8
14.3
9.5
California Coastal Sage, Chaparral,
and Oak Woodlands
11.1.1
21
0
0.0
0.0
0.0
0.0
0.0
Northern Basin and Range
10.1.3
20
1
5.0
5.0
0.0
5.0
0.0
Mississippi Alluvial Plain
8.5.2
19
1
5.3
15.8
10.5
15.8
10.5
Interior River Valleys and Hills
8.3.2
18
0
0.0
11.1
11.1
11.1
11.1
Western Gulf Coastal Plain
9.5.1
16
0
0.0
0.0
0.0
0.0
0.0
Central Basin and Range
10.1.5
16
0
0.0
0.0
0.0
0.0
0.0
Driftless Area
8.1.5
15
0
0.0
0.0
0.0
0.0
0.0
Erie Drift Plain
8.1.10
14
0
0.0
7.1
7.1
7.1
7.1
Eastern Corn Belt Plains
8.2.4
14
0
0.0
14.3
14.3
14.3
14.3
East Central Texas Plains
8.3.8
10
1
10.0
10.0
0.0
10.0
0.0
Southeastern Wisconsin Till Plains
8.2.1
10
0
0.0
0.0
0.0
0.0
0.0
Flint Hills
9.4.4
7
2
28.6
28.6
0.0
28.6
0.0
Central Great Plains
9.4.2
5
0
0.0
0.0
0.0
0.0
0.0
Wyoming Basin
10.1.4
3
0
0.0
0.0
0.0
0.0
0.0
Texas Blackland Prairies
9.4.7
3
0
0.0
0.0
0.0
0.0
0.0
Northern Minnesota Wetlands
5.2.2
2
0
0.0
50.0
50.0
50.0
50.0
Snake River Plain
10.1.8
2
0
0.0
0.0
0.0
0.0
0.0
Central California Valley
11.1.2
2
0
0.0
0.0
0.0
0.0
0.0
Columbia Plateau
10.1.2
2
0
0.0
0.0
0.0
0.0
0.0
Central Corn Belt Plains
8.2.3
2
0
0.0
0.0
0.0
0.0
0.0
Northwestern Glaciated Plains
9.3.1
2
0
0.0
0.0
0.0
0.0
0.0
Colorado Plateaus
10.1.6
1
0
0.0
0.0
0.0
0.0
0.0
5A-80
-------
Table 5A-22. Percent exceedances of CLs for S only and ANC threshold of 50 jieq/L for
deposition years of 2010-12, 2006-08 and 2001-03 in 69 ecoregions.
Ecoregion (n=69)
Sulfur only - ANC = 50 ueq/L
Number of CLs
% Exceedances
Total
Number
CL<0
2010-2012
2006-2008
2001-2003
Name
Code
No.
%
All
CL>0
All
CL>0
All
CL>0
Northern Appalachian and
Atlantic Maritime Highlands
5.3.1
2851
153
5
15.0
9.7
28.0
22.6
32.0
26.6
Blue Ridge
8.4.4
1972
103
5
26.6
21.4
55.5
50.3
63.1
57.9
Ridge and Valley
8.4.1
1292
28
2
17.3
15.2
40.3
38.2
48.5
46.3
Northern Lakes and Forests
5.2.1
839
11
1
13.6
12.3
20.0
18.7
26.7
25.4
Northeastern Coastal Zone
8.1.7
565
9
2
5.3
3.7
15.0
13.5
16.5
14.9
Piedmont
8.3.4
508
6
1
6.9
5.7
20.9
19.7
25.0
23.8
Middle Rockies
6.2.10
496
16
3
5.6
2.4
6.3
3.0
5.8
2.6
Acadian Plains and Hills
8.1.8
494
29
6
10.9
5.1
18.6
12.8
17.6
11.7
Southeastern Plains
8.3.5
390
21
5
16.2
10.8
25.4
20.0
29.5
24.1
Central Appalachians
8.4.2
372
22
6
33.6
27.7
55.1
49.2
63.2
57.3
Southern Rockies
6.2.14
372
30
8
12.1
4.0
12.9
4.8
12.9
4.8
Sierra Nevada
6.2.12
353
90
25
30.0
4.5
30.0
4.5
30.0
4.5
Atlantic Coastal Pine Barrens
8.5.4
234
28
12
33.8
21.8
53.0
41.0
61.1
49.1
Northern Piedmont
8.3.1
231
3
1
3.5
2.2
8.2
6.9
10.4
9.1
North Central Appalachians
5.3.3
216
5
2
14.4
12.0
34.3
31.9
43.1
40.7
Northern Allegheny Plateau
8.1.3
199
4
2
4.0
2.0
11.6
9.5
14.6
12.6
Idaho Batholith
6.2.15
188
9
5
10.1
5.3
9.6
4.8
7.4
2.7
Cascades
6.2.7
179
21
12
12.8
1.1
14.0
2.2
13.4
1.7
North Cascades
6.2.5
162
4
2
3.7
1.2
3.7
1.2
3.7
1.2
South Central Plains
8.3.7
153
8
5
19.6
14.4
24.2
19.0
24.8
19.6
Southern Coastal Plain
8.5.3
142
12
8
33.8
25.4
40.8
32.4
53.5
45.1
Southwestern Appalachians
8.4.9
117
0
0
2.6
2.6
39.3
39.3
53.8
53.8
Coast Range
7.1.8
115
0
0
0.0
0.0
0.0
0.0
0.0
0.0
Middle Atlantic Coastal Plain
8.5.1
105
3
3
9.5
6.7
26.7
23.8
29.5
26.7
Wasatch and Uinta Mountains
6.2.13
96
1
1
1.0
0.0
1.0
0.0
1.0
0.0
North Central Hardwood Forests
8.1.4
94
1
1
4.3
3.2
6.4
5.3
10.6
9.6
Columbia Mountains/Northern
Rockies
6.2.3
86
4
5
5.8
1.2
5.8
1.2
5.8
1.2
Eastern Great Lakes Lowlands
8.1.1
83
1
1
2.4
1.2
6.0
4.8
6.0
4.8
Klamath Mountains
6.2.11
81
0
0
0.0
0.0
0.0
0.0
0.0
0.0
Interior Plateau
8.3.3
71
2
3
8.5
5.6
11.3
8.5
15.5
12.7
Blue Mountains
6.2.9
63
0
0
0.0
0.0
0.0
0.0
0.0
0.0
Ozark Highlands
8.4.5
56
1
2
5.4
3.6
5.4
3.6
5.4
3.6
Ouachita Mountains
8.4.8
42
0
0
7.1
7.1
11.9
11.9
11.9
11.9
Mississippi Valley Loess Plains
8.3.6
41
1
2
19.5
17.1
19.5
17.1
22.0
19.5
Strait of Georgia/Puget Lowland
7.1.7
38
1
3
2.6
0.0
2.6
0.0
5.3
2.6
5A-81
-------
Ecoregion (n=69)
Sulfur only - ANC = 50 ueq/L
Number of CLs
% Exceedances
Total
Number
CL<0
2010-2012
2006-2008
2001-2003
Name
Code
No.
%
All
CL>0
All
CL>0
All
CL>0
Western Allegheny Plateau
8.4.3
35
0
0
11.4
11.4
25.7
25.7
37.1
37.1
Southern Michigan/Northern
Indiana Drift Plains
8.1.6
33
1
3
15.2
12.1
27.3
24.2
27.3
24.2
Arkansas Valley
8.4.7
31
0
0
6.5
6.5
6.5
6.5
9.7
9.7
Canadian Rockies
6.2.4
31
1
3
3.2
0.0
3.2
0.0
3.2
0.0
Eastern Cascades Slopes and
Foothills
6.2.8
27
0
0
0.0
0.0
0.0
0.0
0.0
0.0
Cross Timbers
9.4.5
26
2
8
11.5
3.8
15.4
7.7
19.2
11.5
Western Corn Belt Plains
9.2.3
26
1
4
3.8
0.0
15.4
11.5
15.4
11.5
Arizona/New Mexico Mountains
13.1.1
25
0
0
0.0
0.0
0.0
0.0
0.0
0.0
Willamette Valley
7.1.9
24
0
0
0.0
0.0
0.0
0.0
0.0
0.0
Boston Mountains
8.4.6
23
0
0
4.3
4.3
8.7
8.7
8.7
8.7
Southern and Baja California
Pine-Oak Mountains
11.1.3
22
1
5
9.1
4.5
9.1
4.5
9.1
4.5
Central Irregular Plains
9.2.4
21
1
5
14.3
9.5
14.3
9.5
14.3
9.5
California Coastal Sage,
Chaparral, and Oak Woodlands
11.1.1
21
0
0
0.0
0.0
0.0
0.0
0.0
0.0
Northern Basin and Range
10.1.3
20
1
5
5.0
0.0
5.0
0.0
5.0
0.0
Mississippi Alluvial Plain
8.5.2
19
1
5
15.8
10.5
26.3
21.1
26.3
21.1
Interior River Valleys and Hills
8.3.2
18
0
0
11.1
11.1
22.2
22.2
22.2
22.2
Western Gulf Coastal Plain
9.5.1
16
0
0
0.0
0.0
0.0
0.0
0.0
0.0
Central Basin and Range
10.1.5
16
0
0
0.0
0.0
0.0
0.0
0.0
0.0
Driftless Area
8.1.5
15
0
0
0.0
0.0
0.0
0.0
0.0
0.0
Erie Drift Plain
8.1.10
14
0
0
14.3
14.3
28.6
28.6
35.7
35.7
Eastern Corn Belt Plains
8.2.4
14
0
0
28.6
28.6
28.6
28.6
28.6
28.6
East Central Texas Plains
8.3.8
10
1
10
10.0
0.0
20.0
10.0
20.0
10.0
Southeastern Wisconsin Till
Plains
8.2.1
10
0
0
0.0
0.0
0.0
0.0
0.0
0.0
Flint Hills
9.4.4
7
2
29
28.6
0.0
28.6
0.0
28.6
0.0
Central Great Plains
9.4.2
5
0
0
0.0
0.0
0.0
0.0
0.0
0.0
Wyoming Basin
10.1.4
3
0
0
0.0
0.0
0.0
0.0
0.0
0.0
Texas Blackland Prairies
9.4.7
3
0
0
0.0
0.0
0.0
0.0
0.0
0.0
Northern Minnesota Wetlands
5.2.2
2
0
0
50.0
50.0
50.0
50.0
50.0
50.0
Snake River Plain
10.1.8
2
0
0
0.0
0.0
0.0
0.0
0.0
0.0
Central California Valley
11.1.2
2
0
0
0.0
0.0
0.0
0.0
0.0
0.0
Columbia Plateau
10.1.2
2
0
0
0.0
0.0
0.0
0.0
0.0
0.0
Central Corn Belt Plains
8.2.3
2
0
0
0.0
0.0
0.0
0.0
0.0
0.0
Northwestern Glaciated Plains
9.3.1
2
0
0
0.0
0.0
0.0
0.0
0.0
0.0
Colorado Plateaus
10.1.6
1
0
0
0.0
0.0
0.0
0.0
0.0
0.0
5A-82
-------
Table 5A-23. Percent exceedances of aquatic CLs for S only and ANC threshold of 50/20
jieq/L for deposition years of 2018-20 and 2014-16 in 69 ecoregions.
Ecoregion (n=69)
Sulfur only - ANC = 50/20 ueq/L
Number of CLs
% Exceedances
Total
Number
CL<0
2018-2020
2014-2016
Name
Code
n
%
All
CL>0
All
CL>0
Northern Appalachian and Atlantic
Maritime Highlands
5.3.1
2851
153
5.4
9.7
4.3
11.9
6.6
Blue Ridge
8.4.4
1972
103
5.2
12.7
7.5
15.1
9.8
Ridge and Valley
8.4.1
1292
28
2.2
5.3
3.1
10.1
8.0
Northern Lakes and Forests
5.2.1
839
11
1.3
4.9
3.6
9.3
8.0
Northeastern Coastal Zone
8.1.7
565
9
1.6
2.8
1.2
3.4
1.8
Piedmont
8.3.4
508
6
1.2
3.5
2.4
4.5
3.3
Middle Rockies
6.2.10
496
0
0.0
0.2
0.2
0.6
0.6
Acadian Plains and Hills
8.1.8
494
29
5.9
7.9
2.0
8.9
3.0
Southeastern Plains
8.3.5
390
21
5.4
12.1
6.7
13.8
8.5
Central Appalachians
8.4.2
372
22
5.9
11.6
5.6
19.6
13.7
Southern Rockies
6.2.14
372
1
0.3
0.5
0.3
1.9
1.6
Sierra Nevada
6.2.12
353
11
3.1
4.2
1.1
5.9
2.8
Atlantic Coastal Pine Barrens
8.5.4
234
28
12.0
24.8
12.8
27.8
15.8
Northern Piedmont
8.3.1
231
3
1.3
1.7
0.4
2.6
1.3
North Central Appalachians
5.3.3
216
5
2.3
4.2
1.9
6.9
4.6
Northern Allegheny Plateau
8.1.3
199
4
2.0
2.5
0.5
2.5
0.5
Idaho Batholith
6.2.15
188
1
0.5
0.5
0.0
0.5
0.0
Cascades
6.2.7
179
4
2.2
3.9
1.7
5.0
2.8
North Cascades
6.2.5
162
1
0.6
0.6
0.0
0.6
0.0
South Central Plains
8.3.7
153
8
5.2
19.0
13.7
19.6
14.4
Southern Coastal Plain
8.5.3
142
12
8.5
29.6
21.1
33.1
24.6
Southwestern Appalachians
8.4.9
117
0
0.0
0.0
0.0
0.9
0.9
Coast Range
7.1.8
115
0
0.0
0.0
0.0
0.0
0.0
Middle Atlantic Coastal Plain
8.5.1
105
3
2.9
5.7
2.9
5.7
2.9
Wasatch and Uinta Mountains
6.2.13
96
0
0.0
0.0
0.0
0.0
0.0
North Central Hardwood Forests
8.1.4
94
1
1.1
2.1
1.1
3.2
2.1
Columbia Mountains/Northern Rockies
6.2.3
86
1
1.2
1.2
0.0
2.3
1.2
Eastern Great Lakes Lowlands
8.1.1
83
1
1.2
2.4
1.2
2.4
1.2
Klamath Mountains
6.2.11
81
0
0.0
0.0
0.0
0.0
0.0
Interior Plateau
8.3.3
71
2
2.8
7.0
4.2
7.0
4.2
Blue Mountains
6.2.9
63
0
0.0
0.0
0.0
0.0
0.0
Ozark Highlands
8.4.5
56
1
1.8
3.6
1.8
3.6
1.8
Ouachita Mountains
8.4.8
42
0
0.0
2.4
2.4
2.4
2.4
Mississippi Valley Loess Plains
8.3.6
41
1
2.4
19.5
17.1
19.5
17.1
Strait of Georgia/Puget Lowland
7.1.7
38
0
0.0
0.0
0.0
0.0
0.0
Western Allegheny Plateau
8.4.3
35
0
0.0
0.0
0.0
2.9
2.9
5A-83
-------
Ecoregion (n=69)
Sulfur only - ANC = 50/20 ueq/L
Number of CLs
% Exceedances
Total
Number
CL<0
2018-2020
2014-2016
Name
Code
n
%
All
CL>0
All
CL>0
Southern Michigan/Northern Indiana
Drift Plains
8.1.6
33
1
3.0
6.1
3.0
9.1
6.1
Arkansas Valley
8.4.7
31
0
0.0
6.5
6.5
3.2
3.2
Canadian Rockies
6.2.4
31
0
0.0
0.0
0.0
0.0
0.0
Eastern Cascades Slopes and Foothills
6.2.8
27
0
0.0
0.0
0.0
0.0
0.0
Cross Timbers
9.4.5
26
2
7.7
11.5
3.8
11.5
3.8
Western Corn Belt Plains
9.2.3
26
1
3.8
3.8
0.0
3.8
0.0
Arizona/New Mexico Mountains
13.1.1
25
0
0.0
0.0
0.0
0.0
0.0
Willamette Valley
7.1.9
24
0
0.0
0.0
0.0
0.0
0.0
Boston Mountains
8.4.6
23
0
0.0
0.0
0.0
0.0
0.0
Southern and Baja California Pine-Oak
Mountains
11.1.3
22
0
0.0
0.0
0.0
4.5
4.5
Central Irregular Plains
9.2.4
21
1
4.8
9.5
4.8
14.3
9.5
California Coastal Sage, Chaparral, and
Oak Woodlands
11.1.1
21
0
0.0
0.0
0.0
0.0
0.0
Northern Basin and Range
10.1.3
20
0
0.0
0.0
0.0
0.0
0.0
Mississippi Alluvial Plain
8.5.2
19
1
5.3
15.8
10.5
15.8
10.5
Interior River Valleys and Hills
8.3.2
18
0
0.0
11.1
11.1
11.1
11.1
Western Gulf Coastal Plain
9.5.1
16
0
0.0
0.0
0.0
0.0
0.0
Central Basin and Range
10.1.5
16
0
0.0
0.0
0.0
0.0
0.0
Driftless Area
8.1.5
15
0
0.0
0.0
0.0
0.0
0.0
Erie Drift Plain
8.1.10
14
0
0.0
7.1
7.1
7.1
7.1
Eastern Corn Belt Plains
8.2.4
14
0
0.0
14.3
14.3
14.3
14.3
East Central Texas Plains
8.3.8
10
1
10.0
10.0
0.0
10.0
0.0
Southeastern Wisconsin Till Plains
8.2.1
10
0
0.0
0.0
0.0
0.0
0.0
Flint Hills
9.4.4
7
2
28.6
28.6
0.0
28.6
0.0
Central Great Plains
9.4.2
5
0
0.0
0.0
0.0
0.0
0.0
Wyoming Basin
10.1.4
3
0
0.0
0.0
0.0
0.0
0.0
Texas Blackland Prairies
9.4.7
3
0
0.0
0.0
0.0
0.0
0.0
Northern Minnesota Wetlands
5.2.2
2
0
0.0
50.0
50.0
50.0
50.0
Snake River Plain
10.1.8
2
0
0.0
0.0
0.0
0.0
0.0
Central California Valley
11.1.2
2
0
0.0
0.0
0.0
0.0
0.0
Columbia Plateau
10.1.2
2
0
0.0
0.0
0.0
0.0
0.0
Central Corn Belt Plains
8.2.3
2
0
0.0
0.0
0.0
0.0
0.0
Northwestern Glaciated Plains
9.3.1
2
0
0.0
0.0
0.0
0.0
0.0
Colorado Plateaus
10.1.6
1
0
0.0
0.0
0.0
0.0
0.0
5A-84
-------
Table 5A-24. Percent exceedances of CLs for S only and ANC threshold of 50/20 jieq/L
for deposition years of 2010-12, 2006-08 and 2001-03 in 69 ecoregions.
Ecoregion (n=69)
Sulfur only - ANC = 50/20 ueq/L
Number of CLs
% Exceedances
Total
Number
CL<0
2010-2012
2006-2008
2001-2003
Name
Code
No.
%
All
CL>0
All
CL>0
All
CL>0
Northern Appalachian and
Atlantic Maritime Highlands
5.3.1
2851
153
5.4
15.0
9.7
28.0
22.6
32.0
26.6
Blue Ridge
8.4.4
1972
103
5.2
26.6
21.4
55.5
50.3
63.1
57.9
Ridge and Valley
8.4.1
1292
28
2.2
17.3
15.2
40.3
38.2
48.5
46.3
Northern Lakes and Forests
5.2.1
839
11
1.3
13.6
12.3
20.0
18.7
26.7
25.4
Northeastern Coastal Zone
8.1.7
565
9
1.6
5.3
3.7
15.0
13.5
16.5
14.9
Piedmont
8.3.4
508
6
1.2
6.9
5.7
20.9
19.7
25.0
23.8
Middle Rockies
6.2.10
496
0
0.0
0.8
0.8
0.8
0.8
0.8
0.8
Acadian Plains and Hills
8.1.8
494
29
5.9
10.9
5.1
18.6
12.8
17.6
11.7
Southeastern Plains
8.3.5
390
21
5.4
16.2
10.8
25.4
20.0
29.5
24.1
Central Appalachians
8.4.2
372
22
5.9
33.6
27.7
55.1
49.2
63.2
57.3
Southern Rockies
6.2.14
372
1
0.3
2.2
1.9
2.7
2.4
2.7
2.4
Sierra Nevada
6.2.12
353
11
3.1
5.1
2.0
5.1
2.0
6.2
3.1
Atlantic Coastal Pine Barrens
8.5.4
234
28
12.0
33.8
21.8
53.0
41.0
61.1
49.1
Northern Piedmont
8.3.1
231
3
1.3
3.5
2.2
8.2
6.9
10.4
9.1
North Central Appalachians
5.3.3
216
5
2.3
14.4
12.0
34.3
31.9
43.1
40.7
Northern Allegheny Plateau
8.1.3
199
4
2.0
4.0
2.0
11.6
9.5
14.6
12.6
Idaho Batholith
6.2.15
188
1
0.5
0.5
0.0
0.5
0.0
0.5
0.0
Cascades
6.2.7
179
4
2.2
3.9
1.7
3.9
1.7
3.9
1.7
North Cascades
6.2.5
162
1
0.6
0.6
0.0
0.6
0.0
0.6
0.0
South Central Plains
8.3.7
153
8
5.2
19.6
14.4
24.2
19.0
24.8
19.6
Southern Coastal Plain
8.5.3
142
12
8.5
33.8
25.4
40.8
32.4
53.5
45.1
Southwestern Appalachians
8.4.9
117
0
0.0
2.6
2.6
39.3
39.3
53.8
53.8
Coast Range
7.1.8
115
0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Middle Atlantic Coastal Plain
8.5.1
105
3
2.9
9.5
6.7
26.7
23.8
29.5
26.7
Wasatch and Uinta Mountains
6.2.13
96
0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
North Central Hardwood Forests
8.1.4
94
1
1.1
4.3
3.2
6.4
5.3
10.6
9.6
Columbia Mountains/Northern
Rockies
6.2.3
86
1
1.2
3.5
2.3
3.5
2.3
3.5
2.3
Eastern Great Lakes Lowlands
8.1.1
83
1
1.2
2.4
1.2
6.0
4.8
6.0
4.8
Klamath Mountains
6.2.11
81
0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Interior Plateau
8.3.3
71
2
2.8
8.5
5.6
11.3
8.5
15.5
12.7
Blue Mountains
6.2.9
63
0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Ozark Highlands
8.4.5
56
1
1.8
5.4
3.6
5.4
3.6
5.4
3.6
Ouachita Mountains
8.4.8
42
0
0.0
7.1
7.1
11.9
11.9
11.9
11.9
Mississippi Valley Loess Plains
8.3.6
41
1
2.4
19.5
17.1
19.5
17.1
22.0
19.5
Strait of Georgia/Puget Lowland
7.1.7
38
0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
5A-85
-------
Ecoregion (n=69)
Sulfur only - ANC = 50/20 ueq/L
Number of CLs
% Exceedances
Total
Number
CL<0
2010-2012
2006-2008
2001-2003
Name
Code
No.
%
All
CL>0
All
CL>0
All
CL>0
Western Allegheny Plateau
8.4.3
35
0
0.0
11.4
11.4
25.7
25.7
37.1
37.1
Southern Michigan/Northern
Indiana Drift Plains
8.1.6
33
1
3.0
15.2
12.1
27.3
24.2
27.3
24.2
Arkansas Valley
8.4.7
31
0
0.0
6.5
6.5
6.5
6.5
9.7
9.7
Canadian Rockies
6.2.4
31
0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Eastern Cascades Slopes and
Foothills
6.2.8
27
0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Cross Timbers
9.4.5
26
2
7.7
11.5
3.8
15.4
7.7
19.2
11.5
Western Corn Belt Plains
9.2.3
26
1
3.8
3.8
0.0
15.4
11.5
15.4
11.5
Arizona/New Mexico Mountains
13.1.1
25
0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Willamette Valley
7.1.9
24
0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Boston Mountains
8.4.6
23
0
0.0
4.3
4.3
8.7
8.7
8.7
8.7
Southern and Baja California
Pine-Oak Mountains
11.1.3
22
0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Central Irregular Plains
9.2.4
21
1
4.8
14.3
9.5
14.3
9.5
14.3
9.5
California Coastal Sage,
Chaparral, and Oak Woodlands
11.1.1
21
0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Northern Basin and Range
10.1.3
20
0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Mississippi Alluvial Plain
8.5.2
19
1
5.3
15.8
10.5
26.3
21.1
26.3
21.1
Interior River Valleys and Hills
8.3.2
18
0
0.0
11.1
11.1
22.2
22.2
22.2
22.2
Western Gulf Coastal Plain
9.5.1
16
0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Central Basin and Range
10.1.5
16
0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Driftless Area
8.1.5
15
0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Erie Drift Plain
8.1.10
14
0
0.0
14.3
14.3
28.6
28.6
35.7
35.7
Eastern Corn Belt Plains
8.2.4
14
0
0.0
28.6
28.6
28.6
28.6
28.6
28.6
East Central Texas Plains
8.3.8
10
1
10.0
10.0
0.0
20.0
10.0
20.0
10.0
Southeastern Wisconsin Till
Plains
8.2.1
10
0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Flint Hills
9.4.4
7
2
28.6
28.6
0.0
28.6
0.0
28.6
0.0
Central Great Plains
9.4.2
5
0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Wyoming Basin
10.1.4
3
0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Texas Blackland Prairies
9.4.7
3
0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Northern Minnesota Wetlands
5.2.2
2
0
0.0
50.0
50.0
50.0
50.0
50.0
50.0
Snake River Plain
10.1.8
2
0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Central California Valley
11.1.2
2
0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Columbia Plateau
10.1.2
2
0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Central Corn Belt Plains
8.2.3
2
0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Northwestern Glaciated Plains
9.3.1
2
0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Colorado Plateaus
10.1.6
1
0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
5A-86
-------
2018 -2020 Sulfur Deposition Ecoregion Exceedances
2014 - 2016 Sulfur Deposition Ecoregion Exceedances
Percent Exceedances
(ANC = 20 Heq/L)
I 0 -10%
10-15%
H >15%
Ecoregions where critical loads are < 50 values
|.~ ^ Ecoregions with high level of natural acidity
| Areas without critical loads
Figure 5A-32. Percent of CLs exceeded per ecoregion for S only deposition from 2018-20
(top) and 2014-16 (bottom) for an ANC threshold of 20 jieq/L. The
Southern Coastal Plan (8.5.3) and Atlantic Coastal Pine Barrens (8.5.4)
ecoregions are cross hatched to indicate natural high level of acidity.
5A-87
-------
2010 -2012 Sulfur Deposition Ecoregion Exceedances
2006 - 2008 Sulfur Deposition Ecoregion Exceedances
Percent Exceedances
(ANC = 20 Heq/L)
] o-10%
| 10-15%
>15%
Ecoregions where critical loads are < 50 values
Ecoregions with high level of natural acidity
| Areas without critical loads
Figure 5A-33. Percent of CLs exceeded per ecoregion for S only deposition from 2010-12
(top) and 2006-08 (bottom) for an ANC threshold of 20 jieq/L. The
Southern Coastal Plan (8.5.3) and Atlantic Coastal Pine Barrens (8.5.4)
ecoregions are cross hatched to indicate natural high level of acidity.
5A-88
-------
2001 - 2003 Sulfur Deposition Ecoregion Exceedances
Percent Exceedances
(ANC = 20 Heq/L)
~i 0-10%
H 10 - 15%
| >15%
Ecoregions where critical loads are < 50 values
KHKH Ecoregions with high level of natural acidity
Areas without critical loads
Figure 5A-34. Percent of CLs exceeded per ecoregion for S only deposition front 2001-02
for an ANC threshold of 20 jieq/L. The Southern Coastal Plan (8.5.3) and
Atlantic Coastal Pine Barrens (8.5.4) ecoregions are cross hatched to
indicate natural high level of acidity.
5A-89
-------
2018 -2020 Sulfur Deposition Ecoregion Exceedances
2014 - 2016 Sulfur Deposition Ecoregion Exceedances
Percent Exceedances
(ANC = 30 Heq/L)
I 0 -10%
10-15%
H >15%
Ecoregions where critical loads are < 50 values
|.~ ^ Ecoregions with high level of natural acidity
| Areas without critical loads
Figure 5A-35. Percent of CLs exceeded per ecoregion for S only deposition from 2018-20
(top) and 2014-16 (bottom) for an ANC threshold of 30 fieq/L. The
Southern Coastal Plan (8.5.3) and Atlantic Coastal Pine Barrens (8.5.4)
ecoregions are cross hatched to indicate natural high level of acidity.
5A-90
-------
2010 -2012 Sulfur Deposition Ecoregion Exceedances
2006 - 2008 Sulfur Deposition Ecoregion Exceedances
Percent Exceedances
(ANC = 30 Heq/L)
] o-10%
| 10-15%
>15%
Ecoregions where critical loads are < 50 values
Ecoregions with high level of natural acidity
| Areas without critical loads
Figure 5A-36. Percent of CLs exceeded per ecoregion for S only deposition from 2010-12
(top) and 2006-08 (bottom) for an ANC threshold of 30 fieq/L. The
Southern Coastal Plan (8.5.3) and Atlantic Coastal Pine Barrens (8.5.4)
ecoregions are cross hatched to indicate natural high level of acidity.
5 A-91
-------
2001 - 2003 Sulfur Deposition Ecoregion Exceedances
Percent Exceedances
(ANC = 30 (jeq/L)
~~| 0-10%
| 10-15%
| >15%
Ecoregions where critical loads are < 50 values
Ecoregions with high level of natural acidity
| | Areas without critical loads
Figure 5A-37. Percent of CLs exceeded per ecoregion for S only deposition from 2001-03
for an ANC threshold of 30 jieq/L. The Southern Coastal Plan (8.5.3) and
Atlantic Coastal Pine Barrens (8.5.4) ecoregions are cross hatched to
indicate natural high level of acidity.
5A-92
-------
2018 -2020 Sulfur Deposition Ecoregion Exceedances
2014 - 2016 Sulfur Deposition Ecoregion Exceedances
Percent Exceedances
(ANC = 50 (jeq/L)
| I 0 -10%
10-15%
H >15%
Ecoregions where critical loads are < 50 values
Ecoregions with high level of natural acidity
| Areas without critical loads
Figure 5A-38. Percent of CLs exceeded per ecoregion for S only deposition from 2018-20
(top) and 2014-16 (bottom) for an ANC threshold of 50 jieq/L. The
Southern Coastal Plan (8.5.3) and Atlantic Coastal Pine Barrens (8.5.4)
ecoregions are cross hatched to indicate natural high level of acidity.
5A-93
-------
2010 -2012 Sulfur Deposition Ecoregion Exceedances
2006 - 2008 Sulfur Deposition Ecoregion Exceedances
Percent Exceedances
(ANC = 50 (jeq/L)
0-10%
10-15%
>15%
Ecoregions where critical loads are < 50 values
.;] Ecoregions with high level of natural acidity
| Areas without critical loads
Figure 5A-39. Percent of CLs exceeded per ecoregion for S only deposition from 2010-12
(top) and 2006-08 (bottom) for an ANC threshold of 50 fieq/L. The
Southern Coastal Plan (8.5.3) and Atlantic Coastal Pine Barrens (8.5.4)
ecoregions are cross hatched to indicate natural high level of acidity.
5A-94
-------
2001 - 2003 Sulfur Deposition Ecoregion Exceedances
Percent Exceedances
(ANC = 50 \ieqlL)
~i 0-10%
| 10 -15%
>15%
Ecoregions where critical loads are < 50 values
Ecoregions with high level of natural acidity
Areas without critical loads
Figure 5A-40. Percent of CLs exceeded per ecoregion for S only deposition from 2001-03
for an ANC threshold of 50 (teq/L. The Southern Coastal Plan (8,5.3) and
Atlantic Coastal Pine Barrens (8.5.4) ecoregions are cross hatched to
indicate natural high level of acidity.
5A-95
-------
2018 -2020 Sulfur Deposition Ecoregion Exceedances
2014 - 2016 Sulfur Deposition Ecoregion Exceedances
Percent Exceedances
(ANC = 50/20 |jeq/L)
0-10%
10-15%
H >15%
Ecoregions where critical loads are < 50 values
.;] Ecoregions with high level of natural acidity
| Areas without critical loads
Figure 5A-41. Percent of CLs exceeded per ecoregion for S only deposition from 2018-20
(top) and 2014-16 (bottom) for an ANC threshold of 50 fieq/L for East and
20 jieq/L for the West. The Southern Coastal Plan (8.5.3) and Atlantic
Coastal Pine Barrens (8.5.4) ecoregions are cross hatched to indicate
natural high level of acidity.
5A-96
-------
2010 -2012 Sulfur Deposition Ecoregion Exceedances
2006 - 2008 Sulfur Deposition Ecoregion Exceedances
Percent Exceedances
(ANC = 50/20 peq/L)
o-10%
I 10 - 15%
| >15%
Ecoregions where critical loads are < 50 values
Ecoregions with high level of natural acidity
| Areas without critical loads
Figure 5A-42. Percent of CLs exceeded per ecoregion for S only deposition from 2010-12
(top) and 2006-08 (bottom) for an ANC threshold of 50 fieq/L for East and
20 jieq/L for the West. The Southern Coastal Plan (8.5.3) and Atlantic
Coastal Pine Barrens (8.5.4) ecoregions are cross hatched to indicate
natural high level of acidity.
5A-97
-------
2001 - 2003 Sulfur Deposition Ecoregion Exceedances
Percent Exceedances
(ANC = 50/20 |jeq/L)
~~| 0-10%
| 10-15%
| >15%
Ecoregions where critical loads are < 50 values
Ecoregions with high level of natural acidity
| | Areas without critical loads
Figure 5A-43. Percent of CLs exceeded per ecoregion for S only deposition from 2001-03
for an ANC threshold of 50 fieq/L for East and 20 fieq/L for the West. The
Southern Coastal Plan (8.5.3) and Atlantic Coastal Pine Barrens (8.5.4)
ecoregions are cross hatched to indicate natural high level of acidity.
5A.2.2.2 Ecoregion Summary - Percent Exceedances as a Function of Total S
deposition
In this section, the results for the deposition estimates across the five deposition periods
(2001-03, 2006-08, 2010-12, 2014-06, 2018-20) are summarized by the number of ecoregions
with over 10, 15, 20, 25, and 30% of their CLs estimated to be exceeded. Ecoregions included in
this analysis are those for which there are at least 50 waterbodies with CLs and that (1) are not
one of the three ecoregions identified as naturally acidic (see 5A.2.2.1 above), and (2) had
waterbodies with a CL greater than zero (for ANC of 50 [teq/L in the East and 20 jieq/L in the
West) that was exceeded during any of the five time periods. These criteria yield a total of 25
ecoregions across the CONUS with 18 and 7 ecoregions in the eastern and western U.S.,
respectively.
5A-98
-------
In the discussions below, ecoregion S deposition for each time period is represented by
the median across waterbodies with CLs in the ecoregion. Table 5A-25 provides the minimum,
maximum, and median of these ecoregion medians. Deposition levels were summarized for the
five deposition periods and three ANC thresholds (20, 30, and 50 |ieq/L) for the eastern and
western U.S. separately and together. Deposition for ecoregions in the eastern U.S. ranged from
a median value (across waterbodies with CLs) of 11.08 kg S/ha-yr in 2001-03 to one of 2.04 kg
S/ha-yr in 2018-20. Total S deposition for ecoregions in the western U.S. was lower, ranging
from a median of 1.40 kg S/ha-yr in 2001-03 to 0.87 kg S/ha-yr in 2018-20.
Table 5A-25. Minimum, maximum, and median S deposition for 25 ecoregions in
analysis. Ecoregion deposition values are medians of deposition at sites
with CLs in the ecoregion.
Median Sulfur Deposition, kg S/ha-yr
2001-03
2006-08
2010-12
2014-16
2018-20
All 18 Eastern Ecoregions
Minimum
4.01
3.10
2.34
1.88
1.31
Maximum
17.27
14.44
7.25
4.58
3.88
Median
11.08
9.36
4.76
2.97
2.04
All 1 Western Ecoregions
Minimum
1.18
1.22
1.02
1.08
0.62
Maximum
1.94
1.83
1.47
1.56
1.19
Median
1.40
1.52
1.29
1.17
0.87
All 25 Ecoregions in Analysis
Minimum
1.18
1.22
1.02
1.08
0.62
Maximum
17.27
14.44
7.25
4.58
3.88
Median
7.77
6.50
3.71
2.32
1.73
The summaries below are intended to look at the percent exceedances per ecoregion as a
function of annual average total S deposition. For example, for estimated deposition at or below
2 kg S/ha-yr across all ecoregions and deposition periods, there are no ecoregions that have more
than 10% of sites exceeding their CLs for an ANC threshold of 50 [j,eq/L (Table 5A-26). Among
the ecoregion-time period combinations with S deposition at or below 3 kg S/ha-yr, there is only
one such ecoregion. However, for deposition at or below 10 kg S/ha-yr, there are 22 ecoregion-
time periods with >10% EX and 1 with >30% EX. At or below 6, 10, and 15 kg S/ha-yr, there
were 13, 22, and 33 ecoregion-time periods, respectively, with >10% of sites exceeding CLs, and
2, 6, and 14 ecoregion-time periods with >20% of sites exceeding CLs. These summaries were
done for ANC thresholds of 20, 30, and 50 [j,eq/L for the eastern U.S., 20 [j,eq/L western U.S.,
and combined 50/20, 30/20, and 20 [j,eq/L for both eastern and western U.S. Results are
summarized in Tables 5A-26, 5A-28, 5A-30, 5A-32, 5A-34, 5A-36, and 5A-38.
5A-99
-------
The cumulative percentages of ecoregion-time periods achieving the various ANC
thresholds were also determined and graphed as a function of deposition bin. For example, for
ANC of 50 [j,eq/L and for the eastern U.S, 100% of ecoregions-time period combinations with S
deposition at/below 2 kg/ha-yr have less than 10% of sites exceeding their CLs while 60% of
ecoregion-time period combinations with S deposition at/below 18 kg S/ha-yr have less than
10% of sites exceeding their CLs, i.e., 40% have > 10% EX (Table 5A-27, Figure 5A-44).
Results for the other ANC thresholds are summarized in Tables 5A-29, 5 A-31, 5A-33, 5A-35,
5A-37, 5A-39. These cumulative results are graphed in Figures 5A-44 to 5A-49.
Table 5A-26. Number of ecoregion-time period combinations with more than 10,15, 20,
25 and 30% of waterbodies exceeding their CLs for ANC target of 50
jieq/L. Includes 18 ecoregions in the eastern U.S.
Total Sulfur
Deposition
(kg S/ha-yr)
No. Eastern
Ecoregion-
Time Period
Combinations
Number of Ecoregion-Time Period Combinations with
Specified Percent of Waterbodies Exceeding their CLs
10%
15%
20%
25%
30%
<2
10
0
0
0
0
0
<3
29
1
0
0
0
0
<4
41
3
1
0
0
0
<5
51
9
3
2
1
0
<6
59
13
4
2
1
0
<7
63
14
5
3
1
0
<8
67
18
9
5
3
0
<9
69
19
9
5
3
0
<10
73
22
11
6
4
1
<11
76
24
13
7
4
1
<12
79
27
15
9
6
3
<13
81
28
16
10
6
3
<14
84
31
18
12
8
5
<15
86
33
20
14
10
7
<16
88
34
21
15
11
8
<17
88
34
21
15
11
8
<18*
90
36
23
17
13
10
* Highest ecoregion median (across sites with CLs) S deposition estimate across the five time periods
is 17.27 kg/ha-yr.
5A-100
-------
Table 5A-27. Cumulative percentage of ecoregion-time period combinations with less
than 10,15, 20, 25, and 30% of waterbodies per ecoregion exceeding their
CLs for the ANC target of 50 jieq/L as a function of total S deposition.
100% indicates there were no ecoregion-time period combinations that had
percent exceedances above specified value. For the 18 eastern U.S.
ecoregions and five deposition periods (2001-03, 2006-08, 2010-12, 2014-06,
2018-20) (See Table 5A-26 for data).
Total Sulfur
Deposition
(kg S/ha-yr)
Percent of Exceedances Across the 5 deposition Periods
10%
15%
20%
25%
30%
<2
100%
100%
100%
100%
100%
<3
97%
100%
100%
100%
100%
<4
93%
98%
100%
100%
100%
<5
82%
94%
96%
98%
100%
<6
78%
93%
97%
98%
100%
<7
78%
92%
95%
98%
100%
<8
73%
87%
93%
96%
100%
<9
72%
87%
93%
96%
100%
<10
70%
85%
92%
95%
99%
<11
68%
83%
91%
95%
99%
<12
66%
81%
89%
92%
96%
<13
65%
80%
88%
93%
96%
<14
63%
79%
86%
90%
94%
<15
62%
77%
84%
88%
92%
<16
61%
76%
83%
88%
91%
<17
61%
76%
83%
88%
91%
<18
60%
74%
81%
86%
89%
5A-101
-------
ANC 50 peq/L-18 Eastern Ecoregions
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
Figure 5A-44.
cn
TD
O
CD
Q_
-------
Table 5A-28. Number of ecoregion-time period combinations with >10, >15, >20, >25,
>30% of waterbodies exceeding their CLs for ANC target of 30 jieq/L as a
function of total S deposition across all 5 deposition periods (2001-03, 2006-
08, 2010-12, 2014-06, 2018-20). Includes 18 ecoregions in the eastern U.S.
Total Sulfur
Deposition
(kg S/ha-yr)
Number of Ecoregion-Time Period Combinations
with Specified Percent of Waterbodies Exceeding
their CLs
10%
15%
20%
25%
30%
<2
0
0
0
0
0
<3
0
0
0
0
0
<4
2
0
0
0
0
<5
4
1
0
0
0
<6
7
1
0
0
0
<7
8
2
0
0
0
<8
12
6
1
0
0
<9
13
6
1
0
0
<10
16
8
2
1
1
<11
18
9
3
1
1
<12
21
11
5
3
3
<13
22
12
5
3
3
<14
25
14
7
5
4
<15
27
16
9
7
6
<16
28
17
10
8
7
<17
28
17
10
8
7
<18
30
19
12
10
9
* Highest ecoregion median (across sites with CLs) S deposition estimate across
the five time periods is 17.27 kg/ha-yr.
5A-103
-------
Table 5A-29. Cumulative percent of ecoregion-time period combinations with less than
10,15, 20, 25 and 30% of waterbodies per ecoregion exceeding their CLs
for the ANC target of 30 jieq/L as a function of total S deposition. 100%
indicates there were no ecoregion-time period combinations that had
percent exceedances above the specified values. Critical load exceedances
for 18 eastern U.S. ecoregions and five deposition periods (2001-03, 2006-
08, 2010-12, 2014-06, 2018-20) (See Table 5A-28 for data).
Total Sulfur
Deposition
(kg S/ha-yr)
Number of Ecoregion with Percent of Exceedances Across
the 5 deposition Periods
10%
15%
20%
25%
30%
<2
100%
100%
100%
100%
100%
<3
100%
100%
100%
100%
100%
<4
95%
100%
100%
100%
100%
<5
92%
98%
100%
100%
100%
<6
88%
98%
100%
100%
100%
<7
87%
97%
100%
100%
100%
<8
82%
91%
99%
100%
100%
<9
81%
91%
99%
100%
100%
<10
78%
89%
97%
99%
99%
<11
76%
88%
96%
99%
99%
<12
73%
86%
94%
96%
96%
<13
73%
85%
94%
96%
96%
<14
70%
83%
92%
94%
95%
<15
69%
81%
90%
92%
93%
<16
68%
81%
89%
91%
92%
<17
68%
81%
89%
91%
92%
<18
67%
79%
87%
89%
90%
5A-104
-------
CO
"O
o
a3
a.
CD
c
o
CD
CD
100%
90%
80%
70%
60%
50%
40%
ANC 30 |jeq/L-18 Eastern Ecoregions
£ 30%
o
o
LU
20%
10%
0%
•—
I t
• <30% exceedances
<25% exceedances
• <20% exceedances
m s. | a-/,,
—•—<10% ex
;eeuances
;eedances
4 8 12 16
Highest Ecoregion Median Sulfur Deposition (kg S/ha-yr)
20
Figure 5A-45.
Cumulative percent of ecoregion-time period combinations with CL
exceedances below 10,15, 20, 25, 30%. 100% indicates there was no
ecoregion that had a percent exceedance above 10,15, 20, 25, or 30% for a
given deposition level bin. Critical load exceedances based on ANC target
of 30 jieq/L for the 18 eastern U.S. ecoregions five deposition periods (2001-
03, 2006-08, 2010-12, 2014-06, 2018-20) (See Table 5A-29 for values).
5A-105
-------
Table 5A-30. Number of ecoregion-time period combinations with >10, >15, >20, >25,
>30% of waterbodies exceeding their CLs for ANC target of 20 jieq/L as a
function of total S deposition across all 5 deposition periods (2001-03, 2006-
08, 2010-12, 2014-06, 2018-20). Includes 18 ecoregions in the eastern U.S.
Total Sulfur
Deposition
(kg S/ha-yr)
Number of Ecoregion-Time Period Combinations with
Specified Percent of Waterbodies Exceeding their CLs
10%
15%
20%
25%
30%
<2
0
0
0
0
0
<3
0
0
0
0
0
<4
0
0
0
0
0
<5
2
1
0
0
0
<6
4
1
0
0
0
<7
5
1
0
0
0
<8
9
4
0
0
0
<9
9
4
0
0
0
<10
11
6
1
1
1
<11
13
7
2
1
1
<12
15
9
4
3
2
<13
16
10
4
3
2
<14
19
12
6
4
3
<15
21
14
8
6
4
<16
22
15
9
7
5
<17
22
15
9
7
5
<18
24
17
11
9
7
* Highest ecoregion median (across sites with CLs) S deposition estimate across the five time
periods is 17.27 kg/ha-yr.
5A-106
-------
Table 5A-31. Cumulative percent of ecoregion-time period combinations with less than
10,15, 20, 25 and 30% of waterbodies per ecoregion exceeding their CLs
for the ANC target of 20 jieq/L as a function of total S deposition. 100%
indicates there were no ecoregion-time period combinations that had
percent exceedances above the specified values. Critical load exceedances
for 18 eastern ecoregions and five deposition periods (2001-03, 2006-08,
2010-12, 2014-06, 2018-20) (See Table 5A-30 for data).
Total Sulfur
Deposition
(kg S/ha-yr)
Number of Ecoregion with Percent of Exceedances Across
the 5 deposition Periods
10%
15%
20%
25%
30%
<2
100%
100%
100%
100%
100%
<3
100%
100%
100%
100%
100%
<4
100%
100%
100%
100%
100%
<5
96%
98%
100%
100%
100%
<6
93%
98%
100%
100%
100%
<7
92%
98%
100%
100%
100%
<8
87%
94%
100%
100%
100%
<9
87%
94%
100%
100%
100%
<10
85%
92%
99%
99%
99%
<11
83%
91%
97%
99%
99%
<12
81%
89%
95%
96%
97%
<13
80%
88%
95%
96%
98%
<14
77%
86%
93%
95%
96%
<15
76%
84%
91%
93%
95%
<16
75%
83%
90%
92%
94%
<17
75%
83%
90%
92%
94%
<18
73%
81%
88%
90%
92%
5A-107
-------
ANC 20 |jeq/L-18 Eastern Ecoregions
C/5
"O
O
cd
Q_
CD
o
en
cd
o
o
LU
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
Figure 5A-46.
W W 1
i
t W
• <30% exceedances
<25% exceedances
—•—<20% exceedances
• s. 1 J /0 tJAV
—•— < 0% exc
.,eeudiiot;;5
;eedances
4 8 12 16
Highest Ecoregion Median Sulfur Deposition (kg S/ha-yr)
20
Cumulative percent of ecoregion-time period combinations with CL
exceedances below 10,15, 20, 25, or 30%. 100% indicates there was no
ecoregion that had a percent exceedance above 10,15, 20, 25, or30%.
Critical load exceedances based on ANC target of 20 fieq/L for the 18
eastern U.S. ecoregions across all 5 deposition periods (2001-03, 2006-08,
2010-12, 2014-06, 2018-20) (See Table 5A-31 for values).
5A-108
-------
Table 5A-32. Number of ecoregion-time period combinations with >10, >15, >20, >25,
>30% of waterbodies exceeding their CLs for ANC target of 20 jieq/L as a
function of total S deposition across all 5 deposition periods (2001-03, 2006-
08, 2010-12, 2014-06, 2018-20) for 7 ecoregions in the western U.S.
Total Sulfur
Deposition
(kg S/ha-yr)
Number of Ecoregion-Time Period Combinations with
Specified Percent of Waterbodies Exceeding their
CLs
10%
15%
20%
25%
30%
¦k
cm,
vl
0
0
0
0
0
* Highest ecoregion median (across sites with CLs) S deposition estimate for 7
western ecoregions across the five time periods is 1.94 kg/ha-yr.
Table 5A-33. Cumulative percent of waterbodies in ecoregions meeting the target ANC
values as a function of total S deposition across all 5 deposition periods
(2001-03, 2006-08, 2010-12, 2014-06, 2018-20). 100% indicates there were
no ecoregions that had percent exceedances above >10, >15, >20, >25,
>30% for a given deposition level. Critical load exceedances based on ANC
target of 20 jieq/L for the western U.S. (See Table 5A-32 for data).
Total Sulfur
Deposition
(kg S/ha-yr)
Number of Ecoregion with Percent of Exceedances Across
the 5 deposition Periods
10%
15%
20%
25%
30%
<2
100%
100%
100%
100%
100%
5A-109
-------
Table 5A-34. Number of ecoregion-time period combinations with >10, >15, >20, >25,
>30% of waterbodies exceeding their CLs for ANC target of 50 jieq/L for
the east and 20 jieq/L for the west as a function of total S deposition across
all 5 deposition periods (2001-03, 2006-08, 2010-12, 2014-06, 2018-20).
Includes 25 ecoregions across the U.S.
Total Sulfur
Deposition
(kg S/ha-yr)
N
Com
V\
umber of Ecoregion-Time Period
binations with Specified Percent of
faterbodies Exceeding their CLs
10%
15%
20%
25%
30%
<2
0
0
0
0
0
<3
1
0
0
0
0
<4
3
1
0
0
0
<5
9
3
2
1
0
<6
13
4
2
1
0
<7
14
5
3
1
0
<8
18
9
5
3
0
<9
19
9
5
3
0
<10
22
11
6
4
1
<11
24
13
7
4
1
<12
27
15
9
6
3
<13
28
16
10
6
3
<14
31
18
12
8
5
<15
33
20
14
10
7
<16
34
21
15
11
8
<17
34
21
15
11
8
<18
36
23
17
13
10
* Highest ecoregion median (across sites with CLs) S deposition estimate
across the five time periods is 17.27 kg/ha-yr.
5A-110
-------
Table 5A-35. Cumulative percent of ecoregion-time period combinations with less than
10,15, 20, 25 and 30% of waterbodies per ecoregion exceeding their CLs
for the ANC target of 50 jieq/L for the east and 20 jieq/L for the west as a
function of total S deposition. 100% indicates there were no ecoregion-time
period combinations that had percent exceedances above the specified
values. Critical load exceedances for 18 eastern and 7 western ecoregions
and five deposition periods (2001-03, 2006-08, 2010-12, 2014-06, 2018-20)
(See Table 5A-34 for data).
Total Sulfur
Deposition
(kg S/ha-yr)
Number of Ecoregion with Percent of Exceedances Across the
5 deposition Periods
10%
15%
20%
25%
30%
<2
100%
100%
100%
100%
100%
<3
98%
100%
100%
100%
100%
<4
96%
99%
100%
100%
100%
<5
90%
97%
98%
99%
100%
<6
86%
96%
98%
99%
100%
<7
86%
95%
97%
99%
100%
<8
82%
91%
95%
97%
100%
<9
82%
91%
95%
97%
100%
<10
80%
90%
94%
96%
99%
<11
78%
88%
94%
96%
99%
<12
76%
87%
92%
95%
97%
<13
76%
86%
91%
95%
97%
<14
74%
85%
90%
93%
96%
<15
73%
83%
88%
92%
94%
<16
72%
83%
88%
91%
93%
<17
72%
83%
88%
91%
93%
<18
71%
82%
86%
90%
92%
5A-111
-------
ANC 50 peq/L-18 Eastern Ecoregions
ANC 20 jjeq/L- 7 West Ecoregions
Figure 5A-47. Cumulative percent of ecoregion-time period combinations with CL
exceedances below 10,15, 20, 25, or 30%. 100% indicates there was no
ecoregion that had a percent exceedance above 10,15, 20, 25, or 30% for a
given deposition level bin. Critical load exceedances based on ANC target
of 50 jieq/L for the 18 east ecoregions and 20 j.ieq/L for the 7 west
ecoregions and five deposition periods (2001-03, 2006-08, 2010-12, 2014-06,
2018-20) (See Table 5A-35 for values).
5 A-112
-------
Table 5A-36. Number of ecoregion-time period combinations with >10, >15, >20, >25,
>30% of waterbodies exceeding their CLs for ANC target of 30 jieq/L for
the east and 20 jieq/L for the west as a function of total S deposition across
all 5 deposition periods (2001-03, 2006-08, 2010-12, 2014-06, 2018-20).
Includes 25 ecoregions across the U.S.
Total Sulfur
Deposition
(kg S/ha-yr)
N
Com
V\
umber of Ecoregion-Time Period
binations with Specified Percent of
faterbodies Exceeding their CLs
10%
15%
20%
25%
30%
<2
0
0
0
0
0
<3
0
0
0
0
0
<4
2
0
0
0
0
<5
4
1
0
0
0
<6
7
1
0
0
0
<7
8
2
0
0
0
<8
12
6
1
0
0
<9
13
6
1
0
0
<10
16
8
2
1
1
<11
18
9
3
1
1
<12
21
11
5
3
3
<13
22
12
5
3
3
<14
25
14
7
5
4
<15
27
16
9
7
6
<16
28
17
10
8
7
<17
28
17
10
8
7
<18
30
19
12
10
9
* Highest ecoregion median (across sites with CLs) S deposition estimate
across the five time periods is 17.27 kg/ha-yr.
5A-113
-------
Table 5A-37. Cumulative percent of ecoregion-time period combinations with less than
10,15, 20, 25 and 30% of waterbodies per ecoregion exceeding their CLs
for the ANC target of 30 jieq/L for the east and 20 jieq/L for the west as a
function of total S deposition. 100% indicates there were no ecoregion-time
period combinations that had percent exceedances above the specified
values. Critical load exceedances for the 18 eastern and 7 western
ecoregions and five deposition periods (2001-03, 2006-08, 2010-12, 2014-06,
2018-20) (See Table 5A-36 for data).
Total Sulfur
Deposition
(kg S/ha-yr)
Number of Ecoregion with Percent of Exceedances
Across the 5 deposition Periods
10%
15%
20%
25%
30%
<2
100%
100%
100%
100%
100%
<3
100%
100%
100%
100%
100%
<4
97%
100%
100%
100%
100%
<5
95%
99%
100%
100%
100%
<6
93%
99%
100%
100%
100%
<7
92%
98%
100%
100%
100%
<8
88%
94%
99%
100%
100%
<9
88%
94%
99%
100%
100%
<10
85%
93%
98%
99%
99%
<11
84%
92%
97%
99%
99%
<12
82%
90%
96%
97%
97%
<13
81%
90%
96%
97%
97%
<14
79%
88%
94%
96%
97%
<15
78%
87%
93%
94%
95%
<16
77%
86%
92%
93%
94%
<17
77%
86%
92%
93%
94%
<18
76%
85%
90%
92%
93%
5A-114
-------
ANC 30 |jeq/L-18 Eastern Ecoregions
ANC 20 |jeq/L- 7 West Ecoregions
100%
90%
80%
70%
60%
CO
-O
o
a5
Q_
E 50%
CD
a)
40%
30%
20%
10%
0%
— —
^i § a g m m—
—~—<30% exceedances
<25% exceedances
—<20% exceedances
• — IO /u cAL
—•—<10% exc
eedances
4 8 12 16
Highest Ecoregion Median Sulfur Deposition (kg S/ha-yr)
20
Figure 5A-48.
Cumulative percent of ecoregion-tinie period combinations with CL
exceedances belowlO, 15, 20, 25, or 30%. 100% indicates there was no
ecoregion that had a percent exceedance above 10,15, 20, 25, or 30% for a
given deposition level bin. Critical load exceedances based on ANC target
of 30 jneq/L for the 18 east ecoregions and 20 jieq/L for the 7 west
ecoregions and five deposition periods (2001-03, 2006-08, 2010-12, 2014-06,
2018-20) (See Table 5A-37 for values).
5A-115
-------
Table 5A-38. Number of ecoregion-time period combinations with >10, >15, >20, >25,
>30% of waterbodies exceeding their CLs for ANC target of 20 jieq/L for
both the east and west as a function of total S deposition across all 5
deposition periods (2001-03, 2006-08, 2010-12, 2014-06, 2018-20). Includes
25 ecoregions across the U.S.
Total Sulfur
Deposition
(kg S/ha-yr)
N
Com
V\
umber of Ecoregion-Time Period
binations with Specified Percent of
faterbodies Exceeding their CLs
10%
15%
20%
25%
30%
<2
0
0
0
0
0
<3
0
0
0
0
0
<4
0
0
0
0
0
<5
2
1
0
0
0
<6
4
1
0
0
0
<7
5
1
0
0
0
<8
9
4
0
0
0
<9
9
4
0
0
0
<10
11
6
1
1
1
<11
13
7
2
1
1
<12
15
9
4
3
2
<13
16
10
4
3
2
<14
19
12
6
4
3
<15
21
14
8
6
4
<16
22
15
9
7
5
<17
22
15
9
7
5
<18
24
17
11
9
7
* Highest ecoregion median (across sites with CLs) S deposition estimate
across the five time periods is 17.27 kg/ha-yr.
5A-116
-------
Table 5A-39. Cumulative percent of ecoregion-time period combinations with less than
10,15, 20, 25 and 30% of waterbodies per ecoregion exceeding their CLs
for the ANC target of 20 jieq/L as a function of total S deposition. 100%
indicates there were no ecoregion-time period combinations that had
percent exceedances above the specified values. Critical load exceedances
for 18 eastern and 7 western ecoregions and five deposition periods (2001-
03, 2006-08, 2010-12, 2014-06, 2018-20) (See Table 5A-38 for data).
Total Sulfur
Deposition
(kg S/ha-yr)
Number of Ecoregion with Percent of Exceedances
Across the 5 deposition Periods
10%
15%
20%
25%
30%
<2
100%
100%
100%
100%
100%
<3
100%
100%
100%
100%
100%
<4
100%
100%
100%
100%
100%
<5
98%
99%
100%
100%
100%
<6
96%
99%
100%
100%
100%
<7
95%
99%
100%
100%
100%
<8
91%
96%
100%
100%
100%
<9
91%
96%
100%
100%
100%
<10
90%
94%
99%
99%
99%
<11
88%
94%
98%
99%
99%
<12
87%
92%
96%
97%
98%
<13
86%
91%
97%
97%
98%
<14
84%
90%
95%
97%
97%
<15
83%
88%
93%
95%
97%
<16
82%
88%
93%
94%
96%
<17
82%
88%
93%
94%
96%
<18
81%
86%
91%
93%
94%
5A-117
-------
ANC 20 peq/L-18 East and 7 West Ecoregions
100%
90%
80%
70%
CO
1 60%
CD
! 50%
I—
I
c=
'§> 40%
o
o
111
^ 30%
20%
10%
0%
0 4 8 12 16 20
Highest Ecoregion Median Sulfur Deposition (kg S/ha-yr)
Figure 5A-49. Cumulative percent of ecoregion-time period combinations with CL
exceedances below 10,15, 20, 25, or 30%. 100% indicates there was no
ecoregion that had a percent exceedance above 10,15, 20, 25, or 30% for a
given deposition level bin. Critical load exceedances based on ANC target
of 20 jieq/L for the 18 east ecoregions and 7 west ecoregions and five
deposition periods (2001-03, 2006-08, 2010-12, 2014-06, 2018-20) (See Table
5A-39 for values).
Figure 5A-50 summarizes the percentage of waterbodies per each of the 25 ecoregions
that were estimated to achieve ANC values of 20, (E&W), 30 (E only) and 50 (E only) jieq/L,
based on CLs greater than zero and annual average S deposition for 2018-2020 and 2014-2016.
These percentages per ecoregion are graphed versus that ecoregion's median deposition (across
sites with CLs). For 2014-16 deposition estimates, more than 10% of waterbodies in two
ecoregions exceed their CLs for ANC of 50 („ieq/L and just one ecoregion for ANC of 20 u.eq/L.
In terms of percentage of waterbodies estimated to achieve the ANC targets, this means that
more than 80% of waterbodies in each of the 25 ecoregions were estimated to achieve an ANC at
or above 50 |ieq/L. For the 2018-20 deposition estimates at or above 90% of waterbodi es in each
— —'
>-=-•—•—«—•—•-=—
—•—<30% exceedances
<25% exceedances
» <20% exceedances
¦ 1070 exc
• <10% exc
eeuariues
eedances
5A-118
-------
of the 25 ecoregions were estimated to achieve an ANC at or above 20 |ieq/L and at or above
90% in all but one ecoregion were estimated to achieve an ANC level at or above 20 ueq/L.
100
90
80
70
60
50
40
30
20
10
0
100
90
I 80
-------
. i'J9'~ _ - ~
/yy- -
ANC Target:
20 yeq/L (East and West)
Ecoregion
-e- 5.2.1
6.2.15
-€k 8.3.5
5.3.1 -e-
8.1.1
8.3.7
•W 5.3.3
8.1.3
8.4.1
6.2.3
8.1.4
8.4.2
6.2.5
8.1.7
-e- 8.4.4
6.2.7
8.1.8
8.4.5
-e- 6.2.10
8.3.1
8.4.9
-e- 6.2.12
8.3.3
-e- 6.2.14
8.3.4
0-
2001-2003
-a***
ANC Target:
50 peq/L (East and West)
Ecoregion
-©- 5.2.1
6.2.15
B.3.5
5.3.1 -e-
8.1.1
;
B.3.7
-e- 5.3.3 ->-
8.1.3
:
8.4.1
6.2.3
6.2.5
8.1 4
8.1.7
-0- i
-0- I
B.4.2
8.4.4
6.2.7
8.1.8
i
B.4.5
6.2.10
8.3.1
i
B.4.9
-©- 6.2.12
8.3.3
-©- 6.2.14
8.3.4
i t
0-
2001-2003
ANC Target:
30 |jeq/L (East and West)
Ecoregion
-©- 5.2.1
6.2.15 -©-
8.3.5
5.3.1 -e-
8.1.1
8.3.7
o 5.3.3 •
B.1.3
8.4.1
6.2.3
5.1.4 -e-
8.4.2
6.2.5
B.1.7 -e-
8.4.4
6.2.7
B.1.8
8.4.5
-o- 6.2.10
8.3.1 -©-
8.4.9
-S- 6.2.12
B.3.3
-e- 6.2.14
B.3.4
«-
-s=8
ANC Target:
50 (jeq/L (East)
20 jJeq/L (West)
Ecoregion
-e- 5.2.1
6.2.15
-©- 8.3.
5.3.1 -e~
8.1.1
8.3.
5.3.3
6.2.3
8.1.3
8.1.4
8.4.
-©- 8.4.
6.2.5
8.1.7
-©- 8.4.
6.2.7
8.1.8
8.4.
6.2.10
8.3.1
8.4.
-e- 6.2.12
8.3.3
6.2.14
8.3.4
Figure 5A-51.
Percent of waterbodies per ecoregion estimated to achieve ANC of 20 jieq/L (top left), 30 fieq/L (top right), 50
jieq/L (bottom left) and 50/20 (Jieq/L in EAV (2001-2020). Bold text, solid lines indicate western ecoregions.
5A-120
-------
5A.2.3 Case Study Analysis of Acidification Risk
The areas included in the case study analysis represent geographic diverse acid sensitive
areas across the CONUS that have sufficient data to complete a quantitative analysis (Figure 5A-
53). This includes the necessary air quality information to assess varying levels of deposition,
including monitoring and deposition information. In addition, recent deposition levels across this
set of case studies generally reflect variation also observed across the CONUS (Table 5A-40).
Five case study areas were identified that meet the criteria (Figure 5A-52). Three of the areas,
Northern Minnesota (NOMN), Shenandoah Valley (SUVA) and White Mountain National Forest
(WHMT), are in the eastern U.S. and two areas are in the western U.S. (Rocky Mountain
National Park [ROMG] and Sierra Nevada Mountains [SINE]). Two of the five areas - WHMT
and ROMO - are made up completely of one park or forest. Two other areas, NOMN and SINE,
are made up of several contiguous parks, forests, and wilderness areas. The fifth area, SHVA,
includes multiple parks or forest areas that are non-contiguous, so the case study boundary was
defined by a rectangle incorporating the areas of interest.
Figure 5A-52. Location of the case study areas.
5 A-121
-------
Table 5A-40. Estimated annual deposition in five case study areas (2018-2020 average).
Northern
Minnesota
Shenandoah
Valley
White Mountain
National Forest
Rocky Mountain
National Park
Sierra Nevada
Mountain
S Deposition,
kg S/ha-yr
mean (range)
1.0
(0.8-1.5)
1.6
(1.1-2.0)
1.3
(1.0-1.6)
0.7
(0.4 -0.9)
1.0
(0.5-1.9)
N Deposition,
kg S/ha-yr
mean (range)
5.7
(3.7-6.9)
9.4
(6.6-13.4)
4.9
(3.7-6.1)
4.5
(3.2-6.1)
6.7
(2.3-31.8)
NHX,
as fraction of N
mean (range)
0.6
(0.6-0.7).
0.7
(.5-0.8)
0.5
(0.5-0.5)
0.6
(0.5-0.6)
0.5
(0.3 - 0.8)
nh3,
as fraction of N
mean (range)
0.3
(0.03-0.5).
0.5
(0.2 - 0.8)
0.2
(0.2-0.3)
0.3
(0.2 - 0.3).
0.2
(0.05-0.6).
NOTE: All estimates from TDEP. The fraction of dry NH3 is not available from TDEP and was calculated as Dry NH3
deposition/Total N Deposition.
5A.2.3.1 Descriptive Information for Case Study Areas
A broad sampling of ecoregions in the U.S. (as specified by the level I classification,
Omernik 1987; Omernik and Griffith, 2014) are represented by the five case studies, as are a
wide array of land uses (based on the 2016 National Land Cover Database [NLCD]).14 While
some of the case study areas fall within a single ecoregion, parts of some other areas are in a
second ecoregion. Some land cover types are much more widespread than others (e.g., evergreen
forests) but less common land cover types are represented as well. All of the natural land cover
types were represented at some level by the case study areas. This excludes the four developed
land cover types and two agricultural types, although the latter two were represented to some
degree in SHVA. As noted above some land cover types, particularly forests are very high
percentages of the total area which is what is seen in the case study areas. But less common land
cover types like wetlands are also represented within the case study areas as well, including
perennial ice and snow areas. This indicates that overall, the case study areas provide a relative
broad coverage of the land cover types. The ecoregions and land uses of the five case studies are
summarized briefly below, beginning with the three eastern areas and followed by the two in the
West. Of the three eastern case study areas, the White Mountain National Forest (WHMT case
study) occurs in two different ecoregions: Northeastern Coastal Zone (8.1.7) and Northern
Appalachian and Atlantic Maritime Highlands15 (5.3.1) (Figure 5A-53). Within the WHMT are
14 We used the 2016 National Land Cover Database (NLCD), developed by the U.S. Geological Survey, in
partnership with several other federal agencies, to assess the general landcover represented in each of the case
study areas. The National Land Cover Database (NLCD) provides nationwide data on land cover and land cover
change at a 30m resolution with a 16-class legend based on a modified Anderson Level II classification system
(Anderson et al., 1976; Jin et al., 2019; https://www.mrlc.gov/data/nlcd-2016-land-cover-conus).
15 The U.S. portion of the ecoregion is referred to as Northeastern Highlands as shown in Figure 5A-52.
5A-122
-------
six wilderness areas and two are Class I areas (Great Gulf and Presidential Range-Dry River
Wilderness Areas, established in 1964 and 1975, respectively). The WHMT case study location
is dominated by forested areas, with a mixture of evergreen, deciduous and mixed forest cover
(Figure 5A-54; Table 5A-41).
Omernik Level III Ecoregions
Northeastern Coastal Zone
Northeastern Highlands
CD White Mountain National Forest
0 3 75 7 5
H H I-
3 Miles
Sources Esri, HERE. Garrnin. Inter map, increment P Corp., GEBCO, USGS, FAO, NPS, NRCAN, GeoBase,
(GN. Kadaster NL, CrQnanceSurvey, Esri Japar?,' MEJJ.Esri China (Hong Kong), 1 cjOpenSbeetMap
contributor^end the GIS User Community
Figure 5A-53. Level III ecoregions in which WHMT occurs.
5 A-123
-------
National Land Cover (2016)
Open Water
| Perennial Ice/Snot
| Developed, Open Space
Developed, Low Intensity
Developed, Medium Intensity
Developed, High Intensity
Barren Land (Rocks/Sand/Clay)
Deciduous Forest
Evergreen Forest
Mixed Forest
Shrub/Scrub
| Grassland/Herbaceous
~ Pasture/Hay
Cultivated Crops
| Woody Wetlands
Emergent Herbaceous Wetlands
^ White Mountain National Forest
Figure 5A-54. Types of land cover in WIIMT based on 2016 IN LCD.
Table 5A-41. Distribution of land cover types in WHMT based on the 2016 NLCD.
Land Cover
Great Gulf Wilderness
Presidential Range Wilderness
Total
Open Water
0%
<0.1%
0,3%
Perennial Ice/Snow
0%
0%
0%
Developed, Open Space
0,9%
0%
1,4%
Developed, Low Intensity
0.8%
0%
0.3%
Developed, Medium Intensity
0.3%
0%
0.1%
Developed, High Intensity
<0.1%
0%
<0.1%
Barren Land (Rock/Sand/Clay)
13.0%
1,0%
0,4%
Deciduous Forest
4.7%
20.3%
34.9 %
Evergreen Forest
67.6%
58,0%
28.9%
Mixed Forest
10.8%
19.5%
29.9%
Shrub/Scrub
0.5%
0.8%
1.5%
Grassland/Herbaceous
1.4%
0.4%
0.4%
Pasture/Hay
0%
<0,1%
0.2%
Cultivated Crops
0%
0%
<0,1%
Woody Wetlands
0%
<0,1%
1.5%
Emergent Herbaceous Wetlands
0%
0%
0.1%
5 A-124
-------
The SHVA case study, which includes the city of Harrisonburg and smaller towns, occurs
in three different ecoregions: Blue Ridge (8.4.4), Northern Piedmont (8.3.1) and Ridge and
Valley (8.4.1) (Figure 5 A-55). This case study area is dominated by deciduous forest (-44%) and
mixed forests (-14%) but also includes nearly 29% of the land cover in pasture, hay, and
cultivated crops (Figure 5A-56, Table 5A-42). This case study area includes a National Park, a
National Forest and two Virginia State Parks (Shenandoah River and Seven Bends State Parks).
In Shenandoah National Park, nearly 84% of the area is deciduous forests. In George
Washington National Forest nearly 86% of the area is deciduous or mixed forest. This area also
includes portions of the Blue Ridge Parkway and Appalachian National Scenic Trail, managed
by the National Park Service.
Omernik Level III Ecoregions
Hj Blue Ridge
H Northern Piedmont
I Ridge and Valley
CD Shenandoah Valley Area
Soirees Esri, HERE. Garmin, Inter map, intrement PCorp.. GEBCO. USGS, FAO, NPS. NRCAN, GeoBase.
K3N. Keaaster NL. Oranance Survey, Esri Japan. METI.Esri China (Hong Kong), |c} OpenStreetMap
contributors, and the GIS User Community
3 Miles
Figure 5A-55. Level III ecoregions in which SIIVA occurs.
5A-125
-------
National Land Cover (2016)
¦ Open Water
~ Perennial Ice/Snot
| Developed. Open Space
jj Developed, Low Intensity
¦ Developed. Medium Intensity
¦ Developed. High Intensity
| Barren Land (Rocks/Sand/Clay)
¦ Deciduous Forest
¦ Evergreen Forest
| Mixed Forest
| Shrub/Scrub
| Grassland/Herbaceous
Hi Pasture/Hay
¦ Cultivated Crops
| Woody Wetlands
¦ Emergent Herbaceous Wetlands
~ Shenandoah Valley Area
0 3.5 Z 14 21 28
Sources Esri, HERE, Garmin. Intermap. increment P Corp.. GEBCO. USGS, FAO, MPS, NRCAN, GeoBsse
IGN. Kadaster NL, Ordnance .Sirvey. Esri Japan, METI, Esri China !Hong Kong). (c)' OpenStreetMap
contributors, and the GIS User Community
Figure 5A-56. Types of land cover in SHVA based on the 2016 NLCD.
Table 5A-42. Distribution of land cover types in SHVA based on the 2016 NLCD.
Land Cover
Shenandoah
National Park
Washington
National Forest
Virginia
State Parks
Total
Open Water
0%
0,5%
1.1%
0.6%
Perennial Ice/Snow
0%
0%
0%
0%
Developed, Open Space
1.2%
2.9%
3.2%
4,9%
Developed, Low Intensity
0,1%
0.2%
0.1%
2.5%
Developed, Medium Intensity
<0.1%
<0,1%
<0.1%
0.7%
Developed, High Intensity
0%
<0,1%
0%
0,3%
Barren Land (Rock/Sand/Clay)
<0.1%
<0,1%
<0.1%
0.1%
Deciduous Forest
84.0%
60.9%
50,2%
44.5%
Evergreen Forest
1.6%
2.6%
0.7%
2,7%
Mixed Forest
12.7%
25,6%
32,2%
14.4%
Shrub/Scrub
0.1%
0,2%
0.6%
0.2%
Grassland/Herbaceous
0,3%
0,7%
0.2%
0,5%
Pasture/Hay
<0.1%
6,0%
9.7%
24.5%
Cultivated Crops
0%
0.2%
2.0%
4,1%
Woody Wetlands
<0.1%
<0,1%
0%
<0,1%
Emergent Herbaceous Wetlands
<0.1%
0%
0%
<0.1%
5 A-126
-------
The NOMN case study, which is composed of Voyageurs National Park and Superior
National Forest, and borders Minnesota and Canada, occurs in two different ecoregions:
Northern Lakes and Forest (5.2.1) and Northern Minnesota Wetlands (5.2.2) (Figure 5A-57). The
Northern Minnesota area has approximately 10% open water and 50% evergreen and mixed
forest areas (Figure 5A-58 and Table 5A-43). The area also has a significant amount of woody
wetlands (-29%). Voyageurs National Park and Boundary Waters-Canoe Wilderness have more
open water than the entire area, but generally similar patterns of land cover.
vHoo*
Sources Esri. HERE. Garmin, Inter map, increment P Corp.. GEBCO. USGS. FAO, NPS, NRCAN. GecBase.
IGN. Kadaster NL. CrGnance Survey, Esri Japan, MET I. Esri China IHong Kong), jc) OpenStreetMap
contributors, and the GIS User Community
Omernik Level III Ecoregions
¦ Northern Lakes and Forests
¦ Northern Minnesota Wetlands
^ Northern Minnesota
0 5 10 20 30 40
l—l l—l l— l ' ' l 1 Miles
Figure 5A-57. Level III ecoregions in which NOMN occurs.
5 A-127
-------
National Land Cover (2016)
¦ Open Water
~ Perennial Ice/Snot
| Developed. Open Space
] Developed, Low Intensity
¦ Developed. Medium Intensity
¦ Developed. High Intensity
| Barren Land (Rocks/Sand/Clay)
¦ Deciduous Forest
¦ Evergreen Forest
| Mixed Forest
| Shrub/Scrub
| Grassland/Herbaceous
Hi Pasture/Hay
¦ Cultivated Crops
| Woody Wetlands
¦ Emergent HeibaceousWetlands
~ Northern Minnesota
3 Miles
Figure 5A-58. Types of land cover in NOMN based on the 2016 NLCD.
Table 5A-43. Distribution of land cover types in NOMN based on the 2016 NLCD.
Land Cover
Voyageurs
National Park
Superior
National Forest
Boundary Waters-
Canoe Wilderness
Total
Open Water
36,6%
9.5%
18.2%
10.8%
Perennial Ice/Snow
0%
0%
0%
0%
Developed, Open Space
<0.1%
1.1%
0,1%
1,1%
Developed, Low Intensity
<0,1%
0,2%
0%
<0.1%
Developed, Medium Intensity
<0.1%
0.1%
0%
0.1%
Developed, High Intensity
0%
<0.1%
0%
<0,1%
Barren Land (Rock/Sand/Clay)
0%
0.2%
<0.1%
0.2%
Deciduous Forest
7.7%
7,0%
3,2%
7,0%
Evergreen Forest
8.6%
16.3%
20,6%
15,9%
Mixed Forest
28.1%
26,2%
28,9%
26.3%
Shrub/Scrub
0.9%
5.2%
2.4%
5.0%
Grassland/Herbaceous
0.7%
2,6%
4,6%
2,5%
Pasture/Hay
<0.1%
0.1%
0%
0.1%
Cultivated Crops
0%
<0.1%
0%
0%
Woody Wetlands
12.5%
30.0%
20,6%
29,2%
Emergent Herbaceous Wetlands
4.9%
1,7%
1,5%
1,8%
5 A-128
-------
In the western U.S. the northwestern forested mountain ecoregions are relatively well
represented as is the Sierra. The Great Plains and intermountain west are not well represented by
our case study areas. The ROMO case study occurs in only one ecoregion, the Northwestern
Forested Mountains, Southern Rockies ecoregion (6.2.14). This case study is composed of Rocky
Mountain National Park, which was designated one of the first World Biosphere Reserves by the
United Nations Educational, Scientific and Cultural Organization in 1977, is a Class I area under
the Clean Air Act.16 The dominant land cover in Rocky Mountain National Park is evergreen
forest (-54%), but there is also substantial shrub (-22%) and herbaceous/grasslands (-13%)
(Figure 5A-59 and Table 5A-44). The area also has high elevation barren areas (5%) and some
areas with perennial snow.
National Land Cover (2016)
¦ Open Water
| Perennial Ice/Snot
| Developed. Open Space
I Developed, Low Intensity
¦ Developed, Medium Intensity
¦ Developed, High Intensity
| Barren Land (Rocks/Sand/Clay)
¦ Deciduous Forest
¦ Evergreen Forest
| Mixed Forest
Shrub/Scrub
~ Grass la nd/H erbaceous
~ Pasture /Hay
J Cultivated Crops
j Woody Wetlands
¦ Emergent Herbaceous Wetlands
" Haw>n
S
-------
Table 5A-44. Distribution of land cover types in ROMO based on the 2016 NLCD.
Land Cover
Total
Open Water
0.3%
Perennial Ice/Snow
0.6%
Developed, Open Space
0.3%
Developed, Low Intensity
0.1%
Developed, Medium Intensity
<0.1%
Developed, High Intensity
<0.1%
Barren Land (Rock/Sand/Clay)
5.2%
Deciduous Forest
0.3%
Evergreen Forest
54.7%
Mixed Forest
<0.1%
Shrub/Scrub
22.2%
Grassland/Herbaceous
13.3%
Pasture/Hay
<0.1%
Cultivated Crops
0%
Woody Wetlands
1.4%
Emergent Herbaceous Wetlands
1.6%
The SINE case study area incorporates a large area within the Sierra Nevada Mountains
of eastern Calif, such that it occurs in four different ecoregions: Central Basin and Range
(10.1.5), Mojave Basin and Range (10.2.1), Sierra Nevada (6.2.12) and Southern and Central
California Chapparal and Oak Woodland (11.1.1) (Figure 5A-60). The Sierra Nevada area is
mostly dominated by evergreen forests (-39%) and shrub/scrub (-38%) (Figure 5A-61, Table
5A-45). The case study area extends from Yosemite national Park in the north to Sequoia
National Park in the south and includes Kings Canyon National Park (contiguous with Sequoia
National Park)17 and two wilderness areas that connect these parks (Ansel Adams and John Muir
Wilderness Areas, both of which are Class I areas). The John Muir and Pacific Crest Trails pass
through these areas. Across the five parks and two wilderness areas the dominant land cover is
similar, with primarily differences in the percent cover of barren lands.
17 The area comprised of these two parks was designated a UNESCO Biosphere Reserve in 1976
(https://www.nps.gov/seki/learn/news/anick-fact-sheet.htin'). and Yosemite National Park was designated a World
Heritage site in 1984 (https://www.nps.gov/articles/nps-geodiversity-atlas-yosemite-national-park.htm).
5A-130
-------
Omernik Level III Ecoregions
M Central Basin and Range
M Mojave Basin and Range
I Sierra N evada
I Southern and Central California Chaparral and Oak Woodlands
] Sierra Nevada Mountains
0 5 10
H M I-
20
30
40
3 Miles
T{/ Souces . Esri, HERE. Garmin. Inter map, increment P Corp.. GEBCO, USGS. FAG.
NPS; NRCAN. GecBase. IGt-4. Kadaster NL, Ordnance Survey, Esri Japan. METI.
t^fiChina :Hcng Kong), :c$ OpenStreetMap contributors, and iheG'S,User
Community
Figure 5A-60. Level III ecoregions in which SINE occurs.
5 A-131
-------
r'b
-------
Table 5A-45. Distribution of land cover types in SINE based on the 2016 NLCD.
Yosemite
National
Sequoia
National
Kings
Canyon
Ansel
Adams
John Muir
Land Cover
Park
Park
National Park
Wilderness
Wilderness
Total
Open Water
1.0%
0.5%
1.2%
0.9%
1.1%
1.0%
Perennial Ice/Snow
0.1%
0.1%
0.2%
0.3%
0.3%
0.2%
Developed, Open Space
0.3%
0.2%
0.1%
<0.1%
<0.1%
0.1%
Developed, Low Intensity
<0.1%
<0.1%
<0.1%
0%
0%
<0.1%
Developed, Medium Intensity
0%
0%
0%
0%
0%
0%
Developed, High Intensity
0%
0%
0%
0%
0%
0%
Barren Land (Rock/Sand/Clay)
5.0%
21.2%
22.5%
8.3%
21.5%
15.3%
Deciduous Forest
<0.1%
0.6%
0.1%
0.1%
0.1%
0.1%
Evergreen Forest
47.4%
43.1%
28.6%
43.3%
31.1%
38.8%
Mixed Forest
0.1%
0.8%
0.1%
0.14%
0.1%
0.2%
Shrub/Scrub
37.6%
30.5%
41.4%
44.8%
40.1%
38.4%
Grassland/Herbaceous
7.8%
2.5%
5.6%
1.9%
5.2%
5.3%
Pasture/Hay
0%
0%
0%
0%
0%
0%
Cultivated Crops
0%
0%
0%
0%
0%
0%
Woody Wetlands
0.3%
0.2%
0.1%
0.1%
0.2%
0.2%
Emergent Herbaceous Wetlands
0.5%
0.4%
0.1%
0.2%
0.4%
0.3%
5A.2.3.2 Case Study Air Quality
To relate air quality to deposition, we identified a set of monitors within an area of
influence (maximum radius of 500 km), described in section 5A.2.3.2.2 below.18 The monitors
identified included monitors collecting and chemically speciating particulate matter with mass
median diameter of 2.5 microns (PM2.5) and Federal Reference Method (FRM) PM2.5 monitors
(used to inform compliance with the NAAQS) for which there are data in the EPA's Air Quality
System (AQS) database, and Clean Air Status and Trends Network (CASTNET) monitoring
sites.19 Some monitors are sited specifically for tracking local sources and are not representative
of regional conditions. Monitors that are designated in AQS with a measurement scale listed as
"microscale" or "middle scale", which indicates that the monitor is strongly influenced by local
emission sources, are also excluded from this analysis. Only a few monitors were removed from
consideration in this step. Among the FRM PM2.5 monitors, the monitor with the maximum
annual average PM2.5 design value is selected. The air quality monitors used in each case study
area are listed in Table 5A-46 and shown in Figures 5A-62 to 5A-66. These included National
Atmospheric Deposition Program (NADP) wet deposition monitors that are part of the National
Trends Network (NTN), described further in Chapter 2, section 2.3.4, as well as CASTNET total
S and total NO3" monitors, and PM2.5 chemical composition monitors.
18 This approach for relating monitor locations and concentrations to deposition estimates (described further in
section 5A.2.3.2.2) differs from the approaches for analyses presented in Chapter 6.
19 These different monitor types and their networks are described in Chapter 2, sections 2.3.3 and 2.3.4.
5A-133
-------
Table 5A-46. Air quality and wet deposition monitors used to assess the relationship between air concentration and
deposition, and trends.
Case Study
Areas
Wet Deposition
(NADP)
Total S and Total N
(CASTNET)
PM2.5 Mass
(FRM)
PM2.5 Composition
Northern
Minnesota
NTN MN32
NTNMN18
NTN MN08
NTN MN99
NTNMN16
NTN MI97:
Voyageurs Nat Pk, MN
Fernberg, MN
Hovland, MN
Wolfland, MN
Marcell Exp Forest
Isle Royale, Ml
VOY413: Voyageurs National
Park, MN
RED004: Red Lake Band of
Chippewa Indians
27-053-0963:
Minneapolis,
MN
27-137-0034
27-075-0005
26-083-9000
27-053-0963
Voyageurs National Park, MN
Boundary Waters, MN
Isle Royale National Park, Ml
Minneapolis, MN
Rocky
Mountain
National Park
(RMNP)
NTN C098: RMNP-Loch Vale
NTN C019: RMNP-Beaver Meadows
ROM406: RMNP
08-031-0002:
Denver, CO
08-069-0007
08-123-0008
08-001-0006
RMNP
Platteville, CO
Commerce City, CO
Shenandoah
Valley Area
NTN VA28: Shenandoah Nat Park
NTN MD08: Frostburg, MD
NTN WV18: Parsons, WV
NTN VAOO: Charlottesville, VA
NTN MD99: Beltsville, MD
SHN418: Shenandoah
National Park
PAR207: Parsons, WV
LRL117: Laurel Hill, PA
ARE128: Arendtsville, PA
BEL116: Beltsville, MD
42-003-0008:
Pittsburgh,
PA
51-113-0003
54-093-9000
24-023-0002
42-129-0008
42-003-0008
42-125-5001
24-033-0030
39-099-0014
Shenandoah National Park
Tucker County, WV
Grantsville, MD
Greensburg, PA
Pittsburgh, PA
Washington County, PA
Beltsville, MD
Youngstown, OH
Sierra Nevada
Mountains
NTN CA99: Yosemite National Park
NTN CA75: Sequoia National Park
YOS404: Yosemite Nat Pk
SEK430: Sequoia National
Park - Ash Mountain
SEK402: Sequoia National
Park - Lookout Pt
06-107-2002:
Visalia, CA
06-107-1001
06-043-0003
06-107-2002
06-099-0005
06-029-0014
Sequoia National Pk-Ash Mtn
Yosemite National Park
Visalia, CA
Modesto, CA
Bakersfield, CA
White
Mountain
National
Forest
NTN NH02: Hubbard Brook, NH
WST109: Woodstock, NH
HBR183: Hubbard Brook, NH
25-025-0042:
Boston, MA
33-007-4002
33-011-5001
50-007-0012
25-015-4002
25-025-0042
Coos County, NH
Peterborough, NH
Burlington, VT
Ware, MA
Boston, MA
5A-134
-------
Monitoring Sites
CASTNET
• NADP NTN
¦ PM2!
F1~R~
120
3 Miles
HERE. Garmin. Intermsp, increment P Corp.. GEBCO.USGS, FAO, NPS. NRCAN.
ce Survey, Esri Japan, METI, EsriCJiins (Hong Kong), |c}
GIS User Community
Northern Minnesota Area
RED004
Figure 5A-62. Monitoring sites used for NOMN to analyze relationships and trends.
Monitoring Sites
CASTNET
• NADP NTN
¦ PM2.5
Rocky Mountain National Park
0 3.75 7.5
H H I-
NPS. NRd&J?-"
sin
.08-123-0008
Figure 5A-63. Monitoring sites used for ROMO to analyze relationships and trends.
5A-135
-------
Mliav*
,£RE128
»b«stoU"
LRL117
24-023-0002 NTf
• " ' " covumto'8
NTN MD99BEL116
•t-033-Oi
WV "" tgiN WV18RAR207
54-093-9000
"N VA28SHM418
051/113-0003
Shenandoah Valley Area
42-003-0008
42-125-50B1M'"1 42-129-0008
flea*"15
Monitoring Sites
CASTNET
# NADPNTN
¦ PMls
„ Cliart"!0"
0 10 20
H H I—-
NJNVAOO
Soirees: EsriTHERE, Garmin. Inter map. imrementf Corp . GEBCO. USGS. FAO, NPS. NRCAN,
GeoBase, IGN. Kacaster NL. Ordnance Survey. Esri Japan. METI. Esri China (Hong Kong), (cj
Figure 5A-64. Monitoring sites used for SHVA to analyze relationships and trends.
Monitoring Sites
CASTNET
• NADPNTN
¦ PMm
60 ***01,,,75
"WO
3 Miles
06-029-0014
^•*#1
in. Intermap, increment P Corp.. GEBCP, USGS, FAO. NPS. NRCAN.
GeoBase, IGN. Kacaster NL. Ordnance Survey. Esri Japan. METI. Esri China,I Hong Kong), lc}
OpenStreetMap contributors, and the GIS User Community
Siera
^ 06-099-0005
Figure 5A-65. Monitoring sites used for SINE to analyze relationships and trends.
5A-136
-------
5A.2.3.2.1 Correlation of Deposition and Air Quality
In this subsection, we consider the extent to which the maximum annual average PM2.5
concentration in the area of influence is linked to the annual deposition in the case study area by
analyzing the correlation between annual average PM2.5 (at the FRM monitor with highest
concentration) and annual total S and N deposition, as estimated by TDEP methods, averaged
spatially across the case study area.
Table 5A-47 shows the correlation between PM2.5 at the maximum monitor and the S and
N total deposition estimated using the TDEP method. The highest correlations, for both S and N
deposition are seen for the two farthest east areas. A high correlation is also seen for S deposition
in the northern Minnesota case study area, which has a less strong correlation for N deposition.
Correlations are lower for both S and N in the two western locations; in the Sierra Nevada case
study area, there is no discernable correlation between PM2.5 and S deposition and the correlation
coefficient for N deposition is below 0.5 (Table 5 A-47).
5A-137
-------
Table 5A-47. Correlation coefficients for TDEP total deposition estimates with annual
average concentrations at the PM2.5 over the period 2000-2019.
Case Study Areas
Correlation between total S
deposition and PM2.5 mass
Correlation between total N
deposition and PM2.5 mass
Northern Minnesota
0.96
0.70
Rocky Mountain National Park
0.64
0.68
Shenandoah Valley Area
0.97
0.93
Sierra Nevada Mountains
-0.02
0.35
White Mountain National Forest
0.97
0.89
The relationship between air concentration and deposition depends on several factors,
including the chemical form of sulfur and nitrogen, the vertical distribution in the atmosphere,
and the frequency of precipitation (See Chapter 2 and Chapter 6 of main document). Each of
these vary across the different case study areas. In the eastern U.S., where SO2 and N oxides
emissions have declined the most, measurements of PM2.5 and wet deposition show a strong
correlation. In the western U.S., where dry deposition and ammonia play a larger role, in some
cases there is no correlation between measured wet deposition and surface PM2.5 mass
concentrations (see Chapter 6 of main document).
5A.2.3.2.2 Air Quality Scenarios
In this case study analysis, critical load exceedances were calculated for several air
quality scenarios that reflected an area meeting the most controlling20 current secondary NAAQS
for that area (of the standards for SO2, NO2 and PM), which in all cases was that for PM2.5. For
each case study area, historic air quality was examined to find a time when the monitors within
or near the area influencing the case study area21 had design values that were within 10% of the
current standard level (i.e., 15 |ig/m3). To examine how changing air quality and corresponding
deposition could affect these estimated exceedances, additional scenarios for air quality at these
locations in other years were also analyzed with the aim of having similar maximum PM2.5
annual design values across the case studies. For these additional scenarios, time periods were
selected where the highest monitor in the area of influence was within 10% of 12 |ig/m3 and 10
|ig/m3. For some locations, it was not possible to select a 3-year historical period as PM2.5
concentrations, currently and in the past, have not been as high as the threshold for that scenario.
20 The scenarios selected had air quality for which the PM2 5 design value for the highest monitor was just equal to
the current annual secondary standard.
21 The premise for the area of influence definition is a region where a change in emissions could be expected to lead
to a change in deposition at the case study area. A recent study of Class I areas found that the area of influence for
nitrogen deposition can vary, and the radius was estimated to range between 500 - 1200 km (Lee et al., 2016). In
identifying locations for emissions and concentrations in the area of influence that could be expected to be
relevant, this analysis uses a maximum radius of 500 km.
5A-138
-------
For each of the selected air quality periods, the TDEP data were extracted for S and N. The air
quality periods analyzed, and associated deposition levels are shown in Tables 5A-48 and 5A-49.
For one case study area, the Sierra Nevada, there is no historical period that is at or near
the target PM2.5 concentration (the annual average exceeds 15 ug/m3 by more than 10%
throughout the historical period). So it is not possible to use a historical dataset of deposition. As
an alternative, the air quality and TDEP data (from the 2014-16 time period) were adjusted
downwards to reflect each air quality scenario based on a regression-based analysis. An
approximation of the change in deposition due to a change in PM2.5 concentration at the
maximum monitor is used based on a regression based on CMAQ modeling. A linear model was
fit using air concentration and total (wet plus dry) deposition from a 21-year CMAQ model
simulation. First, the air concentration and deposition values were normalized by their mean
value. A linear model was fit to predict total deposition from air concentration. The slope was an
estimate of the change in deposition due to a change in PM2.5 concentration. The linear model
was used to calculate the percent change in deposition (from the 2014-16 TDEP estimate) when
the PM2.5 concentration at the highest monitor was reduced from the 3-yr annual average
concentration (in 2014-16) to 10 |ig m"3, 12 |ig m"3, and 15 |ig m"3. The prediction interval at
each of these concentration levels was 40%, which indicates that there are a range of deposition
levels that are consistent with these air concentration targets. The predicted deposition change for
nitrogen and sulfur were different by a small amount, reflecting differences in the relationship
between PM2.5 and deposition. To clarify, this is not a prediction, but used as a plausible
deposition scenario associated with maximum PM2.5 concentrations for each target level.
Table 5A-48. The 3-year historical periods used for each case study area.
Case Study Area
TDEP years for 15 |jg m-3
TDEP years for 12 |jg m-3
TDEP years for 10 |jg m-3
Northern
Minnesota
PM2.5 concentrations have not
been this high
2000-2002
2007-2009
Rocky Mountain
National Park
PM2.5 concentrations have not
been this high
PM2.5 concentrations have not
been this high
2000-2002
Shenandoah Valley
2005-2007
2009-2011
2014-2016
Sierra Nevada
S deposition:
2014-16 (multiplied by 0.70)*
N deposition:
2014-16 (multiplied by 0.72)*
S deposition:
2014-16 (multiplied by 0.56)*
N deposition:
2014-16 (multiplied by 0.57)*
S deposition:
2014-16 (multiplied by 0.46)*
N deposition:
2014-16 (multiplied by 0.48)*
White Mountain
National Forest
2000-2002
2005-2007
2009-2011
*The air quality and associated deposition estimates for Sierra Nevada case study are based on a linear regression-based "roll
down" approach. The S and N deposition estimate assigned to each scenario (15,12 and 10 pg/m3) was derived by multiplying
the factor shown here by the 2014-2016 TDEP estimates. The factors shown here were derived by multiplying the unit S or N
deposition per unit PM2.5 concentration (from a regression based on 21-year CMAQ simulation) by a factor equal to the air
quality scenario PM2.5 concentration (15,12 and 10 pg/m3) by the 2014-16 PM2.5 concentration at the highest monitor.
5A-139
-------
Table 5A-49. For each 3-year period described in Table 5A-48, this is the estimated 3-
year average annual average deposition, based on spatial averaging of
TDEP dataset estimates across the case study area, for N and S deposition.
Case study
Mean N deposition, kg N ha1 year1
(min-max)
Mean S deposition, kg S ha1 year1
(min-max)
15 |jg/m3
12 [jg/m3
10 |jg/m3
15 |jg/m3
12 [jg/m3
10 |jg/m3
Northern Minnesota
NA
6.8
(4.1-8.7)
6.0
(3.7-8.1)
NA
3.4
(2.5-5.4)
3.0
(2.0-4.4)
Rocky Mountain National Park
NA
NA
6.6
(4.4-9.5)
NA
NA
2.3
(1.4-4.6)
Shenandoah National Park
11
(7.8-16)
8.7
(6.4-13)
8.3
(6.6-10)
10
(8.0-13.4)
5.0
(3.4-6.3)
3.1
(2.4-3.8)
Sierra Nevada*
4.9*
(2.2-9.9)
3.9*
(1.8-7.8)
3.3*
(1.5-6.6)
0.80*
(0.40-1.5)
0.64*
(0.32-1.2)
0.53*
(0.27-1.0)
White Mountain National Forest
(New Hampshire)
7.6
(5.6-10)
6.7
(5.2-8.9)
5.2
(3.9-7.0)
7.2
(4.9-11)
7.1
(5.2-9.9)
3.8
(2.8-5.5)
Increased uncertainty is recognized for this case study due the approach used to assign deposition estimates to the three air
quality scenarios, which is described in the text and table above.
5A.2.3.3 Critical Loads Analysis
This section describes the findings of the case study analyses. Using the methodology
described in section 5 A. 1.2 above, CL exceedances were estimated for waterbodies in the five
case study areas based on the deposition estimates for three air quality scenarios. Aquatic CLs
and exceedances are summarized in the subsections below using the following steps:
(1) CLs were extracted from the NCLD for each of the case study areas for the following
ANC thresholds: 20, 30, and 50 [j,eq/L.
(2) CLs were summarized for each area in terms of the average, 70th and 90th percentile.
(3) Exceedances were calculated for each of the air quality scenarios for all three ANC
thresholds for S only and N+S.
(4) The exceedances were summarized in terms of counts and percent of all CL sites in each
study area.
5A.2.3.3.1 Case Study Waterbody Critical Loads
A total of 523 CLs were found in the 5 case study areas, excluding SHVA which had
complete coverage (4977 CLs in total, with 704 CLs in sensitive sites). The ROMO, SINE,
NOMN, and WHMT areas had 119, 139, 190, and 75 CLs respectively (Figure 5A-67). Despite
the relatively high number of aquatic CLs for these five case studies, they do not represent a
complete coverage of water resources. This summary of the CLs and exceedances only
represents the waterbodies that have been modelled. Table 5A-50 provides average, 10th and
5A-140
-------
30th percentile CLs for S only for each case study areas in units of kg S/ha-yr. Table 5A-51
provides the same information for S only CLs in units of meq/m2-yr, and also provide this
information for S plus N CLs. Critical loads for S only were found to be similar for the
waterbodies modelled among the case study areas with higher CL values for the lower ANC
thresholds. Average S only CL values for an ANC threshold of 50 [j,eq/L range from 6.6 to 9.8
kg S/ha-yr or 41.3 to 61.3 meq/m2-yr. For an ANC threshold of 20 [j,eq/L, the 10th percentile
CLs for S only 1.8 to 7.1 kg S/ha-yr (3.6 to 7.1, excluding SINE).
Table 5A-50. Average, 10th and 30th percentile of CLs for kg S in each case study area.
ANC of 20 |jeq/L
ANC of 30 |jeq/L
ANC of 50 |jeq/L
Ave.
30th
10th
Ave.
30th
10th
Ave.
30th
10th
Sulfur (S) only CLs
kg S/ha-yr)
ROMO
9.5
5.4
3.6
8.5
4.5
2.6
6.6
2.7
0.5
SINE
12.0
4.1
1.8
11.0
2.8
0.5
9.3
0.6
0.1
NOMN
10.8
5.5
4.2
10.4
5.3
3.9
9.8
4.7
3.2
WHMT
10.6
6.9
4.4
9.6
6.1
3.3
7.4
4.1
0.7
SHVA
12.4
9.4
7.1
11.4
8.4
6.3
9.4
6.3
4.1
Note: CL units are kg S/ha-yr, and
Table 5A-51. Average, 10th and 30th percentile of CLs for meq S in each case study area.
ANC of 20 |jeq/L
ANC of 30 |jeq/L
ANC of 50 |jeq/L
Ave.
30th
10th
Ave.
30th
10th
Ave.
30th
10th
Sulfur (S
only C
_s (mec
/m2-yr)
ROMO
59.1
34.0
22.6
5.30
28.4
16.1
41.2
16.7
3.4
SINE
75.0
25.4
11.0
68.7
17.3
2.9
58.4
3.5
0.1
NOMN
67.4
34.5
26.0
65.3
32.4
29.1
61.0
29.3
20.1
WHMT
66.3
43.4
27.8
59.7
38.3
20.8
46.3
25.6
4.4
SHVA
77.4
58.9
44.6
71.3
52.4
39.1
59
39.5
25.8
10th and 30th percenti
es refer
o the 10th and 30th percentile lowest CL values.
5A-141
-------
tNorfhem Minnesota (NOMN)
Rocky Mountain
National Park(ROMO)
White Mountain
National Forest
[WHMT)
Sierra Nevada
Mountains (SINE)
Shenandoah
Valley Area (SHVA)
Critical Load
A NClim it = 20 peq/L
• >201
Northern Minnesota (NOMN)
Rocky Mountain
National Park (ROMO)
White Mountain
National Forest
(WHMT)
Sierra Nevada
Mountains (SINE)
Shenandoah
Valley Area (SHVA)
Critical Load
ANC limit = 50 ueq/L
• 0-50
5A-67. Case study area CL maps for sulfur (meq/m2-yr) using an ANC threshold
of 20 jieq/L (upper) and 50 jiieq/L (lower).
Figure
5A-142
-------
5A.2.3.3.2 Case Study Critical Load Exceedances
For the N and S deposition associated with these air quality scenarios, critical load
exceedances were calculated for S, and for N and S combined, for each waterbody in each case
study area. Exceedances forN and/or S were calculated for all case study areas except for
SHVA. Table 5A-52 contains percent exceedances (number waterbodies exceeding the CL
divided by the total number of waterbodies with CLs in the case study area times 100) and the
absolute number of waterbodies that exceed the CL. All four ANC thresholds were evaluated.
Unlike the CLs, exceedances are not consistent among the case study areas. Percent exceedances
were similar between CL values determined for S only and forN and/or S deposition. The
highest percent exceedances occurred for the ANC value of 50 [j,eq/L while lower percent
exceedances occurred for ANC of 20 [j,eq/L, as expected, for all scenarios.
Table 5A-52. Number and percent of case study waterbodies estimated to exceed their
CLs for specified ANC values and air quality scenario.
Air
Quality
Scenario
|jg/m3
Areas
Sulfur Only
No. | Percent
Sulfur and
Nitrogen
No. | Percent
Sulfur Only
No. | Percent
Sulfur and
Nitrogen
No. |Percent
Sulfur Only
No. | Percent
Sulfur and
Nitrogen
No. | Percent
ANC of20jjeq/L
ANC of 30 iJteq/L
ANC of 50 fjeq
/L
10
ROMO
3
2%
6
5%
6
5%
16
13%
25
21%
37
31%
SINE*
1
1%
1
1%
3
2%
3
2%
13
9%
13
9%
NOMN
2
1%
2
1%
2
1%
2
1%
3
2%
4
2%
WHMT
3
4%
5
7%
9
12%
10
14%
18
24%
19
26%
SHVA
9
2%
11
2%
20
4%
12
ROMO
SINE*
1
1%
1
1%
9
6%
9
6%
34
24%
34
24%
NOMN
2
1%
6
3%
2
1%
11
6%
6
3%
21
11%
WHMT
21
28%
30
41%
25
33%
36
49%
37
50%
48
65%
SHVA
16
3%
19
4%
68
15%
15
ROMO
SINE*
2
1%
2
1%
11
8%
11
8%
38
27%
38
27%
NOMN
WHMT
23
31%
35
47%
27
36%
41
55%
38
51%
49
66%
SHVA
156
34%
202
44%
279
60%
'The air quality and associated deposition estimates for all air quality scenarios in the Sierra Nevada case study are based on a
'roll down" approach. The highest PM2.5 DVs in the area were rolled down to equal the specified value for each scenario (15,12
and 10 |jg/m3) and a unit S or N deposition per unit PM2.5 concentration (from a regression based on 21-year CMAQ simulation)
was applied to derive the associated deposition estimates presented here.
5A-143
-------
5A.3 KEY UNCERTAINTIES
In this section, we characterize the nature and magnitude of uncertainties associated with
this aquatic acidification REA and their impact on the REA estimates. A summary of the overall
characterization of uncertainty for the current deposition-related S exposure and aquatic
acidification risk analysis is provided in Table 5A-53 below. This summary is followed by
subsections describing quantitative analyses that inform our understanding of the variability and
uncertainty associated with the CL estimates developed in this assessment and support the
uncertainty characterization regarding the influence of a number of factors. Three sets of
analyses are presented in the following subsections. The first, described in section 5A.3.1, is a
sensitivity analysis using Monte Carlo techniques to quantify CL estimate uncertainty associated
with several model inputs. Section 5A.3.2 describes calculation of confidence intervals forNCb"
flux estimates in New England and Adirondacks lakes and Appalachian streams. Lastly, 5A.3.3
describes an analysis of the variation in CL estimates among the three primary modeling
approaches on which the CLs used in this assessment were based.
The mainly qualitative approach used here and in quantitative analyses in other NAAQS
reviews,22 also informed by quantitative sensitivity analyses, is described by WHO (2008).
Briefly, with this approach, we have identified key aspects of the assessment approach that may
contribute to uncertainty in the conclusions and provided the rationale for their inclusion. Then,
we characterized the magnitude and direction of the influence on the assessment for each of
these identified sources of uncertainty. Consistent with the WHO (2008) guidance, we scaled the
overall impact of the uncertainty by considering the degree of uncertainty as implied by the
relationship between the source of uncertainty and the exposure and risk estimates. A qualitative
characterization of low, moderate, and high was assigned to the magnitude of influence and
knowledge base uncertainty descriptors, using quantitative observations relating to understanding
the uncertainty, where possible. Where the magnitude of uncertainty was rated low, it was judged
that large changes within the source of uncertainty would have only a small effect on the
assessment results (e.g., an impact of few percentage points upwards to a factor of two). A
designation of medium implies that a change within the source of uncertainty would likely have a
moderate (or proportional) effect on the results (e.g., a factor of two or more). A characterization
of high implies that a change in the source would have a large effect on results (e.g., an order of
magnitude). We also included the direction of influence, whether the source of uncertainty was
judged to potentially over-estimate ("over"), under-estimate ("under"), or have an unknown
impact to exposure/risk estimates.
22 This approach to uncertainty characterization has been utilized in welfare and health REAs for reviews of the
ozone, NO2, SO2, and carbon monoxide NAAQS (e.g., U.S. EPA 2014, 2018).
5A-144
-------
Table 5A-53. Characterization of key uncertainties in exposure and risk analyses for aquatic acidification.
Uncertainty Characterization
Sources of Uncertainty
Influence of Uncertainty
on Exposure | Risk
Estimates*
Knowledge-
base
Uncertainty
Comments
Category
Element
Direction
Magnitude
Representativeness
of National-scale
analysis
Over
Unknown
Medium
The analysis may overrepresent the more intensely sampled areas which are likely to be the acid-sensitive sites.
There is also potential for uncertainty contributed by sites impacted by loading other than atmospheric deposition
(e.g., acid mine drainage).
Representativeness
of Ecoregion-scale
analysis
Both
Low
Low
Although the delineation of ecoregions takes into account geology and soil type, there is still variation within
ecoregions with regard to acid sensitivity (e.g. type of bedrock), which is an important influence on waterbody
acidification from deposition.
Wide variation in number, and geographic distribution, of CL sites within an ecoregion, contributes to variation
among ecoregions with regard to uncertainty in their risk characterizations. Some ecoregions are well represented
with many CLs while other have few.
The ecoregion-scale analyses focus on ecoregions with at least 50 CLs and this focus reduces somewhat any
impact of poorly characterized (or sampled) regions. However, the lack of consideration of spatial distribution (e.g.,
not necessarily uniform distribution of CL sites within an ecoregion or similar type of distribution across all
ecoregions) contributes uncertainty.
General Aspects
of Assessment
Design
CLs based primarily
on steady-state
modeling
Over or both
Low
Low
Nearly all CLs for locations outside of the Adirondacks are based on steady-state modeling (a version of SSWC).
Many CLs at Adirondack sites are based on dynamic modeling although most in the Adirondacks are based on
steady state modeling.
Comparison of CLs using SSWC with F-factor to those with the dynamic MAGIC found generally good correlation
(r2 values greater than 0.95, with slight downward bias for New England lakes and upward bias for Appalachian
streams, see section 5A.3.3.2).
Monte Carlo analyses described in 5A.3.1 indicate potential magnitude of uncertainty ranging from 0.37 to 33.2
meq/m2/yr (or 0.1 to 5.3 kg S/ha-yr). The higher values were generally in areas with few water quality data and
variable runoff, e.g., the Midwest, South and along the CA to WA coast (section 5A.3.1.2).
Focus on S
deposition only
Under
Low
Medium
Although omitting the contribution of N deposition to reduced ANC has the potential to contribute to underestimates
of risk (CL exceedances), assessment of the contribution of N deposition (2000-2020) to % exceedances in this
assessment indicates relatively negligible contribution (section 5A.2.1). This may be due to lower N deposition
since the latter half of the last century (1970s-1990s) and there may also be an influence of the relatively greater
contribution of reduced N to total N deposition over the past 20 years (see Chapter 6, section 6.2.1).
Uncertainty in the estimation of N contribution to acidification is likely greater than that associated with estimation
of S contribution to acidification given that only a subset of deposited N compounds play a direct role. Uncertainty
in the factors influencing the amount of N deposition contributing acidity increases the uncertainty for N deposition
(e.g., see entry for N leaching estimate below).
5A-145
-------
Uncertainty Characterization
Sources of Uncertainty
Influence of Uncertainty
on Exposure | Risk
Estimates*
Knowledge-
base
Uncertainty
Comments
Category
Element
Direction
Magnitude
ANC as indicator of
acidification risk
Over
Unknown
Medium
ANC is an indicator of water quality acidification risk, such as conditions in which there is increased toxicity due to
dissolved Al concentrations and pH. While studies of acidified water bodies in the Northeast have reported
associations of different aquatic effects (e.g., species prevalence) with ANC, there is uncertainty in the
relationships on a site-specific basis, related to site-specific factors including deposition and acidification history, as
well as site geology.
The approach used for estimating ANC in this assessment has the potential to overestimate this risk in
waterbodies with appreciable organic acids which can bind dissolved Al, reducing toxicity (ISA, Appendix 8, section
8.3.6.2). However, this is only the case when organic acids are high (>5-8 mg/L) in the surface waters. Organic
acids are low in most surface waters across the U.S. except for northern regions of New England, New York, and
upper midwest. In addition, levels were low during the height of acidification (1980-90's) and have increased as
acidic deposition has decreased, although aluminum toxicity has declined (ISA, Appendix 7, section 7.1.5.1.1).
In the ecoregion assessment, three ecoregions were omitted from the focus group of 25 in light of a general
recognition of naturally occurring acidity, e.g., associated with organic acids, that reduces waterbody response to
reduced acid deposition. While this is not considered to play a role in many of the waterbodies historically
recognized as sensitive, the extent of such conditions in other areas is unknown.
The parameter, ANC, is also recognized as an indicator of risk of episodic acidification events, although the
uncertainty associated with this varies among waterbodies based on historical and recovery status.
Approach for
selection of CL
None
Unknown
Low
At waterbody sites for which multiple CL estimates were available, the most recent was selected. When multiple
estimates were available for the most recent period, they were averaged. Use of CLs based on the most recent
modeling analyses is not expected to directionally contribute uncertainty.
CLs based on
Steady State or
Dynamic Models
Non-anthropogenic
deposition of base
cations (BCdep)
None or
Over
Low-
Medium
Low-Medium
Estimates of BCdep (Ca, Mg, Na, K) are based on deposition estimates to the watershed and waterbody. Wet
deposition component is based on measurements of precipitation from the NADP deposition network and is well
known. However, the dry deposition fraction is not well known and based on an uncertain relationship to wet
deposition. When used, the F-factor approach draws on site-specific surface water chemistry data to estimate the
faction of base cation deposition from surface water chemistry data. The surface water chemistry data in the CL
used estimates vary in collection date from relatively recent to much older (e.g., 2010s to 1980s) and the CL
estimates used a range of sample sizes from a single measurement to multiple years of measurements.
5A-146
-------
Uncertainty Characterization
Sources of Uncertainty
Influence of Uncertainty
on Exposure | Risk
Estimates*
Knowledge-
base
Uncertainty
Comments
Category
Element
Direction
Magnitude
Base cation
weathering (BCw)
and flux estimates
Both
Medium
Medium
Dynamic and SSWC model applications used relied on estimates of base cation weathering (BCw) rates or base
cation flux. The ISA describes the BCw parameter as "one of the most influential yet difficult to estimate
parameters in the calculation of critical acid loads of N and S deposition for protection against terrestrial
acidification" (ISA, section IS.14.2.2.1). Obtaining accurate estimates of BCw is difficult because weathering is a
process that occurs over very long periods of time, and the accuracy of estimates of an ecosystem's ability to
buffer acid deposition relies on accurate estimates of weathering rates within the watershed. Dynamic models use
calibrated watershed biogeochemical models that estimate BCw using complex biogeochemical relationships
based on soil and water quality measurements, among other factors. These models provide the best estimate of
BCw because they take into account the complex nature of the watershed and are calibrated to environmental
conditions. In the F-factor approach used to estimate base-cation flux in CL estimates based on SSWC modeling,
the components of BCw are estimated as part of the total base cation flux from the watershed. This approach has
been widely published and analyzed in Canada and Europe, and has been applied in the U.S. (e.g., Dupont et al.,
2005 and others). As described in section 5A.1.5.1, this approach is based on quantitative relationships to water
chemistry and site-specific surface water chemistry data for key base cations (Ca, Mg, Na, K) fluxes. The surface
water chemistry data vary in collection date from relatively recent to much older (e.g., 2010s to 1980s) and the CL
estimates used a range of sample sizes from a single measurement to multiple years of measurements. Although
the F-factor approach to estimate base-cation flux has been widely published and analyzed in Canada, Europe,
and US, the uncertainty in this estimate hasn't been widely analyzed. Monte Carlo analyses described in 5A.3.1
indicate potential magnitude of uncertainty ranging from 0.37 to 33.2 meq/m2/yr (or 0.1 to 5.3 kg S/ha-yr). The
higher values were generally in areas with few water quality data and variable runoff (e.g., the midwest, south and
along the CA to WA coast (section 5A.3.1.2).
Long-term average
uptake of base
cations in biomass
(harvesting) (BCu)
Over or
none
Unknown
Low
This factor in the CL equation is generally set to zero in applications used in this assessment. Loss of base cations
occurs when trees are removed from the watershed from logging. In watersheds where logging is important, BCu
was set to 0 and is assumed to have a low bias. The subset of CL estimates from Sullivan et al. (2012b) address
this bias through the use of nonzero values drawn from McNulty et al. (2007) for sites outside of protected areas
(e.g., national parks) that the authors classified as "no harvest."
N leaching estimate
Both
Unknown
High
In CLs based on both N and S (used in preliminary analyses of this assessment), the amount of N deposition that
contributes to acidification was estimated based on water quality measurements of nitrate and annual runoff
(section 5A.1.6.2). Estimating the contribution of N deposition to acidification of surface waters is difficult and
uncertain because N cycling in an ecosystem is inherently variable and data for modeling are limited across the
U.S. The surface water chemistry data also vary in collection date from relatively recent to much older (e.g., 2010s
to 1980s). Use of CLs based on older measurements may have no bias or may overestimate current risk as
conditions may have improved (or stayed the same). Analyses in section 5A.3.2 indicate flux estimates to have
declined over the period from 1990 to 2018 and for higher values in the Adirondack lakes compared to
Appalachian streams, with still lower values in New England lakes.
The CL estimates in the main assessment focused on S only and thus, didn't depend on this variable.
5A-147
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Sources of Uncertainty
Uncertainty Characterization
Influence of Uncertainty
on Exposure | Risk
Estimates*
Knowledge-
base
Uncertainty
Comments
Category
Element
Direction
Magnitude
Exceedance
Calculation
Both
Unknown
Medium
The uncertainty of CL exceedances (deposition > CL) is a function of both the deposition and CL estimate
uncertainties. Monte Carlo analyses described in 5A.3.1 indicate potential magnitude of uncertainty in CL
estimates to range from 0.37 to 33.2 meq/m2/yr (or 0.1 to 5.3 kg S/ha-yr) with an average value of 7.68 meq S/m2-
yr or 1.3 kg S/ha/yr. Uncertainty in the TDep deposition estimates are characterized in Chapter 6, Table 6-13.
TDEP-
Estimation of total
deposition
See these entries in Chapter 6, Table 6-13.
* Influence on direction of exposure or risk estimates means would the exposure (deposition estimate) be potentially biased high or low; or would the risk estimate (probability a CL exceedance) be
potentially biased high or low. If the element is concluded to contribute uncertainty with the potential to underestimate a CL, this would be represented by Over in the Direction column as it would have
a potential to bias high the associated risk estimate.
5A-148
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5A.3.1 Quantitative Uncertainty Analyses on Model Inputs
The vast majority of CLs relied on in this assessment are derived using the steady-state
model. The strength of the CL estimate and the exceedance calculation relies heavily on model
inputs, and particularly estimates of the catchment-average base-cation supply (i.e., input of base
cations from weathering of bedrock and soils and air), runoff, and surface water chemistry. The
uncertainty associated with runoff and surface water measurements is among the uncertainties
characterized in Table 5A-53 above, based on previously available information. The analysis
described here is focused on analysis of uncertainty in CL estimates associated with uncertainty
in the estimates of the catchment supply of base cations to the waterbodies.
The catchment supply of base cations from the long-term weathering of bedrock and soils
is the model input that has been previously recognized as having the most influence on the CL
calculation and also has the largest uncertainty (Li and McNulty, 2007; ISA, section
IS. 14.2.2.1)). Although the approach to estimating base-cation supply used in the SSWC model
which was employed in most of the CLs used in this assessment, the F-factor approach, has been
widely published and analyzed in Canada and Europe, and has been applied in the CONUS (e.g.,
Dupont et al., 2005), the uncertainty in this estimate is variable and could be large in some cases.
The F-factor is commonly used in the SSWC to account for changes in nonmarine base cation
concentrations in a waterbody resulting from strong acid anion concentrations in the derivation
of a steady-state, long-term estimate of base cation weathering (2008 ISA, pp. 39-40; Henriksen
et al., 2002). Other approaches include empirical estimates and dynamic modeling with the
MAGIC (section 5A.1.5 above). Use of the F-factor approach is limited to locations for which
acid-base stream chemistry measurements are available. Values for F-factors have been reported
to vary over time in response to changing phases of acidification and recovery (Sullivan et al.,
2012a). Recognizing this source of uncertainty to the CL estimates, a quantitative uncertainty
analysis of the state-steady CL model was completed to evaluate the uncertainty in the CL and
exceedance estimation that is associated with the estimation of base-cation supply.
5A.3.1.1 Method
A probabilistic analysis using a range of parameter uncertainties was used for CLs
determined by the SSWC model using the F-factor approach to assess (1) the confidence interval
of the CL, (2) the degree of confidence in the exceedance values and (3) coefficient of variation
(CV) of the critical load. The probabilistic framework is Monte Carlo, whereby each steady-state
input parameter being assessed varies according to distribution specified as to shape, minimum
and maximum (Table 5A-54). The purpose of the Monte Carlo method was to propagate the
uncertainty in the steady-state CL estimates by modeling each value many times to describe the
distribution around the CL.
5A-149
-------
Model calculations were performed for each CL waterbody site with values for the F-
factor approach selected via Monte Carlo sampling. At each site, enough model simulations were
performed (i.e., 5,000 times) to capture the range of behaviors represented by the SSWC model
parameters analyzed (see equation 5A-2, section 5A. 1.5.1, for model details). The parameters
analyzed were surface water runoff (m/yr), dissolved surface water concentrations of seven
chemicals (Table 5A-54). The distributions sampled for these parameters were determined by
various methods. For runoff (Q), the minimum and maximum values for annual runoff (m/yr)
during the period 1972-2016, available from Wieczorek et al. (2018) were used for each
waterbody.23 The distribution for each of the chemical concentrations at each waterbody site was
defined by a normal distribution with the minimum and maximum equal to the minimum and
maximum concentration reported for that waterbody, for each waterbody where 6-years of water
quality data was available. For waterbodies with fewer than six years of water quality data, the
minimum and maximum values were based on a range determined from regional long-term water
quality data. Decade averages for the water quality parameters were calculated for sites with
long-term data within the region of the site without sufficient data and the minimum and
maximum values were used for the decade that matched the water quality data used to calculate
the CL. The decades ranged from 1980-90 to 2010-2020. Water quality data used were from the
EPA's Long-term Monitoring (LTM) program and that are part of the NCLD.24
Table 5A-54. Parameters varied in the Monte Carlo analysis.
Parameter
Units
Distribution Used for Monte Carlo Sampling
Surface water runoff, Q
m/yr
Normal
Calcium, Ca
|jeq/L
Normal
Magnesium, Mg
|jeq/L
Normal
Chlorine, CI
|jeq/L
Normal
Sodium, Na
|jeq/L
Normal
Potassiun (K)
|jeq/L
Normal
no3-
|jeq/L
Normal
S042"
|jeq/L
Normal
The Monte Carlo analysis for the parameters in Table 5A-2 was done in R. A total of
14,943 waterbodies in the CONUS were analyzed. Results of this analysis are described in
section 5A.3.1.2 below.
23 Values were drawn from the U.S. Geological Survey data in Version 3.0 (January 2021) of Select Attributes for
NHDPlus Version 2.1 Reach Catchments and Modified Network Routed Upstream Watersheds for the
Conterminous United States. This data source is Wieczorek et al. (2018), Select Attributes for NHDPlus Version
2.1 Reach Catchments and Modified Network Routed Upstream Watersheds for the Conterminous United States:
U.S. Geological Survey data release, https://doi.org/10.5066/F7765D7V.
24 See https://www.epa.gOv/power-sector/monitoring-surface-water-chemistry#tab-6 and obtain data from
https://doi.org/10.23719/1518546. Date of data download was 10/21/2020. For NCLD see
https://nadp.slh.wisc.edu/clad-national-critical-load-database/
5A-150
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5A.3.1.2 Results
Based on the Monte Carlo analysis for the F-factor approach parameters, we have
described the uncertainty around the CL associated with these parameters in terms of the
confidence interval around the mean result from the Monte Carlo simulations for each waterbody
site. Figure 5A-68 indicates locations for which the variation in the CL estimate, based on the
relative size of the range of Monte Carlo outputs, is relatively larger (red and orange dots) and
smaller (blue and green).
o 0.5 - 1.0 kg/ha/yr (3.0625 - 6.125 meq/m2/yr)
• 1.0-2.0 kg/ha/yr (6.125-12.25 meq/m2/yr)
• >2.0 kg/ha/yr (>12.25 meq/m2/yr)
Figure 5A-68. Critical load uncertainty analysis for 14,943 values across the CON US of
the SSWC model. Blue and green dots have the lowest confidence interval
and orange, and red dots have the highest confidence interval.
The range of the confidence interval size, based on the 5th to 95th percentile, was 0.37-
33.2 meq/m2-yr or 0.1-5.3 kg S/ha-yr. Sixty-one percent of CL values had a low confidence level
of less than 3.0325 meq/m2-yr or 0.5 kg S/ha-yr, while 26% had levels greater than 6.25 meq/m2-
yr or 1.0 kg S/ha-yr (Table 5A-55). Low confidence intervals were associated with CLs
determined with long-term water quality data and low variability in runoff measurements. CL
values determined by a single water quality measurement and in areas where runoff is variable
(e.g., the western U.S.) had high uncertainty. CLs with the lowest uncertainty occurred in the
5A-151
-------
eastern U.S., particularly along the Appalachian Mountains, upper Midwest, and Rocky
Mountains (Figure 5A-68). Less certain CLs were found in the Midwest and South and along the
CA to WA coast. Most of the CLs in the Midwest are based on a single or few water quality
measurements while variability in runoff in CA to WA coast account for those high uncertainty
values. On average the magnitude of the confidence interval for all SSWC CLs was 7.68 meq
S/m2-yr or 1.3 kg S/ha-yr, giving a confidence level of ±3.84 meq/m2-yr or ±0.65 kg S/ha-yr.
Table 5A-55. Results of the Monte Carlo analysis for uncertainty broken down by
confidence interval.
Range of
Confidence interval
kg/ha-yr
#.
Values
Percent
0.0-0.25
5462
37%
37%
0.25-0.5
3612
24%
61%
0.5-1.0
1994
13%
74%
O
csj
I
o
903
6%
80%
>2.0
2972
20%
100%
Total
14943
Table 5A-56 shows the average and 5th to 95th percentiles of the spread of the confidence
interval around the mean of the Monte Carlo simulation CLs for sites in each ecoregion. Fifty-
one ecoregions had a sufficient number of sites analyzed to allow for the calculation of a 5th and
95th percentile. Ecoregions in the Appalachian Mountains on average (e.g., Northern
Appalachian and Atlantic Maritime Highlands (5.3.1), Blue Ridge (8.4.4), Northern Lakes and
Forests (5.2.1), and North Central Appalachians (5.3.3) and Rockies (e.g. Sierra Nevada (6.2.14),
Southern Rockies (6.2.14), and Idaho Batholith (6.2.15) had lower uncertainty (smaller
confidence intervals around the mean CL from the Monte Carlo simulations), while Northeastern
Coastal Zone (8.1.7), Cascades (6.2.7), Coast Range (7.1.8), Interior Plateau (8.3.3), and
Klamath Mountains/California High North Coast Range (6.2.11) had on average higher
uncertainty.
Table 5A-56. Results of the Monte Carlo analysis for uncertainty by ecoregion.
Ecoregion
No.
Values
Average Confidence Interval, CI, on the Mean
(5th-95th percentile CI)
Code
Name
kg S/ha-yr
meq/m2-yr
5.3.1
Northern Appalachian and Atlantic
Maritime Highlands
2804
0.59 (0.05-2.07)
3.71 (0.32-12.96)
8.4.4
Blue Ridge
2500
0.32 (0.06-0.9)
2(0.39-5.61)
8.4.1
Ridge and Valley
1497
1.64 (0.05-8.16)
10.25 (0.33-50.98)
5.2.1
Northern Lakes and Forests
894
0.47 (0.02-2.04)
2.94 (0.12-12.76)
8.3.4
Piedmont
573
1.29 (0.2-3.24)
8.09 (1.24-20.28)
6.2.12
Sierra Nevada
566
0.41 (0.03-1.66)
2.57 (0.18-10.39)
6.2.10
Middle Rockies
552
0.95 (0.08-5.08)
5.95(0.53-31.76)
5A-152
-------
Ecoregion
No.
Values
Average Confidence Interval, CI, on the Mean
(5th-95th percentile CI)
Code
Name
kg S/ha-yr
meq/m2-yr
6.2.14
Southern Rockies
444
0.58(0.1 -2.1)
3.62 (0.64-13.16)
8.3.5
Southeastern Plains
413
1.59 (0.15-5.63)
9.94 (0.95-35.2)
8.4.2
Central Appalachians
399
1.31 (0.08-3.4)
8.18 (0.47-21.27)
8.1.8
Acadian Plains and Hills
371
1.2(0.09-4.17)
7.47 (0.54-26.09)
8.1.7
Northeastern Coastal Zone
323
2.38(0.19-7.54)
14.87 (1.18-47.14)
8.3.1
Northern Piedmont
265
4.1 (0.79-11.5)
25.6 (4.96-71.89)
8.5.4
Atlantic Coastal Pine Barrens
233
1.1 (0.17-3.52)
6.87 (1.06-21.98)
6.2.7
Cascades
229
3.68 (0.05-2.86)
22.97 (0.29- 17.89)
5.3.3
North Central Appalachians
222
0.6 (0.09- 1.99)
3.75(0.54-12.47)
8.1.3
Northern Allegheny Plateau
217
1.46 (0.29-4.77)
9.11 (1.79-29.79)
6.2.15
Idaho Batholith
212
0.51 (0.13-1.75)
3.21 (0.8-10.95)
6.2.5
North Cascades
169
1.08(0.15-4.73)
6.75 (0.96-29.55)
8.3.7
South Central Plains
157
1.19 (0.32-3.09)
7.45(2.03-19.34)
8.5.3
Southern Coastal Plain
149
0.76 (0.1 -2.89)
4.72 (0.6- 18.09)
8.4.9
Southwestern Appalachians
127
1.2(0.18-4.71)
7.52 (1.15-29.46)
7.1.8
Coast Range
119
5.88(1.82-15.45)
36.77 (11.37-96.59)
8.5.1
Middle Atlantic Coastal Plain
118
2.55 (0.26-9.04)
15.96 (1.63-56.48)
6.2.13
Wasatch and Uinta Mountains
114
1.19(0.15-7.11)
7.46 (0.95-44.44)
8.1.4
North Central Hardwood Forests
101
2.3 (0.07 - 4.89)
14.4(0.45 - 30.59)
6.2.3
Northern Rockies
96
1.14(0.19-4.84)
7.13 (1.18-30.27)
8.3.3
Interior Plateau
89
5.44 (0.54-12.54)
34.01 (3.36 - 78.36)
6.2.11
Klamath Mountains/California High
North Coast Range
85
6.85 (0.43-18.46)
42.82 (2.67-115.34)
8.1.1
Eastern Great Lakes Lowlands
72
2.69 (0.23-8.69)
16.83(1.43-54.29)
6.2.9
Blue Mountains
65
1.33 (0.26-4.22)
8.3(1.62-26.37)
8.4.5
Ozark Highlands
61
5.77 (1.22-9.5)
36.07 (7.6-59.39)
8.4.8
Ouachita Mountains
51
0.94 (0.2-3.41)
5.88 (1.26-21.29)
8.3.6
Mississippi Valley Loess Plains
41
3.1 (0.26-24.02)
19.39 (1.63-150.14)
8.4.7
Arkansas Valley
39
1.31 (0.21 -4.98)
8.2(1.32-31.11)
7.1.7
Puget Lowland
39
2.03 (0.29-5.77)
12.71 (1.81 -36.08)
8.4.3
Western Allegheny Plateau
37
2.03(0.41 -4.89)
12.69 (2.55-30.55)
8.1.6
Southern Michigan/Northern Indiana
Drift Plains
36
2.9(0.75-5.21)
18.12 (4.66-32.56)
6.2.4
Canadian Rockies
32
2.5(0.2-7.23)
15.59 (1.22-45.2)
6.2.8
Eastern Cascades Slopes and Foothills
32
1.52 (0.21 -4.84)
9.51 (1.33-30.24)
9.4.5
Cross Timbers
31
3.72 (1.58-11.31)
23.24 (9.89 - 70.66)
9.2.3
Western Corn Belt Plains
27
3.91 (1.55-9.16)
24.43 (9.67 - 57.28)
13.1.1
Arizona/New Mexico Mountains
27
3.22 (0.28- 10.53)
20.12 (1.74-65.79)
8.4.6
Boston Mountains
26
0.89 (0.23-4.12)
5.56 (1.42-25.72)
11.1.1
Central California Foothills and Coastal
Mountains
25
10.79 (0.5-54.47)
67.41 (3.1 -340.46)
9.2.4
Central Irregular Plains
24
3.08(1.1 -4.94)
19.27 (6.89-30.88)
7.1.9
Willamette Valley
24
3.43 (0.95-7.06)
21.45 (5.97-44.11)
11.1.3
Southern California Mountains
22
10.21 (1.5-20.12)
63.84 (9.4-125.78)
8.5.2
Mississippi Alluvial Plain
21
3.85(0.95-9.94)
24.09 (5.91 -62.1)
10.1.3
Northern Basin and Range
20
1.92 (0.35-8.81)
12.01 (2.18-55.05)
5A-153
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Ecoregion
Average Confidence Interval, CI, on the Mean
No.
(5th-95th percentile CI)
Code
Name
Values
kg S/ha-yr
meq/m2-yr
8.3.2
Interior River Valleys and Hills
19
4(1.57-10.46)
25 (9.78-65.39)
10.1.5
Central Basin and Range
17
N/A*
N/A*
8.2.4
Eastern Corn Belt Plains
16
N/A
N/A
9.5.1
Western Gulf Coastal Plain
16
N/A
N/A
8.1.5
Driftless Area
15
N/A
N/A
8.1.10
Erie Drift Plain
14
N/A
N/A
8.3.8
East Central Texas Plains
11
N/A
N/A
8.2.1
Southeastern Wisconsin Till Plains
11
N/A
N/A
9.4.4
Flint Hills
9
N/A
N/A
9.4.2
Central Great Plains
5
N/A
N/A
10.1.4
Wyoming Basin
4
N/A
N/A
9.4.7
Texas Blackland Prairies
3
N/A
N/A
5.2.2
Northern Minnesota Wetlands
2
N/A
N/A
11.1.2
Central California Valley
2
N/A
N/A
10.1.8
Snake River Plain
2
N/A
N/A
10.1.2
Columbia Plateau
2
N/A
N/A
8.2.3
Central Corn Belt Plains
2
N/A
N/A
9.3.1
Northwestern Glaciated Plains
2
N/A
N/A
10.1.6
Colorado Plateaus
1
N/A
N/A
* N/A inc
icates there was not a sufficient number of sites in the analysis to support calculation of percentiles.
5A.3.2 Uncertainty Analysis for N Leaching Estimates
An analysis of uncertainty associated with NO3" flux used to estimate N leaching into
lakes or streams was performed using water quality data from EPA's Long-term Monitoring
(LTM) program25 over the past 28 years. In EPA's LTM program, lakes or streams are sampled
weekly to quarterly depending on the site and individual project. Annual flux of NO3" was
calculated using annual concentration of NCb'for a given monitoring site and multiplied by
annual runoff, in m/yr (Wieczorek et al., 2018) for the watershed and year. Confidence intervals
were calculated for monitoring sites for a given region (i.e., New England, Adirondacks
Mountains, and Appalachian Mountains) and for four time periods (i.e., 1990-2018, 1990-1999,
2000-2009, 2010-2018).
The results of this analysis are summarized by region and time period in Table 5A-57.
Nitrate flux varied between regions with Adirondacks lakes having the highest annual fluxes and
New England Lakes with the lowest fluxes. Average values ranged from 0.36 to 11.71 meq/m2-
yr as NCbtO.Ol to .37 kg N/ha-yr). The ranges of confidence interval for the NO3" flux differed
across the monitoring sites from 0.15 to 1.62 meq/m2-yr as NO3" (0.01 to 0.05 kg N/ha-yr). A
25 The EPA's Long-Term Monitoring program tracks changes in surface water chemistry in the four regions shown
below, known to be sensitive to acid rain: New England, the Adirondack Mountains, the Northern Appalachian
Plateau, and the central Appalachians (https://www.epa.gov/power-sector/monitoring-surface-water-
chemistrv#tab-6). Data from this program are available at: https://doi.org/10.23719/1518546.
5A-154
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combined S and N confidence interval was ± 3.87 to 4.20 meq/m2-yr which is equivalent to 0.61
to 0.672 kg S/ha-yr or 0.54 to 0.58 kg N/ha-yr.
Table 5A-57. Uncertainty analysis of NO3" flux estimates based on data from EPA's
Long-term Monitoring Program.
Average
(meq/m2-yr)
S.D.
(meq/m27yr)
5th to 95th
(meq/m2-yr)
Magnitude &
Confidence
Interval
(meq/m2-yr)
New England Lakes
All Years
0.7
1.05
0.01-2.87
0.15(0.62-0.78)
1990 to 1999
0.8
1.17
0.00-3.10
0.30 (0.64-0.95)
2000 to 2009
0.92
1.18
0.01-3.88
0.29 (0.78- 1.07)
2010 to 2018
0.36
0.59
0.01-1.48
0.15(0.29-0.44)
Adirondacks Lakes
All Years
8.82
7.79
0.13-23.52
0.77 (8.44-9.21)
1990 to 1999
11.71
9.01
0.72-27.83
1.62 (10.89- 12.52)
2000 to 2009
9.28
7.11
0.68-21.2
1.16 (8.70-9.86)
2010 to 2018
5.73
6.01
0.00-17.91
1.03 (5.21 -6.24)
Appalachian Streams
All Years
3.27
5.77
0.03-13.68
0.52 (3.00-3.53)
1990 to 1999
5.05
7.29
0.43-20.18
1.14(4.48-5.61)
2000 to 2009
2.43
4.75
0.00-11.61
0.73 (2.06-2.79)
2010 to 2018
2.27
4.30
0.00-10.82
0.70 (1.92-2.62)
5A.3.3 Variation in Critical Load Estimates Associated with Modeling Approach
To consider the influence of modeling approach on CL estimates, we compared estimates
derived using three types of approaches: (1) the steady-state approach, based on the SSWC
model with F-Factor approach for estimating base cation weathering; (2) the steady-state
approach with statistical regression model for estimating base cation weathering; and, (3) the
dynamic model, MAGIC. The CLs used in this REA are nearly all based on the first of these
(SSWC with F-Factor approach), although many of the CLs in the Adirondacks were derived
using the MAGIC model. The analyses described here provide a sense of the variation in CL
estimates based on these three approaches.
5A.3.3.1 Method
Critical loads used in the national assessment analysis used different methods (see
methods for more details). To understand differences in the CLs calculated with different
methods, waterbodies where methods overlap were compared. There are three main CL
approaches that have been applied in the literature, all based on watershed mass-balance
approach where acid-base inputs are balanced. The three approaches include: (1) SSWC model
and F-Factor (SSWC F-Factor) that is based on quantitative relationships to water chemistry
5A-155
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(Scheffe et al., 2014; Lynch et al., 2022), (2) Steady State model with Statistical Regression
Model (Regional Regression) that extrapolated weathering rates across the landscape using water
quality or landscape factors (Sullivan et al., 2012b; McDonnell et al., 2014), and (3) Dynamic
Models (MAGIC) (U.S. EPA, 2009). Critical load values were compared among the three
models applying these approaches to determine model biases.
Comparisons of CLs among the approaches (e.g., SSWC F-Factor, Regional Regression,
and MAGIC) were completed for the lakes in New England and the Adirondacks and streams in
the Appalachian Mountains that each had CLs based on three different approaches (drawn from
NCLD). A total of 114 and 77 CLs were compared between SSWC Factor and MAGIC
approaches for New England and Adirondacks lakes, respectively. A total of 1129 CLs were
compared between SSWC Factor and Regional Regression based on CLs from Sullivan et al.
(2012a) for lakes in the Adirondacks. For streams in Appalachian Mountains, 66 CLs were
compared between SSWC Factor and MAGIC approaches and 43 between SSWC Factor and
Regional Regression based on CLs from McDonnell et al. (2014).
5A.3.3.2 Results
Results from the comparison between different CL methods are summarized below for
lakes in New England and the Adirondacks and streams in the Appalachian Mountains. For New
England and Adirondacks lakes, the MAGIC and the SSWC F-Factor (Lynch et al., 2022;
Scheffe et al., 2014) CL values were comparable with a R2=0.979 and R2=0.9587 and RMSE of
15 and 21 meq/m2-yr, respectively (Figure 5A-69).
Across CLs for all sites in the three regions, the Regional Regression (Sullivan et al.,
2014) CL estimates were strongly correlated with those from the SSWC F-Factor model, with
R2= 0.9815 and a bias towards higher values for the Regional Regression approach (Figure 5A-
70a). For all CLs in the three regions within the range of 0 to 150 meq/m2-yr, the correlation was
slightly lower (r2=0,8922) and the regression coefficient closer to one (1.0365) (Figure 5A-70b).
For streams in the Appalachian Mountains, general agreement was found between the
SSWC F-Factor, Regional Regression, and MAGIC approaches; with the MAGIC approach
showing better correlation (than the Regional Regression approach) with the SSWC F-factor.,
For example, CLs determined by the MAGIC approach were highly correlated with CLs derived
with the SSWC F-factor approach, with a R2=0.9887 and RMSE of 24 meq/m2-yr (Figure 5A-
71a). However, the correlation was not as strong (R2=0.8861) between CLs based on Regional
Regression approach (McDonnell et al., 2014) and the SSWC F-Factor model (Lynch et al.,
2022; Scheffe et al., 2014), with the SSWC F-factor CLs generally lower than those based on the
Regional Regression approach as indicated by a regression coefficient of 0.7396 (Figure 5A-
71b). We additionally note that McDonnell et al. (2014) reported a highly correlated relationship
5A-156
-------
(R2 = 0.92 and RMSE = 9-11 meq/m2-yr) with the MAGIC approach. Overall, generally good
agreement has been found between the three methods used to calculate CLs that were used in this
assessment, indicating that they would be expected to produce comparable results when used
together.
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Figure 5A-70. Critical load comparison between values based on Regional Regression
model (Sullivan et al., 2014) (y-axis) and values based on the SSWC F-factor
model (Lynch et al., 2022) for the full range of CLs (a) and for the range from
0 to 150 meq/m2-yr (b). Units are meq/m2-yr.
5A-158
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250
Figure 5A-71.Critical load comparisons: (a.) between values based on MAGIC (y-axis) and
values based on the SSYVC F-factor model (Lynch et al., 2022) (x-axis); and
(b.) between values based on Regional Regression model (McDonnell et al.,
2014) (y-axis) and values based on the SSWC F-factor model (Lynch et al.,
2022) (x-axis). Units are meq/m2-yr.
5A-159
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5A-167
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APPENDIX 5B
ADDITIONAL DETAIL RELATED TO KEY
TERRESTRIAL ECOSYSTEM STUDIES
TABLE OF CONTENTS
5B.1 Introduction 5B-1
5B.2 Tree Growth and Survival 5B-2
5B.2.1. Addition Studies 5B-2
5B.2.2. Gradient or Observational Studies 5B-4
5B.2.2.1. Dietze andMoorcroft (2011) 5B-7
5B.2.2.2. Thomas et al. (2010) 5B-9
5B.2.2.3. Horn etal. (2018) 5B-11
5B.2.3. Tree Growth and Survival: Key Observations, Uncertainties and
Limitations 5B-20
5B.3 Species Richness of Herb and Shrub Communities 5B-30
5B.3.1. Experimental Addition Studies 5B-30
5B.3.2. Gradient or Observational Studies 5B-32
5B.4 Lichen Community composition 5B-35
5B.4.1. Studies Investigating Direct Effects of Pollutants in Ambient Air 5B-36
5B.4.2. Observational Studies Investigating Relationships with Atmospheric
Deposition 5B-37
References 5B-39
TABLE OF TABLES
Table 5B-1. Experimental addition studies assessing tree growth and/or survival 5B-3
Table 5B-2. Recent gradient/observational studies of associations between tree growth
and survival or mortality and S or N deposition: smaller-scale studies 5B-6
Table 5B-3. Recent gradient/observational studies of associations between tree growth
and survival or mortality and S or N deposition: larger-scale FIA data
studies 5B-7
Table 5B-4. Influence of three air pollutants on pattern of tree mortality for 10 plant
functional groups in the eastern and central U.S. (drawn from Dietze and
Moorcrolt. 2011) 5B-9
Table 5B-5. Species with significant growth or survival associations with S or N
deposition for which FIA sites are only in western states (drawn from Horn et
al., 2018) 5B-14
5B-i
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Table 5B-6. Significant associations in the three studies using USFS tree measurements.. 5B-24
Table 5B-7. Experimental addition studies assessing herb and shrub community
responses 5B-30
Table 5B-8. Key aspects of analysis by Simkin et al. (2016) 5B-32
Table 5B-9. Lichen endpoints and associated deposition estimates 5B-38
TABLE OF FIGURES
Figure 5B-1. Study areas of three observational studies utilizing FIA plot data. The
western extent of Dietze and Moorcroft (2011) is a rough approximation 5B-5
Figure 5B-2. Location of FIA plots, based on survival analysis of Horn et al. (2018) 5B-12
Figure 5B-3. Average measurement interval S deposition at sites of species with negative
growth associations with S deposition metric (drawn from Horn et al., 2018).5B-15
Figure 5B-4. Average measurement-interval S deposition at sites of species with negative
survival associations with S deposition metric (drawn from Horn et al.,
2018) 5B-16
Figure 5B-5. Average measurement-interval deposition at sites of species with negative
associations of growth with N deposition metric at median (drawn from Horn
et al., 2018) 5B-17
Figure 5B-6. Average measurement-interval deposition at sites of species with positive
associations of growth with N deposition metric at median (drawn from Horn
et al., 2018) 5B-18
Figure 5B-7. Average measurement-interval deposition at sites of species with negative
associations of survival with N deposition metric (drawn from Horn et al.,
2018) 5B-19
Figure 5B-8. Average measurement-interval deposition at sites of species with positive
associations of survival with N deposition metric (drawn from Horn et al.,
2018) 5B-20
Figure 5B-9. Annual mean wet SO42" deposition in the U.S. for 1989-1991 (top panel) and
2014-2016 (bottom panel) (U.S. EPA, 2023; NADP, 2018) 5B-26
Figure 5B-10. Annual mean wet NO3" deposition in the U.S. for 1989-1991 (top panel) and
2014-2016 (bottom panel) (U.S. EPA, 2023; NADP, 2018) 5B-27
Figure 5B-1 l.Wet plus dry deposition of total sulfur over 3-year periods. Top: 2000-2002;
Bottom: 2016-2018. Drawn from the ISA, Figure 2-70 5B-28
Figure 5B-12.Wet plus dry deposition of total nitrogen over 3-year periods. Top: 2000-
2002; Bottom: 2016-2018. Drawn from the ISA, Figure 2-51 5B-29
Figure 5B-13.Sites included in analysis by Simkin et al. (2016) 5B-33
ATTACHMENTS
5B-ii
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1. Species by Plant Functional Group, Drawn from Dietze and Moorcroft (2011) "Tree
mortality in the eastern and central United States: patterns and drivers"
2A. Species-specific Sample Distribution across Ecoregions for Species with Statistically
Significant Associations of Growth with N/S, from Horn et al. (2018) Supplemental
Information Dataset
2B. Species-specific Sample Distribution across Ecoregions for Species with Statistically
Significant Associations of Survival with N/S, from Horn et al. (2018) Supplemental
Information Dataset
5B-iii
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5B.1 INTRODUCTION
This appendix summarizes salient aspects of key studies investigating responses of
terrestrial ecosystem components (trees, communities of herbs and shrubs, and lichens) to sulfur
and nitrogen deposition, and direct effects of the pollutants in ambient air. The effects may relate
to ecosystem acidification (e.g., acidification of soils in which plants are growing) or nutrient
enrichment (e.g., through changes in competitive advantages of nitrogen-limited species) or both.
The studies described here vary in the extent to which they clarify which factors may be eliciting
the responses. Two general types of studies are described in the sections that follow: controlled
addition experiments and observational (or gradient) studies. Each has strengths, limitations and
uncertainties associated with interpretation.
The strengths of the controlled addition study design include its ability to elucidate N- or
S-related factors and circumstances (e.g., chemical form, duration, concentration) that elicit a
response in the exposed plants (e.g., changes in growth rates of individual species, changes in
productivity of a forest plot, changes in community composition). The scope of impacts that can
be studied, however, is generally limited in the species included, and the size of terrestrial
community. Observational studies, in contrast, can include a large number and range of species
and terrestrial communities, including species less amenable to maintenance in controlled
experimental conditions. These studies, also called gradient studies as they provide for
consideration of observations across a gradient of pollutant concentrations, provide for the
assessment of numerous species and communities across large areas, including across
ecoregions.1 Further, controlled addition studies, which generally include controls that have not
received additions, may be limited to assessment of responses to the addition of the specific
study chemicals. An observational study by its very nature involves the combined impact of
historical and contemporaneous atmospheric deposition in the study areas, which then poses
challenges to disentangling the effects of historic versus recent deposition and of the various
chemicals deposited, as well as the effects of the soil chemistry and geology.2 Further, the
1 Ecoregions are areas where ecosystems (and the type, quality, and quantity of environmental resources) are
generally similar. The ecoregion framework referenced in this document is derived from Omernik (1987) and
from mapping done in collaboration with EPA regional offices, other Federal agencies, state resource
management agencies, and neighboring North American countries. Designed to serve as a spatial framework for
ecosystems and ecosystem components, ecoregions denote areas of similarity in the mosaic of biotic, abiotic,
terrestrial, and aquatic ecosystem components with humans being considered as part of the biota.
2 In context of 2015 ozone NAAQS review, and regarding potential use for predictive purposes in that review of a
single-species O3 gradient study involving tree seedlings planted in fields with transplanted soil at locations along
a gradient in O3 concentrations, CASAC, while noting it to provide important results, cautioned care in
consideration for predictions in other circumstances of this single study that used a gradient methodology without
experimental control of the pollutant exposures (Frey et al., 2014).
5B-1
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observational studies do not generally include measurements or assessments of the site soil
chemistry or geology. Rather, they utilize atmospheric deposition estimates at assessment sites as
surrogates for exposure conditions. These various strengths and limitations inform consideration
of the studies below.
5B.2 TREE GROWTH AND SURVIVAL
As described in the ISA, acidic deposition, which can be comprised of S and N
compounds, can contribute to acidification of soils in which trees grow (ISA, section IS.5).
Deposition of N can also contribute to N enrichment of soil, which can increase the growth of N-
limited trees. In a mixed forest, this can contribute to competitive advantages (depending on
species' growth rates), and potentially reducing the growth rate of out-competed species (ISA,
section IS.5.2). The relationship between deposition and these effects depends on soil status with
regard to acidification and N content, and accordingly is influenced by historic deposition and
the soil characteristics important to soil responses. As noted in the ISA, "[i]n areas where N and
S deposition has decreased, chemical recovery must first create physical and chemical conditions
favorable for growth, survival, and reproduction" for biological recovery to occur (ISA, p. IS-
102). For example, although fewer studies have tracked potential recovery of terrestrial than
aquatic ecosystems, modeling studies in the southern Appalachian Mountains "suggest current
stress and recovery likely to take decades even under scenarios of large reductions in S
deposition" (ISA, p. 4-99). In the subsections below, we provide details of several key studies in
the current ISA that evaluate relationships between N and S deposition on tree growth and
survival.
5B.2.1. Addition Studies
Several experiments involving S or N additions have been reported in the ISA focused on
study areas in the eastern U.S. These studies involve appreciable annual additions of S and/or N
compounds to experimental forest plots. While some study durations are limited to fewer than
five years, others extend appreciably longer than 10 years, providing the time to affect chemical
pools within the soil and the associated soil characteristics linked to acidification or nutrient
enrichment effects (e.g., Ca:Al ratio or NO3" leaching). Among the studies summarized in Table
5B-1 below are addition studies that found species-specific results for growth and survival for
several eastern species including oaks, spruce, maples and pines. (Magill et al., 2004; McNulty et
al., 2005; Pregitzer et al., 2008; Wallace et al., 2007). Further, some multiyear S/N addition (>20
kg/ha-yr) experiments with a small set of eastern species including sugar maple, aspen, white
spruce, yellow poplar, and black cherry, have not reported growth effects (Bethers et al., 2009;
Moore and Houle, 2013; Jung and Chang, 2012; Jensen et al., 2014).
5B-2
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Table 5B-1. Experimental addition studies assessing tree growth and/or survival.
Location,
Reference
Description
Additions
Tree specific Findings
Michigan
(Pregitzer et
al„ 2008)
Four study areas across a 500 km
gradient in temperature and N deposition
in NW Michigan. Forests (approx 90
years old) dominated by sugar maple
(82% by basal area). Study assessed soil
biogeochemical properties, microbial
communities, tree growth/ mortality.
30 kg N ha-1yr1 for 10 years
starting in 1994 (as NaNOs).
Background deposition ranged
from 6.8 to 11.8 kgNha1yr1-
Increased growth (total live woody
biomass) and mortality.
Total deposition estimates: 36.8-
41.8 kg/ha-yr.
Mt. Ascutney,
VT
(McNulty et
al„ 2005)
Six study plots in montane spruce-fir
forests. Assessed soil biogeochemical
properties, microbial communities and
tree growth and mortality.
15.7 and 31.4 kg N ha1yr1 over
14 years starting in 1988 (as
NH4CI).
Background deposition was 10
kg N ha-1yr1
Reductions in total live basal area
(low N-4,18%; high N J,40% vs
control|9%), indicating reduced
growth rates; increased red spruce
mortality in high N.
Bear Brook,
ME
(Elviret al.,
2003;
Bethers et
al., 2009)
Two experimental watersheds (1 control
and 1 treatment), each with softwood,
mixed wood, and hard wood forest.
Studies assessed soil biogeochemical
properties, microbial communities and
tree growth.
25.2 kg N ha1yr1 and 28.8 kg S
ha-1yr1 (as (NH4)2S04) starting
in 1989; assessed after 10 yrs.
Initial background deposition
was 8.4 kg N ha1yr1 and 14.4
kg S ha-1yr1.
Increased growth rates for sugar
maple, but not for red spruce.
No effect on sugar maple seedling
density.
Northern
Quebec,
Canada
(Moore and
Houle, 2013)
Eight year N addition (approximately 3x
and 10x estimates of concurrent
deposition), beginning in 2001, across 9
plots in boreal forests with sugar maple,
yellow birch and American beech.
Studies assessed soil chemistry, foliar
chemistry and tree growth and crown
dieback.
26 and 85 kg N ha1yr1 (from
ammonium nitrate additions as
NH4NO3)
Background wet deposition of
8.5 kg N ha-1yr1
After 8 years, no effect on sugar
maple basal area growth or crown
dieback.
Harvard
Forest, MA
(Magill et al.,
2004)
Eight plots, four in a red pine plantation
and four in a hardwood forest stand
dominated by red and black oak, were
assessed for tree growth and mortality.
50 and 150 kg N ha-1yr1 for 14
years starting in 1988 (as
NH4NO3).
Background deposition was 9
kg N ha-1yr1
Increased growth (stand-level
biomass), but no change in
mortality in the hardwood forest.
Decreased growth and increased
mortality in the red pine plantation.
Canada
(Jung and
Chang et al.,
2012)
At study plots near Atasca oil sands,
assessed above ground tree biomass.
Main canopy species were quaking
aspen and white spruce. Also included
balsam fir, balsam poplar, black spruce
and paper birch
30 kg N/ha-yr, 30 kg S /ha-yr
and 30 kg N+30 kg S /ha-yr
from 2006-2009
Biomass was increased in N-only
treatment and was highest in the
N+S treatment. Understory biomass
unaffected. No evidence of
increased NO3- leaching
Millbrook, NY
(Wallace et
al., 2007)
Six pairs of plots in an upland mixed-oak
forest dominated by chestnut oak,
northern red oak and hickories at the
Institute of Ecosystem Studies where
studies assessed NOr leaching, tree
growth and mortality.
100 kgNha-1yr1 (1996 to
1999), then 50 kg N ha-1yr1
(2000 to 2003) (as NH4NO3).
Background deposition was 10
kg N ha-1yr1
Increased growth rates across
species (oaks and hickories) and
increased mortality in oaks.
Fernow
Forest, WV
(May et al.,
2005; Jensen
et al., 2014)
Two paired watersheds, one control and
one treatment. The most abundant
species were red maple, tulip poplar and
black cherry. Studies assessed soil
biogeochemical properties and tree
growth and mortality.
35 kg N ha1yr1 and 40 kg S ha-
1yr1 starting in 1989 (as
(NH4)2S04)
Background deposition was
approximately 15 kg N ha1yr1
and 20 kg S ha1yr1
Reduced growth (stem diameter) in
all 3 species (red maple, tulip poplar
and black cherry) based on
measurements taken in 1999 and
2001 (after 10 years of treatments).
No difference in growth (basal area)
for tulip poplar and black cherry
after 22 yrs.
5B-3
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5B.2.2. Gradient or Observational Studies
The evidence newly available in this review includes observational or gradient studies
that investigated the existence of statistically significant associations of tree growth and survival
or mortality with S or N deposition (Table 5B-2; ISA, Appendix 5, section 5.5.2 and Appendix 6,
sections 6.2.3.1, 6.3.3 and 6.6.1). In general, these studies utilized measurements of tree growth
and survival or mortality across multiyear intervals at designated plots, and estimates of average
S and/or N deposition (or in some cases, emissions estimates) in the same locations. Statistical
models were employed in the analyses and took into account the influence of different sets of
additional factors (e.g., related to climate, other air pollutants, topography and stand
characteristics).
Tables 5B-2 and 5B-3 below summarize these studies, some of which focused on regions
within a state and others which encompassed multistate regions. The three larger studies utilized
data from the USFS Forest Inventory and Analysis (FIA) program in which measurements are
taken at multiyear intervals at designated plots in forests across the U.S. The three studies have
utilized USFS-FIA data for different, but overlapping, study areas (Figure 5B-1) and species.
More detailed descriptions of these studies and their findings are provided in sections 5B.2.2.1
through 5B.2.2.3 below.
5B-4
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Thomas et al. (2010)
Dietze and Moorcraft (2011)
Horn et al. (2018)
Figure 5B-1. Study areas of three observational studies utilizing FIA plot data. The western
extent of Dietze and Moorcroft (2011) is a rough approximation.
Other observational studies in the recently available evidence have investigated
relationships of tree growth with estimates of SOx and N oxide emissions. For example,
increases in eastern red cedar growth in West Virginia have been associated with reductions in
SO2 emissions and increases in atmospheric CO2 concentrations (Thomas et al., 2013). In a
North Carolina high-elevation forest, increases in red spruce radial growth since the late 1980s
has been associated with declining SOx and N oxide emissions from SE utilities, as well as
increasing temperatures and CO2 (Soule, 2011). Recent studies in areas of Europe where SO2
concentrations are generally higher than in the U.S. have also reported increased growth of some
conifer species (e.g., silver fir) to be related to reductions in SO2 concentrations (ISA, Appendix
3, section 3.2).
5B-5
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Table 5B-2. Recent gradient/observational studies of associations between tree growth and
survival or mortality and S or N deposition: smaller-scale studies.
Study Description Summary
Smaller Regional Scales
Bedison
and
McNeil
(2009)
32 plots in northern hardwood and subalpine spruce-fir
dominated forest plots in Adirondack Park, NY. Trees were
measured in 1984 and 2004. The spatial pattern of inorganic N
deposition in wet deposition was estimated across the plot
locations by multiple regression. Analyses performed for
growth of both individual species and all individuals within
each plot. Potential influence of S deposition was not
assessed.
At the species level, positive
associations of growth with N
deposition were found for maple,
spruce and fir species, with the
largest growth increases in red
maple, balsam fir and red spruce.
Responses varied by forest type
and size class.
Sullivan
et al.
(2013)
Study focused on 50 plots in western Adirondack region with
commonly occurring sugar maple and a 10-fold range of Ca
availability (based on previous stream and soil studies). Plant
measures included DBH of all trees > 10 cm within plots,
assessment of sugar maple canopy condition and vigor,
dendrochronology of sugar maple trees, and seedling and
sappling counts in subplots. Soil chemistry measurements
included base saturation, exchangeable calcium,
exchangeable magnesium and soil pH. Total S and inorganic
(nitrate and ammonium) N deposition estimated using
empirically based GIS model. Average annual (based on the
period 2000-2004) N deposition was calculated as the product
of estimated average annual precipitation from PRISM5, based
on 30-year normals (1970-2000) and kriged S and N
precipitation chemistry from NADP locations. Dry deposition of
SO4-S, HNO3-N, and particulate NO3-N and NH4-N across
Adirondack region calculated as products of air concentrations,
based on the average of 2000-2004 CASTNET air chemistry
data, and vegetation cover deposition velocities per CASTNET
protocols.
Plots with lower soil base
saturation did not have sugar maple
regeneration, with the proportion of
sugar maple seedlings dropping off
substantially from at/above
approximately 60% for base
saturation levels at/above 20% to
at/below approximately 20% for
base saturation at/below about
10%.
Canopy vigor was positively
correlated with soil pH and
exchangeable Ca, Mg.
Mean growth rates (BAI) were
positively correlated with
exchangeable Ca and base
saturation at the watershed level.
Sugar maple distribution negatively
associated with estimated average
2000-04 N+S deposition (750-1120
eq/ha/yr)
5B-6
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Table 5B-3. Recent gradient/observational studies of associations between tree growth and
survival or mortality and S or N deposition: larger-scale FIA data studies.
Study Description | Summary
Larger Regional and National Scales
fand using USFS FIA data)
Thomas
et al.
(2010)
Assessed 24 of the most common northeastern
tree species using 20,067 FIA plots in 19 states
from 1978 to 2001, with the measurement interval
varying from 8.3 to 14.4 across states. Tree
growth and survival were assessed with regard to
association with N deposition (mean annual total
N deposition, 2000-04).
Growth of 11 species was positively associated
with N deposition (including all species with
arbuscular mycorrhizal fungi associations).
Growth of 3 species was negatively associated
with N deposition. Survival of 8 species was
negatively associated with N deposition, with
positive associations for 3 species.
Dietze
and
Moorcroft
(2011)
Assessed influence of patterns of SO42- and NO3-
wet deposition (1994-2005 average), O3 (1996-
2006) and climate, topographic and tree stand
factors on observed variation in tree mortality at
FIA plots in the eastern and central U.S. from
1971 to 2005, binning the 267 species into 10
plant functional types.
Mortality in 7 of the functional groups was
positively associated with both SO42" and O3;
and negatively associated in 1 group.
Mortality in 9 of the 10 functional groups was
negatively associated with NO3-, and positively
associated in 1 functional group.
Horn et
al.
(2018)
At USFS/FIA plots across the continental U.S.,
analyzed potential for associations of growth and
survival across a measurement interval (of
generally 10 years) with estimates of average N
and S deposition for the same interval, all within
the period, 2000-2013. Other factors included in
the analysis were temperature, precipitation, and
terms representing the influence of tree size and
competition on growth and survival. Deposition
estimates were drawn from TDep dataset of
NADP's Science Committee on Total Deposition
for the measurement interval of each plot. The
analyses focused on 71 species that met criteria
for sample size (>2000 trees for both growth and
survival datasets) and for collinearity (correlation
among the independent variables) of N or S,
separately, with the other three independent
variables (S or N, temperature and precipitation)
for growth or survival (Variance Inflation Factor <
3).
Growth in 31 species was negatively associated
with S deposition.
Survival in 40 species was negatively associated
with S deposition.
Growth in 20 species was positively associated
with N deposition and in 2 species (yellow birch
and eastern hemlock) was negatively
associated. Growth in 17 other species was
positively associated with N deposition at lower
levels and negatively associated at higher levels.
Survival of 1 species was positively associated
with N deposition and in 6 species was
negatively associated.
Survival in 25 other species was positively
associated with N deposition at lower N
deposition and negatively associated at higher
levels.
5B.2.2.1. Dietze and Moorcroft (2011)
The study by Dietze and Moorcroft (2011) statistically analyzed patterns of tree mortality
in the eastern and central U.S. using FIA data from 1971 to 2005. The total sample size was 3.4
million tree measurements and 750,000 plot level measurements. Mortality was quantified as a
binary metric (lived or died) based on resampling of FIA plots after intervals of 5 to 15 years.
5B-7
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Climate data were extracted from the database maintained by the PRISM database.3 Using data
from 1971 to 2000, the annual average precipitation, average monthly minimum temperature
across December, January and February, and the average monthly maximum temperature across
June, July and August were calculated. Air quality data were obtained from the National
Atmospheric Deposition Program (NADP) for estimates of wet deposition (in kg ha~'yr~') for
ammonium (NH4+), nitrate (NO3"), hydrogen ion (H+) and sulfate (SO42") for the period of 1994-
2005 and from the EPA's AIRDATA database for ozone for the period of 1996-2006. The ranges
of sulfate and nitrate wet deposition estimates across the study area were 4 to 30 kg/ha-yr and 6
to 16 kg/ha-yr, respectively (Dietze and Moorcroft, 2011). There were 267 tree species sampled
in the study region. The species were classified into 10 different plant functional types to
facilitate analyses (see Attachment 1). The mortality analysis utilized a logistic regression model
for binary mortality probability, relating the mortality probability (live or dead) to a linear model
of the covariates.
All 13 covariates4 were found to be statistically significant predictors of mortality for
more than one of the plant functional types. Sulfate deposition demonstrated a significant
positive effect on mortality in seven of the 10 plant functional groups and a slight negative effect
in one group (Table 5B-4). Nitrate deposition demonstrated a significant negative effect on
mortality in 9 of the 10 plant functional groups and a positive effect in the tenth. Of note is that
ozone exhibited the same pattern of effects as SO42" (Table 5B-4). The authors also noted
correlations between the nitrate and sulfate wet deposition estimates (correlation coefficient of
0.82), and that the highest deposition estimates were for the Ohio River valley and the
northeastern United States (Dietze and Moorcroft, 2011).
3 The PRISM (Parameter-elevation Regressions on Independent Slopes Model) database is maintained by the
PRISM Climate Group, who compile data from multiple monitoring networks and develop spatial climate
datasets to investigate short- and long-term climate patterns, https://prism.oregonstate.edu/
4 There were 13 covariates in 4 categories: climate (mean annual precipitation, mean summer maximum
temperature, mean winter temperature), air pollutants (NO3", SO42", O3), topography (topographic convergence
index, elevation, slope, radiation index), and stand characteristics (stand basal area, stand age, and focal tree
DBH).
5B-8
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Table 5B-4. Influence of three air pollutants on pattern of tree mortality for 10 plant
functional groups in the eastern and central U.S. (drawn from Dietze and
Moorcroft, 2011).
Plant Functional Group
Sulfate,
wet deposition
Nitrate,
wet deposition
Ozone
Early Successional. Hardwood
Pos
Neg
Pos
Evergreen Hardwood
Pos
Neg
Pos
Hydric
Pos
Neg
Pos
Late Successional Conifer
Neg
Neg
Neg
Late Successional Hardwood
Pos
Neg
Pos
Midsuccessional Conifer
Neg
Northern Midsuccessional Hardwood
Pos
Northern Pine
Pos
Neg
Pos
Southern Midsuccessional Hardwood
Pos
Neg
Pos
Southern Pine
Pos
Neg
Pos
In this study, which was limited to the eastern and central U.S., the deposition metrics
were based on wet deposition estimates for SO42", as an indicator of acid deposition,5 and NO3",
as an indicator of wet deposition of total N (Dietze and Moorcroft, 2011).6 As noted by the
authors, "[t]he impacts of both acidification and nitrogen deposition on tree mortality result from
cumulative, long-term deposition, and the patterns presented here should be interpreted in that
light," further noting that "these relationships are not intended to assess the impacts of
interannual variability in deposition nor the efficacy of NO3" or SO42" regulation" (Dietze and
Moorcroft, 2011). Different patterns and associations might be found for analyses utilizing total
deposition (wet and dry) and for species and locations in the western U.S., with its differing
species, soils, climate and historic deposition patterns. In order to utilize all the measurements,
including those for species with lower sample sizes, the tree species were categorized into plant
functional groups; accordingly, variation in mortality at species level was not assessed.
5B.2.2.2. Thomas et al. (2010)
The study by Thomas et al. (2010) statistically analyzed relationships of growth and
survival to N deposition for 24 commonly occurring tree species in a 19-state region of the U.S.
The study region included USFS FIA program plots in 19 states, bounded by Maine in the
Northeast to Virginia and Kentucky in the South, and west to Wisconsin and Illinois. Data were
extracted for the 24 tree species at 20,067 plots. Two measurements were taken at these plots
5 Preliminary analyses indicated stronger relationship for tree mortality with SO42" than with hydrogen ion (Dietze
and Moorcroft, 2011).
6 Preliminary analyses indicated a stronger relationship for tree mortality with NO3" than with NH4 or total N (Dietze
and Moorcroft, 2011).
5B-9
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during the period from the 1978 to 2001, with the measurement interval varying across the 19
states from 8.3 to 14.4 years (Thomas et al., 2010, Supplemental Information).
Nitrogen deposition was estimated using NADP wet deposition estimates and CASTNET
dry deposition estimates for the period from 2000 through 2004. Total N deposition estimates at
the study plots for this period ranged from 3 to 11 kg N/ha-yr (Thomas et al., 2010,
Supplemental Information). Precipitation and temperature were calculated from PRISM with plot
specific values for the span of years from first measurement to second measurement. The
statistical analyses tested a suite of alternate regression models for growth and survival response
to N deposition, precipitation and temperature. The Akaike Information Criteria (AIC) were used
to select the most parsimonious model (i.e., the best model fit for the fewest parameters).
Variation in tree growth for 14 of the 24 species was found to be significantly associated
with N deposition, with positive associations (greater growth at sites with greater N deposition)
found for 11 species and negative associations for three species. All three species with negative
associations were evergreen conifers (red pine, red spruce, and white cedar) that varied widely in
the amount of growth variation per kg N/ha-yr from -9% for red pine to -0.1% and -0.01% for
the other two species, respectively (Thomas et al., 2010). Three of the four most abundant
species (red maple, sugar maple and northern red oak) exhibited strong positive associations. The
largest variation in growth per unit variation in the N deposition metric was observed for black
cherry, tulip poplar, scarlet oak, white ash and balsam fir (18 to 12.3% difference in growth per
kg N/ha-yr).
With regard to probability of tree survival, variation in survival probability across the
study area was significantly associated with the N deposition metric for 11 of 24 species
examined. The association was negative for eight species, with the largest survival variation per
kg N/ha-yr observed for scarlet oak (-1.67%) and quaking aspen (-1.3%). The association was
positive for three species (red maple, paper birch, and black cherry), with only one of the three
having a survival variation per kg N/ha-yr above 1%, black cherry (Thomas et al., 2010).
The authors also suggest that the type of mycorrhizal fungi association with the tree
species may influence its response to N deposition as all five species with arbuscular mycorrhizal
fungi associations had positive associations of growth with N deposition and all 8 of the species
with negative associations of survival with N deposition had ectomycorrhizal fungi associations
(Thomas et al., 2010). Mycorrhizal fungi are important for supplying nutrients and water to
plants, influencing soil C sequestration, and producing mushrooms (ISA, p. ES-16). Mycorrhizal
fungi have long been observed to be sensitive to increased forest N availability (ISA, Appendix
6, section 6.2.3.2).
Not included in the analysis were several factors with the potential to influence tree
growth and survival, including competition, soil chemistry, S deposition and ozone. Accordingly,
5B-10
-------
there was also no analysis of collinearity between such parameters. Most notably, there was no
assessment of the extent of N deposition correlation with S deposition and/or ozone. The study
area and species list was the most limited of the three observational studies relying on USFS-FIA
data.
5B.2.2.3. Horn et al. (2018)
The most recent analysis utilizing the USFS-FIA data, by Horn et al. (2018) also covers
the largest area. This study relies on tree measurements taken for approximately 1.4 million trees
across approximately 70,000 FIA plots. The plots are scattered across 47 states of the contiguous
U.S., excluding Wyoming7 (Figure 5B-2; Horn et al., 2018, Supplemental Data). The eastern
U.S. has many more plots than the West and the areas with highest densities of plots (and
associated measurements) include Wisconsin, northern Michigan and Minnesota and New
England (Figure 5B-2).8
The study investigated associations between variation in tree growth and survival and
atmospheric deposition of N and S across the plots for each species using an approach somewhat
similar to Thomas et al. (2010). The tree growth and survival measurements were those collected
by the FIA generally within the years from 2000 to 2016, with the remeasurement interval for
each plot from which measurements were used in the analysis varying by state and inventory
cycle from 8.8 to 12.1 years (Horn et al., 2018, Supplemental Data). The most common
measurement interval across all plots in the study dataset was 10 years (Horn et al., 2018).
7 The lack of plots in Wyoming resulted because when the researchers obtained the FIA in January 2017, although
there were FIA plots in Wyoming, there were no re-measured plots which is a requirement to assess rates of
growth and survival.
8 This observation is the result of there being more plots in the eastern US due to greater forested area. Within all
U.S. forested areas, plot density is the same by the FIA design (Bechteld and Patterson, 2005).
5B-11
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Figure 5B-2. Location of FIA plots, based on survival analysis of Horn et al. (2018).
Individual tree data were available for a total of 151 species, with 94 species meeting the
study threshold of 2000 individual trees for both growth and survival data (Horn et al., 2018).
Tree growth values were in terms of biomass gains based on measurements of indivi dual trees at
the USFS FIA plots during initial and follow-up visits. Survival was assessed by observing
whether a tree observed on an initial visit was still alive at the follow-up visit (e.g., survived or
not). Thus, survival is a probability metric of the tree surviving and the relationship of survival
(y/n) with the average deposition at that site across years between visits was statistically
analyzed (along with other co-factors like temperature, precipitation, size, competition, and N or
S deposition).
The N and S deposition estimates for each plot's measurement interval were derived from
spatially modeled N and S deposition estimates available from the U.S. National Atmospheric
Deposition Program's Total Deposition Science Committee (stored on the U.S. EPA's FTP
server). Average N deposition and S deposition for each plot were derived from the annual
deposition estimates for the years included in the measurement interval (from year of first
measurement to year of follow-up measurement) for the plot. The plot-level deposition estimates
5B-12
-------
were assigned to all the trees in that plot. Temperature and precipitation values were obtained
from PRISM Climate Group9 and assigned to individual plot values, as for N and S deposition.
In addition to temperature and precipitation, other parameters analyzed in the statistical
models included tree size and competition. A total of 5 different models of growth as a function
of various sets of the 7 parameters were examined: 1) a full model with the size, competition,
climate, S deposition, and N deposition terms; 2) a model with all terms except the N deposition
term; 3) a model with all terms except the S deposition term; 4) a model with all terms but
without S and N deposition terms; and 5) a null model that estimated a single parameter for the
mean growth parameter. For survival, a total of 9 different models were examined, the same 5 as
for growth plus additional models using 2 different size estimates. S deposition was constrained
to have a flat or decreasing response while N deposition could have flat, increasing or decreasing
effects. The models selected to describe growth and survival for each species were the simplest
models (i.e., the one with the fewest parameters) that were within 2.0 AIC units of the best
model (i.e. the model with the lowest AIC) following Thomas et al. (2010).
To quantify collinearity of N and S deposition against other environmental variables, the
study calculated variance inflation factors (VIF). This was done for each tree species and for
both growth and survival. While VIF values from 3 to 10 have been presented in the literature as
a threshold for high collinearity, the authors used VIF < 3 as a criterion for species inclusion
(Horn et al., 2018). The growth and/or survival models for 71 of the 94 species analyzed met this
criterion. Although not utilized in selecting the model for each species, correlation coefficients
were calculated for N and S deposition across the plots assessed for that species (Horn et al.,
2018, Supplemental Information).
Of the 71 species, growth of 31 and survival probability for 40 were negatively
associated with the S deposition metric values. For 21 species, both growth and survival were
negatively associated with S deposition. No statistically significant association was observed for
growth or survival in 5 of the 71 species (Horn et al., 2018). With regard to N, among the
statistically significant models for growth and survival for some species were hump-shaped
relationships, with positive associations in the lower part of the range of N deposition estimates
for a species and negative associations in the upper part of the range. This was the case for
growth and N deposition for 17 species and for survival of 25 species. Growth for two species
and survival for six was negatively associated with the N deposition metric across their ranges.
Conversely, positive associations across the full range were found for growth of 20 species and
9 The PRISM climate group at Oregon State University, supported by the USD A, collects climate data and applies
modeling techniques to develop publicly available datasets covering the period from 1895 to the present. The
Parameter-elevation Relationships on Independent Slopes Model (PRISM) is an interpolation method used in
developing the data. (PRISM Climate Group, Oregon State University, https://prism.oregonstate.edu).
5B-13
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survival of one species, black locust (Robinia pseudoacacia),10 which was also among the 20
species with positive growth associations.
Multiple factors with potential impacts on tree growth/survival were not assessed,
including ozone and others, such as disturbance history (Latty et al., 2004) and insect infestation
(Eshleman et al., 1998, 2004). Further, the influence of soil characteristics on growth or survival
was also not analyzed. Whether these factors may be correlated with the N/S deposition metrics
values and any effect on the reported associations is unknown. Significantly, the study does not
account for the influence at the FIA plots of higher historical deposition. So the extent to which
observed associations relate to historically higher deposition is unclear. Thus, the extent to which
relationships reported for N and S deposition could have had unaccounted for influences of these
variables and associated impacts is unknown.
The authors express strongest confidence in findings from this gradient analysis for the
Eastern U.S., noting the smaller gradients in deposition and smaller number of different species
at western plots (Horn et al., 2018). Plots for some species (e.g., Utah juniper, Douglas fir) were
only in the West (Table 5B-5), FIA plots for some other species are predominantly in the Eastern
U.S. (northeast, mid-atlantic or south), or in the Midwest (e.g., upper Great Lakes areas). Given
the lesser confidence for species only at western plots, we have focused discussion below on the
species for which the sample sites were not limited to the western U.S.
Table 5B-5. Species with significant growth or survival associations with S or N deposition
for which FIA sites are only in western states (drawn from Horn et al., 2018).
All FIA assessment sites in western states
Genus species
Common name
Juniperus osteosperma
Utah juniper
Lithocarpus densiflorus
Tanoak
Pinus monophyla
Singleleaf pinyon
Pseudotsuga menziesii
Douglas fir
Tsuga heterophylla
Western hemlock
Examination of the correlation coefficients additionally indicates relatively high N/S
correlations for some species, complicating interpretation. For example, across the 71 species,
the two highest correlation coefficients are those for eastern hemlock (0.78) and American beech
(0.76), and four of the six species with the next highest coefficients are also for species whose
ranges are concentrated in the eastern U.S. (pond cypress [0.71], yellow birch [0.7], sugar maple
[0.67],and pitch pine [0.66]) (Horn et al., 2018, Supplemental Information). Differences in
10 More than 90% of sample sites for this species were in ecoregions 8.1- 8.4, with more than 50% in 8.4 (Ozark,
Ouachita-Appalachian Forests), regions heavily impacted by SO2 and acid deposition in the past (ISA, Figure 2-
70); the N/S correlation coefficient for these sampling sites was 0.18 (Horn et al., 2018, Supplemental Figures).
5B-14
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quantitative relationships among species may reflect, in part, differences in geographic
distribution of sampling locations, with some species' sites largely concentrated in just a couple
of ecoregions (e.g., paper birch in the far north Great Lakes and Appalachians). Thus, differences
in geographic distributions of the species contribute to differences in ranges of deposition
history, geochemistry, etc. and may contribute to findings reported for some species.
Across sites of species with statistically significant associations of growth or survival
with the S deposition metric, the median average measurement-interval S deposition value,11
with a few exceptions, ranged from 5 to 12 kg S ha~'yr~'. Focusing first on association for
growth, the median S deposition metric values for the species for which growth was negatively
associated with S deposition (excluding the two species with samples only in the west) ranged
from 4 to 12 kg S ha"'yr"', with values below 5 kg S ha^yr"1 for two species, paper birch and
white spruce (for which 75-80% of sites were in the Northern Forests ecoregion12), and above 10
kg S ha'Vr"1 for two species, black locust and sweet birch, which have 70% to more than 90% of
their sites in the Eastern Temperate Forests ecoregion15 (Figure 5B-3; distribution of
measurement sites shown in Attachment 2A).
CO
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in
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30 -
20 -
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o
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Figure 5B-3. Average measurement interval S deposition at sites of species with negative
growth associations with S deposition metric (drawn from Horn et al., 2018).
11 Median average measurement-interval S and N deposition values cited in this document are rounded to whole
numbers.
12 The Northern Forests is the level 1 ecoregion (5.0), which in the U.S. is located in northern Michigan, Wisconsin
and Mimiesota (https://www.epa.gov/eco-researcli/ecoregions-north-america).
13 Eastern Temperate Forests is the level 1 ecoregion (5.0), which includes most of the eastern U.S.
(https://www.epa.gov/eco-researcli/ecoregions-north-america).
5B-15
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The median deposition metric values for the 40 species for which survival probability
was negatively associated with S deposition ranged from 3 to 12 kg S ha lur'yr"1 (Figure 5B-4).
Values for ten species were at or above 10 and for two were below 5 kg S ha ha^yr"'. The two
values below 5 were for paper birch, for which nearly 80% of the measurement sites were in the
Northern Forests ecoregion, and quaking aspen, for which more than 60% of the sites were in the
Northern Forests ecoregion and another 16% were in the Southern Rockies and Wasatch and
Uinta Mountains (see sample distribution in Attachment).
C/)
CD
c
o
cn
o
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Figure 5B-5. Average measurement-interval deposition at sites of species with negative
associations of growth with N deposition metric at median (drawn from Horn
et al., 2018). Blue asterisks indicate species with hump shape associations.
Of the remaining 35 species with significant associations of growth with measurement-
interval N deposition, the association was positive across the full deposition range of their sites
for 20 species. The median N deposition metric values for the 17 nonwestern species15 of these
20 species ranged from 7 kg N ha ha"1 yr"1 (for a number of species) up to 12 kg N ha ha"1 yr"1 for
silver maple, hackberry and black walnut (Figure 5B-6). For the 15 species with significant
associations of growth with measurement-interval N deposition that were positive at the median
average measurement-interval deposition for the species, one was a western species, western
hemlock (Table 5B-5). The median average measurement-interval deposition metric values for
the other 14 species ranged from 7 to 11 kg N ha ha'Vr"1 (Figure 5B-6).
15 Three western species, Utah juniper, Douglas fir and western hemlock (Table 5B-5) had positive growth
association across range of N deposition metric values.
5B-17
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I
Sk -
Figure 5B-6. Average measurement-interval deposition at sites of species with positive
associations of growth with N deposition metric at median (drawn from Horn
et al., 2018). Blue asterisks indicate species with hump shape associations.
Of the six species with negative associations of survival with the N deposition metric
across the full range of the N deposition metric (water oak, southern red oak, winged elm, scarlet
oak, mockernut hickory and American elm), the median deposition values ranged from 8 to 11
kg N ha ha'Vr"1.(Figure 5B-7). The median deposition values for all of the 21 other species with
hump shape functions that were negative at the median deposition value ranged from 3 to 11 kg
N ha ha'Vr"1 (Figure 5B-7; see blue asterisks). The values for the 19 species for which sample
sites were not limited to the western U.S. ranged from 7 to 12 kg N ha ha^yr"1. The four values
below 9 were for quaking aspen (75% sites in Northern Forests, Wasatch and Uinta Mountains
and Southern Rockies), slash pine (-60% sites in southern coastal plain), eastern hemlock (—50%
sites in Northern Forests and -30% in Mixed Wood Plains) and red pine (nearly 70% in Northern
Forests).
5B-18
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1
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40 -
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Figure 5B-8. Average measurement-interval deposition at sites of species with positive
associations of survival with N deposition metric (drawn from Horn et al.,
2018). Blue asterisks indicate species with hump shape associations.
5B.2.3. Tree Growth and Survival: Key Observations, Uncertainties and
Limitations
Looking across the array of experimental addition studies and the three recent
observational (or gradient) studies, we note a number of key observations and associated
uncertainties and limitations:
Experimental Addition Studies of Tree Growth/Survival
• Some studies additionally reported soil chemistry and/or tree cellular responses,
which can inform interpretation of responses that may relate to geology and soil
chemistry in those locations.
• S or S + N addition: Some multiyear S or S+N addition experiments (>20 kg/ha-yr)
with a small set of eastern species, including sugar maple, aspen, white spruce,
yellow poplar, black cherry, have not reported detrimental growth effect (Table 5B-1;
Bethers et al., 2009; Moore and Houle 2013; Jung and Chang, 2012; Jensen et al.,
2014). Some reported increased growth (25.2 kgN + 28.8 kg S/ha-yr for 10 years
[Bethers et al., 2009]), while one reported reduced growth in three species after 10
years that resolved in two of the species after 22 years (Jensen et al., 2014).
• N addition: Several studies found mixed results for growth and survival for several
eastern species including oaks, spruce, maples and pines (Table 5B-1; Magill et al.,
2004; McNulty et al., 2005; Pregitzer et al., 2008; Wallace et al., 2007).
5B-20
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Observational/Gradient Studies of Tree Growth/ Survival
Newly available in this review are three large observational studies of tree
growth/survival and S/N deposition.
Although ozone was analyzed in one of the three studies, soil characteristics and other
factors with potential to impact tree growth and survival (other than climate) were not
assessed.
S deposition: Two large studies that analyzed growth and/or survival measurements
in 94 and 267 species, respectively, at sites across the country, or in the eastern half
of the country, describe negative associations of tree survival and growth with the S
deposition metric for nearly half the species individually and negative associations of
tree survival for 9 of the 10 species' functional type groupings (Dietze and Moorcroft,
2011; Horn et al., 2018). Survival for the same 9 species groups was also negatively
associated with long-term average ozone (Dietze and Moorcroft, 2011).
- The S deposition metrics were derived from estimates for total S or sulfate in
overlapping time periods of roughly 10 years and include areas, particularly in
the eastern U.S., that have experienced decades of much higher deposition.
o The full range of average SO42" deposition estimated for the 1994-2005
time period and eastern U.S. study area assessed by Dietze and
Moorcroft (2011) is 4 to 30 kg S ha^yi""1-
o The full range of average total S deposition estimates for the 2000-
2013 time period and sites across the U.S. assessed by Horn et al.
(2018) is 0.2 to 45 kg S ha'Vi""1 (Horn et al., 2018, Supplemental
Information).
¦ The median S deposition for sites of nonwestern species with neg
associations with growth or survival ranged from 5 to 12 kg S ha"
Vr"1, with few exceptions (Horn et al., 2018).
- The extent to which the differences in growth or survival across sites with
different deposition estimates relate to historically higher deposition at the
sites (e.g., versus the deposition metrics analyzed) is unknown. There are few
available studies describing recovery of historically impacted sites (e.g., ISA,
section IS.4.1, IS.5.1, IS.11.2).
N deposition: Three large studies that analyzed growth and/or survival measurements
in 24 to 267 species at sites in the northeastern or eastern U.S., or across the country,
describe associations of tree survival and growth with several N deposition metrics
(Dietze and Moorcroft, 2011; Thomas et al., 2010; Horn et al., 2018).
- The N deposition metrics were derived from estimates for total N or nitrate in
overlapping time periods and include areas that have experienced decades of
much higher deposition.
o The full range of average NO3" deposition estimated for the 1994-2005
time period ) and eastern U.S. study area assessed by Dietze and
Moorcroft (2011) is 6 to 16 kg N ha^yi""1-
5B-21
-------
o The full range of average total N deposition estimates for the 2000-
2013 time period and sites across the U.S. assessed by Horn et al.
(2018) is 0.9 to 55 kg N ha'Vr"1 (Horn et al., 2018, Supplemental
Information).
¦ The median N deposition for sites of nonwestern species for which
associations with growth or survival were negative (either over full
range or at median for species) ranged from 7 to 12 kg N ha"1 yr"1
(Horn et al., 2018).
¦ The median N deposition for sites of nonwestern species for which
associations with growth or survival were positive (either over full
range or at median for species) ranged from 7 to 12 kg N ha"1 yr"1
(Horn et al., 2018).
o The extent to which the associations of growth or survival with site-
specific N deposition estimates relate to historic patterns of N or S
deposition at the sites (e.g., versus the specific magnitude of the N
deposition metrics analyzed) is unknown.
Only a very small subset of the 71 species of Horn et al. (2018) have been previously
studied with regard to S deposition and growth or survival, although the study by Dietze and
Moorcroft (2011) included these species in its groupings by plant functional type (Table 5B-6).
With regard to relationships of tree growth or survival with N deposition metrics, some of the
Horn et al. (2018) species were also assessed in the study by Thomas et al. (2010), as well as all
of the species being included in the groupings of Dietze and Moorcroft (2011). Table 5B-6
indicates a similarity in the findings, particularly of Horn et al. (2018) and Dietze and Moorcroft
(2011), although the time period and estimation approach for S and N deposition differ.
Given the role of deposition in causing soil conditions that affect tree growth and
survival, and a general similarity of spatial variation of recent deposition to historic deposition,
the similarity in the two studies' findings may indicate the two different metrics to both be
reflecting geographic variation in impacts stemming from historic deposition. Although the
spatial patterns are somewhat similar, the magnitudes of S and N deposition in the U.S. have
changed appreciably over the time period covered by these studies. An example of this is
illustrated by the patterns of wet deposition of SO42" and NO3" in Figures 5B-9 and 5B-10,
respectively, and patterns of total S and N deposition in Figures 5B-11 and 5B-12, respectively.
The appreciable differences in magnitude across the time periods contribute uncertainty to
interpretations related to specific magnitudes of deposition associated with patterns of tree
growth and survival.
Differences in findings of Thomas et al. (2010) may be related to the much shorter N
deposition time period used, as compared to those of Horn et al. (2018) and Dietze and
Moorcroft (2011). The findings of unimodal or hump-shape associations for Horn et al. (2018)
for species with positive or negative associations in Thomas et al. (2010) may also reflect
5B-22
-------
different time periods assessed. The time period for the deposition metric in Thomas et al.
(2010), 2000-2004, overlaps with the earliest five years of the longer time period within which
the measurement intervals for Horn et al. (2018) fall. Further, the occurrence of negative and
positive survival or growth associations from Thomas et al. (2010) and Horn et al. (2018) for
species in a plant functional grouping for which Dietze and Moorcroft (2011) found negative
association may reflect difference in study areas, e.g., early successional hardwood, which had a
positive association of survival with N, includes quaking aspen for which Thomas et al. (2010)
reported negative survival association. The study area of Thomas et al. (2010) was limited to the
Northeast, however, while aspen is prevalent in the Northern Forests ecoregion, which is
included in Dietze and Moorcroft (2011) study area.
5B-23
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Table 5B-6. Significant associations in the three studies using USFS tree measurements.
S Deposition
N Deposition
Dietz and
Moorcroft
Horn et al.
(2018)
Dietze and
Moorcroft
Thomas et al.
(2010)
(total N, 2000-2004,
FlA data, 1970s-90s)
Horn et al.
(2018)
Species
(2011)
(SO42", wet,
1994-2005)
(total S,
-2000-
2013)
(2011)
(NO3-, wet,
1994-2005)
(total N,
-2000-
2013)
Positive (|) or negative
X) association for growth (G) or survival (Su)
Early Successional Hardwood
|Su
|Su
Betula alleghaniensis, yellow birch
iSu
Small XSu
XG
Betula lenta
XSu 1G
USu
Betula papyrifera, paper birch
XSu 1G
Small |Su
USu
Gleditsia triacanthos
1G
Liquidambar styraciflua
ISu
USu
Madura pomifera
1G
Populus grandidentata, bigtooth aspen
XSu 1G
Small XSu
USu
Populus tremuloides, quaking aspen
ISu
XSu |G
USu UG
Prunus serotina, black cherry
|Su |G
USu UG
Salix nigra
1G
UG
Late Successional Hardwood
|Su
|Su
Acer negundo, boxelder
XSu XG
Acerrubrum, red maple
XSu XG
small |Su |G
TG
Acersaccharum, sugar maple
XSu
TG
Acersaccharinum, silver maple
XG
TG
Carpinus caroliniana, American hornbeam
XSu XG
Oxydendrum arboreum, sourwood
XSu
USu
Tilia americana, American basswood
XSu XG
Small XSu
TG
Northern Midsuccessional Hardwood
XSu
Celtis occidentalism hackberry
XSu
UG
Fraxinus americana, white ash
TG
USu |G
Fraxinus pennsylvanica, green ash
XSu XG
USu UG
Juglans nigra, black walnut
XG
USu
Quercus alba, white oak
XSu
USu
Quercus ellipsoidalis, northern pin oak
XG
TG
Quercus rubra, northern red oak
small XSu |G
USu |G
Quercus velutina, black oak
XSu
TG
Sassafras albidum, sassafras
XSu
TG
Ulmus americana, American elm
XSuXG
XSu |G
Ulmus rubra, slippery elm
XSu XG
Hydric
|Su
|Su
Nyssa aguatica
XG
Nyssa biflora
XSu
UG
Taxodium ascendens
TG
Taxodium distichum
XSu XG
5B-24
-------
Species
S Deposition
N Deposition
Dietz and
Moorcroft
(2011)
(SO42-, wet,
1994-2005)
Horn et al.
(2018)
(total S,
-2000-
2013)
Dietze and
Moorcroft
(2011)
(NO3-, wet,
1994-2005)
Thomas et al.
(2010)
(total N, 2000-2004,
FlA data, 1970s-90s)
Horn et al.
(2018)
(total N,
-2000-
2013)
Positive (|) or negative
4) association for growth (G) or survival (Su)
Southern Midsuccessional Hardwood
iSu
|Su
Carya alba, mockernut hickory
ISu
Carya glabra, pignut hickory
jSu
TG
Carya texana, black hickory
TG
Liriodendron tulipifera, yellow poplar
jSu
TG
TG
Nyssa sylvatica, black gum
USu
Quercus coccinea, scarlet oak
jSu |G
USu UG
Quercus falcata, southern red oak
ISu
Quercus laurifolia, laurel oak
ISu
Quercus muelenbergii, chinkapin oak
USu
Quercus nigra, water oak
ISu 1G
XSuUG
Quercus prinus, chestnut oak
jSu
Small jSu
UG
Quercus stellata, post oak
USu
Ulmus alata, winged elm
ISu
Evergreen Hardwood
iSu
|Su
Magnolia Virginia
ISu
UG
Midsuccessional Conifer
|Su
Picea rubens, red spruce
Small |G
Picea glauca, white spruce
1G
Pseudotsuga menziesii, Douglas fir
1G
USu |G
Late Successional Conifer
Weak t Su
|Su
Abies balsamea, balsam fir
TG
Juniperus virginiana, eastern redcedar
ISu 1G
USu
Thuja occidentalis, northern white cedar
Small B|G
Tsuga canadensis, eastern hemlock
1G
Northern Pine
ISu
|Su
Pinus resinosa, red pine
jSu |G
4G
U Su, |G
Pinus regida, pitch pine
ISu
UG
Pinus strobus, eastern white pine
jSu |G
small jSu small |G
TG
Southern Pine
ISu
|Su
Pinus echinata, shortleaf pine
ISu 1G
Pinus elliotti, slash pine
ISu 1G
US
Pinus palustris, longleaf pine
ISu
UG
Pinus taeda, loblolly pine
ISu 1G
Pinus virginiana, Virginia pine
ISu
USu
A For Dietze and Moorcroft (2011), an up arrow is shown for survival if they reported a negative association with mortality.
B For Thomas et al. (2010), "small" used when growth or survival response per unit N is <1%e.
For Horn et al. (2018) "U" used for unimodal (or hump-shaped) associations (positive at lower deposition values and negative at higher).
5B-25
-------
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Figure 5B-9. Annual mean wet SO42" deposition in the U.S. for 1989-1991 (top panel) and
2014-2016 (bottom panel) (U.S. EPA, 2023; NADP, 2018).
5B-26
-------
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Figure 5B-10. Annual mean wet NO3" deposition in the U.S. for 1989-1991 (top panel) and
2014-2016 (bottom panel) (U.S. EPA, 2023; NADP, 2018).
5B-27
-------
Source: CASTNET/CMAQ/NADP
Total S
(kg-S/ha)
¥
-6
-8
-10
-12
P
¦L>20
Total deposition of sulfur 0002
USEPA 09/12/18
of sulfur 1618
USEPA 10/21/19
Source: CASTNET/CMAQ/NADP
Total deposition
Total S
(kg-S/ha)
-0
-2
-4
-6
-8
-10
-12
-14
-16
-18
1
->20
Figure 5B-11. Wet plus dry deposition of total sulfur over 3-year periods. Top: 2000-2002;
Bottom: 2016-2018. Drawn from the ISA, Figure 2-70.
5B-28
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Total deposition of nitrogen 0002
USEPA 02'19/19
Source: CA.STNET/CMAQ/NADP
Total N
(kg-N/ha)
Total N
(kg-N/ha)
Figure 5B-12. Wet plus dry deposition of total nitrogen over 3-year periods. Top: 2000-
2002; Bottom: 2016-2018. Drawn from the ISA, Figure 2-51.
Source: CASTNET/CMAQ/NADP
Tota] deposition of nitrogen 1618
USEPA 10/21/19
5B-29
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5B.3 SPECIES RICHNESS OF HERB AND SHRUB COMMUNITIES
The subsections below summarize salient aspects of studies that have assessed herb and
shrub community metrics and their relationship to N deposition. The addition studies in section
5B.3.1 below evaluated the impact of fertilizer treatments using ammonium nitrate. Section
5B.3.2 summarizes the few recent observational studies that statistically analyze variation in
species richness metrics with variation in N deposition, while also providing detailed information
regarding the largest such study (Simkin et al., 2016). We note that as species richness is the
number of species and does not convey information about species composition, an increase in
species richness may reflect only the addition of new species or a combination of additions and
subtractions, with a net positive result. The extent to which the observational studies account for
potential influence of S deposition varies.
5B.3.1. Experimental Addition Studies
A number of experimental addition studies focused on N (e.g., through addition of
ammonium nitrate fertilizer) are discussed in the ISA and summarized in Table 5B-7 below.
Table 5B-7. Experimental addition studies assessing herb and shrub community responses.
Location
Description
Additions
Findings
Joshua Tree
National Park,
in Mojave
desert, CA
(Allen et al.,
2009)
Assessed biomass and
% cover responses of
native and non-native
grasses to two
fertilization levels at four
sites
5 and 30 kg N ha-1yr1 as
ammonium nitrate
(NH4NO3) fertilizer over 2
years
Ambient air deposition
was estimated to be
approximately 5 - 8 kg N
ha-1yr1
In 1st year, non-native grass biomass
increased significantly at three of the
four study sites receiving 30 kg
N/ha/yr. No significant change with 5
kg N/ha/yr; of with either dose in 2nd
year. No change in % cover.
Native grass species richness
increased with 30 kg N/ha-yr at 1 site
that authors judged related to lower
nonnative species presence.
Prairie
grasslands in
Cedar Creek
Ecosystem
Science
Reserve, MN
(Clark and
Tillman, 2008)
Study plots in two prairie-
like successional
grasslands and one
native savanna
grassland. The soils
were limed to maintain
constant pH (and avoid
acidification).
10, 20, 34, 54 and 95 kg
N ha-1yr1 (ammonium
nitrate addition) over 23
years (1982 to 2004).
Background wet
deposition of N was
estimated to have
averaged 6 kg N ha-1yr1
wet deposition.
Species numbers declined with
increasing chronic addition, including
at the lowest addition (10 kg N/ha/yr).
In a subset of plots for which
additions were ceased after 10 years,
relative species numbers increased,
converging with controls after 13
years. Little recovery species
composition was observed.
Dry sedge
meadow in
Rocky Mountain
National Park,
CO
Five replicate plots (20
total) in a dry meadow
community. Study
assessed plant species
richness, cover of
vascular plants, above
5,10 and 30 kg N ha-1yr1
(ammonium nitrate
addition) over 4 years
starting in 2006.
No significant effect on plant species
richness or diversity.
No significant effect on foliar % N or
above ground biomass. Based on
Carex rupestris increasing in cover
from 34 to 125% in response to
additions, authors estimated 3 kg
5B-30
-------
Location
Description
Additions
Findings
(Bowman et al.,
2012)
ground biomass, and soil
chemistry.
Background deposition
was estimated to be 4 kg
N ha-1yr1
N/ha-yr as deposition associated with
an increase in C rupestris cover and
9 -14 kg N/ha-yr with NO^ leaching
in soil solution.
Santa Margarita
Ecological
Reserve,
Riverside,
California
(Vourlitis, 2017)
Study of long term
effects of N deposition
on native and exotic
plant cover in coastal
sage scrub communities.
4 control and 4 addition
plots (10 x 10 m)
50 kg N ha-1yr1 over 13
years.
Background deposition
estimated at 4 - 6 kg N
ha-1yr1
Increase in the native shrub
Artemesia californica in the 4th
and 5-9th yr of the 13-yr
experiment; decrease in the
native shrub Salvia mellifera in
the 4th and 11-13th yr;
increase in the exotic plant
Brassica nigra in the 11-13th
yr
Santa Margarita
Ecological
Reserve,
Riverside,
California and
Sky Oaks Field
Station, San
Diego County,
CA
(Vourlitis and
Pasquini, 2009)
Study of effects of N
deposition on plant
community composition
in coastal sage scrub
and chaparral
communities.
4 control and 4 addition
plots (10 x 10 m) at each
site (16 total)
50 kg N ha-1yr1 for 5
years as granular NH4NO3
(2003-2006) or
(NH4)2S04 (2007-2008).
Background deposition
estimated as 6-8 kg N ha-
1yr1
Dry season addition of N significantly
changed community composition in
coastal sage scrub communities, but
not in chaparral communities
Great Basin,
California
(Concilio and
Loik, 2013)
Study effects of elevated
N deposition on
sagebrush steppe
communities in 54 paired
plots (half control, half
with additions).
50 kg N ha-1yr1 for 4
years starting in 2007.
Background deposition
estimated as 1 - 3 kg N
ha-1yr1
Community composition (native
species diversity and abundance of
the invasive grass Bromus tectorum)
differed by disturbance history (e.g.
fire), but was not affected by N
deposition.
Sevilleta
National Wildlife
Refuge, New
Mexico
(Collins et al.,
2017)
Study of the effect of
nighttime warming,
winter precipitation and
N deposition in 40 plots
(3.0 x 3.5 m each)
randomly crossed across
treatment effect.
20 kg N ha-1yr1 for 7
years starting in 2006. A
wildfire burned the plots
after the second year.
Ambient air deposition
was approximately 3 kg N
ha-1yr1A
Native desert grass communities
were affected by N deposition in the
3 years following the fire, but not in
the two years preceding the fire or
the last year of the experiment.
Arches National
Park, Colorado
Plateau, Utah
(McHugh et al.,
2017)
Study of community
composition in a semi-
arid grassland
0, 2, 5 and 8 kg N ha-1yr1
for 2 years starting in
2011.
Background deposition
was estimated as 2 - 3 kg
N ha-1yr1
No significant change in community
composition or species richness, but
did find a strong connection between
composition and soil microbial
community structure.
A As the background deposition was not reported in this publication, we have estimated it as the 2007-09 average deposition
based on TDep version 2018.02, using EPA's CL Mapper Tool at: https://www.epa.gov/gcx/about-cl-mapper.
5B-31
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5B.3.2. Gradient or Observational Studies
Recent gradient studies have included analyses investigating the potential of N
enrichment in southern California to alter plant community composition through increases in the
presence of invasive annual species (ISA, Appendix 6, section 6.3.6). A recent study by Cox et
al. (2014) utilized a landscape-level analysis of vegetation change since the 1930s to investigate
risk of conversion of coastal sage scrub vegetation to exotic annual grassland and any association
with N deposition. The authors concluded that sites with 2002 N deposition estimates (based on
CMAQ modeling [Tonnesen et al., 2007]) less than 11 kg N ha'Vi""1 were less likely to have
converted from Coastal sage scrub to non-native grasslands (ISA, Appendix 6, section 6.3.6; Cox
et al., 2014). The authors also evaluated the circumstances associated with recovery of coastal
sage scrub communities from exotic annual grassland that was observed in the 1930s maps, and
reported that plots in areas where surrounding plots had little or no exotic grassland and 60%
cover by coastal sage scrub had increased probability of recovery (Cox et al., 2014). A second
study across the same gradient of 2002 N deposition estimates (6.6 to 20.2 kg N ha~'yr~')
reported similar observations, finding that sites with N deposition above 10 kg N/ha-yr had lower
native species richness (Fenn et al., 2010).
One of the largest studies, by Simkin et al. (2016), analyzed relationships between
observed variation in herb and shrub species richness and average N deposition, soil pH, and
annual average temperature and precipitation at more than 15,000 forest, woodland, shrubland
and grassland sites in multiple regions of the U.S. (Figure 5B-13; Table 5B-8). The study
categorized sites into open-canopy and closed-canopy communities and, in a "national" analysis,
investigated quantitative relationships between site variation in species richness, assessed over
the 23-year period from 1990 to 2013, and in estimates of average N deposition for the "modern"
period of 1985 to 2011 (Simkin et al., 2016, Supplemental Information, SI Methods).
Table 5B-8. Key aspects of analysis by Simkin et al. (2016)
Study Area
Community
assessments
N Deposition estimates
Other variables considered
Northwestern U.S. (predominantly
WA, OR, far north CA, western
MT, NV, UT), northeastern CO,
Ml, mid-Atlantic (MD, VA) and
Southeast (NC, SC, GA, FL)
Assessments...
>15,000 sites
10-yr average (2002-11)
dry deposition from
CMAQ added to 27-year
average (1985-2011) wet
deposition from NADP.
Soil pH, precipitation and
temperature (1981-2010)
The site assessments were drawn from seven databases of biological survey data sources,
with varied distribution across the states represented. For example, more than a third of the sites
were in Minnesota and the Pacific Northwest (WA and OR) and another third in the Carolinas
and Virginia; about 100 sites are in the northeastern U.S. (Simkin et al., 2016, Supplemental
Information, Table SI; Figure 5B-13).
5B-32
-------
Canopy Type
x Closed-canopy
Open-canopy
Figure 5B-13. Sites included in analysis by Sim kin et al. (2016). Based on dataset available at
https ://datadry ad. org/ stash/dataset/doi: 10.5061/dryad. 7kn5 3
When sites were grouped as closed-canopy (forested) sites versus open-canopy
(woodland, shrubland and grassland) sites, a statistical relationship was observed for variation in
herbaceous species richness (number of herbaceous species) with variation in N deposition (and
soil pH, followed by temperature and precipitation). Different quantitative relationships were
observed for the two categories of sites. In open-canopy ecosystems, there was a positive
relationship between herbaceous species richness and N deposition at the low end of the
deposition range (sites with higher N deposition had more species), then a negative relationship
with N deposition at higher deposition rates, with the deposition magnitude at the inflecting point
varying with pH (Simkin et al., 2016). For example, in soils with pH of 4.5, the inflection point
was 6.5 kg N ha'V1""1 and in pH 7 soils, it was 8.8 kg N ha_1yr 1 In closed-canopy ecosystems, the
variation in forest understory species richness with variation in N deposition was more strongly
dependent on soil pH. At closed-canopy sites with low pH (4.5), a negative relationship was
observed for species richness with N deposition above 11.6 kg N luf'yr"1. At closed-canopy sites
with soil pH greater than 8.0, no negative association of species richness with N deposition was
observed across the full range of N deposition estimates, which extended up to about 20 kg N/ha-
yr (Simkin et al., 2016).
5B-33
-------
The statistical models for the two categories of sites were then applied to the pH,
temperature and precipitation for each site to predict N deposition values expected to be
associated with a difference in species count from the predicted optimal for a site of that soil pH,
temperature and precipitation. For the forested (closed-canopy sites), the inflection points above
which a lower species richness would be expected ranged from 7.9 to 19.6 kg N ha^yr"1, across
pH, temperature and precipition of the assessed sites, with a mean of 13.4 kg N ha^yr"1. Across
the open-canopy sites, these N deposition inflection points ranged from 7.4 to 10.3 kg N ha^yr"1,
with a mean of 8.7 kg N ha^yr"1 (Simkin et al., 2016).
Simkin et al. (2016) also performed regional gradient analyses for a set of sites for which
the data were judged sufficient. This involved 44 gradients for a subset of 26 vegetation types
that spanned a range in N deposition estimates the authors judged to be adequate. Of the 44
gradients, a negative association of species richness with N dep was observed at 16 (36.5%), a
positive association at 8 (18%), and no association found for the remaining 20 (45%). Among the
8 gradients showing positive associations, most had N deposition estimates averaging at or below
3 kg N ha^yr"1. Overall, a negative association of species richness with N deposition estimates
was more common for gradients involving soil that was acidic, or had higher precipitation or
warmer temperatures (Simkin et al., 2016).
In summary, the national-scale analysis of herbaceous species richness by Simkin et al.
(2016) indicated that N deposition effects on forest closed-canopy species richness is highly
dependent on soil pH (ISA, Appendix 6, section 6.3.3.2). At open-canopy sites (e.g., grasslands,
shrublands, and woodlands) with low rates of N deposition (e.g., below 6.5 kg N ha"1 yr"1 for soil
pH of 4.5 and below 8.8 kg N ha"1 yr"1 for soil pH of 7), relatively higher N deposition was
generally associated with higher plant species richness (Simkin et al., 2016; ISA, Appendix 6,
section 6.3.5). The open-canopy site-level N deposition above which a negative association was
found for species richness with N deposition (higher deposition lower species count) ranged
from 7.4 to 10.3 kg N ha"1 yr"1, with an average of 8.7 kg N ha"1 yr"1. At forested sites, relatively
higher N deposition was associated with higher plant species richness for sites with soil pH of
4.5 and N deposition estimates below 11.6 kg N ha'Vr"1. With N deposition above this level the
association was negative (higher deposition, lower species richness). At forested sites with the
most basic soil (pH of 8.2), there was no value of N deposition that was negatively associated
with species richness. At both the national and gradient analyses, few sites with N deposition
estimates at or below 3 kg N ha"1 yr"1 showed a negative relationship of species richness with N
deposition (Simkin et al., 2016).
Study limitations with regard to interpretations specific for N deposition include that no
other pollutants with potential to affect species richness (and which may covary in many places
with N deposition), including sulfate and ozone, were considered. Further, the "modern" N
5B-34
-------
deposition estimates (1985-2011) were correlated with both shorter duration more recent
estimates and with longer duration historical estimates, introducing uncertainty with regard to the
particular deposition of interest with greatest influence on the results. This correlation coupled
with the variation in magnitude of the deposition estimates for the various periods also
contributes uncertainty regarding identification of what might be termed N deposition thresholds
that might contribute to different types of relationships with species richness. Further, the study
does not provide information on the species that are absent versus present, or their role in the
community, across the varying species richness values. Additionally, site distribution varied
across parts of the U.S. (as a result of combining species richness assessment surveys conducted
in different contexts, for different purposes). For example, the most densely sampled closed
canopy areas were in the southern Appalachians and Virginia, and Minnesota, areas of
historically high and low deposition, respectively (Figure 5B-13). With regard to herb and shrub
communities, there was appreciable representation in Minnesota and virtually no representation
in Mediterranean California or the Great Plains. The potential influence of the relative
distribution of sites across areas of greater versus lesser historical deposition is unclear.
5B.4 LICHEN COMMUNITY COMPOSITION
Lichens absorb N, S, and other elements from the air and from material deposited on their
surfaces. Accordingly, lichens can be sensitive to air pollution and are frequently used as
indicators of air quality, and associated deposition (2008 ISA, section 3.3.5.1), on forest
ecosystems. Shifts in lichen community composition to greater presence of more N tolerant
species have been associated with areas that have received high acidifying deposition and high
concentrations of SO2, N oxides and reduced N, such as the eastern U.S. (2008 ISA, section
3.2.2.3).
Research in the late 1970s-early 1980s reported inverse associations of lichen cover with
atmospheric oxidants in the San Bernardino Mountains just outside Los Angeles, California.
Studies in this region have reported a reduction in lichen species by about 50% since the early
1900s, with elevated HNO3 identified as a contributor to lichen community declines in the Los
Angeles basin dating back to the 1970s. Studies since the 2008 ISA indicate these communities
have not yet recovered (ISA, Appendix 3, section 3.3). Surveys of urban and industrial areas in
the 1970s and 80s (e.g., in urban areas of Great Britain) also identified SO2 as a factor in lichen
community declines observed lichen deaths (ISA, Appendix 3, section 3.2; Hutchinson et al.,
5B-35
-------
199616). The relative influences of airborne versus deposited air pollutants in such impacts is
unclear.
5B.4.1. Studies Investigating Direct Effects of Pollutants in Ambient Air
Sulfur oxides and oxides of N have been associated with effects on lichens (ISA,
Appendix 3, section 3.2 and 3.3). In laboratory experiments involving daily HNO3 exposures,
with peaks near 50 ppb, over durations of 18 to 78 days, effects on lichen photosynthesis were
reported, among other effects (ISA, Appendix 6, section 6.2.3.3; Riddell et al., 2012). Based on
studies extending back to the 1980s, HNO3 has been suspected to have had an important role in
the dramatic declines of lichen communities that occurred in the Los Angeles basin (ISA,
Appendix 3, section 3.4; Nash and Sigal, 1999; Riddell et al., 2008; Riddell et al., 2012). For
example, lichen transplanted from clean air habitats to analogous habitats in the Los Angeles
basin in 1985-86 were affected in a few weeks by mortality and appreciable accumulation of H+
and N03"(ISA, Appendix 3, section 3.4; Boonpragob et al., 1989).
Air monitoring data summarized in Chapter 2 indicate areas of the U.S. experiencing
appreciably higher annual mean NO2 concentrations in the 1980s compared to more recent years
(Figure 2-22). For example the 95th percentile of U.S. sites ranged from just over 50 ppb to just
over 60 ppb during the 1980s (Figure 2-22). During the 1980s and earlier, the Los Angeles
metropolitan statistical area had some of the highest annual average NO2 concentrations. For
example, the annual average NO2 concentration in Los Angeles was 0.078 ppm in 1979, 0.071
ppm in 1980, 0.058 ppm in 1985 and 0.057 ppm in 1989 (U.S. EPA, 1983, 1987, 1991).
Concentrations of O3 in Los Angeles were also quite high during this time (U.S. EPA, 1983,
1987, 1991); however, while O3 impacts on plants are well established, research with lichens
indicates a lesser sensitivity. This contributes to the evidence for NO2, and particularly, HNO3, as
"the main agent of decline of lichen in the Los Angeles basin" (ISA, Appendix 3, p. 3-15).
Co-occurring elevations in SO2 and ozone contribute uncertainty to identification of a
threshold concentration of N oxides likely to elicit lichen community changes such as those that
occurred in the Los Angeles basin. More recent studies indicate variation in eutrophic lichen
abundance to be associated with variation in N deposition metrics (ISA, Appendix 6, section
6.2.3.3). The extent to which these associations are influenced by residual impacts of historic air
quality is unclear.
16 The publication by Hutchinson et al. (1996) cited in ISA, cites to Seaward (1987) as the support for its
characterization; the characterization summarized here is also drawing on the specific details provided by
Seaward (1987).
5B-36
-------
5B.4.2. Observational Studies Investigating Relationships with Atmospheric
Deposition
Several recent studies have reported negative associations of lichen community
composition/abundance and N deposition (and S deposition) metric values in areas of the
Northwest, California and at some sites in the northeast (Table 5B-9; ISA, Appendix 6, section
6.5). For example, analyses of surveys in 1990s report species richness differences among sites
in the Pacific NW to vary with estimates of N deposition (and N-PM2.5) across sample sites
ranging from approximately 8.2 to <1 kg N ha"1 yr"1 and 10 to <1 kg dissolved inorganic N ha"'yr"
1 (Geiser et al., 2010; Root et al., 2015, Appendix B, Table B.l). The study by Geiser et al.
(2010) analyzed relationships between lichen community composition and several N deposition
metrics at sites in Western Oregon and Washington forests. At other sites in the western U.S.,
Root et al. (2015) analyzed relationships between lichen community/abundance metrics and
lichen N concentrations and N deposition estimates extrapolated from lichen N concentrations.
Statistical modeling was used to identify N deposition estimates associated with a change in
lichen community/abundance metric(s) for sites in 2 ecoregions. Both papers utilized a linear
regression approach. Geiser et al. (2010) used the regression to relate community composition to
an "air score," while Root et al. (2015) used it to relate a community-based index to air
concentrations of nitrogen in fine PM, which was then related to N deposition.
There are several limitations associated with use of these studies' findings for purposes of
interpreting potential risk to lichens of recent N deposition. For example, the estimates of
deposition utilized different methods than the current commonly accepted methods. The potential
role of other unaccounted environmental factors (including ozone, SO2 and S deposition) has not
been addressed in these observational/gradient, uncontrolled studies, and there is a scarcity of
controlled N addition experiments that might augment conclusions. The significance of findings
of the western studies is unclear for other areas of the U.S., and there is uncertainty concerning
the independence of any effect of the deposition levels analyzed from residual effects of pastN
deposition. Further, the extent to which these observations reflect communities still exhibiting
impacts of much higher pollution of the 1970s-80s is unknown. Although some studies have
investigated historical impacts, there remain uncertainties as to the extent to which impacts on
lichen communities noted in recent studies reflect recent N deposition. And there are few
controlled N addition experiments that might augment or inform interpretation of the findings of
observational/gradient studies. Other studies in Europe and Canada have not reported such
associations with relatively large N deposition gradients.
5B-37
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Table 5B-9. Lichen endpoints and associated deposition estimates.
Description
Deposition
Estimates
Findings
Cleavitt et al. (2011) analyzed 4 plots
distributed across a gradient in
estimated S deposition in Acadia
National Park, ME
12 to 18 kg S/ha-yr
Rather than relate deposition to lichen distribution, this study
reported that throughfall chemistry influenced bark pH and
that influenced the suitability of tree boles as habitat for
lichen. Epiphytic lichen species richness and presence of
pollution-sensitive epiphytes were greater on red maple
trees, which have a higher pH in the bark relative to red
spruce trees.
Cleavitt et al. (2015) analyzed 24 sites
in 4 Class I areas in Northeastern U.S.
(Lye Brook Wilderness, VT, Great Gulf
and Presidential Range-Dry River
Wiildernesses, NH, and Acadia National
Park, ME); assessed multiple metrics
for lichen status associations with
concurrent (2-yr ave) and cumulative
(2000-13) S and N deposition
estimates. Cumulative and 2-yr average
recent N deposition were tightly
correlated (r2=0.90 p,
0.0001);cumulative and recent S
deposition were not correlated. Aerosol
NOx declined from -0.7-0.9 to -0.25-
0.5 ug/m3 across 14-yr period.
Total S deposition
of-6-15 kg S/ha-yr
across the 4 areas
in 2000; with
subsequent
reductions to -3-6
kg S/ha-yr by
2013. Total N
deposition of -4-15
kg N/ha-yr across
4 areas In 2000;
with subsequent
reductions to -3-8
kg N/ha-yr
(Cleavitt et al.,
2015, Figure 4).
Negative associations of lichen species richness,
abundance of N-sensitive species, and poorer thallus
condition with annual mean and cumulative N deposition.
Cumulative dry deposition of S yielded best fit to decreases
in thallus condition, poorer community-based S Index
values, and absence of many S-sensitive species, indicating
stronger role for legacy of historical deposition than recent
deposition patterns.
"Lichen metrics were generally better correlated with
cumulative deposition than annual deposition." "In our study,
dry S deposition related more closely to patterns in lichen
metrics than total or wet S deposition. Dry deposition of S
may be more harmful to lichens, both because it has the
potential to become highly concentrated when the thallus is
rehydrated, and because it largely originates from SO2,
which has a long history of toxicity to lichens."
Geiser et al. (2010) analyzed data at
sites in Western OR and WA forests,
calculating different N metrics (total, dry
and wet N deposition; wet N03"+NH4+
deposition; and PM2.5-N, dry N
deposition for specified breakpoint in
"air scores." Statistical modeling of FIA
plot air scores based on aspects of
lichen community composition and
lichen N/S concentrations (assessed
1994-2002) for data subset, considering
elevation, precip (1961-90), hardwood
basal area (Geiser and Neitlich, 2007).
Then model used to predict scores for
remaining plots. Range of scores
divided into six bins from "best' (lowest
bin) to "worst" (highest bin).
Average 1990-99
N deposition
estimated from
CMAQ modeling
(0.8 - 8.2 kg/ha-yr
across all sites);
NADP wet
deposition and
IMPROVE
particulate N for
1994-2002
For breakpoint between 3rd and 4th air scores,total N
deposition ranged from about 3 to 9 kg N/ha-yr
The score equal to the breakpoint between the 3rd and 4th
bins ("fair" and "degraded") was associated with 33-43%
fewer oligotrophic species and 3 to 4 fold more eutrophic
species than scores in the "best" bin.
Per Geiser & Neitlich 2007 for same areas: "Ozone is
potentially adversely affecting Pacific Northwest lichens."
"Ambient [air] concentrations of NOx often correlate with
SO2, making it difficult to separate SO2 effects on lichen
communities from NOx effects."
Root et al. (2015) analyzed data for
sites in WA, northern ID, NW MT, OR
and far NE CA for relationship between
lichen community metrics (assessed
1993-2011) and lichen N concentrations
(samples 1993-2001) and N deposition
estimated from lichen N. Created lichen
index relating lichen N to species
frequency (excluding uncommon
species and species with "ambiguous
relationships").
Inorganic N
deposition
extrapolated from
lichen N
concentrations,
estimated to range
from 0.174 to 9.49
kg N/ha-yr across
sampling plots
Based on a judgment that "[l]ichen communities did not
appear to be strongly impacted by N concentration below
0.378 ug N/m3/year" which was the lowest N-PM2.5
concentration near "known N pollution sources," and the
associated lichen N concentration estimated by linear
regression, the throughfall N deposition was estimated to be
2.5 kg Ha-yr. Throughfall N deposition estimated from the
lichen index value estimated for the chosen N-PM2.5 and its
estimated relationship with throughfall N, was estimated to
be 1.5 kg N/ha-yr.
5B-38
-------
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5B-43
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Attachments
-------
Attachment 1
Species by Plant Functional Group
Drawn from Dietze and Moorcroft (2011) "Tree mortality in the eastern and
central United States: patterns and drivers"
Plant Functional Group
Genus Species
Common Name
Genus Species
Common Name
Ailanthus altissima
ailanthus
Populus alba
silver poplar
Albizia julibrissin
Mimosa
Populus balsamifera
balsam poplar
Alnus
alder
Populus deltoides
eastern cottonwood
Betula
Birch
Populus deltoides sub
monilifera
plains cottonwood
Betula alleqhaniensis
yellow birch
Populus grandidentata
bigtooth aspen
Betula lenta
sweet birch
Populus tremuloides
quaking aspen
Betula nigra
river birch
Prosopis pubescens
screwbean mesquite
Early Successional
Hardwood
Betula papyrifera
paper birch
Prunus
cherry
Betula populifolia
gray birch
Prunus americana
American plum
Large positive
Bursera simaruba
gumbo limbo
Prunus aviumPRAV
sweet cherry
influence of
Catalpa
catalpa
Prunus nigra
Canada plum
SO42" deposition
Catalpa bignoniodes
southern catalpa
Prunus pensylvanica
pin cherry
on mortality
negative
influence of NO3-
deposition on
mortality
Catalpa speciosa
northern catalpa
Prunus serotina
black cherry
Elaeagnus angustifolia
Russian-olive
Prunus virginiana
chokecherry
Ficus aurea
Florida strangler fig
Robinia pseudoacacia
black locust
Gleditsia triacanthos
honeylocust
Salix
willow
Gymnocladus dioicus
Kentucky coffeetree
Salix alba
white willow
Larix laricina
tamarack
Salix bebbiana
Bebb willow
Larix spp
Larch spp
Salix caroliniana
costal plain willow
Liquidambar styraciflua
sweetgum
Salix nigra
black willow
Madura pomifera
Osage-orange
Salix sepulcralis
weeping willow
Melia azedarach
Chinaberrytree
Sideroxylon lanuginosum
ssp. lanuginosum
gum bully
Paulownia tomentosa
paulownia
Vernicia fordii
tung-oil-tree
Populus
poplar
Evergreen Hardwoods
- Large positive
influence of SO42"
deposition on
mortality
- negative influence
Avicennia germinans
Black-mangrove
Magnolia grandifolia
southern magnolia
Casuarina lepidophloia
belah
Magnolia virginiana
sweetbay
Cinnamomum camphora
camphor tree
Melaleuca guinguenervia
melaleuca
Conocarpus erectus
buttonwood mangrove
Persea borbonia
redbay
Eucalyptus
eucalyptus
Quercus margarettiae
dwarf live oak
Eucalyptus grandis
grand eucalyptus
Quercus virginiana
live oak
of NO3- deposition
Gordonia lasianthus
loblolly-bay
Rhizophora mangle
American mangrove
on mortality
Ilex opaca
American holly
Umbellularia californica
California laurel
Laguncularia racemosa
white -mangrove
Hydric
Carya aguatica
water hickory
Planera aguatica
water elm
- Large positive
Citrus
Citrus
Populus heterophylla
swamp cottonwood
influence of SO42-
Eugenia rhombea
red stopper
Quercus lyrata
overcup oak
5B-Attachment 1-1
-------
Plant Functional Group
Genus Species
Common Name
Genus Species
Common Name
deposition on
mortality
- negative influence
of N03"deposition
on mortality
Gleditsia aquatica
waterlocust
Sabal palmetto
cabbage palmetto
Metopium toxiferum
Florida poisontree
Salix amygdaloides
peachleaf willow
NULL
palm, other
Taxodium ascendens
pondcypress
Nyssa aquatica
water tupelo
Taxodium distichum
baldcypress
Nyssa biflora
swamp tupelo
Thrinax morrisii
key thatch palm
Nyssa oqeche
Ogechee tupelo
Late Successional
Conifer
- negative influence
of NO3- deposition
on mortality
- weakly negative
influence of SO42"
deposition on
mortality
Abies balsamea
Balsam fir
Juniperus virginiana var
silicicola
Southern redcedar
Chamaecyparis thyoides
Atlantic white-cedar
Thuja occidentalis
northern white-cedar
Juniperus
juniper
Tsuga
hemlock
Juniperus ashei
Ashe juniper
Tsuga canadensis
eastern hemlock
Juniperus scopulorum
Rocky Mountain juniper
Tsuga caroliniana
Carolina hemlock
Juniperus virginiana
eastern redcedar
Late Successional
Hardwood
- Large positive
influence of SO42"
deposition on
mortality
- negative influence
of N03"deposition
on mortality
Acer
Maple
Carpinus caroliniana
hornbeam
Acer barbatum
Florida maple
Castanea dentata
American chestnut
Acer leucoderme
chalk maple
Cornus florida
Flowering dogwood
Acernegundo
boxelder
Diospyros
persimmon
Acer nigrum
black maple
Diospyros virginiana
common persimmon
Acer pensylvanicum
striped maple
Fagus grandifolia
beech
Acer platanoides
Norway maple
Halesia
silverbell
Acer rubrum
red maple
Halesia Carolina
Carolina silverbell
Acer saccharinum
silver maple
Halesia parviflora
two-wing silverbel
Acer saccharum
sugar maple
Oxydendrum arboreum
sourwood
Acer spicatum
mountain maple
Platanus
sycamore
Aesculus
buckeye
Sapindus saponaria var
drummondii
western soapberry
Aesculus flava
yellow buckeye
Tilia
basswood
Aesculus glabra
Ohio buckeye
Tilia americana
american basswood
Aesculus glabra var
arguta
Texas buckeye
Tilia americana var
caroliniana
Carolina basswood
Alnus glutinosa
European alder
Tilia americana var.
heterophylla
American basswood
Midsuccessional
conifer
- negative influence
of NO3- deposition
on mortality
Abies
fir spp.
Picea glauca
white spruce
Abies concolor
white fir
Picea mariana
black spruce
Abies fraseri
Fraser fir
Picea pungens
Blue spruce
Picea
spruce
Picea rubens
red spruce
Picea abies
Norway Spruce
Pseudotsuga menziesii
Douglas-fir
Northern
Midsuccessional
Hardwood
- positive influence
of N03"deposition
on mortality
Amelanchier
serviceberry
Morus alba
white mulberry
Amelanchier arborea
Downy serviceberry
Morus rubra
red mulberry
Carya
hickory
Ostrya virginiana
eastern
hophornbeam
Carya cordiformis
bitternut hickory
Quercus alba
white oak
Carya ovalis
red hickory
Quercus bicolor
swamp white oak
Carya ovata
shagbark hickory
Quercus ellipsoidalis
northern pin oak
Celtis laevigata var
reticulata
netleaf hackberry
Quercus ilicifolia
scrub oak
Celtis occidentalis
hackberry
Quercus macrocarpa
bur oak
5B-Attachment 1-2
-------
Plant Functional Group
Genus Species
Common Name
Genus Species
Common Name
Cladrastis kentukea
yellowwood
Quercus palustris
pin oak
Crataegus
hawthorn
Quercus prinoides
swarf chinakapin oak
Crataegus crus-galli
cockspur hawthorn
Quercus rubra
northern red oak
Crataegus mollis
downy hawthorn
Quercus velutina
black oak
Fraxinus americana
white ash
Sassafras albidum
sassafras
Fraxinus nigra
black ash
Sorbus americana
American mountain-
ash
Fraxinus pennsylvanica
green ash
Sorbus aucuparia
European mountain-
ash
Fraxinus profunda
pumpkin ash
Ulmus
elm
Juglans
walnut
Ulmus americana
American elm
Juglans cinera
butternut
Ulmus pumila
Siberian elm
Juglans nigra
black walnut
Ulmus rubra
slippery elm
Malus
apple spp.
Ulmus thomasii
rock elm
Malus coronaria
sweet crabapple
Unknown
Unknown dead
hardwood
Malus ioensis
prairie crabapple
Northern Pine
- Large positive
influence of SO42"
on mortality
- negative influence
of N03"on mortality
Pinus banksiana
jack pine
Pinus rigida
pitch pine
Pinus nigra
Austrian pine
Pinus strobus
white pine
Pinus ponderosa
Ponderosa pine
Pinus sylvestris
Scotch pine
Pinus resinosa
red pine
Southern
Midsuccessional
Hardwood
- Large positive
influence of SO42-
deposition on
mortality
- negative influence
of N03"deposition
on mortality
Asimina triloba
pawpaw
Morus
mulberry
Car/a alba
mockernut hickory
Nyssa sylvatica
blackgum
Carya carolinae-
septentrionalis
southern shagbark
hickory
Quercus
oak spp. -
Deciduous
Carya glabra
pignut hickory
Quercus buckleyi
Buckley oak
Carya illinoinensis
pecan
Quercus coccinia
scarlet oak
Carya laciniosa
shellbark hickory
Quercus falcata
southern red oak
Carya myristiciformis
nutmeg hickory
Quercus imbricaria
shingle oak
Carya pallida
sand hickory
Quercus incana
bluejack oak
Carya texana
black hickory
Quercus laevis
turkey oak
Castanea mollissima
Chinese chestnut
Quercus laurifolia
laurel oak
Castanea pumila
Chinkapin
Quercus margarettiae
runner oak
Castanea pumila var
ozarkensis
Ozark chinkapin
Quercus marilandica
blackjack oak
Celtis
hackberry
Quercus michauxii
swamp chestnut oak
Celtis laevigata
sugarberry
Quercus muehlenbergii
chinkapin oak
Cercis canadensis
eastern redbud
Quercus nigra
water oak
Cotinus obovatus
smoketree
Quercus oglethorpensis
Oglethorpe oak
Fraxinus
ash
Quercus pagoda
cherrybark oak
Fraxinus caroliniana
Carolina ash
Quercus phellos
willow oak
Fraxinus guadrangulata
blue ash
Quercus prinus
chestnut oak
Liriodendron tulipifera
yellow-poplar
Quercus shumardii
Shumard's oak
Magnolia
magnolia
Quercus similis
Delta post oak
Magnolia acuminata
cucumbertree
Quercus sinuata var
sinuata
Durand oak
Magnolia fraseri
mountain magnolia
Quercus stellata
post oak
5B-Attachment 1-3
-------
Plant Functional Group
Genus Species
Common Name
Genus Species
Common Name
Magnolia macrophylla
bigleaf magnolia
Triadica sebifera
Chinese tallowtree
Magnolia tripetala
umbrella magnolia
Ulmus alata
winged elm
Malus angustifolia
southern crabapple
Ulmus crassifolia
cedar elm
Ulmus serotina
September elm
Southern Pine
- Large positive
influence of SO42"
deposition on
mortality
- negative influence
of N03"deposition
on mortality
Pinus clausa
Sand pine
Pinus echinata
shortleaf pine
Pinus elliottii
slash pine
Pinus glabra
spruce pine
Pinus palustris
longleaf pine
Pinus pungens
Table Mountain pine
Pinus serotina
pond pine
Pinus taeda
loblolly pine
Pinus virginiana
Virginia pine
5B-Attachment 1-4
-------
Attachment 2A
Species-specific Sample Distribution across Ecoregions
for Species with Statistically Significant Associations of Growth with N/S
from Horn et al. (2018) Supplemental Information Dataset
Key:
NA_L3 = North American Ecoregion, code for level 3
US_L3NAME = Name of Ecoregion at level 3
See: https://www.epa.gov/eco-research/ecoregions
Median = Tree-specific median S and/orN deposition for the species samples
Assoc = U= unimodal, t=positive, j=negative
N/S = elation coefficient for N and S deposition values for the species samples
Count = number of species' tree samples assessed in all plots in that ecoregion
% = percent of species' tree samples in that ecoregion
5B-Attachment 2A
-------
NA L3
CODE
US L3NAME
boxelder
Median S=6
Assoc S-J,
N/S =0.14
red maple
Median N=9, S=7
Assoc N-|, S-J,
N/S = 0.6
silver maple
Median
N=12,S=8
Assoc N-|, S-J,
N/S =0.27
yellow birch
Median N=7
Assoc N-U
N/S =0.7
sweet birch
Median S=12
Assoc S-J,
N/S =0.58
paper birch
Median S=4
Assoc S-J,
N/S = 0.42
count
%
count
%
count
%
count
%
count
%
count
%
5.2.1
Northern Lakes and Forests
85
1.4%
23972
23.6%
324
7.1%
3282
23.9%
9247
50.1%
5.2.2
Northern Minnesota Wetlands
53
0.9%
93
0.1%
1
0.0%
547
3.0%
5.3.1
Northeastern Highlands
17
0.3%
13245
13.1%
27
0.6%
6357
46.3%
1322
14.8%
4824
26.1%
5.3.3
North Central Appalachians
4883
4.8%
363
2.6%
1299
14.6%
93
0.5%
6.2.3
Northern Rockies
129
0.7%
6.2.4
Canadian Rockies
6
0.0%
6.2.5
North Cascades
1
0.0%
6.2.7
Cascades
6.2.8
Eastern Cascades Slopes and Foothills
6.2.9
Blue Mountains
4
0.0%
6.2.10
Middle Rockies
16
0.1%
6.2.11
Klamath Mountains
6.2.12
Sierra Nevada
6.2.13
Wasatch and Uinta Mountains
6.2.14
Southern Rockies
6.2.15
Idaho Batholith
6
0.0%
7.1.7
Puget Lowland
18
0.1%
7.1.8
Coast Range
7.1.9
Willamette Valley
8.1.1
Eastern Great Lakes Lowlands
86
1.4%
1618
1.6%
293
6.4%
204
1.5%
49
0.6%
57
0.3%
8.1.3
Northern Allegheny Plateau
8
0.1%
3565
3.5%
9
0.2%
394
2.9%
543
6.1%
82
0.4%
8.1.4
North Central Hardwood Forests
594
9.8%
4062
4.0%
448
9.8%
418
3.0%
978
5.3%
5B-Attachment 2A-1
-------
NA L3
CODE
US L3NAME
boxelder
Median S=6
Assoc S-J,
N/S =0.14
red maple
Median N=9, S=7
Assoc N-|, S-J,
N/S = 0.6
silver maple
Median
N=12,S=8
Assoc N-|, S-J,
N/S =0.27
yellow birch
Median N=7
Assoc N-U
N/S =0.7
sweet birch
Median S=12
Assoc S-J,
N/S =0.58
paper birch
Median S=4
Assoc S-J,
N/S = 0.42
count
%
count
%
count
%
count
%
count
%
count
%
8.1.5
Driftless Area
934
15.4%
631
0.6%
387
8.5%
31
0.2%
651
3.5%
8.1.6
Southern Michigan/Northern Indiana Drift Plains
106
1.7%
1463
1.4%
516
11.3%
36
0.3%
29
0.2%
8.1.7
Northeastern Coastal Zone
6
0.1%
4309
4.2%
18
0.4%
256
1.9%
975
10.9%
134
0.7%
8.1.8
Acadian Plains and Hills
5025
5.0%
9
0.2%
1236
9.0%
1437
7.8%
8.1.10
Erie Drift Plain
8
0.1%
1811
1.8%
83
1.8%
112
0.8%
6
0.1%
8.2.1
Southeastern Wisconsin Till Plains
338
5.6%
156
0.2%
124
2.7%
55
0.4%
55
0.3%
8.2.2
Huron/Erie Lake Plains
82
1.4%
1123
1.1%
195
4.3%
6
0.0%
123
0.7%
8.2.3
Central Corn Belt Plains
76
1.3%
13
0.0%
101
2.2%
8.2.4
Eastern Corn Belt Plains
202
3.3%
333
0.3%
197
4.3%
1
0.0%
8.3.1
Northern Piedmont
71
1.2%
566
0.6%
38
0.8%
4
0.0%
109
1.2%
8.3.2
Interior River Valleys and Hills
296
4.9%
423
0.4%
625
13.7%
8.3.3
Interior Plateau
469
7.7%
1061
1.0%
82
1.8%
8.3.4
Piedmont
135
2.2%
3119
3.1%
26
0.3%
8.3.5
Southeastern Plains
154
2.5%
4363
4.3%
11
0.2%
8.3.6
Mississippi Valley Loess Plains
192
3.2%
195
0.2%
35
0.8%
8.3.7
South Central Plains
89
1.5%
742
0.7%
10
0.2%
8.3.8
East Central Texas Plains
5
0.1%
4
0.0%
8.4.1
Ridge and Valley
100
1.6%
4942
4.9%
18
0.4%
166
1.2%
1866
21.0%
12
0.1%
8.4.2
Central Appalachians
17
0.3%
4912
4.8%
3
0.1%
495
3.6%
1170
13.1%
8.4.3
Western Allegheny Plateau
193
3.2%
3926
3.9%
103
2.3%
17
0.1%
230
2.6%
8.4.4
Blue Ridge
18
0.3%
3707
3.7%
3
0.1%
283
2.1%
1280
14.4%
8.4.5
Ozark Highlands
105
1.7%
185
0.2%
47
1.0%
5B-Attachment 2A-2
-------
NA L3
CODE
US L3NAME
boxelder
Median S=6
Assoc S-J,
N/S =0.14
red maple
Median N=9, S=7
Assoc N-|, S-J,
N/S = 0.6
silver maple
Median
N=12,S=8
Assoc N-|, S-J,
N/S =0.27
yellow birch
Median N=7
Assoc N-U
N/S =0.7
sweet birch
Median S=12
Assoc S-J,
N/S =0.58
paper birch
Median S=4
Assoc S-J,
N/S = 0.42
count
%
count
%
count
%
count
%
count
%
count
%
8.4.6
Boston Mountains
174
0.2%
8.4.7
Arkansas Valley
28
0.5%
56
0.1%
43
0.9%
8.4.8
Ouachita Mountains
2
0.0%
156
0.2%
3
0.1%
8.4.9
Southwestern Appalachians
25
0.4%
1401
1.4%
1
0.0%
24
0.3%
8.5.1
Middle Atlantic Coastal Plain
19
0.3%
2982
2.9%
15
0.3%
8.5.2
Mississippi Alluvial Plain
396
6.5%
471
0.5%
83
1.8%
8.5.3
Southern Coastal Plain
4
0.1%
1425
1.4%
8.5.4
Atlantic Coastal Pine Barrens
1
0.0%
256
0.3%
6
0.1%
9.2.1
Northern Glaciated Plains
140
2.3%
4
0.0%
9.2.2
Lake Agassiz Plain
200
3.3%
3
0.0%
6
0.0%
9.2.3
Western Corn Belt Plains
555
9.1%
21
0.0%
420
9.2%
3
0.0%
6
0.0%
9.2.4
Central Irregular Plains
157
2.6%
0
0.0%
273
6.0%
9.3.1
Northwestern Glaciated Plains
17
0.3%
2
0.0%
9.3.3
Northwestern Great Plains
13
0.2%
9.3.4
Nebraska Sand Hills
1
0.0%
9.4.1
High Plains
3
0.0%
9.4.2
Central Great Plains
48
0.8%
14
0.3%
9.4.3
Southwestern Tablelands
4
0.1%
9.4.4
Flint Hills
6
0.1%
1
0.0%
9.4.5
Cross Timbers
3
0.0%
1
0.0%
9.4.6
Edwards Plateau
9.4.7
Texas Blackland Prairies
5B-Attachment 2A-3
-------
NA L3
CODE
US L3NAME
boxelder
Median S=6
Assoc S-J,
N/S =0.14
red maple
Median N=9, S=7
Assoc N-|, S-J,
N/S = 0.6
silver maple
Median
N=12,S=8
Assoc N-|, S-J,
N/S =0.27
yellow birch
Median N=7
Assoc N-U
N/S =0.7
sweet birch
Median S=12
Assoc S-J,
N/S =0.58
paper birch
Median S=4
Assoc S-J,
N/S = 0.42
count
%
count
%
count
%
count
%
count
%
count
%
9.5.1
Western Gulf Coastal Plain
8
0.0%
9.6.1
Southern Texas Plains
10.1.2
Columbia Plateau
10.1.3
Northern Basin and Range
10.1.4
Wyoming Basin
10.1.5
Central Basin and Range
10.1.6
Colorado Plateaus
10.1.7
Arizona/New Mexico Plateau
10.1.8
Snake River Plain
10.2.1
Mojave Basin and Range
10.2.2
Sonoran Basin and Range
10.2.10
Chihuahuan Deserts
11.1.1
Southern and Central California Chaparral and
Oak Woodlands
11.1.2
Central California Valley
8
0.1%
11.1.3
Southern California Mountains
12.1.1
Madrean Archipelago
13.1.1
Arizona/New Mexico Mountains
1
0.0%
15.4.1
Southern Florida Coastal Plain
34
0.0%
Total Tree Counts
6070
101434
4561
13721
8905
18465
5B-Attachment 2A-4
-------
NA L3
CODE
US_L3NAME
American
hornbeam
Median S=7
Assoc S-J,
N/S = 0.23
black hickory
Median N=10
Assoc N-|
N/S =0.17
hackberry
Median N=11
Assoc N-U
N/S =0.17
American
beech
Median
N=8,S=7
Assoc N-U,S-J,
N/S = 0.76
white ash
Median N=10
Assoc N-|
N/S =0.54
green ash
Median N=10, =6
Assoc N-U, S-J,
N/S = 0.45
count
%
count
%
count
%
count
%
count
%
count
%
5.2.1
Northern Lakes and Forests
9
0.4%
1378
6.6%
1273
7.4%
1807
11.6%
5.2.2
Northern Minnesota Wetlands
1
0.0%
200
1.3%
5.3.1
Northeastern Highlands
9
0.4%
8502
40.7%
2438
14.1%
39
0.3%
5.3.3
North Central Appalachians
45
2.1%
1520
7.3%
455
2.6%
6.2.3
Northern Rockies
6.2.4
Canadian Rockies
6.2.5
North Cascades
6.2.7
Cascades
6.2.8
Eastern Cascades Slopes and Foothills
6.2.9
Blue Mountains
6.2.10
Middle Rockies
1
0.0%
24
0.2%
6.2.11
Klamath Mountains
6.2.12
Sierra Nevada
6.2.13
Wasatch and Uinta Mountains
6.2.14
Southern Rockies
6.2.15
Idaho Batholith
7.1.7
Puget Lowland
7.1.8
Coast Range
7.1.9
Willamette Valley
8.1.1
Eastern Great Lakes Lowlands
7
0.3%
3
0.1%
282
1.3%
652
3.8%
479
3.1%
8.1.3
Northern Allegheny Plateau
28
1.3%
1192
5.7%
1721
10.0%
76
0.5%
5B-Attachment 2A-5
-------
NA L3
CODE
US_L3NAME
American
hornbeam
Median S=7
Assoc S-J,
N/S = 0.23
black hickory
Median N=10
Assoc N-|
N/S =0.17
hackberry
Median N=11
Assoc N-U
N/S =0.17
American
beech
Median
N=8,S=7
Assoc N-U,S-J,
N/S = 0.76
white ash
Median N=10
Assoc N-|
N/S =0.54
green ash
Median N=10, =6
Assoc N-U, S-J,
N/S = 0.45
count
%
count
%
count
%
count
%
count
%
count
%
8.1.4
North Central Hardwood Forests
4
0.2%
46
0.9%
155
0.7%
595
3.4%
1429
9.2%
8.1.5
Driftless Area
2
0.1%
235
4.8%
0
0.0%
354
2.1%
187
1.2%
8.1.6
Southern Michigan/Northern Indiana Drift Plains
5
0.2%
35
0.7%
143
0.7%
265
1.5%
913
5.9%
8.1.7
Northeastern Coastal Zone
2
0.1%
5
0.1%
327
1.6%
399
2.3%
58
0.4%
8.1.8
Acadian Plains and Hills
1470
7.0%
758
4.4%
31
0.2%
8.1.10
Erie Drift Plain
10
0.5%
3
0.1%
290
1.4%
465
2.7%
128
0.8%
8.2.1
Southeastern Wisconsin Till Plains
23
0.5%
43
0.2%
177
1.0%
675
4.3%
8.2.2
Huron/Erie Lake Plains
1
0.0%
13
0.3%
29
0.1%
84
0.5%
667
4.3%
8.2.3
Central Corn Belt Plains
3
0.1%
75
1.5%
45
0.3%
102
0.7%
8.2.4
Eastern Corn Belt Plains
9
0.4%
271
5.5%
110
0.5%
708
4.1%
286
1.8%
8.3.1
Northern Piedmont
8
0.4%
29
0.6%
76
0.4%
256
1.5%
50
0.3%
8.3.2
Interior River Valleys and Hills
13
0.6%
79
2.0%
591
12.1%
120
0.6%
477
2.8%
547
3.5%
8.3.3
Interior Plateau
72
3.4%
24
0.6%
1031
21.0%
735
3.5%
1408
8.2%
714
4.6%
8.3.4
Piedmont
252
11.8%
65
1.3%
521
2.5%
291
1.7%
481
3.1%
8.3.5
Southeastern Plains
595
27.8%
7
0.2%
44
0.9%
609
2.9%
110
0.6%
1054
6.8%
8.3.6
Mississippi Valley Loess Plains
175
8.2%
26
0.7%
8
0.2%
102
0.5%
82
0.5%
254
1.6%
8.3.7
South Central Plains
469
21.9%
190
4.8%
9
0.2%
152
0.7%
140
0.8%
561
3.6%
8.3.8
East Central Texas Plains
2
0.1%
87
2.2%
2
0.0%
36
0.2%
168
1.1%
8.4.1
Ridge and Valley
19
0.9%
138
2.8%
434
2.1%
909
5.3%
196
1.3%
8.4.2
Central Appalachians
36
1.7%
5
0.1%
1403
6.7%
408
2.4%
44
0.3%
8.4.3
Western Allegheny Plateau
30
1.4%
70
1.4%
678
3.2%
1138
6.6%
106
0.7%
8.4.4
Blue Ridge
15
0.7%
5
0.1%
294
1.4%
317
1.8%
43
0.3%
5B-Attachment 2A-6
-------
NA L3
CODE
US_L3NAME
American
hornbeam
Median S=7
Assoc S-J,
N/S = 0.23
black hickory
Median N=10
Assoc N-|
N/S =0.17
hackberry
Median N=11
Assoc N-U
N/S =0.17
American
beech
Median
N=8,S=7
Assoc N-U,S-J,
N/S = 0.76
white ash
Median N=10
Assoc N-|
N/S =0.54
green ash
Median N=10, =6
Assoc N-U, S-J,
N/S = 0.45
count
%
count
%
count
%
count
%
count
%
count
%
8.4.5
Ozark Highlands
5
0.2%
1863
46.6%
262
5.3%
558
3.2%
155
1.0%
8.4.6
Boston Mountains
4
0.2%
681
17.0%
20
0.4%
55
0.3%
79
0.5%
18
0.1%
8.4.7
Arkansas Valley
10
0.5%
576
14.4%
32
0.7%
65
0.4%
137
0.9%
8.4.8
Ouachita Mountains
24
1.1%
385
9.6%
2
0.0%
8
0.0%
23
0.1%
80
0.5%
8.4.9
Southwestern Appalachians
27
1.3%
1
0.0%
19
0.4%
152
0.7%
214
1.2%
119
0.8%
8.5.1
Middle Atlantic Coastal Plain
101
4.7%
21
0.4%
89
0.4%
34
0.2%
369
2.4%
8.5.2
Mississippi Alluvial Plain
28
1.3%
21
0.5%
55
1.1%
9
0.0%
9
0.1%
717
4.6%
8.5.3
Southern Coastal Plain
108
5.1%
9
0.2%
2
0.0%
6
0.0%
440
2.8%
8.5.4
Atlantic Coastal Pine Barrens
2
0.1%
0
0.0%
14
0.1%
7
0.0%
9.2.1
Northern Glaciated Plains
14
0.3%
337
2.2%
9.2.2
Lake Agassiz Plain
254
1.6%
9.2.3
Western Corn Belt Plains
571
11.6%
82
0.5%
416
2.7%
9.2.4
Central Irregular Plains
45
1.1%
779
15.9%
216
1.3%
354
2.3%
9.3.1
Northwestern Glaciated Plains
14
0.3%
81
0.5%
9.3.3
Northwestern Great Plains
3
0.1%
360
2.3%
9.3.4
Nebraska Sand Hills
6
0.1%
25
0.2%
9.4.1
High Plains
2
0.0%
18
0.1%
9.4.2
Central Great Plains
235
4.8%
1
0.0%
268
1.7%
9.4.3
Southwestern Tablelands
10
0.2%
9
0.1%
9.4.4
Flint Hills
131
2.7%
1
0.0%
42
0.3%
9.4.5
Cross Timbers
12
0.3%
9
0.2%
7
0.0%
24
0.2%
9.4.6
Edwards Plateau
5B-Attachment 2A-7
-------
NA L3
CODE
US_L3NAME
American
hornbeam
Median S=7
Assoc S-J,
N/S = 0.23
black hickory
Median N=10
Assoc N-|
N/S =0.17
hackberry
Median N=11
Assoc N-U
N/S =0.17
American
beech
Median
N=8,S=7
Assoc N-U,S-J,
N/S = 0.76
white ash
Median N=10
Assoc N-|
N/S =0.54
green ash
Median N=10, =6
Assoc N-U, S-J,
N/S = 0.45
count
%
count
%
count
%
count
%
count
%
count
%
9.4.7
Texas Blackland Prairies
3
0.0%
9.5.1
Western Gulf Coastal Plain
8
0.4%
28
0.2%
9.6.1
Southern Texas Plains
10.1.2
Columbia Plateau
10.1.3
Northern Basin and Range
10.1.4
Wyoming Basin
10.1.5
Central Basin and Range
10.1.6
Colorado Plateaus
10.1.7
Arizona/New Mexico Plateau
10.1.8
Snake River Plain
10.2.1
Mojave Basin and Range
10.2.2
Sonoran Basin and Range
10.2.10
Chihuahuan Deserts
11.1.1
Southern and Central California Chaparral and
Oak Woodlands
11.1.2
Central California Valley
11.1.3
Southern California Mountains
12.1.1
Madrean Archipelago
13.1.1
Arizona/New Mexico Mountains
15.4.1
Southern Florida Coastal Plain
3
0.0%
Total Tree Counts
2137
3997
4902
20894
17266
15573
5B-Attachment 2A-8
-------
NA L3
CODE
US_L3NAME
honeylocust
Median S=6
Assoc S-J,
N/S = 0.27
black walnut
Median
N=12,S=9
Assoc N-|, S-J,
N/S =0.08
Utah juniper
Median N=3, S=1
Assoc N-|, S-J,
N/S =0.71
eastern
redcedar
Median S=7
Assoc S-J,
N/S =0.3
sweetgum
Median N=9, S=7
Assoc N-f, S-J,
N/S =0.37
yellow-poplar
Median N=10
Assoc N-|
N/S = 0.41
count
%
count
%
count
%
count
%
count
%
count
%
5.2.1
Northern Lakes and Forests
2
0.0%
5.2.2
Northern Minnesota Wetlands
5.3.1
Northeastern Highlands
6
0.1%
16
0.1%
91
0.4%
5.3.3
North Central Appalachians
4
0.0%
47
0.2%
6.2.3
Northern Rockies
6.2.4
Canadian Rockies
6.2.5
North Cascades
6.2.7
Cascades
6.2.8
Eastern Cascades Slopes and Foothills
6.2.9
Blue Mountains
6.2.10
Middle Rockies
33
0.3%
6.2.11
Klamath Mountains
6.2.12
Sierra Nevada
6.2.13
Wasatch and Uinta Mountains
698
6.3%
6.2.14
Southern Rockies
110
1.0%
6.2.15
Idaho Batholith
7.1.7
Puget Lowland
7.1.8
Coast Range
7.1.9
Willamette Valley
8.1.1
Eastern Great Lakes Lowlands
30
0.5%
29
0.2%
8.1.3
Northern Allegheny Plateau
34
0.6%
11
0.1%
5B-Attachment 2A-9
-------
NA L3
CODE
US L3NAME
honeylocust
Median S=6
Assoc S-J,
N/S = 0.27
black walnut
Median
N=12,S=9
Assoc N-|, S-J,
N/S =0.08
Utah juniper
Median N=3, S=1
Assoc N-|, S-J,
N/S =0.71
eastern
redcedar
Median S=7
Assoc S-J,
N/S =0.3
sweetgum
Median N=9, S=7
Assoc N-f, S-J,
N/S =0.37
yellow-poplar
Median N=10
Assoc N-|
N/S = 0.41
count
%
count
%
count
%
count
%
count
%
count
%
8.1.4
North Central Hardwood Forests
13
0.2%
109
0.8%
8.1.5
Driftless Area
6
0.3%
404
7.1%
276
1.9%
8.1.6
Southern Michigan/Northern Indiana Drift Plains
2
0.1%
119
2.1%
24
0.2%
58
0.2%
8.1.7
Northeastern Coastal Zone
14
0.2%
84
0.6%
2
0.0%
46
0.2%
8.1.8
Acadian Plains and Hills
8.1.10
Erie Drift Plain
1
0.0%
50
0.9%
1
0.0%
160
0.7%
8.2.1
Southeastern Wisconsin Till Plains
1
0.0%
80
1.4%
88
0.6%
8.2.2
Huron/Erie Lake Plains
7
0.3%
35
0.6%
1
0.0%
4
0.0%
8.2.3
Central Corn Belt Plains
72
3.6%
130
2.3%
8
0.1%
3
0.0%
8.2.4
Eastern Corn Belt Plains
130
6.5%
417
7.4%
142
1.0%
62
0.2%
183
0.8%
8.3.1
Northern Piedmont
1
0.0%
142
2.5%
221
1.5%
37
0.1%
659
2.7%
8.3.2
Interior River Valleys and Hills
203
10.1%
457
8.1%
622
4.3%
444
1.5%
377
1.6%
8.3.3
Interior Plateau
166
8.3%
796
14.0%
3325
23.1%
791
2.7%
2259
9.3%
8.3.4
Piedmont
13
0.6%
153
2.7%
1031
7.2%
5544
19.0%
5178
21.4%
8.3.5
Southeastern Plains
11
0.5%
54
1.0%
645
4.5%
9331
32.0%
3421
14.2%
8.3.6
Mississippi Valley Loess Plains
32
1.6%
24
0.4%
163
1.1%
1538
5.3%
272
1.1%
8.3.7
South Central Plains
58
2.9%
14
0.2%
167
1.2%
4762
16.3%
13
0.1%
8.3.8
East Central Texas Plains
17
0.8%
4
0.1%
122
0.8%
209
0.7%
8.4.1
Ridge and Valley
10
0.5%
353
6.2%
722
5.0%
546
1.9%
1657
6.9%
8.4.2
Central Appalachians
4
0.2%
50
0.9%
41
0.3%
94
0.3%
2997
12.4%
8.4.3
Western Allegheny Plateau
15
0.7%
386
6.8%
44
0.3%
14
0.0%
2390
9.9%
8.4.4
Blue Ridge
6
0.3%
65
1.1%
32
0.2%
65
0.2%
2779
11.5%
5B-Attachment 2A-10
-------
NA L3
CODE
US L3NAME
honeylocust
Median S=6
Assoc S-J,
N/S = 0.27
black walnut
Median
N=12,S=9
Assoc N-|, S-J,
N/S =0.08
Utah juniper
Median N=3, S=1
Assoc N-|, S-J,
N/S =0.71
eastern
redcedar
Median S=7
Assoc S-J,
N/S =0.3
sweetgum
Median N=9, S=7
Assoc N-f, S-J,
N/S =0.37
yellow-poplar
Median N=10
Assoc N-|
N/S = 0.41
count
%
count
%
count
%
count
%
count
%
count
%
8.4.5
Ozark Highlands
180
9.0%
710
12.5%
3519
24.5%
146
0.5%
4
0.0%
8.4.6
Boston Mountains
6
0.3%
28
0.5%
285
2.0%
172
0.6%
8.4.7
Arkansas Valley
15
0.7%
9
0.2%
606
4.2%
226
0.8%
8.4.8
Ouachita Mountains
17
0.8%
4
0.1%
210
1.5%
311
1.1%
8.4.9
Southwestern Appalachians
4
0.2%
37
0.7%
370
2.6%
620
2.1%
1035
4.3%
8.5.1
Middle Atlantic Coastal Plain
1
0.0%
12
0.2%
13
0.1%
2428
8.3%
467
1.9%
8.5.2
Mississippi Alluvial Plain
100
5.0%
7
0.1%
19
0.1%
574
2.0%
10
0.0%
8.5.3
Southern Coastal Plain
2
0.1%
26
0.2%
1111
3.8%
43
0.2%
8.5.4
Atlantic Coastal Pine Barrens
2
0.0%
10
0.1%
60
0.2%
16
0.1%
9.2.1
Northern Glaciated Plains
9
0.4%
9.2.2
Lake Agassiz Plain
9.2.3
Western Corn Belt Plains
345
17.2%
325
5.7%
318
2.2%
9.2.4
Central Irregular Plains
496
24.7%
617
10.9%
381
2.6%
9.3.1
Northwestern Glaciated Plains
118
0.8%
9.3.3
Northwestern Great Plains
1
0.0%
82
0.6%
9.3.4
Nebraska Sand Hills
92
0.6%
9.4.1
High Plains
1
0.0%
23
0.2%
9.4.2
Central Great Plains
46
2.3%
22
0.4%
275
1.9%
9.4.3
Southwestern Tablelands
6
0.1%
8
0.1%
9.4.4
Flint Hills
30
1.5%
45
0.8%
55
0.4%
9.4.5
Cross Timbers
3
0.1%
10
0.2%
17
0.1%
9.4.6
Edwards Plateau
5B-Attachment 2A-11
-------
NA L3
CODE
US L3NAME
honeylocust
Median S=6
Assoc S-J,
N/S = 0.27
black walnut
Median
N=12,S=9
Assoc N-|, S-J,
N/S =0.08
Utah juniper
Median N=3, S=1
Assoc N-|, S-J,
N/S =0.71
eastern
redcedar
Median S=7
Assoc S-J,
N/S =0.3
sweetgum
Median N=9, S=7
Assoc N-f, S-J,
N/S =0.37
yellow-poplar
Median N=10
Assoc N-|
N/S = 0.41
count
%
count
%
count
%
count
%
count
%
count
%
9.4.7
Texas Blackland Prairies
12
0.1%
9.5.1
Western Gulf Coastal Plain
11
0.1%
92
0.3%
9.6.1
Southern Texas Plains
10.1.2
Columbia Plateau
10.1.3
Northern Basin and Range
405
3.7%
10.1.4
Wyoming Basin
66
0.6%
10.1.5
Central Basin and Range
3112
28.1%
10.1.6
Colorado Plateaus
3935
35.5%
10.1.7
Arizona/New Mexico Plateau
1601
14.4%
10.1.8
Snake River Plain
10.2.1
Mojave Basin and Range
115
1.0%
10.2.2
Sonoran Basin and Range
10.2.10
Chihuahuan Deserts
11.1.1
Southern and Central California Chaparral and
Oak Woodlands
11.1.2
Central California Valley
11.1.3
Southern California Mountains
12.1.1
Madrean Archipelago
1
0.0%
13.1.1
Arizona/New Mexico Mountains
1008
9.1%
15.4.1
Southern Florida Coastal Plain
Total Tree Count
2009
5666
11084
14379
29180
24169
5B-Attachment 2A-12
-------
NA L3
CODE
US L3NAME
tanoak
Median N=4
Assoc N-U
N/S = 0.57
Osage-orange
Median S=5
Assoc S-J,
N/S =0.36
sweetbay
Median N=7
Assoc N-U
N/S =0.34
water tupelo
Median S=8
Assoc S-J,
N/S =0.5
swamp tupelo
Median N=7
Assoc N-U
N/S =0.47
white spruce
Median S=4
Assoc S-J,
N/S =
count
%
count
%
count
%
count
%
count
%
count
%
5.2.1
Northern Lakes and Forests
3739
63.0%
5.2.2
Northern Minnesota Wetlands
245
4.1%
5.3.1
Northeastern Highlands
716
12.1%
5.3.3
North Central Appalachians
28
0.5%
6.2.3
Northern Rockies
6.2.4
Canadian Rockies
6.2.5
North Cascades
6.2.7
Cascades
1
0.0%
6.2.8
Eastern Cascades Slopes and Foothills
6.2.9
Blue Mountains
6.2.10
Middle Rockies
194
3.3%
6.2.11
Klamath Mountains
1561
51.9%
6.2.12
Sierra Nevada
116
3.9%
6.2.13
Wasatch and Uinta Mountains
6.2.14
Southern Rockies
6.2.15
Idaho Batholith
7.1.7
Puget Lowland
7.1.8
Coast Range
1276
42.4%
7.1.9
Willamette Valley
8.1.1
Eastern Great Lakes Lowlands
2
0.0%
8.1.3
Northern Allegheny Plateau
7
0.1%
5B-Attachment 2A-13
-------
NA L3
CODE
US_L3NAME
tanoak
Median N=4
Assoc N-U
N/S = 0.57
Osage-orange
Median S=5
Assoc S-J,
N/S =0.36
sweetbay
Median N=7
Assoc N-U
N/S =0.34
water tupelo
Median S=8
Assoc S-J,
N/S =0.5
swamp tupelo
Median N=7
Assoc N-U
N/S =0.47
white spruce
Median S=4
Assoc S-J,
N/S =
count
%
count
%
count
%
count
%
count
%
count
%
8.1.4
North Central Hardwood Forests
127
2.1%
8.1.5
Driftless Area
43
0.7%
8.1.6
Southern Michigan/Northern Indiana Drift Plains
3
0.1%
30
0.5%
8.1.7
Northeastern Coastal Zone
2
0.0%
8.1.8
Acadian Plains and Hills
754
12.7%
8.1.10
Erie Drift Plain
17
0.7%
8.2.1
Southeastern Wisconsin Till Plains
25
0.4%
8.2.2
Huron/Erie Lake Plains
2
0.0%
8.2.3
Central Corn Belt Plains
73
3.1%
8.2.4
Eastern Corn Belt Plains
139
5.8%
8.3.1
Northern Piedmont
8
0.3%
1
0.0%
8.3.2
Interior River Valleys and Hills
231
9.7%
33
1.3%
8.3.3
Interior Plateau
281
11.8%
2
0.1%
1
0.0%
8.3.4
Piedmont
2
0.1%
34
1.0%
56
0.7%
8.3.5
Southeastern Plains
72
3.0%
1848
56.6%
686
26.3%
3615
45.6%
8.3.6
Mississippi Valley Loess Plains
5
0.2%
11
0.3%
59
2.3%
5
0.1%
8.3.7
South Central Plains
81
3.4%
188
5.8%
147
5.6%
43
0.5%
8.3.8
East Central Texas Plains
47
2.0%
8.4.1
Ridge and Valley
49
2.1%
2
0.1%
1
0.0%
8.4.2
Central Appalachians
8.4.3
Western Allegheny Plateau
57
2.4%
4
0.1%
8.4.4
Blue Ridge
5B-Attachment 2A-14
-------
NA L3
CODE
US_L3NAME
tanoak
Median N=4
Assoc N-U
N/S = 0.57
Osage-orange
Median S=5
Assoc S-J,
N/S =0.36
sweetbay
Median N=7
Assoc N-U
N/S =0.34
water tupelo
Median S=8
Assoc S-J,
N/S =0.5
swamp tupelo
Median N=7
Assoc N-U
N/S =0.47
white spruce
Median S=4
Assoc S-J,
N/S =
count
%
count
%
count
%
count
%
count
%
count
%
8.4.5
Ozark Highlands
105
4.4%
8.4.6
Boston Mountains
1
0.0%
8.4.7
Arkansas Valley
16
0.7%
2
0.0%
8.4.8
Ouachita Mountains
24
1.0%
8.4.9
Southwestern Appalachians
2
0.1%
8.5.1
Middle Atlantic Coastal Plain
170
5.2%
540
20.7%
1491
18.8%
8.5.2
Mississippi Alluvial Plain
682
26.2%
24
0.3%
8.5.3
Southern Coastal Plain
993
30.4%
391
15.0%
2697
34.0%
8.5.4
Atlantic Coastal Pine Barrens
10
0.3%
9.2.1
Northern Glaciated Plains
9.2.2
Lake Agassiz Plain
4
0.1%
9.2.3
Western Corn Belt Plains
141
5.9%
12
0.2%
9.2.4
Central Irregular Plains
758
31.8%
9.3.1
Northwestern Glaciated Plains
9.3.3
Northwestern Great Plains
9.3.4
Nebraska Sand Hills
9.4.1
High Plains
9.4.2
Central Great Plains
88
3.7%
9.4.3
Southwestern Tablelands
7
0.3%
9.4.4
Flint Hills
154
6.5%
9.4.5
Cross Timbers
22
0.9%
9.4.6
Edwards Plateau
5B-Attachment 2A-15
-------
NA L3
CODE
US_L3NAME
tanoak
Median N=4
Assoc N-U
N/S = 0.57
Osage-orange
Median S=5
Assoc S-J,
N/S =0.36
sweetbay
Median N=7
Assoc N-U
N/S =0.34
water tupelo
Median S=8
Assoc S-J,
N/S =0.5
swamp tupelo
Median N=7
Assoc N-U
N/S =0.47
white spruce
Median S=4
Assoc S-J,
N/S =
count
%
count
%
count
%
count
%
count
%
count
%
9.4.7
Texas Blackland Prairies
1
0.0%
9.5.1
Western Gulf Coastal Plain
2
0.1%
1
0.0%
65
2.5%
9.6.1
Southern Texas Plains
10.1.2
Columbia Plateau
10.1.3
Northern Basin and Range
10.1.4
Wyoming Basin
10.1.5
Central Basin and Range
10.1.6
Colorado Plateaus
10.1.7
Arizona/New Mexico Plateau
10.1.8
Snake River Plain
10.2.1
Mojave Basin and Range
10.2.2
Sonoran Basin and Range
10.2.10
Chihuahuan Deserts
11.1.1
Southern and Central California Chaparral and
Oak Woodlands
55
1.8%
11.1.2
Central California Valley
11.1.3
Southern California Mountains
12.1.1
Madrean Archipelago
13.1.1
Arizona/New Mexico Mountains
15.4.1
Southern Florida Coastal Plain
6
0.2%
1
0.0%
Total Tree Count
3009
2384
3263
2607
7936
5935
5B-Attachment 2A-16
-------
NA L3
CODE
US_L3NAME
shortleaf pine
Median S=6
Assoc S-J,
N/S =0.16
slash pine
Median S=5
Assoc S-J,
N/S =0.46
singleleaf
pinyon
Median N=3
Assoc N-|
N/S =0.58
longleaf pine
Median N=8
Assoc N-U
N/S = 0.45
red pine
Median N=8, S=5
Assoc N-f, S-J,
N/S =0.53
pitch pine
Median N=10
Assoc N-U
N/S = 0.66
count
%
count
%
count
%
count
%
count
%
count
%
5.2.1
Northern Lakes and Forests
5823
65.3%
5.2.2
Northern Minnesota Wetlands
192
2.2%
5.3.1
Northeastern Highlands
151
1.7%
81
3.1%
5.3.3
North Central Appalachians
35
0.4%
37
1.4%
6.2.3
Northern Rockies
6.2.4
Canadian Rockies
6.2.5
North Cascades
6.2.7
Cascades
6.2.8
Eastern Cascades Slopes and Foothills
6.2.9
Blue Mountains
6.2.10
Middle Rockies
6.2.11
Klamath Mountains
6.2.12
Sierra Nevada
109
3.0%
6.2.13
Wasatch and Uinta Mountains
11
0.3%
6.2.14
Southern Rockies
6.2.15
Idaho Batholith
7.1.7
Puget Lowland
7.1.8
Coast Range
7.1.9
Willamette Valley
8.1.1
Eastern Great Lakes Lowlands
66
0.7%
6
0.2%
8.1.3
Northern Allegheny Plateau
120
1.3%
2
0.1%
5B-Attachment 2A-17
-------
NA L3
CODE
US L3NAME
shortleaf pine
Median S=6
Assoc S-J,
N/S =0.16
slash pine
Median S=5
Assoc S-J,
N/S =0.46
singleleaf
pinyon
Median N=3
Assoc N-|
N/S =0.58
longleaf pine
Median N=8
Assoc N-U
N/S = 0.45
red pine
Median N=8, S=5
Assoc N-f, S-J,
N/S =0.53
pitch pine
Median N=10
Assoc N-U
N/S = 0.66
count
%
count
%
count
%
count
%
count
%
count
%
8.1.4
North Central Hardwood Forests
1456
16.3%
8.1.5
Driftless Area
327
3.7%
8.1.6
Southern Michigan/Northern Indiana Drift Plains
367
4.1%
1
0.0%
8.1.7
Northeastern Coastal Zone
7
0.1%
88
3.4%
8.1.8
Acadian Plains and Hills
159
1.8%
3
0.1%
8.1.10
Erie Drift Plain
12
0.1%
8.2.1
Southeastern Wisconsin Till Plains
80
0.9%
8.2.2
Huron/Erie Lake Plains
51
0.6%
8.2.3
Central Corn Belt Plains
5
0.1%
8.2.4
Eastern Corn Belt Plains
6
0.1%
8.3.1
Northern Piedmont
7
0.1%
8.3.2
Interior River Valleys and Hills
19
0.1%
21
0.2%
2
0.1%
8.3.3
Interior Plateau
191
1.4%
2
0.0%
5
0.2%
8.3.4
Piedmont
2037
15.3%
16
0.2%
203
4.4%
17
0.7%
8.3.5
Southeastern Plains
1451
10.9%
3418
34.4%
2729
58.9%
1
0.0%
8.3.6
Mississippi Valley Loess Plains
94
0.7%
2
0.0%
1
0.0%
8.3.7
South Central Plains
1336
10.1%
190
1.9%
244
5.3%
8.3.8
East Central Texas Plains
23
0.2%
6
0.1%
8.4.1
Ridge and Valley
291
2.2%
87
1.9%
6
0.1%
363
14.1%
8.4.2
Central Appalachians
23
0.2%
11
0.1%
50
1.9%
8.4.3
Western Allegheny Plateau
39
0.3%
18
0.2%
81
3.1%
8.4.4
Blue Ridge
275
2.1%
210
8.1%
5B-Attachment 2A-18
-------
NA L3
CODE
US L3NAME
shortleaf pine
Median S=6
Assoc S-J,
N/S =0.16
slash pine
Median S=5
Assoc S-J,
N/S =0.46
singleleaf
pinyon
Median N=3
Assoc N-|
N/S =0.58
longleaf pine
Median N=8
Assoc N-U
N/S = 0.45
red pine
Median N=8, S=5
Assoc N-f, S-J,
N/S =0.53
pitch pine
Median N=10
Assoc N-U
N/S = 0.66
count
%
count
%
count
%
count
%
count
%
count
%
8.4.5
Ozark Highlands
2400
18.1%
8.4.6
Boston Mountains
567
4.3%
8.4.7
Arkansas Valley
1059
8.0%
8.4.8
Ouachita Mountains
3137
23.6%
8.4.9
Southwestern Appalachians
251
1.9%
9
0.2%
9
0.3%
8.5.1
Middle Atlantic Coastal Plain
25
0.2%
105
1.1%
301
6.5%
33
1.3%
8.5.2
Mississippi Alluvial Plain
11
0.1%
8.5.3
Southern Coastal Plain
2
0.0%
6030
60.6%
1054
22.7%
8.5.4
Atlantic Coastal Pine Barrens
38
0.3%
1589
61.6%
9.2.1
Northern Glaciated Plains
9.2.2
Lake Agassiz Plain
9.2.3
Western Corn Belt Plains
9.2.4
Central Irregular Plains
2
0.0%
9.3.1
Northwestern Glaciated Plains
9.3.3
Northwestern Great Plains
9.3.4
Nebraska Sand Hills
9.4.1
High Plains
9.4.2
Central Great Plains
9.4.3
Southwestern Tablelands
9.4.4
Flint Hills
9.4.5
Cross Timbers
2
0.0%
9.4.6
Edwards Plateau
5B-Attachment 2A-19
-------
NA L3
CODE
US L3NAME
shortleaf pine
Median S=6
Assoc S-J,
N/S =0.16
slash pine
Median S=5
Assoc S-J,
N/S =0.46
singleleaf
pinyon
Median N=3
Assoc N-|
N/S =0.58
longleaf pine
Median N=8
Assoc N-U
N/S = 0.45
red pine
Median N=8, S=5
Assoc N-f, S-J,
N/S =0.53
pitch pine
Median N=10
Assoc N-U
N/S = 0.66
count
%
count
%
count
%
count
%
count
%
count
%
9.4.7
Texas Blackland Prairies
9.5.1
Western Gulf Coastal Plain
7
0.2%
9.6.1
Southern Texas Plains
10.1.2
Columbia Plateau
10.1.3
Northern Basin and Range
42
1.2%
10.1.4
Wyoming Basin
10.1.5
Central Basin and Range
2988
83.5%
10.1.6
Colorado Plateaus
46
1.3%
10.1.7
Arizona/New Mexico Plateau
86
2.4%
10.1.8
Snake River Plain
10.2.1
Mojave Basin and Range
153
4.3%
10.2.2
Sonoran Basin and Range
10.2.10
Chihuahuan Deserts
11.1.1
Southern and Central California Chaparral and
Oak Woodlands
11.1.2
Central California Valley
11.1.3
Southern California Mountains
127
3.5%
12.1.1
Madrean Archipelago
3
0.1%
13.1.1
Arizona/New Mexico Mountains
14
0.4%
15.4.1
Southern Florida Coastal Plain
178
1.8%
Total Tree Count
13278
9945
3579
4635
8917
2578
5B-Attachment 2A-20
-------
NA L3
CODE
US_L3NAME
eastern white
pine
Median N=8, S=6
Assoc N-f, S-J,
N/S = 0.59
loblolly pine
Median S=7
Assoc S-J,
N/S =0.32
bigtooth aspen
Median S=6
Assoc S-J,
N/S =0.57
quaking
aspen
Median N=7
Assoc N-U
N/S =0.6
black cherry
Median N=11
Assoc N-U
N/S =0.33
Douglas-fir
Median N=3, S=1
Assoc N-f, S-J,
N/S = 0.65
count
%
count
%
count
%
count
%
count
%
count
%
5.2.1
Northern Lakes and Forests
3921
19.2%
6123
61.0%
23006
55.1%
1726
8.4%
5.2.2
Northern Minnesota Wetlands
34
0.2%
13
0.1%
2488
6.0%
5.3.1
Northeastern Highlands
3744
18.3%
397
4.0%
961
2.3%
1284
6.3%
5.3.3
North Central Appalachians
525
2.6%
89
0.9%
136
0.3%
1260
6.2%
6.2.3
Northern Rockies
44
0.1%
4096
10.4%
6.2.4
Canadian Rockies
74
0.2%
627
1.6%
6.2.5
North Cascades
2101
5.3%
6.2.7
Cascades
8882
22.6%
6.2.8
Eastern Cascades Slopes and Foothills
20
0.0%
1394
3.5%
6.2.9
Blue Mountains
6
0.0%
2946
7.5%
6.2.10
Middle Rockies
264
0.6%
3404
8.6%
6.2.11
Klamath Mountains
3
0.0%
5771
14.7%
6.2.12
Sierra Nevada
21
0.1%
880
2.2%
6.2.13
Wasatch and Uinta Mountains
2195
5.3%
653
1.7%
6.2.14
Southern Rockies
3606
8.6%
1841
4.7%
6.2.15
Idaho Batholith
3
0.0%
1294
3.3%
7.1.7
Puget Lowland
335
0.9%
7.1.8
Coast Range
3526
9.0%
7.1.9
Willamette Valley
155
0.4%
8.1.1
Eastern Great Lakes Lowlands
515
2.5%
71
0.7%
266
0.6%
344
1.7%
8.1.3
Northern Allegheny Plateau
769
3.8%
138
1.4%
340
0.8%
697
3.4%
8
0.0%
5B-Attachment 2A-21
-------
NA L3
CODE
US L3NAME
eastern white
pine
Median N=8, S=6
Assoc N-f, S-J,
N/S = 0.59
loblolly pine
Median S=7
Assoc S-J,
N/S =0.32
bigtooth aspen
Median S=6
Assoc S-J,
N/S =0.57
quaking
aspen
Median N=7
Assoc N-U
N/S =0.6
black cherry
Median N=11
Assoc N-U
N/S =0.33
Douglas-fir
Median N=3, S=1
Assoc N-f, S-J,
N/S = 0.65
count
%
count
%
count
%
count
%
count
%
count
%
8.1.4
North Central Hardwood Forests
1587
7.8%
935
9.3%
3258
7.8%
608
3.0%
8.1.5
Driftless Area
379
1.9%
425
4.2%
383
0.9%
774
3.8%
8.1.6
Southern Michigan/Northern Indiana Drift Plains
320
1.6%
4
0.0%
308
3.1%
250
0.6%
1233
6.0%
1
0.0%
8.1.7
Northeastern Coastal Zone
2299
11.2%
124
1.2%
152
0.4%
244
1.2%
8.1.8
Acadian Plains and Hills
2113
10.3%
513
5.1%
970
2.3%
136
0.7%
8.1.10
Erie Drift Plain
25
0.1%
77
0.8%
143
0.3%
727
3.6%
8.2.1
Southeastern Wisconsin Till Plains
129
0.6%
22
0.2%
233
0.6%
442
2.2%
8.2.2
Huron/Erie Lake Plains
103
0.5%
177
1.8%
308
0.7%
133
0.7%
8.2.3
Central Corn Belt Plains
23
0.1%
16
0.0%
308
1.5%
8.2.4
Eastern Corn Belt Plains
45
0.2%
15
0.1%
9
0.0%
510
2.5%
8.3.1
Northern Piedmont
55
0.3%
30
0.0%
14
0.1%
6
0.0%
208
1.0%
2
0.0%
8.3.2
Interior River Valleys and Hills
14
0.1%
34
0.1%
6
0.1%
485
2.4%
8.3.3
Interior Plateau
91
0.4%
514
0.9%
21
0.2%
803
3.9%
8.3.4
Piedmont
353
1.7%
13499
22.4%
7
0.1%
781
3.8%
8.3.5
Southeastern Plains
1
0.0%
20564
34.1%
5
0.0%
929
4.5%
8.3.6
Mississippi Valley Loess Plains
1302
2.2%
217
1.1%
8.3.7
South Central Plains
12048
20.0%
120
0.6%
8.3.8
East Central Texas Plains
120
0.2%
4
0.0%
8.4.1
Ridge and Valley
1155
5.6%
1343
2.2%
81
0.8%
17
0.0%
1179
5.8%
1
0.0%
8.4.2
Central Appalachians
198
1.0%
4
0.0%
77
0.8%
19
0.0%
1189
5.8%
8.4.3
Western Allegheny Plateau
340
1.7%
98
0.2%
398
4.0%
90
0.2%
2285
11.2%
8.4.4
Blue Ridge
1531
7.5%
217
0.4%
1
0.0%
334
1.6%
5B-Attachment 2A-22
-------
NA L3
CODE
US L3NAME
eastern white
pine
Median N=8, S=6
Assoc N-f, S-J,
N/S = 0.59
loblolly pine
Median S=7
Assoc S-J,
N/S =0.32
bigtooth aspen
Median S=6
Assoc S-J,
N/S =0.57
quaking
aspen
Median N=7
Assoc N-U
N/S =0.6
black cherry
Median N=11
Assoc N-U
N/S =0.33
Douglas-fir
Median N=3, S=1
Assoc N-f, S-J,
N/S = 0.65
count
%
count
%
count
%
count
%
count
%
count
%
8.4.5
Ozark Highlands
20
0.0%
392
1.9%
8.4.6
Boston Mountains
48
0.1%
80
0.4%
8.4.7
Arkansas Valley
170
0.3%
62
0.3%
8.4.8
Ouachita Mountains
666
1.1%
83
0.4%
8.4.9
Southwestern Appalachians
112
0.5%
1288
2.1%
211
1.0%
8.5.1
Middle Atlantic Coastal Plain
6065
10.0%
1
0.0%
192
0.9%
8.5.2
Mississippi Alluvial Plain
87
0.1%
29
0.1%
8.5.3
Southern Coastal Plain
1960
3.2%
64
0.3%
8.5.4
Atlantic Coastal Pine Barrens
93
0.5%
2
0.0%
2
0.0%
26
0.1%
9.2.1
Northern Glaciated Plains
301
0.7%
9.2.2
Lake Agassiz Plain
1548
3.7%
2
0.0%
9.2.3
Western Corn Belt Plains
1
0.0%
71
0.2%
180
0.9%
9.2.4
Central Irregular Plains
159
0.8%
9.3.1
Northwestern Glaciated Plains
9
0.0%
9.3.3
Northwestern Great Plains
24
0.1%
1
0.0%
190
0.5%
9.3.4
Nebraska Sand Hills
9.4.1
High Plains
9.4.2
Central Great Plains
9.4.3
Southwestern Tablelands
9.4.4
Flint Hills
9.4.5
Cross Timbers
2
0.0%
9.4.6
Edwards Plateau
5B-Attachment 2A-23
-------
NA L3
CODE
US L3NAME
eastern white
pine
Median N=8, S=6
Assoc N-|, S-J,
N/S = 0.59
loblolly pine
Median S=7
Assoc S-J,
N/S =0.32
bigtooth aspen
Median S=6
Assoc S-J,
N/S =0.57
quaking
aspen
Median N=7
Assoc N-U
N/S =0.6
black cherry
Median N=11
Assoc N-U
N/S =0.33
Douglas-fir
Median N=3, S=1
Assoc N-f, S-J,
N/S = 0.65
count
%
count
%
count
%
count
%
count
%
count
%
9.4.7
Texas Blackland Prairies
54
0.1%
9.5.1
Western Gulf Coastal Plain
237
0.4%
3
0.0%
9.6.1
Southern Texas Plains
10.1.2
Columbia Plateau
29
0.1%
102
0.3%
10.1.3
Northern Basin and Range
80
0.2%
102
0.3%
10.1.4
Wyoming Basin
25
0.1%
10.1.5
Central Basin and Range
44
0.1%
21
0.1%
10.1.6
Colorado Plateaus
187
0.4%
314
0.8%
10.1.7
Arizona/New Mexico Plateau
14
0.0%
10.1.8
Snake River Plain
24
0.1%
10.2.1
Mojave Basin and Range
10.2.2
Sonoran Basin and Range
10.2.10
Chihuahuan Deserts
11.1.1
Southern and Central California Chaparral and
Oak Woodlands
173
0.4%
11.1.2
Central California Valley
11.1.3
Southern California Mountains
12.1.1
Madrean Archipelago
20
0.1%
13.1.1
Arizona/New Mexico Mountains
144
0.3%
486
1.2%
15.4.1
Southern Florida Coastal Plain
Total Tree Count
20474
60374
10041
41748
20446
39364
5B-Attachment 2A-24
-------
NA L3
CODE
US_L3NAME
scarlet oak
Median N=10
Assoc N-U
N/S = 0.37
northern pin
oak
Median
N=10,S=5
Assoc N-|, S-J,
N/S =0.41
southern red
oak
Median N=9
Assoc N-U
N/S =0.36
bur oak
Median N=9
Assoc N-J,
N/S = 0.59
water oak
Median N=8, S=7
Assoc N-U, S-J,
N/S =0.26
chestnut oak
Median N=9
Assoc N-U
N/S = 0.45
count
%
count
%
count
%
count
%
count
%
count
%
5.2.1
Northern Lakes and Forests
1942
53.7%
2075
28.9%
5.2.2
Northern Minnesota Wetlands
1
0.0%
128
1.8%
5.3.1
Northeastern Highlands
104
1.1%
2
0.0%
349
1.7%
5.3.3
North Central Appalachians
228
2.5%
1067
5.2%
6.2.3
Northern Rockies
6.2.4
Canadian Rockies
6.2.5
North Cascades
6.2.7
Cascades
6.2.8
Eastern Cascades Slopes and Foothills
6.2.9
Blue Mountains
6.2.10
Middle Rockies
128
1.8%
6.2.11
Klamath Mountains
6.2.12
Sierra Nevada
6.2.13
Wasatch and Uinta Mountains
6.2.14
Southern Rockies
6.2.15
Idaho Batholith
7.1.7
Puget Lowland
7.1.8
Coast Range
7.1.9
Willamette Valley
8.1.1
Eastern Great Lakes Lowlands
1
0.0%
31
0.4%
3
0.0%
5B-Attachment 2A-25
-------
NA L3
CODE
US_L3NAME
scarlet oak
Median N=10
Assoc N-U
N/S = 0.37
northern pin
oak
Median
N=10,S=5
Assoc N-|, S-J,
N/S =0.41
southern red
oak
Median N=9
Assoc N-U
N/S =0.36
bur oak
Median N=9
Assoc N-J,
N/S = 0.59
water oak
Median N=8, S=7
Assoc N-U, S-J,
N/S =0.26
chestnut oak
Median N=9
Assoc N-U
N/S = 0.45
count
%
count
%
count
%
count
%
count
%
count
%
8.1.3
Northern Allegheny Plateau
20
0.2%
3
0.0%
216
1.0%
8.1.4
North Central Hardwood Forests
1158
32.0%
1503
20.9%
8.1.5
Driftless Area
220
6.1%
713
9.9%
8.1.6
Southern Michigan/Northern Indiana Drift Plains
6
0.1%
124
3.4%
41
0.6%
8.1.7
Northeastern Coastal Zone
567
6.2%
8
0.1%
174
0.8%
8.1.8
Acadian Plains and Hills
8.1.10
Erie Drift Plain
4
0.0%
8
0.1%
5
0.0%
8.2.1
Southeastern Wisconsin Till Plains
65
1.8%
129
1.8%
8.2.2
Huron/Erie Lake Plains
3
0.0%
65
1.8%
42
0.6%
8.2.3
Central Corn Belt Plains
2
0.1%
38
0.5%
8.2.4
Eastern Corn Belt Plains
4
0.0%
23
0.3%
4
0.0%
8.3.1
Northern Piedmont
56
0.6%
75
1.0%
331
1.6%
8.3.2
Interior River Valleys and Hills
64
0.7%
50
0.7%
43
0.6%
80
0.4%
8.3.3
Interior Plateau
476
5.2%
404
5.4%
8
0.1%
59
0.5%
1010
4.9%
8.3.4
Piedmont
786
8.6%
1431
19.1%
1374
11.1%
1180
5.7%
8.3.5
Southeastern Plains
263
2.9%
1906
25.5%
5531
44.8%
231
1.1%
8.3.6
Mississippi Valley Loess Plains
8
0.1%
217
2.9%
1
0.0%
454
3.7%
8.3.7
South Central Plains
1
0.0%
1292
17.3%
2058
16.7%
8.3.8
East Central Texas Plains
152
2.0%
1
0.0%
145
1.2%
8.4.1
Ridge and Valley
1507
16.4%
1
0.0%
320
4.3%
1
0.0%
123
1.0%
7106
34.3%
8.4.2
Central Appalachians
745
8.1%
31
0.4%
2161
10.4%
8.4.3
Western Allegheny Plateau
479
5.2%
11
0.1%
1127
5.4%
5B-Attachment 2A-26
-------
NA L3
CODE
US_L3NAME
scarlet oak
Median N=10
Assoc N-U
N/S = 0.37
northern pin
oak
Median
N=10,S=5
Assoc N-|, S-J,
N/S =0.41
southern red
oak
Median N=9
Assoc N-U
N/S =0.36
bur oak
Median N=9
Assoc N-J,
N/S = 0.59
water oak
Median N=8, S=7
Assoc N-U, S-J,
N/S =0.26
chestnut oak
Median N=9
Assoc N-U
N/S = 0.45
count
%
count
%
count
%
count
%
count
%
count
%
8.4.4
Blue Ridge
1507
16.4%
225
3.0%
20
0.2%
4206
20.3%
8.4.5
Ozark Highlands
1365
14.9%
494
6.6%
12
0.2%
1
0.0%
8.4.6
Boston Mountains
70
0.9%
6
0.1%
8.4.7
Arkansas Valley
126
1.7%
1
0.0%
109
0.9%
8.4.8
Ouachita Mountains
107
1.4%
62
0.5%
8.4.9
Southwestern Appalachians
502
5.5%
266
3.6%
72
0.6%
1358
6.6%
8.5.1
Middle Atlantic Coastal Plain
75
0.8%
190
2.5%
991
8.0%
24
0.1%
8.5.2
Mississippi Alluvial Plain
1
0.0%
58
0.8%
209
1.7%
8.5.3
Southern Coastal Plain
1
0.0%
17
0.2%
1039
8.4%
8.5.4
Atlantic Coastal Pine Barrens
394
4.3%
21
0.3%
79
0.4%
9.2.1
Northern Glaciated Plains
340
4.7%
9.2.2
Lake Agassiz Plain
527
7.3%
9.2.3
Western Corn Belt Plains
29
0.8%
486
6.8%
9.2.4
Central Irregular Plains
1
0.0%
8
0.2%
1
0.0%
151
2.1%
9.3.1
Northwestern Glaciated Plains
212
3.0%
9.3.3
Northwestern Great Plains
421
5.9%
9.3.4
Nebraska Sand Hills
16
0.2%
9.4.1
High Plains
3
0.0%
9.4.2
Central Great Plains
35
0.5%
9.4.3
Southwestern Tablelands
9.4.4
Flint Hills
43
0.6%
9.4.5
Cross Timbers
1
0.0%
5B-Attachment 2A-27
-------
NA L3
CODE
US_L3NAME
scarlet oak
Median N=10
Assoc N-U
N/S = 0.37
northern pin
oak
Median
N=10,S=5
Assoc N-|, S-J,
N/S =0.41
southern red
oak
Median N=9
Assoc N-U
N/S =0.36
bur oak
Median N=9
Assoc N-J,
N/S = 0.59
water oak
Median N=8, S=7
Assoc N-U, S-J,
N/S =0.26
chestnut oak
Median N=9
Assoc N-U
N/S = 0.45
count
%
count
%
count
%
count
%
count
%
count
%
9.4.6
Edwards Plateau
9.4.7
Texas Blackland Prairies
1
0.0%
3
0.0%
9.5.1
Western Gulf Coastal Plain
14
0.2%
102
0.8%
9.6.1
Southern Texas Plains
10.1.2
Columbia Plateau
10.1.3
Northern Basin and Range
10.1.4
Wyoming Basin
10.1.5
Central Basin and Range
10.1.6
Colorado Plateaus
10.1.7
Arizona/New Mexico Plateau
10.1.8
Snake River Plain
10.2.1
Mojave Basin and Range
10.2.2
Sonoran Basin and Range
10.2.10
Chihuahuan Deserts
11.1.1
Southern and Central California Chaparral and
Oak Woodlands
11.1.2
Central California Valley
11.1.3
Southern California Mountains
12.1.1
Madrean Archipelago
13.1.1
Arizona/New Mexico Mountains
15.4.1
Southern Florida Coastal Plain
Total Tree Count
9167
3616
7479
7180
12352
20711
5B-Attachment 2A-28
-------
northern red
oak
Median N=10
Assoc N-|
N/S = 0.42
black oak
Median N=11
black locust
Median N=11,
S=11
Assoc N-|, S-J,
N/S =0.18
black willow
Median
N=10,S=7
Assoc N-U.S-J,
N/S = 0.29
sassafras
Median N=11
pondcypress
Median N=7
NA L3
CODE
Assoc N-|
N/S =0.13
Assoc N-|
N/S =0.28
Assoc N-|
N/S = 0.71
US_L3NAME
count
%
count
%
count
%
count
%
count
%
count
%
5.2.1
Northern Lakes and Forests
5778
20.2%
690
3.7%
28
0.7%
28
1.4%
22
0.4%
5.2.2
Northern Minnesota Wetlands
2
0.0%
5.3.1
Northeastern Highlands
2993
10.5%
152
0.8%
28
0.7%
3
0.1%
25
0.5%
5.3.3
North Central Appalachians
1020
3.6%
158
0.9%
6
0.2%
3
0.1%
221
4.4%
6.2.3
Northern Rockies
6.2.4
Canadian Rockies
6.2.5
North Cascades
6.2.7
Cascades
6.2.8
Eastern Cascades Slopes and Foothills
6.2.9
Blue Mountains
6.2.10
Middle Rockies
6.2.11
Klamath Mountains
6.2.12
Sierra Nevada
6.2.13
Wasatch and Uinta Mountains
6.2.14
Southern Rockies
6.2.15
Idaho Batholith
7.1.7
Puget Lowland
7.1.8
Coast Range
7.1.9
Willamette Valley
8.1.1
Eastern Great Lakes Lowlands
223
0.8%
23
0.1%
26
0.7%
40
2.0%
2
0.0%
8.1.3
Northern Allegheny Plateau
822
2.9%
111
0.6%
51
1.3%
28
1.4%
8
0.2%
5B-Attachment 2A-29
-------
northern red
oak
Median N=10
Assoc N-f
N/S = 0.42
black oak
Median N=11
black locust
Median N=11,
S=11
Assoc N-|, S-J,
N/S =0.18
black willow
Median
N=10,S=7
Assoc N-U.S-J,
N/S = 0.29
sassafras
Median N=11
pondcypress
Median N=7
NA L3
CODE
Assoc N-f
N/S =0.13
Assoc N-|
N/S =0.28
Assoc N-|
N/S = 0.71
US L3NAME
count
%
count
%
count
%
count
%
count
%
count
%
8.1.4
North Central Hardwood Forests
1459
5.1%
827
4.5%
118
3.1%
74
3.6%
8.1.5
Driftless Area
1474
5.2%
636
3.4%
110
2.9%
25
1.2%
8.1.6
Southern Michigan/Northern Indiana Drift Plains
630
2.2%
684
3.7%
108
2.8%
68
3.3%
412
8.3%
8.1.7
Northeastern Coastal Zone
1657
5.8%
794
4.3%
52
1.4%
6
0.3%
41
0.8%
8.1.8
Acadian Plains and Hills
846
3.0%
8
0.0%
8.1.10
Erie Drift Plain
180
0.6%
46
0.2%
62
1.6%
29
1.4%
49
1.0%
8.2.1
Southeastern Wisconsin Till Plains
141
0.5%
48
0.3%
88
2.3%
71
3.5%
8.2.2
Huron/Erie Lake Plains
175
0.6%
22
0.1%
6
0.2%
18
0.9%
58
1.2%
8.2.3
Central Corn Belt Plains
60
0.2%
81
0.4%
74
1.9%
20
1.0%
39
0.8%
8.2.4
Eastern Corn Belt Plains
200
0.7%
84
0.5%
148
3.9%
44
2.1%
115
2.3%
8.3.1
Northern Piedmont
150
0.5%
122
0.7%
68
1.8%
12
0.6%
76
1.5%
8.3.2
Interior River Valleys and Hills
461
1.6%
686
3.7%
149
3.9%
170
8.3%
496
10.0%
8.3.3
Interior Plateau
664
2.3%
823
4.4%
338
8.8%
34
1.7%
785
15.8%
8.3.4
Piedmont
773
2.7%
635
3.4%
68
1.8%
61
3.0%
52
1.0%
8.3.5
Southeastern Plains
165
0.6%
350
1.9%
24
0.6%
274
13.4%
111
2.2%
487
14.1%
8.3.6
Mississippi Valley Loess Plains
39
0.1%
65
0.4%
36
0.9%
105
5.1%
88
1.8%
8.3.7
South Central Plains
6
0.0%
60
0.3%
7
0.2%
114
5.6%
85
1.7%
4
0.1%
8.3.8
East Central Texas Plains
4
0.0%
2
0.1%
16
0.8%
15
0.3%
8.4.1
Ridge and Valley
2335
8.2%
1371
7.4%
548
14.3%
8
0.4%
527
10.6%
8.4.2
Central Appalachians
1114
3.9%
573
3.1%
397
10.4%
9
0.4%
381
7.7%
8.4.3
Western Allegheny Plateau
793
2.8%
843
4.5%
437
11.4%
20
1.0%
668
13.4%
8.4.4
Blue Ridge
1240
4.3%
501
2.7%
428
11.2%
7
0.3%
161
3.2%
5B-Attachment 2A-30
-------
northern red
oak
Median N=10
Assoc N-f
N/S = 0.42
black oak
Median N=11
black locust
Median N=11,
S=11
Assoc N-|, S-J,
N/S =0.18
black willow
Median
N=10,S=7
Assoc N-U.S-J,
N/S = 0.29
sassafras
Median N=11
pondcypress
Median N=7
NA L3
CODE
Assoc N-|
N/S =0.13
Assoc N-|
N/S =0.28
Assoc N-|
N/S = 0.71
US L3NAME
count
%
count
%
count
%
count
%
count
%
count
%
8.4.5
Ozark Highlands
1331
4.7%
6319
34.0%
20
0.5%
9
0.4%
269
5.4%
8.4.6
Boston Mountains
631
2.2%
418
2.3%
51
1.3%
29
0.6%
8.4.7
Arkansas Valley
147
0.5%
112
0.6%
4
0.1%
22
1.1%
4
0.1%
8.4.8
Ouachita Mountains
364
1.3%
153
0.8%
4
0.1%
0
0.0%
1
0.0%
8.4.9
Southwestern Appalachians
273
1.0%
442
2.4%
38
1.0%
5
0.2%
110
2.2%
8.5.1
Middle Atlantic Coastal Plain
18
0.1%
67
0.4%
24
0.6%
71
3.5%
45
0.9%
109
3.2%
8.5.2
Mississippi Alluvial Plain
7
0.0%
11
0.1%
9
0.2%
412
20.1%
12
0.2%
37
1.1%
8.5.3
Southern Coastal Plain
2
0.0%
34
1.7%
2281
65.9%
8.5.4
Atlantic Coastal Pine Barrens
13
0.0%
236
1.3%
7
0.2%
10
0.5%
59
1.2%
9.2.1
Northern Glaciated Plains
9.2.2
Lake Agassiz Plain
2
0.0%
1
0.0%
9.2.3
Western Corn Belt Plains
168
0.6%
67
0.4%
53
1.4%
72
3.5%
9.2.4
Central Irregular Plains
198
0.7%
293
1.6%
160
4.2%
72
3.5%
3
0.1%
9.3.1
Northwestern Glaciated Plains
9.3.3
Northwestern Great Plains
9.3.4
Nebraska Sand Hills
2
0.1%
9.4.1
High Plains
1
0.0%
9.4.2
Central Great Plains
42
1.1%
22
1.1%
9.4.3
Southwestern Tablelands
3
0.1%
0
0.0%
9.4.4
Flint Hills
3
0.0%
2
0.1%
9.4.5
Cross Timbers
2
0.0%
42
0.2%
7
0.3%
2
0.0%
9.4.6
Edwards Plateau
5B-Attachment 2A-31
-------
northern red
oak
Median N=10
Assoc N-|
N/S = 0.42
black oak
Median N=11
black locust
Median N=11,
S=11
Assoc N-|, S-J,
N/S =0.18
black willow
Median
N=10,S=7
Assoc N-U.S-J,
N/S = 0.29
sassafras
Median N=11
pondcypress
Median N=7
NA L3
CODE
Assoc N-|
N/S =0.13
Assoc N-|
N/S =0.28
Assoc N-|
N/S = 0.71
US L3NAME
count
%
count
%
count
%
count
%
count
%
count
%
9.4.7
Texas Blackland Prairies
9.5.1
Western Gulf Coastal Plain
20
1.0%
9.6.1
Southern Texas Plains
10.1.2
Columbia Plateau
10.1.3
Northern Basin and Range
10.1.4
Wyoming Basin
10.1.5
Central Basin and Range
10.1.6
Colorado Plateaus
10.1.7
Arizona/New Mexico Plateau
10.1.8
Snake River Plain
10.2.1
Mojave Basin and Range
10.2.2
Sonoran Basin and Range
10.2.10
Chihuahuan Deserts
11.1.1
Southern and Central California Chaparral and
Oak Woodlands
11.1.2
Central California Valley
11.1.3
Southern California Mountains
12.1.1
Madrean Archipelago
13.1.1
Arizona/New Mexico Mountains
15.4.1
Southern Florida Coastal Plain
541
15.6%
Total Tree Count
28557
18559
3822
2049
4971
3459
5B-Attachment 2A-32
-------
NA L3
CODE
US_L3NAME
baldcypress
Median S=6
Assoc S-J,
N/S =0.54
American
basswood
Median N=9, S=5
Assoc N-f, S-J,
N/S =0.4
eastern
hemlock
Median N=8
Assoc N-J,
N/S = 0.78
western
hemlock
Median N=3
Assoc N-U
N/S =0.34
American elm
Median N=11,S=6
Assoc N-|, S-J,
N/S = 0.25
slippery elm
Median S=8
Assoc S-J,
N/S =0.09
count
%
count
%
count
%
count
%
count
%
count
%
5.2.1
Northern Lakes and Forests
5591
44.4%
3098
13.5%
1082
7.6%
26
0.6%
5.2.2
Northern Minnesota Wetlands
139
1.1%
97
0.7%
5.3.1
Northeastern Highlands
200
1.6%
7154
31.3%
162
1.1%
3
0.1%
5.3.3
North Central Appalachians
148
1.2%
1271
5.6%
19
0.1%
2
0.0%
6.2.3
Northern Rockies
705
7.5%
6.2.4
Canadian Rockies
1
0.0%
6.2.5
North Cascades
1747
18.6%
6.2.7
Cascades
4379
46.5%
6.2.8
Eastern Cascades Slopes and Foothills
59
0.6%
6.2.9
Blue Mountains
6.2.10
Middle Rockies
2
0.0%
6.2.11
Klamath Mountains
28
0.3%
6.2.12
Sierra Nevada
6.2.13
Wasatch and Uinta Mountains
6.2.14
Southern Rockies
6.2.15
Idaho Batholith
7.1.7
Puget Lowland
179
1.9%
7.1.8
Coast Range
2307
24.5%
7.1.9
Willamette Valley
10
0.1%
8.1.1
Eastern Great Lakes Lowlands
276
2.2%
923
4.0%
358
2.5%
13
0.3%
8.1.3
Northern Allegheny Plateau
325
2.6%
2010
8.8%
80
0.6%
1
0.0%
5B-Attachment 2A-33
-------
NA L3
CODE
US L3NAME
baldcypress
Median S=6
Assoc S-J,
N/S =0.54
American
basswood
Median N=9, S=5
Assoc N-f, S-J,
N/S =0.4
eastern
hemlock
Median N=8
Assoc N-J,
N/S = 0.78
western
hemlock
Median N=3
Assoc N-U
N/S =0.34
American elm
Median N=11,S=6
Assoc N-|, S-J,
N/S = 0.25
slippery elm
Median S=8
Assoc S-J,
N/S =0.09
count
%
count
%
count
%
count
%
count
%
count
%
8.1.4
North Central Hardwood Forests
1698
13.5%
548
2.4%
1033
7.3%
131
3.2%
8.1.5
Driftless Area
969
7.7%
1512
10.6%
473
11.6%
8.1.6
Southern Michigan/Northern Indiana Drift Plains
352
2.8%
38
0.2%
566
4.0%
56
1.4%
8.1.7
Northeastern Coastal Zone
44
0.3%
1195
5.2%
119
0.8%
1
0.0%
8.1.8
Acadian Plains and Hills
44
0.3%
2657
11.6%
53
0.4%
8.1.10
Erie Drift Plain
107
0.9%
383
1.7%
234
1.6%
45
1.1%
8.2.1
Southeastern Wisconsin Till Plains
288
2.3%
14
0.1%
396
2.8%
54
1.3%
8.2.2
Huron/Erie Lake Plains
183
1.5%
7
0.0%
305
2.1%
28
0.7%
8.2.3
Central Corn Belt Plains
40
0.3%
125
0.9%
39
1.0%
8.2.4
Eastern Corn Belt Plains
232
1.8%
474
3.3%
140
3.4%
8.3.1
Northern Piedmont
13
0.1%
9
0.0%
78
0.5%
20
0.5%
8.3.2
Interior River Valleys and Hills
7
0.2%
77
0.6%
907
6.4%
286
7.0%
8.3.3
Interior Plateau
2
0.1%
100
0.8%
1
0.0%
674
4.7%
437
10.7%
8.3.4
Piedmont
4
0.1%
10
0.1%
26
0.1%
273
1.9%
142
3.5%
8.3.5
Southeastern Plains
549
19.0%
23
0.2%
9
0.0%
371
2.6%
168
4.1%
8.3.6
Mississippi Valley Loess Plains
56
1.9%
1
0.0%
233
1.6%
127
3.1%
8.3.7
South Central Plains
458
15.8%
9
0.1%
287
2.0%
75
1.8%
8.3.8
East Central Texas Plains
4
0.0%
43
0.3%
8.4.1
Ridge and Valley
295
2.3%
966
4.2%
166
1.2%
141
3.4%
8.4.2
Central Appalachians
556
4.4%
908
4.0%
87
0.6%
92
2.3%
8.4.3
Western Allegheny Plateau
154
1.2%
347
1.5%
659
4.6%
498
12.2%
8.4.4
Blue Ridge
209
1.7%
1018
4.5%
16
0.1%
13
0.3%
5B-Attachment 2A-34
-------
NA L3
CODE
US L3NAME
baldcypress
Median S=6
Assoc S-J,
N/S =0.54
American
basswood
Median N=9, S=5
Assoc N-f, S-J,
N/S =0.4
eastern
hemlock
Median N=8
Assoc N-J,
N/S = 0.78
western
hemlock
Median N=3
Assoc N-U
N/S =0.34
American elm
Median N=11,S=6
Assoc N-|, S-J,
N/S = 0.25
slippery elm
Median S=8
Assoc S-J,
N/S =0.09
count
%
count
%
count
%
count
%
count
%
count
%
8.4.5
Ozark Highlands
21
0.2%
687
4.8%
387
9.5%
8.4.6
Boston Mountains
20
0.2%
27
0.2%
35
0.9%
8.4.7
Arkansas Valley
37
0.3%
16
0.4%
8.4.8
Ouachita Mountains
2
0.0%
20
0.1%
13
0.3%
8.4.9
Southwestern Appalachians
54
0.4%
282
1.2%
35
0.2%
21
0.5%
8.5.1
Middle Atlantic Coastal Plain
344
11.9%
0
0.0%
107
0.8%
35
0.9%
8.5.2
Mississippi Alluvial Plain
525
18.2%
2
0.0%
449
3.2%
203
5.0%
8.5.3
Southern Coastal Plain
793
27.4%
1
0.0%
205
1.4%
14
0.3%
8.5.4
Atlantic Coastal Pine Barrens
0
0.0%
1
0.0%
9.2.1
Northern Glaciated Plains
9
0.1%
18
0.1%
9.2.2
Lake Agassiz Plain
102
0.8%
103
0.7%
1
0.0%
9.2.3
Western Corn Belt Plains
232
1.8%
654
4.6%
190
4.6%
9.2.4
Central Irregular Plains
48
0.4%
1001
7.0%
126
3.1%
9.3.1
Northwestern Glaciated Plains
4
0.0%
47
0.3%
2
0.0%
9.3.3
Northwestern Great Plains
63
0.4%
9.3.4
Nebraska Sand Hills
1
0.0%
9.4.1
High Plains
7
0.0%
9.4.2
Central Great Plains
174
1.2%
14
0.3%
9.4.3
Southwestern Tablelands
7
0.0%
9.4.4
Flint Hills
5
0.0%
103
0.7%
10
0.2%
9.4.5
Cross Timbers
32
0.2%
3
0.1%
9.4.6
Edwards Plateau
5B-Attachment 2A-35
-------
NA L3
CODE
US L3NAME
baldcypress
Median S=6
Assoc S-J,
N/S =0.54
American
basswood
Median N=9, S=5
Assoc N-|, S-J,
N/S =0.4
eastern
hemlock
Median N=8
Assoc N-J,
N/S = 0.78
western
hemlock
Median N=3
Assoc N-U
N/S =0.34
American elm
Median N=11,S=6
Assoc N-|, S-J,
N/S = 0.25
slippery elm
Median S=8
Assoc S-J,
N/S =0.09
count
%
count
%
count
%
count
%
count
%
count
%
9.4.7
Texas Blackland Prairies
2
0.0%
9.5.1
Western Gulf Coastal Plain
16
0.6%
20
0.1%
5
0.1%
9.6.1
Southern Texas Plains
10.1.2
Columbia Plateau
10.1.3
Northern Basin and Range
10.1.4
Wyoming Basin
10.1.5
Central Basin and Range
10.1.6
Colorado Plateaus
10.1.7
Arizona/New Mexico Plateau
10.1.8
Snake River Plain
10.2.1
Mojave Basin and Range
10.2.2
Sonoran Basin and Range
10.2.10
Chihuahuan Deserts
11.1.1
Southern and Central California Chaparral and
Oak Woodlands
11.1.2
Central California Valley
11.1.3
Southern California Mountains
12.1.1
Madrean Archipelago
13.1.1
Arizona/New Mexico Mountains
15.4.1
Southern Florida Coastal Plain
138
4.8%
Total Tree Count
2892
12587
22864
9415
14210
4087
5B-Attachment 2A-36
-------
Attachment 2B
Species-specific Sample Distribution across Ecoregions
for Species with Statistically Significant Associations of Survival with N/S
from Horn et al. (2018) Supplemental Information Dataset
Key:
NA L3 = North American Ecoregion, code for level III
US_L3NAME = Name of Ecoregion at level III
See: https://www.epa.gov/eco-research/ecoregions
Median = Tree-specific median S and/orN deposition for the species samples
Assoc = U= unimodal, t=positive, j=negative
N/S = correlation coefficient for N and S deposition values for the species samples
Count = number of species' tree samples assessed in all plots in that ecoregion
% = percent of species' tree samples in that ecoregion
5B-Attachment 2B
-------
NA L3
CODE
US_L3NAME
boxelder
Median S = 6
Assoc S-J,
N/S = 0.13
red maple
Median N=9, S=7
Assoc N-U, S-J,
N/S = 0.59
sugar maple
Median S=8
Assoc S-J,
N/S = 0.67
yellow birch
Median S = 5
Assoc S-J,
N/S = 0.71
sweet birch
Median N=10,S =13
Assoc N-U, S-J,
N/S = 0.57
paper birch
Median N=7, S=4
Assoc N-U, S-J,
NS = 0.42
count
%
count
%
count
%
count
%
count
%
count
%
5.2.1
Northern Lakes and Forests
97
1.3%
26666
22.0%
31512
42.2%
3912
24.0%
12403
50.0%
5.2.2
Northern Minnesota Wetlands
61
0.8%
98
0.1%
77
0.1%
2
0.0%
657
2.6%
5.3.1
Northeastern Highlands
24
0.3%
15529
12.8%
12843
17.2%
7577
46.6%
1471
14.4%
6728
27.1%
5.3.3
North Central Appalachians
5908
4.9%
2186
2.9%
452
2.8%
1477
14.5%
122
0.5%
6.2.3
Northern Rockies
219
0.9%
6.2.4
Canadian Rockies
10
0.0%
6.2.5
North Cascades
2
0.0%
6.2.7
Cascades
6.2.8
Eastern Cascades Slopes and Foothills
6.2.9
Blue Mountains
4
0.0%
6.2.10
Middle Rockies
28
0.1%
6.2.11
Klamath Mountains
6.2.12
Sierra Nevada
6.2.13
Wasatch and Uinta Mountains
6.2.14
Southern Rockies
6.2.15
Idaho Batholith
12
0.0%
7.1.7
Puget Lowland
24
0.1%
7.1.8
Coast Range
7.1.9
Willamette Valley
8.1.1
Eastern Great Lakes Lowlands
111
1.5%
1943
1.6%
1122
1.5%
240
1.5%
56
0.5%
80
0.3%
8.1.3
Northern Allegheny Plateau
10
0.1%
4347
3.6%
3421
4.6%
466
2.9%
626
6.1%
110
0.4%
8.1.4
North Central Hardwood Forests
684
9.1%
4556
3.8%
3346
4.5%
488
3.0%
1249
5.0%
5B-Attachment 2B-1
-------
NA L3
CODE
US_L3NAME
boxelder
Median S = 6
Assoc S-J,
N/S = 0.13
red maple
Median N=9, S=7
Assoc N-U, S-J,
N/S = 0.59
sugar maple
Median S=8
Assoc S-J,
N/S = 0.67
yellow birch
Median S = 5
Assoc S-J,
N/S = 0.71
sweet birch
Median N=10,S =13
Assoc N-U, S-J,
N/S = 0.57
paper birch
Median N=7, S=4
Assoc N-U, S-J,
NS = 0.42
count
%
count
%
count
%
count
%
count
%
count
%
8.1.5
Driftless Area
1095
14.6%
750
0.6%
916
1.2%
32
0.2%
891
3.6%
8.1.6
Southern Michigan/Northern Indiana Drift Plains
142
1.9%
1923
1.6%
631
0.8%
42
0.3%
39
0.2%
8.1.7
Northeastern Coastal Zone
7
0.1%
5239
4.3%
510
0.7%
300
1.8%
1095
10.7%
181
0.7%
8.1.8
Acadian Plains and Hills
5482
4.5%
1267
1.7%
1347
8.3%
1784
7.2%
8.1.10
Erie Drift Plain
8
0.1%
2203
1.8%
984
1.3%
124
0.8%
10
0.1%
8.2.1
Southeastern Wisconsin Till Plains
404
5.4%
198
0.2%
229
0.3%
61
0.4%
72
0.3%
8.2.2
Huron/Erie Lake Plains
103
1.4%
1283
1.1%
117
0.2%
10
0.1%
157
0.6%
8.2.3
Central Corn Belt Plains
103
1.4%
17
0.0%
63
0.1%
8.2.4
Eastern Corn Belt Plains
251
3.3%
462
0.4%
1233
1.6%
2
0.0%
8.3.1
Northern Piedmont
92
1.2%
759
0.6%
99
0.1%
4
0.0%
144
1.4%
8.3.2
Interior River Valleys and Hills
370
4.9%
588
0.5%
1377
1.8%
8.3.3
Interior Plateau
600
8.0%
1367
1.1%
4029
5.4%
8.3.4
Piedmont
195
2.6%
4099
3.4%
14
0.0%
2
0.0%
29
0.3%
8.3.5
Southeastern Plains
207
2.8%
5825
4.8%
47
0.1%
8.3.6
Mississippi Valley Loess Plains
252
3.4%
261
0.2%
74
0.1%
8.3.7
South Central Plains
120
1.6%
1001
0.8%
2
0.0%
8.3.8
East Central Texas Plains
7
0.1%
5
0.0%
8.4.1
Ridge and Valley
131
1.7%
6002
4.9%
1707
2.3%
201
1.2%
2133
20.9%
20
0.1%
8.4.2
Central Appalachians
25
0.3%
5895
4.9%
2534
3.4%
594
3.7%
1347
13.2%
8.4.3
Western Allegheny Plateau
249
3.3%
4725
3.9%
2779
3.7%
17
0.1%
265
2.6%
8.4.4
Blue Ridge
23
0.3%
4545
3.7%
397
0.5%
395
2.4%
1524
14.9%
8.4.5
Ozark Highlands
121
1.6%
219
0.2%
531
0.7%
5B-Attachment 2B-2
-------
NA L3
CODE
US_L3NAME
boxelder
Median S = 6
Assoc S-J,
N/S = 0.13
red maple
Median N=9, S=7
Assoc N-U, S-J,
N/S = 0.59
sugar maple
Median S=8
Assoc S-J,
N/S = 0.67
yellow birch
Median S = 5
Assoc S-J,
N/S = 0.71
sweet birch
Median N=10,S =13
Assoc N-U, S-J,
N/S = 0.57
paper birch
Median N=7, S=4
Assoc N-U, S-J,
NS = 0.42
count
%
count
%
count
%
count
%
count
%
count
%
8.4.6
Boston Mountains
223
0.2%
26
0.0%
8.4.7
Arkansas Valley
36
0.5%
68
0.1%
1
0.0%
8.4.8
Ouachita Mountains
5
0.1%
197
0.2%
8.4.9
Southwestern Appalachians
27
0.4%
1760
1.5%
527
0.7%
2
0.0%
30
0.3%
8.5.1
Middle Atlantic Coastal Plain
23
0.3%
4009
3.3%
8.5.2
Mississippi Alluvial Plain
524
7.0%
716
0.6%
8
0.0%
8.5.3
Southern Coastal Plain
4
0.1%
1984
1.6%
8.5.4
Atlantic Coastal Pine Barrens
1
0.0%
325
0.3%
8
0.1%
9.2.1
Northern Glaciated Plains
157
2.1%
6
0.0%
9.2.2
Lake Agassiz Plain
221
2.9%
3
0.0%
39
8
0.0%
9.2.3
Western Corn Belt Plains
692
9.2%
30
0.0%
78
0.1%
3
0.0%
9
0.0%
9.2.4
Central Irregular Plains
202
2.7%
2
0.0%
34
0.0%
9.3.1
Northwestern Glaciated Plains
21
0.3%
9.3.3
Northwestern Great Plains
14
0.2%
9.3.4
Nebraska Sand Hills
1
0.0%
9.4.1
High Plains
4
0.1%
9.4.2
Central Great Plains
62
0.8%
9.4.3
Southwestern Tablelands
4
0.1%
9.4.4
Flint Hills
8
0.1%
9.4.5
Cross Timbers
4
0.1%
9.4.6
Edwards Plateau
9.4.7
Texas Blackland Prairies
5B-Attachment 2B-3
-------
NA L3
CODE
US_L3NAME
boxelder
Median S = 6
Assoc S-J,
N/S = 0.13
red maple
Median N=9, S=7
Assoc N-U, S-J,
N/S = 0.59
sugar maple
Median S=8
Assoc S-J,
N/S = 0.67
yellow birch
Median S = 5
Assoc S-J,
N/S = 0.71
sweet birch
Median N=10,S =13
Assoc N-U, S-J,
N/S = 0.57
paper birch
Median N=7, S=4
Assoc N-U, S-J,
NS = 0.42
count
%
count
%
count
%
count
%
count
%
count
%
9.5.1
Western Gulf Coastal Plain
11
0.0%
9.6.1
Southern Texas Plains
10.1.2
Columbia Plateau
10.1.3
Northern Basin and Range
10.1.4
Wyoming Basin
10.1.5
Central Basin and Range
10.1.6
Colorado Plateaus
10.1.7
Arizona/New Mexico Plateau
10.1.8
Snake River Plain
10.2.1
Mojave Basin and Range
10.2.2
Sonoran Basin and Range
10.2.10
Chihuahuan Deserts
11.1.1
Southern and Central California Chaparral and
Oak Woodlands
11.1.2
Central California Valley
10
0.1%
11.1.3
Southern California Mountains
12.1.1
Madrean Archipelago
13.1.1
Arizona/New Mexico Mountains
1
0.0%
15.4.1
Southern Florida Coastal Plain
65
0.1%
Total Tree Count
7513
121288
74760
16273
10215
24815
5B-Attachment 2B-4
-------
NA L3
CODE
US L3NAME
American
hornbeam
Median S=7
Assoc S-J,
N/S = 0.26
mockernut
hickory
Median N=10
Assoc N-J,
N/S =0.15
pignut hickory
Median S=10
Assoc S-J,
N/S = 0.4
hackberry
Median S=7
Assoc S-J,
N/S =0.18
American beech
Median N=8, S=7
Assoc N-U, S-J,
N/S =0.76
white ash
Median N=10
Assoc N-U
N/S = 0.53
count
%
count
%
count
%
count
%
count
%
count
%
5.2.1
Northern Lakes and Forests
14
0.4%
1565
6.4%
1415
7.0%
5.2.2
Northern Minnesota Wetlands
1
0.0%
5.3.1
Northeastern Highlands
14
0.4%
30
0.3%
171
1.4%
9630
39.5%
2787
13.8%
5.3.3
North Central Appalachians
51
1.6%
13
0.1%
97
0.8%
1747
7.2%
541
2.7%
6.2.3
Northern Rockies
6.2.4
Canadian Rockies
6.2.5
North Cascades
6.2.7
Cascades
6.2.8
Eastern Cascades Slopes and Foothills
6.2.9
Blue Mountains
6.2.10
Middle Rockies
1
0.0%
6.2.11
Klamath Mountains
6.2.12
Sierra Nevada
6.2.13
Wasatch and Uinta Mountains
6.2.14
Southern Rockies
6.2.15
Idaho Batholith
7.1.7
Puget Lowland
7.1.8
Coast Range
7.1.9
Willamette Valley
8.1.1
Eastern Great Lakes Lowlands
9
0.3%
20
0.2%
4
0.1%
324
1.3%
757
3.7%
8.1.3
Northern Allegheny Plateau
35
1.1%
9
0.1%
97
0.8%
1415
5.8%
1958
9.7%
8.1.4
North Central Hardwood Forests
6
0.2%
49
0.9%
198
0.8%
657
3.2%
5B-Attachment 2B-5
-------
NA L3
CODE
US L3NAME
American
hornbeam
Median S=7
Assoc S-J,
N/S = 0.26
mockernut
hickory
Median N=10
Assoc N-J,
N/S =0.15
pignut hickory
Median S=10
Assoc S-J,
N/S = 0.4
hackberry
Median S=7
Assoc S-J,
N/S =0.18
American beech
Median N=8, S=7
Assoc N-U, S-J,
N/S =0.76
white ash
Median N=10
Assoc N-U
N/S = 0.53
count
%
count
%
count
%
count
%
count
%
count
%
8.1.5
Driftless Area
2
0.1%
251
4.5%
399
2.0%
8.1.6
Southern Michigan/Northern Indiana Drift Plains
12
0.4%
20
0.2%
138
1.1%
39
0.7%
177
0.7%
370
1.8%
8.1.7
Northeastern Coastal Zone
6
0.2%
60
0.5%
253
2.1%
5
0.1%
375
1.5%
503
2.5%
8.1.8
Acadian Plains and Hills
0
0.0%
1672
6.9%
788
3.9%
8.1.10
Erie Drift Plain
14
0.4%
5
0.0%
23
0.2%
4
0.1%
358
1.5%
558
2.8%
8.2.1
Southeastern Wisconsin Till Plains
0
0.0%
26
0.5%
47
0.2%
196
1.0%
8.2.2
Huron/Erie Lake Plains
2
0.1%
3
0.0%
13
0.1%
14
0.3%
33
0.1%
135
0.7%
8.2.3
Central Corn Belt Plains
3
0.1%
8
0.1%
3
0.0%
89
1.6%
55
0.3%
8.2.4
Eastern Corn Belt Plains
12
0.4%
47
0.4%
115
0.9%
309
5.6%
135
0.6%
885
4.4%
8.3.1
Northern Piedmont
13
0.4%
197
1.7%
230
1.9%
32
0.6%
97
0.4%
321
1.6%
8.3.2
Interior River Valleys and Hills
17
0.5%
296
2.6%
673
5.5%
637
11.4%
155
0.6%
591
2.9%
8.3.3
Interior Plateau
95
3.0%
782
6.9%
2090
17.2%
1210
21.7%
914
3.7%
1695
8.4%
8.3.4
Piedmont
406
12.6%
1261
11.1%
1301
10.7%
80
1.4%
626
2.6%
347
1.7%
8.3.5
Southeastern Plains
879
27.3%
1163
10.2%
982
8.1%
63
1.1%
758
3.1%
129
0.6%
8.3.6
Mississippi Valley Loess Plains
253
7.9%
211
1.9%
263
2.2%
10
0.2%
134
0.5%
96
0.5%
8.3.7
South Central Plains
708
22.0%
497
4.4%
68
0.6%
9
0.2%
207
0.8%
163
0.8%
8.3.8
East Central Texas Plains
6
0.2%
29
0.3%
6
0.0%
2
0.0%
37
0.2%
8.4.1
Ridge and Valley
23
0.7%
1025
9.0%
1455
11.9%
151
2.7%
508
2.1%
1076
5.3%
8.4.2
Central Appalachians
53
1.6%
580
5.1%
786
6.5%
6
0.1%
1724
7.1%
511
2.5%
8.4.3
Western Allegheny Plateau
44
1.4%
815
7.2%
952
7.8%
79
1.4%
829
3.4%
1367
6.7%
8.4.4
Blue Ridge
23
0.7%
511
4.5%
804
6.6%
5
0.1%
353
1.4%
395
1.9%
8.4.5
Ozark Highlands
9
0.3%
1530
13.4%
664
5.4%
294
5.3%
1
0.0%
649
3.2%
5B-Attachment 2B-6
-------
NA L3
CODE
US L3NAME
American
hornbeam
Median S=7
Assoc S-J,
N/S = 0.26
mockernut
hickory
Median N=10
Assoc N-J,
N/S =0.15
pignut hickory
Median S=10
Assoc S-J,
N/S = 0.4
hackberry
Median S=7
Assoc S-J,
N/S =0.18
American beech
Median N=8, S=7
Assoc N-U, S-J,
N/S =0.76
white ash
Median N=10
Assoc N-U
N/S = 0.53
count
%
count
%
count
%
count
%
count
%
count
%
8.4.6
Boston Mountains
5
0.2%
619
5.4%
24
0.2%
24
0.4%
65
0.3%
102
0.5%
8.4.7
Arkansas Valley
14
0.4%
288
2.5%
1
0.0%
40
0.7%
79
0.4%
8.4.8
Ouachita Mountains
37
1.2%
565
5.0%
2
0.0%
6
0.1%
9
0.0%
29
0.1%
8.4.9
Southwestern Appalachians
38
1.2%
536
4.7%
754
6.2%
35
0.6%
199
0.8%
240
1.2%
8.5.1
Middle Atlantic Coastal Plain
170
5.3%
110
1.0%
58
0.5%
26
0.5%
109
0.4%
41
0.2%
8.5.2
Mississippi Alluvial Plain
39
1.2%
85
0.7%
29
0.2%
71
1.3%
11
0.0%
9
0.0%
8.5.3
Southern Coastal Plain
186
5.8%
14
0.1%
96
0.8%
10
0.2%
3
0.0%
16
0.1%
8.5.4
Atlantic Coastal Pine Barrens
3
0.1%
4
0.0%
6
0.0%
0
0.0%
19
0.1%
7
0.0%
9.2.1
Northern Glaciated Plains
17
0.3%
9.2.2
Lake Agassiz Plain
9.2.3
Western Corn Belt Plains
2
0.0%
2
0.0%
651
11.7%
104
0.5%
9.2.4
Central Irregular Plains
69
0.6%
7
0.1%
855
15.4%
243
1.2%
9.3.1
Northwestern Glaciated Plains
16
0.3%
9.3.3
Northwestern Great Plains
3
0.1%
9.3.4
Nebraska Sand Hills
7
0.1%
9.4.1
High Plains
2
0.0%
9.4.2
Central Great Plains
260
4.7%
2
0.0%
9.4.3
Southwestern Tablelands
11
0.2%
9.4.4
Flint Hills
151
2.7%
2
0.0%
9.4.5
Cross Timbers
5
0.0%
1
0.0%
10
0.2%
8
0.0%
9.4.6
Edwards Plateau
9.4.7
Texas Blackland Prairies
1
0.0%
3
0.0%
5B-Attachment 2B-7
-------
NA L3
CODE
US L3NAME
American
hornbeam
Median S=7
Assoc S-J,
N/S = 0.26
mockernut
hickory
Median N=10
Assoc N-J,
N/S =0.15
pignut hickory
Median S=10
Assoc S-J,
N/S = 0.4
hackberry
Median S=7
Assoc S-J,
N/S =0.18
American beech
Median N=8, S=7
Assoc N-U, S-J,
N/S =0.76
white ash
Median N=10
Assoc N-U
N/S = 0.53
count
%
count
%
count
%
count
%
count
%
count
%
9.5.1
Western Gulf Coastal Plain
13
0.4%
2
0.0%
1
0.0%
9.6.1
Southern Texas Plains
10.1.2
Columbia Plateau
10.1.3
Northern Basin and Range
10.1.4
Wyoming Basin
10.1.5
Central Basin and Range
10.1.6
Colorado Plateaus
10.1.7
Arizona/New Mexico Plateau
10.1.8
Snake River Plain
10.2.1
Mojave Basin and Range
10.2.2
Sonoran Basin and Range
10.2.10
Chihuahuan Deserts
11.1.1
Southern and Central California Chaparral and
Oak Woodlands
11.1.2
Central California Valley
11.1.3
Southern California Mountains
12.1.1
Madrean Archipelago
13.1.1
Arizona/New Mexico Mountains
15.4.1
Southern Florida Coastal Plain
Total Tree Count
3214
11392
12185
5565
24397
20266
5B-Attachment 2B-8
-------
NA L3
CODE
US_L3NAME
green ash
Median
N=10,S=6
Assoc N-U, S-J,
N/S = 0.45
black walnut
Median N=12
Assoc N-U
N/S =0.13
Utah juniper
Median N=3
Assoc N-U
N/S =0.72
eastern
redcedar
Median
N=11,S=7
Assoc N-U,S-J,
N/S Cor r= 0.3
sweetgum
Median N=9, S=7
Assoc N-U, S-J,
N/S =0.37
yellow poplar
Median S=11
Assoc S-J,
N/S =0.4
count
%
count
%
count
%
count
%
count
%
count
%
5.2.1
Northern Lakes and Forests
1941
10.3%
4
0.1%
2
0.0%
5.2.2
Northern Minnesota Wetlands
216
1.1%
5.3.1
Northeastern Highlands
45
0.2%
6
0.1%
28
0.2%
112
0.4%
5.3.3
North Central Appalachians
1
0.0%
4
0.0%
57
0.2%
6.2.3
Northern Rockies
6.2.4
Canadian Rockies
6.2.5
North Cascades
6.2.7
Cascades
6.2.8
Eastern Cascades Slopes and Foothills
6.2.9
Blue Mountains
6.2.10
Middle Rockies
25
0.1%
60
0.3%
6.2.11
Klamath Mountains
6.2.12
Sierra Nevada
6.2.13
Wasatch and Uinta Mountains
1089
5.8%
6.2.14
Southern Rockies
219
1.2%
6.2.15
Idaho Batholith
7.1.7
Puget Lowland
7.1.8
Coast Range
7.1.9
Willamette Valley
8.1.1
Eastern Great Lakes Lowlands
546
2.9%
40
0.6%
48
0.3%
9
0.0%
5B-Attachment 2B-9
-------
NA L3
CODE
US_L3NAME
green ash
Median
N=10,S=6
Assoc N-U, S-J,
N/S = 0.45
black walnut
Median N=12
Assoc N-U
N/S =0.13
Utah juniper
Median N=3
Assoc N-U
N/S =0.72
eastern
redcedar
Median
N=11,S=7
Assoc N-U,S-J,
N/S Cor r= 0.3
sweetgum
Median N=9, S=7
Assoc N-U, S-J,
N/S =0.37
yellow poplar
Median S=11
Assoc S-J,
N/S =0.4
count
%
count
%
count
%
count
%
count
%
count
%
8.1.3
Northern Allegheny Plateau
99
0.5%
40
0.6%
21
0.1%
7
0.0%
8.1.4
North Central Hardwood Forests
1561
8.3%
13
0.2%
123
0.7%
8.1.5
Driftless Area
217
1.2%
448
6.8%
313
1.8%
8.1.6
Southern Michigan/Northern Indiana Drift Plains
1128
6.0%
145
2.2%
27
0.2%
71
0.3%
8.1.7
Northeastern Coastal Zone
62
0.3%
23
0.3%
172
1.0%
2
0.0%
58
0.2%
8.1.8
Acadian Plains and Hills
31
0.2%
8.1.10
Erie Drift Plain
162
0.9%
63
1.0%
1
0.0%
184
0.7%
8.2.1
Southeastern Wisconsin Till Plains
735
3.9%
88
1.3%
102
0.6%
8.2.2
Huron/Erie Lake Plains
813
4.3%
42
0.6%
1
0.0%
4
0.0%
8.2.3
Central Corn Belt Plains
126
0.7%
148
2.2%
8
0.0%
6
0.0%
8.2.4
Eastern Corn Belt Plains
359
1.9%
469
7.1%
161
0.9%
84
0.2%
206
0.7%
8.3.1
Northern Piedmont
61
0.3%
171
2.6%
277
1.6%
63
0.2%
804
2.9%
8.3.2
Interior River Valleys and Hills
666
3.5%
514
7.8%
738
4.3%
569
1.5%
436
1.6%
8.3.3
Interior Plateau
904
4.8%
931
14.1%
3994
23.2%
1003
2.7%
2609
9.4%
8.3.4
Piedmont
621
3.3%
192
2.9%
1329
7.7%
6822
18.3%
5776
20.9%
8.3.5
Southeastern Plains
1302
6.9%
66
1.0%
873
5.1%
11773
31.6%
4012
14.5%
8.3.6
Mississippi Valley Loess Plains
322
1.7%
29
0.4%
208
1.2%
2090
5.6%
327
1.2%
8.3.7
South Central Plains
711
3.8%
24
0.4%
249
1.4%
6197
16.7%
13
0.0%
8.3.8
East Central Texas Plains
191
1.0%
4
0.1%
173
1.0%
275
0.7%
8.4.1
Ridge and Valley
262
1.4%
414
6.3%
883
5.1%
690
1.9%
1880
6.8%
8.4.2
Central Appalachians
52
0.3%
64
1.0%
47
0.3%
118
0.3%
3411
12.3%
8.4.3
Western Allegheny Plateau
134
0.7%
454
6.9%
63
0.4%
15
0.0%
2714
9.8%
5B-Attachment 2B-10
-------
NA L3
CODE
US_L3NAME
green ash
Median
N=10,S=6
Assoc N-U, S-J,
N/S = 0.45
black walnut
Median N=12
Assoc N-U
N/S =0.13
Utah juniper
Median N=3
Assoc N-U
N/S =0.72
eastern
redcedar
Median
N=11,S=7
Assoc N-U,S-J,
N/S Cor r= 0.3
sweetgum
Median N=9, S=7
Assoc N-U, S-J,
N/S =0.37
yellow poplar
Median S=11
Assoc S-J,
N/S =0.4
count
%
count
%
count
%
count
%
count
%
count
%
8.4.4
Blue Ridge
61
0.3%
82
1.2%
40
0.2%
85
0.2%
3203
11.6%
8.4.5
Ozark Highlands
176
0.9%
810
12.3%
3903
22.6%
168
0.5%
4
0.0%
8.4.6
Boston Mountains
21
0.1%
35
0.5%
333
1.9%
218
0.6%
8.4.7
Arkansas Valley
166
0.9%
10
0.2%
727
4.2%
268
0.7%
8.4.8
Ouachita Mountains
101
0.5%
5
0.1%
260
1.5%
386
1.0%
8.4.9
Southwestern Appalachians
154
0.8%
49
0.7%
415
2.4%
752
2.0%
1154
4.2%
8.5.1
Middle Atlantic Coastal Plain
530
2.8%
14
0.2%
21
0.1%
3130
8.4%
551
2.0%
8.5.2
Mississippi Alluvial Plain
1109
5.9%
9
0.1%
22
0.1%
877
2.4%
19
0.1%
8.5.3
Southern Coastal Plain
630
3.3%
43
0.2%
1422
3.8%
55
0.2%
8.5.4
Atlantic Coastal Pine Barrens
1
0.0%
3
0.0%
16
0.1%
85
0.2%
19
0.1%
9.2.1
Northern Glaciated Plains
383
2.0%
1
0.0%
9.2.2
Lake Agassiz Plain
280
1.5%
9.2.3
Western Corn Belt Plains
502
2.7%
381
5.8%
384
2.2%
9.2.4
Central Irregular Plains
408
2.2%
701
10.6%
435
2.5%
9.3.1
Northwestern Glaciated Plains
96
0.5%
130
0.8%
9.3.3
Northwestern Great Plains
417
2.2%
1
0.0%
95
0.6%
9.3.4
Nebraska Sand Hills
29
0.2%
104
0.6%
9.4.1
High Plains
27
0.1%
1
0.0%
33
0.2%
9.4.2
Central Great Plains
337
1.8%
27
0.4%
319
1.8%
9.4.3
Southwestern Tablelands
12
0.1%
7
0.1%
8
0.0%
9.4.4
Flint Hills
50
0.3%
50
0.8%
73
0.4%
9.4.5
Cross Timbers
25
0.1%
13
0.2%
19
0.1%
5B-Attachment 2B-11
-------
NA L3
CODE
US_L3NAME
green ash
Median
N=10,S=6
Assoc N-U, S-J,
N/S = 0.45
black walnut
Median N=12
Assoc N-U
N/S =0.13
Utah juniper
Median N=3
Assoc N-U
N/S =0.72
eastern
redcedar
Median
N=11,S=7
Assoc N-U,S-J,
N/S Cor r= 0.3
sweetgum
Median N=9, S=7
Assoc N-U, S-J,
N/S =0.37
yellow poplar
Median S=11
Assoc S-J,
N/S =0.4
count
%
count
%
count
%
count
%
count
%
count
%
9.4.6
Edwards Plateau
9.4.7
Texas Blackland Prairies
1
0.0%
13
0.1%
9.5.1
Western Gulf Coastal Plain
39
0.2%
11
0.1%
118
0.3%
9.6.1
Southern Texas Plains
10.1.2
Columbia Plateau
10.1.3
Northern Basin and Range
605
3.2%
10.1.4
Wyoming Basin
189
1.0%
10.1.5
Central Basin and Range
4944
26.5%
10.1.6
Colorado Plateaus
7696
41.2%
10.1.7
Arizona/New Mexico Plateau
2211
11.8%
10.1.8
Snake River Plain
10.2.1
Mojave Basin and Range
207
1.1%
10.2.2
Sonoran Basin and Range
2
0.0%
10.2.10
Chihuahuan Deserts
11.1.1
Southern and Central California Chaparral and
Oak Woodlands
11.1.2
Central California Valley
11.1.3
Southern California Mountains
12.1.1
Madrean Archipelago
2
0.0%
13.1.1
Arizona/New Mexico Mountains
1457
7.8%
15.4.1
Southern Florida Coastal Plain
7
0.0%
Total Tree Count
18854
6591
18681
17249
37211
27701
5B-Attachment 2B-12
-------
NA L3
CODE
US_L3NAME
sweetbay
Median S=7
Assoc S-J,
N/S = 0.35
swamp tupelo
Median S=6
Assoc S-J,
N/S =0.46
blackgum
Median N=10
Assoc N-U
N/S =0.42
eastern
hophornbeam
Median S=6
Assoc S-J,
N/S =0.36
sourwood
Median N=9, S=10
Assoc N-U, S-J,
N/S =0.3
shortleaf pine
Median S=6
Assoc S-J,
N/S =0.18
count
%
count
%
count
%
count
%
count
%
count
%
5.2.1
Northern Lakes and Forests
2
0.0%
1051
17.8%
5.2.2
Northern Minnesota Wetlands
5
0.1%
5.3.1
Northeastern Highlands
29
0.2%
703
11.9%
5.3.3
North Central Appalachians
171
1.3%
116
2.0%
6.2.3
Northern Rockies
6.2.4
Canadian Rockies
6.2.5
North Cascades
6.2.7
Cascades
6.2.8
Eastern Cascades Slopes and Foothills
6.2.9
Blue Mountains
6.2.10
Middle Rockies
3
0.1%
6.2.11
Klamath Mountains
6.2.12
Sierra Nevada
6.2.13
Wasatch and Uinta Mountains
6.2.14
Southern Rockies
6.2.15
Idaho Batholith
7.1.7
Puget Lowland
7.1.8
Coast Range
7.1.9
Willamette Valley
8.1.1
Eastern Great Lakes Lowlands
32
0.2%
241
4.1%
8.1.3
Northern Allegheny Plateau
16
0.1%
432
7.3%
5B-Attachment 2B-13
-------
NA L3
CODE
US L3NAME
sweetbay
Median S=7
Assoc S-J,
N/S = 0.35
swamp tupelo
Median S=6
Assoc S-J,
N/S =0.46
blackgum
Median N=10
Assoc N-U
N/S =0.42
eastern
hophornbeam
Median S=6
Assoc S-J,
N/S =0.36
sourwood
Median N=9, S=10
Assoc N-U, S-J,
N/S =0.3
shortleaf pine
Median S=6
Assoc S-J,
N/S =0.18
count
%
count
%
count
%
count
%
count
%
count
%
8.1.4
North Central Hardwood Forests
560
9.5%
8.1.5
Driftless Area
454
7.7%
8.1.6
Southern Michigan/Northern Indiana Drift Plains
43
0.3%
68
1.2%
8.1.7
Northeastern Coastal Zone
121
0.9%
34
0.6%
8.1.8
Acadian Plains and Hills
231
3.9%
8.1.10
Erie Drift Plain
72
0.5%
84
1.4%
1
0.0%
8.2.1
Southeastern Wisconsin Till Plains
34
0.6%
8.2.2
Huron/Erie Lake Plains
37
0.3%
18
0.3%
8.2.3
Central Corn Belt Plains
40
0.3%
13
0.2%
8.2.4
Eastern Corn Belt Plains
47
0.3%
78
1.3%
8.3.1
Northern Piedmont
179
1.3%
4
0.1%
6
0.1%
8
0.0%
8.3.2
Interior River Valleys and Hills
214
1.6%
83
1.4%
26
0.3%
27
0.2%
8.3.3
Interior Plateau
3
0.0%
885
6.6%
201
3.4%
673
7.5%
276
1.6%
8.3.4
Piedmont
42
1.0%
79
0.7%
957
7.1%
85
1.4%
1953
21.8%
2761
16.2%
8.3.5
Southeastern Plains
2345
55.8%
4949
44.5%
2415
17.9%
239
4.0%
678
7.6%
1941
11.4%
8.3.6
Mississippi Valley Loess Plains
12
0.3%
10
0.1%
164
1.2%
176
3.0%
51
0.6%
141
0.8%
8.3.7
South Central Plains
262
6.2%
78
0.7%
1232
9.2%
309
5.2%
6
0.1%
1891
11.1%
8.3.8
East Central Texas Plains
42
0.3%
30
0.2%
8.4.1
Ridge and Valley
3
0.1%
1
0.0%
1668
12.4%
121
2.0%
804
9.0%
383
2.2%
8.4.2
Central Appalachians
606
4.5%
44
0.7%
845
9.4%
47
0.3%
8.4.3
Western Allegheny Plateau
586
4.4%
100
1.7%
504
5.6%
65
0.4%
8.4.4
Blue Ridge
1029
7.6%
50
0.8%
2363
26.4%
376
2.2%
5B-Attachment 2B-14
-------
NA L3
CODE
US L3NAME
sweetbay
Median S=7
Assoc S-J,
N/S = 0.35
swamp tupelo
Median S=6
Assoc S-J,
N/S =0.46
blackgum
Median N=10
Assoc N-U
N/S =0.42
eastern
hophornbeam
Median S=6
Assoc S-J,
N/S =0.36
sourwood
Median N=9, S=10
Assoc N-U, S-J,
N/S =0.3
shortleaf pine
Median S=6
Assoc S-J,
N/S =0.18
count
%
count
%
count
%
count
%
count
%
count
%
8.4.5
Ozark Highlands
660
4.9%
12
0.2%
2867
16.8%
8.4.6
Boston Mountains
333
2.5%
10
0.2%
679
4.0%
8.4.7
Arkansas Valley
5
0.0%
112
0.8%
10
0.2%
1259
7.4%
8.4.8
Ouachita Mountains
288
2.1%
43
0.7%
3804
22.3%
8.4.9
Southwestern Appalachians
547
4.1%
36
0.6%
989
11.1%
381
2.2%
8.5.1
Middle Atlantic Coastal Plain
225
5.4%
2181
19.6%
506
3.8%
11
0.2%
43
0.5%
31
0.2%
8.5.2
Mississippi Alluvial Plain
65
0.6%
33
0.2%
13
0.2%
3
0.0%
16
0.1%
8.5.3
Southern Coastal Plain
1293
30.8%
3738
33.6%
278
2.1%
17
0.3%
1
0.0%
2
0.0%
8.5.4
Atlantic Coastal Pine Barrens
10
0.2%
99
0.7%
40
0.2%
9.2.1
Northern Glaciated Plains
10
0.2%
9.2.2
Lake Agassiz Plain
11
0.2%
9.2.3
Western Corn Belt Plains
160
2.7%
9.2.4
Central Irregular Plains
36
0.6%
9.3.1
Northwestern Glaciated Plains
2
0.0%
9.3.3
Northwestern Great Plains
2
0.0%
9.3.4
Nebraska Sand Hills
9.4.1
High Plains
9.4.2
Central Great Plains
9.4.3
Southwestern Tablelands
9.4.4
Flint Hills
1
0.0%
9.4.5
Cross Timbers
3
0.0%
9.4.6
Edwards Plateau
5B-Attachment 2B-15
-------
NA L3
CODE
US L3NAME
sweetbay
Median S=7
Assoc S-J,
N/S = 0.35
swamp tupelo
Median S=6
Assoc S-J,
N/S =0.46
blackgum
Median N=10
Assoc N-U
N/S =0.42
eastern
hophornbeam
Median S=6
Assoc S-J,
N/S =0.36
sourwood
Median N=9, S=10
Assoc N-U, S-J,
N/S =0.3
shortleaf pine
Median S=6
Assoc S-J,
N/S =0.18
count
%
count
%
count
%
count
%
count
%
count
%
9.4.7
Texas Blackland Prairies
9.5.1
Western Gulf Coastal Plain
1
0.0%
21
0.2%
1
0.0%
9.6.1
Southern Texas Plains
10.1.2
Columbia Plateau
10.1.3
Northern Basin and Range
10.1.4
Wyoming Basin
10.1.5
Central Basin and Range
10.1.6
Colorado Plateaus
10.1.7
Arizona/New Mexico Plateau
10.1.8
Snake River Plain
10.2.1
Mojave Basin and Range
10.2.2
Sonoran Basin and Range
10.2.10
Chihuahuan Deserts
11.1.1
Southern and Central California Chaparral and
Oak Woodlands
11.1.2
Central California Valley
11.1.3
Southern California Mountains
12.1.1
Madrean Archipelago
13.1.1
Arizona/New Mexico Mountains
15.4.1
Southern Florida Coastal Plain
6
0.1%
1
0.0%
Total Tree Count
4199
11110
13464
5912
8946
17028
5B-Attachment 2B-16
-------
NA L3
CODE
US_L3NAME
slash pine
Median N=7, S=5
Assoc N-U, S-J,
N/S = 0.44
longleaf pine
Median S=7
Assoc S-J,
N/S =0.44
red pine
Median N=8, S=5
Assoc N-U, S-J,
N/S =0.54
pitch pine
Median
N=10,S=12
Assoc N-U,S-J,
N/S = 0.65
eastern white pine
Median S=6
Assoc S-J,
N/S = 0.6
loblolly pine
Median S=7
Assoc S-J,
N/S = 0.32
count
%
Count
%
count
%
count
%
count
%
count
%
5.2.1
Northern Lakes and Forests
6593
65.0%
4340
18.4%
5.2.2
Northern Minnesota Wetlands
208
2.1%
38
0.2%
5.3.1
Northeastern Highlands
189
1.9%
88
2.8%
4501
19.1%
5.3.3
North Central Appalachians
44
0.4%
47
1.5%
566
2.4%
6.2.3
Northern Rockies
6.2.4
Canadian Rockies
6.2.5
North Cascades
6.2.7
Cascades
6.2.8
Eastern Cascades Slopes and Foothills
6.2.9
Blue Mountains
6.2.10
Middle Rockies
6.2.11
Klamath Mountains
6.2.12
Sierra Nevada
6.2.13
Wasatch and Uinta Mountains
6.2.14
Southern Rockies
6.2.15
Idaho Batholith
7.1.7
Puget Lowland
7.1.8
Coast Range
7.1.9
Willamette Valley
8.1.1
Eastern Great Lakes Lowlands
79
0.8%
7
0.2%
602
2.6%
8.1.3
Northern Allegheny Plateau
155
1.5%
3
0.1%
955
4.1%
5B-Attachment 2B-17
-------
slash pine
Median N=7, S=5
longleaf pine
Median S=7
red pine
Median N=8, S=5
pitch pine
Median
N=10,S=12
Assoc N-U,S-J,
N/S = 0.65
eastern white pine
Median S=6
loblolly pine
Median S=7
NA L3
CODE
Assoc N-U, S-J,
N/S = 0.44
Assoc S-J,
N/S =0.44
Assoc N-U, S-J,
N/S =0.54
Assoc S-J,
N/S = 0.6
Assoc S-J,
N/S = 0.32
US L3NAME
count
%
Count
%
count
%
count
%
count
%
count
%
8.1.4
North Central Hardwood Forests
1609
15.9%
1751
7.4%
8.1.5
Driftless Area
346
3.4%
436
1.9%
8.1.6
Southern Michigan/Northern Indiana Drift Plains
466
4.6%
2
0.1%
362
1.5%
8.1.7
Northeastern Coastal Zone
13
0.1%
106
3.4%
2711
11.5%
8.1.8
Acadian Plains and Hills
163
1.6%
5
0.2%
2353
10.0%
8.1.10
Erie Drift Plain
20
0.2%
33
0.1%
8.2.1
Southeastern Wisconsin Till Plains
85
0.8%
140
0.6%
8.2.2
Huron/Erie Lake Plains
60
0.6%
128
0.5%
8.2.3
Central Corn Belt Plains
19
0.2%
29
0.1%
8.2.4
Eastern Corn Belt Plains
13
0.1%
47
0.2%
8.3.1
Northern Piedmont
69
0.3%
33
0.0%
8.3.2
Interior River Valleys and Hills
29
0.3%
5
0.2%
16
0.1%
40
0.1%
8.3.3
Interior Plateau
2
0.0%
7
0.2%
106
0.4%
578
0.8%
8.3.4
Piedmont
29
0.2%
224
4.2%
25
0.8%
401
1.7%
15095
21.8%
8.3.5
Southeastern Plains
4035
33.9%
3108
57.8%
1
0.0%
1
0.0%
23675
34.2%
8.3.6
Mississippi Valley Loess Plains
3
0.0%
1
0.0%
1599
2.3%
8.3.7
South Central Plains
235
2.0%
311
5.8%
13971
20.2%
8.3.8
East Central Texas Plains
37
0.3%
151
0.2%
8.4.1
Ridge and Valley
100
1.9%
7
0.1%
472
14.9%
1308
5.6%
1494
2.2%
8.4.2
Central Appalachians
13
0.1%
80
2.5%
228
1.0%
4
0.0%
8.4.3
Western Allegheny Plateau
24
0.2%
107
3.4%
391
1.7%
100
0.1%
8.4.4
Blue Ridge
312
9.9%
1817
7.7%
304
0.4%
5B-Attachment 2B-18
-------
NA L3
CODE
US L3NAME
slash pine
Median N=7, S=5
Assoc N-U, S-J,
N/S = 0.44
longleaf pine
Median S=7
Assoc S-J,
N/S =0.44
red pine
Median N=8, S=5
Assoc N-U, S-J,
N/S =0.54
pitch pine
Median
N=10,S=12
Assoc N-U,S-J,
N/S = 0.65
eastern white pine
Median S=6
Assoc S-J,
N/S = 0.6
loblolly pine
Median S=7
Assoc S-J,
N/S = 0.32
count
%
Count
%
count
%
count
%
count
%
count
%
8.4.5
Ozark Highlands
21
0.0%
8.4.6
Boston Mountains
50
0.1%
8.4.7
Arkansas Valley
205
0.3%
8.4.8
Ouachita Mountains
715
1.0%
8.4.9
Southwestern Appalachians
9
0.2%
27
0.9%
130
0.6%
1486
2.1%
8.5.1
Middle Atlantic Coastal Plain
120
1.0%
353
6.6%
42
1.3%
7035
10.1%
8.5.2
Mississippi Alluvial Plain
103
0.1%
8.5.3
Southern Coastal Plain
7160
60.2%
1258
23.4%
2279
3.3%
8.5.4
Atlantic Coastal Pine Barrens
1827
57.8%
103
0.4%
2
0.0%
9.2.1
Northern Glaciated Plains
9.2.2
Lake Agassiz Plain
9.2.3
Western Corn Belt Plains
9.2.4
Central Irregular Plains
2
0.0%
9.3.1
Northwestern Glaciated Plains
9.3.3
Northwestern Great Plains
9.3.4
Nebraska Sand Hills
9.4.1
High Plains
9.4.2
Central Great Plains
9.4.3
Southwestern Tablelands
9.4.4
Flint Hills
9.4.5
Cross Timbers
9.4.6
Edwards Plateau
5B-Attachment 2B-19
-------
NA L3
CODE
US L3NAME
slash pine
Median N=7, S=5
Assoc N-U, S-J,
N/S = 0.44
longleaf pine
Median S=7
Assoc S-J,
N/S =0.44
red pine
Median N=8, S=5
Assoc N-U, S-J,
N/S =0.54
pitch pine
Median
N=10,S=12
Assoc N-U,S-J,
N/S = 0.65
eastern white pine
Median S=6
Assoc S-J,
N/S = 0.6
loblolly pine
Median S=7
Assoc S-J,
N/S = 0.32
count
%
Count
%
count
%
count
%
count
%
count
%
9.4.7
Texas Blackland Prairies
75
0.1%
9.5.1
Western Gulf Coastal Plain
9
0.2%
306
0.4%
9.6.1
Southern Texas Plains
10.1.2
Columbia Plateau
10.1.3
Northern Basin and Range
10.1.4
Wyoming Basin
10.1.5
Central Basin and Range
10.1.6
Colorado Plateaus
10.1.7
Arizona/New Mexico Plateau
10.1.8
Snake River Plain
10.2.1
Mojave Basin and Range
10.2.2
Sonoran Basin and Range
10.2.10
Chihuahuan Deserts
11.1.1
Southern and Central California Chaparral and
Oak Woodlands
11.1.2
Central California Valley
11.1.3
Southern California Mountains
12.1.1
Madrean Archipelago
13.1.1
Arizona/New Mexico Mountains
15.4.1
Southern Florida Coastal Plain
283
2.4%
Total Tree Count
11902
5373
10139
3163
23562
69321
5B-Attachment 2B-20
-------
NA L3
CODE
US_L3NAME
Virginia pine
Median
N=10,S=11
Assoc N-U, S-J,
N/S = 0.44
bigtooth aspen
Median N=9, S=6
Assoc N-U, S-J,
N/S =0.57
quaking aspen
Median N=7, S=3
Assoc N-U, S-J,
N/S =0.61
black cherry
Median N=11
Assoc N-U
N/S = 0.33
Douglas-fir
Median N=3
Assoc N-U
N/S = 0.65
white oak
Median N=10, S=8
Assoc N-U, S-J,
N/S =0.17
count
%
count
%
count
%
count
%
count
%
count
%
5.2.1
Northern Lakes and Forests
6784
58.8%
27494
52.9%
1989
8.1%
1615
3.4%
5.2.2
Northern Minnesota Wetlands
13
0.1%
2995
5.8%
5.3.1
Northeastern Highlands
496
4.3%
1273
2.5%
1539
6.3%
243
0.5%
5.3.3
North Central Appalachians
115
1.0%
167
0.3%
1516
6.2%
1032
2.2%
6.2.3
Northern Rockies
67
0.1%
4463
9.4%
6.2.4
Canadian Rockies
92
0.2%
800
1.7%
6.2.5
North Cascades
2702
5.7%
6.2.7
Cascades
10680
22.5%
6.2.8
Eastern Cascades Slopes and Foothills
28
0.1%
1631
3.4%
6.2.9
Blue Mountains
7
0.0%
3271
6.9%
6.2.10
Middle Rockies
398
0.8%
3908
8.2%
6.2.11
Klamath Mountains
3
0.0%
6885
14.5%
6.2.12
Sierra Nevada
30
0.1%
1066
2.2%
6.2.13
Wasatch and Uinta Mountains
3024
5.8%
774
1.6%
6.2.14
Southern Rockies
5182
10.0%
2161
4.6%
6.2.15
Idaho Batholith
15
0.0%
1699
3.6%
7.1.7
Puget Lowland
409
0.9%
7.1.8
Coast Range
4823
10.2%
7.1.9
Willamette Valley
184
0.4%
8.1.1
Eastern Great Lakes Lowlands
88
0.8%
384
0.7%
408
1.7%
27
0.1%
8.1.3
Northern Allegheny Plateau
166
1.4%
428
0.8%
867
3.5%
9
0.0%
180
0.4%
5B-Attachment 2B-21
-------
NA L3
CODE
US L3NAME
Virginia pine
Median
N=10,S=11
Assoc N-U, S-J,
N/S = 0.44
bigtooth aspen
Median N=9, S=6
Assoc N-U, S-J,
N/S =0.57
quaking aspen
Median N=7, S=3
Assoc N-U, S-J,
N/S =0.61
black cherry
Median N=11
Assoc N-U
N/S = 0.33
Douglas-fir
Median N=3
Assoc N-U
N/S = 0.65
white oak
Median N=10, S=8
Assoc N-U, S-J,
N/S =0.17
count
%
count
%
count
%
count
%
count
%
count
%
8.1.4
North Central Hardwood Forests
1026
8.9%
4024
7.7%
692
2.8%
1060
2.3%
8.1.5
Driftless Area
540
4.7%
488
0.9%
894
3.7%
1247
2.7%
8.1.6
Southern Michigan/Northern Indiana Drift Plains
396
3.4%
344
0.7%
1508
6.2%
1
0.0%
749
1.6%
8.1.7
Northeastern Coastal Zone
157
1.4%
184
0.4%
312
1.3%
817
1.7%
8.1.8
Acadian Plains and Hills
612
5.3%
1183
2.3%
169
0.7%
17
0.0%
8.1.10
Erie Drift Plain
105
0.9%
202
0.4%
879
3.6%
74
0.2%
8.2.1
Southeastern Wisconsin Till Plains
30
0.3%
296
0.6%
490
2.0%
164
0.3%
8.2.2
Huron/Erie Lake Plains
200
1.7%
421
0.8%
154
0.6%
97
0.2%
8.2.3
Central Corn Belt Plains
1
0.0%
21
0.0%
350
1.4%
128
0.3%
8.2.4
Eastern Corn Belt Plains
3
0.0%
21
0.2%
8
0.0%
603
2.5%
177
0.4%
8.3.1
Northern Piedmont
238
2.6%
18
0.2%
6
0.0%
283
1.2%
9
0.0%
328
0.7%
8.3.2
Interior River Valleys and Hills
78
0.8%
9
0.1%
578
2.4%
1594
3.4%
8.3.3
Interior Plateau
339
3.6%
30
0.3%
976
4.0%
3240
6.9%
8.3.4
Piedmont
3590
38.5%
9
0.1%
970
4.0%
4634
9.9%
8.3.5
Southeastern Plains
412
4.4%
7
0.1%
1163
4.7%
2853
6.1%
8.3.6
Mississippi Valley Loess Plains
258
1.1%
448
1.0%
8.3.7
South Central Plains
1
0.0%
157
0.6%
1221
2.6%
8.3.8
East Central Texas Plains
4
0.0%
8.4.1
Ridge and Valley
1653
17.7%
111
1.0%
24
0.0%
1398
5.7%
1
0.0%
2923
6.2%
8.4.2
Central Appalachians
212
2.3%
101
0.9%
27
0.1%
1396
5.7%
1976
4.2%
8.4.3
Western Allegheny Plateau
574
6.2%
505
4.4%
109
0.2%
2712
11.1%
2442
5.2%
8.4.4
Blue Ridge
946
10.1%
1
0.0%
417
1.7%
1265
2.7%
5B-Attachment 2B-22
-------
NA L3
CODE
US L3NAME
Virginia pine
Median
N=10,S=11
Assoc N-U, S-J,
N/S = 0.44
bigtooth aspen
Median N=9, S=6
Assoc N-U, S-J,
N/S =0.57
quaking aspen
Median N=7, S=3
Assoc N-U, S-J,
N/S =0.61
black cherry
Median N=11
Assoc N-U
N/S = 0.33
Douglas-fir
Median N=3
Assoc N-U
N/S = 0.65
white oak
Median N=10, S=8
Assoc N-U, S-J,
N/S =0.17
count
%
count
%
count
%
count
%
count
%
count
%
8.4.5
Ozark Highlands
3
0.0%
448
1.8%
8989
19.2%
8.4.6
Boston Mountains
96
0.4%
1619
3.5%
8.4.7
Arkansas Valley
73
0.3%
487
1.0%
8.4.8
Ouachita Mountains
106
0.4%
1268
2.7%
8.4.9
Southwestern Appalachians
1155
12.4%
260
1.1%
2152
4.6%
8.5.1
Middle Atlantic Coastal Plain
83
0.9%
2
0.0%
250
1.0%
458
1.0%
8.5.2
Mississippi Alluvial Plain
40
0.2%
83
0.2%
8.5.3
Southern Coastal Plain
14
0.2%
85
0.3%
13
0.0%
8.5.4
Atlantic Coastal Pine Barrens
22
0.2%
3
0.0%
40
0.2%
519
1.1%
9.2.1
Northern Glaciated Plains
377
0.7%
9.2.2
Lake Agassiz Plain
1
0.0%
1773
3.4%
3
0.0%
9.2.3
Western Corn Belt Plains
1
0.0%
86
0.2%
210
0.9%
112
0.2%
9.2.4
Central Irregular Plains
192
0.8%
676
1.4%
9.3.1
Northwestern Glaciated Plains
10
0.0%
12
0.0%
9.3.3
Northwestern Great Plains
30
0.1%
1
0.0%
260
0.5%
9.3.4
Nebraska Sand Hills
9.4.1
High Plains
9.4.2
Central Great Plains
9.4.3
Southwestern Tablelands
9.4.4
Flint Hills
9.4.5
Cross Timbers
2
0.0%
9.4.6
Edwards Plateau
5B-Attachment 2B-23
-------
NA L3
CODE
US L3NAME
Virginia pine
Median
N=10,S=11
Assoc N-U, S-J,
N/S = 0.44
bigtooth aspen
Median N=9, S=6
Assoc N-U, S-J,
N/S =0.57
quaking aspen
Median N=7, S=3
Assoc N-U, S-J,
N/S =0.61
black cherry
Median N=11
Assoc N-U
N/S = 0.33
Douglas-fir
Median N=3
Assoc N-U
N/S = 0.65
white oak
Median N=10, S=8
Assoc N-U, S-J,
N/S =0.17
count
%
count
%
count
%
count
%
count
%
count
%
9.4.7
Texas Blackland Prairies
9.5.1
Western Gulf Coastal Plain
5
0.0%
9.6.1
Southern Texas Plains
10.1.2
Columbia Plateau
37
0.1%
174
0.4%
10.1.3
Northern Basin and Range
112
0.2%
105
0.2%
10.1.4
Wyoming Basin
27
0.1%
10.1.5
Central Basin and Range
62
0.1%
26
0.1%
10.1.6
Colorado Plateaus
282
0.5%
403
0.8%
10.1.7
Arizona/New Mexico Plateau
3
0.0%
17
0.0%
10.1.8
Snake River Plain
32
0.1%
10.2.1
Mojave Basin and Range
10.2.2
Sonoran Basin and Range
10.2.10
Chihuahuan Deserts
11.1.1
Southern and Central California Chaparral and
Oak Woodlands
222
0.5%
11.1.2
Central California Valley
11.1.3
Southern California Mountains
12.1.1
Madrean Archipelago
38
0.1%
13.1.1
Arizona/New Mexico Mountains
218
0.4%
657
1.4%
15.4.1
Southern Florida Coastal Plain
Total Tree Count
9324
11547
51946
24493
47417
46927
5B-Attachment 2B-24
-------
NA L3
CODE
US L3NAME
scarlet oak
Median
N=10,S=10
Assoc N-J,, S-J,
N/S = 0.37
southern red
oak
Median N=9
Assoc N-J,
N/S =0.36
laurel oak
Median S=6
Assoc S-J,
N/S =0.41
chinkapin oak
Median N=11
Assoc N-U
N/S = 0.31
water oak
Median N=8, S=9
Assoc N-J,, S-J,
N/S =0.26
chestnut oak
Median S=12
Assoc S-J,
N/S = 0.44
count
%
count
%
count
%
count
%
count
%
count
%
5.2.1
Northern Lakes and Forests
5.2.2
Northern Minnesota Wetlands
5.3.1
Northeastern Highlands
112
1.1%
386
1.6%
5.3.3
North Central Appalachians
236
2.2%
1155
4.9%
6.2.3
Northern Rockies
6.2.4
Canadian Rockies
6.2.5
North Cascades
6.2.7
Cascades
6.2.8
Eastern Cascades Slopes and Foothills
6.2.9
Blue Mountains
6.2.10
Middle Rockies
6.2.11
Klamath Mountains
6.2.12
Sierra Nevada
6.2.13
Wasatch and Uinta Mountains
6.2.14
Southern Rockies
6.2.15
Idaho Batholith
7.1.7
Puget Lowland
7.1.8
Coast Range
2
0.1%
7.1.9
Willamette Valley
8.1.1
Eastern Great Lakes Lowlands
3
0.0%
8.1.3
Northern Allegheny Plateau
21
0.2%
229
1.0%
8.1.4
North Central Hardwood Forests
5B-Attachment 2B-25
-------
NA L3
CODE
US L3NAME
scarlet oak
Median
N=10,S=10
Assoc N-J,, S-J,
N/S = 0.37
southern red
oak
Median N=9
Assoc N-J,
N/S =0.36
laurel oak
Median S=6
Assoc S-J,
N/S =0.41
chinkapin oak
Median N=11
Assoc N-U
N/S = 0.31
water oak
Median N=8, S=9
Assoc N-J,, S-J,
N/S =0.26
chestnut oak
Median S=12
Assoc S-J,
N/S = 0.44
count
%
count
%
count
%
count
%
count
%
count
%
8.1.5
Driftless Area
17
0.6%
8.1.6
Southern Michigan/Northern Indiana Drift Plains
7
0.1%
3
0.1%
8.1.7
Northeastern Coastal Zone
636
6.0%
4
0.1%
187
0.8%
8.1.8
Acadian Plains and Hills
8.1.10
Erie Drift Plain
5
0.0%
5
0.0%
8.2.1
Southeastern Wisconsin Till Plains
8.2.2
Huron/Erie Lake Plains
5
0.0%
4
0.1%
8.2.3
Central Corn Belt Plains
9
0.3%
8.2.4
Eastern Corn Belt Plains
5
0.0%
104
3.4%
6
0.0%
8.3.1
Northern Piedmont
79
0.7%
90
1.0%
400
1.7%
8.3.2
Interior River Valleys and Hills
72
0.7%
57
0.6%
275
8.9%
86
0.4%
8.3.3
Interior Plateau
612
5.8%
498
5.6%
1
0.0%
1002
32.4%
70
0.5%
1183
5.1%
8.3.4
Piedmont
961
9.0%
1646
18.6%
52
0.9%
2
0.1%
1546
10.6%
1313
5.6%
8.3.5
Southeastern Plains
326
3.1%
2263
25.6%
2724
46.9%
28
0.9%
6454
44.3%
269
1.1%
8.3.6
Mississippi Valley Loess Plains
14
0.1%
278
3.1%
29
0.5%
22
0.7%
605
4.2%
8.3.7
South Central Plains
1
0.0%
1597
18.0%
341
5.9%
11
0.4%
2446
16.8%
8.3.8
East Central Texas Plains
181
2.0%
3
0.1%
203
1.4%
8.4.1
Ridge and Valley
1707
16.0%
385
4.3%
17
0.3%
124
4.0%
149
1.0%
7903
33.7%
8.4.2
Central Appalachians
874
8.2%
34
0.4%
44
1.4%
2496
10.7%
8.4.3
Western Allegheny Plateau
586
5.5%
11
0.1%
53
1.7%
1305
5.6%
8.4.4
Blue Ridge
1749
16.4%
254
2.9%
5
0.2%
22
0.2%
4800
20.5%
8.4.5
Ozark Highlands
1508
14.2%
538
6.1%
2
0.0%
848
27.5%
1
0.0%
5B-Attachment 2B-26
-------
NA L3
CODE
US L3NAME
scarlet oak
Median
N=10,S=10
Assoc N-J,, S-J,
N/S = 0.37
southern red
oak
Median N=9
Assoc N-J,
N/S =0.36
laurel oak
Median S=6
Assoc S-J,
N/S =0.41
chinkapin oak
Median N=11
Assoc N-U
N/S = 0.31
water oak
Median N=8, S=9
Assoc N-J,, S-J,
N/S =0.26
chestnut oak
Median S=12
Assoc S-J,
N/S = 0.44
count
%
count
%
count
%
count
%
count
%
count
%
8.4.6
Boston Mountains
81
0.9%
75
2.4%
8.4.7
Arkansas Valley
156
1.8%
2
0.0%
3
0.1%
140
1.0%
8.4.8
Ouachita Mountains
130
1.5%
5
0.2%
76
0.5%
8.4.9
Southwestern Appalachians
586
5.5%
305
3.4%
2
0.0%
253
8.2%
82
0.6%
1590
6.8%
8.5.1
Middle Atlantic Coastal Plain
102
1.0%
211
2.4%
544
9.4%
1111
7.6%
25
0.1%
8.5.2
Mississippi Alluvial Plain
1
0.0%
75
0.8%
20
0.3%
8
0.3%
286
2.0%
8.5.3
Southern Coastal Plain
1
0.0%
20
0.2%
2003
34.5%
2
0.1%
1233
8.5%
8.5.4
Atlantic Coastal Pine Barrens
432
4.1%
22
0.2%
84
0.4%
9.2.1
Northern Glaciated Plains
9.2.2
Lake Agassiz Plain
9.2.3
Western Corn Belt Plains
47
1.5%
9.2.4
Central Irregular Plains
2
0.0%
1
0.0%
96
3.1%
9.3.1
Northwestern Glaciated Plains
9.3.3
Northwestern Great Plains
9.3.4
Nebraska Sand Hills
9.4.1
High Plains
9.4.2
Central Great Plains
1
0.0%
9.4.3
Southwestern Tablelands
9.4.4
Flint Hills
34
1.1%
9.4.5
Cross Timbers
7
0.2%
9.4.6
Edwards Plateau
9.4.7
Texas Blackland Prairies
1
0.0%
3
0.0%
5B-Attachment 2B-27
-------
NA L3
CODE
US L3NAME
scarlet oak
Median
N=10,S=10
Assoc N-J,, S-J,
N/S = 0.37
southern red
oak
Median N=9
Assoc N-J,
N/S =0.36
laurel oak
Median S=6
Assoc S-J,
N/S =0.41
chinkapin oak
Median N=11
Assoc N-U
N/S = 0.31
water oak
Median N=8, S=9
Assoc N-J,, S-J,
N/S =0.26
chestnut oak
Median S=12
Assoc S-J,
N/S = 0.44
count
%
count
%
count
%
count
%
count
%
count
%
9.5.1
Western Gulf Coastal Plain
21
0.2%
29
0.5%
139
1.0%
9.6.1
Southern Texas Plains
10.1.2
Columbia Plateau
10.1.3
Northern Basin and Range
10.1.4
Wyoming Basin
10.1.5
Central Basin and Range
10.1.6
Colorado Plateaus
10.1.7
Arizona/New Mexico Plateau
10.1.8
Snake River Plain
10.2.1
Mojave Basin and Range
10.2.2
Sonoran Basin and Range
10.2.10
Chihuahuan Deserts
11.1.1
Southern and Central California Chaparral and
Oak Woodlands
11.1.2
Central California Valley
11.1.3
Southern California Mountains
12.1.1
Madrean Archipelago
13.1.1
Arizona/New Mexico Mountains
1
0.0%
15.4.1
Southern Florida Coastal Plain
44
0.8%
Total Tree Count
10640
8855
5813
3089
14566
23425
5B-Attachment 2B-28
-------
NA L3
CODE
US L3NAME
northern red
oak
Median N=10
Assoc N-U
N/S = 0.41
post oak
Median 10
Assoc N-U
N/S =0.14
black oak
Median S=8
Assoc S-J,
N/S =0.15
black locust
Median
N=11,S=12
Assoc N-f.S-J,
N/S =0.19
sassafras
Median S=12
Assoc S-J,
N/S = 0.3
baldcypress
Median S=6
Assoc S-J,
N/S = 0.55
count
%
count
%
count
%
count
%
count
%
count
%
5.2.1
Northern Lakes and Forests
6123
19.3%
743
3.4%
28
0.5%
25
0.4%
5.2.2
Northern Minnesota Wetlands
2
0.0%
5.3.1
Northeastern Highlands
3162
10.0%
173
0.8%
34
0.6%
31
0.5%
5.3.3
North Central Appalachians
1104
3.5%
174
0.8%
12
0.2%
244
3.9%
6.2.3
Northern Rockies
6.2.4
Canadian Rockies
6.2.5
North Cascades
6.2.7
Cascades
6.2.8
Eastern Cascades Slopes and Foothills
6.2.9
Blue Mountains
6.2.10
Middle Rockies
6.2.11
Klamath Mountains
6.2.12
Sierra Nevada
6.2.13
Wasatch and Uinta Mountains
6.2.14
Southern Rockies
6.2.15
Idaho Batholith
7.1.7
Puget Lowland
7.1.8
Coast Range
7.1.9
Willamette Valley
8.1.1
Eastern Great Lakes Lowlands
258
0.8%
24
0.1%
30
0.5%
2
0.0%
8.1.3
Northern Allegheny Plateau
886
2.8%
123
0.6%
65
1.2%
9
0.1%
8.1.4
North Central Hardwood Forests
1604
5.1%
988
4.5%
143
2.6%
5B-Attachment 2B-29
-------
NA L3
CODE
US L3NAME
northern red
oak
Median N=10
Assoc N-U
N/S = 0.41
post oak
Median 10
Assoc N-U
N/S =0.14
black oak
Median S=8
Assoc S-J,
N/S =0.15
black locust
Median
N=11,S=12
Assoc N-f.S-J,
N/S =0.19
sassafras
Median S=12
Assoc S-J,
N/S = 0.3
baldcypress
Median S=6
Assoc S-J,
N/S = 0.55
count
%
count
%
count
%
count
%
count
%
count
%
8.1.5
Driftless Area
1673
5.3%
748
3.4%
128
2.3%
8.1.6
Southern Michigan/Northern Indiana Drift Plains
724
2.3%
820
3.7%
137
2.5%
491
7.8%
8.1.7
Northeastern Coastal Zone
1761
5.6%
911
4.2%
80
1.4%
46
0.7%
8.1.8
Acadian Plains and Hills
868
2.7%
9
0.0%
8.1.10
Erie Drift Plain
233
0.7%
60
0.3%
88
1.6%
60
1.0%
8.2.1
Southeastern Wisconsin Till Plains
157
0.5%
64
0.3%
108
2.0%
8.2.2
Huron/Erie Lake Plains
194
0.6%
25
0.1%
7
0.1%
63
1.0%
8.2.3
Central Corn Belt Plains
73
0.2%
104
0.5%
93
1.7%
43
0.7%
8.2.4
Eastern Corn Belt Plains
250
0.8%
3
0.0%
107
0.5%
184
3.3%
148
2.4%
8.3.1
Northern Piedmont
192
0.6%
10
0.0%
179
0.8%
103
1.9%
91
1.4%
8.3.2
Interior River Valleys and Hills
536
1.7%
551
2.7%
819
3.7%
189
3.4%
600
9.6%
7
0.2%
8.3.3
Interior Plateau
793
2.5%
687
3.4%
1053
4.8%
520
9.4%
1078
17.2%
6
0.1%
8.3.4
Piedmont
901
2.8%
991
4.9%
773
3.5%
120
2.2%
68
1.1%
4
0.1%
8.3.5
Southeastern Plains
200
0.6%
1416
7.0%
445
2.0%
45
0.8%
141
2.2%
712
17.1%
8.3.6
Mississippi Valley Loess Plains
44
0.1%
172
0.8%
81
0.4%
51
0.9%
119
1.9%
88
2.1%
8.3.7
South Central Plains
7
0.0%
1673
8.3%
86
0.4%
14
0.3%
112
1.8%
643
15.4%
8.3.8
East Central Texas Plains
934
4.6%
4
0.0%
3
0.1%
19
0.3%
8.4.1
Ridge and Valley
2603
8.2%
299
1.5%
1593
7.3%
826
14.9%
642
10.2%
8.4.2
Central Appalachians
1336
4.2%
26
0.1%
678
3.1%
603
10.9%
524
8.3%
8.4.3
Western Allegheny Plateau
952
3.0%
46
0.2%
1009
4.6%
605
10.9%
836
13.3%
1
0.0%
8.4.4
Blue Ridge
1439
4.5%
88
0.4%
593
2.7%
752
13.6%
218
3.5%
8.4.5
Ozark Highlands
1437
4.5%
6909
34.1%
7233
33.0%
31
0.6%
334
5.3%
5B-Attachment 2B-30
-------
NA L3
CODE
US L3NAME
northern red
oak
Median N=10
Assoc N-U
N/S = 0.41
post oak
Median 10
Assoc N-U
N/S =0.14
black oak
Median S=8
Assoc S-J,
N/S =0.15
black locust
Median
N=11,S=12
Assoc N-f.S-J,
N/S =0.19
sassafras
Median S=12
Assoc S-J,
N/S = 0.3
baldcypress
Median S=6
Assoc S-J,
N/S = 0.55
count
%
count
%
count
%
count
%
count
%
count
%
8.4.6
Boston Mountains
741
2.3%
996
4.9%
527
2.4%
78
1.4%
36
0.6%
8.4.7
Arkansas Valley
188
0.6%
2263
11.2%
151
0.7%
6
0.1%
4
0.1%
8.4.8
Ouachita Mountains
451
1.4%
1902
9.4%
225
1.0%
5
0.1%
2
0.0%
8.4.9
Southwestern Appalachians
327
1.0%
276
1.4%
547
2.5%
70
1.3%
140
2.2%
8.5.1
Middle Atlantic Coastal Plain
26
0.1%
63
0.3%
73
0.3%
29
0.5%
61
1.0%
456
11.0%
8.5.2
Mississippi Alluvial Plain
8
0.0%
153
0.8%
13
0.1%
18
0.3%
17
0.3%
985
23.7%
8.5.3
Southern Coastal Plain
39
0.2%
2
0.0%
1
0.0%
1044
25.1%
8.5.4
Atlantic Coastal Pine Barrens
16
0.1%
31
0.2%
280
1.3%
10
0.2%
67
1.1%
9.2.1
Northern Glaciated Plains
0
0.0%
9.2.2
Lake Agassiz Plain
2
0.0%
0
0.0%
9.2.3
Western Corn Belt Plains
192
0.6%
7
0.0%
84
0.4%
69
1.2%
9.2.4
Central Irregular Plains
220
0.7%
337
1.7%
336
1.5%
196
3.5%
4
0.1%
9.3.1
Northwestern Glaciated Plains
9.3.3
Northwestern Great Plains
9.3.4
Nebraska Sand Hills
9.4.1
High Plains
9.4.2
Central Great Plains
49
0.9%
9.4.3
Southwestern Tablelands
3
0.1%
9.4.4
Flint Hills
4
0.0%
3
0.0%
9.4.5
Cross Timbers
2
0.0%
372
1.8%
56
0.3%
2
0.0%
9.4.6
Edwards Plateau
0
0.0%
9.4.7
Texas Blackland Prairies
18
0.1%
5B-Attachment 2B-31
-------
NA L3
CODE
US L3NAME
northern red
oak
Median N=10
Assoc N-U
N/S = 0.41
post oak
Median 10
Assoc N-U
N/S =0.14
black oak
Median S=8
Assoc S-J,
N/S =0.15
black locust
Median
N=11,S=12
Assoc N-f.S-J,
N/S =0.19
sassafras
Median S=12
Assoc S-J,
N/S = 0.3
baldcypress
Median S=6
Assoc S-J,
N/S = 0.55
count
%
count
%
count
%
count
%
count
%
count
%
9.5.1
Western Gulf Coastal Plain
12
0.1%
1
0.0%
1
0.0%
29
0.7%
9.6.1
Southern Texas Plains
10.1.2
Columbia Plateau
10.1.3
Northern Basin and Range
10.1.4
Wyoming Basin
10.1.5
Central Basin and Range
10.1.6
Colorado Plateaus
10.1.7
Arizona/New Mexico Plateau
10.1.8
Snake River Plain
10.2.1
Mojave Basin and Range
10.2.2
Sonoran Basin and Range
10.2.10
Chihuahuan Deserts
11.1.1
Southern and Central California Chaparral and
Oak Woodlands
11.1.2
Central California Valley
11.1.3
Southern California Mountains
12.1.1
Madrean Archipelago
13.1.1
Arizona/New Mexico Mountains
15.4.1
Southern Florida Coastal Plain
187
4.5%
Total Tree Count
31689
20277
21914
5533
6278
4162
5B-Attachment 2B-32
-------
NA L3
CODE
US L3NAME
American
basswood
Median S=5
Assoc S-J,
N/S = 0.39
eastern
hemlock
Median N=8
Assoc N-U
N/S =0.78
winged elm
Median N=10
Assoc N-J,
N/S =0.37
American elm
Median
N=11,S=6
Assoc N-J„S-J,
N/S = 0.24
slippery elm
Median N=11,S=8
Assoc N-U, S-J,
N/S =0.07
count
%
count
%
count
%
count
%
count
%
5.2.1
Northern Lakes and Forests
6603
43.4%
3441
13.4%
1340
7.0%
29
0.5%
5.2.2
Northern Minnesota Wetlands
155
1.0%
124
0.6%
5.3.1
Northeastern Highlands
238
1.6%
7956
31.0%
233
1.2%
3
0.1%
5.3.3
North Central Appalachians
192
1.3%
1416
5.5%
23
0.1%
4
0.1%
6.2.3
Northern Rockies
6.2.4
Canadian Rockies
6.2.5
North Cascades
6.2.7
Cascades
6.2.8
Eastern Cascades Slopes and Foothills
6.2.9
Blue Mountains
6.2.10
Middle Rockies
2
0.0%
6.2.11
Klamath Mountains
6.2.12
Sierra Nevada
6.2.13
Wasatch and Uinta Mountains
6.2.14
Southern Rockies
6.2.15
Idaho Batholith
7.1.7
Puget Lowland
7.1.8
Coast Range
7.1.9
Willamette Valley
8.1.1
Eastern Great Lakes Lowlands
343
2.3%
1010
3.9%
508
2.7%
16
0.3%
8.1.3
Northern Allegheny Plateau
398
2.6%
2257
8.8%
136
0.7%
1
0.0%
8.1.4
North Central Hardwood Forests
2072
13.6%
598
2.3%
1420
7.4%
171
3.1%
5B-Attachment 2B-33
-------
NA L3
CODE
US L3NAME
American
basswood
Median S=5
Assoc S-J,
N/S = 0.39
eastern
hemlock
Median N=8
Assoc N-U
N/S =0.78
winged elm
Median N=10
Assoc N-J,
N/S =0.37
American elm
Median
N=11,S=6
Assoc N-J„S-J,
N/S = 0.24
slippery elm
Median N=11,S=8
Assoc N-U, S-J,
N/S =0.07
count
%
count
%
count
%
count
%
count
%
8.1.5
Driftless Area
1198
7.9%
0
0.0%
2130
11.1%
653
11.9%
8.1.6
Southern Michigan/Northern Indiana Drift Plains
437
2.9%
39
0.2%
895
4.7%
107
1.9%
8.1.7
Northeastern Coastal Zone
54
0.4%
1322
5.1%
169
0.9%
2
0.0%
8.1.8
Acadian Plains and Hills
51
0.3%
2848
11.1%
67
0.4%
8.1.10
Erie Drift Plain
129
0.8%
423
1.6%
355
1.9%
64
1.2%
8.2.1
Southeastern Wisconsin Till Plains
333
2.2%
14
0.1%
591
3.1%
86
1.6%
8.2.2
Huron/Erie Lake Plains
214
1.4%
7
0.0%
4
0.1%
444
2.3%
44
0.8%
8.2.3
Central Corn Belt Plains
50
0.3%
167
0.9%
67
1.2%
8.2.4
Eastern Corn Belt Plains
278
1.8%
612
3.2%
200
3.6%
8.3.1
Northern Piedmont
15
0.1%
10
0.0%
117
0.6%
33
0.6%
8.3.2
Interior River Valleys and Hills
100
0.7%
156
2.3%
1168
6.1%
393
7.1%
8.3.3
Interior Plateau
126
0.8%
1
0.0%
718
10.6%
834
4.4%
572
10.4%
8.3.4
Piedmont
13
0.1%
29
0.1%
1071
15.8%
344
1.8%
182
3.3%
8.3.5
Southeastern Plains
32
0.2%
12
0.0%
763
11.3%
496
2.6%
211
3.8%
8.3.6
Mississippi Valley Loess Plains
2
0.0%
524
7.8%
311
1.6%
166
3.0%
8.3.7
South Central Plains
11
0.1%
1437
21.3%
391
2.0%
103
1.9%
8.3.8
East Central Texas Plains
5
0.0%
258
3.8%
58
0.3%
8.4.1
Ridge and Valley
367
2.4%
1269
4.9%
143
2.1%
235
1.2%
177
3.2%
8.4.2
Central Appalachians
742
4.9%
1032
4.0%
8
0.1%
110
0.6%
128
2.3%
8.4.3
Western Allegheny Plateau
189
1.2%
385
1.5%
2
0.0%
869
4.5%
655
11.9%
8.4.4
Blue Ridge
251
1.6%
1305
5.1%
7
0.1%
24
0.1%
21
0.4%
8.4.5
Ozark Highlands
33
0.2%
457
6.8%
821
4.3%
468
8.5%
5B-Attachment 2B-34
-------
NA L3
CODE
US L3NAME
American
basswood
Median S=5
Assoc S-J,
N/S = 0.39
eastern
hemlock
Median N=8
Assoc N-U
N/S =0.78
winged elm
Median N=10
Assoc N-J,
N/S =0.37
American elm
Median
N=11,S=6
Assoc N-J„S-J,
N/S = 0.24
slippery elm
Median N=11,S=8
Assoc N-U, S-J,
N/S =0.07
count
%
count
%
count
%
count
%
count
%
8.4.6
Boston Mountains
21
0.1%
115
1.7%
36
0.2%
43
0.8%
8.4.7
Arkansas Valley
2
0.0%
310
4.6%
62
0.3%
24
0.4%
8.4.8
Ouachita Mountains
3
0.0%
327
4.8%
28
0.1%
15
0.3%
8.4.9
Southwestern Appalachians
66
0.4%
302
1.2%
94
1.4%
43
0.2%
29
0.5%
8.5.1
Middle Atlantic Coastal Plain
20
0.3%
139
0.7%
49
0.9%
8.5.2
Mississippi Alluvial Plain
3
0.0%
223
3.3%
579
3.0%
255
4.6%
8.5.3
Southern Coastal Plain
1
0.0%
24
0.4%
266
1.4%
20
0.4%
8.5.4
Atlantic Coastal Pine Barrens
1
0.0%
9.2.1
Northern Glaciated Plains
13
0.1%
30
0.2%
9.2.2
Lake Agassiz Plain
122
0.8%
139
0.7%
1
0.0%
9.2.3
Western Corn Belt Plains
300
2.0%
960
5.0%
290
5.3%
9.2.4
Central Irregular Plains
54
0.4%
27
0.4%
1250
6.5%
172
3.1%
9.3.1
Northwestern Glaciated Plains
4
0.0%
65
0.3%
2
0.0%
9.3.3
Northwestern Great Plains
69
0.4%
9.3.4
Nebraska Sand Hills
1
0.0%
9.4.1
High Plains
9
0.0%
9.4.2
Central Great Plains
230
1.2%
16
0.3%
9.4.3
Southwestern Tablelands
10
0.1%
0
0.0%
9.4.4
Flint Hills
5
0.0%
129
0.7%
13
0.2%
9.4.5
Cross Timbers
47
0.7%
36
0.2%
5
0.1%
9.4.6
Edwards Plateau
9.4.7
Texas Blackland Prairies
5
0.1%
3
0.0%
5B-Attachment 2B-35
-------
NA L3
CODE
US L3NAME
American
basswood
Median S=5
Assoc S-J,
N/S = 0.39
eastern
hemlock
Median N=8
Assoc N-U
N/S =0.78
winged elm
Median N=10
Assoc N-J,
N/S =0.37
American elm
Median
N=11,S=6
Assoc N-J„S-J,
N/S = 0.24
slippery elm
Median N=11,S=8
Assoc N-U, S-J,
N/S =0.07
count
%
count
%
count
%
count
%
count
%
9.5.1
Western Gulf Coastal Plain
20
0.3%
29
0.2%
6
0.1%
9.6.1
Southern Texas Plains
10.1.2
Columbia Plateau
10.1.3
Northern Basin and Range
10.1.4
Wyoming Basin
10.1.5
Central Basin and Range
10.1.6
Colorado Plateaus
10.1.7
Arizona/New Mexico Plateau
10.1.8
Snake River Plain
10.2.1
Mojave Basin and Range
10.2.2
Sonoran Basin and Range
10.2.10
Chihuahuan Deserts
11.1.1
Southern and Central California Chaparral and
Oak Woodlands
11.1.2
Central California Valley
11.1.3
Southern California Mountains
12.1.1
Madrean Archipelago
13.1.1
Arizona/New Mexico Mountains
15.4.1
Southern Florida Coastal Plain
Total Tree Count
15225
25676
6760
19107
5497
5B-Attachment 2B-36
-------
APPENDIX 6A
DERIVATION OF THE ECOREGION AIR QUALITY
METRICS
TABLE OF CONTENTS
6A. 1. Introduction 6A-1
6A.2. HYSPLIT Trajectory Methodology 6A-1
6A.3. Estimation of Ecoregion Air Quality Metrics 6A-26
6A.4. Combined EAQM and Deposition Data 6A-28
6A.5. Impacts of Three Key Aspects of Methodology on Findings 6A-41
6A.6. Results of HYSPLIT EAQM Analyses 6A-44
6A.6.1. SO: 3-hr Metric - 120-hr 6A-44
6A.6.2. SO: 3-hr Metric - 48-hr 6A-60
6A.6.3. SO2 Annual Metric - 120-hr 6A-64
6A.6.4. SO2 Annual Metric - 48-hr 6A-80
6A.6.5. NO2 Annual Metric - 120-hr 6A-85
6A.6.6. NO2 Annual Metric - 48-hr 6A-95
6A.6.7. PM2.5 Annual Metric - 120-hr 6A-100
6A.6.7.1. Nitrogen 6A-100
6A.6.7.2. Sulfur 6A-110
6A.6.8. PM2.5 Annual Metric - 48-hr 6A-120
6A.6.8.1. Nitrogen 6A-120
6A.6.8.2. Sulfur 6A-122
TABLE OF TABLES
Table 6A-1. EAQM-TDep table for a weighted annual SO2 and S deposition 6A-29
Table 6A-2. Correlation coefficients of TDep-estimated S deposition and 3-hr SO2 EAQMs
generated by HYSPLIT analysis at three monitor inclusion criteria, 120-hr
trajectories 6A-44
Table 6A-3. Correlation coefficients of TDep estimates of sulfur deposition and 3-hr SO2
EAQMs generated by HYSPLIT analysis, 48-hr trajectories. Data are also
split by year and by region (East/West) 6A-60
6A-i
-------
Table 6A-4. Correlation coefficients of TDep estimates of sulfur deposition and annual
SO2 EAQMs generated by HYSPLIT analysis at three different monitor
inclusion criteria, 120-hr trajectories 6A-64
Table 6A-5. Correlation coefficients of TDep estimates of sulfur deposition and annual
SO2 EAQMs generated by HYSPLIT analysis. Data are also split by year and
by region (East/West), 48-hr trajectories 6A-80
Table 6A-6. Correlation coefficients of TDep estimates of nitrogen deposition and annual
NO2 EAQMs generated by HYSPLIT analysis, 120-hr trajectories. Data are
also split by year and by region (East/West) 6A-85
Table 6A-7. Correlation coefficients of TDep estimates of nitrogen deposition and annual
NO2 EAQMs generated by HYSPLIT analysis, 48-hr trajectories. Data are
also split by year and by region (East/West) 6A-95
Table 6A-8. Correlation coefficients of TDep estimates of nitrogen deposition and annual
PM2.5 EAQMs generated by HYSPLIT analysis, 120-hr trajectories. Data are
also split by year and by region (East/West) 6A-100
Table 6A-9. Correlation coefficients of Tdep estimates of sulfur deposition and annual
PM2.5 EAQMs generated by HYSPLIT analysis, 120-hr trajectories. Data are
also split by year and by region (East/West) 6A-110
Table 6A-10. Correlation coefficients of TDep estimates of nitrogen deposition and annual
PM2.5 EAQMs generated by HYSPLIT analysis, 48-hr trajectories. Data are
also split by year and by region (East/West) 6A-120
Table 6A-11. Correlation coefficients of TDep estimates of sulfur deposition and annual
PM2.5 EAQMs generated by HYSPLIT analysis, 48-hr trajectories. Data are
also split by year and by region (East/West) 6A-122
TABLE OF FIGURES
Figure 6A-1. 2016 annual average maximum temperatures by U.S. divisions binned across
seven categories based on how 2016 differed from the 1895-2016
climatological average. (Source: NOAA/NCEI) 5
Figure 6A-2. 2016 annual average precipitation amounts by U.S. divisions binned across
seven categories based on how 2016 differed from the 1895-2016
climatological average. (Source: NOAA/NCEI) 6
Figure 6A-3. Monitoring site locations for which daily HYSPLIT trajectories were
generated for the four air quality metrics 7
Figure 6A-4. Map of PM2.5 monitoring sites of potential influence (red circles) for
ecoregion 8.3.3 (purple shaded region) based on the original trajectories and a
1% hit rate as criterion for monitoring site inclusion. Other PM monitoring
sites that did not meet the criterion are shown as gray circles 9
Figure 6A-5. Monitoring sites (annual SO2 metric) of potential influence for ecoregion 5.2.1
(red shaded region) 11
Figure 6A-6. Monitoring sites (annual SO2 metric) of potential influence for ecoregion 5.3.1
(red shaded region) 12
6A-ii
-------
Figure 6A-7. Monitoring sites (annual SO2 metric) of potential influence for ecoregion 6.2.7
(red shaded region) 13
Figure 6A-8. Monitoring sites (annual SO2 metric) of potential influence for ecoregion
6.2.12 (red shaded region) 14
Figure 6A-9. Monitoring sites (annual SO2 metric) of potential influence for ecoregion
6.2.14 (red shaded region) 15
Figure 6A-10. Monitoring sites (annual SO2 metric) of potential influence for ecoregion
6.2.15 (red shaded region) 16
Figure 6A-11. Monitoring sites (annual SO2 metric) of potential influence for ecoregion
8.1.1 (red shaded region) 17
Figure 6A-12. Monitoring sites (annual SO2 metric) of potential influence for ecoregion
8.1.4 (red shaded region) 18
Figure 6A-13. Monitoring sites (annual SO2 metric) of potential influence for ecoregion
8.3.1 (red shaded region) 19
Figure 6A-14. Monitoring sites (annual SO2 metric) of potential influence for ecoregion
8.3.7 (red shaded region) 20
Figure 6A-15. Monitoring sites (annual SO2 metric) of potential influence for ecoregion
8.4.1 (red shaded region) 21
Figure 6A-16. Monitoring sites (annual SO2 metric) of potential influence for ecoregion
8.4.2 (red shaded region) 22
Figure 6A-17. Monitoring sites (annual SO2 metric) of potential influence for ecoregion
9.4.2 (red shaded region) 23
Figure 6A-18. Monitoring sites (annual SO2 metric) of potential influence for ecoregion
11.1.3 (red shaded region) 24
Figure 6A-19. Monitoring sites (annual SO2 metric) of potential influence for ecoregion
13.1.1 (red shaded region) 25
Figure 6A-20. Monitoring sites (annual SO2 metric) of potential influence for ecoregion
15.4.1 (red shaded region) 26
Figure 6A-21. Annual SO2 EAQM-weighted values and TDep S deposition in 84
ecoregions (48-hr trajectories, NARR-32, 1% monitor inclusion criteria) 42
Figure 6A-22. Annual SO2 EAQM-weighted values and TDep S deposition in 84
ecoregions (120-hr trajectories, NAM-12, 0.5% monitor inclusion criteria) 42
Figure 6A-23. Annual SO2 EAQM-weighted values and TDep S deposition in eastern
ecoregions (48-hr trajectories, NARR-32, 1% monitor inclusion criteria) 43
Figure 6A-24. Annual SO2 EAQM-weighted values and TDep S deposition in eastern
ecoregions (120-hr trajectories, NAM-12, 0.5% monitor inclusion criteria) 43
Figure 6A-25. The 3-hr SO2 EAQM-max values and TDep S deposition in 84 ecoregions
(120-hr trajectories, NAM-12, 1% monitor inclusion criteria): all values
(upper), outliers excluded (lower) 46
6A-iii
-------
Figure 6A-26. The 3-hr SO2 EAQM-max values and TDep S deposition in eastern
ecoregions (120-hr trajectories, NAM-12, 1% monitor inclusion criteria): all
values (upper), outliers excluded (lower) 47
Figure 6A-27. The 3-hr SO2 EAQM-max values and TDep S deposition in western
ecoregions (120-hr trajectories, NAM-12, 1% monitor inclusion criteria) 48
Figure 6A-28. The 3-hr SO2 EAQM-max values and TDep S deposition in 84 ecoregions
(120-hr trajectories, NAM-12, 0.5% monitor inclusion criteria): all values
(upper), outliers excluded (lower) 49
Figure 6A-29. The 3-hr SO2 EAQM-max values and TDep S deposition in eastern
ecoregions (120-hr trajectories, NAM-12, 0.5% monitor inclusion criteria): all
values (upper), outliers excluded (lower) 50
Figure 6A-30. The 3-hr SO2 EAQM-max values and TDep S deposition in western
ecoregions (120-hr trajectories, NAM-12, 0.5% monitor inclusion criteria) 51
Figure 6A-31. The 3-hr SO2 EAQM-max values and TDep S deposition in 84 ecoregions
(120-hr trajectories, NAM-12, 0.1% monitor inclusion criteria): all values
(upper), outliers excluded (lower) 52
Figure 6A-32. The 3-hr SO2 EAQM-max values and TDep S deposition in eastern
ecoregions (120-hr trajectories, NAM-12, 0.1% monitor inclusion criteria): all
values (upper), outliers excluded (lower) 53
Figure 6A-33. The 3-hr SO2 EAQM-max values and TDep S deposition in western
ecoregions (120-hr trajectories, NAM-12, 0.1% monitor inclusion criteria): all
values (upper), outliers excluded (lower) 54
Figure 6A-34. The 3-hr SO2 EAQM-weighted values and TDep S deposition in 84
ecoregions (120-hr trajectories, NAM-12, 1% monitor inclusion criteria) 55
Figure 6A-35. The 3-hr SO2 EAQM-weighted values and TDep S deposition in eastern
ecoregions (120-hr trajectories, NAM-12, 1% monitor inclusion criteria) 55
Figure 6A-36. The 3-hr SO2 EAQM-weighted values and TDep S deposition in western
ecoregions (120-hr trajectories, NAM-12, 1% monitor inclusion criteria) 56
Figure 6A-37. The 3-hr SO2 EAQM-weighted values and TDep S deposition in 84
ecoregions (120-hr trajectories, NAM-12, 0.5% monitor inclusion criteria) 56
Figure 6A-38. The 3-hr SO2 EAQM-weighted values and TDep S deposition in eastern
ecoregions (120-hr trajectories, NAM-12, 0.5% monitor inclusion criteria) 57
Figure 6A-39. The 3-hr SO2 EAQM-weighted values and TDep S deposition in western
ecoregions (120-hr trajectories, NAM-12, 0.5% monitor inclusion criteria) 57
Figure 6A-40. The 3-hr SO2 EAQM-weighted values and TDep S deposition in 84
ecoregions (120-hr trajectories, NAM-12, 0.1% monitor inclusion criteria) 58
Figure 6A-41. The 3-hr SO2 EAQM-weighted values and TDep S deposition in eastern
ecoregions (120-hr trajectories, NAM-12, 0.1% monitor inclusion criteria) 58
Figure 6A-42. The 3-hr SO2 EAQM-weighted values and TDep S deposition in western
ecoregions (120-hr trajectories, NAM-12, 0.1% monitor inclusion criteria) 59
6A-iv
-------
Figure 6A-43. The 3-hr SO2 EAQM-max values and TDep S deposition in 84 ecoregions
(48-hr trajectories, NARR-32, 1% monitor inclusion criteria) 61
Figure 6A-44. The 3-hr SO2 EAQM-max values and TDep S deposition in eastern
ecoregions (48-hr trajectories, NARR-32, 1% monitor inclusion criteria) 61
Figure 6A-45. The 3-hr SO2 EAQM-max values and TDep S deposition in western
ecoregions (48-hr trajectories, NARR-32, 1% monitor inclusion criteria) 62
Figure 6A-46. The 3-hr SO2 EAQM-weightedvalues and TDep S deposition in 84
ecoregions (48-hr trajectories, NARR-32, 1% monitor inclusion criteria) 62
Figure 6A-47. The 3-hr SO2 EAQM-weighted values and TDep S deposition in eastern
ecoregions (48-hr trajectories, NARR-32, 1% monitor inclusion criteria) 63
Figure 6A-48. The 3-hr SO2 EAQM-weighted values and TDep S deposition in western
ecoregions (48-hr trajectories, NARR-32, 1% monitor inclusion criteria) 63
Figure 6A-49. Annual SO2 EAQM-max values and TDep S deposition in 84 ecoregions
(120-hr trajectories, NAM-12, 1% monitor inclusion criteria): all values
(upper), outliers excluded (lower) 66
Figure 6A-50. Annual SO2 EAQM-max values and TDep S deposition in eastern
ecoregions (120-hr trajectories, NAM-12, 1% monitor inclusion criteria): all
values (upper), outliers excluded (lower) 67
Figure 6A-51. Annual SO2 EAQM-max values and TDep S deposition in western
ecoregions (120-hr trajectories, NAM-12, 1% monitor inclusion criteria) 68
Figure 6A-52. Annual SO2 EAQM-max values and TDep S deposition in 84 ecoregions
(120-hr trajectories, NAM-12, 0.5% monitor inclusion criteria): all values
(upper), outliers excluded (lower) 69
Figure 6A-53. Annual SO2 EAQM-max values and TDep S deposition in eastern
ecoregions (120-hr trajectories, NAM-12, 0.5% monitor inclusion criteria): all
values (upper), outliers excluded (lower) 70
Figure 6A-54. Annual SO2 EAQM-max values and TDep S deposition in western
ecoregions (120-hr trajectories, NAM-12, 0.5% monitor inclusion criteria) 71
Figure 6A-55. Annual SO2 EAQM-max values and TDep S deposition in 84 ecoregions
(120-hr trajectories, NAM-12, 0.1% monitor inclusion criteria): all values
(upper), outliers excluded (lower) 72
Figure 6A-56. Annual SO2 EAQM-max values and TDep S deposition in eastern
ecoregions (120-hr trajectories, NAM-12, 0.1% monitor inclusion criteria): all
values (upper), outliers excluded (lower) 73
Figure 6A-57. Annual SO2 EAQM-max values and TDep S deposition in western
ecoregions (120-hr trajectories, NAM-12, 0.1% monitor inclusion criteria): all
values (upper), outliers excluded (lower) 74
Figure 6A-58. Annual SO2 EAQM-weighted values and TDep S deposition in 84
ecoregions (120-hr trajectories, NAM-12, 1% monitor inclusion criteria) 75
6A-v
-------
Figure 6A-59. Annual SO2 EAQM-weighted values and TDep S deposition in eastern
ecoregions (120-hr trajectories, NAM-12, 1% monitor inclusion criteria) 75
Figure 6A-60. Annual SO2 EAQM-weighted values and TDep S deposition in western
ecoregions (120-hr trajectories, NAM-12, 1% monitor inclusion criteria) 76
Figure 6A-61. Annual SO2 EAQM-weighted values and TDep S deposition in 84
ecoregions (120-hr trajectories, NAM-12, 0.5% monitor inclusion criteria) 76
Figure 6A-62. Annual SO2 EAQM-weighted values and TDep S deposition in eastern
ecoregions (120-hr trajectories, NAM-12, 0.5% monitor inclusion criteria) 77
Figure 6A-63. Annual SO2 EAQM-weighted values and TDep S deposition in western
ecoregions (120-hr trajectories, NAM-12, 0.5% monitor inclusion criteria) 77
Figure 6A-64. Annual SO2 EAQM-weighted values and TDep S deposition in 84
ecoregions (120-hr trajectories, NAM-12, 0.1% monitor inclusion criteria) 78
Figure 6A-65. Annual SO2 EAQM-weighted values and TDep S deposition in eastern
ecoregions (120-hr trajectories, NAM-12, 0.1% monitor inclusion criteria) 78
Figure 6A-66. Annual SO2 EAQM-weighted values and TDep S deposition in western
ecoregions (120-hr trajectories, NAM-12, 0.1% monitor inclusion criteria) 79
Figure 6A-67. Annual SO2 EAQM-max values and TDep S deposition in 84 ecoregions
(48-hr trajectories, NARR-32, 1% monitor inclusion criteria): all values
(upper), outliers excluded (lower) 81
Figure 6A-68. Annual SO2 EAQM-max values and TDep S deposition in eastern
ecoregions (48-hr trajectories, NARR-32, 1% monitor inclusion criteria): all
values (upper), outliers excluded (lower) 82
Figure 6A-69. Annual SO2 EAQM-max values and TDep S deposition in western
ecoregions (48-hr trajectories, NARR-32, 1% monitor inclusion criteria) 83
Figure 6A-70. Annual SO2 EAQM-weighted values and TDep S deposition in 84
ecoregions (48-hr trajectories, NARR-32, 1% monitor inclusion criteria) 83
Figure 6A-71. Annual SO2 EAQM-weighted values and TDep S deposition in eastern
ecoregions (48-hr trajectories, NARR-32, 1% monitor inclusion criteria) 84
Figure 6A-72. Annual SO2 EAQM-weighted values and TDep S deposition in western
ecoregions (48-hr trajectories, NARR-32, 1% monitor inclusion criteria) 84
Figure 6A-73. Annual NO2 EAQM-max values and TDep N deposition in 84 ecoregions
(120-hr trajectories, NAM-12, 1% monitor inclusion criteria) 86
Figure 6A-74. Annual NO2 EAQM-max values and TDep N deposition in eastern
ecoregions (120-hr trajectories, NAM-12, 1% monitor inclusion criteria) 87
Figure 6A-75. Annual NO2 EAQM-max values and TDep N deposition in western
ecoregions (120-hr trajectories, NAM-12, 1% monitor inclusion criteria) 87
Figure 6A-76. Annual NO2 EAQM-max values and TDep N deposition in 84 ecoregions
(120-hr trajectories, NAM-12, 0.5% monitor inclusion criteria) 88
6A-vi
-------
Figure 6A-77. Annual NO2 EAQM-max values and TDep N deposition in eastern
ecoregions (120-hr trajectories, NAM-12, 0.5% monitor inclusion criteria) 88
Figure 6A-78. Annual NO2 EAQM-max values and TDep N deposition in western
ecoregions (120-hr trajectories, NAM-12, 0.5% monitor inclusion criteria) 89
Figure 6A-79. Annual NO2 EAQM-max values and TDep N deposition in 84 ecoregions
(120-hr trajectories, NAM-12, 0.1% monitor inclusion criteria) 89
Figure 6A-80. Annual NO2 EAQM-max values and TDep N deposition in eastern
ecoregions (120-hr trajectories, NAM-12, 0.1% monitor inclusion criteria) 90
Figure 6A-81. Annual NO2 EAQM-max values and TDep N deposition in western
ecoregions (120-hr trajectories, NAM-12, 0.1% monitor inclusion criteria) 90
Figure 6A-82. Annual NO2 EAQM-weighted values and TDep N deposition in 84
ecoregions (120-hr trajectories, NAM-12, 1% monitor inclusion criteria) 91
Figure 6A-83. Annual NO2 EAQM-weighted values and TDep N deposition in eastern
ecoregions (120-hr trajectories, NAM-12, 1% monitor inclusion criteria) 91
Figure 6A-84. Annual NO2 EAQM-weighted values and TDep N deposition in western
ecoregions (120-hr trajectories, NAM-12, 1% monitor inclusion criteria) 92
Figure 6A-85. Annual NO2 EAQM-weighted values and TDep N deposition in 84
ecoregions (120-hr trajectories, NAM-12, 0.5% monitor inclusion criteria) 92
Figure 6A-86. Annual NO2 EAQM-max values and TDep N deposition in eastern
ecoregions (120-hr trajectories, NAM-12, 0.5% monitor inclusion criteria) 93
Figure 6A-87. Annual NO2 EAQM-max values and TDep N deposition in western
ecoregions (120-hr trajectories, NAM-12, 0.5% monitor inclusion criteria) 93
Figure 6A-88. Annual NO2 EAQM-weighted values and TDep N deposition in 84
ecoregions (120-hr trajectories, NAM-12, 0.1% monitor inclusion criteria) 94
Figure 6A-89. Annual NO2 EAQM-weighted values and TDep N deposition in eastern
ecoregions (120-hr trajectories, NAM-12, 0.1% monitor inclusion criteria) 94
Figure 6A-90. Annual NO2 EAQM-weighted values and TDep N deposition in western
ecoregions (120-hr trajectories, NAM-12, 0.1% monitor inclusion criteria) 95
Figure 6A-91. Annual NO2 EAQM-max values and TDep N deposition in 84 ecoregions
(48-hr trajectories, NARR-32, 1% monitor inclusion criteria) 96
Figure 6A-92. Annual NO2 EAQM-max values and TDep N deposition in eastern
ecoregions (48-hr trajectories, NARR-32, 1% monitor inclusion criteria) 97
Figure 6A-93. Annual NO2 EAQM-max values and TDep N deposition in western
ecoregions (48-hr trajectories, NARR-32, 1% monitor inclusion criteria) 97
Figure 6A-94. Annual NO2 EAQM-weighted values and TDep N deposition in 84
ecoregions (48-hr trajectories, NARR-32, 1% monitor inclusion criteria) 98
Figure 6A-95. Annual NO2 EAQM-weighted values and TDep N deposition in eastern
ecoregions (48-hr trajectories, NARR-32, 1% monitor inclusion criteria) 98
6A-vii
-------
Figure 6A-96. Annual NO2 EAQM-weighted values and TDep N deposition in western
ecoregions (48-hr trajectories, NARR-32, 1% monitor inclusion criteria) 99
Figure 6A-97. Annual PM2.5 EAQM-max values and TDep N deposition in 84 ecoregions
(120-hr trajectories, NAM-12, 1% monitor inclusion criteria) 101
Figure 6A-98. Annual PM2.5 EAQM-max values and TDep N deposition in eastern
ecoregions (120-hr trajectories, NAM-12, 1% monitor inclusion criteria) 102
Figure 6A-99. Annual PM2.5 EAQM-max values and TDep N deposition in western
ecoregions (120-hr trajectories, NAM-12, 1% monitor inclusion criteria) 102
Figure 6A-100. Annual PM2.5 EAQM-max values and TDep N deposition in 84 ecoregions
(120-hr trajectories, NAM-12, 0.5% monitor inclusion criteria) 103
Figure 6A-101. Annual PM2.5 EAQM-max values and TDep N deposition in eastern
ecoregions (120-hr trajectories, NAM-12, 0.5% monitor inclusion criteria) 103
Figure 6A-102. Annual PM2.5 EAQM-max values and TDep N deposition in western
ecoregions (120-hr trajectories, NAM-12, 0.5% monitor inclusion criteria) 104
Figure 6A-103. Annual PM2.5 EAQM-max values and TDep N deposition in 84 ecoregions
(120-hr trajectories, NAM-12, 0.1% monitor inclusion criteria) 104
Figure 6A-104. Annual PM2.5 EAQM-max values and TDep N deposition in eastern
ecoregions (120-hr trajectories, NAM-12, 0.1% monitor inclusion criteria) 105
Figure 6A-105. Annual PM2.5 EAQM-max values and TDep N deposition in western
ecoregions (120-hr trajectories, NAM-12, 0.1% monitor inclusion criteria) 105
Figure 6A-106. Annual PM2.5 EAQM-weighted values and TDep N deposition in 84
ecoregions (120-hr trajectories, NAM-12, 1% monitor inclusion criteria) 106
Figure 6A-107. Annual PM2.5 EAQM-weighted values and TDep N deposition in eastern
ecoregions (120-hr trajectories, NAM-12, 1% monitor inclusion criteria) 106
Figure 6A-108. Annual PM2.5 EAQM-weighted values and TDep N deposition in west
ecoregions (120-hr trajectories, NAM-12, 1% monitor inclusion criteria) 107
Figure 6A-109. Annual PM2.5 EAQM-weighted values and TDep N deposition in 84
ecoregions (120-hr trajectories, NAM-12, 0.5% monitor inclusion criteria) 107
Figure 6A-110. Annual PM2.5 EAQM-weighted values and TDep N deposition in eastern
ecoregions (120-hr trajectories, NAM-12, 0.5% monitor inclusion criteria) 108
Figure 6A-111. Annual PM2.5 EAQM-weighted values and TDep N deposition in western
ecoregions (120-hr trajectories, NAM-12, 0.5% monitor inclusion criteria) 108
Figure 6A-112. Annual PM2.5 EAQM-weighted values and TDep N deposition in 84
ecoregions (120-hr trajectories, NAM-12, 0.1% monitor inclusion criteria) 109
Figure 6A-113. Annual PM2.5 EAQM-weighted values and TDep N deposition in eastern
ecoregions (120-hr trajectories, NAM-12, 0.1% monitor inclusion criteria) 109
Figure 6A-114. Annual PM2.5 EAQM-weighted values and TDep N deposition in western
ecoregions (120-hr trajectories, NAM-12, 0.1% monitor inclusion criteria) 110
6A-viii
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Figure 6A-115. Annual PM2.5 EAQM-max values and TDep S deposition in 84 ecoregions
(120-hr trajectories, NAM-12, 1% monitor inclusion criteria) 112
Figure 6A-116. Annual PM2.5 EAQM-max values and TDep S deposition in eastern
ecoregions (120-hr trajectories, NAM-12, 1% monitor inclusion criteria) 112
Figure 6A-117. Annual PM2.5 EAQM-max values and TDep S deposition in western
ecoregions (120-hr trajectories, NAM-12, 1% monitor inclusion criteria) 113
Figure 6A-118. Annual PM2.5 EAQM-max values and TDep S deposition in 84 ecoregions
(120-hr trajectories, NAM-12, 0.5% monitor inclusion criteria) 113
Figure 6A-119. Annual PM2.5 EAQM-max values and TDep S deposition in eastern
ecoregions (120-hr trajectories, NAM-12, 0.5% monitor inclusion criteria) 114
Figure 6A-120. Annual PM2.5 EAQM-max values and TDep S deposition in western
ecoregions (120-hr trajectories, NAM-12, 0.5% monitor inclusion criteria) 114
Figure 6A-121. Annual PM2.5 EAQM-max values and Tdep S deposition in 84 ecoregions
(120-hr trajectories, NAM-12, 0.1% monitor inclusion criteria) 115
Figure 6A-122. Annual PM2.5 EAQM-weighted values and TDep S deposition in 84
ecoregions (120-hr trajectories, NAM-12, 1% monitor inclusion criteria) 115
Figure 6A-123. Annual PM2.5 EAQM-weighted values and TDep S deposition in eastern
ecoregions (120-hr trajectories, NAM-12, 1% monitor inclusion criteria) 116
Figure 6A-124. Annual PM2.5 EAQM-weighted values and TDep S deposition in western
ecoregions (120-hr trajectories, NAM-12, 1% monitor inclusion criteria) 116
Figure 6A-125. Annual PM2.5 EAQM-weighted values and TDep S deposition in 84
ecoregions (120-hr trajectories, NAM-12, 0.5% monitor inclusion criteria) 117
Figure 6A-126. Annual PM2.5 EAQM-weighted values and TDep S deposition in eastern
ecoregions (120-hr trajectories, NAM-12, 0.5% monitor inclusion criteria) 117
Figure 6A-127. Annual PM2.5 EAQM-weighted values and TDep S deposition in western
ecoregions (120-hr trajectories, NAM-12, 0.5% monitor inclusion criteria) 118
Figure 6A-128. Annual PM2.5 EAQM-weighted values and TDep S deposition in 84
ecoregions (120-hr trajectories, NAM-12, 0.1% monitor inclusion criteria) 118
Figure 6A-129. Annual PM2.5 EAQM-weighted values and TDep S deposition in eastern
ecoregions (120-hr trajectories, NAM-12, 0.1% monitor inclusion criteria) 119
Figure 6A-130. Annual PM2.5 EAQM-weighted values and TDep S deposition in western
ecoregions (120-hr trajectories, NAM-12, 0.1% monitor inclusion criteria) 119
Figure 6A-131. Annual PM2.5 EAQM-max values and TDep N deposition in 84 ecoregions
(48-hr trajectories, NARR-32, 1% monitor inclusion criteria) 121
Figure 6A-132. Annual PM2.5 EAQM-weighted values and TDep N deposition in 84
ecoregions (48-hr trajectories, NARR-32, 1% monitor inclusion criteria) 121
Figure 6A-133. Annual PM2.5 EAQM-max values and Tdep S deposition in 84 ecoregions
(48-hr trajectories, NARR-32, 1% monitor inclusion criteria) 123
6A-ix
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Figure 6A-134. Annual PM2.5 EAQM-weighted values and TDep S deposition in 84
ecoregions (48-hr trajectories, NARR-32, 1% monitor inclusion criteria)
ATTACHMENT
Maps Showing Monitor Sites of Influence for NO2, PM2.5, and SO2 (3-hour metric) Based
Different Inclusion Criteria for 16 Example Ecoregions
6A-x
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6A.1. INTRODUCTION
In order to better understand the relationship between air quality concentrations and
downwind nitrogen (N) and sulfur (S) deposition, as described in Chapter 6, we conducted air
parcel trajectory modeling, using the Hybrid Single-Particle Lagrangian Integrated Trajectory
(HYSPLIT) model,1 to help identify the meteorological patterns that determine the transport of
pollutant material from source to receptor. Using ambient air quality monitoring sites as
trajectory starting points, we estimated potential "sites of influence" for each of the 84 Level III
ecoregions in the contiguous U.S. The "sites of influence" are used to identify upwind
geographic areas from which emissions potentially contribute to N and S deposition in each
ecoregion. The air quality design values (DV)2 for each ecoregion's set of "sites of influence"
were then used to estimate an Ecoregion Air Quality Metric (EAQM). The EAQM values were
calculated for each ecoregion and for three separate pollutants: NO2, SO2, and PM2.5. Further, we
derived two sets of EAQM values for SO2, one reflecting the DV for the current 3-hour
secondary standard (averaged over three years) and a second for an annual average metric
(averaged over three years). The EAQM values provide a perspective of air quality levels in the
upwind regions that could potentially contribute to downwind deposition levels.
This Appendix describes the methodology used to calculate the air parcel trajectories that
identified sites of influence (6A.2), the methodology used to estimate the EAQM values for each
ecoregion-pollutant metric combination using DVs, or design value-like metrics, based on
historical air quality data (6A.3), as well as the method by which EAQM values and TDep
estimates were linked (6A.4). The appendix then briefly summarizes the results of a series of
sensitivity analyses on several aspects of the EAQM methodology (6A.5). Finally, all of the plots
and tables generated within this analysis are provided (6A.6).
6A.2. HYSPLIT TRAJECTORY METHODOLOGY
The HYSPLIT model is commonly used to compute simple air parcel trajectories using
historical meteorological data. HYSPLIT can simulate the trajectory of air parcels as they are
1 Stein, A.F., Draxler, R.R, Rolph, G.D., Stunder, B.J.B., Cohen, M.D., and Ngan, F„ (2015). NOAA's HYSPLIT
atmospheric transport and dispersion modeling system. Bull. Amer. Meteor. Soc., 96, 2059-2077,
http://dx.doi.Org/10.1175/BAMS-D-14-00110.l."
2 A design value is a statistic that summarizes the air quality data for a given area in terms of the indicator, averaging
time, and form of the standard. Design values can be compared to the level of the standard and are typically used
to designate areas as meeting or not meeting the standard and assess progress towards meeting the NAAQS.
Design values are computed and published annually by EPA (https://www.epa.gov/air-trends/air-qualitv-design-
values). It should be noted that not all of the air quality metrics considered here are existing NAAQS. In those
cases we are using the term "design value" as a proxy for design value-like metrics of the air quality data at a
location.
6A-1
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transported through the atmosphere for a given set of meteorological conditions. One common
application of HYSPLIT is to apply the model in a forward-trajectory mode to evaluate the
transport of hypothetical emissions releases from a specific origin. When trajectories are
calculated over a large number of time periods with representative meteorological conditions,
one can develop a potential zone of influence, or "footprint," for any emissions source from a
specific location. In this exercise, HYSPLIT was used to estimate the frequency at which
simulated air transport trajectories from individual monitoring sites could plausibly have
impacted a downwind ecoregion. In this way, the upwind monitor sites of influence for each of
the 84 ecoregions in the contiguous U.S. were established, indicating the areas where emissions
potentially contribute to deposition in a downwind ecoregion.
In a subsequent step, we investigated relationships between pollutant concentrations at
the upwind sites of influence and N or S deposition estimates for each of the ecoregions. The air
quality metrics utilized for each of the pollutants in this step included DVs for existing standards,
or a DV-like metric for other considered pollutant/averaging time combinations. As explained in
more detail below, multiple HYSPLIT trajectories were generated and analyzed to determine
potential sites of influence for each region, and then all of the valid data from those monitors
were assessed to generate an EAQM for multiple ecoregion-pollutant metric pairs.
Two sets of trajectories were generated, an original analysis and a final analysis. The
basic configurations of the two sets of HYSPLIT simulations were as follows:
• Forward trajectories (i.e., where the air will go from its source)
• Trajectory origin: monitor sites with any valid DV (or DV-like metric) for the
pollutant (in the 2000-2020 period)
• Trajectory length: 48-hour (original analysis), 120-hour (final analysis)
• Trajectory start time: 1800 GMT (i.e., intended to be mid-day)
• Trajectories per site-day: 1
• Trajectory start height: 500 meters
• Trajectory output tracking: every 10 minutes
• Meteorological year: 2016
• Meteorological data:
o Original analysis: 32-km North American Regional Reanalysis (NARR-
32)3;
3 National Centers for Environmental Prediction/National Weather Service/NOAA/U.S. Department of Commerce.
2005, updated monthly. NCEP North American Regional Reanalysis (NARR). Research Data Archive at the
National Center for Atmospheric Research, Computational and Information Systems Laboratory.
https://rda.ucar.edu/datasets/ds608.0/. Accessed 25 May 2017.
6A-2
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o Final analysis: 12-km North American Mesoscale Forecast System (NAM-
12)4
In all, 568,398 individual trajectories were generated. There are several assumptions built into
this HYSPLIT application that kept the exercise manageable, but that may also influence the
outcome. We discuss the rationale for this particular configuration below and introduce
sensitivity analyses conducted for several aspects of this application.
This analysis used a single year of meteorology in 2016. While no single year can be
considered truly representative of all possible wind trajectories and their frequency at any given
location, we note that 2016 marked the transition from a strongly positive Oceanic Nino Index
(ONI) to a weakly negative one by the end of the year5. Using meteorology from a year that
captures each phase of the ONI is presumed to be more likely to represent a broader variety of
wind and transport patterns than a year which captures only a single phase. To compare 2016 to
other meteorological years, Figure 6A-1 illustrates how the average maximum temperatures
across the U.S. in 2016 ranked across the entire 1895-2016 climatological period. The 2016
annual maximum temperatures were higher than the climatological average in the longer record,
due to the changing climate in recent decades, but the difference is generally consistent across
the country (i.e., most areas are "much above average"). Figure 6A-2 shows how annual
precipitation amounts in 2016 compared to the longer-term climatological average. There were
parts of the northeast and southeastern U.S. where 2016 was an anomalously dry year, while
other parts of the U.S. were much wetter than average (e.g., WI, MN, ND). However, a large part
of the U.S. experienced relatively normal precipitation levels in 2016. Based on this cursory
evaluation of the meteorological conditions in 2016, we see no evidence that suggests this year
of meteorology would yield unrepresentative trajectory patterns. We also note that a single
meteorological year, and specifically the year 2016, has also been used in EPA regulatory actions
where transport patterns are evaluated to assess how upwind emissions may impact downwind
areas (e.g., the 2015 Ozone NAAQS Good Neighbor Plan final rulemaking6). We recognize that
the use of a single meteorological year may add uncertainty to the identification of monitoring
4 National Centers for Environmental Prediction, National Weather Service, NOAA, U.S. Department of Commerce.
2015, updated daily. North American Mesoscale Forecast System (NAM). Research Data Archive at the National
Center for Atmospheric Research, Computational and Information Systems Laboratory.
https://www.ncei.noaa.gov/data/north-american-mesoscale-model/access. Accessed 3 July 2023
5 National Weather Service, Climate Prediction Center.
https://origin.cpc.ncep.noaa.gov/products/analvsis monitoring/ensostuff/ONI v5.php. Accessed 06 October
2023.
6 USEPA, Air Quality Modeling Final Rule Technical Support Document - 2015 Ozone NAAQS Good Neighbor
Plan. https://www.epa.gov/svstem/files/documents/2023-03/AO%20Modeling%20Final%20Rule%20TSD.pdf.
Accessed 10/06/2023.
6A-3
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sites with ambient air concentrations that may contribute to downwind deposition, but we expect
that uncertainty is relatively small. And, as noted in Chapter 6, the purpose of these trajectory-
based assessments of upwind concentrations and downwind deposition is to identify the pollutant
and metrics for which such relationships are most evident. This analysis was not designed to be
predictive of any such association.
This analysis evaluated one trajectory per day from each of the monitoring site locations
with a valid DV (or DV-like metric) for a given pollutant across the 2000-2020 period. The
locations of the monitoring sites for each pollutant are shown in Figure 6A-3. The daily
trajectories were initiated at 1800 GMT which is generally midday over the U.S. This
methodological decision ensures that the trajectories start in a period in which the planetary
boundary layer is generally well-mixed and therefore representative of all emissions within the
boundary layer (i.e., ground-level sources and sources with elevated stacks). Additionally, the
decision was made to initiate all of the daily trajectories at 500 meters. Again, this is designed to
ensure that trajectories are generated that are representative of the entire mixed layer where the
most significant transport takes place (as opposed to the shallow surface layer that may exist at
night or during temperature inversion conditions). We note that this choice of trajectory height is
consistent with past EPA practice (e.g., designations guidance7) and consistent with the
recommendations of HYSPLIT developers ("if only starting at one height, then a good choice
might be one half of the planetary boundary layer"8). Again, these methodological choices, while
sensible and consistent with past practice, do have the potential to affect the ultimate
identification of the sites of influence. That said, we think the uncertainty associated with these
choices is relatively low and entirely consistent with the intended use of these data (i.e., inform
illustrative relationships).
7 Memorandum from Gina McCarthy to USEPA Regional Administrators,
https://www3.epa.gov/pmdesignations/2012standards/docs/april2013guidance.pdf. Accessed 10/06/2023.
8 NOAA, HYSPLIT Cheat Sheet. https://www.readv.noaa.gov/documents/ppts/Cheat Sheet 2020.pdf. Accessed
10/06/2023.
6A-4
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Divisional Maximum Temperature Ranks
January-December 2016
Period: 1895-2016
Record Much Beto.v Near
Coldest Below Average Average
Average
Above Much Record
Average Above Warmest
Average
Figure 6A-1. 2016 annual average maximum temperatures by U.S. divisions binned
across seven categories based on how 2016 differed from the 1895-2016
climatological average. (Source: NOAA/NCEI)
6A-5
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Divisional Precipitation Ranks
January-December 2016
Period: 1895-2016
Naaonal Centers for
Environmental
Information
42017
Record Much Betow Near Above Much Recort
Dnesl Betow Average Average Average Above Wetley
Average Average
Figure 6A-2. 2016 annual average precipitation amounts by U.S. divisions binned across
seven categories based on how 2016 differed from the 1895-2016
climatological average. (Source: NOAA/NCEI)
6A-6
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6A-7
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As noted above, two sets of analyses were generated: the original analyses and the final
analyses. Forward trajectories were calculated of a specified duration (48- or 120-hours) with an
initial plume height of 500 m and a single year (2016) of meteorological data. For the original
analyses, the meteorological input were from the 32-km resolution North American Regional
Reanalysis (NARR-32). The NARR-32 dataset is one of several meteorological input options for
HYSPLIT. Because the resolution of the meteorological data governing the forward trajectories
was a relatively coarse 32 km for the original 48-hr modeling, there can be some uncertainty in
cases where a trajectory only interacts with the periphery of an ecoregion, as to whether or not
the upwind site should be considered as a site of influence. For the final analyses, we used
meteorological data from the finer-resolution 12-km North American Mesoscale System (NAM-
12) for the 120-hr HYSPLIT modeling. While the finer resolution meteorological data is
expected to allow for more precision, this choice is not expected to significantly affect the
results, and the same considerations with respect to the periphery of an ecoregion would likely
still apply. The meteorological data source and the trajectory length were the only inputs that
differed between the original and final analyses.
Each trajectory was divided into sequential segments corresponding to 10 minutes of the
trajectory length (i.e., 288 segments for a 48-hour trajectory) to trace the trajectory at a relatively
fine temporal frequency. Using geospatial tools, we assessed the number of forward trajectory
segments for a day's trajectory from an individual monitoring site that fell into each of
ecoregions. If at any point, the trajectory crossed into the boundary of the ecoregion, this
trajectory site-day was counted as a "hit." The analysis evaluated the frequency of trajectory
"hits" for each monitoring site / ecoregion pair. In the initial analysis, if more than 1% of the
total hits for an ecoregion could be tracked back to a monitoring site, then that site was
considered to be potentially representative of the air quality concentrations that influence
deposition in that ecoregion. Figure 6A-4 depicts the outcome of this analysis using this
"monitor inclusion criterion" of 1% and a 48-hour trajectory duration for one ecoregion-pollutant
metric pair. For this ecoregion in central Kentucky (8.3.3), given the prevailing winds, the
original trajectory analysis indicates that PM2.5 from sites within the ecoregion itself, along with
some sites in surrounding upwind areas (e.g., Southwest IN, Central TN) may contribute to N
and S deposition within the ecoregion, given the analysis parameters.
6A-8
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Figure 6A-4. Map of PM2.5 monitoring sites of potential influence (red circles) for
ecoregion 8.3.3 (purple shaded region) based on the original trajectories
and a 1% hit rate as criterion for monitoring site inclusion. Other PM
monitoring sites that did not meet the criterion are shown as gray circles.
We then considered whether a longer duration trajectory might be more appropriate for
evaluating the totality of transport paths of S and N emissions that can contribute to downwind
deposition. The final trajectory analysis was borne out of sensitivity testing that considered 5-day
(120-hour) trajectories. Additionally, we conducted sensitivity tests to assess how different
values for the monitoring site inclusion criterion could affect the determination of potential sites
of influence. These sensitivity analysis were designed to enable consideration of more distant
monitoring locations that could also be considered as part of an ecoregion's set of sites of
influence given the relatively long atmospheric lifetimes of some pollutants and potential long
transport distances that can contribute to deposition. We considered three different hit rates as
criteria for monitoring site inclusion (1.0%, 0.5%, and 0.1%).
A set of sample sensitivity results are shown in Figures 6A-5 through 6A-20. These maps
illustrate the impact, for 16 example ecoregions, of using different trajectory hit rates as criteria
for monitoring site inclusion using the monitoring sites for the annual SO2 metric. Sample results
6A-9
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for these 16 ecoregions for the other three metrics are included in Attachment 1 to Appendix 6A.
In each figure, the black circles represent sites contributing at least 1.0% of the total trajectory
"hits" to the ecoregion. Dark blue circles are sites contributing 0.5% to 1% of the ecoregion's
trajectory hits. Light blue circles are sites contributing 0.1 to 0.5% of the total trajectory hits.
Other monitoring sites contributing less than 0.1% of an ecoregion's total trajectory hits are
shown as gray circles.
Looking at the Northern Lakes and Forests Ecoregion (5.2.1), Figure 6A-5 shows that the
upwind sites contributing at least 1.0 percent of the total trajectory hits are located in the
ecoregion itself or in an area in close proximity to the ecoregion. These locations are depicted by
the black circles and suggest that transport of air pollution into this ecoregion is generally from
the south and the west. Reducing the hit rate inclusion criterion to include sites contributing as
low as 0.5%) of the ecoregion's total hits increases the number of sites of influence. Specifically,
including sites with hit rates at or above 0.5% (dark blue and black circles) results in sites of
influence in an area extending from North Dakota to northern Oklahoma, as well as many more
sites in the northern Mississippi River Valley (Figure 6A-5). Finally, we examined the sites of
influence associated with a hit rate at or above 0.1% (light blue, dark blue and black circles). For
this inclusion criterion, the analysis indicates trajectories reaching the Northern Lakes and
Forests ecoregion from as far away as California and southern Texas (Figure 6A-5). The
remainder of the figures show the sensitivity results for 15 other ecoregions.
It is not possible to determine with certainty which monitor inclusion criterion is most
appropriate for identifying the possible sites of influence (over which the EAQM is calculated).
Based on the results of our sensitivity analyses, using 120-hour trajectories, the 0.5% threshold
appears to be a better match with pollutant deposition lifetimes and therefore appropriate for
considering how transport from one upwind area may affect a downwind area. As a result, the
final analysis has used this threshold value (0.5%).
6 A-10
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~ >1% ofecoregion hits
~ 0.5% to <1% ofecoregion hits
~ 0.1% to <0.5% ofecoregion hits
o <0.1% of ecoregion hits
I Northern Lakes and Forests (Ecoregion 5.2.1)
Figure 6A-5. Monitoring sites (annual SO2 metric) of potential influence for ecoregion
5.2.1 (red shaded region).
6 A-11
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S02 Monitor Sites of Influence
(Annual)
• >1 % of ec oregion hits
• 0.5% to <1% of ecoregion nits
• 0.1% to <0.5% of ecoregion hits
® <0.1% of ecoregion hits
Northeastern Highlands (Ecoregion 5.3.1)
Figure 6A-6. Monitoring sites (annual SO2 metric) of potential influence for ecoregion
5.3.1 (red shaded region).
6A-12
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S02 Monitor Sites of Influence
(Annual)
• >1% ofecoregion hits
• 0 5% to <1% ofecoregion hits
• 0.1% to <0.5% ofecoregion hits
® <0.1% of ecoregion hits
3 Cascades (Ecoregion 6.2.7)
Figure 6A-7. Monitoring sites (annual SO2 metric) of potential
6.2.7 (red shaded region).
6A-13
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S02 Monitor Sites of Influence
(Annual)
• >1% ofecoreglori hits
• 0.5% to <1% of ecoregior hits
• 0.1% to <0.5% of ecoregion hits
® <0.1% of ecoregion hits
Sierra Nevada (Ecoregion 6.2.12)
Figure 6A-8. Monitoring sites (annual SO2 metric) of potential
6.2.12 (red shaded region).
6A-14
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S02 Monitor Sites of Influence
(Annual)
• >1% of ecoregion hits
• 0.5% to <1% of ecoregion hits
• 0.1% to <0.5% of ecoregion hits
® <0.1% of ecoregion hits
12 Southern Rockies (Ecoregion 6.2.14)
Figure 6A-9. Monitoring sites (annual SO2 metric) of potential
6.2.14 (red shaded region).
6A-15
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S02 Monitor Sites of Influence
(Annual)
• >1% of ecoregiori hits
• 0.5% to <1% of ecoregiori hits
• 0.1% to <0.5% of ecoregion hits
® <0.1% of ecoregion hits
I Idaho Batholith (Ecoregion 6.2.15)
Figure 6A-10. Monitoring sites (annual SO2 metric) of potential influence for ecoregion
6.2.15 (red shaded region).
6A-16
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S02 Monitor Sites of
(Annual)
• >1% ofecoregion hits
• 0.5% to <1% of ecoregion hits
• 0.1% to <0.5% ot ecoregion hits
o <0.1% ofecoregion hits
^ Eastern Great Lakes Lowlands (Ecoregion 8.1.1)
Figure 6A-11. Monitoring sites (annual SO2 metric) of potential influence for ecoregion
8.1.1 (red shaded region).
6A-17
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S02 Monitor Sites of Influence
(Annual)
• >1% of ecoregion hits
• 0-5% to <1% of ecoregion hits
• 01% to <0 5% of ecoregion hits
® <0.1% of ecoregion hits
HI North Central Hardwood Forests (Ecoregion 8.1.4)
Figure 6A-12. Monitoring sites (annual SO2 metric) of potential influence for ecoregion
8.1.4 (red shaded region).
6A-18
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S02 Monitor Sites of Influence
(Annual)
• >1% of ecoregion hits
• 0.5% to <1% of ecoregion hits
• 0.1% to <0.5% of ecoregion hits
o <0.1% of ecoregion hits
HI Northern Piedmont (Ecoregion 8.3.1)
Figure 6A-13. Monitoring sites (annual SO2 metric) of potential influence for ecoregion
8.3.1 (red shaded region).
6A-19
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S02 Monitor Sites of Influence
(Annual)
• >1% of ecoregion hits
• 0.5% to <1% of ecoregion hits
* 0.1% to <0.5% of ecoregion hits
* <0.1% ofecoiegion hits
I I South Central Plains (Ecoregion 8.3.7)
Figure 6A-14. Monitoring sites (annual SO2 metric) of potential influence for ecoregion
8.3.7 (red shaded region).
6A-20
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S02 Monitor Sites of Influence
(Annual)
• >1% of ecoreglon hits
• 0.5% to <1% of ecoregion hits
• 0.1% to <0.5% of ecoregion hits
• <0.1% of ecoregion hits
HI Ridge and Valley (Ecoregion 8.4.1)
L %° \
0
Figure 6A-15. Monitoring sites (annual SO2 metric) of potential influence for ecoregion
8.4.1 (red shaded region).
6A-21
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S02 Monitor Sites of Influence
(Annual)
• >1% of ecoregion hits
• 0.5% to <1% of ecoregion hits
• 0.1% to <0.5% of ecoregion hits
o <0.1% of ecoregion hits
I I Central Appalachians (Ecoregion 8.4.2)
Figure 6A-16. Monitoring sites (annual SO2 metric) of potential
8.4.2 (red shaded region).
6A-22
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S02 Monitor Sites of Influence
(Annual)
• >1% ofecoreglon hits
• 0.5% to <1% of ecoregion hits
• 0.1% to <0.5% ofecoreglon hits
® <0.1% of ecoregion hits
| Central Great Plains (Ecoregion 9.4.2)
Figure 6A-17. Monitoring sites (annual SO2 metric) of potential influence
9.4.2 (red shaded region).
6A-23
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• >1% ofecoregion hits
• 0.5% to <1% of ecoregion hits
• 0.1% to <0.5% ofecoregion hits
o <0.1% of ecoregion hits
Southern California Mountains (Ecoregion 11.1.3)
Figure 6A-18. Monitoring sites (annual SO2 metric) of potential influence for ecoregion
11.1.3 (red shaded region).
6A-24
-------
• >1% ofecoregion hits
• 0.5% to <1% of ecoregion hits
• 0.1% to <0.5% of ecoregion hits
o <0.1% of ecoregion hits
HI Arizona/New Mexico Mountains (Ecoregion 13.1.1)
Figure 6A-19. Monitoring sites (annual SO2 metric) of potential influence for ecoregion
13.1.1 (red shaded region).
6A-25
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StfcA, o
S02 Monitor Sites of Influence1-
(Annual)
* >1% of ecoregion hits
• 0.5% to <1% of ecoregion hits
» 0.1 % to <0.5% of ecoreg ion hits
0 <0.1% of ecoregion hits
Southern Florida Coastal Plain (Ecoregion 15.4.1)
Figure 6A-20. Monitoring sites (annual SO2 metric) of potential influence for ecoregion
15.4.1 (red shaded region).
6A.3. ESTIMATION OF ECOREGION AIR QUALITY METRICS
After the trajectories were generated and the air quality monitoring sites of influence
were identified for each ecoregion-pollutant metric pair, the next step in this analysis was to
investigate the relationship between air quality levels at the upwind sites and deposition levels in
the downwind ecoregion. For each pollutant metric, two types of EAQMs were derived for each
ecoregion based on the air quality data for that ecoregion's contributing monitors:
• EAOM-max: the highest value from any monitor wi thin the sites of influence, and
• EAOM-weightsd: a weighted average, where each monitor value is weighted by the
percentage of HYSPLIT hits to the ecoregion.
Both versions of EAQMs have value. EAQM-max represents the highest EAQM within
the upwind region potentially contributing to deposition in an ecoregion, and as such it enables
an assessment of the relationship between deposition levels and worst-case m onitored air quality
that is associated with that level of deposition. Given that EAQM-weighted considers the relative
contributions from different upwind directions, it is presumed to represent thq general-ccise
upwind air quality that is associated with downwind deposition. Design values at sites closer to
6A-26
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the ecoregion itself will have more weight as impactful trajectories from these locations are more
common. Both types of EAQMs have inherent uncertainties related to the trajectories
themselves, the methodology used to link upwind regions to downwind receptors (e.g., monitor
inclusion criterion), and the density of the existing monitoring network. All EAQM values are
averaged over 3 years. EAQMs were generated for the following periods: 2001-2003, 2006-
2008, 2010-2012, 2014-2016, and 2018-2020. Both types of EAQMs were generated for each of
the 84 Ecoregion III areas for four separate combinations of pollutant and averaging time:
• SO2: annual 2nd high of individual 3-hour averages, averaged over 3-year periods
• SO2: annual average of hourly data, averaged over 3-year periods
• NO2: annual average of hourly data, averaged over 3-year periods
• PM2.5: annual average of hourly data, averaged over 3-year periods
To provide further explanation of the EAQM calculation, we consider a specific example
EAQM value and the individual steps that lead to its calculation. For this example, we will
consider the annual SO2 metric in 2020 for Ecoregion 5.2.1 (Northern Lakes and Forests).
• Step 1: Identify sites with valid annual SO2 data for any year between 2000 and 2020.
• Step 2: Apply HYSPLIT to simulate 120-hour forward trajectories from these locations.
• Step 3: Evaluate the number of trajectory segments that reside in Ecoregion 5.2.1 and
determine which sites contribute at least 0.5% of the total trajectory segments that impact
the ecoregion. For this example, 74 sites meet the 0.5% criterion and are plotted as either
black or dark blue circles in Figure 6A-5. The site with the most frequent trajectory
impacts was site 55-041-0007 in Forest County, WI. The site of influence with the lowest
hit percentage above 0.5% was 29-093-0034 in Iron County, MO.
• Step 4: For EAQM-max, determine which of the 74 potential sites of influence had the
highest annual SO2 metric value in each year. For 2018 through 2020, the maximum
values were 3.31 ppb (Macon County, IL), 2.01 ppb (Mercer County, ND) and 2.13 ppb
(Macon County, IL). The average of these three values (EAQM-max for 2018-2020) was
then paired with the 2018-2020 3-year average deposition estimates from the ecoregion.
Accordingly, for the analyses in Chapter 6 of the PA that consider the relationships
between upwind air quality concentrations (i.e., EAQMs) and downwind deposition in an
ecoregion, in this example for the 2018-2020 period we are pairing 2.49 ppb, as the 3-
year average of the highest annual average SO2 concentrations at sites of influence with
the 2018-2020, 3-year-average annual total S deposition in Ecoregion 5.2.1. We
recognize, however, that the monitoring network density may not always allow for the
capture of all pollution concentrations that ultimately contribute to downwind deposition,
and that atmospheric loading (and consequently subsequent deposition) is more a
6A-27
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function of the uniform distribution of air concentrations. As such, the EAQM-max
associations should consider that caveat.
• Step 5: To calculate the EAQM-weighted value for the same time period, access the 2018,
2019 and 2020 annual average concentrations from the 74 potential sites of influence and
derive a weighted average for each year from the site-specific concentrations weighted by
the fraction of impacting trajectories arising from that site. Then take the 3-year average
of the EAQM-weighted values for the three years. The resulting EAQM-weighted SO2
for Ecoregion 5.2.1 in 2018-2020 was 0.77 ppb. For the analyses in Chapter 6 of the PA
that consider the relationships between upwind air quality concentrations (i.e., EAQMs)
and downwind deposition in an ecoregion, in this example we are concluding that as a
regional generality, the annual average SO2 value contributing to 2018-2020 total S
deposition in Ecoregion 5.2.1 is 0.77 ppb.
As discussed further in Chapter 6, this analytical work culminates in a series of plots
which display how the upwind EAQMs are related to median S and N deposition values for the
downwind ecoregions. Again, the goal of this exercise is to examine the strengths of these
associations and not to establish predictive relationships between EAQM values and deposition.
The findings of this analysis are intended to help inform conclusions regarding the pollutants and
concentration averaging times most strongly associated with eventual downwind deposition and
might be useful in identifying policy options for controlling deposition with the potential for
welfare effects.
6A.4. COMBINED EAQM AND DEPOSITION DATA
Linking the EAQM and ecoregion deposition data for the analysis in Chapter 6 was
straightforward. As noted above, 8 sets of EAQM values were generated (i.e., 2 types for 4
different pollutant-forms) for five separate 3-year time periods between 2000 and 2020. Median
ecoregion S and N deposition estimates (averages for the five 3-year periods) were then linked to
the EAQM values for each ecoregion. The median TDep deposition value for a given year is
derived from the estimates for all of the grid cells that comprise the ecoregion (at level III
delineation).9 Median deposition values were calculated using the zonal statistics tool in
ArcMap. Grid cells from the TDep dataset were included if the centroid of the grid was within
the ecoregion boundary.
The result was eight tables (one for each combination of the type of EAQM and the four
pollutant metrics) that contain an EAQM value and TDep-based deposition value for each
9 Deposition estimates for S and N were based on TDep v.2018.02 in all EAQM analyses (see
https ://nadp. slh. wise, edu/committees/tdep/).
6A-28
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ecoregion and each of the 3-year time periods. The SO2 tables (annual and 3-hour) include S
deposition values. All other tables (i.e., NO2, and PM2.5) include N deposition values. An
example EAQM table is shown in Table 6A-1.
Table 6A-1. EAQM-TDep table for a weighted annual SO2 and S deposition.
Ecoregion
EAQM-weighted
annual SO2 (ppb)
Median ecoregion
S dep (kg S/ha-yr)
Period
10.1.2
1.8
0.5
2001-2003
10.1.3
1.9
0.3
2001-2003
10.1.4
3.0
0.6
2001-2003
10.1.5
1.6
0.5
2001-2003
10.1.6
2.0
0.7
2001-2003
10.1.7
2.2
0.8
2001-2003
10.1.8
2.3
0.5
2001-2003
10.2.1
1.3
0.6
2001-2003
10.2.2
2.1
0.5
2001-2003
10.2.4
1.7
1.2
2001-2003
11.1.1
1.5
1.1
2001-2003
11.1.2
1.5
1.1
2001-2003
11.1.3
1.3
1.2
2001-2003
12.1.1
2.6
1.2
2001-2003
13.1.1
2.3
1.4
2001-2003
15.4.1
1.9
6.0
2001-2003
5.2.1
2.4
4.3
2001-2003
5.2.2
1.9
2.3
2001-2003
5.3.1
3.8
6.5
2001-2003
5.3.3
8.4
18.1
2001-2003
6.2.10
3.0
1.0
2001-2003
6.2.11
1.5
0.9
2001-2003
6.2.12
1.4
1.3
2001-2003
6.2.13
2.1
1.4
2001-2003
6.2.14
2.4
1.1
2001-2003
6.2.15
2.5
0.9
2001-2003
6.2.3
2.2
0.9
2001-2003
6.2.4
2.4
1.2
2001-2003
6.2.5
1.7
1.6
2001-2003
6.2.7
1.7
1.7
2001-2003
6A-29
-------
Ecoregion
EAQM-weighted
annual SO2 (ppb)
Median ecoregion
S dep (kg S/ha-yr)
Period
6.2.8
1.5
0.4
2001-2003
6.2.9
1.7
0.5
2001-2003
7.1.7
1.8
2.1
2001-2003
7.1.8
1.5
2.4
2001-2003
7.1.9
1.8
1.6
2001-2003
8.1.1
6.6
11.0
2001-2003
8.1.10
7.7
18.4
2001-2003
8.1.3
6.6
11.9
2001-2003
8.1.4
2.0
4.6
2001-2003
8.1.5
2.6
5.4
2001-2003
8.1.6
4.2
9.6
2001-2003
8.1.7
4.7
9.6
2001-2003
8.1.8
3.9
4.5
2001-2003
8.2.1
3.3
7.0
2001-2003
8.2.2
4.7
9.9
2001-2003
8.2.3
3.9
9.8
2001-2003
8.2.4
5.1
14.8
2001-2003
8.3.1
6.4
14.9
2001-2003
8.3.2
4.3
10.5
2001-2003
8.3.3
4.7
13.5
2001-2003
8.3.4
3.9
11.7
2001-2003
8.3.5
2.9
9.7
2001-2003
8.3.6
3.4
8.6
2001-2003
8.3.7
2.7
7.3
2001-2003
8.3.8
2.2
6.4
2001-2003
8.4.1
5.6
14.1
2001-2003
8.4.2
6.3
16.2
2001-2003
8.4.3
8.3
20.4
2001-2003
8.4.4
4.1
11.1
2001-2003
8.4.5
3.1
6.3
2001-2003
8.4.6
2.6
6.0
2001-2003
8.4.7
2.6
5.5
2001-2003
8.4.8
2.6
6.2
2001-2003
8.4.9
4.0
14.7
2001-2003
8.5.1
3.7
10.5
2001-2003
8.5.2
3.3
7.4
2001-2003
6A-30
-------
Ecoregion
EAQM-weighted
annual SO2 (ppb)
Median ecoregion
S dep (kg S/ha-yr)
Period
8.5.3
2.6
7.9
2001-2003
8.5.4
5.4
14.0
2001-2003
9.2.1
2.2
2.0
2001-2003
9.2.2
1.8
2.0
2001-2003
9.2.3
2.2
4.5
2001-2003
9.2.4
2.6
5.8
2001-2003
9.3.1
2.5
1.6
2001-2003
9.3.3
2.8
1.2
2001-2003
9.3.4
2.6
1.7
2001-2003
9.4.1
2.5
1.6
2001-2003
9.4.2
2.3
3.1
2001-2003
9.4.3
2.2
1.3
2001-2003
9.4.4
2.6
4.4
2001-2003
9.4.5
2.1
4.6
2001-2003
9.4.6
2.0
3.1
2001-2003
9.4.7
1.9
6.1
2001-2003
9.5.1
2.4
6.9
2001-2003
9.6.1
1.7
3.7
2001-2003
10.1.2
1.2
0.4
2006-2008
10.1.3
1.4
0.4
2006-2008
10.1.4
1.8
0.7
2006-2008
10.1.5
1.3
0.4
2006-2008
10.1.6
1.5
0.7
2006-2008
10.1.7
1.8
0.8
2006-2008
10.1.8
1.7
0.7
2006-2008
10.2.1
1.1
0.4
2006-2008
10.2.2
2.0
0.5
2006-2008
10.2.4
1.8
1.1
2006-2008
11.1.1
1.1
1.0
2006-2008
11.1.2
1.1
0.9
2006-2008
11.1.3
1.0
1.1
2006-2008
12.1.1
2.7
1.1
2006-2008
13.1.1
2.1
1.4
2006-2008
15.4.1
0.9
5.2
2006-2008
5.2.1
2.2
3.2
2006-2008
5.2.2
1.6
2.1
2006-2008
6A-31
-------
Ecoregion
EAQM-weighted
annual SO2 (ppb)
Median ecoregion
S dep (kg S/ha-yr)
Period
5.3.1
3.0
5.8
2006-2008
5.3.3
6.1
15.1
2006-2008
6.2.10
1.8
1.1
2006-2008
6.2.11
1.1
1.1
2006-2008
6.2.12
1.1
1.1
2006-2008
6.2.13
1.6
1.4
2006-2008
6.2.14
1.7
1.2
2006-2008
6.2.15
1.5
1.2
2006-2008
6.2.3
1.4
1.0
2006-2008
6.2.4
1.5
1.3
2006-2008
6.2.5
1.2
1.6
2006-2008
6.2.7
1.2
1.7
2006-2008
6.2.8
1.1
0.5
2006-2008
6.2.9
1.2
0.5
2006-2008
7.1.7
1.2
1.6
2006-2008
7.1.8
1.1
2.1
2006-2008
7.1.9
1.3
1.5
2006-2008
8.1.1
4.8
8.8
2006-2008
8.1.10
5.7
15.1
2006-2008
8.1.3
4.8
10.2
2006-2008
8.1.4
1.9
3.4
2006-2008
8.1.5
2.3
5.0
2006-2008
8.1.6
3.5
8.3
2006-2008
8.1.7
3.4
8.4
2006-2008
8.1.8
2.9
4.6
2006-2008
8.2.1
3.0
6.4
2006-2008
8.2.2
4.2
8.6
2006-2008
8.2.3
3.3
9.0
2006-2008
8.2.4
4.0
12.0
2006-2008
8.3.1
5.2
12.6
2006-2008
8.3.2
3.5
9.3
2006-2008
8.3.3
4.1
11.0
2006-2008
8.3.4
3.5
9.6
2006-2008
8.3.5
2.7
8.1
2006-2008
8.3.6
3.2
6.7
2006-2008
8.3.7
2.3
6.8
2006-2008
6A-32
-------
Ecoregion
EAQM-weighted
annual SO2 (ppb)
Median ecoregion
S dep (kg S/ha-yr)
Period
8.3.8
1.8
5.1
2006-2008
8.4.1
4.8
11.9
2006-2008
8.4.2
5.2
13.3
2006-2008
8.4.3
6.1
16.4
2006-2008
8.4.4
3.8
9.3
2006-2008
8.4.5
2.6
5.8
2006-2008
8.4.6
2.4
5.7
2006-2008
8.4.7
2.3
5.2
2006-2008
8.4.8
2.3
5.8
2006-2008
8.4.9
3.9
11.6
2006-2008
8.5.1
2.9
9.3
2006-2008
8.5.2
3.2
6.1
2006-2008
8.5.3
1.6
6.0
2006-2008
8.5.4
3.9
12.3
2006-2008
9.2.1
1.2
2.1
2006-2008
9.2.2
1.2
2.0
2006-2008
9.2.3
1.9
4.3
2006-2008
9.2.4
2.5
5.3
2006-2008
9.3.1
1.2
1.6
2006-2008
9.3.3
1.5
1.3
2006-2008
9.3.4
1.5
2.0
2006-2008
9.4.1
1.7
1.5
2006-2008
9.4.2
1.7
3.0
2006-2008
9.4.3
1.6
1.2
2006-2008
9.4.4
2.1
4.0
2006-2008
9.4.5
1.7
4.0
2006-2008
9.4.6
1.5
2.8
2006-2008
9.4.7
1.6
4.9
2006-2008
9.5.1
1.8
5.6
2006-2008
9.6.1
1.3
3.0
2006-2008
10.1.2
0.9
0.4
2010-2012
10.1.3
1.1
0.5
2010-2012
10.1.4
1.5
0.5
2010-2012
10.1.5
1.0
0.5
2010-2012
10.1.6
1.2
0.6
2010-2012
10.1.7
1.3
0.6
2010-2012
6A-33
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Ecoregion
EAQM-weighted
annual SO2 (ppb)
Median ecoregion
S dep (kg S/ha-yr)
Period
10.1.8
1.2
0.6
2010-2012
10.2.1
0.8
0.4
2010-2012
10.2.2
1.4
0.5
2010-2012
10.2.4
1.3
1.1
2010-2012
11.1.1
0.8
0.9
2010-2012
11.1.2
0.8
0.8
2010-2012
11.1.3
0.7
1.1
2010-2012
12.1.1
2.2
0.9
2010-2012
13.1.1
1.6
1.2
2010-2012
15.4.1
0.6
4.2
2010-2012
5.2.1
1.5
2.4
2010-2012
5.2.2
1.1
1.5
2010-2012
5.3.1
2.0
3.0
2010-2012
5.3.3
3.2
7.2
2010-2012
6.2.10
1.3
0.9
2010-2012
6.2.11
0.9
1.0
2010-2012
6.2.12
0.8
1.2
2010-2012
6.2.13
1.3
1.2
2010-2012
6.2.14
1.2
0.9
2010-2012
6.2.15
1.1
1.1
2010-2012
6.2.3
1.0
0.8
2010-2012
6.2.4
1.1
1.0
2010-2012
6.2.5
0.9
1.3
2010-2012
6.2.7
0.9
1.4
2010-2012
6.2.8
0.8
0.5
2010-2012
6.2.9
0.9
0.5
2010-2012
7.1.7
1.0
1.4
2010-2012
7.1.8
0.9
2.0
2010-2012
7.1.9
0.9
1.4
2010-2012
8.1.1
2.7
4.0
2010-2012
8.1.10
3.2
8.1
2010-2012
8.1.3
2.7
4.8
2010-2012
8.1.4
1.3
2.6
2010-2012
8.1.5
1.4
3.4
2010-2012
8.1.6
2.1
5.3
2010-2012
8.1.7
1.7
3.8
2010-2012
6A-34
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Ecoregion
EAQM-weighted
annual SO2 (ppb)
Median ecoregion
S dep (kg S/ha-yr)
Period
8.1.8
1.7
2.4
2010-2012
8.2.1
1.8
4.0
2010-2012
8.2.2
2.7
5.2
2010-2012
8.2.3
2.0
5.4
2010-2012
8.2.4
2.4
7.1
2010-2012
8.3.1
2.6
5.3
2010-2012
8.3.2
2.3
6.2
2010-2012
8.3.3
2.2
6.2
2010-2012
8.3.4
1.5
4.3
2010-2012
8.3.5
1.6
4.3
2010-2012
8.3.6
2.2
4.6
2010-2012
8.3.7
1.7
4.9
2010-2012
8.3.8
1.2
3.8
2010-2012
8.4.1
2.7
5.3
2010-2012
8.4.2
2.9
7.0
2010-2012
8.4.3
3.3
8.3
2010-2012
8.4.4
1.7
4.4
2010-2012
8.4.5
1.9
4.6
2010-2012
8.4.6
1.7
4.5
2010-2012
8.4.7
1.7
4.2
2010-2012
8.4.8
1.6
4.7
2010-2012
8.4.9
2.1
5.5
2010-2012
8.5.1
1.8
5.1
2010-2012
8.5.2
2.4
4.2
2010-2012
8.5.3
1.1
4.4
2010-2012
8.5.4
2.2
5.6
2010-2012
9.2.1
1.0
1.7
2010-2012
9.2.2
0.9
1.4
2010-2012
9.2.3
1.3
3.0
2010-2012
9.2.4
1.7
4.1
2010-2012
9.3.1
1.0
1.4
2010-2012
9.3.3
1.1
1.0
2010-2012
9.3.4
1.2
1.5
2010-2012
9.4.1
1.3
1.3
2010-2012
9.4.2
1.2
2.2
2010-2012
9.4.3
1.2
1.0
2010-2012
6A-35
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Ecoregion
EAQM-weighted
annual SO2 (ppb)
Median ecoregion
S dep (kg S/ha-yr)
Period
9.4.4
1.5
2.9
2010-2012
9.4.5
1.1
3.0
2010-2012
9.4.6
1.1
2.2
2010-2012
9.4.7
1.0
3.8
2010-2012
9.5.1
1.2
4.3
2010-2012
9.6.1
0.9
2.5
2010-2012
10.1.2
0.7
0.5
2014-2016
10.1.3
0.9
0.5
2014-2016
10.1.4
1.4
0.6
2014-2016
10.1.5
0.7
0.5
2014-2016
10.1.6
1.0
0.6
2014-2016
10.1.7
1.5
0.6
2014-2016
10.1.8
1.0
0.6
2014-2016
10.2.1
0.6
0.4
2014-2016
10.2.2
1.5
0.4
2014-2016
10.2.4
1.6
1.2
2014-2016
11.1.1
0.6
0.8
2014-2016
11.1.2
0.8
0.8
2014-2016
11.1.3
0.4
1.0
2014-2016
12.1.1
2.3
0.9
2014-2016
13.1.1
2.0
1.0
2014-2016
15.4.1
0.4
4.3
2014-2016
5.2.1
1.1
1.9
2014-2016
5.2.2
0.8
1.1
2014-2016
5.3.1
1.0
2.0
2014-2016
5.3.3
1.7
4.1
2014-2016
6.2.10
1.1
0.9
2014-2016
6.2.11
0.6
1.1
2014-2016
6.2.12
0.8
1.1
2014-2016
6.2.13
0.9
1.3
2014-2016
6.2.14
1.4
0.8
2014-2016
6.2.15
0.8
0.9
2014-2016
6.2.3
0.8
0.8
2014-2016
6.2.4
0.8
1.0
2014-2016
6.2.5
0.6
1.4
2014-2016
6.2.7
0.7
1.5
2014-2016
6A-36
-------
Ecoregion
EAQM-weighted
annual SO2 (ppb)
Median ecoregion
S dep (kg S/ha-yr)
Period
6.2.8
0.7
0.5
2014-2016
6.2.9
0.7
0.6
2014-2016
7.1.7
0.6
2.1
2014-2016
7.1.8
0.5
2.0
2014-2016
7.1.9
0.6
1.7
2014-2016
8.1.1
1.4
2.7
2014-2016
8.1.10
1.6
5.0
2014-2016
8.1.3
1.5
2.8
2014-2016
8.1.4
1.0
2.0
2014-2016
8.1.5
1.1
2.6
2014-2016
8.1.6
1.3
3.3
2014-2016
8.1.7
0.8
2.4
2014-2016
8.1.8
0.9
1.6
2014-2016
8.2.1
1.2
2.7
2014-2016
8.2.2
1.5
3.2
2014-2016
8.2.3
1.3
4.1
2014-2016
8.2.4
1.3
4.1
2014-2016
8.3.1
1.3
3.3
2014-2016
8.3.2
1.5
4.3
2014-2016
8.3.3
1.3
4.2
2014-2016
8.3.4
0.8
2.6
2014-2016
8.3.5
0.9
3.5
2014-2016
8.3.6
1.2
4.0
2014-2016
8.3.7
0.9
4.7
2014-2016
8.3.8
0.6
4.4
2014-2016
8.4.1
1.4
3.2
2014-2016
8.4.2
1.4
4.1
2014-2016
8.4.3
1.5
4.8
2014-2016
8.4.4
0.8
2.6
2014-2016
8.4.5
1.0
3.2
2014-2016
8.4.6
0.9
3.3
2014-2016
8.4.7
0.9
3.4
2014-2016
8.4.8
0.9
4.1
2014-2016
8.4.9
1.2
3.5
2014-2016
8.5.1
0.9
3.4
2014-2016
8.5.2
1.3
3.9
2014-2016
6A-37
-------
Ecoregion
EAQM-weighted
annual SO2 (ppb)
Median ecoregion
S dep (kg S/ha-yr)
Period
8.5.3
0.8
3.9
2014-2016
8.5.4
1.1
3.8
2014-2016
9.2.1
0.8
1.3
2014-2016
9.2.2
0.7
1.2
2014-2016
9.2.3
0.9
2.6
2014-2016
9.2.4
0.9
3.0
2014-2016
9.3.1
0.9
1.2
2014-2016
9.3.3
1.0
0.9
2014-2016
9.3.4
1.3
1.4
2014-2016
9.4.1
1.4
1.3
2014-2016
9.4.2
1.0
2.2
2014-2016
9.4.3
1.4
1.1
2014-2016
9.4.4
0.9
2.5
2014-2016
9.4.5
0.6
3.1
2014-2016
9.4.6
0.8
2.5
2014-2016
9.4.7
0.6
4.0
2014-2016
9.5.1
0.6
4.7
2014-2016
9.6.1
0.6
3.1
2014-2016
10.1.2
0.6
0.3
2018-2020
10.1.3
0.8
0.3
2018-2020
10.1.4
1.2
0.4
2018-2020
10.1.5
0.6
0.3
2018-2020
10.1.6
0.7
0.3
2018-2020
10.1.7
0.9
0.3
2018-2020
10.1.8
0.8
0.4
2018-2020
10.2.1
0.6
0.3
2018-2020
10.2.2
0.9
0.3
2018-2020
10.2.4
1.3
0.9
2018-2020
11.1.1
0.5
0.7
2018-2020
11.1.2
0.6
0.7
2018-2020
11.1.3
0.5
0.8
2018-2020
12.1.1
1.4
0.5
2018-2020
13.1.1
1.1
0.6
2018-2020
15.4.1
0.7
3.8
2018-2020
5.2.1
0.7
1.3
2018-2020
5.2.2
0.8
0.9
2018-2020
6A-38
-------
Ecoregion
EAQM-weighted
annual SO2 (ppb)
Median ecoregion
S dep (kg S/ha-yr)
Period
5.3.1
0.6
1.3
2018-2020
5.3.3
0.9
2.4
2018-2020
6.2.10
1.1
0.7
2018-2020
6.2.11
0.5
0.9
2018-2020
6.2.12
0.5
1.0
2018-2020
6.2.13
0.7
0.8
2018-2020
6.2.14
0.9
0.5
2018-2020
6.2.15
0.6
0.6
2018-2020
6.2.3
0.8
0.5
2018-2020
6.2.4
0.8
0.8
2018-2020
6.2.5
0.9
1.1
2018-2020
6.2.7
0.6
1.2
2018-2020
6.2.8
0.6
0.5
2018-2020
6.2.9
0.6
0.4
2018-2020
7.1.7
0.9
1.2
2018-2020
7.1.8
0.5
1.5
2018-2020
7.1.9
0.5
1.1
2018-2020
8.1.1
0.8
1.6
2018-2020
8.1.10
0.8
2.8
2018-2020
8.1.3
0.7
1.7
2018-2020
8.1.4
0.7
1.4
2018-2020
8.1.5
0.8
1.9
2018-2020
8.1.6
0.8
2.2
2018-2020
8.1.7
0.5
1.9
2018-2020
8.1.8
0.5
1.2
2018-2020
8.2.1
0.7
2.0
2018-2020
8.2.2
0.9
2.1
2018-2020
8.2.3
0.9
2.4
2018-2020
8.2.4
1.2
2.6
2018-2020
8.3.1
0.8
2.1
2018-2020
8.3.2
1.4
3.0
2018-2020
8.3.3
1.4
2.7
2018-2020
8.3.4
0.8
1.9
2018-2020
8.3.5
0.8
2.6
2018-2020
8.3.6
1.9
3.2
2018-2020
8.3.7
1.1
3.6
2018-2020
6A-39
-------
Ecoregion
EAQM-weighted
annual SO2 (ppb)
Median ecoregion
S dep (kg S/ha-yr)
Period
8.3.8
0.6
3.6
2018-2020
8.4.1
1.1
2.1
2018-2020
8.4.2
1.0
2.3
2018-2020
8.4.3
1.0
2.9
2018-2020
8.4.4
0.9
1.9
2018-2020
8.4.5
1.4
2.6
2018-2020
8.4.6
1.1
2.8
2018-2020
8.4.7
1.1
3.0
2018-2020
8.4.8
1.1
3.5
2018-2020
8.4.9
1.2
2.6
2018-2020
8.5.1
0.7
2.4
2018-2020
8.5.2
1.9
3.2
2018-2020
8.5.3
0.8
3.2
2018-2020
8.5.4
0.6
2.7
2018-2020
9.2.1
0.9
1.2
2018-2020
9.2.2
0.9
1.1
2018-2020
9.2.3
0.8
1.9
2018-2020
9.2.4
0.7
2.3
2018-2020
9.3.1
1.0
1.1
2018-2020
9.3.3
1.0
0.8
2018-2020
9.3.4
1.1
1.4
2018-2020
9.4.1
1.1
1.0
2018-2020
9.4.2
1.0
1.8
2018-2020
9.4.3
1.1
0.6
2018-2020
9.4.4
0.8
1.9
2018-2020
9.4.5
0.7
2.6
2018-2020
9.4.6
0.8
2.1
2018-2020
9.4.7
0.6
3.4
2018-2020
9.5.1
0.6
4.3
2018-2020
9.6.1
0.7
2.4
2018-2020
6A-40
-------
6A.5. IMPACTS OF THREE KEY ASPECTS OF METHODOLOGY
ON FINDINGS
As noted earlier in this appendix, three aspects of the analytical methodology used to
compare upwind air quality (EAQM) and downwind deposition in an ecoregion were examined
with regard to their influence on analysis findings. Specifically, we examined two durations for
the forward parcel trajectories (48-hours and 120-hours), two different meteorological input data
sets (NARR-32 and NAM-12) with differing resolution, and three different monitor inclusion
criteria (hit rates) ranging from from 1% of total hits to 0.1% of total hits. Each of these
methodological changes, when moving from the original analysis to the final analysis, had the
effect of allowing more distant upwind sites to be included in the EAQM calculations of air
quality across potential sites of influence. Again, like other elements of the EAQM analysis,
these methodological assumptions about the potential scope of the sites of influence introduce
uncertainty. Sensitivity analyses were performed to evaluate the effect of these changes (length
of trajectory plus finer resolution meteorological data, hit threshold) on what the EAQM
approach concluded about the association between upwind air quality and downwind deposition.
Figure 6A-21 shows the association between annual SO2 EAQM values and S deposition
across the 84 ecoregions and 5 time periods, based on a 48-hour duration for the trajectory
analysis, the NARR-32 inputs, and a monitor inclusion criterion of 1%. Figure 6A-22 shows the
association between annual SO2 EAQM values and S deposition across the 84 ecoregions and 5
time periods, based on 120-hour duration for the trajectory analysis, the NAM-12 input data, and
a minimum hit rate of 0.5% for monitoring site inclusion criterion. In both analyses, similar
themes emerge. It is clear from both figures that the EAQM SO2 and TDep S deposition
association is strongest for the 47 eastern ecoregions and their upwind monitoring sites of
influence, and essentially non-existent for the 37 western ecoregions and their upwind
monitoring sites of influence. In both cases, we can conclude that the relationship between
upwind air quality and downwind deposition was stronger in the earlier periods than the most
recent 2018-2020 period. It can be noted that the r-value improves slightly with the inclusion of
more distant sites (i.e., the final analysis configuration), from 0.45 to 0.56. Figures 6A-23 and
6A-24 limit the EAQM-TDep comparisons to sites in the eastern U.S. and the associations are
equally strong in both iterations of the methodology (r-values = 0.85, slopes ~ 2.2). We also
looked at how the results varied by methodology for other associations (e.g., annual NO2 and N
deposition) and concluded that the overall strength of association between upwind air quality and
downwind deposition were not strongly affected by the choice of trajectory length,
meteorological inputs, or monitor inclusion criteria. All of the outputs, both original analysis and
final analysis (for 3 different monitor inclusion criteria) are shown in 6A.6.
6A-41
-------
20-
co
co 15
03
U)
as
10
0
GO
rc
CD
10-
o
d
Q
£ 5-
0-
• /
Time Period
• 2001-2003
• 2006-2008
% /
• 2010-2012
• 2014-2016
• • **7
2018-2020
* /
•• /
s •/ •
Region
0 East
9 / •
• • /
A West
IJ.
*
Monitor Inclusion Criterion: 0.5%
r= 0.56 (p<0.05)
::
slope= 2.36 (p<0.05)
0 5 10 15 20
Annual Average S02 (Weighted), 3-yr average (ppb)
Figure 6A-22. Annual SO2 EAQM-weighted values and TDep S deposition in 84
ecoregions (120-hr trajectories, NAM-12, 0.5% monitor inclusion criteria).
6A-42
-------
20-
co
co 15
03
U)
as
10
-------
6A.6. RESULTS OF HYSPLIT EAQM ANALYSES
6A.6.I.SO2 3-hr Metric - 120-hr
Table 6A-2. Correlation coefficients of TDep-estimated S deposition and 3-hr SO2
EAQMs generated by HYSPLIT analysis at three monitor inclusion criteria,
120-hr trajectories.
Sulfur Deposition and S02(3-hr Standard)
3-hr Max-All
Correlation
3-hr Max-All
Correlation
3-hr Max-All
Correlation
Ecoregions- Monitor
Coefficient
Ecoregions- Monitor
Coefficient
Ecoregions- Monitor
Coefficient
Inclusion Criteria: 1%
(r) = 0.29*
Inclusion Criteria: 0.5%
(r) = 0.51*
Inclusion Criteria: 0.1%
(r) = 0.50*
Year
r
Year
r
Year
r
2001 - 2003
0.12
2001 - 2003
0.49*
2001 - 2003
0.84*
2006 - 2008
0.49*
2006 - 2008
0.69*
2006 - 2008
0.84*
2010 -2012
0.25*
2010 -2012
0.25*
2010 -2012
-0.12
2014-2016
0.15
2014-2016
0.23*
2014-2016
0.57*
2018-2020
0.17
2018-2020
0.54*
2018-2020
0.76*
3-hr Max-All
3-hr Max-All
3-hr Max-All
Ecoregions- Monitor
Ecoregions- Monitor
Ecoregions- Monitor
Inclusion Criteria: 1%a
r = 0.29*
Inclusion Criteria: 0.5%3
r = 0.52*
Inclusion Criteria: 0.1 %a
r = 0.59*
Year
r
Year
r
Year
r
2001 - 2003
0.12
2001 - 2003
0.49*
2001 - 2003
0.84*
2006 - 2008
0.49*
2006 - 2008
0.69*
2006 - 2008
0.84*
2010 -2012
0.25*
2010 -2012
0.25*
2010 -2012
-0.12
2014-2016
0.15
2014-2016
0.23*
2014-2016
0.57*
2018-2020
0.10
2018-2020
0.40*
2018-2020
-0.09*
3-hr Max-East
3-hr Max-East
3-hr Max-East
Ecoregions- Monitor
Ecoregions- Monitor
Ecoregions- Monitor
Inclusion Criteria: 1%
r = 0.42*
Inclusion Criteria: 0.5%
r = 0.32*
Inclusion Criteria: 0.1%
r = -0.11
Year
r
Year
r
Year
r
2001 - 2003
0.29
2001 - 2003
0.28
2001 - 2003
0.63*
2006 - 2008
0.26
2006 - 2008
0.05
2006 - 2008
0.56*
2010 -2012
0.05
2010 -2012
-0.29*
2010 -2012
-0.18
2014-2016
0.15
2014-2016
-0.38*
2014-2016
-0.18
2018-2020
0.18
2018-2020
0.33*
2018-2020
NA
3-hr Max-East
3-hr Max-East
3-hr Max-East
Ecoregions- Monitor
Ecoregions- Monitor
Ecoregions- Monitor
Inclusion Criteria: 1%a
r = 0.45*
Inclusion Criteria: 0.5%a
r = 0.42*
Inclusion Criteria: 0.1 %a
r = 0.47*
Year
r
Year
r
Year
r
2001 - 2003
0.29
2001 - 2003
0.28
2001 - 2003
0.63*
2006 - 2008
0.26
2006 - 2008
0.05
2006 - 2008
0.56*
2010 -2012
0.06
2010 -2012
-0.29*
2010 -2012
-0.18
2014-2016
0.15
2014-2016
-0.38*
2014-2016
-0.18
2018-2020
0.11
2018-2020
0.04
2018-2020
NA
3-hr Max-West
3-hr Max-West
3-hr Max-West
Ecoregions- Monitor
Ecoregions- Monitor
Ecoregions- Monitor
Inclusion Criteria: 1%
r = -0.01
Inclusion Criteria: 0.5%
r = 0.07
Inclusion Criteria: 0.1%
r = -0.01
Year
r
Year
r
Year
r
2001 - 2003
-0.18
2001 - 2003
-0.06
2001 - 2003
0.06
6A-44
-------
2006 - 2008
0.06
2006 - 2008
0.18
2006 - 2008
0.15
2010 -2012
-0.16
2010 -2012
-0.09
2010 -2012
0.09
2014-2016
-0.15
2014-2016
-0.09
2014-2016
-0.14
2018-2020
0.21
2018-2020
0.10
2018-2020
-0.03
3-hr Max-West
Ecoregions- Monitor
Inclusion Criteria: 1%a
No outliers
in West
dataset
3-hr Max-West
Ecoregions- Monitor
Inclusion Criteria: 0.5%3
No outliers
in West
dataset
3-hr Max-West
Ecoregions- Monitor
Inclusion Criteria: 0.1%3
r = -0.02
Year
r
Year
r
Year
r
2001 - 2003
2001 - 2003
2001 - 2003
0.06
2006 - 2008
2006 - 2008
2006 - 2008
0.15
2010 -2012
2010 -2012
2010 -2012
0.09
2014-2016
2014-2016
2014-2016
-0.14
2018-2020
2018-2020
2018-2020
-0.15
Weighted 3-hr
Average-All
Ecoregions- Monitor
Inclusion Criteria: 1%
r = 0.60*
Weighted 3-hr Average-
All Ecoregions- Monitor
Inclusion Criteria: 0.5%
—?
II
O
O
*
Weighted 3-hr Average-
All Ecoregions- Monitor
Inclusion Criteria: 0.1%
r = 0.72*
Year
r
Year
r
Year
r
2001 - 2003
0.74*
2001 - 2003
0.86*
2001 - 2003
0.90*
2006 - 2008
0.81*
2006 - 2008
0.89*
2006 - 2008
0.91*
2010 -2012
0.64*
2010 -2012
0.77*
2010 -2012
0.81*
2014-2016
0.34*
2014-2016
0.38*
2014-2016
0.42*
2018-2020
0.38*
2018-2020
0.54*
2018-2020
0.58*
Weighted 3-hr
Average-East
Ecoregions- Monitor
Inclusion Criteria: 1%
r = 0.77*
Weighted 3-hr Average-
East Ecoregions-
Monitor Inclusion
Criteria: 0.5%
r = 0.83*
Weighted 3-hr Average-
East Ecoregions-
Monitor Inclusion
Criteria: 0.1%
r = 0.84*
Year
r
Year
r
Year
r
2001 - 2003
0.70*
2001 - 2003
0.86*
2001 - 2003
0.92*
2006 - 2008
0.63*
2006 - 2008
0.78*
2006 - 2008
0.86*
2010 -2012
0.57*
2010 -2012
0.76*
2010 -2012
0.78*
2014-2016
0.43*
2014-2016
0.24
2014-2016
0.24
2018-2020
0.32*
2018-2020
0.41*
2018-2020
0.43*
Weighted 3-hr
Average-West
Ecoregions- Monitor
Inclusion Criteria: 1%
r = 0.19*
Weighted 3-hr Average-
West Ecoregions-
Monitor Inclusion
Criteria: 0.5%
r = 0.20*
Weighted 3-hr Average-
West Ecoregions-
Monitor Inclusion
Criteria: 0.1%
r = 0.21*
Year
r
Year
r
Year
r
2001 - 2003
0.16
2001 - 2003
0.15
2001 - 2003
0.15
2006 - 2008
0.23
2006 - 2008
0.31
2006 - 2008
0.32
2010 -2012
0.05
2010 -2012
0.06
2010 -2012
0.07
2014-2016
-0.17
2014-2016
-0.16
2014-2016
-0.19
2018-2020
0.15
2018-2020
0.15
2018-2020
0.12
*p< 0.05
a Note: There are several outlier points in this comparison where the EAQM-max annual average SO2 value exceeds 20 ppb in
the 2018-2020 period. These points have been removed from these analyses. These data are driven by a monitor in
southeastern MO where annual average SO2 has exceeded 20 ppb in recent years. Any downwind ecoregion that is linked to
this upwind monitor will have an EAQM-max with this value. A preliminary analysis suggests that these observed SO2 data are
due to a new source that was not modeled in the CMAQ simulation that informed the TDep estimates of deposition. As there
is no deposition monitor in the immediate vicinity of the source it is unlikely that the TDep estimates are capturing the impacts
of this source. For that reason, we concluded it was appropriate not to consider these data in our evaluation of the
concentration-deposition relationship.
6A-45
-------
20 H Monitor Inclusion Criterion: 1%
r= 0.29 (p<0.05)
slope= 0.01 (p<0.05)
C/) 15-
O)
Time Period
• 2001-2003
• 2006-2008
• 2010-2012
• 2014-2016
2018-2020
Region
o East
A West
0 200 400
Secondary S02 Design Value (Max), 3-yr average (ppb)
0 200 400
Secondary S02 Design Value (Max), 3-yr average (ppb)
Figure 6A-25. The 3-hr SO2 EAQM-max values and TDep S deposition in 84 ecoregions
(120-hr trajectories, NAM-12, 1% monitor inclusion criteria): all values
(upper), outliers excluded (lower).
6A-46
-------
03
£
CO
U)
0
Q
CO
Monitor Inclusion Criterion: 1%
r= 0.42 (p<0.05)
slope= 0.02 (p<0.05)
Time Period
• 2001-2003
• 2006-2008
• 2010-2012
• 2014-2016
2018-2020
Region
o East
0 200 400
Secondary S02 Design Value (Max), 3-yr average (ppb)
20-Monitor Inclusion Criterion: 1°/
r= 0.45 (p<0.05)
slope= 0.02 (p<0.05)
Time Period
• 2001-2003
• 2006-2008
• 2010-2012
• 2014-2016
2018-2020
a)
Q
Outliers Excluded; see Table 6A-2
0-
200 400
Secondary S02 Design Value (Max), 3-yr average (ppb)
Figure 6A-26. The 3-hr SO2 EAQM-max values and TDep S deposition in eastern
ecoregions (120-hr trajectories, NAM-12,1% monitor inclusion criteria):
all values (upper), outliers excluded (lower).
6A-47
-------
20-
jz
V) 15
U)
0
Q
£ 5
CO
0-
Monitor Inclusion Criterion: 1%
Time Period
• 2001-2003
• 2006-2008
• 2010-2012
• 2014-2016
2018-2020
Region
A West
200 400
Secondary S02 Design Value (Max), 3-yr average (ppb)
Figure 6A-27. The 3-hr SO2 EAQM-max values and TDep S deposition in western
ecoregions (120-hr trajectories, NAM-12,1% monitor inclusion criteria).
6A-48
-------
CO
U)
20-
CO
15-
Time Period
• 2001-2003
• 2006-2008
• 2010-2012
• 2014-2016
2018-2020
Region
o East
A West
Monitor Inclusion Criterion: 0.5%
r= 0.51 (p<0.05)
slope= 0.07 (p<0.05)
10-
<1>
Q
£ 5
0-
0 200 400
Secondary S02 Design Value (Max), 3-yr average (ppb)
20
CO 15-
10-
d)
Q
£ 5-
0-
Time Period
• 2001-2003
• 2006-2008
• 2010-2012
• 2014-2016
2018-2020
Monitor Inclusion Criterion: 0.5%
r= 0.52 (p<0.05)
slope= 0.02 (p<0.05)
Outliers Excluded; see Table 6A-2
200 400
Secondary S02 Design Value (Max), 3-yr average (ppb)
Figure 6A-28. The 3-hr SO2 EAQM-max values and TDep S deposition in 84 ecoregions
(120-hr trajectories, NAM-12, 0.5% monitor inclusion criteria): all values
(upper), outliers excluded (lower).
6A-49
-------
CO
U)
20-
CO
15-
Time Period
• 2001-2003
• 2006-2008
• 2010-2012
• 2014-2016
2018-2020
Region
o East
Monitor Inclusion Criterion: 0.5%
r= 0.32 (p<0.05)
slope= 0.01 (p<0.05)
10-
<1>
Q
£ 5-
'«° •
0-
0 200 400
Secondary S02 Design Value (Max), 3-yr average (ppb)
20
CO 15"
10-
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Q
^ 5-
0-
Time Period
• 2001-2003
• 2006-2008
• 2010-2012
• 2014-2016
2018-2020
Region
o East
9 %
Monitor Inclusion Criterion: 0.5%
r= 0.42 (p<0.05)
slope= 0.02 (p<0.05)
Outliers Excluded; see Table 6A-2
°®c|d
i ° ® o
200 400
Secondary S02 Design Value (Max), 3-yr average (ppb)
Figure 6A-29. The 3-hr SO2 EAQM-max values and TDep S deposition in eastern
ecoregions (120-hr trajectories, NAM-12, 0.5% monitor inclusion criteria):
all values (upper), outliers excluded (lower).
6 A-50
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20-
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20-
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20-
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15-
10
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10-
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Time Period
•
• 2001-2003
./
• 2006-2008
• 2010-2012
• 2014-2016
• A
2018-2020
• % /
• A
"• /
9®
Region
0 East
®y©/
A West
Of
Monitor Inclusion Criterion: 1%
ff *1
r= 0.60 (p<0.05)
slope= 0.13 (p<0.05)
9 vti® #
Mo
0 200 400
Secondary S02 Design Value (Weighted), 3-yr average (ppb)
Figure 6A-34. The 3-hr SO2 EAQM-weighted values and TDep S deposition in 84
ecoregions (120-hr trajectories, NAM-12,1% monitor inclusion criteria).
CO
O)
2
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W
a
15-
10-
£ 5-
0-
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Time Period
• 2001-2003
• /
• 2006-2008
• 2010-2012
© /•
• 2014-2016
• ^ /
2018-2020
• /•
Region
•e J
0 East
•P
•• 7*
Monitor Inclusion Criterion: 1%
* >
J
r= 0.77 (p<0.05)
slope= 0.13 (p<0.05)
• • •
9H0
*Ro
0 200 400
Secondary S02 Design Value (Weighted), 3-yr average (ppb)
Figure 6A-35. The 3-hr SO2 EAQM-weighted values and TDep S deposition in eastern
ecoregions (120-hr trajectories, NAM-12,1% monitor inclusion criteria).
6A-55
-------
20-
cb
CO
U)
15-
10-
<1>
Q
£ 5
0-
Time Period
• 2001-2003
• 2006-2008
• 2010-2012
• 2014-2016
2018-2020
Region
A West
Monitor Inclusion Criterion: 1%
r= 0.19 (p<0.05)
slope= 0.01 (p<0.05)
0 200 400
Secondary S02 Design Value (Weighted), 3-yr average (ppb)
Figure 6A-36. The 3-hr SO2 EAQM-weighted values and TDep S deposition in western
ecoregions (120-hr trajectories, NAM-12,1% monitor inclusion criteria).
CO
O)
15-
10-
w
a
£ 5-
0-
Time Period
• 2001-2003
• 2006-2008
• 2010-2012
• 2014-2016
2018-2020
Region
o East
A West
Monitor Inclusion Criterion: 0.5%
r= 0.70 (p<0.05)
slope= 0.16 (p<0.05)
0 200 400
Secondary S02 Design Value (Weighted), 3-yr average (ppb)
Figure 6A-37. The 3-hr SO2 EAQM-weighted values and TDep S deposition in 84
ecoregions (120-hr trajectories, NAM-12, 0.5% monitor inclusion criteria).
6A-56
-------
20-
cb
CO
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15-
10-
<1>
Q
£ 5
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Time Period
• 2001-2003
•./
• 2006-2008
• 2010-2012
• 2014-2016
H /
2018-2020
• *7
•/
Region
•v
0 East
tr
Monitor Inclusion Criterion: 0.5%
® qM
r= 0.83 (p<0.05)
slope= 0.16 (p<0.05)
0 200 400
Secondary S02 Design Value (Weighted), 3-yr average (ppb)
Figure 6A-38. The 3-hr SO2 EAQM-weighted values and TDep S deposition in eastern
ecoregions (120-hr trajectories, NAM-12, 0.5% monitor inclusion criteria).
CO
O)
2
(D
>
CO
W
a
15-
10-
£ 5-
0-
Time Period
• 2001-2003
• 2006-2008
• 2010-2012
• 2014-2016
2018-2020
Region
A West
Monitor Inclusion Criterion: 0.5%
r= 0.20 (p<0.05)
slope= 0.008 (p<0.05)
0 200 400
Secondary S02 Design Value (Weighted), 3-yr average (ppb)
Figure 6A-39. The 3-hr SO2 EAQM-weighted values and TDep S deposition in western
ecoregions (120-hr trajectories, NAM-12, 0.5% monitor inclusion criteria).
6A-57
-------
20-
cb
CO
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15-
10-
<1>
Q
£ 5
0-
Time Period
•
• 2001-2003
• 2006-2008
I /
• 2010-2012
• 2014-2016
»• /
2018-2020
/
• • /
•• /
s® /
Region
0 East
w
A West
Monitor Inclusion Criterion: 0.1%
r= 0.72 (p<0.05)
:
slope- 0.16 (p<0.05)
w*
0 200 400
Secondary S02 Design Value (Weighted), 3-yr average (ppb)
Figure 6A-40. The 3-hr SO2 EAQM-weighted values and TDep S deposition in 84
ecoregions (120-hr trajectories, NAM-12, 0.1% monitor inclusion criteria).
CO
O)
2
(D
>
CO
W
a
15-
10-
£ 5-
0-
• /
Time Period
J
• 2001-2003
i /
• 2006-2008
• 2010-2012
®» /
• 2014-2016
/
• */
•7
2018-2020
Region
o East
qP
Monitor Inclusion Criterion: 0.1%
r= 0.84 (p<0.05)
r
slope= 0.17 (p<0.05)
ir
w°*
0 200 400
Secondary S02 Design Value (Weighted), 3-yr average (ppb)
Figure 6A-41. The 3-hr SO2 EAQM-weighted values and TDep S deposition in eastern
ecoregions (120-hr trajectories, NAM-12, 0.1% monitor inclusion criteria).
6A-58
-------
CO
U)
20-
CO
15-
10-
<1>
Q
£ 5
0-
Time Period
• 2001-2003
• 2006-2008
• 2010-2012
• 2014-2016
2018-2020
Region
A West
Monitor Inclusion Criterion: 0.1%
slope= 0.01 (p<0.05)
0 200 400
Secondary S02 Design Value (Weighted), 3-yr average (ppb)
Figure 6A-42. The 3-hr SO2 EAQM-weighted values and TDep S deposition in western
ecoregions (120-hr trajectories, NAM-12, 0.1% monitor inclusion criteria).
6A-59
-------
6A.6.2.SO2 3-hr Metric - 48-hr
Table 6A-3. Correlation coefficients of TDep estimates of sulfur deposition and 3-hr SO2
EAQMs generated by HYSPLIT analysis, 48-hr trajectories. Data are also
split by year and by region (East/West).
Sulfur Deposition and S02(3-hr Standard)
Annual Max-All Ecoregions-
Monitor Inclusion Criteria: 1%
Correlation
Coefficient (r) =
0.39*
Weighted Annual Average-All
Ecoregions- Monitor Inclusion
Criteria: 1%
Correlation
Coefficient (r) =
0.59*
Year
r
Year
r
2001 - 2003
0.27*
2001 - 2003
0.72*
2006 - 2008
0.69*
2006 - 2008
0.80*
2010-2012
0.32*
2010-2012
0.64*
2014-2016
0.17
2014-2016
0.32*
2018-2020
0.32*
2018-2020
0.38*
Annual Max-East Ecoregions-
Monitor Inclusion Criteria: 1%
r = 0.44*
Weighted Annual Average-East
Ecoregions- Monitor Inclusion
Criteria: 1%
r = 0.80*
Year
r
Year
r
2001 - 2003
0.29*
2001 - 2003
0.80*
2006 - 2008
0.33*
2006 - 2008
0.68*
2010-2012
0.22
2010-2012
0.67*
2014-2016
0.11
2014-2016
0.39*
2018-2020
0.18
2018-2020
0.35*
Annual Max-West Ecoregions-
Monitor Inclusion Criteria: 1%
r = -0.07
Weighted Annual Average-
West Ecoregions- Monitor
Inclusion Criteria: 1%
r = 0.14
Year
r
Year
r
2001 - 2003
-0.31
2001 - 2003
0.05
2006 - 2008
-0.03
2006 - 2008
0.11
2010-2012
-0.20
2010-2012
0.01
2014-2016
-0.33*
2014-2016
-0.24
2018-2020
0.10
2018-2020
0.07
*p< 0.05
6A-60
-------
CO
U)
20-
CO
15-
10
-------
20-
cb
CO
U)
15-
10
-------
20-
cb
CO
U)
15-
10
CO
W
a
15-
10-
£ 5-
0-
Time Period
• 2001-2003
• 2006-2008
• 2010-2012
• 2014-2016
2018-2020
Region
A West
Monitor Inclusion Criterion: 1%
A 1a A
A
30 60 90
Secondary S02 Design Value (Weighted), 3-yr average (ppb)
120
Figure 6A-48. The 3-hr SO2 EAQM-weighted values and TDep S deposition in western
ecoregions (48-hr trajectories, NARR-32,1% monitor inclusion criteria).
6A-63
-------
6A.6.3.SO2 Annual Metric - 120-hr
Table 6A-4. Correlation coefficients of TDep estimates of sulfur deposition and annual
SO2 EAQMs generated by HYSPLIT analysis at three different monitor
inclusion criteria, 120-hr trajectories.
Sulfur Deposition and SO2
Annual Max-All
Correlation
Annual Max-All
Correlation
Annual Max-All
Correlation
Ecoregions- Monitor
Coefficient
Ecoregions- Monitor
Coefficient
Ecoregions- Monitor
Coefficient
Inclusion Criteria: 1%
(r) = 0.24*
Inclusion Criteria: 0.5%
(r) = 0.48*
Inclusion Criteria: 0.1%
(r) = 0.51*
Year
r
Year
r
Year
r
2001 - 2003
0.33*
2001 - 2003
0.62*
2001 - 2003
0.87*
2006 - 2008
0.48*
2006 - 2008
0.69*
2006 - 2008
0.87*
2010-2012
0.19
2010-2012
0.28*
2010-2012
0.72*
2014-2016
-0.24*
2014-2016
-0.05
2014-2016
-0.28*
2018-2020
-0.34*
2018-2020
0.19
2018-2020
0.76*
Annual Max-All
Annual Max-All
Annual Max-All
Ecoregions- Monitor
Ecoregions- Monitor
Ecoregions- Monitor
Inclusion Criteria: 1%3
r = 0.24*
Inclusion Criteria: 0.5%3
r = 0.49*
Inclusion Criteria: 0.1%3
r = 0.61*
Year
r
Year
r
Year
r
2001 - 2003
0.33*
2001 - 2003
0.62*
2001 - 2003
0.87*
2006 - 2008
0.48*
2006 - 2008
0.69*
2006 - 2008
0.87*
2010-2012
0.19*
2010-2012
0.28*
2010-2012
0.72*
2014-2016
-0.24*
2014-2016
-0.05
2014-2016
-0.28*
2018-2020
-0.45*
2018-2020
-0.10
2018-2020
-0.10
Annual Max-East
Annual Max-East
Annual Max-East
Ecoregions- Monitor
Ecoregions- Monitor
Ecoregions- Monitor
Inclusion Criteria: 1%
r = 0.65*
Inclusion Criteria: 0.5%
r = 0.53*
Inclusion Criteria: 0.1%
—?
II
O
O
cn
Year
r
Year
r
Year
r
2001 - 2003
0.69*
2001 - 2003
0.78*
2001 - 2003
0.73*
2006 - 2008
0.55*
2006 - 2008
0.59*
2006 - 2008
0.74*
2010-2012
0.18
2010-2012
-0.43*
2010-2012
0.01
2014-2016
0.02
2014-2016
-0.44*
2014-2016
-0.09
2018-2020
0.23
2018-2020
0.23
2018-2020
0.17
Annual Max-East
Annual Max-East
Annual Max-East
Ecoregions- Monitor
Ecoregions- Monitor
Ecoregions- Monitor
Inclusion Criteria: 1%a
r = 0.68*
Inclusion Criteria: 0.5%a
r = 0.65*
Inclusion Criteria: 0.1 %a
r = 0.72*
Year
r
Year
r
Year
r
2001 - 2003
0.69*
2001 - 2003
0.78*
2001 - 2003
0.73*
2006 - 2008
0.56*
2006 - 2008
0.59*
2006 - 2008
0.74*
2010-2012
0.18
2010-2012
-0.43*
2010-2012
-0.01
2014-2016
0.12
2014-2016
-0.44*
2014-2016
-0.09
N/A (only 2
2018-2020
0.15
2018-2020
-0.13
2018-2020
datapoints)
Annual Max-West
Annual Max-West
Annual Max-West
Ecoregions- Monitor
Ecoregions- Monitor
Ecoregions- Monitor
Inclusion Criteria: 1%
r = -0.07
Inclusion Criteria: 0.5%
—?
II
O
O
Inclusion Criteria: 0.1%
—?
II
O
O
Year
r
Year
r
Year
r
2001 - 2003
-0.19
2001 - 2003
-0.11
2001 - 2003
-0.09
2006 - 2008
-0.07
2006 - 2008
0.12
2006 - 2008
0.05
2010-2012
-0.26
2010-2012
-0.07
2010-2012
-0.12
2014-2016
-0.19
2014-2016
-0.06
2014-2016
-0.27
6A-64
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2018-2020
-0.07
2018-2020
0.03
2018-2020
-0.09
Annual Max-West
Ecoregions (outliers
excluded)- Monitor
Inclusion Criteria: 1%
No outliers
in West
dataset
Annual Max-West
Ecoregions (outliers
excluded)- Monitor
Inclusion Criteria: 0.5%
No outliers
in West
dataset
Annual Max-West
Ecoregions (outliers
excluded)- Monitor
Inclusion Criteria: 0.1 %a
—?
II
O
O
CO
Year
r
Year
r
Year
r
2001 - 2003
2001 - 2003
2001 - 2003
-0.09
2006 - 2008
2006 - 2008
2006 - 2008
0.05
2010-2012
2010-2012
2010-2012
-0.12
2014-2016
2014-2016
2014-2016
-0.27
2018-2020
2018-2020
2018-2020
-0.23
Weighted Annual
Average-All Ecoregions-
Monitor Inclusion
Criteria: 1%
r = 0.47*
Weighted Annual
Average-All Ecoregions-
Monitor Inclusion
Criteria: 0.5%
r = 0.56*
Weighted Annual
Average-All Ecoregions-
Monitor Inclusion
Criteria: 0.1%
r = 0.59*
Year
r
Year
r
Year
r
2001 - 2003
0.66*
2001 - 2003
0.77*
2001 - 2003
0.85*
2006 - 2008
0.72*
2006 - 2008
0.81*
2006 - 2008
0.86*
2010-2012
0.58*
2010-2012
0.71*
2010-2012
0.75*
2014-2016
0.07
2014-2016
0.16
2014-2016
0.24*
2018-2020
-0.04
2018-2020
0.22*
2018-2020
0.34
Weighted Annual
Average-East
Ecoregions- Monitor
Inclusion Criteria: 1%
r = 0.85*
Weighted Annual
Average-East
Ecoregions- Monitor
Inclusion Criteria: 0.5%
r = 0.85*
Weighted Annual
Average-East
Ecoregions- Monitor
Inclusion Criteria: 0.1%
r = 0.84*
Year
r
Year
r
Year
r
2001 - 2003
0.90*
2001 - 2003
0.89*
2001 - 2003
0.84*
2006 - 2008
0.88*
2006 - 2008
0.9*
2006 - 2008
0.85*
2010-2012
0.75*
2010-2012
0.75*
2010-2012
0.72*
2014-2016
0.32*
2014-2016
0.19
2014-2016
0.2
2018-2020
0.21
2018-2020
0.30*
2018-2020
0.31*
Weighted Annual
Average-West
Ecoregions- Monitor
Inclusion Criteria: 1%
r = 0.14
Weighted Annual
Average-West
Ecoregions- Monitor
Inclusion Criteria: 0.5%
r = 0.19*
Weighted Annual
Average-West
Ecoregions- Monitor
Inclusion Criteria: 0.1%
r = 0.20*
Year
r
Year
r
Year
r
2001 - 2003
-0.01
2001 - 2003
0.04
2001 - 2003
0.07
2006 - 2008
-0.17
2006 - 2008
-0.07
2006 - 2008
0.06
2010-2012
-0.23
2010-2012
-0.12
2010-2012
-0.11
2014-2016
-0.19
2014-2016
-0.14
2014-2016
-0.17
2018-2020
0.04
2018-2020
0.04
2018-2020
0.02
*p< 0.05
a Note: There are several outlier points in this comparison where the EAQM-max annual average SO2 value exceeds 20 ppb in
the 2018-2020 period. These points have been removed from these analyses. These data are driven by a monitor in
southeastern MO where annual average SO2 has exceeded 20 ppb in recent years. Any downwind ecoregion that is linked to
this upwind monitor will have an EAQM-max with this value. A preliminary analysis suggests that these observed SO2 data are
due to a new source that was not modeled in the CMAQ simulation that informed the TDep estimates of deposition. As there is
no deposition monitor in the immediate vicinity of the source it is unlikely that the TDep estimates are capturing the impacts of
this source. For that reason, we concluded it was appropriate not to consider these data in our evaluation of the concentration-
deposition relationship.
6A-65
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20-
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-C
0)
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15-
10-
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£ 5-
Time Period
• 2001-2003
• 2006-2008
• 2010-2012
• 2014-2016
2018-2020
0-
Monitor Inclusion Criterion: 1%
r= 0.24 (p<0.05)
slope= 0.50 (p<0.05)
0 5 10 15 20
Annual Average S02 (Max), 3-yr average (ppb)
20
CO 15-
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10-
£ 5-
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Time Period
• 2001-2003
• 2006-2008
• 2010-2012
• 2014-2016
2018-2020
• t
Region
® •
0 East
A West
•
•• ®***
5-0° •
• ®
©
° s s
Monitor Inclusion Criterion: 1%
r= 0.24 (p<0.05)
slope= 0.65 (p<0.05)
Outliers Excluded;
see Table 6A-2
5 10 15 20
Annual Average S02 (Max), 3-yr average (ppb)
Figure 6A-49. Annual SO2 EAQM-inax values and TDep S deposition in 84 ecoregions
(120-hr trajectories, NAM-12,1% monitor inclusion criteria): all values
(upper), outliers excluded (lower).
6A-66
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20-
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-C
0)
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£ 5-
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20
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0-
Time Period
• 2001-2003
• 2006-2008
• 2010-2012
• 2014-2016
2018-2020
Region
o East
Monitor Inclusion Criterion: 1%
r= 0.65 (p<0.05)
slope= 0.63 (p<0.05)
0 5 10 15 20
Annual Average S02 (Max), 3-yr average (ppb)
•
Time Period
•
2001-2003
•
•
•
2006-2008
•
2010-2012
• •
•
2014-2016
• f
•
©
2018-2020 .
•
O y
Region ° ® ©
0 East &
•
• y
•
• • .*•*
/ © |
Monitor Inclusion Criterion: 1%
0
• •
©
oG
oc
©
r= 0.68 (p<0.05)
slope= 0.96 (p<0.05)
Outliers Excluded;
o/g
8
see Table 6A-2
0
5
10
1
5 20
Annual Average S02 (Max), 3-yr average (ppb)
Figure 6A-50. Annual SO2 EAQM-inax values and TDep S deposition in eastern
ecoregions (120-hr trajectories, NAM-12,1% monitor inclusion criteria):
all values (upper), outliers excluded (lower).
6A-67
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20-
cb
-C
0)
U)
15-
10
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£ 5-
0-
Time Period
• 2001-2003
• 2006-2008
• 2010-2012
• 2014-2016
2018-2020
Region
A West
Monitor Inclusion Criterion: 1%
5 10 15 20
Annual Average S02 (Max), 3-yr average (ppb)
Figure 6A-51. Annual SO2 EAQM-max values and TDep S deposition in western
ecoregions (120-hr trajectories, NAM-12,1% monitor inclusion criteria).
6A-68
-------
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cb
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10
a>
Q
£ 5-
0-
20
CO 15-
10-
a>
Q
£ 5-
0-
Monitor Inclusion Criterion: 0.5%
r= 0.48 (p<0.05)
slope= 0.45 (p<0.05)
Time Period
• 2001-2003
• 2006-2008
• 2010-2012
• 2014-2016
2018-2020
5 10 15 20
Annual Average S02 (Max), 3-yr average (ppb)
Time Period
• 2001-2003
• 2006-2008
• 2010-2012
• 2014-2016
2018-2020
Region
o East
A West
Monitor Inclusion Criterion: 0.5%
r= 0.49 (p<0.05)
slope= 0.80 (p<0.05)
Outliers Excluded;
see Table 6A-2
5 10 15 20
Annual Average S02 (Max), 3-yr average (ppb)
Figure 6A-52. Annual SO2 EAQM-max values and TDep S deposition in 84 ecoregions
(120-hr trajectories, NAM-12, 0.5% monitor inclusion criteria): all values
(upper), outliers excluded (lower).
6A-69
-------
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r= 0.53 (p<0.05)
slope= 0.36 (p<0.05)
15-
10-
a>
a
£ 5-
0-
# •
' •
> •
Time Period
• 2001-2003
• 2006-2008
• 2010-2012
• 2014-2016
2018-2020
0 5 10 15 20
Annual Average S02 (Max), 3-yr average (ppb)
20
CO 15"
10-
d)
Q
^ 5-
0-
Time Period
• 2001-2003
2006-2008
• 2010-2012
• 2014-2016
2018-2020
Region
o East
Monitor Inclusion Criterion: 0.5%
r= 0.65 (p<0.05)
slope= 0.95 (p<0.05)
Outliers Excluded;
see Table 6A-2
5 10 15 20
Annual Average S02 (Max), 3-yr average (ppb)
Figure 6A-53. Annual SO2 EAQM-inax values and TDep S deposition in eastern
ecoregions (120-hr trajectories, NAM-12, 0.5% monitor inclusion criteria):
all values (upper), outliers excluded (lower).
6 A-70
-------
Monitor Inclusion Criterion: 0.5%
Time Period
• 2001-2003
• 2006-2008
• 2010-2012
• 2014-2016
2018-2020
Region
A West
i
t 1 1 1 r
0 5 10 15 20
Annual Average S02 (Max), 3-yr average (ppb)
Figure 6A-54. Annual SO2 EAQM-max values and TDep S deposition in western
ecoregions (120-hr trajectories, NAM-12, 0.5% monitor inclusion criteria).
6 A-71
-------
20-
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cb
CO 15
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10
a>
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0-
Monitor Inclusion Criterion: 0.1%
r= 0.51 (p<0.05)
slope= 0.2 (p<0.05)
Time Period
• 2001-2003
• 2006-2008
• 2010-2012
• 2014-2016
2018-2020
m-tf'
5 10 15 20
Annual Average S02 (Max), 3-yr average (ppb)
20-
CO 15"
Time Period
• 2001-2003
• 2006-2008
• 2010-2012
• 2014-2016
2018-2020
Region
o East
A West
10-
d)
Q
£ 5-
0-
Monitor Inclusion Criterion: 0
0 5 10 15 20
Annual Average S02 (Max), 3-yr average (ppb)
Figure 6A-55. Annual SO2 EAQM-inax values and TDep S deposition in 84 ecoregions
(120-hr trajectories, NAM-12, 0.1% monitor inclusion criteria): all values
(upper), outliers excluded (lower).
6 A-72
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CO 15
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10
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£ 5-
0-
Time Period
• 2001-2003
• 2006-2008
• 2010-2012
• 2014-2016
2018-2020
Region
o East
5 10 15 20
Annual Average S02 (Max), 3-yr average (ppb)
20
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Time Period
• 2001-2003
• 2006-2008
• 2010-2012
• 2014-2016
t
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2018-2020
Region
0 East
• •
•
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© • •
s •
8
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0
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Outliers Excluded;
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r= 0.72 (p<0.05)
slope= 0.84 (p<0.05)
0
0 5 10 1
5 20
Annual Average S02 (Max), 3-yr average (ppb)
Figure 6A-56. Annual SO2 EAQM-inax values and TDep S deposition in eastern
ecoregions (120-hr trajectories, NAM-12, 0.1% monitor inclusion criteria):
all values (upper), outliers excluded (lower).
6 A-73
-------
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CD
20-
CO
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15-
10
a>
Q
£ 5-
0-
Monitor Inclusion Criterion: 0.1%
Time Period
• 2001-2003
• 2006-2008
• 2010-2012
• 2014-2016
2018-2020
Region
A West
A A
&
A*#
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5 10 15 20
Annual Average S02 (Max), 3-yr average (ppb)
20-
15-
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Time Period
• 2001-2003
• 2006-2008
• 2010-2012
• 2014-2016
2018-2020
Region
A West
Monitor Inclusion Criterion: 0.1%
Outliers Excluded;
see Table 6A-2
<*» r
0
10 1
5 20
Annual Average S02 (Max), 3-yr average (ppb)
Figure 6A-57. Annual SO2 EAQM-max values and TDep S deposition in western
ecoregions (120-hr trajectories, NAM-12, 0.1% monitor inclusion criteria):
all values (upper), outliers excluded (lower).
6 A-74
-------
20-
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10
a>
Q
£ 5-
0-
Monitor Inclusion Criterion: 1%
r= 0.47 (p<0.05)
slope= 2.34 (p<0.05)
Time Period
• 2001-2003
• 2006-2008
• 2010-2012
• 2014-2016
2018-2020
Region
o East
A West
5 10 15 20
Annual Average S02 (Weighted), 3-yr average (ppb)
Figure 6A-58. Annual SO2 EAQM-weighted values and TDep S deposition in 84
ecoregions (120-hr trajectories, NAM-12,1% monitor inclusion criteria).
CO
O)
2
CO
0
Q
15-
10-
£ 5-
0-
Monitor Inclusion Criterion: 1%
slope= 2.27 (p<0.05)
• /
/ •
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• /
• /•
• L
Time Period
•#/•
& •
• 2001-2003
• ©7#
• 2006-2008
© 9®
• 2010-2012
• 2014-2016
2018-2020
Jr s
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Region
0 East
("fXjjfa CD
0
5 10 15 20
Annual Average S02 (Weighted), 3-yr average (ppb)
Figure 6A-59. Annual SO2 EAQM-weighted values and TDep S deposition in eastern
ecoregions (120-hr trajectories, NAM-12,1% monitor inclusion criteria).
6A-75
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20-
>>
cb
CO 15
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10
a>
Q
£ 5-
0-
Monitor Inclusion Criterion: 1%
Time Period
• 2001-2003
• 2006-2008
• 2010-2012
• 2014-2016
2018-2020
Region
A West
5 10 15 20
Annual Average S02 (Weighted), 3-yr average (ppb)
Figure 6A-60. Annual SO2 EAQM-weighted values and TDep S deposition in western
ecoregions (120-hr trajectories, NAM-12,1% monitor inclusion criteria).
CO
O)
2
CO
0
Q
15-
10-
£ 5-
0-
Time Period
• 2001-2003
• 2006-2008
• 2010-2012
• 2014-2016
2018-2020
Region
o East
A West
Monitor Inclusion Criterion: 0.5%
r= 0.56 (p<0.05)
slope= 2.36 (p<0.05)
5 10 15 20
Annual Average S02 (Weighted), 3-yr average (ppb)
Figure 6A-61. Annual SO2 EAQM-weighted values and TDep S deposition in 84
ecoregions (120-hr trajectories, NAM-12, 0.5% monitor inclusion criteria).
6 A-76
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20-
>>
cb
CO 15
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10
a>
Q
£ 5-
0-
Time Period
2001-2003
2006-2008
2010-2012
2014-2016
2018-2020
Region
o East
Monitor Inclusion Criterion: 0.5%
r= 0.85 (p<0.05)
slope= 2.22 (p<0.05)
5 10 15 20
Annual Average S02 (Weighted), 3-yr average (ppb)
Figure 6A-62. Annual SO2 EAQM-weighted values and TDep S deposition in eastern
ecoregions (120-hr trajectories, NAM-12, 0.5% monitor inclusion criteria).
CO
O)
15-
10-
0
Q
£ 5-
0-
Time Period
• 2001-2003
• 2006-2008
• 2010-2012
• 2014-2016
2018-2020
Region
A West
Monitor Inclusion Criterion: 0.5%
r= 0.19 (p<0.05)
slope= 0.14 (p<0.05)
5 10 15 20
Annual Average S02 (Weighted), 3-yr average (ppb)
Figure 6A-63. Annual SO2 EAQM-weighted values and TDep S deposition in western
ecoregions (120-hr trajectories, NAM-12, 0.5% monitor inclusion criteria).
6A-77
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20-
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10
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• /
Time Period
t /
• 2001-2003
• 2006-2008
•• /
. * •/
• 2010-2012
• 2014-2016
2018-2020
»/
8 /
9® /
Region
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• •/ •
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0 East
A West
d''
& •
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Monitor Inclusion Criterion: 0.1%
r= 0.59 (p<0.05)
isn
slope= 2.5 (p<0.05)
0 5 10 15 20
Annual Average S02 (Weighted), 3-yr average (ppb)
Figure 6A-64. Annual SO2 EAQM-weighted values and TDep S deposition in 84
ecoregions (120-hr trajectories, NAM-12, 0.1% monitor inclusion criteria).
CO
O)
2
CO
0
Q
15-
10-
£ 5-
0-
Time Period
• 2001-2003
• 2006-2008
• 2010-2012
• 2014-2016
2018-2020
Region
o East
Monitor Inclusion Criterion: 0.1%
r= 0.84 (p<0.05)
slope= 2.32 (p<0.05)
10 15 20
Annual Average S02 (Weighted), 3-yr average (ppb)
Figure 6A-65. Annual SO2 EAQM-weighted values and TDep S deposition in eastern
ecoregions (120-hr trajectories, NAM-12, 0.1% monitor inclusion criteria).
6A-78
-------
Time Period Monitor inclusion Criterion: 0.1%
• 2001-2003 r= 0.20 (p<0.05)
• 2006-2008 slope= 0.16 (p<0.05)
• 2010-2012
• 2014-2016
2018-2020
Region
A West
0 5 10 15 20
Annual Average S02 (Weighted), 3-yr average (ppb)
Figure 6A-66. Annual SO2 EAQM-weighted values and TDep S deposition in western
ecoregions (120-hr trajectories, NAM-12, 0.1% monitor inclusion criteria).
6 A-79
-------
6A.6.4.SO2 Annual Metric - 48-hr
Table 6A-5. Correlation coefficients of TDep estimates of sulfur deposition and annual
SO2 EAQMs generated by HYSPLIT analysis. Data are also split by year and
by region (East/West), 48-hr trajectories.
Sulfur Deposition and SO2
Annual Max-All
Ecoregions- Monitor
Inclusion Criteria: 1%
Correlation
Coefficient (r)
= 0.32*
Annual Max-All
Ecoregions- Monitor
Inclusion Criteria: 1%a
Correlation
Coefficient
(r) = 0.32*
Weighted Annual
Average-All Ecoregions-
Monitor Inclusion
Criteria: 1%
Correlation
Coefficient
(r) = 0.45*
Year
r
Year
r
Year
r
2001 - 2003
0.55*
2001 - 2003
0.55*
2001 - 2003
0.63*
2006 - 2008
0.59*
2006 - 2008
0.59*
2006 - 2008
0.71*
2010-2012
0.24*
2010-2012
0.24*
2010-2012
0.56*
2014-2016
-0.21
2014-2016
-0.21
2014-2016
0.04
2018-2020
-0.22*
2018-2020
-0.39*
2018-2020
-0.01
Annual Max-East
Ecoregions- Monitor
Inclusion Criteria: 1%
r = 0.67*
Annual Max-East
Ecoregions- Monitor
Inclusion Criteria: 1%a
—?
II
O
CO
*
Weighted Annual
Average-East
Ecoregions- Monitor
Inclusion Criteria: 1%
r = 0.85*
Year
r
Year
r
Year
r
2001 - 2003
0.75*
2001 - 2003
0.75*
2001 - 2003
0.88*
2006 - 2008
0.69*
2006 - 2008
0.69*
2006 - 2008
0.89*
2010-2012
0.23
2010-2012
0.23
2010-2012
0.75*
2014-2016
-0.02
2014-2016
-0.02
2014-2016
0.33*
2018-2020
0.09
2018-2020
-0.05
2018-2020
0.23
Annual Max-West
Ecoregions- Monitor
Inclusion Criteria: 1%
r = -0.13
Annual Max-West
Ecoregions- Monitor
Inclusion Criteria: 1%
No outliers
in West
dataset
Weighted Annual
Average-West
Ecoregions- Monitor
Inclusion Criteria: 1%
r = 0.07
Year
r
Year
r
Year
r
2001 - 2003
-0.25
2001 - 2003
2001 - 2003
-0.14
2006 - 2008
-0.22
2006 - 2008
2006 - 2008
-0.23
2010-2012
-0.27
2010-2012
2010-2012
-0.27
2014-2016
-0.21
2014-2016
2014-2016
-0.29
2018-2020
-0.25
2018-2020
2018-2020
-0.06
*p< 0.05
a Note: There are several outlier points in this comparison where the EAQM-max annual average SO2 value exceeds 20 ppb in the
2018-2020 period. These points have been removed from these analyses. These data are driven by a monitor in southeastern
MO where annual average SO2 has exceeded 20 ppb in recent years. Any downwind ecoregion that is linked to this upwind
monitor will have an EAQM-max with this value. A preliminary analysis suggests that these observed SO2 data are due to a new
source that was not modeled in the CMAQ simulation that informed the TDep estimates of deposition. As there is no deposition
monitor in the immediate vicinity of the source it is unlikely that the TDep estimates are capturing the impacts of this source. For
that reason, we concluded it was appropriate not to consider these data in our evaluation of the concentration-deposition
relationship.
6 A-80
-------
Time Period
20-
>>
cb
CO 15
U)
10-
a>
a
£ 5-
0-
Monitor Inclusion Criterion: 1%
r= 0.32 (p<0.05)
slope= 0.36 (p<0.05)
0 10 20
Annual S02 Design Value (Max), 3-yr average (ppb)
30
20-
CO 15-
10-
d)
Q
^ 5-
0-
Time Period
• 2001-2003
• 2006-2008
• 2010-2012
• 2014-2016
2018-2020
Region
o East
A West
5 10 15 20
Annual Average S02 (Max), 3-yr average (ppb)
Figure 6A-67. Annual SO2 EAQM-inax values and TDep S deposition in 84 ecoregions
(48-hr trajectories, NARR-32,1% monitor inclusion criteria): all values
(upper), outliers excluded (lower).
6 A-81
-------
CO
CD
20-
CO
-C
0)
U)
15-
10
a>
Q
£ 5-
0-
Time Period
• 2001-2003
• 2006-2008
• 2010-2012
• 2014-2016
2018-2020
Region
o East
Monitor Inclusion Criterion: 1%
r= 0.67 (p<0.05)
slope= 0.36 (p<0.05)
20-
15-
0 10 20
Annual S02 Design Value (Max), 3-yr average (ppb)
Time Period
• 2001-2003
• 2006-2008
• 2010-2012
• 2014-2016
2018-2020
Region
o East
30
10
<1)
o
£ 5
0-
5 10 15 20
Annual Average S02 (Max), 3-yr average (ppb)
Figure 6A-68. Annual SO2 EAQM-max values and TDep S deposition in eastern
ecoregions (48-hr trajectories, NARR-32,1% monitor inclusion criteria):
all values (upper), outliers excluded (lower).
6A-82
-------
20-
>>
cb
CO 15
U)
10
a>
Q
£ 5-
0-
Time Period
• 2001-2003
• 2006-2008
• 2010-2012
• 2014-2016
2018-2020
Region
A West
Monitor Inclusion Criterion: 1%
0 10 20 30
Annual S02 Design Value (Max), 3-yr average (ppb)
Figure 6A-69. Annual SO2 EAQM-max values and TDep S deposition in western
ecoregions (48-hr trajectories, NARR-32,1% monitor inclusion criteria).
20-
CO 15-
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20-
C0 15
CT>
10
• 1
2018-2020
Region
fy*
9/ •
8 /
0 East
* a? *
A9-
Monitor Inclusion Criterion: 1%
*££* *
r= 0.85 (p<0.05)
© cgSw
slope= 2.24 (p<0.05)
gpikt-
0 5
10 1
5 20
Annual Average S02 (Weighted), 3-yr average (ppb)
Figure 6A-71. Annual SO2 EAQM-weighted values and TDep S deposition in eastern
ecoregions (48-hr trajectories, NARR-32,1% monitor inclusion criteria).
20
CO 15
-------
6A.6.5.NO2 Annual Metric - 120-hr
Table 6A-6. Correlation coefficients of TDep estimates of nitrogen deposition and annual
NO2 EAQMs generated by HYSPLIT analysis, 120-hr trajectories. Data are
also split by year and by region (East/West).
Nitrogen Deposition and NO2
Annual Max-All
Correlation
Annual Max-All
Correlation
Annual Max-All
Correlation
Ecoregions- Monitor
Coefficient
Ecoregions- Monitor
Coefficient
Ecoregions- Monitor
Coefficient
Inclusion Criteria: 1%
(r) = -0.12*
Inclusion Criteria: 0.5%
(r) = -0.17*
Inclusion Criteria: 0.1%
(r) = 0.02
Year
r
Year
r
Year
r
2001 - 2003
-0.14
2001 - 2003
-0.31*
2001 - 2003
-0.67*
2006 - 2008
-0.06
2006 - 2008
0.05
2006 - 2008
0.65*
2010-2012
-0.26*
2010-2012
-0.26*
2010-2012
-0.06
2014-2016
-0.28*
2014-2016
-0.41*
2014-2016
-0.65*
2018-2020
-0.37*
2018-2020
-0.58*
2018-2020
-0.58*
Annual Max-East
Annual Max-East
Annual Max-East
Ecoregions- Monitor
Ecoregions- Monitor
Ecoregions- Monitor
Inclusion Criteria: 1%
r = 0.42*
Inclusion Criteria: 0.5%
r = 0.35*
Inclusion Criteria: 0.1%
r = 0.44*
Year
r
Year
r
Year
r
2001 - 2003
0.41*
2001 - 2003
0.24*
2001 - 2003
-0.12
2006 - 2008
0.47*
2006 - 2008
0.35*
2006 - 2008
0.38*
2010-2012
0.29*
2010-2012
0.15
2010-2012
0.06
2014-2016
0.20
2014-2016
0.03
2014-2016
0.16
2018-2020
0.11
2018-2020
0.02
2018-2020
0.29*
Annual Max-West
Annual Max-West
Annual Max-West
Ecoregions- Monitor
Ecoregions- Monitor
Ecoregions- Monitor
Inclusion Criteria: 1%
r = 0.02
Inclusion Criteria: 0.5%
—?
II
O
O
Inclusion Criteria: 0.1%
r = 0.07
Year
r
Year
r
Year
r
2001 - 2003
-0.00
2001 - 2003
-0.12
2001 - 2003
-0.32
2006 - 2008
-0.02
2006 - 2008
-0.05
2006 - 2008
0.24
2010-2012
-0.16
2010-2012
0.02
2010-2012
-0.16
2014-2016
-0.00
2014-2016
-0.19
2014-2016
-0.15
2018-2020
-0.00
2018-2020
-0.25
2018-2020
0.0
Weighted Annual
Weighted Annual
Weighted Annual
Average-All Ecoregions-
Average-All Ecoregions-
Average-All Ecoregions-
Monitor Inclusion
Monitor Inclusion
Monitor Inclusion
Criteria: 1%
r = 0.03
Criteria: 0.5%
r = -0.06
Criteria: 0.1%
r = -0.10*
Year
r
Year
r
Year
r
2001 - 2003
0.0
2001 - 2003
-0.1
2001 - 2003
-0.22*
2006 - 2008
-0.08
2006 - 2008
-0.21
2006 - 2008
-0.35*
2010-2012
-0.03
2010-2012
-0.14
2010-2012
-0.26*
2014-2016
-0.07
2014-2016
-0.20
2014-2016
-0.34*
2018-2020
-0.2
2018-2020
-0.37*
2018-2020
-0.52*
Weighted Annual
Weighted Annual
Weighted Annual
Average-East
Average-East
Average-East
Ecoregions- Monitor
Ecoregions- Monitor
Ecoregions- Monitor
Inclusion Criteria: 1%
r = 0.55*
Inclusion Criteria: 0.5%
r = 0.48*
Inclusion Criteria: 0.1%
r = 0.45*
Year
r
Year
r
Year
r
2001 - 2003
0.62*
2001 - 2003
0.61*
2001 - 2003
0.62*
2006 - 2008
0.54*
2006 - 2008
0.39*
2006 - 2008
0.45*
2010-2012
0.42*
2010-2012
0.32*
2010-2012
0.26
6A-85
-------
2014-2016
0.38*
2014-2016
0.21
2014-2016
0.07
2018-2020
0.09
2018-2020
-0.03
2018-2020
-0.14
Weighted Annual
Average-V\/est
Ecoregions- Monitor
Inclusion Criteria: 1%
r = -0.12
V/eighted Annual
Average-West
Ecoregions- Monitor
Inclusion Criteria: 0.5%
r = -0.17*
Weighted Annual
Average-West
Ecoregions- Monitor
Inclusion Criteria: 0.1%
r = -0.11
Year
r
Year
r
Year
r
2001 - 2003
-0.01
2001 - 2003
-0.22
2001 - 2003
-0.23
2006 - 2008
-0.21
2006 - 2008
-0.23
2006 - 2008
-0.27
2010-2012
-0.28
2010-2012
-0.34*
2010-2012
-0.34*
2014-2016
-0.16
2014-2016
-0.28
2014-2016
-0.27
2018-2020
-0.10
2018-2020
-0.26
2018-2020
-0.25
*p< 0.05
Annual Average N02 Design Value (Max), 3-yr average (ppb)
Figure 6A-73. Annual NO2 EAQM-inax values and TDep N deposition in 84 ecoregions
(120-hr trajectories, NAM-12,1% monitor inclusion criteria).
6A-86
-------
15-
10-
-------
15-
10-
-------
15-
10-
-------
15-
10-
e
Monitor Inclusion Criterion: 0.1%
r= 0.44 (p<0.05)
slope= 0.17 (p<0.05)
10
20
30
40
Annual Average N02 Design Value (Max), 3-yr average (ppb)
Figure 6A-80. Annual NO2 EAQM-max values and TDep N deposition in eastern
ecoregions (120-hr trajectories, NAM-12, 0.1% monitor inclusion criteria).
15-
10-
Q
5-
Time Period
. 2001-2003
• 2006-2008
• 2010-2012
• 2014-2016
2018-2020
Region
A West
6*
A*
A
0-
10 20 30 40
Annual Average N02 Design Value (Max), 3-yr average (ppb)
Figure 6A-81. Annual NO2 EAQM-inax values and TDep N deposition in western
ecoregions (120-hr trajectories, NAM-12, 0.1% monitor inclusion criteria).
6A-90
-------
15-
10-
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15-
10-
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10-
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15-
10-
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15-
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cb
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CO
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Figure 6A-90. Annual NOi EAQM-weighted values and TDep N deposition in western
ecoregions (120-hr trajectories, NAM-12, 0.1% monitor inclusion criteria).
6A.6.6.NO2 Annual Metric - 48-hr
Table 6A-7. Correlation coefficients of TDep estimates of nitrogen deposition and annual
NO2 EAQMs generated by HYSPLIT analysis, 48-hr trajectories. Data are
also split by year and by region (East/West).
Nitrogen Deposition and NO2
Annual Max-All tcoregions-
Monitor Inclusion Criteria: 1%
Correlation
Coefficient (r)
= -0.06
Weighted Annual Average-All
Ecoregions- Monitor Inclusion
Criteria: 1%
Correlation
Coefficient (r)
= 0.06
Year
r
Year
r
2001 - 2003
-0.05
2001 - 2003
0.01
2006 - 2008
0,01
2006 - 2008
-0,03
2010-2012
-0,16
2010-2012
0.01
2014-2016
-0.25*
2014-2016
-0,05
2018-2020
-0.36*
2018-2020
-0.15
Annual Max-East Ecoregions-
Monitor Inclusion Criteria: 1%
r = 0.42*
Weighted Annual Average-East
Ecoregions- Monitor Inclusion
Criteria: 1%
r = 0,56*
Year
r
Year
r
2001 - 2003
0.38*
2001 - 2003
0,63*
2006 - 2008
0,35*
2006 - 2008
0.55*
Time Period
2001-2003
2006-2008
2010-2012
2014-2016
2018-2020
Region
A West
Monitor Inclusion Criterion: 0.1%
0 10 20 30 40
Annual Average NO, Design Value (Weighted), 3-yr average (ppb)
6A-95
-------
2010-2012
0,34*
2010-2012
0.48*
2014-2016
0,29*
2014-2016
0.40*
2018-2020
0,13
2018-2020
0.13
Annual Max-West Ecoregions-
Monitor Inclusion Criteria: 1%
r = -0.06
Weighted Annual Average-West
Ecoregions- Monitor Inclusion
Criteria: 1%
r = -0,13
Year
r
Year
r
2001 - 2003
-0.20
2001 - 2003
-0,17
2006 - 2008
-0.11
2006 - 2008
-0.26
2010-2012
-0.18
2010-2012
-0.24
2014-2016
-0,08
2014-2016
-0.15
2018-2020
-0.01
2018-2020
-0,06
*p< 0.05
15
Z
o>
10-
-------
15-
10-
-------
15-
10-
£•$>** A
n o o - .A
: •
• •
Time Period
•
• 2001-2003
• 2006-2008
•
•
• 2010-2012
O A A
• 2014-2016
3 w
>
2018-2020
•
>
•
Region
A
0 East
~
A West
•
Q)
Q
5-
0-
Monitor Inclusion Criterion: 1%
10 20 30 40
N02 Design Value (Weighted), 3-yr average (ppb)
Figure 6A-94. Annual NCte EAQM-weighted values and TDep N deposition in 84
ecoregions (48-hr trajectories, NARR-32,1% monitor inclusion criteria).
15-
2006-2008
2010-2012
2014-2016
2018-2020
10
o
w
o
CL
-------
15-
10-
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6A.6.7.PM2.5 Annual Metric - 120-hr
6A.6.7.1. Nitrogen
Table 6A-8. Correlation coefficients of TDep estimates of nitrogen deposition and annual
PM2.5 EAQMs generated by HYSPLIT analysis, 120-hr trajectories. Data are
also split by year and by region (East/West).
Nitrogen Deposition and PM2.5
Annual Max-All
Correlation
Annual Max-All
Correlation
Annual Max-All
Correlation
Ecoregions- Monitor
Coefficient
Ecoregions- Monitor
Coefficient
Ecoregions- Monitor
Coefficient
Inclusion Criteria: 1%
(r) = -0.13*
Inclusion Criteria: 0.5%
(r) = -0.22*
Inclusion Criteria: 0.1%
(r) = -0.38*
Year
r
Year
r
Year
r
2001 - 2003
-0.01
2001 - 2003
-0.12
2001 - 2003
-0.67*
2006 - 2008
0.03
2006 - 2008
-0.30*
2006 - 2008
-0.73*
2010-2012
0.16
2010-2012
-0.14
2010-2012
-0.75*
2014-2016
-0.37*
2014-2016
-0.46*
2014-2016
-0.76*
2018-2020
-0.47*
2018-2020
-0.49*
2018-2020
-0.72*
Annual Max-East
Annual Max-East
Annual Max-East
Ecoregions- Monitor
Ecoregions- Monitor
Ecoregions- Monitor
Inclusion Criteria: 1%
r = 0.47*
Inclusion Criteria: 0.5%
r = 0.53*
Inclusion Criteria: 0.1%
r = 0.29*
Year
r
Year
r
Year
r
2001 - 2003
0.53*
2001 - 2003
0.64*
2001 - 2003
0.08
2006 - 2008
0.29
2006 - 2008
0.34*
2006 - 2008
-0.17
2010-2012
0.38*
2010-2012
0.46*
2010-2012
-0.20
2014-2016
0.35*
2014-2016
0.27
2014-2016
-0.26
2018-2020
0.18
2018-2020
0.26
2018-2020
-0.09
Annual Max-West
Annual Max-West
Annual Max-West
Ecoregions- Monitor
Ecoregions- Monitor
Ecoregions- Monitor
Inclusion Criteria: 1%
r = -0.11
Inclusion Criteria: 0.5%
r = -0.12
Inclusion Criteria: 0.1%
r = -0.01
Year
r
Year
r
Year
r
2001 - 2003
-0.05
2001 - 2003
-0.18
2001 - 2003
0.07
2006 - 2008
-0.1
2006 - 2008
-0.22
2006 - 2008
-0.28
2010-2012
-0.21
2010-2012
-0.13
2010-2012
-0.23
2014-2016
-0.36*
2014-2016
-0.24
2014-2016
-0.37
2018-2020
-0.11
2018-2020
-0.07
2018-2020
-0.37*
Weighted Annual
Weighted Annual
Weighted Annual
Average-All Ecoregions-
Average-All Ecoregions-
Average-All Ecoregions-
Monitor Inclusion
Monitor Inclusion
Monitor Inclusion
Criteria: 1%
r = 0.42*
Criteria: 0.5%
r = 0.45*
Criteria: 0.1%
r = 0.39*
Year
r
Year
r
Year
r
2001 - 2003
0.64*
2001 - 2003
0.65*
2001 - 2003
0.71*
2006 - 2008
0.60*
2006 - 2008
0.64*
2006 - 2008
0.67*
2010-2012
0.69*
2010-2012
0.75*
2010-2012
0.77*
2014-2016
0.39*
2014-2016
0.45*
2014-2016
0.40*
2018-2020
0.23
2018-2020
-0.09
2018-2020
-0.29*
Weighted Annual
Weighted Annual
Weighted Annual
Average-East
Average-East
Average-East
Ecoregions- Monitor
Ecoregions- Monitor
Ecoregions- Monitor
Inclusion Criteria: 1%
r = 0.57*
Inclusion Criteria: 0.5%
r = 0.62*
Inclusion Criteria: 0.1%
r = 0.53*
Year
r
Year
r
Year
r
2001 - 2003
0.80*
2001 - 2003
0.85*
2001 - 2003
0.81*
2006 - 2008
0.70*
2006 - 2008
0.67*
2006 - 2008
0.53*
6A-100
-------
2010-2012
0.57*
2010-2012
0.60*
2010-2012
0.52*
2014-2016
0.37*
2014-2016
0.42*
2014-2016
0.29
2018-2020
0.26
2018-2020
0.27
2018-2020
0.17
Weighted Annual
Average-West
Ecoregions- Monitor
Inclusion Criteria: 1%
r = 0.06
Weighted Annual
Average-West
Ecoregions- Monitor
Inclusion Criteria: 0.5%
r = 0.02
Weighted Annual
Average-West
Ecoregions- Monitor
Inclusion Criteria: 0.1%
—s
II
O
O
cn
Year
r
Year
r
Year
r
2001 - 2003
0.08
2001 - 2003
-0.03
2001 - 2003
0.06
2006 - 2008
-0.08
2006 - 2008
-0.14
2006 - 2008
-0.09
2010-2012
0,11
2010-2012
0,09
2010-2012
0,15
2014-2016
-0.13
2014-2016
-0.16
2014-2016
-0.16
2018-2020
-0.03
2018-2020
-0.02
2018-2020
-0.00
*p< 0.05
Annual Average PM2 5 Design Value (Max), 3-yr average (|jg/m3)
Figure 6A-97. Annual PM2.5 EAQM-max values and TDep N deposition in 84 ecoregions
(120-hr trajectories, NAM-12,1% monitor inclusion criteria).
6 A-101
-------
15-
10-
o
CL
cu
Q
0-
Time Period
• 2001-2003
• 2006-2008
• 2010-2012
• 2014-2016
2018-2020
Monitor Inclusion Criterion: 1%
r= 0.47 (p<0.05)
slope= 0.36 (p<0.05)
0 10 20 30
Annual Average PM2 5 Design Value (Max), 3-yr average (ng/m3)
Figure 6A-98. Annual PM2.5 EAQM-max values and TDep N deposition in eastern
ecoregions (120-hr trajectories, NAM-12,1% monitor inclusion criteria).
15
-------
d)
O)
o
Annual Average PM2 5 Design Value (Max), 3-yr average (|jg/m3)
Figure 6A-100. Annual PM2.5 EAQM-max values and TDep N deposition in 84 ecoregions
(120-hr trajectories, NAM-12, 0,5% monitor inclusion criteria).
Annual Average PM2 5 Design Value (Max), 3-yr average ((jg/m3)
Figure 6A-101. Annual PM2.5 EAQM-max values and TDep N deposition in eastern
ecoregions (120-hr trajectories, NAM-12, 0.5% monitor inclusion criteria).
Monitor Inclusion Criterion: 0.5%
r= -0.22 (p<0.05)
slope= -0.21 (p<0.05)
Time Period
• 2001-2003
• 2006-2008
• 2010-2012 0
• 2014-2016
2018-2020 o
Region
o East
A West
6A-103
-------
03
x:
Z
CO
c
o
0)
O)
o
0 10 20
Annual Average PM2 5 Design Value (Max), 3-yr average (pg/m3)
Figure 6A-102. Annual PM2.5 EAQM-max values and TDep N deposition in western
ecoregions (120-hr trajectories, NAM-12, 0.5% monitor inclusion criteria).
!
0 10 20 30^
Annual Average PM2 5 Design Value (Max), 3-yr average (|jg/m3)
Figure 6A-103. Annual PM2.5 EAQM-max values and TDep N deposition in 84 ecoregions
(120-hr trajectories, NAM-12, 0.1% monitor inclusion criteria).
6A-104
-------
15-
10-
a>
O)
o
0-
Time Period
• 2001-2003
• 2006-2008
• 2010-2012
• 2014-2016
2018-2020
Region
o East
^oo
Monitor Inclusion Criterion: 0.1%
r= 0.29 (p<0.05)
slope= 0.14 (p<0.05)
10
20
30
Annual Average PM2 5 Design Value (Max), 3-yr average (pg/m3)
Figure 6A-104. Annual PM2.5 EAQM-max values and TDep N deposition in eastern
ecoregions (120-hr trajectories, NAM-12, 0.1% monitor inclusion criteria).
15-1
10-
5-
-------
15-
03
X!
z
CO
c
o
0)
O)
o
10-
0-
Monitor Inclusion Criterion: 1%
r= 0.42 (p<0.05)
slope= 0.67 (p<0.05)
Time Period
• 2001-2003
• 2006-2008
• 2010-2012
• 2014-2016
2018-2020
Region
o East
A West
10
20
30
Annual Average PM2 5 Design Value (Weighted), 3-yr average (pg/m3)
Figure 6A-106. Annual PM2.5 EAQM-weighted values and TDep N deposition in 84
ecoregions (120-hr trajectories, NAM-12,1% monitor inclusion criteria).
15
10-
co
c
.0
CO
o
Q.
0
• 2010-2012
• 2014-2016
2018-2020
Region
Monitor Inclusion Criterion: 1%
r= 0.57 (p<0.05)
slope= 0.52 (p<0.05)
0 East
20
Annual Average PM2 5 Design Value (Weighted), 3-yr average (pg/m3)
Figure 6A-107. Annual PM2.5 EAQM-weighted values and TDep N deposition in eastern
ecoregions (120-hr trajectories, NAM-12,1% monitor inclusion criteria).
6A-106
-------
15-
10-
a>
O)
o
0-
Time Period
• 2001-2003
• 2006-2008
• 2010-2012
• 2014-2016
2018-2020
Monitor Inclusion Criterion: 1%
slope= 0.24 (p<0.05)
0 10 20 30
Annual Average PM2 5 Design Value (Weighted), 3-yr average (pg/m3)
Figure 6A-108. Annual PM2.5 EAQM-weighted values and TDep N deposition in west
ecoregions (120-hr trajectories, NAM-12,1% monitor inclusion criteria).
-------
15-
03
X!
z
CO
c
o
0)
O)
o
10-
0-
Time Period
• 2001-2003
• 2006-2008
• 2010-2012
• 2014-2016
2018-2020
Region
0 East
©
Monitor Inclusion Criterion: 0.5%
r= 0.62 (p<0.05)
slope= 0.63 (p<0.05)
0 10 20 30
Annual Average PM2 5 Design Value (Weighted), 3-yr average (pg/m3)
Figure 6A-110. Annual PM2.5 EAQM-weighted values and TDep N deposition in eastern
ecoregions (120-hr trajectories, NAM-12, 0.5% monitor inclusion criteria).
15-
CO
-C
2
O)
03
>»
O
Q.
<1)
Q
c
Q)
O)
O
10-
0-
Time Period
• 2001-2003
• 2006-2008
• 2010-2012
• 2014-2016
2018-2020
Region
A West
Monitor Inclusion Criterion: 0.5%
slope= 0.19 (p<0.05)
0 10 20 30
Annual Average PM2 5 Design Value (Weighted), 3-yr average (pg/m3)
Figure 6A- 111. Annual PM2.5 EAQM-weighted values and TDep N deposition in western
ecoregions (120-hr trajectories, NAM-12, 0.5% monitor inclusion criteria).
6A-108
-------
15-
10-
5-
-------
Annual Average PM2 5 Design Value (Weighted), 3-yr average (|jg/m3)
Figure 6A-114. Annual PM2.5 EAQM-weighted values and TDep N deposition in western
ecoregions (120-hr trajectories, NAM-12, 0.1% monitor inclusion criteria).
6A.6.7.2. Sulfur
Table 6A-9. Correlation coefficients of Tdep estimates of sulfur deposition and annual
PM2.5 EAQMs generated by HYSPLIT analysis, 120-hr trajectories. Data are
also split by year and by region (East/West).
Sulfur Deposition and PM2.5
Annual Max-All
Correlation
Annual Max-All
Correlation
Annual Max-All
Correlation
Ecoregions- Monitor
Coefficient
Ecoregions- Monitor
Coefficient
Ecoregions- Monitor
Coefficient
Inclusion Criteria: 1%
(r) = -0,14*
inclusion Criteria: 0.5%
(r) = -0.22*
inclusion Criteria: 0.1%
(r) = -0.31*
Year
r
Year
r
Year
r
2001 - 2003
-0.13
2001 - 2003
-0.21
2001 - 2003
-0.73*
2006 - 2008
-0.05
2006 - 2008
-0.37*
2006 - 2008
-0,73*
2010-2012
0.07
2010-2012
-0.22*
2010-2012
-0.78*
2014-2016
-0.44*
2014-2016
-0.53*
2014-2016
-0.84*
2018-2020
-0.40*
2018-2020
-0.54*
2018-2020
-0.80*
Annual Max-East
Annual Max-East
Annual Max-East
Ecoregions- Monitor
Ecoregions- Monitor
Ecoregions- Monitor
inclusion Criteria: 1%
r = 0.74*
Inclusion Criteria: 0.5%
r = 0,83*
Inclusion Criteria: 0.1%
r = 0.58*
Year
r
Year
r
Year
r
2001 - 2003
0.69*
2001 - 2003
0.73*
2001 - 2003
0.02
2006 - 2008
0.39*
2006 - 2008
0.53*
2006 - 2008
0.10
2010-2012
0.47*
2010-2012
0.70*
2010-2012
-0,04
2014-2016
0.41*
2014-2016
0.43*
2014-2016
-0,47*
6A-110
-------
2018-2020
0.61*
2018-2020
0.53*
2018-2020
-0.30*
Annual Max-West
Annual Max-West
Annual Max-West
Ecoregions- Monitor
Inclusion Criteria: 1%
r = -0.36*
Ecoregions- Monitor
Inclusion Criteria: 0.5%
r = -0.33*
Ecoregions- Monitor
Inclusion Criteria: 0.1%
r = 0.19*
Year
r
Year
r
Year
r
2001 - 2003
-0.45*
2001 - 2003
-0.48*
2001 - 2003
-0.17
2006 - 2008
-0.47*
2006 - 2008
-0.61*
2006 - 2008
-0.31
2010-2012
-0.64*
2010-2012
-0.56*
2010-2012
-0.23
2014-2016
-0.73*
2014-2016
-0.53*
2014-2016
-0.48*
2018-2020
-0.20
2018-2020
-0.37*
2018-2020
-0.39*
Weighted Annual
Average-All Ecoregions-
Monitor Inclusion
Weighted Annual
Average-All Ecoregions-
Monitor Inclusion
Weighted Annual
Average-All Ecoregions-
Monitor Inclusion
Criteria: 1%
r = 0.43*
Criteria: 0.5%
r = 0.48*
Criteria: 0.1%
r = 0.46*
Year
r
Year
r
Year
r
2001 - 2003
0.51*
2001 - 2003
0.55*
2001 - 2003
0.62*
2006 - 2008
0.46*
2006 - 2008
0.56*
2006 - 2008
0.64*
2010-2012
0.60*
2010-2012
0.70*
2010-2012
0.76*
2014-2016
0.33*
2014-2016
0.43*
2014-2016
0.38*
2018-2020
-0.00
2018-2020
-0.07
2018-2020
-0.22*
Weighted Annual
Average-East
Ecoregions- Monitor
Inclusion Criteria: 1%
—?
II
O
CO
o
*
Weighted Annual
Average-East
Ecoregions- Monitor
Inclusion Criteria: 0.5%
r = 0.90*
Weighted Annual
Average-East
Ecoregions- Monitor
Inclusion Criteria: 0.1%
r = 0.89*
Year
r
Year
r
Year
r
2001 - 2003
0.83*
2001 - 2003
0.88*
2001 - 2003
0.88*
2006 - 2008
0.73*
2006 - 2008
0.86*
2006 - 2008
0.93*
2010-2012
0.64*
2010-2012
0.84*
2010-2012
0.89*
2014-2016
0.52*
2014-2016
0.65*
2014-2016
0.53*
2018-2020
0.63*
2018-2020
0.69*
2018-2020
0.72*
Weighted Annual
Average-West
Ecoregions- Monitor
Inclusion Criteria: 1%
r = -0.19*
Weighted Annual
Average-West
Ecoregions- Monitor
Inclusion Criteria: 0.5%
r = -0.22*
Weighted Annual
Average-West
Ecoregions- Monitor
Inclusion Criteria: 0.1%
r = -0.13
Year
r
Year
r
Year
r
2001 - 2003
-0.30
2001 - 2003
-0.44*
2001 - 2003
-0.37*
2006 - 2008
-0.52*
2006 - 2008
-0.62*
2006 - 2008
-0.60*
2010-2012
-0.30
2010-2012
-0.40*
2010-2012
-0.35*
2014-2016
-0.48*
2014-2016
-0.55*
2014-2016
-0.58*
2018-2020
-0.20
2018-2020
-0.25
2018-2020
-0.24
*p< 0.05
6A-111
-------
20-
cn
g> 15-
10-
^ 5-
0-
Time Period
• 2001-2003
# • 2006-2008
• 2010-2012
• 2014-2016
©
2018-2020
•
•
©
Region
0 East
0® •
A West
• •
9 *o
•v/-
»2-1.
•
r%& ¦.'
Monitor Inclusion Criterion: 1%
r= -0.22 (p<0.05)
slope= -0.20 (p<0.05)
bsi- £ 1
4
10
20
Annual Average PM2 5 Design Value (Max), 3-yr average (pg/m3
30
Figure 6A-115. Annual PM2.5 EAQM-max values and TDep S deposition in 84 ecoregions
(120-hr trajectories, NAM-12,1% monitor inclusion criteria).
20
O) 15
U)
TO
10
0
Q
Time Period
• 2001-2003
• 2006-2008
• 2010-2012
• 2014-2016
2018-2020
Region
o East
ee •
Monitor Inclusion Criterion: 1%
r= 0.46 (p<0.05)
10
20
30
Annual Average PM2 5 Design Value (Max), 3-yr average (|jg/m3)
Figure 6A-116. Annual PM2.5 EAQM-max values and TDep S deposition in eastern
ecoregions (120-hr trajectories, NAM-12,1% monitor inclusion criteria).
6A-112
-------
20-
c/)
cn
15-
10-
*= 5-
0-
Time Period
• 2001-2003
• 2006-2008
• 2010-2012
• 2014-2016
2018-2020
Region
A West
Monitor Inclusion Criterion: 1%
r= -0.36 (p<0.05)
slope= -0.03 (p<0.05)
&
A
-A-
0 10 20 30
Annual Average PM2 5 Design Value (Max), 3-yr average (pg/m3)
Figure 6A-117. Annual PM2.5 EAQM-inax values and TDep S deposition in western
ecoregions (120-hr trajectories, NAM-12,1% monitor inclusion criteria)
20
C/)
& 15
CD
U)
CO
CO
10
CD
Q
^ 5
Time Period
•
• 2001-2003
• 2006-2008
• #
• 2010-2012
• 2014-2016
• ®
2018-2020
Region
©
©
• #
• •
•
0 East
A West
•
• 0
%
A»
•
0 ©&@ | Monitor Inclusion Criterion: 0.5%
S to ! r- -0.22 (p<0.05)
liri * l
10
20
30
Annual Average PM2 5 Design Value (Max), 3-yr average (pg/m3)
Figure 6A-118. Annual PM2.5 EAQM-max values and TDep S deposition in 84 ecoregions
(120-hr trajectories, NAM-12, 0.5% monitor inclusion criteria).
6A-113
-------
20-
c/)
cn
15-
10-
*= 5-
0-
Time Period
• 2001-2003
• 2006-2008
• 2010-2012
• 2014-2016
2018-2020
Region
o East
Monitor Inclusion Criterion: 0.5%
r= 0.83 (p<0.05)
slope= 1.12 (p<0.05)
0 10 20 30
Annual Average PM2 5 Design Value (Max), 3-yr average (pg/m3)
Figure 6A-119. Annual PM2.5 EAQM-max values and TDep S deposition in eastern
ecoregions (120-hr trajectories, NAM-12, 0.5% monitor inclusion criteria).
20-
if)
o) 15
U)
TO
10-
0
Q
Time Period
• 2001-2003
• 2006-2008
• 2010-2012
• 2014-2016
2018-2020
Region
A West
Monitor Inclusion Criterion: 0.5%
r= -0.33 (p<0.05)
slope= -0.03 (p<0.05)
ft
10
20
30
Annual Average PM2 5 Design Value (Max), 3-yr average (|jg/m3)
Figure 6A-120. Annual PM2.5 EAQM-max values and TDep S deposition in western
ecoregions (120-hr trajectories, NAM-12, 0.5% monitor inclusion criteria).
6A-114
-------
20-
cn
o) 15
10-
3 ®
CO
0-
Time Period
• 2001-2003
• 2006-2008
• 2010-2012
• 2014-2016
2018-2020
Region
o East
A West
Monitor Inclusion Criterion: 0.1%
r= -0.31 (p<0.05)
10
20
30
Annual Average PM2 5 Design Value (Max), 3-yr average (pg/m3)
Figure 6A-121. Annual PM2.5 EAQM-max values and Tdep S deposition in 84 ecoregions
(120-hr trajectories, NAM-12, 0.1% monitor inclusion criteria).
20-
>»
CO
jC
CO
D) 15
10-
u ®
if)
0-
Time Period /
• 2001-2003
• 2006-2008
• /
• 2010-2012
• 2014-2016
• © /
2018-2020
© /
• /
• /
Region
© /
0 East
/
A West
© /
/
0
• /
0
•
•
Jr
3* Monitor Inclusion Criterion: 1%
sM$S°
£ r= 0.43 (p<0.05)
©• slope= 0.83 (p<0.05)
A AA
20
Annual Average PM2 5 Design Value (Weighted), 3-yr average (pg/m3)
Figure 6A-122. Annual PM2.5 EAQM-weighted values and TDep S deposition in 84
ecoregions (120-hr trajectories, NAM-12,1% monitor inclusion criteria).
6A-115
-------
20-
c/)
cn
15-
10-
*= 5-
0-
Time Period
•
2001-2003
•
2006-2008
•
•
2010-2012
•
2014-2016
© ®
2018-2020
©
•
•
Region
©
© •
0
East
©
©
•
©• ,
° /
© /
<# /
• /
>/•
V
Monitor Inclusion Criterion: 1%
&o0©
r= 0.80 (p<0.05)
slope= 1.14 (p<0.05)
°/
0 10 20 30
Annual Average PM2 5 Design Value (Weighted), 3-yr average (pg/m3)
Figure 6A-123. Annual PM2.5 EAQM-weighted values and TDep S deposition in eastern
ecoregions (120-hr trajectories, NAM-12,1% monitor inclusion criteria).
20-
if)
o) 15
U)
TO
10
0
Q
Time Period
• 2001-2003
• 2006-2008
• 2010-2012
© 2014-2016
2018-2020
Region
A West
Monitor Inclusion Criterion: 1%
r= -0.19 (p<0.05)
slope= -0.04 (p<0.05)
±
A «
0
10 20 30
Annual Average PM2 5 Design Value (Weighted), 3-yr average (|jg/m3)
Figure 6A-124. Annual PM2.5 EAQM-weighted values and TDep S deposition in western
ecoregions (120-hr trajectories, NAM-12,1% monitor inclusion criteria).
6A-116
-------
20-
cn
o) 15
10-
3 ®
CO
0-
Time Period
• 2001-2003
• 2006-2008
• 2010-2012
• 2014-2016
2018-2020
Region
o East
A West
Monitor Inclusion Criterion: 0.5%
r= 0.48 (p<0.05)
slope= 1.1 (p<0.05)
0 10 20 30
Annual Average PM2 5 Design Value (Weighted), 3-yr average (pg/m3)
Figure 6A-125. Annual PM2.5 EAQM-weighted values and TDep S deposition in 84
ecoregions (120-hr trajectories, NAM-12, 0.5% monitor inclusion criteria).
20-
if)
o) 15
U)
TO
10-
0
Q
Time Period
•
• 2001-2003
• 2006-2008
• 2010-2012
• 2014-2016
% /
2018-2020
•*• /
Region
0 East
• /
• * /
'V
• ©>*
•• 7 •
c
0 j?
e
•yF •
Monitor Inclusion Criterion: 0.5%
*
r= 0.90 (p<0.05)
0
© S$
yo
10
20
30
Annual Average PM2 5 Design Value (Weighted), 3-yr average (|jg/m3)
Figure 6A-126. Annual PM2.5 EAQM-weighted values and TDep S deposition in eastern
ecoregions (120-hr trajectories, NAM-12, 0.5% monitor inclusion criteria).
6A-117
-------
20-
c/)
cn
15-
10-
*= 5-
0-
Time Period
• 2001-2003
• 2006-2008
• 2010-2012
• 2014-2016
2018-2020
Region
A West
Monitor Inclusion Criterion: 0.5%
r= -0.22 (p<0.05)
slope= -0.04 (p<0.05)
0 10 20 30
Annual Average PM2 5 Design Value (Weighted), 3-yr average (pg/m3)
Figure 6A-127. Annual PM2.5 EAQM-weighted values and TDep S deposition in western
ecoregions (120-hr trajectories, NAM-12, 0.5% monitor inclusion criteria).
20-
C/)
2 15
CD
U)
CO
10-
CD
O
^ 5
Time Period
• 2001-2003
• 2006-2008
• 2010-2012
• 2014-2016
2018-2020
Region
o East
A West
Monitor Inclusion Criterion: 0.1%
r= 0.46 (p<0.05)
slope= 1.2 (p<0.05)
0 10 20 30
Annual Average PM2 5 Design Value (Weighted), 3-yr average (pg/rn3)
Figure 6A-128. Annual PM2.5 EAQM-weighted values and TDep S deposition in 84
ecoregions (120-hr trajectories, NAM-12, 0.1% monitor inclusion criteria).
6A-118
-------
20-
c/)
cn
15-
10-
*= 5-
0-
Time Period
•
•
2001-2003
•
2006-2008
••
•
2010-2012
•
2014-2016
• •
2018-2020
*.• /
Region
0 East
• /
»• /
V
G A
®z*.
e
Oft*
d 0
* '
Monitor Inclusion Criterion: 0.1%
r= 0.89 (p<0.05)
slope= 1.5 (p<0.05)
JPK*
0
0 10 20 30
Annual Average PM2 5 Design Value (Weighted), 3-yr average (pg/m3)
Figure 6A-129. Annual PM2.5 EAQM-weighted values and TDep S deposition in eastern
ecoregions (120-hr trajectories, NAM-12, 0.1% monitor inclusion criteria).
20
if)
o) 15
U)
TO
10
0
Q
Time Period
• 2001-2003
• 2006-2008
• 2010-2012
• 2014-2016
2018-2020
Region
A West
Monitor Inclusion Criterion: 0.1%
A a A
SpfA a aa
~
0
10 20 30
Annual Average PM2 5 Design Value (Weighted), 3-yr average (|jg/m3)
Figure 6A-130. Annual PM2.5 EAQM-weighted values and TDep S deposition in western
ecoregions (120-hr trajectories, NAM-12, 0.1% monitor inclusion criteria).
6A-119
-------
6A.6.8.PM2.5 Annual Metric - 48-hr
6A.6.8.1. Nitrogen
Table 6A-10. Correlation coefficients of TDep estimates of nitrogen deposition and annual
PM2.5 EAQMs generated by HYSPLIT analysis, 48-hr trajectories. Data are
also split by year and by region (East/West).
Nitrogen Deposition and PM2.5
Annual Max-All Ecoregions-
Monitor Inclusion Criteria: 1%
Correlation
Coefficient (r)
= 0.05
Weighted Annual Average-All
Ecoregions- Monitor Inclusion
Criteria: 1%
Correlation
Coefficient (r)
= 0.50*
Year
r
Year
r
2001 - 2003
0.28*
2001 - 2003
0.69*
2006 - 2008
0.24*
2006 - 2008
0.64*
2010-2012
0.30*
2010-2012
0.71*
2014-2016
-0.18
2014-2016
0.44*
2018-2020
-0.39*
2018-2020
0.003
Annual Max-East Ecoregions-
Monitor Inclusion Criteria: 1%
¦k
LO
O
II
Weighted Annual Average-East
Ecoregions- Monitor Inclusion
Criteria: 1%
r = 0.61*
Year
r
Year
r
2001 - 2003
0.60*
2001 - 2003
0.81*
2006 - 2008
0.30*
2006 - 2008
0.63*
2010-2012
0.36*
2010-2012
0.57*
2014-2016
0.25
2014-2016
0.39*
2018-2020
0.38*
2018-2020
0.21
Annual Max-West Ecoregions-
Monitor Inclusion Criteria: 1%
r = -0.08
Weighted Annual Average-West
Ecoregions- Monitor Inclusion
Criteria: 1%
r = 0.04
Year
r
Year
r
2001 - 2003
-0.07
2001 - 2003
0.02
2006 - 2008
-0.11
2006 - 2008
-0.08
2010-2012
-0.04
2010-2012
0.13
2014-2016
-0.23
2014-2016
-0.11
2018-2020
-0.12
2018-2020
-0.007
*p< 0.05
6A-120
-------
20-
15-
CO
>*
CO
c
o
w
o
Ql
0
O
c
-------
6A.6.8.2. Sulfur
Table 6A-11. Correlation coefficients of TDep estimates of sulfur deposition and annual
PM2.5 EAQMs generated by HYSPLIT analysis, 48-hr trajectories. Data are
also split by year and by region (East/West).
Sulfur Deposition and PM2.5
Annual Max-All Ecoregions-
Monitor Inclusion Criteria: 1%
Correlation
Coefficient (r)
= 0.05
Weighted Annual Average-All
Ecoregions- Monitor Inclusion
Criteria: 1%
Correlation
Coefficient (r)
= 0.51*
Year
r
Year
r
2001 - 2003
0.16
2001 - 2003
0.59*
2006 - 2008
0.15
2006 - 2008
0.53*
2010-2012
0.23*
2010-2012
0.64*
2014-2016
-0.24*
2014-2016
0.43*
2018-2020
-0.40*
2018-2020
0.05
Annual Max-East Ecoregions-
Monitor Inclusion Criteria: 1%
r = 0.80*
Weighted Annual Average-East
Ecoregions- Monitor Inclusion
Criteria: 1%
r = 0.85*
Year
r
Year
r
2001 - 2003
0.62*
2001 - 2003
0.83*
2006 - 2008
0.42*
2006 - 2008
0.75*
2010-2012
0.61*
2010-2012
0.67*
2014-2016
0.48*
2014-2016
0.62*
2018-2020
0.52*
2018-2020
0.61*
Annual Max-West Ecoregions-
Monitor Inclusion Criteria: 1%
r = -0.37*
Weighted Annual Average-West
Ecoregions- Monitor Inclusion
Criteria: 1%
r = -0.21*
Year
r
Year
r
2001 - 2003
-0.50*
2001 - 2003
-0.38*
2006 - 2008
-0.46*
2006 - 2008
-0.53*
2010-2012
-0.50*
2010-2012
-0.28*
2014-2016
-0.61*
2014-2016
-0.47*
2018-2020
-0.26
2018-2020
-0.19
*p< 0.05
6A-122
-------
20-
C/)
O)
15-
10-
*= 5-
0-
Time Period
• 2001-2003
• 2006-2008
• 2010-2012
• 2014-2016
2018-2020
Region
o East
A West
• §
Monitor Inclusion Criterion: 1°/
48 hr Trajectory
• slope= 0.22 (p<0.05)
0 10 20 30
Annual Average PM2 5 Design Value (Max), 3-yr average (|jg/m3)
Figure 6A-133. Annual PM2.5 EAQM-inax values and Tdep S deposition in 84 ecoregions
(48-hr trajectories, NARR-32,1% monitor inclusion criteria).
20-
if)
o) 15
U)
aj
CO
10
-------
Attachment
-------
Attachment
Maps Showing Monitor Sites of Influence for INO2, PM2.5, and SO2 (3-hour
metric) Based on Different Inclusion Criteria for 16 Example Ecoregions
As described in Appendix 6-A, we considered the effect, at 16 representative ecoregions across
the U.S., of using different trajectory hit rates as criteria for monitoring site inclusion. Figures
6A-5 through 6A-20 of Appendix 6A show the results of those sensitivity tests for the annual
SO2 metric and 16 ecoregions. The following maps similar show this effect over the same 16
ecoregions for the annual NO2, annual PM2.5, and the 3-hour SO2 metrics.
N02 Monitor Sites of Influence
* >1% of ecoregion hits
* 0.5% to <1% of ecoregion hits
* 0.1% to <0.5% of ecoregion hits
o <0.1% of ecoregion hits
I] Northern Lakes and Forests (Ecoregion 5.2.1)
Figure 1. Monitoring sites (annual NO2 metric) of potential influence for ecoregion 5.2.1
(red shaded region).
6 A-Attachment-1
-------
Figure 2. Monitoring sites (annual NO2 metric) of potential influence for ecoregion 5.3.1
(red shaded region).
6A-Attachment-2
-------
N02 Monitor Sites of Influence
~ >1% of ecoregion hits
~ 0.5% to <1% of ecoregion hits
~ 0.1% to <0.5% of ecoregion hits
o <0.1% of ecoregion hits
^ Cascades (Ecoregion 6.2.7)
Figure 3. Monitoring sites (annual NO2 metric) of potential influence for ecoregion 6.2.7
(red shaded region).
6A-Attachment-3
-------
N02 Monitor Sites of Influence
• >1% ofecoregion hits
• 0.5% to <1% ofecoregion hits
• 0.1% to <0.5% ofecoregion hits
o <0.1% of ecoregion hits
I] Sierra Nevada (Ecoregion 6.2.12)
Figure 4. Monitoring sites (annual NO2 metric) of potential influence for ecoregion 6.2.12
(red shaded region).
6A-Attachment-4
-------
N02 Monitor Sites of Influence
• >1% ofecoregion hits
• 0.5% to <1% of ecoregion hits
• 0.1% to <0.5% ofecoregion hits
o <0.1% of ecoregion hits
I I Southern Rockies (Ecoregion 6.2.14)
Figure 5. Monitoring sites (annual NO2 metric) of potential influence for ecoregion 6.2.14
(red shaded region).
6A-Attachment-5
-------
N02 Monitor Sites of Influence
• >1 % of ec oregion hits
• 0.5% to <1% of ecoregion hits
• 0.1% to <0.5% of ecoregion hits
o <0.1% of ecoregion hits
HI Idaho Batholith (Ecoregion 6.2.15)
Figure 6. Monitoring sites (annual NO2 metric) of potential influence for ecoregion 6.2.15
(red shaded region).
6A-Attachment-6
-------
NO2 Monitor Sites of Influence
~ >1% ofecoregion hits
~ 0.5% to <1% of ecoregion hits
~ 0.1% to <0.5% ofecoregion hits
o <0.1% of ecoregion hits
I Eastern Great Lakes Lowlands (Ecoregion 8.1.1)
Figure 7. Monitoring sites (annual NO2 metric) of potential influence for ecoregion 8.1.1
(red shaded region).
6A-Attachment-7
-------
N02 Monitor Sites of Influence
>1% ofecoregion hits
0.5% to <1% ofecoregion hits
0.1% to <0.5% ofecoregion hits
<0.1% of ecoregion hits
) North Central Hardwood Forests (Ecoregion 8.1.4)
Figure 8. Monitoring sites (annual NO2 metric) of potential influence for ecoregion 8.1.4
(red shaded region).
6A-Attachment-8
-------
N02 Monitor Sites of Influence
~ >1% of ecoregion hits
~ 0.5% to < 1 % of ecoregion hits
~ 0.1% to <0.5% ofecoregion hits
o <0.1% of ecoregion hits
Northern Piedmont (Ecoregion 8.3.1)
Figure 9. Monitoring sites (annual NO2 metric) of potential influence for ecoregion 8.3.1
(red shaded region).
6A-Attachment-9
-------
N02 Monitor Sites of Influence
* >1% ofecoregion hits
* 0.5% to <1% ofecoregion hits
* 0.1% to <0.5% ofecoregion hits
o <0.1% of ecoregion hits
H South Central Plains (Ecoregion 8.3.7)
Figure 10. Monitoring sites (annual NO2 metric) of potential influence for ecoregion 8.3.7
(red shaded region).
6 A- Attachment-10
-------
N02 Monitor Sites of Influence
• >1% ofecoregion hits
* 0.5% to <1% ofecoregion hits
~ 0.1% to <0.5% ofecoregion hits
o <0.1% of ecoregion hits
Ridge and Valley (Ecoregion 8.4.1)
Figure 11. Monitoring sites (annual NO2 metric) of potential influence for ecoregion 8.4.1
(red shaded region).
6A-Attachment-11
-------
N02 Monitor Sites of Influence
~ >1% of ecoregion hits
~ 0.5% to < 1 % of ecoregion hits
~ 0.1% to <0.5% of ecoregion hits
o <0.1% of ecoregion hits
I Central Appalachians (Ecoregion 8.4.2)
Figure 12. Monitoring sites (annual NO2 metric) of potential influence for ecoregion 8.4.2
(red shaded region).
6 A- Attachment-12
-------
N02 Monitor Sites of Influence
* >1% ofecoregion hits
~ 0.5% to <1% of ecoregion hits
• 0.1% to <0.5% ofecoregion hits
o <0.1% of ecoregion hits
HI Central Great Plains (Ecoregion 9.4.2)
Figure 13. Monitoring sites (annual NO2 metric) of potential influence for ecoregion 9.4.2
(red shaded region).
6 A- Attachment-13
-------
N02 Monitor Sites of Influence
>1% ofecoregion hits
0.5% to <1% ofecoregion hits
0.1% to <0.5% ofecoregion hits
<0.1% of ecoregion hits
] Southern California Mountains (Ecoregion 11.1.3)
Figure 14. Monitoring sites (annual NO2 metric) of potential influence for ecoregion 11.1.3
(red shaded region).
6 A-Attachment-14
-------
N02 Monitor Sites of Influence
>1% ofecoregion hits
0.5% to <1% ofecoregion hits
0.1% to <0.5% ofecoregion hits
<0.1% ofecoregion hits
Arizona/New Mexico Mountains (Ecoregion 13.1.1)
Figure 15. Monitoring sites (annual NO2 metric) of potential influence for ecoregion 13.1.1
(red shaded region).
6 A- Attachment-15
-------
N02 Monitor Sites of Influence
>1% ofecoregion hits
0.5% to <1% ofecoregion hits
0.1% to <0.5% ofecoregion hits
<0.1% of ecoregion hits
Southern Florida Coastal Plain (Ecoregion 15.4.1)
Figure 16. Monitoring sites (annual NO2 metric) of potential influence for ecoregion 15.4.1
(red shaded region).
6 A- Attachment-16
-------
PM2.5 Monitor Sites of Influence
* >1% ofecoregion hits
* 0.5% to <1% ofecoregion hits
* 0.1% to <0.5% ofecoregion hits
o <0.1% ofecoregion hits
I Northern Lakes and Forests (Ecoregion 5.2.1)
Figure 17. Monitoring sites (annual PM2.5 metric) of potential influence for ecoregion 5.2.1
(red shaded region).
6 A- Attachment-17
-------
PM2.5 Monitor Sites of Influence
* >1% ofecoregion hits
* 0.5% to <1% of ecoregion hits
* 0.1% to <0.5% ofecoregion hits
o <0.1% of ecoregion hits
~ I Northeastern Highlands (Ecoregion 5.3.1)
Figure 18. Monitoring sites (annual PM2.5 metric) of potential influence for ecoregion 5.3.1
(red shaded region).
6 A- Attachment-18
-------
PM2.5 Monitor Sites of Influence
~ >1% ofecoregion hits
~ 0.5% to <1% ofecoregion hits
~ 0.1% to <0.5% ofecoregion hits
o <0.1% of ecoregion hits
HI Cascades (Ecoregion 6.2.7)
Figure 19. Monitoring sites (annual PM2.5 metric) of potential influence for ecoregion 6.2.7
(red shaded region).
6 A- Attachment-19
-------
PM2.5 Monitor Sites of Influence
* >1% ofecoregion hits
* 0.5% to <1% ofecoregion hits
* 0.1% to <0.5% of ecoregion hits
o <0.1% of ecoregion hits
HI Sierra Nevada (Ecoregion 6.2.12)
Figure 20. Monitoring sites (annual PM2.5 metric) of potential influence for ecoregion
6.2.12 (red shaded region).
6A-Attachment-20
-------
PM2.5 Monitor Sites of Influence
* >1% ofecoregion hits
~ 0.5% to <1% of ecoregion hits
• 0.1 % to <0.5% of ecoregion hits
o <0.1% of ecoregion hits
I] Southern Rockies (Ecoregion 6.2.14)
Figure 21. Monitoring sites (annual PM2.5 metric) of potential influence for ecoregion
6.2.14 (red shaded region).
6 A- Attachment-21
-------
PM2.5 Monitor Sites of Influence
* >1% ofecoregion hits
* 0.5% to <1% of ecoregion hits
* 0.1% to <0.5% ofecoregion hits
o <0.1% of ecoregion hits
I I Idaho Batholith (Ecoregion 6.2.15)
Figure 22. Monitoring sites (annual PM2.5 metric) of potential influence for ecoregion
6.2.15 (red shaded region).
6A-Attachment-22
-------
PMa.5 Monitor Sites of Influence
~ >1% ofecoregion hits
~ 0.5% to <1% of ecoregion hits
~ 0.1% to <0.5% ofecoregion hits
o <0.1% of ecoregion hits
I Eastern Great Lakes Lowlands (Ecoregion 8.1.1)
Figure 23. Monitoring sites (annual PM2.5 metric) of potential influence for ecoregion 8.1.1
(red shaded region).
6A-Attachment-23
-------
PM2.5 Monitor Sites of Influence 1
>1% ofecoregion hits
0.5% to <1% ofecoregion hits
0.1% to <0.5% ofecoregion hits
<0.1% of ecoregion hits
1 North Central Hardwood Forests (Ecoregion 8.1.4)
Figure 24. Monitoring sites (annual PM2.5 metric) of potential influence for ecoregion 8.1.4
(red shaded region).
6A-Attachment-24
-------
PM25 Monitor Sites of Influence
* >1% ofecoregion hits
* 0.5% to <1% of ecoregion hits
* 0.1% to <0.5% ofecoregion hits
o <0.1% of ecoregion hits
Northern Piedmont (Ecoregion 8.3.1)
Figure 25. Monitoring sites (annual PM2.5 metric) of potential influence for ecoregion 8.3.1
(red shaded region).
6A-Attachment-25
-------
PM2,5 Monitor Sites of Influence
* >1% ofecoregion hits
* 0.5% to <1% ofecoregion hits
* 0.1% to <0.5% ofecoregion hits
o <0.1% of ecoregion hits
I South Central Plains (Ecoregion 8.3.7)
Figure 26. Monitoring sites (annual PM2.5 metric) of potential influence for ecoregion 8.3.7
(red shaded region).
6A-Attachment-26
-------
PM25 Monitor Sites of Influence
• >1% of ecoregion hits
* 0.5% to <1% of ecoregion hits
~ 0.1% to <0.5% of ecoregion hits
o <0.1% of ecoregion hits
[HH Ridge and Valley (Ecoregion 8.4.1)
Figure 27. Monitoring sites (annual PM2.5 metric) of potential influence for ecoregion 8.4.1
(red shaded region).
6A-Attachment-27
-------
PM2.5 Monitor Sites of Influence
~ >1% of ecoregion hits
~ 0.5% to < 1 % of ecoregion hits
~ 0.1% to <0.5% ofecoregion hits
o <0.1% of ecoregion hits
I I Central Appalachians (Ecoregion 8.4.2)
Figure 28. Monitoring sites (annual PM2.5 metric) of potential influence for ecoregion 8.4.2
(red shaded region).
6A-Attachment-28
-------
PM2.5 Monitor Sites of Influence
• >1% ofecoregion hits
• 0.5% to <1% ofecoregion hits
• 0.1% to <0.5% ofecoregion hits
o <0.1% of ecoregion hits
HI Central Great Plains (Ecoregion 9.4.2)
Figure 29. Monitoring sites (annual PM2.5 metric) of potential influence for ecoregion 9.4.2
(red shaded region).
6A-Attachment-29
-------
PM2.5 Monitor Sites of Influence
* >1% ofecoregion hits
* 0.5% to <1% ofecoregion hits
* 0.1% to <0.5% ofecoregion hits
o <0.1% of ecoregion hits
~] Southern California Mountains (Ecoregion 11.1.3)
Figure 30. Monitoring sites (annual PM2.5 metric) of potential influence for ecoregion
11.1.3 (red shaded region).
6 A-Attachment-3 0
-------
PM2.5 Monitor Sites of Influence
~ >1% ofecoregion hits
~ 0.5% to <1% of ecoregion hits
~ 0.1% to <0.5% of ecoregion hits
o <0.1% of ecoregion hits
HI Arizona/New Mexico Mountains (Ecoregion 13.1.1)
Figure 31. Monitoring sites (annual PM2.5 metric) of potential influence for ecoregion
13.1.1 (red shaded region).
6A-Attachment-31
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PM2.5 Monitor Sites of Influence
• >1% ofecoregiori hits
• 0.5% to <1% of ecoregion hits
• 0.1% to <0.5% of ecoregion hits
o <0.1% of ecoregion hits
Z2 Southern Florida Coastal Plain (Ecoregion 15.4.1)
Figure 32. Monitoring sites (annual PM2.5 metric) of potential influence for ecoregion
15.4.1 (red shaded region).
6 A-Attachment-3 2
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S02 Monitor Sites of
(3-hour)
~ >1% ofecoregion hits
~ 0.5% to <1% ofecoregion hits
~ 0.1% to <0.5% ofecoregion hits
o <0.1% of ecoregion hits
HI Northern Lakes and Forests (Ecoregion 5.2.1)
Figure 33. Monitoring sites (3-hour SO2 metric) of potential influence for ecoregion 5.2.1
(red shaded region).
6 A-Attachment-3 3
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S02 Monitor Sites of Influence
(3-hour)
~ >1% ofecoregion hits
~ 0.5% to <1% of ecoregion hits
~ 0.1% to <0.5% ofecoregion hits
o <0.1% of ecoregion hits
¦I Northeastern Highlands (Ecoregion 5.3.1)
Figure 34. Monitoring sites (3-hour SO2 metric) of potential influence for ecoregion 5.3.1
(red shaded region).
6A-Attachment-34
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SO2 Monitor Sites of Influence
(3-hour)
~ >1% of ecoregion hits
~ 0.5% to <1% of ecoregion hits
~ 0.1% to <0.5% of ecoregion hits
o <0.1% of ecoregion hits
^ Cascades (Ecoregion 6.2.7)
Figure 35. Monitoring sites (3-hour SO2 metric) of potential influence for ecoregion 6.2.7
(red shaded region).
6 A- Attachment-3 5
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S02 Monitor Sites of Influence
(3-hour)
• >1% ofecoregion hits
• 0.5% to <1% ofecoregion hits
• 0.1% to <0.5% ofecoregion hits
o <0.1% of ecoregion hits
I] Sierra Nevada (Ecoregion 6.2.12)
Figure 36. Monitoring sites (3-hour SO2 metric) of potential influence for ecoregion 6.2.12
(red shaded region).
6 A-Attachment-3 6
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S02 Monitor Sites of Influence
(3-hour)
• >1% ofecoregion hits
• 0.5% to <1% of ecoregion hits
• 0.1% to <0.5% ofecoregion hits
o <0.1% of ecoregion hits
I I Southern Rockies (Ecoregion 6.2.14)
Figure 37. Monitoring sites (3-hour SO2 metric) of potential influence for ecoregion 6.2.14
(red shaded region).
6 A- Attachment-3 7
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SO2 Monitor Sites of Influence
(3-hour)
• >1 % of ec oregion hits
• 0.5% to <1% of ecoregion hits
• 0.1% to <0.5% of ecoregion hits
o <0.1% of ecoregion hits
HI Idaho Batholith (Ecoregion 6.2.15)
Figure 38. Monitoring sites (3-hour SO2 metric) of potential influence for ecoregion 6.2.15
(red shaded region).
6 A- Attachment-3 8
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S02 Monitor Sites of
(3-hour)
~ >1% ofecoregion hits
~ 0.5% to <1% of ecoregion hits
~ 0.1% to <0.5% ofecoregion hits
o <0.1% of ecoregion hits
I Eastern Great Lakes Lowlands (Ecoregion 8.1.1)
Figure 39. Monitoring sites (3-hour SO2 metric) of potential influence for ecoregion 8.1.1
(red shaded region).
6 A-Attachment-3 9
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S02 Monitor Sites of
(3-hour)
>1% ofecoregion hits
0.5% to <1% ofecoregion hits
0.1% to <0.5% ofecoregion hits
<0.1% of ecoregion hits
North Central Hardwood Forests (Ecoregion 8.1.4)
Figure 40. Monitoring sites (3-hour SO2 metric) of potential influence for ecoregion 8.1.4
(red shaded region).
6A-Attachment-4Q
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S02 Monitor Sites of Influence
(3-hour)
~ >1% ofecoregion hits
~ 0.5% to < 1 % of ecoregion hits
~ 0.1% to <0.5% ofecoregion hits
o <0.1% of ecoregion hits
Northern Piedmont (Ecoregion 8.3.1)
Figure 41. Monitoring sites (3-hour SO2 metric) of potential influence for ecoregion 8.3.1
(red shaded region).
6 A- Attachment-41
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Figure 42. Monitoring sites (3-hour SO2 metric) of potential influence for ecoregion 8.3.7
(red shaded region).
6A-Attachment-42
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S02 Monitor Sites of Influence
(3-hour)
~ >1% ofecoregion hits
~ 0.5% to <1% of ecoregion hits
~ 0.1% to <0.5% ofecoregion hits
o <0.1% of ecoregion hits
I: "I Ridge and Valley (Ecoregion 8.4.1)
Figure 43. Monitoring sites (3-hour SO2 metric) of potential influence for ecoregion 8.4.1
(red shaded region).
6A-Attachment-43
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Figure 44. Monitoring sites (3-hour SO2 metric) of potential influence for ecoregion 8.4.2
(red shaded region).
6A-Attachment-44
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S02 Monitor Sites of Influence
(3-hour)
• >1% ofecoregion hits
• 0.5% to <1% ofecoregion hits
• 0.1% to <0.5% ofecoregion hits
o <0.1% of ecoregion hits
I Central Great Plains (Ecoregion 9.4.2)
Figure 45. Monitoring sites (3-hour SO2 metric) of potential influence for ecoregion 9.4.2
(red shaded region).
6A-Attachment-45
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* >1% of ecoregion hits
• 0.5% to <1% of ecoregion hits
~ 0.1% to <0.5% of ecoregion hits
o <0.1% of ecoregion hits
HI Southern California Mountains (Ecoregion 11.1.3)
Figure 46. Monitoring sites (3-hour SO2 metric) of potential influence for ecoregion 11.1.3
(red shaded region).
6A-Attachment-46
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0.5% to <1% of ecoregion hits
0.1% to <0.5% of ecoregion hits
<0.1% of ecoregion hits
] Arizona/New Mexico Mountains (Ecoregion 13.1.1)
Figure 47. Monitoring sites (3-hour SO2 metric) of potential influence for ecoregion 13.1.1
(red shaded region).
6A-Attachment-47
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>1% ofecoregion hits
0.5% to <1% ofecoregion hits
0.1% to <0.5% ofecoregion hits
<0.1% of ecoregion hits
] Southern Florida Coastal Plain (Ecoregion 15.4.1)
Figure 48. Monitoring sites (3-hour SO2 metric) of potential influence for ecoregion 15.4.1
(red shaded region).
6A-Attachment-48
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United States Office of Air Quality Planning and Standards Publication No. EPA-452/R-24-003
Environmental Protection Health and Environmental Impacts Division January 2024
Agency Research Triangle Park, NC
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